From b2e85bff39a411d02f5a167f7bfab376ae9ccb67 Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Tue, 19 Sep 2023 13:33:01 -0400 Subject: [PATCH 001/111] Update `cugraph-dgl` conv layers to use improved graph class (#3849) This PR: - Removes the usage of the deprecated `StaticCSC` and `SampledCSC` - Support creating CSR and storing edge information in SparseGraph - clean up unit tests - Adds GATv2Conv layer - Adds `pylibcugraphops` as a dependency of `cugraph-dgl` conda package Authors: - Tingyu Wang (https://github.com/tingyu66) Approvers: - Jake Awe (https://github.com/AyodeAwe) - Vibhu Jawa (https://github.com/VibhuJawa) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3849 --- conda/recipes/cugraph-dgl/meta.yaml | 1 + .../cugraph_dgl/nn/conv/__init__.py | 2 + .../cugraph-dgl/cugraph_dgl/nn/conv/base.py | 262 +++++++++++++----- .../cugraph_dgl/nn/conv/gatconv.py | 140 +++++++--- .../cugraph_dgl/nn/conv/gatv2conv.py | 249 +++++++++++++++++ .../cugraph_dgl/nn/conv/relgraphconv.py | 70 ++--- .../cugraph_dgl/nn/conv/sageconv.py | 122 ++++---- .../cugraph_dgl/nn/conv/transformerconv.py | 20 +- python/cugraph-dgl/tests/conftest.py | 3 + python/cugraph-dgl/tests/nn/test_gatconv.py | 100 ++++--- python/cugraph-dgl/tests/nn/test_gatv2conv.py | 147 ++++++++++ .../cugraph-dgl/tests/nn/test_relgraphconv.py | 71 +++-- python/cugraph-dgl/tests/nn/test_sageconv.py | 65 +++-- .../cugraph-dgl/tests/nn/test_sparsegraph.py | 28 +- .../tests/nn/test_transformerconv.py | 41 ++- python/cugraph-dgl/tests/test_dataset.py | 2 +- ...ograph.py => test_from_dgl_heterograph.py} | 0 17 files changed, 978 insertions(+), 345 deletions(-) create mode 100644 python/cugraph-dgl/cugraph_dgl/nn/conv/gatv2conv.py create mode 100644 python/cugraph-dgl/tests/nn/test_gatv2conv.py rename python/cugraph-dgl/tests/{test_from_dgl_hetrograph.py => test_from_dgl_heterograph.py} (100%) diff --git a/conda/recipes/cugraph-dgl/meta.yaml b/conda/recipes/cugraph-dgl/meta.yaml index 2fbc6360c04..9e9fcd2faf1 100644 --- a/conda/recipes/cugraph-dgl/meta.yaml +++ b/conda/recipes/cugraph-dgl/meta.yaml @@ -26,6 +26,7 @@ requirements: - dgl >=1.1.0.cu* - numba >=0.57 - numpy >=1.21 + - pylibcugraphops ={{ version }} - python - pytorch diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/__init__.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/__init__.py index e5acbf34478..3e7f2f076f0 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/__init__.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/__init__.py @@ -13,6 +13,7 @@ from .base import SparseGraph from .gatconv import GATConv +from .gatv2conv import GATv2Conv from .relgraphconv import RelGraphConv from .sageconv import SAGEConv from .transformerconv import TransformerConv @@ -20,6 +21,7 @@ __all__ = [ "SparseGraph", "GATConv", + "GATv2Conv", "RelGraphConv", "SAGEConv", "TransformerConv", diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py index 0eeaed29d86..307eb33078e 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py @@ -17,38 +17,7 @@ torch = import_optional("torch") ops_torch = import_optional("pylibcugraphops.pytorch") - - -class BaseConv(torch.nn.Module): - r"""An abstract base class for cugraph-ops nn module.""" - - def __init__(self): - super().__init__() - self._cached_offsets_fg = None - - def reset_parameters(self): - r"""Resets all learnable parameters of the module.""" - raise NotImplementedError - - def forward(self, *args): - r"""Runs the forward pass of the module.""" - raise NotImplementedError - - def pad_offsets(self, offsets: torch.Tensor, size: int) -> torch.Tensor: - r"""Pad zero-in-degree nodes to the end of offsets to reach size. This - is used to augment offset tensors from DGL blocks (MFGs) to be - compatible with cugraph-ops full-graph primitives.""" - if self._cached_offsets_fg is None: - self._cached_offsets_fg = torch.empty( - size, dtype=offsets.dtype, device=offsets.device - ) - elif self._cached_offsets_fg.numel() < size: - self._cached_offsets_fg.resize_(size) - - self._cached_offsets_fg[: offsets.numel()] = offsets - self._cached_offsets_fg[offsets.numel() : size] = offsets[-1] - - return self._cached_offsets_fg[:size] +dgl = import_optional("dgl") def compress_ids(ids: torch.Tensor, size: int) -> torch.Tensor: @@ -63,8 +32,9 @@ def decompress_ids(c_ids: torch.Tensor) -> torch.Tensor: class SparseGraph(object): - r"""A god-class to store different sparse formats needed by cugraph-ops - and facilitate sparse format conversions. + r"""A class to create and store different sparse formats needed by + cugraph-ops. It always creates a CSC representation and can provide COO- or + CSR-format if needed. Parameters ---------- @@ -89,25 +59,43 @@ class SparseGraph(object): consists of the sources between `src_indices[cdst_indices[k]]` and `src_indices[cdst_indices[k+1]]`. - dst_ids_is_sorted: bool - Whether `dst_ids` has been sorted in an ascending order. When sorted, - creating CSC layout is much faster. + values: torch.Tensor, optional + Values on the edges. + + is_sorted: bool + Whether the COO inputs (src_ids, dst_ids, values) have been sorted by + `dst_ids` in an ascending order. CSC layout creation is much faster + when sorted. formats: str or tuple of str, optional - The desired sparse formats to create for the graph. + The desired sparse formats to create for the graph. The formats tuple + must include "csc". Default: "csc". reduce_memory: bool, optional When set, the tensors are not required by the desired formats will be - set to `None`. + set to `None`. Default: True. Notes ----- For MFGs (sampled graphs), the node ids must have been renumbered. """ - supported_formats = {"coo": ("src_ids", "dst_ids"), "csc": ("cdst_ids", "src_ids")} - - all_tensors = set(["src_ids", "dst_ids", "csrc_ids", "cdst_ids"]) + supported_formats = { + "coo": ("_src_ids", "_dst_ids"), + "csc": ("_cdst_ids", "_src_ids"), + "csr": ("_csrc_ids", "_dst_ids", "_perm_csc2csr"), + } + + all_tensors = set( + [ + "_src_ids", + "_dst_ids", + "_csrc_ids", + "_cdst_ids", + "_perm_coo2csc", + "_perm_csc2csr", + ] + ) def __init__( self, @@ -116,15 +104,19 @@ def __init__( dst_ids: Optional[torch.Tensor] = None, csrc_ids: Optional[torch.Tensor] = None, cdst_ids: Optional[torch.Tensor] = None, - dst_ids_is_sorted: bool = False, - formats: Optional[Union[str, Tuple[str]]] = None, + values: Optional[torch.Tensor] = None, + is_sorted: bool = False, + formats: Union[str, Tuple[str]] = "csc", reduce_memory: bool = True, ): self._num_src_nodes, self._num_dst_nodes = size - self._dst_ids_is_sorted = dst_ids_is_sorted + self._is_sorted = is_sorted if dst_ids is None and cdst_ids is None: - raise ValueError("One of 'dst_ids' and 'cdst_ids' must be given.") + raise ValueError( + "One of 'dst_ids' and 'cdst_ids' must be given " + "to create a SparseGraph." + ) if src_ids is not None: src_ids = src_ids.contiguous() @@ -148,21 +140,40 @@ def __init__( ) cdst_ids = cdst_ids.contiguous() + if values is not None: + values = values.contiguous() + self._src_ids = src_ids self._dst_ids = dst_ids self._csrc_ids = csrc_ids self._cdst_ids = cdst_ids - self._perm = None + self._values = values + self._perm_coo2csc = None + self._perm_csc2csr = None if isinstance(formats, str): formats = (formats,) - - if formats is not None: - for format_ in formats: - assert format_ in SparseGraph.supported_formats - self.__getattribute__(f"_create_{format_}")() self._formats = formats + if "csc" not in formats: + raise ValueError( + f"{self.__class__.__name__}.formats must contain " + f"'csc', but got {formats}." + ) + + # always create csc first + if self._cdst_ids is None: + if not self._is_sorted: + self._dst_ids, self._perm_coo2csc = torch.sort(self._dst_ids) + self._src_ids = self._src_ids[self._perm_coo2csc] + if self._values is not None: + self._values = self._values[self._perm_coo2csc] + self._cdst_ids = compress_ids(self._dst_ids, self._num_dst_nodes) + + for format_ in formats: + assert format_ in SparseGraph.supported_formats + self.__getattribute__(f"{format_}")() + self._reduce_memory = reduce_memory if reduce_memory: self.reduce_memory() @@ -170,8 +181,6 @@ def __init__( def reduce_memory(self): """Remove the tensors that are not necessary to create the desired sparse formats to reduce memory footprint.""" - - self._perm = None if self._formats is None: return @@ -181,16 +190,22 @@ def reduce_memory(self): for t in SparseGraph.all_tensors.difference(set(tensors_needed)): self.__dict__[t] = None - def _create_coo(self): + def src_ids(self) -> torch.Tensor: + return self._src_ids + + def cdst_ids(self) -> torch.Tensor: + return self._cdst_ids + + def dst_ids(self) -> torch.Tensor: if self._dst_ids is None: self._dst_ids = decompress_ids(self._cdst_ids) + return self._dst_ids - def _create_csc(self): - if self._cdst_ids is None: - if not self._dst_ids_is_sorted: - self._dst_ids, self._perm = torch.sort(self._dst_ids) - self._src_ids = self._src_ids[self._perm] - self._cdst_ids = compress_ids(self._dst_ids, self._num_dst_nodes) + def csrc_ids(self) -> torch.Tensor: + if self._csrc_ids is None: + src_ids, self._perm_csc2csr = torch.sort(self._src_ids) + self._csrc_ids = compress_ids(src_ids, self._num_src_nodes) + return self._csrc_ids def num_src_nodes(self): return self._num_src_nodes @@ -198,21 +213,134 @@ def num_src_nodes(self): def num_dst_nodes(self): return self._num_dst_nodes + def values(self): + return self._values + def formats(self): return self._formats - def coo(self) -> Tuple[torch.Tensor, torch.Tensor]: + def coo(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: if "coo" not in self.formats(): raise RuntimeError( "The SparseGraph did not create a COO layout. " - "Set 'formats' to include 'coo' when creating the graph." + "Set 'formats' list to include 'coo' when creating the graph." ) - return (self._src_ids, self._dst_ids) + return self.src_ids(), self.dst_ids(), self._values - def csc(self) -> Tuple[torch.Tensor, torch.Tensor]: + def csc(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: if "csc" not in self.formats(): raise RuntimeError( "The SparseGraph did not create a CSC layout. " - "Set 'formats' to include 'csc' when creating the graph." + "Set 'formats' list to include 'csc' when creating the graph." + ) + return self.cdst_ids(), self.src_ids(), self._values + + def csr(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: + if "csr" not in self.formats(): + raise RuntimeError( + "The SparseGraph did not create a CSR layout. " + "Set 'formats' list to include 'csr' when creating the graph." + ) + csrc_ids = self.csrc_ids() + dst_ids = self.dst_ids()[self._perm_csc2csr] + value = self._values + if value is not None: + value = value[self._perm_csc2csr] + return csrc_ids, dst_ids, value + + +class BaseConv(torch.nn.Module): + r"""An abstract base class for cugraph-ops nn module.""" + + def __init__(self): + super().__init__() + + def reset_parameters(self): + r"""Resets all learnable parameters of the module.""" + raise NotImplementedError + + def forward(self, *args): + r"""Runs the forward pass of the module.""" + raise NotImplementedError + + def get_cugraph_ops_CSC( + self, + g: Union[SparseGraph, dgl.DGLHeteroGraph], + is_bipartite: bool = False, + max_in_degree: Optional[int] = None, + ) -> ops_torch.CSC: + """Create CSC structure needed by cugraph-ops.""" + + if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)): + raise TypeError( + f"The graph has to be either a 'cugraph_dgl.nn.SparseGraph' or " + f"'dgl.DGLHeteroGraph', but got '{type(g)}'." ) - return (self._cdst_ids, self._src_ids) + + # TODO: max_in_degree should default to None in pylibcugraphops + if max_in_degree is None: + max_in_degree = -1 + + if isinstance(g, SparseGraph): + offsets, indices, _ = g.csc() + else: + offsets, indices, _ = g.adj_tensors("csc") + + graph = ops_torch.CSC( + offsets=offsets, + indices=indices, + num_src_nodes=g.num_src_nodes(), + dst_max_in_degree=max_in_degree, + is_bipartite=is_bipartite, + ) + + return graph + + def get_cugraph_ops_HeteroCSC( + self, + g: Union[SparseGraph, dgl.DGLHeteroGraph], + num_edge_types: int, + etypes: Optional[torch.Tensor] = None, + is_bipartite: bool = False, + max_in_degree: Optional[int] = None, + ) -> ops_torch.HeteroCSC: + """Create HeteroCSC structure needed by cugraph-ops.""" + + if not isinstance(g, (SparseGraph, dgl.DGLHeteroGraph)): + raise TypeError( + f"The graph has to be either a 'cugraph_dgl.nn.SparseGraph' or " + f"'dgl.DGLHeteroGraph', but got '{type(g)}'." + ) + + # TODO: max_in_degree should default to None in pylibcugraphops + if max_in_degree is None: + max_in_degree = -1 + + if isinstance(g, SparseGraph): + offsets, indices, etypes = g.csc() + if etypes is None: + raise ValueError( + "SparseGraph must have 'values' to create HeteroCSC. " + "Pass in edge types as 'values' when creating the SparseGraph." + ) + etypes = etypes.int() + else: + if etypes is None: + raise ValueError( + "'etypes' is required when creating HeteroCSC " + "from dgl.DGLHeteroGraph." + ) + offsets, indices, perm = g.adj_tensors("csc") + etypes = etypes[perm].int() + + graph = ops_torch.HeteroCSC( + offsets=offsets, + indices=indices, + edge_types=etypes, + num_src_nodes=g.num_src_nodes(), + num_edge_types=num_edge_types, + dst_max_in_degree=max_in_degree, + is_bipartite=is_bipartite, + ) + + return graph diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/gatconv.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/gatconv.py index 239def5b677..8843e61ad89 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/gatconv.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/gatconv.py @@ -10,13 +10,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Torch Module for graph attention network layer using the aggregation -primitives in cugraph-ops""" -# pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments -from __future__ import annotations + from typing import Optional, Tuple, Union -from cugraph_dgl.nn.conv.base import BaseConv +from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph from cugraph.utilities.utils import import_optional dgl = import_optional("dgl") @@ -32,13 +29,15 @@ class GATConv(BaseConv): Parameters ---------- - in_feats : int, pair of ints + in_feats : int or tuple Input feature size. A pair denotes feature sizes of source and destination nodes. out_feats : int Output feature size. num_heads : int - Number of heads in Multi-Head Attention. + Number of heads in multi-head attention. + feat_drop : float, optional + Dropout rate on feature. Defaults: ``0``. concat : bool, optional If False, the multi-head attentions are averaged instead of concatenated. Default: ``True``. @@ -46,6 +45,15 @@ class GATConv(BaseConv): Edge feature size. Default: ``None``. negative_slope : float, optional LeakyReLU angle of negative slope. Defaults: ``0.2``. + residual : bool, optional + If True, use residual connection. Defaults: ``False``. + allow_zero_in_degree : bool, optional + If there are 0-in-degree nodes in the graph, output for those nodes will + be invalid since no message will be passed to those nodes. This is + harmful for some applications causing silent performance regression. + This module will raise a DGLError if it detects 0-in-degree nodes in + input graph. By setting ``True``, it will suppress the check and let the + users handle it by themselves. Defaults: ``False``. bias : bool, optional If True, learns a bias term. Defaults: ``True``. @@ -81,37 +89,46 @@ class GATConv(BaseConv): [ 1.6477, -1.9986], [ 1.1138, -1.9302]]], device='cuda:0', grad_fn=) """ - MAX_IN_DEGREE_MFG = 200 def __init__( self, in_feats: Union[int, Tuple[int, int]], out_feats: int, num_heads: int, + feat_drop: float = 0.0, concat: bool = True, edge_feats: Optional[int] = None, negative_slope: float = 0.2, + residual: bool = False, + allow_zero_in_degree: bool = False, bias: bool = True, ): super().__init__() self.in_feats = in_feats self.out_feats = out_feats + self.in_feats_src, self.in_feats_dst = dgl.utils.expand_as_pair(in_feats) self.num_heads = num_heads + self.feat_drop = nn.Dropout(feat_drop) self.concat = concat self.edge_feats = edge_feats self.negative_slope = negative_slope + self.allow_zero_in_degree = allow_zero_in_degree if isinstance(in_feats, int): - self.fc = nn.Linear(in_feats, num_heads * out_feats, bias=False) + self.lin = nn.Linear(in_feats, num_heads * out_feats, bias=False) else: - self.fc_src = nn.Linear(in_feats[0], num_heads * out_feats, bias=False) - self.fc_dst = nn.Linear(in_feats[1], num_heads * out_feats, bias=False) + self.lin_src = nn.Linear( + self.in_feats_src, num_heads * out_feats, bias=False + ) + self.lin_dst = nn.Linear( + self.in_feats_dst, num_heads * out_feats, bias=False + ) if edge_feats is not None: - self.fc_edge = nn.Linear(edge_feats, num_heads * out_feats, bias=False) + self.lin_edge = nn.Linear(edge_feats, num_heads * out_feats, bias=False) self.attn_weights = nn.Parameter(torch.Tensor(3 * num_heads * out_feats)) else: - self.register_parameter("fc_edge", None) + self.register_parameter("lin_edge", None) self.attn_weights = nn.Parameter(torch.Tensor(2 * num_heads * out_feats)) if bias and concat: @@ -121,28 +138,40 @@ def __init__( else: self.register_buffer("bias", None) + self.residual = residual and self.in_feats_dst != out_feats * num_heads + if self.residual: + self.lin_res = nn.Linear( + self.in_feats_dst, num_heads * out_feats, bias=bias + ) + else: + self.register_buffer("lin_res", None) + self.reset_parameters() def reset_parameters(self): r"""Reinitialize learnable parameters.""" gain = nn.init.calculate_gain("relu") - if hasattr(self, "fc"): - nn.init.xavier_normal_(self.fc.weight, gain=gain) + if hasattr(self, "lin"): + nn.init.xavier_normal_(self.lin.weight, gain=gain) else: - nn.init.xavier_normal_(self.fc_src.weight, gain=gain) - nn.init.xavier_normal_(self.fc_dst.weight, gain=gain) + nn.init.xavier_normal_(self.lin_src.weight, gain=gain) + nn.init.xavier_normal_(self.lin_dst.weight, gain=gain) nn.init.xavier_normal_( self.attn_weights.view(-1, self.num_heads, self.out_feats), gain=gain ) - if self.fc_edge is not None: - self.fc_edge.reset_parameters() + if self.lin_edge is not None: + self.lin_edge.reset_parameters() + + if self.lin_res is not None: + self.lin_res.reset_parameters() + if self.bias is not None: nn.init.zeros_(self.bias) def forward( self, - g: dgl.DGLHeteroGraph, + g: Union[SparseGraph, dgl.DGLHeteroGraph], nfeat: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], efeat: Optional[torch.Tensor] = None, max_in_degree: Optional[int] = None, @@ -151,18 +180,17 @@ def forward( Parameters ---------- - graph : DGLGraph + graph : DGLGraph or SparseGraph The graph. nfeat : torch.Tensor Input features of shape :math:`(N, D_{in})`. efeat: torch.Tensor, optional Optional edge features. max_in_degree : int - Maximum in-degree of destination nodes. It is only effective when - :attr:`g` is a :class:`DGLBlock`, i.e., bipartite graph. When - :attr:`g` is generated from a neighbor sampler, the value should be - set to the corresponding :attr:`fanout`. If not given, - :attr:`max_in_degree` will be calculated on-the-fly. + Maximum in-degree of destination nodes. When :attr:`g` is generated + from a neighbor sampler, the value should be set to the corresponding + :attr:`fanout`. This option is used to invoke the MFG-variant of + cugraph-ops kernel. Returns ------- @@ -171,49 +199,63 @@ def forward( :math:`H` is the number of heads, and :math:`D_{out}` is size of output feature. """ - if max_in_degree is None: - max_in_degree = -1 - - bipartite = not isinstance(nfeat, torch.Tensor) - offsets, indices, _ = g.adj_tensors("csc") - - graph = ops_torch.CSC( - offsets=offsets, - indices=indices, - num_src_nodes=g.num_src_nodes(), - dst_max_in_degree=max_in_degree, - is_bipartite=bipartite, + if isinstance(g, dgl.DGLHeteroGraph): + if not self.allow_zero_in_degree: + if (g.in_degrees() == 0).any(): + raise dgl.base.DGLError( + "There are 0-in-degree nodes in the graph, " + "output for those nodes will be invalid. " + "This is harmful for some applications, " + "causing silent performance regression. " + "Adding self-loop on the input graph by " + "calling `g = dgl.add_self_loop(g)` will resolve " + "the issue. Setting ``allow_zero_in_degree`` " + "to be `True` when constructing this module will " + "suppress the check and let the code run." + ) + + bipartite = isinstance(nfeat, (list, tuple)) + + _graph = self.get_cugraph_ops_CSC( + g, is_bipartite=bipartite, max_in_degree=max_in_degree ) + if bipartite: + nfeat = (self.feat_drop(nfeat[0]), self.feat_drop(nfeat[1])) + nfeat_dst_orig = nfeat[1] + else: + nfeat = self.feat_drop(nfeat) + nfeat_dst_orig = nfeat[: g.num_dst_nodes()] + if efeat is not None: - if self.fc_edge is None: + if self.lin_edge is None: raise RuntimeError( f"{self.__class__.__name__}.edge_feats must be set to " f"accept edge features." ) - efeat = self.fc_edge(efeat) + efeat = self.lin_edge(efeat) if bipartite: - if not hasattr(self, "fc_src"): + if not hasattr(self, "lin_src"): raise RuntimeError( f"{self.__class__.__name__}.in_feats must be a pair of " f"integers to allow bipartite node features, but got " f"{self.in_feats}." ) - nfeat_src = self.fc_src(nfeat[0]) - nfeat_dst = self.fc_dst(nfeat[1]) + nfeat_src = self.lin_src(nfeat[0]) + nfeat_dst = self.lin_dst(nfeat[1]) else: - if not hasattr(self, "fc"): + if not hasattr(self, "lin"): raise RuntimeError( f"{self.__class__.__name__}.in_feats is expected to be an " f"integer, but got {self.in_feats}." ) - nfeat = self.fc(nfeat) + nfeat = self.lin(nfeat) out = ops_torch.operators.mha_gat_n2n( (nfeat_src, nfeat_dst) if bipartite else nfeat, self.attn_weights, - graph, + _graph, num_heads=self.num_heads, activation="LeakyReLU", negative_slope=self.negative_slope, @@ -224,6 +266,12 @@ def forward( if self.concat: out = out.view(-1, self.num_heads, self.out_feats) + if self.residual: + res = self.lin_res(nfeat_dst_orig).view(-1, self.num_heads, self.out_feats) + if not self.concat: + res = res.mean(dim=1) + out = out + res + if self.bias is not None: out = out + self.bias diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/gatv2conv.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/gatv2conv.py new file mode 100644 index 00000000000..209a5fe1a8d --- /dev/null +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/gatv2conv.py @@ -0,0 +1,249 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional, Tuple, Union + +from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph +from cugraph.utilities.utils import import_optional + +dgl = import_optional("dgl") +torch = import_optional("torch") +nn = import_optional("torch.nn") +ops_torch = import_optional("pylibcugraphops.pytorch") + + +class GATv2Conv(BaseConv): + r"""GATv2 from `How Attentive are Graph Attention Networks? + `__, with the sparse aggregation + accelerated by cugraph-ops. + + Parameters + ---------- + in_feats : int, or pair of ints + Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`. + If the layer is to be applied to a unidirectional bipartite graph, `in_feats` + specifies the input feature size on both the source and destination nodes. + If a scalar is given, the source and destination node feature size + would take the same value. + out_feats : int + Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`. + num_heads : int + Number of heads in Multi-Head Attention. + feat_drop : float, optional + Dropout rate on feature. Defaults: ``0``. + concat : bool, optional + If False, the multi-head attentions are averaged instead of concatenated. + Default: ``True``. + edge_feats : int, optional + Edge feature size. Default: ``None``. + negative_slope : float, optional + LeakyReLU angle of negative slope. Defaults: ``0.2``. + residual : bool, optional + If True, use residual connection. Defaults: ``False``. + allow_zero_in_degree : bool, optional + If there are 0-in-degree nodes in the graph, output for those nodes will + be invalid since no message will be passed to those nodes. This is + harmful for some applications causing silent performance regression. + This module will raise a DGLError if it detects 0-in-degree nodes in + input graph. By setting ``True``, it will suppress the check and let the + users handle it by themselves. Defaults: ``False``. + bias : bool, optional + If set to :obj:`False`, the layer will not learn + an additive bias. (default: :obj:`True`) + share_weights : bool, optional + If set to :obj:`True`, the same matrix for :math:`W_{left}` and + :math:`W_{right}` in the above equations, will be applied to the source + and the target node of every edge. (default: :obj:`False`) + """ + + def __init__( + self, + in_feats: Union[int, Tuple[int, int]], + out_feats: int, + num_heads: int, + feat_drop: float = 0.0, + concat: bool = True, + edge_feats: Optional[int] = None, + negative_slope: float = 0.2, + residual: bool = False, + allow_zero_in_degree: bool = False, + bias: bool = True, + share_weights: bool = False, + ): + super().__init__() + self.in_feats = in_feats + self.out_feats = out_feats + self.in_feats_src, self.in_feats_dst = dgl.utils.expand_as_pair(in_feats) + self.num_heads = num_heads + self.feat_drop = nn.Dropout(feat_drop) + self.concat = concat + self.edge_feats = edge_feats + self.negative_slope = negative_slope + self.allow_zero_in_degree = allow_zero_in_degree + self.share_weights = share_weights + + self.lin_src = nn.Linear(self.in_feats_src, num_heads * out_feats, bias=bias) + if share_weights: + if self.in_feats_src != self.in_feats_dst: + raise ValueError( + f"Input feature size of source and destination " + f"nodes must be identical when share_weights is enabled, " + f"but got {self.in_feats_src} and {self.in_feats_dst}." + ) + self.lin_dst = self.lin_src + else: + self.lin_dst = nn.Linear( + self.in_feats_dst, num_heads * out_feats, bias=bias + ) + + self.attn = nn.Parameter(torch.Tensor(num_heads * out_feats)) + + if edge_feats is not None: + self.lin_edge = nn.Linear(edge_feats, num_heads * out_feats, bias=False) + else: + self.register_parameter("lin_edge", None) + + if bias and concat: + self.bias = nn.Parameter(torch.Tensor(num_heads, out_feats)) + elif bias and not concat: + self.bias = nn.Parameter(torch.Tensor(out_feats)) + else: + self.register_buffer("bias", None) + + self.residual = residual and self.in_feats_dst != out_feats * num_heads + if self.residual: + self.lin_res = nn.Linear( + self.in_feats_dst, num_heads * out_feats, bias=bias + ) + else: + self.register_buffer("lin_res", None) + + self.reset_parameters() + + def reset_parameters(self): + r"""Reinitialize learnable parameters.""" + gain = nn.init.calculate_gain("relu") + nn.init.xavier_normal_(self.lin_src.weight, gain=gain) + nn.init.xavier_normal_(self.lin_dst.weight, gain=gain) + + nn.init.xavier_normal_( + self.attn.view(-1, self.num_heads, self.out_feats), gain=gain + ) + if self.lin_edge is not None: + self.lin_edge.reset_parameters() + + if self.lin_res is not None: + self.lin_res.reset_parameters() + + if self.bias is not None: + nn.init.zeros_(self.bias) + + def forward( + self, + g: Union[SparseGraph, dgl.DGLHeteroGraph], + nfeat: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], + efeat: Optional[torch.Tensor] = None, + max_in_degree: Optional[int] = None, + ) -> torch.Tensor: + r"""Forward computation. + + Parameters + ---------- + graph : DGLGraph or SparseGraph + The graph. + nfeat : torch.Tensor + Input features of shape :math:`(N, D_{in})`. + efeat: torch.Tensor, optional + Optional edge features. + max_in_degree : int + Maximum in-degree of destination nodes. When :attr:`g` is generated + from a neighbor sampler, the value should be set to the corresponding + :attr:`fanout`. This option is used to invoke the MFG-variant of + cugraph-ops kernel. + + Returns + ------- + torch.Tensor + The output feature of shape :math:`(N, H, D_{out})` where + :math:`H` is the number of heads, and :math:`D_{out}` is size of + output feature. + """ + + if isinstance(g, dgl.DGLHeteroGraph): + if not self.allow_zero_in_degree: + if (g.in_degrees() == 0).any(): + raise dgl.base.DGLError( + "There are 0-in-degree nodes in the graph, " + "output for those nodes will be invalid. " + "This is harmful for some applications, " + "causing silent performance regression. " + "Adding self-loop on the input graph by " + "calling `g = dgl.add_self_loop(g)` will resolve " + "the issue. Setting ``allow_zero_in_degree`` " + "to be `True` when constructing this module will " + "suppress the check and let the code run." + ) + + nfeat_bipartite = isinstance(nfeat, (list, tuple)) + graph_bipartite = nfeat_bipartite or self.share_weights is False + + _graph = self.get_cugraph_ops_CSC( + g, is_bipartite=graph_bipartite, max_in_degree=max_in_degree + ) + + if nfeat_bipartite: + nfeat = (self.feat_drop(nfeat[0]), self.feat_drop(nfeat[1])) + nfeat_dst_orig = nfeat[1] + else: + nfeat = self.feat_drop(nfeat) + nfeat_dst_orig = nfeat[: g.num_dst_nodes()] + + if efeat is not None: + if self.lin_edge is None: + raise RuntimeError( + f"{self.__class__.__name__}.edge_feats must be set to " + f"accept edge features." + ) + efeat = self.lin_edge(efeat) + + if nfeat_bipartite: + nfeat = (self.lin_src(nfeat[0]), self.lin_dst(nfeat[1])) + elif graph_bipartite: + nfeat = (self.lin_src(nfeat), self.lin_dst(nfeat[: g.num_dst_nodes()])) + else: + nfeat = self.lin_src(nfeat) + + out = ops_torch.operators.mha_gat_v2_n2n( + nfeat, + self.attn, + _graph, + num_heads=self.num_heads, + activation="LeakyReLU", + negative_slope=self.negative_slope, + concat_heads=self.concat, + edge_feat=efeat, + )[: g.num_dst_nodes()] + + if self.concat: + out = out.view(-1, self.num_heads, self.out_feats) + + if self.residual: + res = self.lin_res(nfeat_dst_orig).view(-1, self.num_heads, self.out_feats) + if not self.concat: + res = res.mean(dim=1) + out = out + res + + if self.bias is not None: + out = out + self.bias + + return out diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/relgraphconv.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/relgraphconv.py index 89e49011cf7..54916674210 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/relgraphconv.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/relgraphconv.py @@ -10,14 +10,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Torch Module for Relational graph convolution layer using the aggregation -primitives in cugraph-ops""" -# pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments -from __future__ import annotations + import math -from typing import Optional +from typing import Optional, Union -from cugraph_dgl.nn.conv.base import BaseConv +from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph from cugraph.utilities.utils import import_optional dgl = import_optional("dgl") @@ -29,13 +26,8 @@ class RelGraphConv(BaseConv): r"""An accelerated relational graph convolution layer from `Modeling Relational Data with Graph Convolutional Networks - `__ that leverages the highly-optimized - aggregation primitives in cugraph-ops. - - See :class:`dgl.nn.pytorch.conv.RelGraphConv` for mathematical model. - - This module depends on :code:`pylibcugraphops` package, which can be - installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`. + `__, with the sparse aggregation + accelerated by cugraph-ops. Parameters ---------- @@ -84,7 +76,6 @@ class RelGraphConv(BaseConv): [-1.4335, -2.3758], [-1.4331, -2.3295]], device='cuda:0', grad_fn=) """ - MAX_IN_DEGREE_MFG = 500 def __init__( self, @@ -148,7 +139,7 @@ def reset_parameters(self): def forward( self, - g: dgl.DGLHeteroGraph, + g: Union[SparseGraph, dgl.DGLHeteroGraph], feat: torch.Tensor, etypes: torch.Tensor, max_in_degree: Optional[int] = None, @@ -167,49 +158,24 @@ def forward( so any input of other integer types will be casted into int32, thus introducing some overhead. Pass in int32 tensors directly for best performance. - max_in_degree : int, optional - Maximum in-degree of destination nodes. It is only effective when - :attr:`g` is a :class:`DGLBlock`, i.e., bipartite graph. When - :attr:`g` is generated from a neighbor sampler, the value should be - set to the corresponding :attr:`fanout`. If not given, - :attr:`max_in_degree` will be calculated on-the-fly. + max_in_degree : int + Maximum in-degree of destination nodes. When :attr:`g` is generated + from a neighbor sampler, the value should be set to the corresponding + :attr:`fanout`. This option is used to invoke the MFG-variant of + cugraph-ops kernel. Returns ------- torch.Tensor New node features. Shape: :math:`(|V|, D_{out})`. """ - offsets, indices, edge_ids = g.adj_tensors("csc") - edge_types_perm = etypes[edge_ids.long()].int() - - if g.is_block: - if max_in_degree is None: - max_in_degree = g.in_degrees().max().item() - - if max_in_degree < self.MAX_IN_DEGREE_MFG: - _graph = ops_torch.SampledHeteroCSC( - offsets, - indices, - edge_types_perm, - max_in_degree, - g.num_src_nodes(), - self.num_rels, - ) - else: - offsets_fg = self.pad_offsets(offsets, g.num_src_nodes() + 1) - _graph = ops_torch.StaticHeteroCSC( - offsets_fg, - indices, - edge_types_perm, - self.num_rels, - ) - else: - _graph = ops_torch.StaticHeteroCSC( - offsets, - indices, - edge_types_perm, - self.num_rels, - ) + _graph = self.get_cugraph_ops_HeteroCSC( + g, + num_edge_types=self.num_rels, + etypes=etypes, + is_bipartite=False, + max_in_degree=max_in_degree, + ) h = ops_torch.operators.agg_hg_basis_n2n_post( feat, diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/sageconv.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/sageconv.py index 60f4c505e19..a3f946d7cb4 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/sageconv.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/sageconv.py @@ -10,11 +10,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Torch Module for GraphSAGE layer using the aggregation primitives in -cugraph-ops""" -# pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments -from __future__ import annotations -from typing import Optional, Union + +from typing import Optional, Tuple, Union from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph from cugraph.utilities.utils import import_optional @@ -27,22 +24,18 @@ class SAGEConv(BaseConv): r"""An accelerated GraphSAGE layer from `Inductive Representation Learning - on Large Graphs `__ that leverages the - highly-optimized aggregation primitives in cugraph-ops. - - See :class:`dgl.nn.pytorch.conv.SAGEConv` for mathematical model. - - This module depends on :code:`pylibcugraphops` package, which can be - installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`. + on Large Graphs `, with the sparse + aggregation accelerated by cugraph-ops. Parameters ---------- - in_feats : int - Input feature size. + in_feats : int or tuple + Input feature size. If a scalar is given, the source and destination + nodes are required to be the same. out_feats : int Output feature size. aggregator_type : str - Aggregator type to use (``mean``, ``sum``, ``min``, ``max``). + Aggregator type to use ("mean", "sum", "min", "max", "pool", "gcn"). feat_drop : float Dropout rate on features, default: ``0``. bias : bool @@ -68,38 +61,57 @@ class SAGEConv(BaseConv): [-1.1690, 0.1952], [-1.1690, 0.1952]], device='cuda:0', grad_fn=) """ - MAX_IN_DEGREE_MFG = 500 + valid_aggr_types = {"mean", "sum", "min", "max", "pool", "gcn"} def __init__( self, - in_feats: int, + in_feats: Union[int, Tuple[int, int]], out_feats: int, aggregator_type: str = "mean", feat_drop: float = 0.0, bias: bool = True, ): super().__init__() - self.in_feats = in_feats - self.out_feats = out_feats - valid_aggr_types = {"max", "min", "mean", "sum"} - if aggregator_type not in valid_aggr_types: + + if aggregator_type not in self.valid_aggr_types: raise ValueError( - f"Invalid aggregator_type. Must be one of {valid_aggr_types}. " + f"Invalid aggregator_type. Must be one of {self.valid_aggr_types}. " f"But got '{aggregator_type}' instead." ) - self.aggr = aggregator_type + + self.aggregator_type = aggregator_type + self._aggr = aggregator_type + self.in_feats = in_feats + self.out_feats = out_feats + self.in_feats_src, self.in_feats_dst = dgl.utils.expand_as_pair(in_feats) self.feat_drop = nn.Dropout(feat_drop) - self.linear = nn.Linear(2 * in_feats, out_feats, bias=bias) + if self.aggregator_type == "gcn": + self._aggr = "mean" + self.lin = nn.Linear(self.in_feats_src, out_feats, bias=bias) + else: + self.lin = nn.Linear( + self.in_feats_src + self.in_feats_dst, out_feats, bias=bias + ) + + if self.aggregator_type == "pool": + self._aggr = "max" + self.pre_lin = nn.Linear(self.in_feats_src, self.in_feats_src) + else: + self.register_parameter("pre_lin", None) + + self.reset_parameters() def reset_parameters(self): r"""Reinitialize learnable parameters.""" - self.linear.reset_parameters() + self.lin.reset_parameters() + if self.pre_lin is not None: + self.pre_lin.reset_parameters() def forward( self, g: Union[SparseGraph, dgl.DGLHeteroGraph], - feat: torch.Tensor, + feat: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], max_in_degree: Optional[int] = None, ) -> torch.Tensor: r"""Forward computation. @@ -108,7 +120,7 @@ def forward( ---------- g : DGLGraph or SparseGraph The graph. - feat : torch.Tensor + feat : torch.Tensor or tuple Node features. Shape: :math:`(|V|, D_{in})`. max_in_degree : int Maximum in-degree of destination nodes. When :attr:`g` is generated @@ -121,36 +133,34 @@ def forward( torch.Tensor Output node features. Shape: :math:`(|V|, D_{out})`. """ - if max_in_degree is None: - max_in_degree = -1 - - if isinstance(g, SparseGraph): - assert "csc" in g.formats() - offsets, indices = g.csc() - _graph = ops_torch.CSC( - offsets=offsets, - indices=indices, - num_src_nodes=g.num_src_nodes(), - dst_max_in_degree=max_in_degree, - ) - elif isinstance(g, dgl.DGLHeteroGraph): - offsets, indices, _ = g.adj_tensors("csc") - _graph = ops_torch.CSC( - offsets=offsets, - indices=indices, - num_src_nodes=g.num_src_nodes(), - dst_max_in_degree=max_in_degree, - ) - else: - raise TypeError( - f"The graph has to be either a 'SparseGraph' or " - f"'dgl.DGLHeteroGraph', but got '{type(g)}'." - ) + feat_bipartite = isinstance(feat, (list, tuple)) + graph_bipartite = feat_bipartite or self.aggregator_type == "pool" + + _graph = self.get_cugraph_ops_CSC( + g, is_bipartite=graph_bipartite, max_in_degree=max_in_degree + ) - feat = self.feat_drop(feat) - h = ops_torch.operators.agg_concat_n2n(feat, _graph, self.aggr)[ + if feat_bipartite: + feat = (self.feat_drop(feat[0]), self.feat_drop(feat[1])) + else: + feat = self.feat_drop(feat) + + if self.aggregator_type == "pool": + if feat_bipartite: + feat = (self.pre_lin(feat[0]).relu(), feat[1]) + else: + feat = (self.pre_lin(feat).relu(), feat[: g.num_dst_nodes()]) + # force ctx.needs_input_grad=True in cugraph-ops autograd function + feat[0].requires_grad_() + feat[1].requires_grad_() + + out = ops_torch.operators.agg_concat_n2n(feat, _graph, self._aggr)[ : g.num_dst_nodes() ] - h = self.linear(h) - return h + if self.aggregator_type == "gcn": + out = out[:, : self.in_feats_src] + + out = self.lin(out) + + return out diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/transformerconv.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/transformerconv.py index 5cd5fbbaebe..8481b9ee265 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/transformerconv.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/transformerconv.py @@ -10,9 +10,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + from typing import Optional, Tuple, Union -from cugraph_dgl.nn.conv.base import BaseConv +from cugraph_dgl.nn.conv.base import BaseConv, SparseGraph from cugraph.utilities.utils import import_optional dgl = import_optional("dgl") @@ -114,7 +115,7 @@ def reset_parameters(self): def forward( self, - g: dgl.DGLHeteroGraph, + g: Union[SparseGraph, dgl.DGLHeteroGraph], nfeat: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], efeat: Optional[torch.Tensor] = None, ) -> torch.Tensor: @@ -130,17 +131,12 @@ def forward( efeat: torch.Tensor, optional Edge feature tensor. Default: ``None``. """ - offsets, indices, _ = g.adj_tensors("csc") - graph = ops_torch.CSC( - offsets=offsets, - indices=indices, - num_src_nodes=g.num_src_nodes(), - is_bipartite=True, - ) - - if isinstance(nfeat, torch.Tensor): + feat_bipartite = isinstance(nfeat, (list, tuple)) + if not feat_bipartite: nfeat = (nfeat, nfeat) + _graph = self.get_cugraph_ops_CSC(g, is_bipartite=True) + query = self.lin_query(nfeat[1][: g.num_dst_nodes()]) key = self.lin_key(nfeat[0]) value = self.lin_value(nfeat[0]) @@ -157,7 +153,7 @@ def forward( key_emb=key, query_emb=query, value_emb=value, - graph=graph, + graph=_graph, num_heads=self.num_heads, concat_heads=self.concat, edge_emb=efeat, diff --git a/python/cugraph-dgl/tests/conftest.py b/python/cugraph-dgl/tests/conftest.py index 6f8690d1140..a3863ed81fa 100644 --- a/python/cugraph-dgl/tests/conftest.py +++ b/python/cugraph-dgl/tests/conftest.py @@ -40,16 +40,19 @@ class SparseGraphData1: nnz = 6 src_ids = torch.IntTensor([0, 1, 2, 3, 2, 5]).cuda() dst_ids = torch.IntTensor([1, 2, 3, 4, 0, 3]).cuda() + values = torch.IntTensor([10, 20, 30, 40, 50, 60]).cuda() # CSR src_ids_sorted_by_src = torch.IntTensor([0, 1, 2, 2, 3, 5]).cuda() dst_ids_sorted_by_src = torch.IntTensor([1, 2, 0, 3, 4, 3]).cuda() csrc_ids = torch.IntTensor([0, 1, 2, 4, 5, 5, 6]).cuda() + values_csr = torch.IntTensor([10, 20, 50, 30, 40, 60]).cuda() # CSC src_ids_sorted_by_dst = torch.IntTensor([2, 0, 1, 5, 2, 3]).cuda() dst_ids_sorted_by_dst = torch.IntTensor([0, 1, 2, 3, 3, 4]).cuda() cdst_ids = torch.IntTensor([0, 1, 2, 3, 5, 6]).cuda() + values_csc = torch.IntTensor([50, 10, 20, 60, 30, 40]).cuda() @pytest.fixture diff --git a/python/cugraph-dgl/tests/nn/test_gatconv.py b/python/cugraph-dgl/tests/nn/test_gatconv.py index 7ed65645a28..ef3047dc2cd 100644 --- a/python/cugraph-dgl/tests/nn/test_gatconv.py +++ b/python/cugraph-dgl/tests/nn/test_gatconv.py @@ -10,69 +10,84 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# pylint: disable=too-many-arguments, too-many-locals import pytest -try: - import cugraph_dgl -except ModuleNotFoundError: - pytest.skip("cugraph_dgl not available", allow_module_level=True) - -from cugraph.utilities.utils import import_optional +from cugraph_dgl.nn.conv.base import SparseGraph +from cugraph_dgl.nn import GATConv as CuGraphGATConv from .common import create_graph1 -torch = import_optional("torch") -dgl = import_optional("dgl") +dgl = pytest.importorskip("dgl", reason="DGL not available") +torch = pytest.importorskip("torch", reason="PyTorch not available") + +ATOL = 1e-6 @pytest.mark.parametrize("bipartite", [False, True]) @pytest.mark.parametrize("idtype_int", [False, True]) @pytest.mark.parametrize("max_in_degree", [None, 8]) @pytest.mark.parametrize("num_heads", [1, 2, 7]) +@pytest.mark.parametrize("residual", [False, True]) @pytest.mark.parametrize("to_block", [False, True]) -def test_gatconv_equality(bipartite, idtype_int, max_in_degree, num_heads, to_block): - GATConv = dgl.nn.GATConv - CuGraphGATConv = cugraph_dgl.nn.GATConv - device = "cuda" - g = create_graph1().to(device) +@pytest.mark.parametrize("sparse_format", ["coo", "csc", None]) +def test_gatconv_equality( + bipartite, idtype_int, max_in_degree, num_heads, residual, to_block, sparse_format +): + from dgl.nn.pytorch import GATConv + + g = create_graph1().to("cuda") if idtype_int: g = g.int() - if to_block: g = dgl.to_block(g) + size = (g.num_src_nodes(), g.num_dst_nodes()) + if bipartite: in_feats = (10, 3) nfeat = ( - torch.rand(g.num_src_nodes(), in_feats[0], device=device), - torch.rand(g.num_dst_nodes(), in_feats[1], device=device), + torch.rand(g.num_src_nodes(), in_feats[0]).cuda(), + torch.rand(g.num_dst_nodes(), in_feats[1]).cuda(), ) else: in_feats = 10 - nfeat = torch.rand(g.num_src_nodes(), in_feats, device=device) + nfeat = torch.rand(g.num_src_nodes(), in_feats).cuda() out_feats = 2 + if sparse_format == "coo": + sg = SparseGraph( + size=size, src_ids=g.edges()[0], dst_ids=g.edges()[1], formats="csc" + ) + elif sparse_format == "csc": + offsets, indices, _ = g.adj_tensors("csc") + sg = SparseGraph(size=size, src_ids=indices, cdst_ids=offsets, formats="csc") + args = (in_feats, out_feats, num_heads) - kwargs = {"bias": False} + kwargs = {"bias": False, "allow_zero_in_degree": True} - conv1 = GATConv(*args, **kwargs, allow_zero_in_degree=True).to(device) + conv1 = GATConv(*args, **kwargs).cuda() out1 = conv1(g, nfeat) - conv2 = CuGraphGATConv(*args, **kwargs).to(device) + conv2 = CuGraphGATConv(*args, **kwargs).cuda() dim = num_heads * out_feats with torch.no_grad(): conv2.attn_weights.data[:dim] = conv1.attn_l.data.flatten() conv2.attn_weights.data[dim:] = conv1.attn_r.data.flatten() if bipartite: - conv2.fc_src.weight.data = conv1.fc_src.weight.data.detach().clone() - conv2.fc_dst.weight.data = conv1.fc_dst.weight.data.detach().clone() + conv2.lin_src.weight.data = conv1.fc_src.weight.data.detach().clone() + conv2.lin_dst.weight.data = conv1.fc_dst.weight.data.detach().clone() else: - conv2.fc.weight.data = conv1.fc.weight.data.detach().clone() - out2 = conv2(g, nfeat, max_in_degree=max_in_degree) + conv2.lin.weight.data = conv1.fc.weight.data.detach().clone() + if residual and conv2.residual: + conv2.lin_res.weight.data = conv1.fc_res.weight.data.detach().clone() - assert torch.allclose(out1, out2, atol=1e-6) + if sparse_format is not None: + out2 = conv2(sg, nfeat, max_in_degree=max_in_degree) + else: + out2 = conv2(g, nfeat, max_in_degree=max_in_degree) + + assert torch.allclose(out1, out2, atol=ATOL) grad_out1 = torch.rand_like(out1) grad_out2 = grad_out1.clone().detach() @@ -81,18 +96,18 @@ def test_gatconv_equality(bipartite, idtype_int, max_in_degree, num_heads, to_bl if bipartite: assert torch.allclose( - conv1.fc_src.weight.grad, conv2.fc_src.weight.grad, atol=1e-6 + conv1.fc_src.weight.grad, conv2.lin_src.weight.grad, atol=ATOL ) assert torch.allclose( - conv1.fc_dst.weight.grad, conv2.fc_dst.weight.grad, atol=1e-6 + conv1.fc_dst.weight.grad, conv2.lin_dst.weight.grad, atol=ATOL ) else: - assert torch.allclose(conv1.fc.weight.grad, conv2.fc.weight.grad, atol=1e-6) + assert torch.allclose(conv1.fc.weight.grad, conv2.lin.weight.grad, atol=ATOL) assert torch.allclose( torch.cat((conv1.attn_l.grad, conv1.attn_r.grad), dim=0), conv2.attn_weights.grad.view(2, num_heads, out_feats), - atol=1e-6, + atol=ATOL, ) @@ -106,10 +121,7 @@ def test_gatconv_equality(bipartite, idtype_int, max_in_degree, num_heads, to_bl def test_gatconv_edge_feats( bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats ): - from cugraph_dgl.nn import GATConv - - device = "cuda" - g = create_graph1().to(device) + g = create_graph1().to("cuda") if to_block: g = dgl.to_block(g) @@ -117,24 +129,30 @@ def test_gatconv_edge_feats( if bipartite: in_feats = (10, 3) nfeat = ( - torch.rand(g.num_src_nodes(), in_feats[0], device=device), - torch.rand(g.num_dst_nodes(), in_feats[1], device=device), + torch.rand(g.num_src_nodes(), in_feats[0]).cuda(), + torch.rand(g.num_dst_nodes(), in_feats[1]).cuda(), ) else: in_feats = 10 - nfeat = torch.rand(g.num_src_nodes(), in_feats, device=device) + nfeat = torch.rand(g.num_src_nodes(), in_feats).cuda() out_feats = 2 if use_edge_feats: edge_feats = 3 - efeat = torch.rand(g.num_edges(), edge_feats, device=device) + efeat = torch.rand(g.num_edges(), edge_feats).cuda() else: edge_feats = None efeat = None - conv = GATConv( - in_feats, out_feats, num_heads, concat=concat, edge_feats=edge_feats, bias=bias - ).to(device) + conv = CuGraphGATConv( + in_feats, + out_feats, + num_heads, + concat=concat, + edge_feats=edge_feats, + bias=bias, + allow_zero_in_degree=True, + ).cuda() out = conv(g, nfeat, efeat=efeat, max_in_degree=max_in_degree) grad_out = torch.rand_like(out) diff --git a/python/cugraph-dgl/tests/nn/test_gatv2conv.py b/python/cugraph-dgl/tests/nn/test_gatv2conv.py new file mode 100644 index 00000000000..cc46a6e4b39 --- /dev/null +++ b/python/cugraph-dgl/tests/nn/test_gatv2conv.py @@ -0,0 +1,147 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest + +from cugraph_dgl.nn.conv.base import SparseGraph +from cugraph_dgl.nn import GATv2Conv as CuGraphGATv2Conv +from .common import create_graph1 + +dgl = pytest.importorskip("dgl", reason="DGL not available") +torch = pytest.importorskip("torch", reason="PyTorch not available") + +ATOL = 1e-6 + + +@pytest.mark.parametrize("bipartite", [False, True]) +@pytest.mark.parametrize("idtype_int", [False, True]) +@pytest.mark.parametrize("max_in_degree", [None, 8]) +@pytest.mark.parametrize("num_heads", [1, 2, 7]) +@pytest.mark.parametrize("residual", [False, True]) +@pytest.mark.parametrize("to_block", [False, True]) +@pytest.mark.parametrize("sparse_format", ["coo", "csc", None]) +def test_gatv2conv_equality( + bipartite, idtype_int, max_in_degree, num_heads, residual, to_block, sparse_format +): + from dgl.nn.pytorch import GATv2Conv + + g = create_graph1().to("cuda") + + if idtype_int: + g = g.int() + if to_block: + g = dgl.to_block(g) + + size = (g.num_src_nodes(), g.num_dst_nodes()) + + if bipartite: + in_feats = (10, 3) + nfeat = ( + torch.rand(g.num_src_nodes(), in_feats[0]).cuda(), + torch.rand(g.num_dst_nodes(), in_feats[1]).cuda(), + ) + else: + in_feats = 10 + nfeat = torch.rand(g.num_src_nodes(), in_feats).cuda() + out_feats = 2 + + if sparse_format == "coo": + sg = SparseGraph( + size=size, src_ids=g.edges()[0], dst_ids=g.edges()[1], formats="csc" + ) + elif sparse_format == "csc": + offsets, indices, _ = g.adj_tensors("csc") + sg = SparseGraph(size=size, src_ids=indices, cdst_ids=offsets, formats="csc") + + args = (in_feats, out_feats, num_heads) + kwargs = {"bias": False, "allow_zero_in_degree": True} + + conv1 = GATv2Conv(*args, **kwargs).cuda() + out1 = conv1(g, nfeat) + + conv2 = CuGraphGATv2Conv(*args, **kwargs).cuda() + with torch.no_grad(): + conv2.attn.data = conv1.attn.data.flatten() + conv2.lin_src.weight.data = conv1.fc_src.weight.data.detach().clone() + conv2.lin_dst.weight.data = conv1.fc_dst.weight.data.detach().clone() + if residual and conv2.residual: + conv2.lin_res.weight.data = conv1.fc_res.weight.data.detach().clone() + + if sparse_format is not None: + out2 = conv2(sg, nfeat, max_in_degree=max_in_degree) + else: + out2 = conv2(g, nfeat, max_in_degree=max_in_degree) + + assert torch.allclose(out1, out2, atol=ATOL) + + grad_out1 = torch.rand_like(out1) + grad_out2 = grad_out1.clone().detach() + out1.backward(grad_out1) + out2.backward(grad_out2) + + assert torch.allclose( + conv1.fc_src.weight.grad, conv2.lin_src.weight.grad, atol=ATOL + ) + assert torch.allclose( + conv1.fc_dst.weight.grad, conv2.lin_dst.weight.grad, atol=ATOL + ) + + assert torch.allclose(conv1.attn.grad, conv1.attn.grad, atol=ATOL) + + +@pytest.mark.parametrize("bias", [False, True]) +@pytest.mark.parametrize("bipartite", [False, True]) +@pytest.mark.parametrize("concat", [False, True]) +@pytest.mark.parametrize("max_in_degree", [None, 8, 800]) +@pytest.mark.parametrize("num_heads", [1, 2, 7]) +@pytest.mark.parametrize("to_block", [False, True]) +@pytest.mark.parametrize("use_edge_feats", [False, True]) +def test_gatv2conv_edge_feats( + bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats +): + g = create_graph1().to("cuda") + + if to_block: + g = dgl.to_block(g) + + if bipartite: + in_feats = (10, 3) + nfeat = ( + torch.rand(g.num_src_nodes(), in_feats[0]).cuda(), + torch.rand(g.num_dst_nodes(), in_feats[1]).cuda(), + ) + else: + in_feats = 10 + nfeat = torch.rand(g.num_src_nodes(), in_feats).cuda() + out_feats = 2 + + if use_edge_feats: + edge_feats = 3 + efeat = torch.rand(g.num_edges(), edge_feats).cuda() + else: + edge_feats = None + efeat = None + + conv = CuGraphGATv2Conv( + in_feats, + out_feats, + num_heads, + concat=concat, + edge_feats=edge_feats, + bias=bias, + allow_zero_in_degree=True, + ).cuda() + out = conv(g, nfeat, efeat=efeat, max_in_degree=max_in_degree) + + grad_out = torch.rand_like(out) + out.backward(grad_out) diff --git a/python/cugraph-dgl/tests/nn/test_relgraphconv.py b/python/cugraph-dgl/tests/nn/test_relgraphconv.py index d2ae6a23978..901f9ba1433 100644 --- a/python/cugraph-dgl/tests/nn/test_relgraphconv.py +++ b/python/cugraph-dgl/tests/nn/test_relgraphconv.py @@ -10,20 +10,17 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# pylint: disable=too-many-arguments, too-many-locals import pytest -try: - import cugraph_dgl -except ModuleNotFoundError: - pytest.skip("cugraph_dgl not available", allow_module_level=True) - -from cugraph.utilities.utils import import_optional +from cugraph_dgl.nn.conv.base import SparseGraph +from cugraph_dgl.nn import RelGraphConv as CuGraphRelGraphConv from .common import create_graph1 -torch = import_optional("torch") -dgl = import_optional("dgl") +dgl = pytest.importorskip("dgl", reason="DGL not available") +torch = pytest.importorskip("torch", reason="PyTorch not available") + +ATOL = 1e-6 @pytest.mark.parametrize("idtype_int", [False, True]) @@ -32,12 +29,17 @@ @pytest.mark.parametrize("regularizer", [None, "basis"]) @pytest.mark.parametrize("self_loop", [False, True]) @pytest.mark.parametrize("to_block", [False, True]) +@pytest.mark.parametrize("sparse_format", ["coo", "csc", None]) def test_relgraphconv_equality( - idtype_int, max_in_degree, num_bases, regularizer, self_loop, to_block + idtype_int, + max_in_degree, + num_bases, + regularizer, + self_loop, + to_block, + sparse_format, ): - RelGraphConv = dgl.nn.RelGraphConv - CuGraphRelGraphConv = cugraph_dgl.nn.RelGraphConv - device = "cuda" + from dgl.nn.pytorch import RelGraphConv in_feat, out_feat, num_rels = 10, 2, 3 args = (in_feat, out_feat, num_rels) @@ -47,34 +49,57 @@ def test_relgraphconv_equality( "bias": False, "self_loop": self_loop, } - g = create_graph1().to(device) - g.edata[dgl.ETYPE] = torch.randint(num_rels, (g.num_edges(),)).to(device) + g = create_graph1().to("cuda") + g.edata[dgl.ETYPE] = torch.randint(num_rels, (g.num_edges(),)).cuda() + if idtype_int: g = g.int() if to_block: g = dgl.to_block(g) - feat = torch.rand(g.num_src_nodes(), in_feat).to(device) + + size = (g.num_src_nodes(), g.num_dst_nodes()) + feat = torch.rand(g.num_src_nodes(), in_feat).cuda() + + if sparse_format == "coo": + sg = SparseGraph( + size=size, + src_ids=g.edges()[0], + dst_ids=g.edges()[1], + values=g.edata[dgl.ETYPE], + formats="csc", + ) + elif sparse_format == "csc": + offsets, indices, perm = g.adj_tensors("csc") + etypes = g.edata[dgl.ETYPE][perm] + sg = SparseGraph( + size=size, src_ids=indices, cdst_ids=offsets, values=etypes, formats="csc" + ) torch.manual_seed(0) - conv1 = RelGraphConv(*args, **kwargs).to(device) + conv1 = RelGraphConv(*args, **kwargs).cuda() torch.manual_seed(0) kwargs["apply_norm"] = False - conv2 = CuGraphRelGraphConv(*args, **kwargs).to(device) + conv2 = CuGraphRelGraphConv(*args, **kwargs).cuda() out1 = conv1(g, feat, g.edata[dgl.ETYPE]) - out2 = conv2(g, feat, g.edata[dgl.ETYPE], max_in_degree=max_in_degree) - assert torch.allclose(out1, out2, atol=1e-06) + + if sparse_format is not None: + out2 = conv2(sg, feat, sg.values(), max_in_degree=max_in_degree) + else: + out2 = conv2(g, feat, g.edata[dgl.ETYPE], max_in_degree=max_in_degree) + + assert torch.allclose(out1, out2, atol=ATOL) grad_out = torch.rand_like(out1) out1.backward(grad_out) out2.backward(grad_out) end = -1 if self_loop else None - assert torch.allclose(conv1.linear_r.W.grad, conv2.W.grad[:end], atol=1e-6) + assert torch.allclose(conv1.linear_r.W.grad, conv2.W.grad[:end], atol=ATOL) if self_loop: - assert torch.allclose(conv1.loop_weight.grad, conv2.W.grad[-1], atol=1e-6) + assert torch.allclose(conv1.loop_weight.grad, conv2.W.grad[-1], atol=ATOL) if regularizer is not None: - assert torch.allclose(conv1.linear_r.coeff.grad, conv2.coeff.grad, atol=1e-6) + assert torch.allclose(conv1.linear_r.coeff.grad, conv2.coeff.grad, atol=ATOL) diff --git a/python/cugraph-dgl/tests/nn/test_sageconv.py b/python/cugraph-dgl/tests/nn/test_sageconv.py index 447bbe49460..e2acf9e6596 100644 --- a/python/cugraph-dgl/tests/nn/test_sageconv.py +++ b/python/cugraph-dgl/tests/nn/test_sageconv.py @@ -10,31 +10,33 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# pylint: disable=too-many-arguments, too-many-locals import pytest -from cugraph.utilities.utils import import_optional from cugraph_dgl.nn.conv.base import SparseGraph from cugraph_dgl.nn import SAGEConv as CuGraphSAGEConv from .common import create_graph1 -torch = import_optional("torch") -dgl = import_optional("dgl") +dgl = pytest.importorskip("dgl", reason="DGL not available") +torch = pytest.importorskip("torch", reason="PyTorch not available") +ATOL = 1e-6 + +@pytest.mark.parametrize("aggr", ["mean", "pool"]) @pytest.mark.parametrize("bias", [False, True]) +@pytest.mark.parametrize("bipartite", [False, True]) @pytest.mark.parametrize("idtype_int", [False, True]) @pytest.mark.parametrize("max_in_degree", [None, 8]) @pytest.mark.parametrize("to_block", [False, True]) @pytest.mark.parametrize("sparse_format", ["coo", "csc", None]) -def test_SAGEConv_equality(bias, idtype_int, max_in_degree, to_block, sparse_format): - SAGEConv = dgl.nn.SAGEConv - device = "cuda" +def test_sageconv_equality( + aggr, bias, bipartite, idtype_int, max_in_degree, to_block, sparse_format +): + from dgl.nn.pytorch import SAGEConv - in_feat, out_feat = 5, 2 - kwargs = {"aggregator_type": "mean", "bias": bias} - g = create_graph1().to(device) + kwargs = {"aggregator_type": aggr, "bias": bias} + g = create_graph1().to("cuda") if idtype_int: g = g.int() @@ -42,7 +44,17 @@ def test_SAGEConv_equality(bias, idtype_int, max_in_degree, to_block, sparse_for g = dgl.to_block(g) size = (g.num_src_nodes(), g.num_dst_nodes()) - feat = torch.rand(g.num_src_nodes(), in_feat).to(device) + + if bipartite: + in_feats = (5, 3) + feat = ( + torch.rand(size[0], in_feats[0], requires_grad=True).cuda(), + torch.rand(size[1], in_feats[1], requires_grad=True).cuda(), + ) + else: + in_feats = 5 + feat = torch.rand(size[0], in_feats).cuda() + out_feats = 2 if sparse_format == "coo": sg = SparseGraph( @@ -52,39 +64,38 @@ def test_SAGEConv_equality(bias, idtype_int, max_in_degree, to_block, sparse_for offsets, indices, _ = g.adj_tensors("csc") sg = SparseGraph(size=size, src_ids=indices, cdst_ids=offsets, formats="csc") - torch.manual_seed(0) - conv1 = SAGEConv(in_feat, out_feat, **kwargs).to(device) - - torch.manual_seed(0) - conv2 = CuGraphSAGEConv(in_feat, out_feat, **kwargs).to(device) + conv1 = SAGEConv(in_feats, out_feats, **kwargs).cuda() + conv2 = CuGraphSAGEConv(in_feats, out_feats, **kwargs).cuda() + in_feats_src = conv2.in_feats_src with torch.no_grad(): - conv2.linear.weight.data[:, :in_feat] = conv1.fc_neigh.weight.data - conv2.linear.weight.data[:, in_feat:] = conv1.fc_self.weight.data + conv2.lin.weight.data[:, :in_feats_src] = conv1.fc_neigh.weight.data + conv2.lin.weight.data[:, in_feats_src:] = conv1.fc_self.weight.data if bias: - conv2.linear.bias.data[:] = conv1.fc_self.bias.data + conv2.lin.bias.data[:] = conv1.fc_self.bias.data + if aggr == "pool": + conv2.pre_lin.weight.data[:] = conv1.fc_pool.weight.data + conv2.pre_lin.bias.data[:] = conv1.fc_pool.bias.data out1 = conv1(g, feat) if sparse_format is not None: out2 = conv2(sg, feat, max_in_degree=max_in_degree) else: out2 = conv2(g, feat, max_in_degree=max_in_degree) - assert torch.allclose(out1, out2, atol=1e-06) + assert torch.allclose(out1, out2, atol=ATOL) grad_out = torch.rand_like(out1) out1.backward(grad_out) out2.backward(grad_out) assert torch.allclose( conv1.fc_neigh.weight.grad, - conv2.linear.weight.grad[:, :in_feat], - atol=1e-6, + conv2.lin.weight.grad[:, :in_feats_src], + atol=ATOL, ) assert torch.allclose( conv1.fc_self.weight.grad, - conv2.linear.weight.grad[:, in_feat:], - atol=1e-6, + conv2.lin.weight.grad[:, in_feats_src:], + atol=ATOL, ) if bias: - assert torch.allclose( - conv1.fc_self.bias.grad, conv2.linear.bias.grad, atol=1e-6 - ) + assert torch.allclose(conv1.fc_self.bias.grad, conv2.lin.bias.grad, atol=ATOL) diff --git a/python/cugraph-dgl/tests/nn/test_sparsegraph.py b/python/cugraph-dgl/tests/nn/test_sparsegraph.py index 3fb01575d66..09c0df202ff 100644 --- a/python/cugraph-dgl/tests/nn/test_sparsegraph.py +++ b/python/cugraph-dgl/tests/nn/test_sparsegraph.py @@ -19,32 +19,42 @@ def test_coo2csc(sparse_graph_1): data = sparse_graph_1 - values = torch.ones(data.nnz).cuda() + g = SparseGraph( - size=data.size, src_ids=data.src_ids, dst_ids=data.dst_ids, formats="csc" + size=data.size, + src_ids=data.src_ids, + dst_ids=data.dst_ids, + values=data.values, + formats=["csc"], ) - cdst_ids, src_ids = g.csc() + cdst_ids, src_ids, values = g.csc() new = torch.sparse_csc_tensor(cdst_ids, src_ids, values).cuda() old = torch.sparse_coo_tensor( - torch.vstack((data.src_ids, data.dst_ids)), values + torch.vstack((data.src_ids, data.dst_ids)), data.values ).cuda() torch.allclose(new.to_dense(), old.to_dense()) -def test_csc2coo(sparse_graph_1): +def test_csc_input(sparse_graph_1): data = sparse_graph_1 - values = torch.ones(data.nnz).cuda() + g = SparseGraph( size=data.size, src_ids=data.src_ids_sorted_by_dst, cdst_ids=data.cdst_ids, - formats="coo", + values=data.values_csc, + formats=["coo", "csc", "csr"], ) - src_ids, dst_ids = g.coo() + src_ids, dst_ids, values = g.coo() new = torch.sparse_coo_tensor(torch.vstack((src_ids, dst_ids)), values).cuda() old = torch.sparse_csc_tensor( - data.cdst_ids, data.src_ids_sorted_by_dst, values + data.cdst_ids, data.src_ids_sorted_by_dst, data.values_csc ).cuda() torch.allclose(new.to_dense(), old.to_dense()) + + csrc_ids, dst_ids, values = g.csr() + + new = torch.sparse_csr_tensor(csrc_ids, dst_ids, values).cuda() + torch.allclose(new.to_dense(), old.to_dense()) diff --git a/python/cugraph-dgl/tests/nn/test_transformerconv.py b/python/cugraph-dgl/tests/nn/test_transformerconv.py index 00476b9f0bb..b2b69cb35ab 100644 --- a/python/cugraph-dgl/tests/nn/test_transformerconv.py +++ b/python/cugraph-dgl/tests/nn/test_transformerconv.py @@ -13,16 +13,14 @@ import pytest -try: - from cugraph_dgl.nn import TransformerConv -except ModuleNotFoundError: - pytest.skip("cugraph_dgl not available", allow_module_level=True) - -from cugraph.utilities.utils import import_optional +from cugraph_dgl.nn.conv.base import SparseGraph +from cugraph_dgl.nn import TransformerConv from .common import create_graph1 -torch = import_optional("torch") -dgl = import_optional("dgl") +dgl = pytest.importorskip("dgl", reason="DGL not available") +torch = pytest.importorskip("torch", reason="PyTorch not available") + +ATOL = 1e-6 @pytest.mark.parametrize("beta", [False, True]) @@ -32,8 +30,16 @@ @pytest.mark.parametrize("num_heads", [1, 2, 3, 4]) @pytest.mark.parametrize("to_block", [False, True]) @pytest.mark.parametrize("use_edge_feats", [False, True]) -def test_TransformerConv( - beta, bipartite_node_feats, concat, idtype_int, num_heads, to_block, use_edge_feats +@pytest.mark.parametrize("sparse_format", ["coo", "csc", None]) +def test_transformerconv( + beta, + bipartite_node_feats, + concat, + idtype_int, + num_heads, + to_block, + use_edge_feats, + sparse_format, ): device = "cuda" g = create_graph1().to(device) @@ -44,6 +50,15 @@ def test_TransformerConv( if to_block: g = dgl.to_block(g) + size = (g.num_src_nodes(), g.num_dst_nodes()) + if sparse_format == "coo": + sg = SparseGraph( + size=size, src_ids=g.edges()[0], dst_ids=g.edges()[1], formats="csc" + ) + elif sparse_format == "csc": + offsets, indices, _ = g.adj_tensors("csc") + sg = SparseGraph(size=size, src_ids=indices, cdst_ids=offsets, formats="csc") + if bipartite_node_feats: in_node_feats = (5, 3) nfeat = ( @@ -71,6 +86,10 @@ def test_TransformerConv( edge_feats=edge_feats, ).to(device) - out = conv(g, nfeat, efeat) + if sparse_format is not None: + out = conv(sg, nfeat, efeat) + else: + out = conv(g, nfeat, efeat) + grad_out = torch.rand_like(out) out.backward(grad_out) diff --git a/python/cugraph-dgl/tests/test_dataset.py b/python/cugraph-dgl/tests/test_dataset.py index 69d50261e55..5db443dc0d8 100644 --- a/python/cugraph-dgl/tests/test_dataset.py +++ b/python/cugraph-dgl/tests/test_dataset.py @@ -123,6 +123,6 @@ def test_homogeneous_sampled_graphs_from_dataframe(return_type, seed_node): assert dgl_block.num_src_nodes() == cugraph_dgl_graph.num_src_nodes() assert dgl_block.num_dst_nodes() == cugraph_dgl_graph.num_dst_nodes() dgl_offsets, dgl_indices, _ = dgl_block.adj_tensors("csc") - cugraph_offsets, cugraph_indices = cugraph_dgl_graph.csc() + cugraph_offsets, cugraph_indices, _ = cugraph_dgl_graph.csc() assert torch.equal(dgl_offsets.to("cpu"), cugraph_offsets.to("cpu")) assert torch.equal(dgl_indices.to("cpu"), cugraph_indices.to("cpu")) diff --git a/python/cugraph-dgl/tests/test_from_dgl_hetrograph.py b/python/cugraph-dgl/tests/test_from_dgl_heterograph.py similarity index 100% rename from python/cugraph-dgl/tests/test_from_dgl_hetrograph.py rename to python/cugraph-dgl/tests/test_from_dgl_heterograph.py From ed7b1a41fe502c9097c4ac9688f08c1d1e5fd33f Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Tue, 19 Sep 2023 13:48:52 -0400 Subject: [PATCH 002/111] New mtmg API for integration (#3521) Creating a new API for integrating multi-threaded multi-GPU programs into the cugraph library. This API will extend our OPG (one [process] per GPU) model to support a single process handling multiple GPUs, and will also ultimately support a multi-node configuration where some compute nodes might not have GPUs. closes https://github.com/rapidsai/graph_dl/issues/241 Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/3521 --- cpp/CMakeLists.txt | 2 + cpp/cmake/thirdparty/get_ucp.cmake | 35 ++ .../mtmg/detail/device_shared_device_span.hpp | 39 ++ .../detail/device_shared_device_vector.hpp | 58 +++ .../mtmg/detail/device_shared_wrapper.hpp | 123 +++++ .../mtmg/detail/per_device_edgelist.hpp | 275 +++++++++++ cpp/include/cugraph/mtmg/edge_property.hpp | 60 +++ .../cugraph/mtmg/edge_property_view.hpp | 33 ++ cpp/include/cugraph/mtmg/edgelist.hpp | 65 +++ cpp/include/cugraph/mtmg/graph.hpp | 136 ++++++ cpp/include/cugraph/mtmg/graph_view.hpp | 34 ++ cpp/include/cugraph/mtmg/handle.hpp | 111 +++++ cpp/include/cugraph/mtmg/instance_manager.hpp | 98 ++++ .../cugraph/mtmg/per_thread_edgelist.hpp | 174 +++++++ cpp/include/cugraph/mtmg/renumber_map.hpp | 40 ++ .../cugraph/mtmg/renumber_map_view.hpp | 32 ++ cpp/include/cugraph/mtmg/resource_manager.hpp | 225 +++++++++ cpp/include/cugraph/mtmg/vertex_result.hpp | 40 ++ .../cugraph/mtmg/vertex_result_view.hpp | 49 ++ cpp/src/link_analysis/pagerank_impl.cuh | 8 +- cpp/src/mtmg/vertex_result.cu | 167 +++++++ cpp/tests/CMakeLists.txt | 8 + cpp/tests/mtmg/threaded_test.cu | 459 ++++++++++++++++++ 23 files changed, 2268 insertions(+), 3 deletions(-) create mode 100644 cpp/cmake/thirdparty/get_ucp.cmake create mode 100644 cpp/include/cugraph/mtmg/detail/device_shared_device_span.hpp create mode 100644 cpp/include/cugraph/mtmg/detail/device_shared_device_vector.hpp create mode 100644 cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp create mode 100644 cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp create mode 100644 cpp/include/cugraph/mtmg/edge_property.hpp create mode 100644 cpp/include/cugraph/mtmg/edge_property_view.hpp create mode 100644 cpp/include/cugraph/mtmg/edgelist.hpp create mode 100644 cpp/include/cugraph/mtmg/graph.hpp create mode 100644 cpp/include/cugraph/mtmg/graph_view.hpp create mode 100644 cpp/include/cugraph/mtmg/handle.hpp create mode 100644 cpp/include/cugraph/mtmg/instance_manager.hpp create mode 100644 cpp/include/cugraph/mtmg/per_thread_edgelist.hpp create mode 100644 cpp/include/cugraph/mtmg/renumber_map.hpp create mode 100644 cpp/include/cugraph/mtmg/renumber_map_view.hpp create mode 100644 cpp/include/cugraph/mtmg/resource_manager.hpp create mode 100644 cpp/include/cugraph/mtmg/vertex_result.hpp create mode 100644 cpp/include/cugraph/mtmg/vertex_result_view.hpp create mode 100644 cpp/src/mtmg/vertex_result.cu create mode 100644 cpp/tests/mtmg/threaded_test.cu diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 69a488de0b8..a6c26ee3b91 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -166,6 +166,7 @@ endif() include(cmake/thirdparty/get_nccl.cmake) include(cmake/thirdparty/get_cuhornet.cmake) +include(cmake/thirdparty/get_ucp.cmake) if(BUILD_TESTS) include(cmake/thirdparty/get_gtest.cmake) @@ -292,6 +293,7 @@ set(CUGRAPH_SOURCES src/community/triangle_count_mg.cu src/traversal/k_hop_nbrs_sg.cu src/traversal/k_hop_nbrs_mg.cu + src/mtmg/vertex_result.cu ) if(USE_CUGRAPH_OPS) diff --git a/cpp/cmake/thirdparty/get_ucp.cmake b/cpp/cmake/thirdparty/get_ucp.cmake new file mode 100644 index 00000000000..dcc4956a34e --- /dev/null +++ b/cpp/cmake/thirdparty/get_ucp.cmake @@ -0,0 +1,35 @@ +#============================================================================= +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +#============================================================================= + +function(find_and_configure_ucp) + + if(TARGET UCP::UCP) + return() + endif() + + rapids_find_generate_module(UCP + HEADER_NAMES ucp.h + LIBRARY_NAMES ucp + INCLUDE_SUFFIXES ucp/api + ) + + # Currently UCP has no CMake build-system so we require + # it built and installed on the machine already + rapids_find_package(UCP REQUIRED) + +endfunction() + +find_and_configure_ucp() diff --git a/cpp/include/cugraph/mtmg/detail/device_shared_device_span.hpp b/cpp/include/cugraph/mtmg/detail/device_shared_device_span.hpp new file mode 100644 index 00000000000..37398891370 --- /dev/null +++ b/cpp/include/cugraph/mtmg/detail/device_shared_device_span.hpp @@ -0,0 +1,39 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { +namespace detail { + +/** + * @brief Wrap an object to be available for each GPU + * + * In the MTMG environment we need the ability to manage a collection of objects + * that are associated with a particular GPU, and fetch the objects from an + * arbitrary GPU thread. This object will wrap any object and allow it to be + * accessed from different threads. + */ +template +using device_shared_device_span_t = device_shared_wrapper_t>; + +} // namespace detail +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/detail/device_shared_device_vector.hpp b/cpp/include/cugraph/mtmg/detail/device_shared_device_vector.hpp new file mode 100644 index 00000000000..7f3992b73bd --- /dev/null +++ b/cpp/include/cugraph/mtmg/detail/device_shared_device_vector.hpp @@ -0,0 +1,58 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { +namespace detail { + +/** + * @brief Wrap an object to be available for each GPU + * + * In the MTMG environment we need the ability to manage a collection of objects + * that are associated with a particular GPU, and fetch the objects from an + * arbitrary GPU thread. This object will wrap any object and allow it to be + * accessed from different threads. + */ +template +class device_shared_device_vector_t : public device_shared_wrapper_t> { + using parent_t = detail::device_shared_wrapper_t>; + + public: + /** + * @brief Create a device_shared_device_span (read only view) + */ + auto view() + { + std::lock_guard lock(parent_t::lock_); + + device_shared_device_span_t result; + + std::for_each(parent_t::objects_.begin(), parent_t::objects_.end(), [&result](auto& p) { + result.set(p.first, raft::device_span{p.second.data(), p.second.size()}); + }); + + return result; + } +}; + +} // namespace detail +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp new file mode 100644 index 00000000000..c4cacb401af --- /dev/null +++ b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp @@ -0,0 +1,123 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +#include +#include + +namespace cugraph { +namespace mtmg { +namespace detail { + +/** + * @brief Wrap an object to be available for each GPU + * + * In the MTMG environment we need the ability to manage a collection of objects + * that are associated with a particular GPU, and fetch the objects from an + * arbitrary GPU thread. This object will wrap any object and allow it to be + * accessed from different threads. + */ +template +class device_shared_wrapper_t { + public: + using wrapped_t = T; + + device_shared_wrapper_t() = default; + device_shared_wrapper_t(device_shared_wrapper_t&& other) : objects_{std::move(other.objects_)} {} + device_shared_wrapper_t& operator=(device_shared_wrapper_t&& other) + { + objects_ = std::move(other.objects_); + return *this; + } + + /** + * @brief Move a wrapped object into the wrapper for this thread + * + * @param handle Handle is used to identify the GPU we associated this object with + * @param obj Wrapped object + */ + void set(cugraph::mtmg::handle_t const& handle, wrapped_t&& obj) + { + std::lock_guard lock(lock_); + + auto pos = objects_.find(handle.get_local_rank()); + CUGRAPH_EXPECTS(pos == objects_.end(), "Cannot overwrite wrapped object"); + + objects_.insert(std::make_pair(handle.get_local_rank(), std::move(obj))); + } + + /** + * @brief Move a wrapped object into the wrapper for this thread + * + * @param local_rank Identify which GPU to associated this object with + * @param obj Wrapped object + */ + void set(int local_rank, wrapped_t&& obj) + { + std::lock_guard lock(lock_); + + auto pos = objects_.find(local_rank); + CUGRAPH_EXPECTS(pos == objects_.end(), "Cannot overwrite wrapped object"); + + objects_.insert(std::make_pair(local_rank, std::move(obj))); + } + + public: + /** + * @brief Get reference to an object for a particular thread + * + * @param handle Handle is used to identify the GPU we associated this object with + * @return Reference to the wrapped object + */ + wrapped_t& get(cugraph::mtmg::handle_t const& handle) + { + std::lock_guard lock(lock_); + + auto pos = objects_.find(handle.get_local_rank()); + CUGRAPH_EXPECTS(pos != objects_.end(), "Uninitialized wrapped object"); + + return pos->second; + } + + /** + * @brief Get the pointer to an object for a particular thread from this wrapper + * + * @param handle Handle is used to identify the GPU we associated this object with + * @return Shared pointer the wrapped object + */ + wrapped_t const& get(cugraph::mtmg::handle_t const& handle) const + { + std::lock_guard lock(lock_); + + auto pos = objects_.find(handle.get_local_rank()); + + CUGRAPH_EXPECTS(pos != objects_.end(), "Uninitialized wrapped object"); + + return pos->second; + } + + protected: + mutable std::mutex lock_{}; + std::map objects_{}; +}; + +} // namespace detail +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp b/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp new file mode 100644 index 00000000000..8011146ee4f --- /dev/null +++ b/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp @@ -0,0 +1,275 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +// FIXME: Could use std::span once compiler supports C++20 +#include + +#include + +namespace cugraph { +namespace mtmg { +namespace detail { + +/** + * @brief An edgelist for each GPU + * + * Manages an edge list for edges associated with a particular GPU. Multiple threads + * can call the append() method, possibly concurrently. To avoid constantly copying + * when the buffers fill up, the class will create a device buffer containing a + * number of elements specified in the constructor. When that device buffer is full + * we will create a new buffer. + * + * When we try and use the edgelist we will consolidate the buffers, since at that + * time we know the entire size required. + * + * Important note, the expectation is that this object will be used in two phases: + * 1) The append() method will be used to fill buffers with edges + * 2) The edges will be consumed to create a graph + * + * These two phases are expected to be disjoint. The calling process is expected to + * manage some barrier so that all threads are guaranteed to be completed before changing + * phases. If an append() call (part of the filling phase) overlaps with calls to + * finalize_buffer(), consolidate_and_shuffle(), get_src(), get_dst(), get_wgt(), + * get_edge_id() and get_edge_type() then the behavior is undefined (data might change + * in some non-deterministic way). + */ +template +class per_device_edgelist_t { + public: + per_device_edgelist_t() = delete; + per_device_edgelist_t(per_device_edgelist_t const&) = delete; + per_device_edgelist_t& operator=(per_device_edgelist_t const&) = delete; + per_device_edgelist_t& operator=(per_device_edgelist_t&&) = delete; + + per_device_edgelist_t(cugraph::mtmg::handle_t const& handle, + size_t device_buffer_size, + bool use_weight, + bool use_edge_id, + bool use_edge_type) + : device_buffer_size_{device_buffer_size}, + current_pos_{0}, + src_{}, + dst_{}, + wgt_{std::nullopt}, + edge_id_{std::nullopt}, + edge_type_{std::nullopt} + { + if (use_weight) { wgt_ = std::make_optional(std::vector>()); } + + if (use_edge_id) { edge_id_ = std::make_optional(std::vector>()); } + + if (use_edge_type) { + edge_type_ = std::make_optional(std::vector>()); + } + + create_new_buffers(handle); + } + + per_device_edgelist_t(per_device_edgelist_t&& other) + : device_buffer_size_{other.device_buffer_size_}, + current_pos_{other.current_pos_}, + src_{std::move(other.src_)}, + dst_{std::move(other.dst_)}, + wgt_{std::move(other.wgt_)}, + edge_id_{std::move(other.edge_id_)}, + edge_type_{std::move(other.edge_type_)} + { + } + + /** + * @brief Append a list of edges to the edge list + * + * @param handle The resource handle + * @param src Source vertex id + * @param dst Destination vertex id + * @param wgt Edge weight + * @param edge_id Edge id + * @param edge_type Edge type + */ + void append(handle_t const& handle, + raft::host_span src, + raft::host_span dst, + std::optional> wgt, + std::optional> edge_id, + std::optional> edge_type) + { + // FIXME: This lock guard could be on a smaller region, but it + // would require more careful coding. The raft::update_device + // calls could be done without the lock if we made a local + // of the values of *.back() and did an increment of current_pos_ + // while we hold the lock. + std::lock_guard lock(lock_); + + size_t count = src.size(); + size_t pos = 0; + + while (count > 0) { + size_t copy_count = std::min(count, (src_.back().size() - current_pos_)); + + raft::update_device( + src_.back().begin() + current_pos_, src.begin() + pos, copy_count, handle.get_stream()); + raft::update_device( + dst_.back().begin() + current_pos_, dst.begin() + pos, copy_count, handle.get_stream()); + if (wgt) + raft::update_device( + wgt_->back().begin() + current_pos_, wgt->begin() + pos, copy_count, handle.get_stream()); + if (edge_id) + raft::update_device(edge_id_->back().begin() + current_pos_, + edge_id->begin() + pos, + copy_count, + handle.get_stream()); + if (edge_type) + raft::update_device(edge_type_->back().begin() + current_pos_, + edge_type->begin() + pos, + copy_count, + handle.get_stream()); + + count -= copy_count; + pos += copy_count; + current_pos_ += copy_count; + + if (current_pos_ == src_.back().size()) { create_new_buffers(handle); } + } + + handle.raft_handle().sync_stream(); + } + + /** + * @brief Mark the edgelist as ready for reading (all writes are complete) + * + * @param handle The resource handle + */ + void finalize_buffer(handle_t const& handle) + { + src_.back().resize(current_pos_, handle.get_stream()); + dst_.back().resize(current_pos_, handle.get_stream()); + if (wgt_) wgt_->back().resize(current_pos_, handle.get_stream()); + if (edge_id_) edge_id_->back().resize(current_pos_, handle.get_stream()); + if (edge_type_) edge_type_->back().resize(current_pos_, handle.get_stream()); + } + + bool use_weight() const { return wgt_.has_value(); } + + bool use_edge_id() const { return edge_id_.has_value(); } + + bool use_edge_type() const { return edge_type_.has_value(); } + + std::vector>& get_src() { return src_; } + std::vector>& get_dst() { return dst_; } + std::optional>>& get_wgt() { return wgt_; } + std::optional>>& get_edge_id() { return edge_id_; } + std::optional>>& get_edge_type() + { + return edge_type_; + } + + /** + * @brief Consolidate edgelists (if necessary) and shuffle to the proper GPU + * + * @param handle The resource handle + */ + void consolidate_and_shuffle(cugraph::mtmg::handle_t const& handle, bool store_transposed) + { + if (src_.size() > 1) { + size_t total_size = std::transform_reduce( + src_.begin(), src_.end(), size_t{0}, std::plus(), [](auto& d_vector) { + return d_vector.size(); + }); + + resize_and_copy_buffers(handle.get_stream(), src_, total_size); + resize_and_copy_buffers(handle.get_stream(), dst_, total_size); + if (wgt_) resize_and_copy_buffers(handle.get_stream(), *wgt_, total_size); + if (edge_id_) resize_and_copy_buffers(handle.get_stream(), *edge_id_, total_size); + if (edge_type_) resize_and_copy_buffers(handle.get_stream(), *edge_type_, total_size); + } + + auto tmp_wgt = wgt_ ? std::make_optional(std::move((*wgt_)[0])) : std::nullopt; + auto tmp_edge_id = edge_id_ ? std::make_optional(std::move((*edge_id_)[0])) : std::nullopt; + auto tmp_edge_type = + edge_type_ ? std::make_optional(std::move((*edge_type_)[0])) : std::nullopt; + + std::tie(store_transposed ? dst_[0] : src_[0], + store_transposed ? src_[0] : dst_[0], + tmp_wgt, + tmp_edge_id, + tmp_edge_type) = + cugraph::detail::shuffle_ext_vertex_pairs_with_values_to_local_gpu_by_edge_partitioning( + handle.raft_handle(), + store_transposed ? std::move(dst_[0]) : std::move(src_[0]), + store_transposed ? std::move(src_[0]) : std::move(dst_[0]), + std::move(tmp_wgt), + std::move(tmp_edge_id), + std::move(tmp_edge_type)); + + if (tmp_wgt) ((*wgt_)[0]) = std::move(*tmp_wgt); + if (tmp_edge_id) ((*edge_id_)[0]) = std::move(*tmp_edge_id); + if (tmp_edge_type) ((*edge_type_)[0]) = std::move(*tmp_edge_type); + } + + private: + template + void resize_and_copy_buffers(rmm::cuda_stream_view stream, + std::vector>& buffer, + size_t total_size) + { + size_t pos = buffer[0].size(); + buffer[0].resize(total_size, stream); + + for (size_t i = 1; i < buffer.size(); ++i) { + raft::copy(buffer[0].data() + pos, buffer[i].data(), buffer[i].size(), stream); + pos += buffer[i].size(); + buffer[i].resize(0, stream); + buffer[i].shrink_to_fit(stream); + } + + std::vector> new_buffer; + new_buffer.push_back(std::move(buffer[0])); + buffer = std::move(new_buffer); + } + + void create_new_buffers(cugraph::mtmg::handle_t const& handle) + { + src_.emplace_back(device_buffer_size_, handle.get_stream()); + dst_.emplace_back(device_buffer_size_, handle.get_stream()); + + if (wgt_) { wgt_->emplace_back(device_buffer_size_, handle.get_stream()); } + + if (edge_id_) { edge_id_->emplace_back(device_buffer_size_, handle.get_stream()); } + + if (edge_type_) { edge_type_->emplace_back(device_buffer_size_, handle.get_stream()); } + + current_pos_ = 0; + } + + mutable std::mutex lock_{}; + + size_t current_pos_{0}; + size_t device_buffer_size_{0}; + + std::vector> src_{}; + std::vector> dst_{}; + std::optional>> wgt_{}; + std::optional>> edge_id_{}; + std::optional>> edge_type_{}; +}; + +} // namespace detail +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/edge_property.hpp b/cpp/include/cugraph/mtmg/edge_property.hpp new file mode 100644 index 00000000000..afa72492b9a --- /dev/null +++ b/cpp/include/cugraph/mtmg/edge_property.hpp @@ -0,0 +1,60 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Edge property object for each GPU + */ +template +class edge_property_t : public detail::device_shared_wrapper_t< + cugraph::edge_property_t> { + public: + using parent_t = detail::device_shared_wrapper_t< + cugraph::edge_property_t>; + + /** + * @brief Return a edge_property_view_t (read only) + */ + auto view() + { + std::lock_guard lock(parent_t::lock_); + + using edge_t = typename graph_view_t::wrapped_t::edge_type; + using buffer_t = + typename cugraph::edge_property_t::buffer_type; + std::vector buffers{}; + using const_value_iterator_t = decltype(get_dataframe_buffer_cbegin(buffers[0])); + + edge_property_view_t result; + + std::for_each(parent_t::objects_.begin(), parent_t::objects_.end(), [&result](auto& p) { + result.set(p.first, p.second.view()); + }); + + return result; + } +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/edge_property_view.hpp b/cpp/include/cugraph/mtmg/edge_property_view.hpp new file mode 100644 index 00000000000..c84a6458e1d --- /dev/null +++ b/cpp/include/cugraph/mtmg/edge_property_view.hpp @@ -0,0 +1,33 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Edge property object for each GPU + */ +template +using edge_property_view_t = + detail::device_shared_wrapper_t>; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/edgelist.hpp b/cpp/include/cugraph/mtmg/edgelist.hpp new file mode 100644 index 00000000000..90c53dfbb64 --- /dev/null +++ b/cpp/include/cugraph/mtmg/edgelist.hpp @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Edgelist object for each GPU + */ +template +class edgelist_t : public detail::device_shared_wrapper_t< + detail::per_device_edgelist_t> { + public: + /** + * @brief Create a per_device_edgelist for this GPU + */ + void set(handle_t const& handle, + size_t device_buffer_size, + bool use_weight, + bool use_edge_id, + bool use_edge_type) + { + detail::per_device_edgelist_t tmp( + handle, device_buffer_size, use_weight, use_edge_id, use_edge_type); + + detail::device_shared_wrapper_t< + detail::per_device_edgelist_t>::set(handle, + std::move(tmp)); + } + + /** + * @brief Stop inserting edges into this edgelist so we can use the edges + */ + void finalize_buffer(handle_t const& handle) { this->get(handle).finalize_buffer(handle); } + + /** + * @brief Consolidate for the edgelist edges into a single edgelist and then + * shuffle across GPUs. + */ + void consolidate_and_shuffle(cugraph::mtmg::handle_t const& handle, bool store_transposed) + { + this->get(handle).consolidate_and_shuffle(handle, store_transposed); + } +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/graph.hpp b/cpp/include/cugraph/mtmg/graph.hpp new file mode 100644 index 00000000000..76a2f401425 --- /dev/null +++ b/cpp/include/cugraph/mtmg/graph.hpp @@ -0,0 +1,136 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Graph object for each GPU + */ +template +class graph_t : public detail::device_shared_wrapper_t< + cugraph::graph_t> { + using parent_t = detail::device_shared_wrapper_t< + cugraph::graph_t>; + + public: + /** + * @brief Create an MTMG graph view (read only) + */ + auto view() + { + std::lock_guard lock(parent_t::lock_); + + cugraph::mtmg::graph_view_t result; + + std::for_each(parent_t::objects_.begin(), parent_t::objects_.end(), [&result](auto& p) { + result.set(p.first, std::move(p.second.view())); + }); + + return result; + } +}; + +/** + * @brief Create an MTMG graph from an edgelist + * + * @param[in] handle Resource handle + * @param[in] edgelist Edgelist + * @param[in] graph_properties Graph properties + * @param[in] renumber If true, renumber graph (must be true for MG) + * @param[out] graph MTMG graph is stored here + * @param[out] edge_weights MTMG edge weights is stored here + * @param[out] edge_ids MTMG edge ids is stored here + * @param[out] edge_types MTMG edge types is stored here + * @param[in] renumber_map MTMG renumber_map is stored here + * @param[in] do_expensive_check A flag to run expensive checks for input arguments (if set to + * `true`). + */ +template +void create_graph_from_edgelist( + handle_t const& handle, + cugraph::mtmg::edgelist_t& edgelist, + graph_properties_t graph_properties, + bool renumber, + cugraph::mtmg::graph_t& graph, + std::optional, + weight_t>>& edge_weights, + std::optional, + edge_id_t>>& edge_ids, + std::optional, + edge_type_t>>& edge_types, + std::optional>& renumber_map, + bool do_expensive_check = false) +{ + if (handle.get_thread_rank() > 0) return; + + CUGRAPH_EXPECTS(renumber_map.has_value() == renumber, + "Renumbering set to true, but no space for renumber map"); + + auto& my_edgelist = edgelist.get(handle); + + CUGRAPH_EXPECTS(my_edgelist.get_src().size() > 0, "Cannot create graph without an edge list"); + CUGRAPH_EXPECTS(my_edgelist.get_src().size() == 1, + "Must consolidate edges into a single list before creating graph"); + + auto [local_graph, local_edge_weights, local_edge_ids, local_edge_types, local_renumber_map] = + cugraph::create_graph_from_edgelist( + handle.raft_handle(), + std::nullopt, + std::move(my_edgelist.get_src()[0]), + std::move(my_edgelist.get_dst()[0]), + my_edgelist.get_wgt() ? std::make_optional(std::move((*my_edgelist.get_wgt())[0])) + : std::nullopt, + my_edgelist.get_edge_id() ? std::make_optional(std::move((*my_edgelist.get_edge_id())[0])) + : std::nullopt, + my_edgelist.get_edge_type() ? std::make_optional(std::move((*my_edgelist.get_edge_type())[0])) + : std::nullopt, + graph_properties, + renumber, + do_expensive_check); + + graph.set(handle, std::move(local_graph)); + if (edge_weights) edge_weights->set(handle, std::move(*local_edge_weights)); + if (edge_ids) edge_ids->set(handle, std::move(*local_edge_ids)); + if (edge_types) edge_types->set(handle, std::move(*local_edge_types)); + if (renumber) renumber_map->set(handle, std::move(*local_renumber_map)); +} + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/graph_view.hpp b/cpp/include/cugraph/mtmg/graph_view.hpp new file mode 100644 index 00000000000..94347e016ea --- /dev/null +++ b/cpp/include/cugraph/mtmg/graph_view.hpp @@ -0,0 +1,34 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Graph view for each GPU + */ +template +using graph_view_t = detail::device_shared_wrapper_t< + cugraph::graph_view_t>; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/handle.hpp b/cpp/include/cugraph/mtmg/handle.hpp new file mode 100644 index 00000000000..f23bce5aeac --- /dev/null +++ b/cpp/include/cugraph/mtmg/handle.hpp @@ -0,0 +1,111 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Resource handler + * + * Multi-threaded resource handler. Every GPU gets a raft::handle object that provides access to + * the GPU resources. In a multi-threaded environment multiple threads will share a particular GPU. + * Following the MPI model, each thread will be assigned to a thread rank. + * + */ +class handle_t { + public: + /** + * @brief Constructor + * + * @param raft_handle Raft handle for the resources + * @param thread_rank Rank for this thread + */ + handle_t(raft::handle_t const& raft_handle, int thread_rank, size_t device_id) + : raft_handle_(raft_handle), + thread_rank_(thread_rank), + local_rank_(raft_handle.get_comms().get_rank()), // FIXME: update for multi-node + device_id_(device_id) + { + } + + /** + * @brief Get the raft handle + * + * @return const reference to a raft handle + */ + raft::handle_t const& raft_handle() const { return raft_handle_; } + + /** + * @brief Get cuda stream + * + * @return cuda stream + */ + rmm::cuda_stream_view get_stream() const + { + return raft_handle_.is_stream_pool_initialized() + ? raft_handle_.get_stream_from_stream_pool(device_id_) + : raft_handle_.get_stream(); + } + + /** + * @brief Get thread rank + * + * @return thread rank + */ + int get_thread_rank() const { return thread_rank_; } + + /** + * @brief Get number of gpus + * + * @return number of gpus + */ + int get_size() const { return raft_handle_.get_comms().get_size(); } + + /** + * @brief Get number of local gpus + * + * @return number of local gpus + */ + // FIXME: wrong for multi-node + int get_local_size() const { return raft_handle_.get_comms().get_size(); } + + /** + * @brief Get gpu rank + * + * @return gpu rank + */ + int get_rank() const { return raft_handle_.get_comms().get_rank(); } + + /** + * @brief Get local gpu rank + * + * @return local gpu rank + */ + int get_local_rank() const { return local_rank_; } + + private: + raft::handle_t const& raft_handle_; + int thread_rank_; + int local_rank_; + size_t device_id_; +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/instance_manager.hpp b/cpp/include/cugraph/mtmg/instance_manager.hpp new file mode 100644 index 00000000000..8bf62b56f4b --- /dev/null +++ b/cpp/include/cugraph/mtmg/instance_manager.hpp @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include + +#include + +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Manages a subset of the cluster for a set of graph computations + */ +class instance_manager_t { + public: + /** + * @brief Constructor + * + * @param handles Vector of RAFT handles, one for each device on this node + */ + instance_manager_t(std::vector>&& handles, + std::vector>&& nccl_comms, + std::vector&& device_ids, + int local_gpu_count) + : thread_counter_{0}, + raft_handle_{std::move(handles)}, + nccl_comms_{std::move(nccl_comms)}, + device_ids_{std::move(device_ids)}, + local_gpu_count_{local_gpu_count} + { + } + + /** + * @brief Get handle + * + * The instance manager will construct a handle appropriate for the thread making + * the request. Threads will be assigned to GPUs in a round-robin fashion to + * spread requesting threads around the GPU resources. + * + * This function will be CPU thread-safe. + * + * @return a handle for this thread. + */ + handle_t get_handle() + { + int local_id = thread_counter_++; + + RAFT_CUDA_TRY(cudaSetDevice(device_ids_[local_id % raft_handle_.size()].value())); + return handle_t(*raft_handle_[local_id % raft_handle_.size()], + local_id / raft_handle_.size(), + static_cast(local_id % raft_handle_.size())); + } + + /** + * @brief Reset the thread counter + * + * After a parallel activity is completed, we need to reset the thread counter so that + * future threads will round robin around the GPUs properly. + */ + void reset_threads() { thread_counter_.store(0); } + + /** + * @brief Number of local GPUs in the instance + */ + int get_local_gpu_count() { return local_gpu_count_; } + + private: + // FIXME: Should this be an std::map<> where the key is the rank? + // On a multi-node system we might have nodes with fewer + // (or no) GPUs, so mapping rank to a handle might be a challenge + // + std::vector> raft_handle_{}; + std::vector> nccl_comms_{}; + std::vector device_ids_{}; + int local_gpu_count_{}; + + std::atomic thread_counter_{0}; +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/per_thread_edgelist.hpp b/cpp/include/cugraph/mtmg/per_thread_edgelist.hpp new file mode 100644 index 00000000000..b672db48719 --- /dev/null +++ b/cpp/include/cugraph/mtmg/per_thread_edgelist.hpp @@ -0,0 +1,174 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Supports creating an edgelist from individual host threads + * + * A cugraph edgelist needs to contain all of the edges necessary to create the graph + * stored in GPU memory (distributed across multiple GPUs in a multi-GPU configuration). + * + * This class provides a mechanism for populating the edgelist object from independent CPU threads. + * + * Calls to the append() method will take edges (in CPU host memory) and append them to a local + * buffer. As the local buffer fills, the buffer will be sent to GPU memory using the flush() + * method. This allows the CPU to GPU transfers to be larger (and consequently more efficient). + */ +template +class per_thread_edgelist_t { + public: + per_thread_edgelist_t() = delete; + per_thread_edgelist_t(per_thread_edgelist_t const&) = delete; + + /** + * @brief Only constructor + * + * @param edgelist The edge list this thread_edgelist_t should be associated with + * @param thread_buffer_size Size of the local buffer for accumulating edges on the CPU + */ + per_thread_edgelist_t( + detail::per_device_edgelist_t& edgelist, + size_t thread_buffer_size) + : edgelist_{edgelist}, + current_pos_{0}, + src_(thread_buffer_size), + dst_(thread_buffer_size), + wgt_{std::nullopt}, + edge_id_{std::nullopt}, + edge_type_{std::nullopt} + { + if (edgelist.use_weight()) wgt_ = std::make_optional(std::vector(thread_buffer_size)); + + if (edgelist.use_edge_id()) + edge_id_ = std::make_optional(std::vector(thread_buffer_size)); + + if (edgelist.use_edge_type()) + edge_type_ = std::make_optional(std::vector(thread_buffer_size)); + } + + /** + * @brief Append an edge to the edge list + * + * @param handle The resource handle + * @param src Source vertex id + * @param dst Destination vertex id + * @param wgt Edge weight + * @param edge_id Edge id + * @param edge_type Edge type + */ + void append(handle_t const& handle, + vertex_t src, + vertex_t dst, + std::optional wgt, + std::optional edge_id, + std::optional edge_type) + { + if (current_pos_ == src_.size()) { flush(handle); } + + src_[current_pos_] = src; + dst_[current_pos_] = dst; + if (wgt) (*wgt_)[current_pos_] = *wgt; + if (edge_id) (*edge_id_)[current_pos_] = *edge_id; + if (edge_type) (*edge_type_)[current_pos_] = *edge_type; + + ++current_pos_; + } + + /** + * @brief Append a list of edges to the edge list + * + * @param handle The resource handle + * @param src Source vertex id + * @param dst Destination vertex id + * @param wgt Edge weight + * @param edge_id Edge id + * @param edge_type Edge type + */ + void append(handle_t const& handle, + raft::host_span src, + raft::host_span dst, + std::optional> wgt, + std::optional> edge_id, + std::optional> edge_type) + { + size_t count = src.size(); + size_t pos = 0; + + while (count > 0) { + size_t copy_count = std::min(count, (src_.size() - current_pos_)); + + std::copy(src.begin() + pos, src.begin() + pos + copy_count, src_.begin() + current_pos_); + std::copy(dst.begin() + pos, dst.begin() + pos + copy_count, dst_.begin() + current_pos_); + if (wgt) + std::copy(wgt.begin() + pos, wgt.begin() + pos + copy_count, wgt_->begin() + current_pos_); + if (edge_id) + std::copy(edge_id.begin() + pos, + edge_id.begin() + pos + copy_count, + edge_id_->begin() + current_pos_); + if (edge_type) + std::copy(edge_type.begin() + pos, + edge_type.begin() + pos + copy_count, + edge_type_->begin() + current_pos_); + + if (current_pos_ == src_.size()) { flush(handle); } + + count -= copy_count; + pos += copy_count; + } + } + + /** + * @brief Flush thread data from host to GPU memory + * + * @param handle The resource handle + */ + void flush(handle_t const& handle) + { + edgelist_.append( + handle, + raft::host_span{src_.data(), current_pos_}, + raft::host_span{dst_.data(), current_pos_}, + wgt_ ? std::make_optional(raft::host_span{wgt_->data(), current_pos_}) + : std::nullopt, + edge_id_ ? std::make_optional(raft::host_span{edge_id_->data(), current_pos_}) + : std::nullopt, + edge_type_ + ? std::make_optional(raft::host_span{edge_type_->data(), current_pos_}) + : std::nullopt); + + current_pos_ = 0; + } + + private: + detail::per_device_edgelist_t& edgelist_; + size_t current_pos_{0}; + std::vector src_{}; + std::vector dst_{}; + std::optional> wgt_{}; + std::optional> edge_id_{}; + std::optional> edge_type_{}; +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/renumber_map.hpp b/cpp/include/cugraph/mtmg/renumber_map.hpp new file mode 100644 index 00000000000..da07d61bd96 --- /dev/null +++ b/cpp/include/cugraph/mtmg/renumber_map.hpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief An MTMG device vector for storing a renumber map + */ +template +class renumber_map_t : public detail::device_shared_device_vector_t { + using parent_t = detail::device_shared_device_vector_t; + + public: + /** + * @brief Return a view (read only) of the renumber map + */ + auto view() { return static_cast>(this->parent_t::view()); } +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/renumber_map_view.hpp b/cpp/include/cugraph/mtmg/renumber_map_view.hpp new file mode 100644 index 00000000000..5ff7ff5e100 --- /dev/null +++ b/cpp/include/cugraph/mtmg/renumber_map_view.hpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief An MTMG device span for storing a renumber map + */ +template +using renumber_map_view_t = detail::device_shared_device_span_t; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/resource_manager.hpp b/cpp/include/cugraph/mtmg/resource_manager.hpp new file mode 100644 index 00000000000..b4633626e7c --- /dev/null +++ b/cpp/include/cugraph/mtmg/resource_manager.hpp @@ -0,0 +1,225 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include + +#include + +#include +#include +#include +#include + +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief Class for managing local and remote GPU resources for use in + * multi-threaded multi-GPU interface. + * + * Each process in a multi-GPU configuration should have an instance of this + * class. The resource manager object should be configured by calling + * register_local_gpu (or register_remote_gpu once we support a multi-node + * configuration) to allocate resources that can be used in the mtmg space. + * + * When we want to execute some graph computations, we need to create an instance for execution. + * Based on how big a subset of the desired compute resources is desired, we can allocate some + * number of GPUs to the problem (up to the total set of managed resources). + * + * The returned instance can be used to create a graph, execute one or more algorithms, etc. Once + * we are done the caller can delete the instance. + * + * At the moment, the caller is assumed to be responsible for scheduling use of the resources. + * + * For our first release, we will only consider a single node multi-GPU configuration, so the remote + * GPU methods are currently disabled via ifdef. + */ +class resource_manager_t { + public: + /** + * @brief Default constructor + */ + resource_manager_t() {} + + /** + * @brief add a local GPU to the resource manager. + * + * @param rank The rank to assign to the local GPU + * @param device_id The device_id corresponding to this rank + */ + void register_local_gpu(int rank, rmm::cuda_device_id device_id) + { + std::lock_guard lock(lock_); + + CUGRAPH_EXPECTS(local_rank_map_.find(rank) == local_rank_map_.end(), + "cannot register same rank multiple times"); + + int num_gpus_this_node; + RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_this_node)); + + CUGRAPH_EXPECTS((device_id.value() >= 0) && (device_id.value() < num_gpus_this_node), + "device id out of range"); + + local_rank_map_.insert(std::pair(rank, device_id)); + + RAFT_CUDA_TRY(cudaSetDevice(device_id.value())); + + // FIXME: There is a bug in the cuda_memory_resource that results in a Hang. + // using the pool resource as a work-around. + // + // There is a deprecated environment variable: NCCL_LAUNCH_MODE=GROUP + // which should temporarily work around this problem. + // + // Ultimately there should be some RMM parameters passed into this function + // (or the constructor of the object) to configure this behavior +#if 0 + auto per_device_it = per_device_rmm_resources_.insert( + std::pair{rank, std::make_shared()}); +#else + auto const [free, total] = rmm::detail::available_device_memory(); + auto const min_alloc = + rmm::detail::align_down(std::min(free, total / 6), rmm::detail::CUDA_ALLOCATION_ALIGNMENT); + + auto per_device_it = per_device_rmm_resources_.insert( + std::pair{rank, + rmm::mr::make_owning_wrapper( + std::make_shared(), min_alloc)}); +#endif + + rmm::mr::set_per_device_resource(device_id, per_device_it.first->second.get()); + } + + /** + * @brief Create an instance using a subset of the registered resources + * + * The selected set of resources will be configured as an instance manager. + * If @ranks_to_include is a proper subset of the registered resources, + * ranks will be renumbered into the range [0, @p ranks_to_use.size()), making + * it a proper configuration. + * + * @param ranks_to_use a vector containing the ranks to include in the instance. + * Must be a subset of the entire set of available ranks. + * @param instance_manager_id a ncclUniqueId that is shared by all processes participating + * in this instance. All processes must use the same ID in this call, it is up + * to the calling code to share this ID properly before the call. + * + * @return unique pointer to instance manager + */ + std::unique_ptr create_instance_manager( + std::vector ranks_to_include, ncclUniqueId instance_manager_id) const + { + std::for_each( + ranks_to_include.begin(), ranks_to_include.end(), [local_ranks = local_rank_map_](int rank) { + CUGRAPH_EXPECTS(local_ranks.find(rank) != local_ranks.end(), + "requesting inclusion of an invalid rank"); + }); + + std::vector> nccl_comms{}; + std::vector> handles{}; + std::vector device_ids{}; + + nccl_comms.reserve(ranks_to_include.size()); + handles.reserve(ranks_to_include.size()); + device_ids.reserve(ranks_to_include.size()); + + // FIXME: not quite right for multi-node + auto gpu_row_comm_size = static_cast(sqrt(static_cast(ranks_to_include.size()))); + while (ranks_to_include.size() % gpu_row_comm_size != 0) { + --gpu_row_comm_size; + } + + // FIXME: not quite right for multi-node + for (size_t i = 0; i < ranks_to_include.size(); ++i) { + int rank = ranks_to_include[i]; + auto pos = local_rank_map_.find(rank); + RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); + + raft::handle_t tmp_handle; + + nccl_comms.push_back(std::make_unique()); + handles.push_back( + std::make_unique(tmp_handle, per_device_rmm_resources_.find(rank)->second)); + device_ids.push_back(pos->second); + } + + std::vector running_threads; + + for (size_t i = 0; i < ranks_to_include.size(); ++i) { + running_threads.emplace_back([instance_manager_id, + idx = i, + gpu_row_comm_size, + comm_size = ranks_to_include.size(), + &ranks_to_include, + &local_rank_map = local_rank_map_, + &nccl_comms, + &handles]() { + int rank = ranks_to_include[idx]; + auto pos = local_rank_map.find(rank); + RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); + + NCCL_TRY(ncclCommInitRank(nccl_comms[idx].get(), comm_size, instance_manager_id, rank)); + + raft::comms::build_comms_nccl_only(handles[idx].get(), *nccl_comms[idx], comm_size, rank); + + cugraph::partition_manager::init_subcomm(*handles[idx], gpu_row_comm_size); + }); + } + + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + + // FIXME: Update for multi-node + return std::make_unique( + std::move(handles), std::move(nccl_comms), std::move(device_ids), ranks_to_include.size()); + } + + /** + * @brief Get a list of all of the currently registered ranks + * + * @return A copy of the list of ranks. + */ + std::vector registered_ranks() const + { + std::lock_guard lock(lock_); + + // + // C++20 mechanism: + // return std::vector{ std::views::keys(local_rank_map_).begin(), + // std::views::keys(local_rank_map_).end() }; + // Would need a bit more complicated to handle remote_rank_map_ also + // + std::vector registered_ranks(local_rank_map_.size()); + std::transform( + local_rank_map_.begin(), local_rank_map_.end(), registered_ranks.begin(), [](auto pair) { + return pair.first; + }); + + return registered_ranks; + } + + private: + mutable std::mutex lock_{}; + std::map local_rank_map_{}; + std::map> per_device_rmm_resources_{}; +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/vertex_result.hpp b/cpp/include/cugraph/mtmg/vertex_result.hpp new file mode 100644 index 00000000000..e8999b35aa9 --- /dev/null +++ b/cpp/include/cugraph/mtmg/vertex_result.hpp @@ -0,0 +1,40 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief An MTMG device vector for storing vertex results + */ +template +class vertex_result_t : public detail::device_shared_device_vector_t { + using parent_t = detail::device_shared_device_vector_t; + + public: + /** + * @brief Create a vertex result view (read only) + */ + auto view() { return static_cast>(this->parent_t::view()); } +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/vertex_result_view.hpp b/cpp/include/cugraph/mtmg/vertex_result_view.hpp new file mode 100644 index 00000000000..7a7070d6f2a --- /dev/null +++ b/cpp/include/cugraph/mtmg/vertex_result_view.hpp @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#pragma once + +#include +#include +#include +#include + +namespace cugraph { +namespace mtmg { + +/** + * @brief An MTMG device span for referencing a vertex result + */ +template +class vertex_result_view_t : public detail::device_shared_device_span_t { + using parent_t = detail::device_shared_device_span_t; + + public: + vertex_result_view_t(parent_t&& other) : parent_t{std::move(other)} {} + + /** + * @brief Gather results from specified vertices into a device vector + */ + template + rmm::device_uvector gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); +}; + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/src/link_analysis/pagerank_impl.cuh b/cpp/src/link_analysis/pagerank_impl.cuh index 3a84cdedfda..92c70fcff20 100644 --- a/cpp/src/link_analysis/pagerank_impl.cuh +++ b/cpp/src/link_analysis/pagerank_impl.cuh @@ -388,9 +388,11 @@ void pagerank(raft::handle_t const& handle, handle, graph_view, edge_weight_view, - std::make_optional(raft::device_span{ - *precomputed_vertex_out_weight_sums, - static_cast(graph_view.local_vertex_partition_range_size())}), + precomputed_vertex_out_weight_sums + ? std::make_optional(raft::device_span{ + *precomputed_vertex_out_weight_sums, + static_cast(graph_view.local_vertex_partition_range_size())}) + : std::nullopt, personalization_vertices ? std::make_optional(std::make_tuple( raft::device_span{*personalization_vertices, diff --git a/cpp/src/mtmg/vertex_result.cu b/cpp/src/mtmg/vertex_result.cu new file mode 100644 index 00000000000..a669a127f41 --- /dev/null +++ b/cpp/src/mtmg/vertex_result.cu @@ -0,0 +1,167 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include +#include +#include + +#include + +#include + +namespace cugraph { +namespace mtmg { + +template +template +rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view) +{ + auto this_gpu_graph_view = graph_view.get(handle); + + rmm::device_uvector local_vertices(vertices.size(), handle.get_stream()); + rmm::device_uvector vertex_gpu_ids(vertices.size(), handle.get_stream()); + rmm::device_uvector vertex_pos(vertices.size(), handle.get_stream()); + rmm::device_uvector result(vertices.size(), handle.get_stream()); + + raft::copy(local_vertices.data(), vertices.data(), vertices.size(), handle.get_stream()); + cugraph::detail::scalar_fill( + handle.get_stream(), vertex_gpu_ids.data(), vertex_gpu_ids.size(), handle.get_rank()); + cugraph::detail::sequence_fill( + handle.get_stream(), vertex_pos.data(), vertex_pos.size(), size_t{0}); + + rmm::device_uvector d_vertex_partition_range_lasts( + this_gpu_graph_view.vertex_partition_range_lasts().size(), handle.get_stream()); + raft::update_device(d_vertex_partition_range_lasts.data(), + this_gpu_graph_view.vertex_partition_range_lasts().data(), + this_gpu_graph_view.vertex_partition_range_lasts().size(), + handle.get_stream()); + + if (renumber_map_view) { + cugraph::renumber_ext_vertices( + handle.raft_handle(), + local_vertices.data(), + local_vertices.size(), + renumber_map_view->get(handle).data(), + this_gpu_graph_view.local_vertex_partition_range_first(), + this_gpu_graph_view.local_vertex_partition_range_last()); + } + + auto const major_comm_size = + handle.raft_handle().get_subcomm(cugraph::partition_manager::major_comm_name()).get_size(); + auto const minor_comm_size = + handle.raft_handle().get_subcomm(cugraph::partition_manager::minor_comm_name()).get_size(); + + std::forward_as_tuple(local_vertices, std::tie(vertex_gpu_ids, vertex_pos), std::ignore) = + groupby_gpu_id_and_shuffle_kv_pairs( + handle.raft_handle().get_comms(), + local_vertices.begin(), + local_vertices.end(), + thrust::make_zip_iterator(vertex_gpu_ids.begin(), vertex_pos.begin()), + cugraph::detail::compute_gpu_id_from_int_vertex_t{ + raft::device_span(d_vertex_partition_range_lasts.data(), + d_vertex_partition_range_lasts.size()), + major_comm_size, + minor_comm_size}, + handle.get_stream()); + + // + // Now gather + // + rmm::device_uvector tmp_result(local_vertices.size(), handle.get_stream()); + + auto& wrapped = this->get(handle); + + auto vertex_partition = vertex_partition_device_view_t( + this_gpu_graph_view.local_vertex_partition_view()); + + auto iter = + thrust::make_transform_iterator(local_vertices.begin(), [vertex_partition] __device__(auto v) { + return vertex_partition.local_vertex_partition_offset_from_vertex_nocheck(v); + }); + + thrust::gather(handle.raft_handle().get_thrust_policy(), + iter, + iter + local_vertices.size(), + wrapped.begin(), + tmp_result.begin()); + + // + // Shuffle back + // + std::forward_as_tuple(std::ignore, std::tie(std::ignore, vertex_pos, tmp_result), std::ignore) = + groupby_gpu_id_and_shuffle_kv_pairs( + handle.raft_handle().get_comms(), + vertex_gpu_ids.begin(), + vertex_gpu_ids.end(), + thrust::make_zip_iterator(local_vertices.begin(), vertex_pos.begin(), tmp_result.begin()), + [] __device__(int gpu) { return gpu; }, + handle.get_stream()); + + // + // Finally, reorder result + // + thrust::scatter(handle.raft_handle().get_thrust_policy(), + tmp_result.begin(), + tmp_result.end(), + vertex_pos.begin(), + result.begin()); + + return result; +} + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + cugraph::mtmg::graph_view_t const& graph_view, + std::optional>& renumber_map_view); + +} // namespace mtmg +} // namespace cugraph diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 5e1e1d6ace3..f08606df8ea 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -419,6 +419,14 @@ ConfigureTest(K_HOP_NBRS_TEST traversal/k_hop_nbrs_test.cpp) # - install tests --------------------------------------------------------------------------------- rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcugraph) +################################################################################################### +# - MTMG tests ------------------------------------------------------------------------- +ConfigureTest(MTMG_TEST mtmg/threaded_test.cu) +target_link_libraries(MTMG_TEST + PRIVATE + UCP::UCP + ) + ################################################################################################### # - MG tests -------------------------------------------------------------------------------------- diff --git a/cpp/tests/mtmg/threaded_test.cu b/cpp/tests/mtmg/threaded_test.cu new file mode 100644 index 00000000000..c5dc2d3c7ce --- /dev/null +++ b/cpp/tests/mtmg/threaded_test.cu @@ -0,0 +1,459 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include + +#include + +#include + +#include +#include + +struct Multithreaded_Usecase { + bool test_weighted{false}; + bool check_correctness{true}; +}; + +template +class Tests_Multithreaded + : public ::testing::TestWithParam> { + public: + Tests_Multithreaded() {} + + static void SetUpTestCase() {} + static void TearDownTestCase() {} + + virtual void SetUp() {} + virtual void TearDown() {} + + std::vector get_gpu_list() + { + int num_gpus_per_node{1}; + RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_per_node)); + + std::vector gpu_list(num_gpus_per_node); + std::iota(gpu_list.begin(), gpu_list.end(), 0); + + return gpu_list; + } + + template + void run_current_test( + std::tuple const& param, + std::vector gpu_list) + { + using edge_type_t = int32_t; + + constexpr bool renumber = true; + constexpr bool do_expensive_check = false; + + auto [multithreaded_usecase, input_usecase] = param; + + raft::handle_t handle{}; + + result_t constexpr alpha{0.85}; + result_t constexpr epsilon{1e-6}; + + size_t device_buffer_size{64 * 1024 * 1024}; + size_t thread_buffer_size{4 * 1024 * 1024}; + + int num_gpus = gpu_list.size(); + int num_threads = num_gpus * 4; + + cugraph::mtmg::resource_manager_t resource_manager; + + std::for_each(gpu_list.begin(), gpu_list.end(), [&resource_manager](int gpu_id) { + resource_manager.register_local_gpu(gpu_id, rmm::cuda_device_id{gpu_id}); + }); + + ncclUniqueId instance_manager_id; + ncclGetUniqueId(&instance_manager_id); + + auto instance_manager = resource_manager.create_instance_manager( + resource_manager.registered_ranks(), instance_manager_id); + + cugraph::mtmg::edgelist_t edgelist; + cugraph::mtmg::graph_t graph; + cugraph::mtmg::graph_view_t graph_view; + cugraph::mtmg::vertex_result_t pageranks; + std::optional> renumber_map = + std::make_optional>(); + + auto edge_weights = multithreaded_usecase.test_weighted + ? std::make_optional, + weight_t>>() + : std::nullopt; + + // + // Simulate graph creation by spawning threads to walk through the + // local COO and add edges + // + std::vector running_threads; + + // Initialize shared edgelist object, one per GPU + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &edgelist, + device_buffer_size, + use_weight = true, + use_edge_id = false, + use_edge_type = false]() { + auto thread_handle = instance_manager->get_handle(); + + edgelist.set(thread_handle, device_buffer_size, use_weight, use_edge_id, use_edge_type); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + // Load SG edge list + auto [d_src_v, d_dst_v, d_weights_v, d_vertices_v, is_symmetric] = + input_usecase.template construct_edgelist( + handle, multithreaded_usecase.test_weighted, false, false); + + auto h_src_v = cugraph::test::to_host(handle, d_src_v); + auto h_dst_v = cugraph::test::to_host(handle, d_dst_v); + auto h_weights_v = cugraph::test::to_host(handle, d_weights_v); + auto unique_vertices = cugraph::test::to_host(handle, d_vertices_v); + + // Load edgelist from different threads. We'll use more threads than GPUs here + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back([&instance_manager, + thread_buffer_size, + &edgelist, + &h_src_v, + &h_dst_v, + &h_weights_v, + i, + num_threads]() { + auto thread_handle = instance_manager->get_handle(); + cugraph::mtmg::per_thread_edgelist_t + per_thread_edgelist(edgelist.get(thread_handle), thread_buffer_size); + + for (size_t j = i; j < h_src_v.size(); j += num_threads) { +#if 0 + if (h_weights_v) { + thread_edgelist.append( + thread_handle, h_src_v[j], h_dst_v[j], (*h_weights_v)[j], std::nullopt, std::nullopt); + } else { + thread_edgelist.append( + thread_handle, h_src_v[j], h_dst_v[j], std::nullopt, std::nullopt, std::nullopt); + } +#endif + per_thread_edgelist.append( + thread_handle, + h_src_v[j], + h_dst_v[j], + h_weights_v ? std::make_optional((*h_weights_v)[j]) : std::nullopt, + std::nullopt, + std::nullopt); + } + + per_thread_edgelist.flush(thread_handle); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph, + &edge_weights, + &edgelist, + &renumber_map, + &pageranks, + is_symmetric = is_symmetric, + renumber, + do_expensive_check]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + std::optional, + edge_t>> + edge_ids{std::nullopt}; + std::optional, + int32_t>> + edge_types{std::nullopt}; + + edgelist.finalize_buffer(thread_handle); + edgelist.consolidate_and_shuffle(thread_handle, true); + + cugraph::mtmg:: + create_graph_from_edgelist( + thread_handle, + edgelist, + cugraph::graph_properties_t{is_symmetric, true}, + renumber, + graph, + edge_weights, + edge_ids, + edge_types, + renumber_map, + do_expensive_check); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + graph_view = graph.view(); + + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back( + [&instance_manager, &graph_view, &edge_weights, &pageranks, alpha, epsilon]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + auto [local_pageranks, metadata] = + cugraph::pagerank( + thread_handle.raft_handle(), + graph_view.get(thread_handle), + edge_weights ? std::make_optional(edge_weights->get(thread_handle).view()) + : std::nullopt, + std::nullopt, + std::nullopt, + std::nullopt, + alpha, + epsilon, + 500, + true); + + pageranks.set(thread_handle, std::move(local_pageranks)); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + std::vector, std::vector>> computed_pageranks_v; + std::mutex computed_pageranks_lock{}; + + auto pageranks_view = pageranks.view(); + auto renumber_map_view = renumber_map ? std::make_optional(renumber_map->view()) : std::nullopt; + + // Load computed_pageranks from different threads. + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph_view, + &renumber_map_view, + &pageranks_view, + &computed_pageranks_lock, + &computed_pageranks_v, + &h_src_v, + &h_dst_v, + &h_weights_v, + &unique_vertices, + i, + num_threads]() { + auto thread_handle = instance_manager->get_handle(); + + auto number_of_vertices = unique_vertices->size(); + + std::vector my_vertex_list; + my_vertex_list.reserve((number_of_vertices + num_threads - 1) / num_threads); + + for (size_t j = i; j < number_of_vertices; j += num_threads) { + my_vertex_list.push_back((*unique_vertices)[j]); + } + + rmm::device_uvector d_my_vertex_list(my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + raft::update_device(d_my_vertex_list.data(), + my_vertex_list.data(), + my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + + auto d_my_pageranks = pageranks_view.gather( + thread_handle, + raft::device_span{d_my_vertex_list.data(), d_my_vertex_list.size()}, + graph_view, + renumber_map_view); + + std::vector my_pageranks(d_my_pageranks.size()); + raft::update_host(my_pageranks.data(), + d_my_pageranks.data(), + d_my_pageranks.size(), + thread_handle.raft_handle().get_stream()); + + { + std::lock_guard lock(computed_pageranks_lock); + computed_pageranks_v.push_back( + std::make_tuple(std::move(my_vertex_list), std::move(my_pageranks))); + } + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + if (multithreaded_usecase.check_correctness) { + // Want to compare the results in computed_pageranks_v with SG results + cugraph::graph_t sg_graph(handle); + std::optional< + cugraph::edge_property_t, weight_t>> + sg_edge_weights{std::nullopt}; + std::optional> sg_renumber_map{std::nullopt}; + + std::tie(sg_graph, sg_edge_weights, std::ignore, std::ignore, sg_renumber_map) = cugraph:: + create_graph_from_edgelist( + handle, + std::nullopt, + std::move(d_src_v), + std::move(d_dst_v), + std::move(d_weights_v), + std::nullopt, + std::nullopt, + cugraph::graph_properties_t{is_symmetric, true}, + true); + + auto [sg_pageranks, meta] = cugraph::pagerank( + handle, + sg_graph.view(), + sg_edge_weights ? std::make_optional(sg_edge_weights->view()) : std::nullopt, + std::nullopt, + std::nullopt, + std::nullopt, + alpha, + epsilon); + + auto h_sg_pageranks = cugraph::test::to_host(handle, sg_pageranks); + auto h_sg_renumber_map = cugraph::test::to_host(handle, sg_renumber_map); + auto compare_functor = cugraph::test::nearly_equal{ + weight_t{1e-3}, + weight_t{(weight_t{1} / static_cast(h_sg_pageranks.size())) * weight_t{1e-3}}}; + + std::for_each( + computed_pageranks_v.begin(), + computed_pageranks_v.end(), + [h_sg_pageranks, compare_functor, h_sg_renumber_map](auto t1) { + std::for_each( + thrust::make_zip_iterator(std::get<0>(t1).begin(), std::get<1>(t1).begin()), + thrust::make_zip_iterator(std::get<0>(t1).end(), std::get<1>(t1).end()), + [h_sg_pageranks, compare_functor, h_sg_renumber_map](auto t2) { + vertex_t v = thrust::get<0>(t2); + weight_t pr = thrust::get<1>(t2); + + auto pos = std::find(h_sg_renumber_map->begin(), h_sg_renumber_map->end(), v); + auto offset = std::distance(h_sg_renumber_map->begin(), pos); + + ASSERT_TRUE(compare_functor(pr, h_sg_pageranks[offset])) + << "vertex " << v << ", SG result = " << h_sg_pageranks[offset] + << ", mtmg result = " << pr << ", renumber map = " << (*h_sg_renumber_map)[offset]; + }); + }); + } + } +}; + +using Tests_Multithreaded_File = Tests_Multithreaded; +using Tests_Multithreaded_Rmat = Tests_Multithreaded; + +// FIXME: add tests for type combinations +TEST_P(Tests_Multithreaded_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam()), std::vector{{0, 1}}); +} + +TEST_P(Tests_Multithreaded_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam()), std::vector{{0, 1}}); +} + +INSTANTIATE_TEST_SUITE_P(file_test, + Tests_Multithreaded_File, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{false, true}, + Multithreaded_Usecase{true, true}), + ::testing::Values(cugraph::test::File_Usecase("karate.csv"), + cugraph::test::File_Usecase("dolphins.csv")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_Multithreaded_Rmat, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{false, true}, Multithreaded_Usecase{true, true}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + +INSTANTIATE_TEST_SUITE_P( + file_benchmark_test, /* note that the test filename can be overridden in benchmarking (with + --gtest_filter to select only the file_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one File_Usecase that differ only in filename + (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_File, + ::testing::Combine( + // disable correctness checks + ::testing::Values(Multithreaded_Usecase{false, false}, Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_benchmark_test, /* note that scale & edge factor can be overridden in benchmarking (with + --gtest_filter to select only the rmat_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one Rmat_Usecase that differ only in scale or edge + factor (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_Rmat, + ::testing::Combine( + // disable correctness checks for large graphs + ::testing::Values(Multithreaded_Usecase{false, false}, Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + +CUGRAPH_TEST_PROGRAM_MAIN() From 686c3727782c6d303385d7ecdb0330d890e8184d Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Tue, 19 Sep 2023 14:20:15 -0500 Subject: [PATCH 003/111] Update to clang 16.0.6. (#3859) This PR updates cugraph to use clang 16.0.6. The previous version 16.0.1 has some minor formatting issues affecting several RAPIDS repos. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Brad Rees (https://github.com/BradReesWork) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3859 --- .pre-commit-config.yaml | 2 +- cpp/src/components/legacy/scc_matrix.cuh | 2 +- cpp/src/cores/core_number_impl.cuh | 2 +- ...er_v_random_select_transform_outgoing_e.cuh | 4 ++-- cpp/src/sampling/random_walks.cuh | 18 +++++++++--------- cpp/src/structure/renumber_edgelist_impl.cuh | 2 +- cpp/src/traversal/bfs_impl.cuh | 2 +- cpp/tests/prims/mg_extract_transform_e.cu | 4 ++-- cpp/tests/traversal/mg_sssp_test.cpp | 2 +- cpp/tests/traversal/sssp_test.cpp | 2 +- cpp/tests/utilities/test_utilities.hpp | 2 +- 11 files changed, 21 insertions(+), 21 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0f05aedf1a1..865d06b20e4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -33,7 +33,7 @@ repos: additional_dependencies: - flake8==6.0.0 - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v16.0.1 + rev: v16.0.6 hooks: - id: clang-format exclude: | diff --git a/cpp/src/components/legacy/scc_matrix.cuh b/cpp/src/components/legacy/scc_matrix.cuh index 3d56bdc5bf4..d044123bed0 100644 --- a/cpp/src/components/legacy/scc_matrix.cuh +++ b/cpp/src/components/legacy/scc_matrix.cuh @@ -68,7 +68,7 @@ struct SCC_Data { SCC_Data(size_t nrows, const IndexT* p_d_r_o, // row_offsets const IndexT* p_d_c_i) - : // column indices + : // column indices nrows_(nrows), p_d_r_o_(p_d_r_o), p_d_c_i_(p_d_c_i), diff --git a/cpp/src/cores/core_number_impl.cuh b/cpp/src/cores/core_number_impl.cuh index b63ae60f052..ea8e2a9c4ee 100644 --- a/cpp/src/cores/core_number_impl.cuh +++ b/cpp/src/cores/core_number_impl.cuh @@ -72,7 +72,7 @@ struct v_to_core_number_t { // a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct mult_degree_by_two_t { - __device__ edge_t operator()(edge_t d) const { return d* edge_t{2}; } + __device__ edge_t operator()(edge_t d) const { return d * edge_t{2}; } }; } // namespace diff --git a/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh b/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh index 3375a651982..e6db21f1c7c 100644 --- a/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh +++ b/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh @@ -287,7 +287,7 @@ rmm::device_uvector get_sampling_index_without_replacement( #ifndef NO_CUGRAPH_OPS edge_t mid_partition_degree_range_last = static_cast(K * 10); // tuning parameter assert(mid_partition_degree_range_last > K); - size_t high_partition_over_sampling_K = K * 2; // tuning parameter + size_t high_partition_over_sampling_K = K * 2; // tuning parameter assert(high_partition_over_sampling_K > K); rmm::device_uvector sample_nbr_indices(frontier_degrees.size() * K, handle.get_stream()); @@ -883,7 +883,7 @@ per_v_random_select_transform_e(raft::handle_t const& handle, sample_nbr_indices); // neighbor index within an edge partition (note that each vertex's // neighbors are distributed in minor_comm_size partitions) std::optional> sample_key_indices{ - std::nullopt}; // relevant only when (minor_comm_size > 1) + std::nullopt}; // relevant only when (minor_comm_size > 1) auto local_frontier_sample_counts = std::vector{}; auto local_frontier_sample_displacements = std::vector{}; if (minor_comm_size > 1) { diff --git a/cpp/src/sampling/random_walks.cuh b/cpp/src/sampling/random_walks.cuh index 46789c6b8bd..6a7334e9f1a 100644 --- a/cpp/src/sampling/random_walks.cuh +++ b/cpp/src/sampling/random_walks.cuh @@ -197,19 +197,19 @@ struct col_indx_extract_t { void operator()( original::device_vec_t const& d_coalesced_src_v, // in: coalesced vector of vertices original::device_vec_t const& - d_v_col_indx, // in: column indices, given by stepper's random engine + d_v_col_indx, // in: column indices, given by stepper's random engine original::device_vec_t& d_v_next_vertices, // out: set of destination vertices, for next step original::device_vec_t& - d_v_next_weights) // out: set of weights between src and destination vertices, for next step + d_v_next_weights) // out: set of weights between src and destination vertices, for next step const { thrust::transform_if( handle_.get_thrust_policy(), thrust::make_counting_iterator(0), - thrust::make_counting_iterator(num_paths_), // input1 - d_v_col_indx.begin(), // input2 - out_degs_, // stencil + thrust::make_counting_iterator(num_paths_), // input1 + d_v_col_indx.begin(), // input2 + out_degs_, // stencil thrust::make_zip_iterator( thrust::make_tuple(d_v_next_vertices.begin(), d_v_next_weights.begin())), // output [max_depth = max_depth_, @@ -575,9 +575,9 @@ struct random_walker_t { d_crt_out_degs, // |current set of vertex out degrees| = nelems, // to be used as stencil (don't scatter if 0) original::device_vec_t const& - d_sizes, // paths sizes used to provide delta in coalesced paths; - // pre-condition: assumed as updated to reflect new vertex additions; - // also, this is the number of _vertices_ in each path; + d_sizes, // paths sizes used to provide delta in coalesced paths; + // pre-condition: assumed as updated to reflect new vertex additions; + // also, this is the number of _vertices_ in each path; // hence for scattering weights this needs to be adjusted; hence the `adjust` parameter index_t stride, // stride = coalesce block size (max_depth for vertices; max_depth-1 for weights) @@ -762,7 +762,7 @@ random_walks_impl( // pre-allocate num_paths * max_depth; // original::device_vec_t d_coalesced_v(num_paths * max_depth, - stream); // coalesced vertex set + stream); // coalesced vertex set original::device_vec_t d_coalesced_w(num_paths * (max_depth - 1), stream); // coalesced weight set original::device_vec_t d_paths_sz(num_paths, stream); // paths sizes diff --git a/cpp/src/structure/renumber_edgelist_impl.cuh b/cpp/src/structure/renumber_edgelist_impl.cuh index d7381ba71af..6bc19ff4fe1 100644 --- a/cpp/src/structure/renumber_edgelist_impl.cuh +++ b/cpp/src/structure/renumber_edgelist_impl.cuh @@ -86,7 +86,7 @@ struct find_unused_id_t { for (size_t i = worker_id; i < sorted_local_vertices.size() + size_t{1}; i += num_workers) { auto start = (i == size_t{0}) ? std::numeric_limits::lowest() : sorted_local_vertices[i - size_t{1}]; - if (start != std::numeric_limits::max()) { ++start; }; // now inclusive + if (start != std::numeric_limits::max()) { ++start; }; // now inclusive auto end = (i == sorted_local_vertices.size()) ? std::numeric_limits::max() : sorted_local_vertices[i]; // exclusive for (vertex_t v = start; v < end; ++v) { diff --git a/cpp/src/traversal/bfs_impl.cuh b/cpp/src/traversal/bfs_impl.cuh index 0402184bd93..437071569bf 100644 --- a/cpp/src/traversal/bfs_impl.cuh +++ b/cpp/src/traversal/bfs_impl.cuh @@ -73,7 +73,7 @@ struct e_op_t { if (*(prev_visited_flags + packed_bool_offset(dst)) & packed_bool_mask(dst)) { // check if unvisited in previous iterations push = false; - } else { // check if unvisited in this iteration as well + } else { // check if unvisited in this iteration as well auto old = visited_flags.atomic_or(dst, true); push = !old; } diff --git a/cpp/tests/prims/mg_extract_transform_e.cu b/cpp/tests/prims/mg_extract_transform_e.cu index b71fe5ddb5e..bca6471a5bb 100644 --- a/cpp/tests/prims/mg_extract_transform_e.cu +++ b/cpp/tests/prims/mg_extract_transform_e.cu @@ -157,8 +157,8 @@ class Tests_MGExtractTransformE // 1. create MG graph constexpr bool is_multi_gpu = true; - constexpr bool renumber = true; // needs to be true for multi gpu case - constexpr bool store_transposed = false; // needs to be false for using extract_transform_e + constexpr bool renumber = true; // needs to be true for multi gpu case + constexpr bool store_transposed = false; // needs to be false for using extract_transform_e if (cugraph::test::g_perf) { RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement handle_->get_comms().barrier(); diff --git a/cpp/tests/traversal/mg_sssp_test.cpp b/cpp/tests/traversal/mg_sssp_test.cpp index b3e96981f96..ea0353c3743 100644 --- a/cpp/tests/traversal/mg_sssp_test.cpp +++ b/cpp/tests/traversal/mg_sssp_test.cpp @@ -214,7 +214,7 @@ class Tests_MGSSSP : public ::testing::TestWithParam> sg_renumber_map, // std::nullopt if the SG graph is not renumbered std::optional> - mg_vertices, // std::nullopt if the entire local vertex partition range is assumed + mg_vertices, // std::nullopt if the entire local vertex partition range is assumed raft::device_span mg_values); template From d93032105ff92a70e28511471444dfcb2557da90 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Wed, 20 Sep 2023 14:19:56 -0700 Subject: [PATCH 004/111] MFG C++ code bug fix (#3865) cugraph::sort_sampled_edgelist currently returns (label, hop) offsets of all zero. This PR fixes this. Authors: - Seunghwa Kang (https://github.com/seunghwak) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3865 --- cpp/src/sampling/sampling_post_processing_impl.cuh | 10 ++++++++-- cpp/tests/sampling/sampling_post_processing_test.cu | 11 +++++++++++ 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/cpp/src/sampling/sampling_post_processing_impl.cuh b/cpp/src/sampling/sampling_post_processing_impl.cuh index ff8da72ff35..0c397d91b20 100644 --- a/cpp/src/sampling/sampling_post_processing_impl.cuh +++ b/cpp/src/sampling/sampling_post_processing_impl.cuh @@ -1619,10 +1619,13 @@ renumber_and_sort_sampled_edgelist( (*edgelist_label_hop_offsets).begin(), (*edgelist_label_hop_offsets).end(), size_t{0}); - thrust::for_each( + // FIXME: the device lambda should be placed in cuda::proclaim_return_type() + // once we update CCCL version to 2.x + thrust::transform( handle.get_thrust_policy(), thrust::make_counting_iterator(size_t{0}), thrust::make_counting_iterator(num_labels * num_hops), + (*edgelist_label_hop_offsets).begin(), [edgelist_label_offsets = edgelist_label_offsets ? thrust::make_optional(std::get<0>(*edgelist_label_offsets)) : thrust::nullopt, @@ -1743,10 +1746,13 @@ sort_sampled_edgelist( (*edgelist_label_hop_offsets).begin(), (*edgelist_label_hop_offsets).end(), size_t{0}); - thrust::for_each( + // FIXME: the device lambda should be placed in cuda::proclaim_return_type() + // once we update CCCL version to 2.x + thrust::transform( handle.get_thrust_policy(), thrust::make_counting_iterator(size_t{0}), thrust::make_counting_iterator(num_labels * num_hops), + (*edgelist_label_hop_offsets).begin(), [edgelist_label_offsets = edgelist_label_offsets ? thrust::make_optional(std::get<0>(*edgelist_label_offsets)) : thrust::nullopt, diff --git a/cpp/tests/sampling/sampling_post_processing_test.cu b/cpp/tests/sampling/sampling_post_processing_test.cu index 422fe953b20..e5267d75ac2 100644 --- a/cpp/tests/sampling/sampling_post_processing_test.cu +++ b/cpp/tests/sampling/sampling_post_processing_test.cu @@ -635,6 +635,12 @@ class Tests_SamplingPostProcessing (*renumbered_and_sorted_edgelist_label_hop_offsets).end())) << "Renumbered and sorted edge list (label,hop) offset array values should be " "non-decreasing."; + + ASSERT_TRUE( + (*renumbered_and_sorted_edgelist_label_hop_offsets).back_element(handle.get_stream()) == + renumbered_and_sorted_edgelist_srcs.size()) + << "Renumbered and sorted edge list (label,hop) offset array's last element should " + "coincide with the number of edges."; } if (renumbered_and_sorted_renumber_map_label_offsets) { @@ -1189,6 +1195,11 @@ class Tests_SamplingPostProcessing (*sorted_edgelist_label_hop_offsets).end())) << "Sorted edge list (label,hop) offset array values should be " "non-decreasing."; + + ASSERT_TRUE((*sorted_edgelist_label_hop_offsets).back_element(handle.get_stream()) == + sorted_edgelist_srcs.size()) + << "Sorted edge list (label,hop) offset array's last element should coincide with the " + "number of edges."; } for (size_t i = 0; i < sampling_post_processing_usecase.num_labels; ++i) { From a53ab34b804af2865d2d210b801a759d2ca29bc6 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Thu, 21 Sep 2023 19:39:18 +0200 Subject: [PATCH 005/111] Refactor python code for similarity algos to use latest CAPI (#3828) This PR - refactors python code for similarity algorithms (Jaccard, Sorensen, Overlap) to use latest CAPI - removes legacy cuda c/c++ code and python wrapper around legacy code - update CAPI tests - remove and update python tests Closes #2546 Closes #2547 Closes #2548 Closes #2549 Closes #2749 Authors: - Naim (https://github.com/naimnv) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3828 --- cpp/CMakeLists.txt | 2 - cpp/src/link_prediction/legacy/jaccard.cu | 429 ------------------ cpp/src/link_prediction/legacy/overlap.cu | 425 ----------------- cpp/tests/c_api/mg_similarity_test.c | 51 ++- cpp/tests/c_api/similarity_test.c | 57 +-- python/cugraph/CMakeLists.txt | 1 - .../cugraph/community/induced_subgraph.py | 9 +- .../cugraph/dask/link_prediction/jaccard.py | 2 +- .../cugraph/dask/link_prediction/overlap.py | 2 +- .../cugraph/dask/link_prediction/sorensen.py | 2 +- .../cugraph/cugraph/experimental/__init__.py | 32 +- .../experimental/link_prediction/__init__.py | 13 - .../experimental/link_prediction/jaccard.py | 255 ----------- .../experimental/link_prediction/overlap.py | 223 --------- .../experimental/link_prediction/sorensen.py | 221 --------- .../cugraph/link_prediction/CMakeLists.txt | 22 - .../cugraph/link_prediction/__init__.py | 23 +- .../cugraph/link_prediction/jaccard.pxd | 35 -- .../cugraph/link_prediction/jaccard.py | 208 ++++++--- .../link_prediction/jaccard_wrapper.pyx | 155 ------- .../cugraph/link_prediction/overlap.pxd | 35 -- .../cugraph/link_prediction/overlap.py | 212 +++++++-- .../link_prediction/overlap_wrapper.pyx | 142 ------ .../cugraph/link_prediction/sorensen.py | 223 ++++++--- .../cugraph/link_prediction/wjaccard.py | 100 ++-- .../cugraph/link_prediction/woverlap.py | 76 ++-- .../cugraph/link_prediction/wsorensen.py | 78 ++-- .../cugraph/cugraph/sampling/random_walks.py | 9 +- .../tests/link_prediction/test_jaccard.py | 315 +++++++------ .../tests/link_prediction/test_overlap.py | 152 ++++--- .../tests/link_prediction/test_sorensen.py | 252 ++++++---- .../tests/link_prediction/test_wjaccard.py | 177 -------- .../tests/link_prediction/test_woverlap.py | 171 ------- .../tests/link_prediction/test_wsorensen.py | 181 -------- python/pylibcugraph/pylibcugraph/__init__.py | 7 + .../pylibcugraph/experimental/__init__.py | 19 +- .../pylibcugraph/jaccard_coefficients.pyx | 12 +- .../pylibcugraph/overlap_coefficients.pyx | 10 +- .../pylibcugraph/sorensen_coefficients.pyx | 10 +- 39 files changed, 1129 insertions(+), 3219 deletions(-) delete mode 100644 cpp/src/link_prediction/legacy/jaccard.cu delete mode 100644 cpp/src/link_prediction/legacy/overlap.cu delete mode 100644 python/cugraph/cugraph/experimental/link_prediction/__init__.py delete mode 100644 python/cugraph/cugraph/experimental/link_prediction/jaccard.py delete mode 100644 python/cugraph/cugraph/experimental/link_prediction/overlap.py delete mode 100644 python/cugraph/cugraph/experimental/link_prediction/sorensen.py delete mode 100644 python/cugraph/cugraph/link_prediction/CMakeLists.txt delete mode 100644 python/cugraph/cugraph/link_prediction/jaccard.pxd delete mode 100644 python/cugraph/cugraph/link_prediction/jaccard_wrapper.pyx delete mode 100644 python/cugraph/cugraph/link_prediction/overlap.pxd delete mode 100644 python/cugraph/cugraph/link_prediction/overlap_wrapper.pyx delete mode 100644 python/cugraph/cugraph/tests/link_prediction/test_wjaccard.py delete mode 100644 python/cugraph/cugraph/tests/link_prediction/test_woverlap.py delete mode 100644 python/cugraph/cugraph/tests/link_prediction/test_wsorensen.py diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index a6c26ee3b91..0d7bd86075d 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -196,8 +196,6 @@ set(CUGRAPH_SOURCES src/utilities/path_retrieval.cu src/structure/legacy/graph.cu src/linear_assignment/legacy/hungarian.cu - src/link_prediction/legacy/jaccard.cu - src/link_prediction/legacy/overlap.cu src/link_prediction/jaccard_sg.cu src/link_prediction/sorensen_sg.cu src/link_prediction/overlap_sg.cu diff --git a/cpp/src/link_prediction/legacy/jaccard.cu b/cpp/src/link_prediction/legacy/jaccard.cu deleted file mode 100644 index d0b240e3c77..00000000000 --- a/cpp/src/link_prediction/legacy/jaccard.cu +++ /dev/null @@ -1,429 +0,0 @@ -/* - * Copyright (c) 2019-2022, NVIDIA CORPORATION. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include -#include -#include - -#include -#include - -#include - -namespace cugraph { -namespace detail { - -// Volume of neighboors (*weight_s) -template -__global__ void jaccard_row_sum( - vertex_t n, edge_t const* csrPtr, vertex_t const* csrInd, weight_t const* v, weight_t* work) -{ - vertex_t row; - edge_t start, end, length; - weight_t sum; - - for (row = threadIdx.y + blockIdx.y * blockDim.y; row < n; row += gridDim.y * blockDim.y) { - start = csrPtr[row]; - end = csrPtr[row + 1]; - length = end - start; - - // compute row sums - if (weighted) { - sum = parallel_prefix_sum(length, csrInd + start, v); - if (threadIdx.x == 0) work[row] = sum; - } else { - work[row] = static_cast(length); - } - } -} - -// Volume of intersections (*weight_i) and cumulated volume of neighboors (*weight_s) -template -__global__ void jaccard_is(vertex_t n, - edge_t const* csrPtr, - vertex_t const* csrInd, - weight_t const* v, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s) -{ - edge_t i, j, Ni, Nj; - vertex_t row, col; - vertex_t ref, cur, ref_col, cur_col, match; - weight_t ref_val; - - for (row = threadIdx.z + blockIdx.z * blockDim.z; row < n; row += gridDim.z * blockDim.z) { - for (j = csrPtr[row] + threadIdx.y + blockIdx.y * blockDim.y; j < csrPtr[row + 1]; - j += gridDim.y * blockDim.y) { - col = csrInd[j]; - // find which row has least elements (and call it reference row) - Ni = csrPtr[row + 1] - csrPtr[row]; - Nj = csrPtr[col + 1] - csrPtr[col]; - ref = (Ni < Nj) ? row : col; - cur = (Ni < Nj) ? col : row; - - // compute new sum weights - weight_s[j] = work[row] + work[col]; - - // compute new intersection weights - // search for the element with the same column index in the reference row - for (i = csrPtr[ref] + threadIdx.x + blockIdx.x * blockDim.x; i < csrPtr[ref + 1]; - i += gridDim.x * blockDim.x) { - match = -1; - ref_col = csrInd[i]; - if (weighted) { - ref_val = v[ref_col]; - } else { - ref_val = 1.0; - } - - // binary search (column indices are sorted within each row) - edge_t left = csrPtr[cur]; - edge_t right = csrPtr[cur + 1] - 1; - while (left <= right) { - edge_t middle = (left + right) >> 1; - cur_col = csrInd[middle]; - if (cur_col > ref_col) { - right = middle - 1; - } else if (cur_col < ref_col) { - left = middle + 1; - } else { - match = middle; - break; - } - } - - // if the element with the same column index in the reference row has been found - if (match != -1) { atomicAdd(&weight_i[j], ref_val); } - } - } - } -} - -// Volume of intersections (*weight_i) and cumulated volume of neighboors (*weight_s) -// Using list of node pairs -template -__global__ void jaccard_is_pairs(edge_t num_pairs, - edge_t const* csrPtr, - vertex_t const* csrInd, - vertex_t const* first_pair, - vertex_t const* second_pair, - weight_t const* v, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s) -{ - edge_t i, idx, Ni, Nj, match; - vertex_t row, col, ref, cur, ref_col, cur_col; - weight_t ref_val; - - for (idx = threadIdx.z + blockIdx.z * blockDim.z; idx < num_pairs; - idx += gridDim.z * blockDim.z) { - row = first_pair[idx]; - col = second_pair[idx]; - - // find which row has least elements (and call it reference row) - Ni = csrPtr[row + 1] - csrPtr[row]; - Nj = csrPtr[col + 1] - csrPtr[col]; - ref = (Ni < Nj) ? row : col; - cur = (Ni < Nj) ? col : row; - - // compute new sum weights - weight_s[idx] = work[row] + work[col]; - - // compute new intersection weights - // search for the element with the same column index in the reference row - for (i = csrPtr[ref] + threadIdx.x + blockIdx.x * blockDim.x; i < csrPtr[ref + 1]; - i += gridDim.x * blockDim.x) { - match = -1; - ref_col = csrInd[i]; - if (weighted) { - ref_val = v[ref_col]; - } else { - ref_val = 1.0; - } - - // binary search (column indices are sorted within each row) - edge_t left = csrPtr[cur]; - edge_t right = csrPtr[cur + 1] - 1; - while (left <= right) { - edge_t middle = (left + right) >> 1; - cur_col = csrInd[middle]; - if (cur_col > ref_col) { - right = middle - 1; - } else if (cur_col < ref_col) { - left = middle + 1; - } else { - match = middle; - break; - } - } - - // if the element with the same column index in the reference row has been found - if (match != -1) { atomicAdd(&weight_i[idx], ref_val); } - } - } -} - -// Jaccard weights (*weight) -template -__global__ void jaccard_jw(edge_t e, - weight_t const* weight_i, - weight_t const* weight_s, - weight_t* weight_j) -{ - edge_t j; - weight_t Wi, Ws, Wu; - - for (j = threadIdx.x + blockIdx.x * blockDim.x; j < e; j += gridDim.x * blockDim.x) { - Wi = weight_i[j]; - Ws = weight_s[j]; - Wu = Ws - Wi; - weight_j[j] = (Wi / Wu); - } -} - -template -int jaccard(vertex_t n, - edge_t e, - edge_t const* csrPtr, - vertex_t const* csrInd, - weight_t const* weight_in, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s, - weight_t* weight_j) -{ - rmm::cuda_stream_view stream_view; - dim3 nthreads, nblocks; - int y = 4; - - // setup launch configuration - nthreads.x = 32; - nthreads.y = y; - nthreads.z = 1; - nblocks.x = 1; - nblocks.y = min((n + nthreads.y - 1) / nthreads.y, vertex_t{CUDA_MAX_BLOCKS}); - nblocks.z = 1; - - // launch kernel - jaccard_row_sum - <<>>(n, csrPtr, csrInd, weight_in, work); - - thrust::fill(rmm::exec_policy(stream_view), weight_i, weight_i + e, weight_t{0.0}); - - // setup launch configuration - nthreads.x = 32 / y; - nthreads.y = y; - nthreads.z = 8; - nblocks.x = 1; - nblocks.y = 1; - nblocks.z = min((n + nthreads.z - 1) / nthreads.z, vertex_t{CUDA_MAX_BLOCKS}); // 1; - - // launch kernel - jaccard_is<<>>( - n, csrPtr, csrInd, weight_in, work, weight_i, weight_s); - - // setup launch configuration - nthreads.x = min(e, edge_t{CUDA_MAX_KERNEL_THREADS}); - nthreads.y = 1; - nthreads.z = 1; - nblocks.x = min((e + nthreads.x - 1) / nthreads.x, edge_t{CUDA_MAX_BLOCKS}); - nblocks.y = 1; - nblocks.z = 1; - - // launch kernel - jaccard_jw - <<>>(e, weight_i, weight_s, weight_j); - - return 0; -} - -template -int jaccard_pairs(vertex_t n, - edge_t num_pairs, - edge_t const* csrPtr, - vertex_t const* csrInd, - vertex_t const* first_pair, - vertex_t const* second_pair, - weight_t const* weight_in, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s, - weight_t* weight_j) -{ - dim3 nthreads, nblocks; - int y = 4; - - // setup launch configuration - nthreads.x = 32; - nthreads.y = y; - nthreads.z = 1; - nblocks.x = 1; - nblocks.y = min((n + nthreads.y - 1) / nthreads.y, vertex_t{CUDA_MAX_BLOCKS}); - nblocks.z = 1; - - // launch kernel - jaccard_row_sum - <<>>(n, csrPtr, csrInd, weight_in, work); - cudaDeviceSynchronize(); - - // NOTE: initilized weight_i vector with 0.0 - // fill(num_pairs, weight_i, weight_t{0.0}); - - // setup launch configuration - nthreads.x = 32; - nthreads.y = 1; - nthreads.z = 8; - nblocks.x = 1; - nblocks.y = 1; - nblocks.z = min((n + nthreads.z - 1) / nthreads.z, vertex_t{CUDA_MAX_BLOCKS}); // 1; - - // launch kernel - jaccard_is_pairs<<>>( - num_pairs, csrPtr, csrInd, first_pair, second_pair, weight_in, work, weight_i, weight_s); - - // setup launch configuration - nthreads.x = min(num_pairs, edge_t{CUDA_MAX_KERNEL_THREADS}); - nthreads.y = 1; - nthreads.z = 1; - nblocks.x = min((num_pairs + nthreads.x - 1) / nthreads.x, (edge_t)CUDA_MAX_BLOCKS); - nblocks.y = 1; - nblocks.z = 1; - - // launch kernel - jaccard_jw - <<>>(num_pairs, weight_i, weight_s, weight_j); - - return 0; -} -} // namespace detail - -template -void jaccard(legacy::GraphCSRView const& graph, WT const* weights, WT* result) -{ - CUGRAPH_EXPECTS(result != nullptr, "Invalid input argument: result pointer is NULL"); - - rmm::device_vector weight_i(graph.number_of_edges); - rmm::device_vector weight_s(graph.number_of_edges); - rmm::device_vector work(graph.number_of_vertices); - - if (weights == nullptr) { - cugraph::detail::jaccard(graph.number_of_vertices, - graph.number_of_edges, - graph.offsets, - graph.indices, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } else { - cugraph::detail::jaccard(graph.number_of_vertices, - graph.number_of_edges, - graph.offsets, - graph.indices, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } -} - -template -void jaccard_list(legacy::GraphCSRView const& graph, - WT const* weights, - ET num_pairs, - VT const* first, - VT const* second, - WT* result) -{ - CUGRAPH_EXPECTS(result != nullptr, "Invalid input argument: result pointer is NULL"); - CUGRAPH_EXPECTS(first != nullptr, "Invalid input argument: first is NULL"); - CUGRAPH_EXPECTS(second != nullptr, "Invalid input argument: second in NULL"); - - rmm::device_vector weight_i(num_pairs, WT{0.0}); - rmm::device_vector weight_s(num_pairs); - rmm::device_vector work(graph.number_of_vertices); - - if (weights == nullptr) { - cugraph::detail::jaccard_pairs(graph.number_of_vertices, - num_pairs, - graph.offsets, - graph.indices, - first, - second, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } else { - cugraph::detail::jaccard_pairs(graph.number_of_vertices, - num_pairs, - graph.offsets, - graph.indices, - first, - second, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } -} - -template void jaccard(legacy::GraphCSRView const&, - float const*, - float*); -template void jaccard( - legacy::GraphCSRView const&, double const*, double*); -template void jaccard(legacy::GraphCSRView const&, - float const*, - float*); -template void jaccard( - legacy::GraphCSRView const&, double const*, double*); -template void jaccard_list( - legacy::GraphCSRView const&, - float const*, - int32_t, - int32_t const*, - int32_t const*, - float*); -template void jaccard_list( - legacy::GraphCSRView const&, - double const*, - int32_t, - int32_t const*, - int32_t const*, - double*); -template void jaccard_list( - legacy::GraphCSRView const&, - float const*, - int64_t, - int64_t const*, - int64_t const*, - float*); -template void jaccard_list( - legacy::GraphCSRView const&, - double const*, - int64_t, - int64_t const*, - int64_t const*, - double*); - -} // namespace cugraph diff --git a/cpp/src/link_prediction/legacy/overlap.cu b/cpp/src/link_prediction/legacy/overlap.cu deleted file mode 100644 index 67d7cd5e4c6..00000000000 --- a/cpp/src/link_prediction/legacy/overlap.cu +++ /dev/null @@ -1,425 +0,0 @@ -/* - * Copyright (c) 2019-2022, NVIDIA CORPORATION. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include -#include -#include -#include - -namespace cugraph { -namespace detail { - -// Volume of neighboors (*weight_s) -// TODO: Identical kernel to jaccard_row_sum!! -template -__global__ void overlap_row_sum( - vertex_t n, edge_t const* csrPtr, vertex_t const* csrInd, weight_t const* v, weight_t* work) -{ - vertex_t row; - edge_t start, end, length; - weight_t sum; - - for (row = threadIdx.y + blockIdx.y * blockDim.y; row < n; row += gridDim.y * blockDim.y) { - start = csrPtr[row]; - end = csrPtr[row + 1]; - length = end - start; - - // compute row sums - if (weighted) { - sum = parallel_prefix_sum(length, csrInd + start, v); - if (threadIdx.x == 0) work[row] = sum; - } else { - work[row] = static_cast(length); - } - } -} - -// Volume of intersections (*weight_i) and cumulated volume of neighboors (*weight_s) -// TODO: Identical kernel to jaccard_row_sum!! -template -__global__ void overlap_is(vertex_t n, - edge_t const* csrPtr, - vertex_t const* csrInd, - weight_t const* v, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s) -{ - edge_t i, j, Ni, Nj; - vertex_t row, col; - vertex_t ref, cur, ref_col, cur_col, match; - weight_t ref_val; - - for (row = threadIdx.z + blockIdx.z * blockDim.z; row < n; row += gridDim.z * blockDim.z) { - for (j = csrPtr[row] + threadIdx.y + blockIdx.y * blockDim.y; j < csrPtr[row + 1]; - j += gridDim.y * blockDim.y) { - col = csrInd[j]; - // find which row has least elements (and call it reference row) - Ni = csrPtr[row + 1] - csrPtr[row]; - Nj = csrPtr[col + 1] - csrPtr[col]; - ref = (Ni < Nj) ? row : col; - cur = (Ni < Nj) ? col : row; - - // compute new sum weights - weight_s[j] = min(work[row], work[col]); - - // compute new intersection weights - // search for the element with the same column index in the reference row - for (i = csrPtr[ref] + threadIdx.x + blockIdx.x * blockDim.x; i < csrPtr[ref + 1]; - i += gridDim.x * blockDim.x) { - match = -1; - ref_col = csrInd[i]; - if (weighted) { - ref_val = v[ref_col]; - } else { - ref_val = 1.0; - } - - // binary search (column indices are sorted within each row) - edge_t left = csrPtr[cur]; - edge_t right = csrPtr[cur + 1] - 1; - while (left <= right) { - edge_t middle = (left + right) >> 1; - cur_col = csrInd[middle]; - if (cur_col > ref_col) { - right = middle - 1; - } else if (cur_col < ref_col) { - left = middle + 1; - } else { - match = middle; - break; - } - } - - // if the element with the same column index in the reference row has been found - if (match != -1) { atomicAdd(&weight_i[j], ref_val); } - } - } - } -} - -// Volume of intersections (*weight_i) and cumulated volume of neighboors (*weight_s) -// Using list of node pairs -// NOTE: NOT the same as jaccard -template -__global__ void overlap_is_pairs(edge_t num_pairs, - edge_t const* csrPtr, - vertex_t const* csrInd, - vertex_t const* first_pair, - vertex_t const* second_pair, - weight_t const* v, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s) -{ - edge_t i, idx, Ni, Nj, match; - vertex_t row, col, ref, cur, ref_col, cur_col; - weight_t ref_val; - - for (idx = threadIdx.z + blockIdx.z * blockDim.z; idx < num_pairs; - idx += gridDim.z * blockDim.z) { - row = first_pair[idx]; - col = second_pair[idx]; - - // find which row has least elements (and call it reference row) - Ni = csrPtr[row + 1] - csrPtr[row]; - Nj = csrPtr[col + 1] - csrPtr[col]; - ref = (Ni < Nj) ? row : col; - cur = (Ni < Nj) ? col : row; - - // compute new sum weights - weight_s[idx] = min(work[row], work[col]); - - // compute new intersection weights - // search for the element with the same column index in the reference row - for (i = csrPtr[ref] + threadIdx.x + blockIdx.x * blockDim.x; i < csrPtr[ref + 1]; - i += gridDim.x * blockDim.x) { - match = -1; - ref_col = csrInd[i]; - if (weighted) { - ref_val = v[ref_col]; - } else { - ref_val = 1.0; - } - - // binary search (column indices are sorted within each row) - edge_t left = csrPtr[cur]; - edge_t right = csrPtr[cur + 1] - 1; - while (left <= right) { - edge_t middle = (left + right) >> 1; - cur_col = csrInd[middle]; - if (cur_col > ref_col) { - right = middle - 1; - } else if (cur_col < ref_col) { - left = middle + 1; - } else { - match = middle; - break; - } - } - - // if the element with the same column index in the reference row has been found - if (match != -1) { atomicAdd(&weight_i[idx], ref_val); } - } - } -} - -// Overlap weights (*weight) -template -__global__ void overlap_jw(edge_t e, - edge_t const* csrPtr, - vertex_t const* csrInd, - weight_t* weight_i, - weight_t* weight_s, - weight_t* weight_j) -{ - edge_t j; - weight_t Wi, Wu; - - for (j = threadIdx.x + blockIdx.x * blockDim.x; j < e; j += gridDim.x * blockDim.x) { - Wi = weight_i[j]; - Wu = weight_s[j]; - weight_j[j] = (Wi / Wu); - } -} - -template -int overlap(vertex_t n, - edge_t e, - edge_t const* csrPtr, - vertex_t const* csrInd, - weight_t const* weight_in, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s, - weight_t* weight_j) -{ - dim3 nthreads, nblocks; - int y = 4; - - // setup launch configuration - nthreads.x = 32; - nthreads.y = y; - nthreads.z = 1; - nblocks.x = 1; - nblocks.y = min((n + nthreads.y - 1) / nthreads.y, vertex_t{CUDA_MAX_BLOCKS}); - nblocks.z = 1; - - // launch kernel - overlap_row_sum - <<>>(n, csrPtr, csrInd, weight_in, work); - cudaDeviceSynchronize(); - fill(e, weight_i, weight_t{0.0}); - - // setup launch configuration - nthreads.x = 32 / y; - nthreads.y = y; - nthreads.z = 8; - nblocks.x = 1; - nblocks.y = 1; - nblocks.z = min((n + nthreads.z - 1) / nthreads.z, vertex_t{CUDA_MAX_BLOCKS}); // 1; - - // launch kernel - overlap_is - <<>>(n, csrPtr, csrInd, weight_in, work, weight_i, weight_s); - - // setup launch configuration - nthreads.x = min(e, edge_t{CUDA_MAX_KERNEL_THREADS}); - nthreads.y = 1; - nthreads.z = 1; - nblocks.x = min((e + nthreads.x - 1) / nthreads.x, edge_t{CUDA_MAX_BLOCKS}); - nblocks.y = 1; - nblocks.z = 1; - - // launch kernel - overlap_jw - <<>>(e, csrPtr, csrInd, weight_i, weight_s, weight_j); - - return 0; -} - -template -int overlap_pairs(vertex_t n, - edge_t num_pairs, - edge_t const* csrPtr, - vertex_t const* csrInd, - vertex_t const* first_pair, - vertex_t const* second_pair, - weight_t const* weight_in, - weight_t* work, - weight_t* weight_i, - weight_t* weight_s, - weight_t* weight_j) -{ - dim3 nthreads, nblocks; - int y = 4; - - // setup launch configuration - nthreads.x = 32; - nthreads.y = y; - nthreads.z = 1; - nblocks.x = 1; - nblocks.y = min((n + nthreads.y - 1) / nthreads.y, vertex_t{CUDA_MAX_BLOCKS}); - nblocks.z = 1; - // launch kernel - - overlap_row_sum - <<>>(n, csrPtr, csrInd, weight_in, work); - cudaDeviceSynchronize(); - fill(num_pairs, weight_i, weight_t{0.0}); - // setup launch configuration - nthreads.x = 32; - nthreads.y = 1; - nthreads.z = 8; - nblocks.x = 1; - nblocks.y = 1; - nblocks.z = min((n + nthreads.z - 1) / nthreads.z, vertex_t{CUDA_MAX_BLOCKS}); // 1; - - // launch kernel - overlap_is_pairs<<>>( - num_pairs, csrPtr, csrInd, first_pair, second_pair, weight_in, work, weight_i, weight_s); - - // setup launch configuration - nthreads.x = min(num_pairs, edge_t{CUDA_MAX_KERNEL_THREADS}); - nthreads.y = 1; - nthreads.z = 1; - nblocks.x = min((num_pairs + nthreads.x - 1) / nthreads.x, edge_t{CUDA_MAX_BLOCKS}); - nblocks.y = 1; - nblocks.z = 1; - // launch kernel - - overlap_jw - <<>>(num_pairs, csrPtr, csrInd, weight_i, weight_s, weight_j); - - return 0; -} -} // namespace detail - -template -void overlap(legacy::GraphCSRView const& graph, WT const* weights, WT* result) -{ - CUGRAPH_EXPECTS(result != nullptr, "Invalid input argument: result pointer is NULL"); - - rmm::device_vector weight_i(graph.number_of_edges); - rmm::device_vector weight_s(graph.number_of_edges); - rmm::device_vector work(graph.number_of_vertices); - - if (weights == nullptr) { - cugraph::detail::overlap(graph.number_of_vertices, - graph.number_of_edges, - graph.offsets, - graph.indices, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } else { - cugraph::detail::overlap(graph.number_of_vertices, - graph.number_of_edges, - graph.offsets, - graph.indices, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } -} - -template -void overlap_list(legacy::GraphCSRView const& graph, - WT const* weights, - ET num_pairs, - VT const* first, - VT const* second, - WT* result) -{ - CUGRAPH_EXPECTS(result != nullptr, "Invalid input argument: result pointer is NULL"); - CUGRAPH_EXPECTS(first != nullptr, "Invalid input argument: first column is NULL"); - CUGRAPH_EXPECTS(second != nullptr, "Invalid input argument: second column is NULL"); - - rmm::device_vector weight_i(num_pairs); - rmm::device_vector weight_s(num_pairs); - rmm::device_vector work(graph.number_of_vertices); - - if (weights == nullptr) { - cugraph::detail::overlap_pairs(graph.number_of_vertices, - num_pairs, - graph.offsets, - graph.indices, - first, - second, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } else { - cugraph::detail::overlap_pairs(graph.number_of_vertices, - num_pairs, - graph.offsets, - graph.indices, - first, - second, - weights, - work.data().get(), - weight_i.data().get(), - weight_s.data().get(), - result); - } -} - -template void overlap(legacy::GraphCSRView const&, - float const*, - float*); -template void overlap( - legacy::GraphCSRView const&, double const*, double*); -template void overlap(legacy::GraphCSRView const&, - float const*, - float*); -template void overlap( - legacy::GraphCSRView const&, double const*, double*); -template void overlap_list( - legacy::GraphCSRView const&, - float const*, - int32_t, - int32_t const*, - int32_t const*, - float*); -template void overlap_list( - legacy::GraphCSRView const&, - double const*, - int32_t, - int32_t const*, - int32_t const*, - double*); -template void overlap_list( - legacy::GraphCSRView const&, - float const*, - int64_t, - int64_t const*, - int64_t const*, - float*); -template void overlap_list( - legacy::GraphCSRView const&, - double const*, - int64_t, - int64_t const*, - int64_t const*, - double*); - -} // namespace cugraph diff --git a/cpp/tests/c_api/mg_similarity_test.c b/cpp/tests/c_api/mg_similarity_test.c index 0ac160245ab..336f6c50519 100644 --- a/cpp/tests/c_api/mg_similarity_test.c +++ b/cpp/tests/c_api/mg_similarity_test.c @@ -160,15 +160,16 @@ int test_jaccard(const cugraph_resource_handle_t* handle) int test_weighted_jaccard(const cugraph_resource_handle_t* handle) { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.357143, 0.208333, 0.0}; return generic_similarity_test(handle, h_src, @@ -216,15 +217,16 @@ int test_sorensen(const cugraph_resource_handle_t* handle) int test_weighted_sorensen(const cugraph_resource_handle_t* handle) { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.526316, 0.344828, 0.000000}; return generic_similarity_test(handle, h_src, @@ -272,15 +274,16 @@ int test_overlap(const cugraph_resource_handle_t* handle) int test_weighted_overlap(const cugraph_resource_handle_t* handle) { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.714286, 0.416667, 0.000000}; return generic_similarity_test(handle, h_src, diff --git a/cpp/tests/c_api/similarity_test.c b/cpp/tests/c_api/similarity_test.c index 20af3f3eccd..52f849ccd28 100644 --- a/cpp/tests/c_api/similarity_test.c +++ b/cpp/tests/c_api/similarity_test.c @@ -161,15 +161,16 @@ int test_jaccard() int test_weighted_jaccard() { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.357143, 0.208333, 0.0}; return generic_similarity_test(h_src, h_dst, @@ -215,15 +216,16 @@ int test_sorensen() int test_weighted_sorensen() { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.526316, 0.344828, 0.000000}; return generic_similarity_test(h_src, h_dst, @@ -269,15 +271,16 @@ int test_overlap() int test_weighted_overlap() { size_t num_edges = 16; - size_t num_vertices = 6; - size_t num_pairs = 10; + size_t num_vertices = 7; + size_t num_pairs = 3; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - vertex_t h_first[] = {0, 0, 0, 1, 1, 1, 2, 2, 2, 3}; - vertex_t h_second[] = {1, 3, 4, 2, 3, 5, 3, 4, 5, 4}; - weight_t h_result[] = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}; // TODO: Fill in + vertex_t h_src[] = {0, 1, 2, 0, 1, 2, 3, 3, 3, 4, 4, 4, 0, 5, 2, 6}; + vertex_t h_dst[] = {3, 3, 3, 4, 4, 4, 0, 1, 2, 0, 1, 2, 5, 0, 6, 2}; + weight_t h_wgt[] = {0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 3.5, 4.0, 4.0}; + + vertex_t h_first[] = {0, 0, 1}; + vertex_t h_second[] = {1, 2, 3}; + weight_t h_result[] = {0.714286, 0.416667, 0.000000}; return generic_similarity_test(h_src, h_dst, @@ -301,8 +304,8 @@ int main(int argc, char** argv) result |= RUN_TEST(test_jaccard); result |= RUN_TEST(test_sorensen); result |= RUN_TEST(test_overlap); - // result |= RUN_TEST(test_weighted_jaccard); - // result |= RUN_TEST(test_weighted_sorensen); - // result |= RUN_TEST(test_weighted_overlap); + result |= RUN_TEST(test_weighted_jaccard); + result |= RUN_TEST(test_weighted_sorensen); + result |= RUN_TEST(test_weighted_overlap); return result; } diff --git a/python/cugraph/CMakeLists.txt b/python/cugraph/CMakeLists.txt index f3b28623b12..ecfcb9b219f 100644 --- a/python/cugraph/CMakeLists.txt +++ b/python/cugraph/CMakeLists.txt @@ -89,7 +89,6 @@ add_subdirectory(cugraph/dask/structure) add_subdirectory(cugraph/internals) add_subdirectory(cugraph/layout) add_subdirectory(cugraph/linear_assignment) -add_subdirectory(cugraph/link_prediction) add_subdirectory(cugraph/structure) add_subdirectory(cugraph/tree) add_subdirectory(cugraph/utilities) diff --git a/python/cugraph/cugraph/community/induced_subgraph.py b/python/cugraph/cugraph/community/induced_subgraph.py index 29fe2f29c1e..3a901199b01 100644 --- a/python/cugraph/cugraph/community/induced_subgraph.py +++ b/python/cugraph/cugraph/community/induced_subgraph.py @@ -25,11 +25,10 @@ ) from cugraph.utilities.utils import import_optional -# FIXME: the networkx.Graph type used in the type annotation for -# induced_subgraph() is specified using a string literal to avoid depending on -# and importing networkx. Instead, networkx is imported optionally, which may -# cause a problem for a type checker if run in an environment where networkx is -# not installed. +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. networkx = import_optional("networkx") diff --git a/python/cugraph/cugraph/dask/link_prediction/jaccard.py b/python/cugraph/cugraph/dask/link_prediction/jaccard.py index b3d688584a0..218e6206fc3 100644 --- a/python/cugraph/cugraph/dask/link_prediction/jaccard.py +++ b/python/cugraph/cugraph/dask/link_prediction/jaccard.py @@ -20,7 +20,7 @@ from cugraph.dask.common.input_utils import get_distributed_data from cugraph.utilities import renumber_vertex_pair -from pylibcugraph.experimental import ( +from pylibcugraph import ( jaccard_coefficients as pylibcugraph_jaccard_coefficients, ) from pylibcugraph import ResourceHandle diff --git a/python/cugraph/cugraph/dask/link_prediction/overlap.py b/python/cugraph/cugraph/dask/link_prediction/overlap.py index c47aeef3c72..5540be28fd1 100644 --- a/python/cugraph/cugraph/dask/link_prediction/overlap.py +++ b/python/cugraph/cugraph/dask/link_prediction/overlap.py @@ -20,7 +20,7 @@ from cugraph.dask.common.input_utils import get_distributed_data from cugraph.utilities import renumber_vertex_pair -from pylibcugraph.experimental import ( +from pylibcugraph import ( overlap_coefficients as pylibcugraph_overlap_coefficients, ) from pylibcugraph import ResourceHandle diff --git a/python/cugraph/cugraph/dask/link_prediction/sorensen.py b/python/cugraph/cugraph/dask/link_prediction/sorensen.py index bb5a3f44f39..24295ac330c 100644 --- a/python/cugraph/cugraph/dask/link_prediction/sorensen.py +++ b/python/cugraph/cugraph/dask/link_prediction/sorensen.py @@ -20,7 +20,7 @@ from cugraph.dask.common.input_utils import get_distributed_data from cugraph.utilities import renumber_vertex_pair -from pylibcugraph.experimental import ( +from pylibcugraph import ( sorensen_coefficients as pylibcugraph_sorensen_coefficients, ) from pylibcugraph import ResourceHandle diff --git a/python/cugraph/cugraph/experimental/__init__.py b/python/cugraph/cugraph/experimental/__init__.py index b96b760e634..2309a529047 100644 --- a/python/cugraph/cugraph/experimental/__init__.py +++ b/python/cugraph/cugraph/experimental/__init__.py @@ -48,30 +48,22 @@ experimental_warning_wrapper(EXPERIMENTAL__find_bicliques) ) -from cugraph.experimental.link_prediction.jaccard import ( - EXPERIMENTAL__jaccard, - EXPERIMENTAL__jaccard_coefficient, -) +from cugraph.gnn.data_loading import EXPERIMENTAL__BulkSampler -jaccard = experimental_warning_wrapper(EXPERIMENTAL__jaccard) -jaccard_coefficient = experimental_warning_wrapper(EXPERIMENTAL__jaccard_coefficient) +BulkSampler = experimental_warning_wrapper(EXPERIMENTAL__BulkSampler) -from cugraph.experimental.link_prediction.sorensen import ( - EXPERIMENTAL__sorensen, - EXPERIMENTAL__sorensen_coefficient, -) -sorensen = experimental_warning_wrapper(EXPERIMENTAL__sorensen) -sorensen_coefficient = experimental_warning_wrapper(EXPERIMENTAL__sorensen_coefficient) +from cugraph.link_prediction.jaccard import jaccard, jaccard_coefficient -from cugraph.experimental.link_prediction.overlap import ( - EXPERIMENTAL__overlap, - EXPERIMENTAL__overlap_coefficient, -) +jaccard = promoted_experimental_warning_wrapper(jaccard) +jaccard_coefficient = promoted_experimental_warning_wrapper(jaccard_coefficient) -overlap = experimental_warning_wrapper(EXPERIMENTAL__overlap) -overlap_coefficient = experimental_warning_wrapper(EXPERIMENTAL__overlap_coefficient) +from cugraph.link_prediction.sorensen import sorensen, sorensen_coefficient -from cugraph.gnn.data_loading import EXPERIMENTAL__BulkSampler +sorensen = promoted_experimental_warning_wrapper(sorensen) +sorensen_coefficient = promoted_experimental_warning_wrapper(sorensen_coefficient) -BulkSampler = experimental_warning_wrapper(EXPERIMENTAL__BulkSampler) +from cugraph.link_prediction.overlap import overlap, overlap_coefficient + +overlap = promoted_experimental_warning_wrapper(overlap) +overlap_coefficient = promoted_experimental_warning_wrapper(overlap_coefficient) diff --git a/python/cugraph/cugraph/experimental/link_prediction/__init__.py b/python/cugraph/cugraph/experimental/link_prediction/__init__.py deleted file mode 100644 index 081b2ae8260..00000000000 --- a/python/cugraph/cugraph/experimental/link_prediction/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022-2023, NVIDIA CORPORATION. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/python/cugraph/cugraph/experimental/link_prediction/jaccard.py b/python/cugraph/cugraph/experimental/link_prediction/jaccard.py deleted file mode 100644 index 2eba73b3824..00000000000 --- a/python/cugraph/cugraph/experimental/link_prediction/jaccard.py +++ /dev/null @@ -1,255 +0,0 @@ -# Copyright (c) 2019-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from cugraph.utilities import ( - ensure_cugraph_obj_for_nx, - df_edge_score_to_dictionary, - renumber_vertex_pair, -) -import cudf -import warnings - -from pylibcugraph.experimental import ( - jaccard_coefficients as pylibcugraph_jaccard_coefficients, -) -from pylibcugraph import ResourceHandle - - -# FIXME: Move this function to the utility module so that it can be -# shared by other algos -def ensure_valid_dtype(input_graph, vertex_pair): - - vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] - vertex_pair_dtypes = vertex_pair.dtypes - - if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: - warning_msg = ( - "Jaccard requires 'vertex_pair' to match the graph's 'vertex' type. " - f"input graph's vertex type is: {vertex_dtype} and got " - f"'vertex_pair' of type: {vertex_pair_dtypes}." - ) - warnings.warn(warning_msg, UserWarning) - vertex_pair = vertex_pair.astype(vertex_dtype) - - return vertex_pair - - -def EXPERIMENTAL__jaccard(G, vertex_pair=None, use_weight=False): - """ - Compute the Jaccard similarity between each pair of vertices connected by - an edge, or between arbitrary pairs of vertices specified by the user. - Jaccard similarity is defined between two sets as the ratio of the volume - of their intersection divided by the volume of their union. In the context - of graphs, the neighborhood of a vertex is seen as a set. The Jaccard - similarity weight of each edge represents the strength of connection - between vertices based on the relative similarity of their neighbors. If - first is specified but second is not, or vice versa, an exception will be - thrown. - - NOTE: If the vertex_pair parameter is not specified then the behavior - of cugraph.jaccard is different from the behavior of - networkx.jaccard_coefficient. - - cugraph.jaccard, in the absence of a specified vertex pair list, will - compute the two_hop_neighbors of the entire graph to construct a vertex pair - list and will return the jaccard coefficient for those vertex pairs. This is - not advisable as the vertex_pairs can grow exponentially with respect to the - size of the datasets - - networkx.jaccard_coefficient, in the absence of a specified vertex - pair list, will return an upper triangular dense matrix, excluding - the diagonal as well as vertex pairs that are directly connected - by an edge in the graph, of jaccard coefficients. Technically, networkx - returns a lazy iterator across this upper triangular matrix where - the actual jaccard coefficient is computed when the iterator is - dereferenced. Computing a dense matrix of results is not feasible - if the number of vertices in the graph is large (100,000 vertices - would result in 4.9 billion values in that iterator). - - If your graph is small enough (or you have enough memory and patience) - you can get the interesting (non-zero) values that are part of the networkx - solution by doing the following: - - >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> pairs = G.get_two_hop_neighbors() - >>> df = cugraph.jaccard(G, pairs) - - But please remember that cugraph will fill the dataframe with the entire - solution you request, so you'll need enough memory to store the 2-hop - neighborhood dataframe. - - - Parameters - ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not supported yet for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - - This implementation only supports undirected, unweighted Graph. - - vertex_pair : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the jaccard coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the jaccard coefficient for all - adjacent vertices in the graph. - - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the Jaccard weights. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - df['first'] : cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - df['second'] : cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - df['jaccard_coeff'] : cudf.Series - The computed jaccard coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import jaccard as exp_jaccard - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_jaccard(G) - - """ - if G.is_directed(): - raise ValueError("Input must be an undirected Graph.") - - if G.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if vertex_pair is None: - # Call two_hop neighbor of the entire graph - vertex_pair = G.get_two_hop_neighbors() - - v_p_num_col = len(vertex_pair.columns) - - if isinstance(vertex_pair, cudf.DataFrame): - vertex_pair = renumber_vertex_pair(G, vertex_pair) - vertex_pair = ensure_valid_dtype(G, vertex_pair) - src_col_name = vertex_pair.columns[0] - dst_col_name = vertex_pair.columns[1] - first = vertex_pair[src_col_name] - second = vertex_pair[dst_col_name] - - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - use_weight = False - first, second, jaccard_coeff = pylibcugraph_jaccard_coefficients( - resource_handle=ResourceHandle(), - graph=G._plc_graph, - first=first, - second=second, - use_weight=use_weight, - do_expensive_check=False, - ) - - if G.renumbered: - vertex_pair = G.unrenumber(vertex_pair, src_col_name, preserve_order=True) - vertex_pair = G.unrenumber(vertex_pair, dst_col_name, preserve_order=True) - - if v_p_num_col == 2: - # single column vertex - vertex_pair = vertex_pair.rename( - columns={src_col_name: "first", dst_col_name: "second"} - ) - - df = vertex_pair - df["jaccard_coeff"] = cudf.Series(jaccard_coeff) - - return df - - -def EXPERIMENTAL__jaccard_coefficient(G, ebunch=None, use_weight=False): - """ - For NetworkX Compatability. See `jaccard` - - Parameters - ---------- - graph : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not supported yet for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - - ebunch : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the jaccard coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the jaccard coefficient for all - adjacent vertices in the graph. - - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the Jaccard weights. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - ddf['first']: dask_cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - ddf['second']: dask_cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - ddf['jaccard_coeff']: dask_cudf.Series - The computed jaccard coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import jaccard_coefficient as exp_jaccard_coefficient - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_jaccard_coefficient(G) - - """ - vertex_pair = None - - G, isNx = ensure_cugraph_obj_for_nx(G) - - # FIXME: What is the logic behind this since the docstrings mention that 'G' and - # 'ebunch'(if not None) are respectively of type cugraph.Graph and cudf.DataFrame? - if isNx is True and ebunch is not None: - vertex_pair = cudf.DataFrame(ebunch) - - df = EXPERIMENTAL__jaccard(G, vertex_pair) - - if isNx is True: - df = df_edge_score_to_dictionary( - df, k="jaccard_coeff", src="first", dst="second" - ) - - return df diff --git a/python/cugraph/cugraph/experimental/link_prediction/overlap.py b/python/cugraph/cugraph/experimental/link_prediction/overlap.py deleted file mode 100644 index 0981ced4835..00000000000 --- a/python/cugraph/cugraph/experimental/link_prediction/overlap.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) 2019-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from cugraph.utilities import ( - ensure_cugraph_obj_for_nx, - df_edge_score_to_dictionary, - renumber_vertex_pair, -) -import cudf -import warnings - -from pylibcugraph.experimental import ( - overlap_coefficients as pylibcugraph_overlap_coefficients, -) -from pylibcugraph import ResourceHandle - - -# FIXME: Move this function to the utility module so that it can be -# shared by other algos -def ensure_valid_dtype(input_graph, vertex_pair): - - vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] - vertex_pair_dtypes = vertex_pair.dtypes - - if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: - warning_msg = ( - "Overlap requires 'vertex_pair' to match the graph's 'vertex' type. " - f"input graph's vertex type is: {vertex_dtype} and got " - f"'vertex_pair' of type: {vertex_pair_dtypes}." - ) - warnings.warn(warning_msg, UserWarning) - vertex_pair = vertex_pair.astype(vertex_dtype) - - return vertex_pair - - -def EXPERIMENTAL__overlap_coefficient(G, ebunch=None, use_weight=False): - """ - For NetworkX Compatability. See `overlap` - - Parameters - ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not supported yet for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - - ebunch : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the Overlap coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the overlap coefficient for all - adjacent vertices in the graph. - - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the overlap weights. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - ddf['first']: dask_cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - ddf['second']: dask_cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - ddf['overlap_coeff']: dask_cudf.Series - The computed overlap coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import overlap_coefficient as exp_overlap_coefficient - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_overlap_coefficient(G) - """ - vertex_pair = None - - G, isNx = ensure_cugraph_obj_for_nx(G) - - # FIXME: What is the logic behind this since the docstrings mention that 'G' and - # 'ebunch'(if not None) are respectively of type cugraph.Graph and cudf.DataFrame? - if isNx is True and ebunch is not None: - vertex_pair = cudf.DataFrame(ebunch) - - df = EXPERIMENTAL__overlap(G, vertex_pair) - - if isNx is True: - df = df_edge_score_to_dictionary( - df, k="overlap_coeff", src="first", dst="second" - ) - - return df - - -def EXPERIMENTAL__overlap(G, vertex_pair=None, use_weight=False): - """ - Compute the Overlap Coefficient between each pair of vertices connected by - an edge, or between arbitrary pairs of vertices specified by the user. - Overlap Coefficient is defined between two sets as the ratio of the volume - of their intersection divided by the smaller of their two volumes. In the - context of graphs, the neighborhood of a vertex is seen as a set. The - Overlap Coefficient weight of each edge represents the strength of - connection between vertices based on the relative similarity of their - neighbors. If first is specified but second is not, or vice versa, an - exception will be thrown. - - cugraph.overlap, in the absence of a specified vertex pair list, will - compute the two_hop_neighbors of the entire graph to construct a vertex pair - list and will return the overlap coefficient for those vertex pairs. This is - not advisable as the vertex_pairs can grow exponentially with respect to the - size of the datasets - - Parameters - ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not supported yet for this algorithm). The - adjacency list will be computed if not already present. - - This implementation only supports undirected, unweighted Graph. - - vertex_pair : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the overlap coefficient is computed for the - given vertex pairs, else, it is computed for all vertex pairs. - - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the Overlap coefficients. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - df['first'] : cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - df['second'] : cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - df['overlap_coeff'] : cudf.Series - The computed overlap coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import overlap as exp_overlap - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_overlap(G) - - """ - - if G.is_directed(): - raise ValueError("Input must be an undirected Graph.") - - if G.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if vertex_pair is None: - # Call two_hop neighbor of the entire graph - vertex_pair = G.get_two_hop_neighbors() - - v_p_num_col = len(vertex_pair.columns) - - if isinstance(vertex_pair, cudf.DataFrame): - vertex_pair = renumber_vertex_pair(G, vertex_pair) - vertex_pair = ensure_valid_dtype(G, vertex_pair) - src_col_name = vertex_pair.columns[0] - dst_col_name = vertex_pair.columns[1] - first = vertex_pair[src_col_name] - second = vertex_pair[dst_col_name] - - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - use_weight = False - first, second, overlap_coeff = pylibcugraph_overlap_coefficients( - resource_handle=ResourceHandle(), - graph=G._plc_graph, - first=first, - second=second, - use_weight=use_weight, - do_expensive_check=False, - ) - - if G.renumbered: - vertex_pair = G.unrenumber(vertex_pair, src_col_name, preserve_order=True) - vertex_pair = G.unrenumber(vertex_pair, dst_col_name, preserve_order=True) - - if v_p_num_col == 2: - # single column vertex - vertex_pair = vertex_pair.rename( - columns={src_col_name: "first", dst_col_name: "second"} - ) - - df = vertex_pair - df["overlap_coeff"] = cudf.Series(overlap_coeff) - - return df diff --git a/python/cugraph/cugraph/experimental/link_prediction/sorensen.py b/python/cugraph/cugraph/experimental/link_prediction/sorensen.py deleted file mode 100644 index ed27e4813d3..00000000000 --- a/python/cugraph/cugraph/experimental/link_prediction/sorensen.py +++ /dev/null @@ -1,221 +0,0 @@ -# Copyright (c) 2021-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from cugraph.utilities import ( - ensure_cugraph_obj_for_nx, - df_edge_score_to_dictionary, - renumber_vertex_pair, -) -import cudf -import warnings -from pylibcugraph.experimental import ( - sorensen_coefficients as pylibcugraph_sorensen_coefficients, -) -from pylibcugraph import ResourceHandle - - -# FIXME: Move this function to the utility module so that it can be -# shared by other algos -def ensure_valid_dtype(input_graph, vertex_pair): - - vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] - vertex_pair_dtypes = vertex_pair.dtypes - - if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: - warning_msg = ( - "Sorensen requires 'vertex_pair' to match the graph's 'vertex' type. " - f"input graph's vertex type is: {vertex_dtype} and got " - f"'vertex_pair' of type: {vertex_pair_dtypes}." - ) - warnings.warn(warning_msg, UserWarning) - vertex_pair = vertex_pair.astype(vertex_dtype) - - return vertex_pair - - -def EXPERIMENTAL__sorensen(G, vertex_pair=None, use_weight=False): - """ - Compute the Sorensen coefficient between each pair of vertices connected by - an edge, or between arbitrary pairs of vertices specified by the user. - Sorensen coefficient is defined between two sets as the ratio of twice the - volume of their intersection divided by the volume of each set. - If first is specified but second is not, or vice versa, an exception will - be thrown. - - cugraph.sorensen, in the absence of a specified vertex pair list, will - compute the two_hop_neighbors of the entire graph to construct a vertex pair - list and will return the sorensen coefficient for those vertex pairs. This is - not advisable as the vertex_pairs can grow exponentially with respect to the - size of the datasets - - Parameters - ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not supported yet for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - - This implementation only supports undirected, unweighted Graph. - - vertex_pair : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the Sorensen coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the Sorensen coefficient for all - adjacent vertices in the graph. - - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the Sorensen index. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - df['first'] : cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - df['second'] : cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - df['sorensen_coeff'] : cudf.Series - The computed sorensen coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import sorensen as exp_sorensen - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_sorensen(G) - - """ - if G.is_directed(): - raise ValueError("Input must be an undirected Graph.") - - if G.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if vertex_pair is None: - # Call two_hop neighbor of the entire graph - vertex_pair = G.get_two_hop_neighbors() - - v_p_num_col = len(vertex_pair.columns) - - if isinstance(vertex_pair, cudf.DataFrame): - vertex_pair = renumber_vertex_pair(G, vertex_pair) - vertex_pair = ensure_valid_dtype(G, vertex_pair) - src_col_name = vertex_pair.columns[0] - dst_col_name = vertex_pair.columns[1] - first = vertex_pair[src_col_name] - second = vertex_pair[dst_col_name] - - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - use_weight = False - first, second, sorensen_coeff = pylibcugraph_sorensen_coefficients( - resource_handle=ResourceHandle(), - graph=G._plc_graph, - first=first, - second=second, - use_weight=use_weight, - do_expensive_check=False, - ) - - if G.renumbered: - vertex_pair = G.unrenumber(vertex_pair, src_col_name, preserve_order=True) - vertex_pair = G.unrenumber(vertex_pair, dst_col_name, preserve_order=True) - - if v_p_num_col == 2: - # single column vertex - vertex_pair = vertex_pair.rename( - columns={src_col_name: "first", dst_col_name: "second"} - ) - - df = vertex_pair - df["sorensen_coeff"] = cudf.Series(sorensen_coeff) - - return df - - -def EXPERIMENTAL__sorensen_coefficient(G, ebunch=None, use_weight=False): - """ - For NetworkX Compatability. See `sorensen` - - Parameters - ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - ebunch : cudf.DataFrame, optional (default=None) - A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the sorensen coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the sorensen coefficient for all - adjacent vertices in the graph. - use_weight : bool, optional (default=False) - Currently not supported - - Returns - ------- - df : cudf.DataFrame - GPU data frame of size E (the default) or the size of the given pairs - (first, second) containing the Sorensen weights. The ordering is - relative to the adjacency list, or that given by the specified vertex - pairs. - - df['first'] : cudf.Series - The first vertex ID of each pair (will be identical to first if specified). - df['second'] : cudf.Series - The second vertex ID of each pair (will be identical to second if - specified). - df['sorensen_coeff'] : cudf.Series - The computed sorensen coefficient between the first and the second - vertex ID. - - Examples - -------- - >>> from cugraph.datasets import karate - >>> from cugraph.experimental import sorensen_coefficient as exp_sorensen_coef - >>> G = karate.get_graph(download=True, ignore_weights=True) - >>> df = exp_sorensen_coef(G) - - """ - vertex_pair = None - - G, isNx = ensure_cugraph_obj_for_nx(G) - - # FIXME: What is the logic behind this since the docstrings mention that 'G' and - # 'ebunch'(if not None) are respectively of type cugraph.Graph and cudf.DataFrame? - if isNx is True and ebunch is not None: - vertex_pair = cudf.DataFrame(ebunch) - - df = EXPERIMENTAL__sorensen(G, vertex_pair) - - if isNx is True: - df = df_edge_score_to_dictionary( - df, k="sorensen_coeff", src="first", dst="second" - ) - - return df diff --git a/python/cugraph/cugraph/link_prediction/CMakeLists.txt b/python/cugraph/cugraph/link_prediction/CMakeLists.txt deleted file mode 100644 index a117cf9afc3..00000000000 --- a/python/cugraph/cugraph/link_prediction/CMakeLists.txt +++ /dev/null @@ -1,22 +0,0 @@ -# ============================================================================= -# Copyright (c) 2022, NVIDIA CORPORATION. -# -# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except -# in compliance with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software distributed under the License -# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing permissions and limitations under -# the License. -# ============================================================================= - -set(cython_sources jaccard_wrapper.pyx overlap_wrapper.pyx) -set(linked_libraries cugraph::cugraph) -rapids_cython_create_modules( - CXX - SOURCE_FILES "${cython_sources}" - LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX link_prediction_ - ASSOCIATED_TARGETS cugraph -) diff --git a/python/cugraph/cugraph/link_prediction/__init__.py b/python/cugraph/cugraph/link_prediction/__init__.py index a6911d3b8ae..a8517ee7c0f 100644 --- a/python/cugraph/cugraph/link_prediction/__init__.py +++ b/python/cugraph/cugraph/link_prediction/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2019-2021, NVIDIA CORPORATION. +# Copyright (c) 2019-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -11,13 +11,26 @@ # See the License for the specific language governing permissions and # limitations under the License. + +from cugraph.utilities.api_tools import deprecated_warning_wrapper from cugraph.link_prediction.jaccard import jaccard from cugraph.link_prediction.jaccard import jaccard_coefficient + +from cugraph.link_prediction.sorensen import sorensen +from cugraph.link_prediction.sorensen import sorensen_coefficient + from cugraph.link_prediction.overlap import overlap +from cugraph.link_prediction.overlap import overlap_coefficient + +# To be deprecated from cugraph.link_prediction.wjaccard import jaccard_w + +jaccard_w = deprecated_warning_wrapper(jaccard_w) + from cugraph.link_prediction.woverlap import overlap_w + +overlap_w = deprecated_warning_wrapper(overlap_w) + from cugraph.link_prediction.wsorensen import sorensen_w -from cugraph.link_prediction.jaccard import jaccard_coefficient -from cugraph.link_prediction.sorensen import sorensen_coefficient -from cugraph.link_prediction.sorensen import sorensen -from cugraph.link_prediction.overlap import overlap_coefficient + +sorensen_w = deprecated_warning_wrapper(sorensen_w) diff --git a/python/cugraph/cugraph/link_prediction/jaccard.pxd b/python/cugraph/cugraph/link_prediction/jaccard.pxd deleted file mode 100644 index 9e8c82ec3d8..00000000000 --- a/python/cugraph/cugraph/link_prediction/jaccard.pxd +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2019-2021, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.structure.graph_primtypes cimport * - - -cdef extern from "cugraph/algorithms.hpp" namespace "cugraph": - - cdef void jaccard[VT,ET,WT]( - const GraphCSRView[VT,ET,WT] &graph, - const WT *weights, - WT *result) except + - - cdef void jaccard_list[VT,ET,WT]( - const GraphCSRView[VT,ET,WT] &graph, - const WT *weights, - ET num_pairs, - const VT *first, - const VT *second, - WT *result) except + diff --git a/python/cugraph/cugraph/link_prediction/jaccard.py b/python/cugraph/cugraph/link_prediction/jaccard.py index 334d57f9d80..27bfa58e6b0 100644 --- a/python/cugraph/cugraph/link_prediction/jaccard.py +++ b/python/cugraph/cugraph/link_prediction/jaccard.py @@ -11,16 +11,54 @@ # See the License for the specific language governing permissions and # limitations under the License. -import cudf -from cugraph.link_prediction import jaccard_wrapper from cugraph.utilities import ( ensure_cugraph_obj_for_nx, df_edge_score_to_dictionary, renumber_vertex_pair, ) +import cudf +import warnings +from typing import Union, Iterable + +from pylibcugraph import ( + jaccard_coefficients as pylibcugraph_jaccard_coefficients, +) +from pylibcugraph import ResourceHandle + +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional + +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + + +# FIXME: Move this function to the utility module so that it can be +# shared by other algos +def ensure_valid_dtype(input_graph, vertex_pair): + vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] + vertex_pair_dtypes = vertex_pair.dtypes + + if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: + warning_msg = ( + "Jaccard requires 'vertex_pair' to match the graph's 'vertex' type. " + f"input graph's vertex type is: {vertex_dtype} and got " + f"'vertex_pair' of type: {vertex_pair_dtypes}." + ) + warnings.warn(warning_msg, UserWarning) + vertex_pair = vertex_pair.astype(vertex_dtype) + + return vertex_pair -def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): +def jaccard( + input_graph: Graph, + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated + use_weight: bool = False, +): """ Compute the Jaccard similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. @@ -36,13 +74,11 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): of cugraph.jaccard is different from the behavior of networkx.jaccard_coefficient. - This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. - cugraph.jaccard, in the absence of a specified vertex pair list, will - use the edges of the graph to construct a vertex pair list and will - return the jaccard coefficient for those vertex pairs. + compute the two_hop_neighbors of the entire graph to construct a vertex pair + list and will return the jaccard coefficient for those vertex pairs. This is + not advisable as the vertex_pairs can grow exponentially with respect to the + size of the datasets networkx.jaccard_coefficient, in the absence of a specified vertex pair list, will return an upper triangular dense matrix, excluding @@ -59,9 +95,9 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): solution by doing the following: >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True) - >>> pairs = G.get_two_hop_neighbors() - >>> df = cugraph.jaccard(G, pairs) + >>> input_graph = karate.get_graph(download=True, ignore_weights=True) + >>> pairs = input_graph.get_two_hop_neighbors() + >>> df = cugraph.jaccard(input_graph, pairs) But please remember that cugraph will fill the dataframe with the entire solution you request, so you'll need enough memory to store the 2-hop @@ -72,10 +108,11 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): ---------- input_graph : cugraph.Graph cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. + as an edge list. The graph should be undirected where an undirected + edge is represented by a directed edge in both direction.The adjacency + list will be computed if not already present. + + This implementation only supports undirected, non-multi Graphs. vertex_pair : cudf.DataFrame, optional (default=None) A GPU dataframe consisting of two columns representing pairs of @@ -84,9 +121,20 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): current implementation computes the jaccard coefficient for all adjacent vertices in the graph. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. + + use_weight : bool, optional (default=False) + Flag to indicate whether to compute weighted jaccard (if use_weight==True) + or un-weighted jaccard (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. + Returns ------- @@ -99,7 +147,7 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): df['first'] : cudf.Series The first vertex ID of each pair (will be identical to first if specified). df['second'] : cudf.Series - the second vertex ID of each pair (will be identical to second if + The second vertex ID of each pair (will be identical to second if specified). df['jaccard_coeff'] : cudf.Series The computed Jaccard coefficient between the first and the second @@ -108,65 +156,101 @@ def jaccard(input_graph, vertex_pair=None, do_expensive_check=True): Examples -------- >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True) - >>> df = cugraph.jaccard(G) + >>> from cugraph import jaccard + >>> input_graph = karate.get_graph(download=True, ignore_weights=True) + >>> df = jaccard(input_graph) """ if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) if input_graph.is_directed(): raise ValueError("Input must be an undirected Graph.") - if type(vertex_pair) == cudf.DataFrame: + + if vertex_pair is None: + # Call two_hop neighbor of the entire graph + vertex_pair = input_graph.get_two_hop_neighbors() + + v_p_num_col = len(vertex_pair.columns) + + if isinstance(vertex_pair, cudf.DataFrame): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) + vertex_pair = ensure_valid_dtype(input_graph, vertex_pair) + src_col_name = vertex_pair.columns[0] + dst_col_name = vertex_pair.columns[1] + first = vertex_pair[src_col_name] + second = vertex_pair[dst_col_name] + elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") + raise ValueError("vertex_pair must be a cudf Dataframe") - df = jaccard_wrapper.jaccard(input_graph, None, vertex_pair) + first, second, jaccard_coeff = pylibcugraph_jaccard_coefficients( + resource_handle=ResourceHandle(), + graph=input_graph._plc_graph, + first=first, + second=second, + use_weight=use_weight, + do_expensive_check=False, + ) if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") + vertex_pair = input_graph.unrenumber( + vertex_pair, src_col_name, preserve_order=True + ) + vertex_pair = input_graph.unrenumber( + vertex_pair, dst_col_name, preserve_order=True + ) + + if v_p_num_col == 2: + # single column vertex + vertex_pair = vertex_pair.rename( + columns={src_col_name: "first", dst_col_name: "second"} + ) + + df = vertex_pair + df["jaccard_coeff"] = cudf.Series(jaccard_coeff) return df -def jaccard_coefficient(G, ebunch=None, do_expensive_check=True): +def jaccard_coefficient( + G: Union[Graph, "networkx.Graph"], + ebunch: Union[cudf.DataFrame, Iterable[Union[int, str, float]]] = None, + do_expensive_check: bool = False, # deprecated +): """ For NetworkX Compatability. See `jaccard` - NOTE: This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. - Parameters ---------- - graph : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. + G : cugraph.Graph or NetworkX.Graph + cuGraph or NetworkX Graph instance, should contain the connectivity + information as an edge list. The graph should be undirected where an + undirected edge is represented by a directed edge in both direction. + The adjacency list will be computed if not already present. - ebunch : cudf.DataFrame, optional (default=None) + This implementation only supports undirected, non-multi Graphs. + + ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None) A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the jaccard coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the jaccard coefficient for all - adjacent vertices in the graph. + vertices or iterable of 2-tuples (u, v) where u and v are nodes in + the graph. + + If provided, the Overlap coefficient is computed for the given vertex + pairs. Otherwise, the current implementation computes the overlap + coefficient for all adjacent vertices in the graph. + + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. Returns ------- @@ -188,10 +272,18 @@ def jaccard_coefficient(G, ebunch=None, do_expensive_check=True): Examples -------- >>> from cugraph.datasets import karate + >>> from cugraph import jaccard_coefficient >>> G = karate.get_graph(download=True) - >>> df = cugraph.jaccard_coefficient(G) + >>> df = jaccard_coefficient(G) """ + if do_expensive_check: + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) + vertex_pair = None G, isNx = ensure_cugraph_obj_for_nx(G) diff --git a/python/cugraph/cugraph/link_prediction/jaccard_wrapper.pyx b/python/cugraph/cugraph/link_prediction/jaccard_wrapper.pyx deleted file mode 100644 index e66d8bf0b5c..00000000000 --- a/python/cugraph/cugraph/link_prediction/jaccard_wrapper.pyx +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright (c) 2019-2022, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.link_prediction.jaccard cimport jaccard as c_jaccard -from cugraph.link_prediction.jaccard cimport jaccard_list as c_jaccard_list -from cugraph.structure.graph_primtypes cimport * -from cugraph.structure import graph_primtypes_wrapper -from libc.stdint cimport uintptr_t -import cudf -import numpy as np - - -def jaccard(input_graph, weights_arr=None, vertex_pair=None): - """ - Call jaccard or jaccard_list - """ - offsets = None - indices = None - - if input_graph.adjlist: - [offsets, indices] = graph_primtypes_wrapper.datatype_cast([input_graph.adjlist.offsets, - input_graph.adjlist.indices], [np.int32]) - elif input_graph.transposedadjlist: - # - # NOTE: jaccard ONLY operates on an undirected graph, so CSR and CSC should be - # equivalent. The undirected check has already happened, so we'll just use - # the CSC as if it were CSR. - # - [offsets, indices] = graph_primtypes_wrapper.datatype_cast([input_graph.transposedadjlist.offsets, - input_graph.transposedadjlist.indices], [np.int32]) - else: - input_graph.view_adj_list() - [offsets, indices] = graph_primtypes_wrapper.datatype_cast([input_graph.adjlist.offsets, - input_graph.adjlist.indices], [np.int32]) - - num_verts = input_graph.number_of_vertices() - num_edges = input_graph.number_of_edges(directed_edges=True) - - first = None - second = None - - cdef uintptr_t c_result_col = NULL - cdef uintptr_t c_first_col = NULL - cdef uintptr_t c_second_col = NULL - cdef uintptr_t c_src_index_col = NULL - cdef uintptr_t c_dst_index_col = NULL - cdef uintptr_t c_weights = NULL - cdef uintptr_t c_offsets = offsets.__cuda_array_interface__['data'][0] - cdef uintptr_t c_indices = indices.__cuda_array_interface__['data'][0] - - cdef GraphCSRView[int,int,float] graph_float - cdef GraphCSRView[int,int,double] graph_double - - weight_type = np.float32 - - if weights_arr is not None: - [weights] = graph_primtypes_wrapper.datatype_cast([weights_arr], [np.float32, np.float64]) - c_weights = weights.__cuda_array_interface__['data'][0] - weight_type = weights.dtype - - if type(vertex_pair) == cudf.DataFrame: - result_size = len(vertex_pair) - result = cudf.Series(np.ones(result_size, dtype=weight_type)) - c_result_col = result.__cuda_array_interface__['data'][0] - - df = cudf.DataFrame() - df['jaccard_coeff'] = result - - cols = vertex_pair.columns.to_list() - first = vertex_pair[cols[0]].astype(np.int32) - second = vertex_pair[cols[1]].astype(np.int32) - - # FIXME: multi column support - df['first'] = first - df['second'] = second - c_first_col = first.__cuda_array_interface__['data'][0] - c_second_col = second.__cuda_array_interface__['data'][0] - - if weight_type == np.float32: - graph_float = GraphCSRView[int,int,float](c_offsets, c_indices, - c_weights, num_verts, num_edges) - c_jaccard_list[int,int,float](graph_float, - c_weights, - result_size, - c_first_col, - c_second_col, - c_result_col) - else: - graph_double = GraphCSRView[int,int,double](c_offsets, c_indices, - c_weights, num_verts, num_edges) - c_jaccard_list[int,int,double](graph_double, - c_weights, - result_size, - c_first_col, - c_second_col, - c_result_col) - - return df - else: - # error check performed in jaccard.py - assert vertex_pair is None - - df = cudf.DataFrame() - df['first'] = cudf.Series(np.zeros(num_edges, indices.dtype)) - df['second'] = indices - - c_src_index_col = df['first'].__cuda_array_interface__['data'][0] - - if weight_type == np.float32: - df['jaccard_coeff'] = cudf.Series(np.ones(num_edges, dtype=np.float32), - nan_as_null=False) - c_result_col = df['jaccard_coeff'].__cuda_array_interface__['data'][0] - - graph_float = GraphCSRView[int,int,float](c_offsets, - c_indices, - c_weights, - num_verts, - num_edges) - c_jaccard[int,int,float](graph_float, - c_weights, - c_result_col) - - graph_float.get_source_indices(c_src_index_col) - else: - df['jaccard_coeff'] = cudf.Series(np.ones(num_edges, dtype=np.float64), - nan_as_null=False) - c_result_col = df['jaccard_coeff'].__cuda_array_interface__['data'][0] - - graph_double = GraphCSRView[int,int,double](c_offsets, - c_indices, - c_weights, - num_verts, - num_edges) - c_jaccard[int,int,double](graph_double, - c_weights, - c_result_col) - - graph_double.get_source_indices(c_src_index_col) - - return df diff --git a/python/cugraph/cugraph/link_prediction/overlap.pxd b/python/cugraph/cugraph/link_prediction/overlap.pxd deleted file mode 100644 index f0654472587..00000000000 --- a/python/cugraph/cugraph/link_prediction/overlap.pxd +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2019-2021, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.structure.graph_primtypes cimport * - - -cdef extern from "cugraph/algorithms.hpp" namespace "cugraph": - - cdef void overlap[VT,ET,WT]( - const GraphCSRView[VT,ET,WT] &graph, - const WT *weights, - WT *result) except + - - cdef void overlap_list[VT,ET,WT]( - const GraphCSRView[VT,ET,WT] &graph, - const WT *weights, - ET num_pairs, - const VT *first, - const VT *second, - WT *result) except + diff --git a/python/cugraph/cugraph/link_prediction/overlap.py b/python/cugraph/cugraph/link_prediction/overlap.py index 9bb7b76b0ca..3a25526679c 100644 --- a/python/cugraph/cugraph/link_prediction/overlap.py +++ b/python/cugraph/cugraph/link_prediction/overlap.py @@ -11,28 +11,120 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.link_prediction import overlap_wrapper -import cudf from cugraph.utilities import ( ensure_cugraph_obj_for_nx, df_edge_score_to_dictionary, renumber_vertex_pair, ) +import cudf +import warnings +from typing import Union, Iterable + +from pylibcugraph import ( + overlap_coefficients as pylibcugraph_overlap_coefficients, +) +from pylibcugraph import ResourceHandle + +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional + +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + +# FIXME: Move this function to the utility module so that it can be +# shared by other algos +def ensure_valid_dtype(input_graph, vertex_pair): + vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] + vertex_pair_dtypes = vertex_pair.dtypes -def overlap_coefficient(G, ebunch=None, do_expensive_check=True): + if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: + warning_msg = ( + "Overlap requires 'vertex_pair' to match the graph's 'vertex' type. " + f"input graph's vertex type is: {vertex_dtype} and got " + f"'vertex_pair' of type: {vertex_pair_dtypes}." + ) + warnings.warn(warning_msg, UserWarning) + vertex_pair = vertex_pair.astype(vertex_dtype) + + return vertex_pair + + +def overlap_coefficient( + G: Union[Graph, "networkx.Graph"], + ebunch: Union[cudf.DataFrame, Iterable[Union[int, str, float]]] = None, + do_expensive_check: bool = False, # deprecated +): """ - For NetworkX Compatability. See `overlap` + Compute overlap coefficient. + + Parameters + ---------- + G : cugraph.Graph or NetworkX.Graph + cuGraph or NetworkX Graph instance, should contain the connectivity + information as an edge list. The graph should be undirected where an + undirected edge is represented by a directed edge in both direction. + The adjacency list will be computed if not already present. + + This implementation only supports undirected, non-multi edge Graph. + + ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None) + A GPU dataframe consisting of two columns representing pairs of + vertices or iterable of 2-tuples (u, v) where u and v are nodes in + the graph. + + If provided, the Overlap coefficient is computed for the given vertex + pairs. Otherwise, the current implementation computes the overlap + coefficient for all adjacent vertices in the graph. + + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. + + Returns + ------- + df : cudf.DataFrame + GPU data frame of size E (the default) or the size of the given pairs + (first, second) containing the overlap weights. The ordering is + relative to the adjacency list, or that given by the specified vertex + pairs. - NOTE: This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. + ddf['first']: dask_cudf.Series + The first vertex ID of each pair (will be identical to first if specified). + ddf['second']: dask_cudf.Series + The second vertex ID of each pair (will be identical to second if + specified). + ddf['overlap_coeff']: dask_cudf.Series + The computed overlap coefficient between the first and the second + vertex ID. + Examples + -------- + >>> from cugraph.datasets import karate + >>> from cugraph import overlap_coefficient + >>> G = karate.get_graph(download=True, ignore_weights=True) + >>> df = overlap_coefficient(G) """ + if do_expensive_check: + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) + vertex_pair = None G, isNx = ensure_cugraph_obj_for_nx(G) + # FIXME: What is the logic behind this since the docstrings mention that 'G' and + # 'ebunch'(if not None) are respectively of type cugraph.Graph and cudf.DataFrame? if isNx is True and ebunch is not None: vertex_pair = cudf.DataFrame(ebunch) @@ -46,7 +138,12 @@ def overlap_coefficient(G, ebunch=None, do_expensive_check=True): return df -def overlap(input_graph, vertex_pair=None, do_expensive_check=True): +def overlap( + input_graph: Graph, + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated + use_weight: bool = False, +): """ Compute the Overlap Coefficient between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. @@ -58,25 +155,39 @@ def overlap(input_graph, vertex_pair=None, do_expensive_check=True): neighbors. If first is specified but second is not, or vice versa, an exception will be thrown. - NOTE: This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. + cugraph.overlap, in the absence of a specified vertex pair list, will + compute the two_hop_neighbors of the entire graph to construct a vertex pair + list and will return the overlap coefficient for those vertex pairs. This is + not advisable as the vertex_pairs can grow exponentially with respect to the + size of the datasets Parameters ---------- input_graph : cugraph.Graph cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - adjacency list will be computed if not already present. + as an edge list. The adjacency list will be computed if not already + present. + This implementation only supports undirected, non-multi edge Graph. vertex_pair : cudf.DataFrame, optional (default=None) A GPU dataframe consisting of two columns representing pairs of vertices. If provided, the overlap coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. + + use_weight : bool, optional (default=False) + Flag to indicate whether to compute weighted overlap (if use_weight==True) + or un-weighted overlap (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. + + Returns ------- @@ -98,35 +209,62 @@ def overlap(input_graph, vertex_pair=None, do_expensive_check=True): Examples -------- >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True) - >>> df = cugraph.overlap(G) + >>> from cugraph import overlap + >>> input_graph = karate.get_graph(download=True, ignore_weights=True) + >>> df = overlap(input_graph) """ if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") - - if type(vertex_pair) == cudf.DataFrame: + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) + + if input_graph.is_directed(): + raise ValueError("Input must be an undirected Graph.") + + if vertex_pair is None: + # Call two_hop neighbor of the entire graph + vertex_pair = input_graph.get_two_hop_neighbors() + + v_p_num_col = len(vertex_pair.columns) + + if isinstance(vertex_pair, cudf.DataFrame): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) + vertex_pair = ensure_valid_dtype(input_graph, vertex_pair) + src_col_name = vertex_pair.columns[0] + dst_col_name = vertex_pair.columns[1] + first = vertex_pair[src_col_name] + second = vertex_pair[dst_col_name] + elif vertex_pair is not None: raise ValueError("vertex_pair must be a cudf dataframe") - df = overlap_wrapper.overlap(input_graph, None, vertex_pair) + first, second, overlap_coeff = pylibcugraph_overlap_coefficients( + resource_handle=ResourceHandle(), + graph=input_graph._plc_graph, + first=first, + second=second, + use_weight=use_weight, + do_expensive_check=False, + ) if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") + vertex_pair = input_graph.unrenumber( + vertex_pair, src_col_name, preserve_order=True + ) + vertex_pair = input_graph.unrenumber( + vertex_pair, dst_col_name, preserve_order=True + ) + + if v_p_num_col == 2: + # single column vertex + vertex_pair = vertex_pair.rename( + columns={src_col_name: "first", dst_col_name: "second"} + ) + + df = vertex_pair + df["overlap_coeff"] = cudf.Series(overlap_coeff) return df diff --git a/python/cugraph/cugraph/link_prediction/overlap_wrapper.pyx b/python/cugraph/cugraph/link_prediction/overlap_wrapper.pyx deleted file mode 100644 index 0f61460a72f..00000000000 --- a/python/cugraph/cugraph/link_prediction/overlap_wrapper.pyx +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) 2019-2022, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.link_prediction.overlap cimport overlap as c_overlap -from cugraph.link_prediction.overlap cimport overlap_list as c_overlap_list -from cugraph.structure.graph_primtypes cimport * -from cugraph.structure import graph_primtypes_wrapper -from libc.stdint cimport uintptr_t -import cudf -import numpy as np - - -def overlap(input_graph, weights_arr=None, vertex_pair=None): - """ - Call overlap or overlap_list - """ - - if not input_graph.adjlist: - input_graph.view_adj_list() - - [offsets, indices] = graph_primtypes_wrapper.datatype_cast([input_graph.adjlist.offsets, input_graph.adjlist.indices], [np.int32]) - - num_verts = input_graph.number_of_vertices() - num_edges = input_graph.number_of_edges(directed_edges=True) - - first = None - second = None - - cdef uintptr_t c_result_col = NULL - cdef uintptr_t c_first_col = NULL - cdef uintptr_t c_second_col = NULL - cdef uintptr_t c_src_index_col = NULL - cdef uintptr_t c_dst_index_col = NULL - cdef uintptr_t c_weights = NULL - cdef uintptr_t c_offsets = offsets.__cuda_array_interface__['data'][0] - cdef uintptr_t c_indices = indices.__cuda_array_interface__['data'][0] - - cdef GraphCSRView[int,int,float] graph_float - cdef GraphCSRView[int,int,double] graph_double - - weight_type = np.float32 - - if weights_arr is not None: - [weights] = graph_primtypes_wrapper.datatype_cast([weights_arr], [np.float32, np.float64]) - c_weights = weights.__cuda_array_interface__['data'][0] - weight_type = weights.dtype - - if type(vertex_pair) == cudf.DataFrame: - result_size = len(vertex_pair) - result = cudf.Series(np.ones(result_size, dtype=np.float32)) - c_result_col = result.__cuda_array_interface__['data'][0] - - df = cudf.DataFrame() - df['overlap_coeff'] = result - - cols = vertex_pair.columns.to_list() - first = vertex_pair[cols[0]] - second = vertex_pair[cols[1]] - - # FIXME: multi column support - df['first'] = first - df['second'] = second - c_first_col = first.__cuda_array_interface__['data'][0] - c_second_col = second.__cuda_array_interface__['data'][0] - - if weight_type == np.float32: - graph_float = GraphCSRView[int,int,float](c_offsets, c_indices, - c_weights, num_verts, num_edges) - c_overlap_list[int,int,float](graph_float, - c_weights, - result_size, - c_first_col, - c_second_col, - c_result_col) - else: - graph_double = GraphCSRView[int,int,double](c_offsets, c_indices, - c_weights, num_verts, num_edges) - c_overlap_list[int,int,double](graph_double, - c_weights, - result_size, - c_first_col, - c_second_col, - c_result_col) - - return df - else: - # error check performed in overlap.py - assert vertex_pair is None - - df = cudf.DataFrame() - df['first'] = cudf.Series(np.zeros(num_edges, indices.dtype)) - df['second'] = indices - - c_src_index_col = df['first'].__cuda_array_interface__['data'][0] - - if weight_type == np.float32: - df['overlap_coeff'] = cudf.Series(np.ones(num_edges, dtype=np.float32), - nan_as_null=False) - c_result_col = df['overlap_coeff'].__cuda_array_interface__['data'][0] - - graph_float = GraphCSRView[int,int,float](c_offsets, - c_indices, - c_weights, - num_verts, - num_edges) - c_overlap[int,int,float](graph_float, - c_weights, - c_result_col) - - graph_float.get_source_indices(c_src_index_col) - else: - df['overlap_coeff'] = cudf.Series(np.ones(num_edges, dtype=np.float64), - nan_as_null=False) - c_result_col = df['overlap_coeff'].__cuda_array_interface__['data'][0] - - graph_double = GraphCSRView[int,int,double](c_offsets, - c_indices, - c_weights, - num_verts, - num_edges) - c_overlap[int,int,double](graph_double, - c_weights, - c_result_col) - - graph_double.get_source_indices(c_src_index_col) - - return df diff --git a/python/cugraph/cugraph/link_prediction/sorensen.py b/python/cugraph/cugraph/link_prediction/sorensen.py index ef2bd8d674d..a8ccced1e68 100644 --- a/python/cugraph/cugraph/link_prediction/sorensen.py +++ b/python/cugraph/cugraph/link_prediction/sorensen.py @@ -11,17 +11,54 @@ # See the License for the specific language governing permissions and # limitations under the License. -import cudf -from cugraph.structure.graph_classes import Graph -from cugraph.link_prediction import jaccard_wrapper from cugraph.utilities import ( ensure_cugraph_obj_for_nx, df_edge_score_to_dictionary, renumber_vertex_pair, ) +import cudf +import warnings +from typing import Union, Iterable + +from pylibcugraph import ( + sorensen_coefficients as pylibcugraph_sorensen_coefficients, +) +from pylibcugraph import ResourceHandle + +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional + +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + + +# FIXME: Move this function to the utility module so that it can be +# shared by other algos +def ensure_valid_dtype(input_graph, vertex_pair): + vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] + vertex_pair_dtypes = vertex_pair.dtypes + + if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: + warning_msg = ( + "Sorensen requires 'vertex_pair' to match the graph's 'vertex' type. " + f"input graph's vertex type is: {vertex_dtype} and got " + f"'vertex_pair' of type: {vertex_pair_dtypes}." + ) + warnings.warn(warning_msg, UserWarning) + vertex_pair = vertex_pair.astype(vertex_dtype) + + return vertex_pair -def sorensen(input_graph, vertex_pair=None, do_expensive_check=True): +def sorensen( + input_graph: Graph, + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated + use_weight: bool = False, +): """ Compute the Sorensen coefficient between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by the user. @@ -30,22 +67,20 @@ def sorensen(input_graph, vertex_pair=None, do_expensive_check=True): If first is specified but second is not, or vice versa, an exception will be thrown. - NOTE: This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. - cugraph.sorensen, in the absence of a specified vertex pair list, will - use the edges of the graph to construct a vertex pair list and will - return the sorensen coefficient for those vertex pairs. + compute the two_hop_neighbors of the entire graph to construct a vertex pair + list and will return the sorensen coefficient for those vertex pairs. This is + not advisable as the vertex_pairs can grow exponentially with respect to the + size of the datasets Parameters ---------- input_graph : cugraph.Graph cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. + as an edge list. The adjacency list will be computed if not already + present. + + This implementation only supports undirected, non-multi edge Graph. vertex_pair : cudf.DataFrame, optional (default=None) A GPU dataframe consisting of two columns representing pairs of @@ -54,9 +89,18 @@ def sorensen(input_graph, vertex_pair=None, do_expensive_check=True): current implementation computes the Sorensen coefficient for all adjacent vertices in the graph. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. + + use_weight : bool, optional (default=False) + Flag to indicate whether to compute weighted sorensen (if use_weight==True) + or un-weighted sorensen (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. Returns ------- @@ -67,79 +111,112 @@ def sorensen(input_graph, vertex_pair=None, do_expensive_check=True): pairs. df['first'] : cudf.Series - The first vertex ID of each pair (will be identical to first if specified) - + The first vertex ID of each pair (will be identical to first if specified). df['second'] : cudf.Series The second vertex ID of each pair (will be identical to second if - specified) - + specified). df['sorensen_coeff'] : cudf.Series - The computed Sorensen coefficient between the first and the second + The computed sorensen coefficient between the first and the second vertex ID. Examples -------- >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True) - >>> df = cugraph.sorensen(G) + >>> from cugraph import sorensen + >>> input_graph = karate.get_graph(download=True, ignore_weights=True) + >>> df = sorensen(input_graph) """ if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") - - if type(input_graph) is not Graph: - raise TypeError("input graph must a Graph") - - if type(vertex_pair) == cudf.DataFrame: + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) + + if input_graph.is_directed(): + raise ValueError("Input must be an undirected Graph.") + + if vertex_pair is None: + # Call two_hop neighbor of the entire graph + vertex_pair = input_graph.get_two_hop_neighbors() + + v_p_num_col = len(vertex_pair.columns) + + if isinstance(vertex_pair, cudf.DataFrame): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) + vertex_pair = ensure_valid_dtype(input_graph, vertex_pair) + src_col_name = vertex_pair.columns[0] + dst_col_name = vertex_pair.columns[1] + first = vertex_pair[src_col_name] + second = vertex_pair[dst_col_name] + elif vertex_pair is not None: raise ValueError("vertex_pair must be a cudf dataframe") - df = jaccard_wrapper.jaccard(input_graph, None, vertex_pair) - df.jaccard_coeff = (2 * df.jaccard_coeff) / (1 + df.jaccard_coeff) - df.rename({"jaccard_coeff": "sorensen_coeff"}, axis=1, inplace=True) + first, second, sorensen_coeff = pylibcugraph_sorensen_coefficients( + resource_handle=ResourceHandle(), + graph=input_graph._plc_graph, + first=first, + second=second, + use_weight=use_weight, + do_expensive_check=False, + ) + if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") + vertex_pair = input_graph.unrenumber( + vertex_pair, src_col_name, preserve_order=True + ) + vertex_pair = input_graph.unrenumber( + vertex_pair, dst_col_name, preserve_order=True + ) + + if v_p_num_col == 2: + # single column vertex + vertex_pair = vertex_pair.rename( + columns={src_col_name: "first", dst_col_name: "second"} + ) + + df = vertex_pair + df["sorensen_coeff"] = cudf.Series(sorensen_coeff) return df -def sorensen_coefficient(G, ebunch=None, do_expensive_check=True): +def sorensen_coefficient( + G: Union[Graph, "networkx.Graph"], + ebunch: Union[cudf.DataFrame, Iterable[Union[int, str, float]]] = None, + do_expensive_check: bool = False, # deprecated +): """ - For NetworkX Compatability. See `sorensen` - - NOTE: This algorithm doesn't currently support datasets with vertices that - are not (re)numebred vertices from 0 to V-1 where V is the total number of - vertices as this creates isolated vertices. + Compute sorensen coefficient. Parameters ---------- - G : cugraph.Graph - cuGraph Graph instance, should contain the connectivity information - as an edge list (edge weights are not used for this algorithm). The - graph should be undirected where an undirected edge is represented by a - directed edge in both direction. The adjacency list will be computed if - not already present. - ebunch : cudf.DataFrame, optional (default=None) + G : cugraph.Graph or NetworkX.Graph + cuGraph or NetworkX Graph instance, should contain the connectivity + information as an edge list. The graph should be undirected where an + undirected edge is represented by a directed edge in both direction. + The adjacency list will be computed if not already present. + + This implementation only supports undirected, non-multi Graphs. + + ebunch : cudf.DataFrame or iterable of node pairs, optional (default=None) A GPU dataframe consisting of two columns representing pairs of - vertices. If provided, the sorensen coefficient is computed for the - given vertex pairs. If the vertex_pair is not provided then the - current implementation computes the sorensen coefficient for all - adjacent vertices in the graph. + vertices or iterable of 2-tuples (u, v) where u and v are nodes in + the graph. + + If provided, the Overlap coefficient is computed for the given vertex + pairs. Otherwise, the current implementation computes the overlap + coefficient for all adjacent vertices in the graph. + + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. Returns ------- @@ -152,7 +229,7 @@ def sorensen_coefficient(G, ebunch=None, do_expensive_check=True): df['first'] : cudf.Series The first vertex ID of each pair (will be identical to first if specified). df['second'] : cudf.Series - the second vertex ID of each pair (will be identical to second if + The second vertex ID of each pair (will be identical to second if specified). df['sorensen_coeff'] : cudf.Series The computed Sorensen coefficient between the first and the second @@ -161,14 +238,24 @@ def sorensen_coefficient(G, ebunch=None, do_expensive_check=True): Examples -------- >>> from cugraph.datasets import karate - >>> G = karate.get_graph(download=True) - >>> df = cugraph.sorensen_coefficient(G) + >>> from cugraph import sorensen_coefficient + >>> G = karate.get_graph(download=True, ignore_weights=True) + >>> df = sorensen_coefficient(G) """ + if do_expensive_check: + warnings.warn( + "do_expensive_check is deprecated since vertex IDs are no longer " + "required to be consecutively numbered", + FutureWarning, + ) + vertex_pair = None G, isNx = ensure_cugraph_obj_for_nx(G) + # FIXME: What is the logic behind this since the docstrings mention that 'G' and + # 'ebunch'(if not None) are respectively of type cugraph.Graph and cudf.DataFrame? if isNx is True and ebunch is not None: vertex_pair = cudf.DataFrame(ebunch) diff --git a/python/cugraph/cugraph/link_prediction/wjaccard.py b/python/cugraph/cugraph/link_prediction/wjaccard.py index e3486473fe5..ec538bbc0ed 100644 --- a/python/cugraph/cugraph/link_prediction/wjaccard.py +++ b/python/cugraph/cugraph/link_prediction/wjaccard.py @@ -11,13 +11,45 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.structure.graph_classes import Graph -from cugraph.link_prediction import jaccard_wrapper +from cugraph.link_prediction import jaccard import cudf -from cugraph.utilities import renumber_vertex_pair +import warnings +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional -def jaccard_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + + +# FIXME: Move this function to the utility module so that it can be +# shared by other algos +def ensure_valid_dtype(input_graph, vertex_pair): + + vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0] + vertex_pair_dtypes = vertex_pair.dtypes + + if vertex_pair_dtypes[0] != vertex_dtype or vertex_pair_dtypes[1] != vertex_dtype: + warning_msg = ( + "Jaccard requires 'vertex_pair' to match the graph's 'vertex' type. " + f"input graph's vertex type is: {vertex_dtype} and got " + f"'vertex_pair' of type: {vertex_pair_dtypes}." + ) + warnings.warn(warning_msg, UserWarning) + vertex_pair = vertex_pair.astype(vertex_dtype) + + return vertex_pair + + +def jaccard_w( + input_graph: Graph, + weights: cudf.DataFrame = None, # deprecated + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated +): """ Compute the weighted Jaccard similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by @@ -55,9 +87,13 @@ def jaccard_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): vertices. If provided, the jaccard coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. Returns ------- @@ -95,47 +131,9 @@ def jaccard_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): >>> df = cugraph.jaccard_w(G, weights) """ - if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") - - if type(input_graph) is not Graph: - raise TypeError("input graph must a Graph") - - if type(vertex_pair) == cudf.DataFrame: - vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - if input_graph.renumbered: - # The 'vertex' column of the cudf 'weights' also needs to be renumbered - # if the graph was renumbered - vertex_size = input_graph.vertex_column_size() - # single-column vertices i.e only one src and dst columns - if vertex_size == 1: - weights = input_graph.add_internal_vertex_id(weights, "vertex", "vertex") - # multi-column vertices i.e more than one src and dst columns - else: - cols = weights.columns[:vertex_size].to_list() - weights = input_graph.add_internal_vertex_id(weights, "vertex", cols) - - jaccard_weights = weights["weight"] - df = jaccard_wrapper.jaccard(input_graph, jaccard_weights, vertex_pair) - - if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") - - return df + warning_msg = ( + "jaccard_w is deprecated. To compute weighted jaccard, please use " + "jaccard(input_graph, vertex_pair=False, use_weight=True)" + ) + warnings.warn(warning_msg, FutureWarning) + return jaccard(input_graph, vertex_pair, do_expensive_check, use_weight=True) diff --git a/python/cugraph/cugraph/link_prediction/woverlap.py b/python/cugraph/cugraph/link_prediction/woverlap.py index d7ebc5fc684..5f43ad0670b 100644 --- a/python/cugraph/cugraph/link_prediction/woverlap.py +++ b/python/cugraph/cugraph/link_prediction/woverlap.py @@ -11,12 +11,26 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.link_prediction import overlap_wrapper +from cugraph.link_prediction import overlap import cudf -from cugraph.utilities import renumber_vertex_pair +import warnings +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional -def overlap_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + + +def overlap_w( + input_graph: Graph, + weights: cudf.DataFrame = None, # deprecated + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated +): """ Compute the weighted Overlap Coefficient between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by @@ -55,9 +69,13 @@ def overlap_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): vertices. If provided, the overlap coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. Returns ------- @@ -96,43 +114,9 @@ def overlap_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): ... len(weights['vertex']))] >>> df = cugraph.overlap_w(G, weights) """ - if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") - - if type(vertex_pair) == cudf.DataFrame: - vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - if input_graph.renumbered: - vertex_size = input_graph.vertex_column_size() - if vertex_size == 1: - weights = input_graph.add_internal_vertex_id(weights, "vertex", "vertex") - else: - cols = weights.columns[:vertex_size].to_list() - weights = input_graph.add_internal_vertex_id(weights, "vertex", cols) - - overlap_weights = weights["weight"] - - overlap_weights = overlap_weights.astype("float32") - - df = overlap_wrapper.overlap(input_graph, overlap_weights, vertex_pair) - - if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") - - return df + warning_msg = ( + " overlap_w is deprecated. To compute weighted overlap, please use " + "overlap(input_graph, vertex_pair=False, use_weight=True)" + ) + warnings.warn(warning_msg, FutureWarning) + return overlap(input_graph, vertex_pair, do_expensive_check, use_weight=True) diff --git a/python/cugraph/cugraph/link_prediction/wsorensen.py b/python/cugraph/cugraph/link_prediction/wsorensen.py index 8337b4602de..ff502b36837 100644 --- a/python/cugraph/cugraph/link_prediction/wsorensen.py +++ b/python/cugraph/cugraph/link_prediction/wsorensen.py @@ -11,13 +11,26 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.structure.graph_classes import Graph -from cugraph.link_prediction import jaccard_wrapper +from cugraph.link_prediction import sorensen import cudf -from cugraph.utilities import renumber_vertex_pair +import warnings +from cugraph.structure import Graph +from cugraph.utilities.utils import import_optional -def sorensen_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + + +def sorensen_w( + input_graph: Graph, + weights: cudf.DataFrame = None, # deprecated + vertex_pair: cudf.DataFrame = None, + do_expensive_check: bool = False, # deprecated +): """ Compute the weighted Sorensen similarity between each pair of vertices connected by an edge, or between arbitrary pairs of vertices specified by @@ -51,9 +64,13 @@ def sorensen_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): vertices. If provided, the sorensen coefficient is computed for the given vertex pairs, else, it is computed for all vertex pairs. - do_expensive_check: bool (default=True) - When set to True, check if the vertices in the graph are (re)numbered - from 0 to V-1 where V is the total number of vertices. + do_expensive_check : bool, optional (default=False) + Deprecated. + This option added a check to ensure integer vertex IDs are sequential + values from 0 to V-1. That check is now redundant because cugraph + unconditionally renumbers and un-renumbers integer vertex IDs for + optimal performance, therefore this option is deprecated and will be + removed in a future version. Returns ------- @@ -93,44 +110,9 @@ def sorensen_w(input_graph, weights, vertex_pair=None, do_expensive_check=True): >>> df = cugraph.sorensen_w(G, weights) """ - if do_expensive_check: - if not input_graph.renumbered: - input_df = input_graph.edgelist.edgelist_df[["src", "dst"]] - max_vertex = input_df.max().max() - expected_nodes = cudf.Series(range(0, max_vertex + 1, 1)).astype( - input_df.dtypes[0] - ) - nodes = ( - cudf.concat([input_df["src"], input_df["dst"]]) - .unique() - .sort_values() - .reset_index(drop=True) - ) - if not expected_nodes.equals(nodes): - raise ValueError("Unrenumbered vertices are not supported.") - - if type(input_graph) is not Graph: - raise TypeError("input graph must a Graph") - - if type(vertex_pair) == cudf.DataFrame: - vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) - elif vertex_pair is not None: - raise ValueError("vertex_pair must be a cudf dataframe") - - if input_graph.renumbered: - vertex_size = input_graph.vertex_column_size() - if vertex_size == 1: - weights = input_graph.add_internal_vertex_id(weights, "vertex", "vertex") - else: - cols = weights.columns[:vertex_size].to_list() - weights = input_graph.add_internal_vertex_id(weights, "vertex", cols) - jaccard_weights = weights["weight"] - df = jaccard_wrapper.jaccard(input_graph, jaccard_weights, vertex_pair) - df.jaccard_coeff = (2 * df.jaccard_coeff) / (1 + df.jaccard_coeff) - df.rename({"jaccard_coeff": "sorensen_coeff"}, axis=1, inplace=True) - - if input_graph.renumbered: - df = input_graph.unrenumber(df, "first") - df = input_graph.unrenumber(df, "second") - - return df + warning_msg = ( + "sorensen_w is deprecated. To compute weighted sorensen, please use " + "sorensen(input_graph, vertex_pair=False, use_weight=True)" + ) + warnings.warn(warning_msg, FutureWarning) + return sorensen(input_graph, vertex_pair, use_weight=True) diff --git a/python/cugraph/cugraph/sampling/random_walks.py b/python/cugraph/cugraph/sampling/random_walks.py index 015c05d1b08..7b04dba82a5 100644 --- a/python/cugraph/cugraph/sampling/random_walks.py +++ b/python/cugraph/cugraph/sampling/random_walks.py @@ -25,11 +25,10 @@ from cugraph.utilities.utils import import_optional from typing import Union, Tuple -# FIXME: the networkx.Graph type used in the type annotation for -# induced_subgraph() is specified using a string literal to avoid depending on -# and importing networkx. Instead, networkx is imported optionally, which may -# cause a problem for a type checker if run in an environment where networkx is -# not installed. +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. networkx = import_optional("networkx") diff --git a/python/cugraph/cugraph/tests/link_prediction/test_jaccard.py b/python/cugraph/cugraph/tests/link_prediction/test_jaccard.py index cd883fb88f2..7ce7d263eda 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_jaccard.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_jaccard.py @@ -11,6 +11,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +# FIXME: Can we use global variables for column names instead of hardcoded ones? + import gc import pytest @@ -20,12 +22,19 @@ import cugraph from cugraph.datasets import netscience from cugraph.testing import utils, UNDIRECTED_DATASETS -from cugraph.experimental import jaccard as exp_jaccard -from cudf.testing import assert_series_equal, assert_frame_equal -from cugraph.experimental import jaccard_coefficient as exp_jaccard_coefficient - +from cudf.testing import assert_series_equal +from cudf.testing.testing import assert_frame_equal -print("Networkx version : {} ".format(nx.__version__)) +SRC_COL = "0" +DST_COL = "1" +VERTEX_PAIR_FIRST_COL = "first" +VERTEX_PAIR_SECOND_COL = "second" +JACCARD_COEFF_COL = "jaccard_coeff" +EDGE_ATT_COL = "weight" +MULTI_COL_SRC_0_COL = "src_0" +MULTI_COL_DST_0_COL = "dst_0" +MULTI_COL_SRC_1_COL = "src_1" +MULTI_COL_DST_1_COL = "dst_1" # ============================================================================= @@ -38,65 +47,79 @@ def setup_function(): # ============================================================================= # Helper functions # ============================================================================= -def compare_jaccard_two_hop(G, Gnx, edgevals=True): + + +def compare_jaccard_two_hop(G, Gnx, use_weight=False): """ Compute both cugraph and nx jaccard after extracting the two hop neighbors from G and compare both results """ pairs = ( G.get_two_hop_neighbors() - .sort_values(["first", "second"]) + .sort_values([VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]) .reset_index(drop=True) ) - nx_pairs = list(pairs.to_records(index=False)) - preds = nx.jaccard_coefficient(Gnx, nx_pairs) - nx_coeff = [] - for u, v, p in preds: - # print(u, " ", v, " ", p) - nx_coeff.append(p) df = cugraph.jaccard(G, pairs) - df = df.sort_values(by=["first", "second"]).reset_index(drop=True) - if not edgevals: - # experimental jaccard currently only supports unweighted graphs - df_exp = exp_jaccard(G, pairs) - df_exp = df_exp.sort_values(by=["first", "second"]).reset_index(drop=True) - assert_frame_equal(df, df_exp, check_dtype=False, check_like=True) + df = df.sort_values(by=[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]).reset_index( + drop=True + ) - assert len(nx_coeff) == len(df) - for i in range(len(df)): - diff = abs(nx_coeff[i] - df["jaccard_coeff"].iloc[i]) - assert diff < 1.0e-6 + if not use_weight: + nx_pairs = list(pairs.to_records(index=False)) + preds = nx.jaccard_coefficient(Gnx, nx_pairs) + nx_coeff = [] + for u, v, p in preds: + nx_coeff.append(p) + + assert len(nx_coeff) == len(df) + for i in range(len(df)): + diff = abs(nx_coeff[i] - df[JACCARD_COEFF_COL].iloc[i]) + assert diff < 1.0e-6 + else: + # FIXME: compare results against resultset api + pass -def cugraph_call(benchmark_callable, graph_file, edgevals=False, input_df=None): +def cugraph_call(benchmark_callable, graph_file, input_df=None, use_weight=False): G = cugraph.Graph() - G = graph_file.get_graph(ignore_weights=not edgevals) + G = graph_file.get_graph(ignore_weights=not use_weight) # If no vertex_pair is passed as input, 'cugraph.jaccard' will # compute the 'jaccard_similarity' with the two_hop_neighbor of the # entire graph while nx compute with the one_hop_neighbor. For better # comparaison, get the one_hop_neighbor of the entire graph for 'cugraph.jaccard' # and pass it as vertex_pair - vertex_pair = input_df.rename(columns={"0": "first", "1": "second"}) - vertex_pair = vertex_pair[["first", "second"]] + if isinstance(input_df, cudf.DataFrame): + vertex_pair = input_df.rename( + columns={SRC_COL: VERTEX_PAIR_FIRST_COL, DST_COL: VERTEX_PAIR_SECOND_COL} + ) + vertex_pair = vertex_pair[[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]] + else: + vertex_pair = cudf.DataFrame( + columns=[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL], + dtype=G.edgelist.edgelist_df["src"].dtype, + ) # cugraph Jaccard Call - df = benchmark_callable(cugraph.jaccard, G, vertex_pair=vertex_pair) + df = benchmark_callable( + cugraph.jaccard, G, vertex_pair=vertex_pair, use_weight=use_weight + ) - df = df.sort_values(["first", "second"]).reset_index(drop=True) + df = df.sort_values([VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]).reset_index( + drop=True + ) return ( - df["first"].to_numpy(), - df["second"].to_numpy(), - df["jaccard_coeff"].to_numpy(), + df[VERTEX_PAIR_FIRST_COL].to_numpy(), + df[VERTEX_PAIR_SECOND_COL].to_numpy(), + df[JACCARD_COEFF_COL].to_numpy(), ) def networkx_call(M, benchmark_callable=None): - - sources = M["0"] - destinations = M["1"] + sources = M[SRC_COL] + destinations = M[DST_COL] edges = [] for i in range(len(M)): edges.append((sources[i], destinations[i])) @@ -108,7 +131,11 @@ def networkx_call(M, benchmark_callable=None): print("Format conversion ... ") Gnx = nx.from_pandas_edgelist( - M, source="0", target="1", edge_attr="weight", create_using=nx.Graph() + M, + source=SRC_COL, + target=DST_COL, + edge_attr=EDGE_ATT_COL, + create_using=nx.Graph(), ) # Networkx Jaccard Call @@ -144,118 +171,130 @@ def read_csv(request): @pytest.mark.sg -def test_jaccard(read_csv, gpubenchmark): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_jaccard(read_csv, gpubenchmark, use_weight): M_cu, M, graph_file = read_csv - cu_src, cu_dst, cu_coeff = cugraph_call(gpubenchmark, graph_file, input_df=M_cu) - nx_src, nx_dst, nx_coeff = networkx_call(M) + cu_src, cu_dst, cu_coeff = cugraph_call( + gpubenchmark, graph_file, input_df=M_cu, use_weight=use_weight + ) + if not use_weight: + nx_src, nx_dst, nx_coeff = networkx_call(M) - # Calculating mismatch - err = 0 - tol = 1.0e-06 + # Calculating mismatch + err = 0 + tol = 1.0e-06 - assert len(cu_coeff) == len(nx_coeff) - for i in range(len(cu_coeff)): - if abs(cu_coeff[i] - nx_coeff[i]) > tol * 1.1: - err += 1 + assert len(cu_coeff) == len(nx_coeff) + for i in range(len(cu_coeff)): + if abs(cu_coeff[i] - nx_coeff[i]) > tol * 1.1: + err += 1 - print("Mismatches: %d" % err) - assert err == 0 + print("Mismatches: %d" % err) + assert err == 0 + else: + G = graph_file.get_graph() + res_w_jaccard = cugraph.jaccard_w(G, vertex_pair=M_cu[[SRC_COL, DST_COL]]) + res_w_jaccard = res_w_jaccard.sort_values( + [VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL] + ).reset_index(drop=True) + res_jaccard = cudf.DataFrame() + res_jaccard[VERTEX_PAIR_FIRST_COL] = cu_src + res_jaccard[VERTEX_PAIR_SECOND_COL] = cu_dst + res_jaccard[JACCARD_COEFF_COL] = cu_coeff + assert_frame_equal( + res_w_jaccard, res_jaccard, check_dtype=False, check_like=True + ) + + # FIXME: compare weighted jaccard results against resultset api @pytest.mark.sg -def test_directed_graph_check(read_csv): +@pytest.mark.parametrize("use_weight", [False, True]) +def test_directed_graph_check(read_csv, use_weight): _, M, _ = read_csv cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 + cu_M[SRC_COL] = cudf.Series(M[SRC_COL]) + cu_M[DST_COL] = cudf.Series(M[DST_COL]) + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) + G1 = cugraph.Graph(directed=True) - G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] - ) + weight = EDGE_ATT_COL if use_weight else None + G1.from_cudf_edgelist(cu_M, source=SRC_COL, destination=DST_COL, weight=weight) + + vertex_pair = cu_M[[SRC_COL, DST_COL]] - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] vertex_pair = vertex_pair[:5] with pytest.raises(ValueError): - cugraph.jaccard(G1, vertex_pair) + cugraph.jaccard(G1, vertex_pair, use_weight) @pytest.mark.sg def test_nx_jaccard_time(read_csv, gpubenchmark): - _, M, _ = read_csv nx_src, nx_dst, nx_coeff = networkx_call(M, gpubenchmark) @pytest.mark.sg @pytest.mark.parametrize("graph_file", [netscience]) -@pytest.mark.skip(reason="Skipping because this datasets is unrenumbered") -def test_jaccard_edgevals(gpubenchmark, graph_file): +@pytest.mark.parametrize("use_weight", [False, True]) +def test_jaccard_edgevals(gpubenchmark, graph_file, use_weight): dataset_path = netscience.get_path() M = utils.read_csv_for_nx(dataset_path) M_cu = utils.read_csv_file(dataset_path) cu_src, cu_dst, cu_coeff = cugraph_call( - gpubenchmark, netscience, edgevals=True, input_df=M_cu + gpubenchmark, netscience, input_df=M_cu, use_weight=use_weight ) - nx_src, nx_dst, nx_coeff = networkx_call(M) + if not use_weight: + nx_src, nx_dst, nx_coeff = networkx_call(M) - # Calculating mismatch - err = 0 - tol = 1.0e-06 - - assert len(cu_coeff) == len(nx_coeff) - for i in range(len(cu_coeff)): - if abs(cu_coeff[i] - nx_coeff[i]) > tol * 1.1: - err += 1 - - print("Mismatches: %d" % err) - assert err == 0 + # Calculating mismatch + err = 0 + tol = 1.0e-06 + assert len(cu_coeff) == len(nx_coeff) + for i in range(len(cu_coeff)): + if abs(cu_coeff[i] - nx_coeff[i]) > tol * 1.1: + err += 1 -@pytest.mark.sg -def test_jaccard_two_hop(read_csv): - - _, M, graph_file = read_csv - - Gnx = nx.from_pandas_edgelist(M, source="0", target="1", create_using=nx.Graph()) - G = graph_file.get_graph(ignore_weights=True) - - compare_jaccard_two_hop(G, Gnx) + print("Mismatches: %d" % err) + assert err == 0 + else: + # FIXME: compare results against resultset api + pass @pytest.mark.sg -def test_jaccard_two_hop_edge_vals(read_csv): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_jaccard_two_hop(read_csv, use_weight): _, M, graph_file = read_csv Gnx = nx.from_pandas_edgelist( - M, source="0", target="1", edge_attr="weight", create_using=nx.Graph() + M, source=SRC_COL, target=DST_COL, create_using=nx.Graph() ) + G = graph_file.get_graph(ignore_weights=not use_weight) - G = graph_file.get_graph() - - compare_jaccard_two_hop(G, Gnx, edgevals=True) + compare_jaccard_two_hop(G, Gnx, use_weight) @pytest.mark.sg def test_jaccard_nx(read_csv): - M_cu, M, _ = read_csv - Gnx = nx.from_pandas_edgelist(M, source="0", target="1", create_using=nx.Graph()) + Gnx = nx.from_pandas_edgelist( + M, source=SRC_COL, target=DST_COL, create_using=nx.Graph() + ) nx_j = nx.jaccard_coefficient(Gnx) nv_js = sorted(nx_j, key=len, reverse=True) - ebunch = M_cu.rename(columns={"0": "first", "1": "second"}) - ebunch = ebunch[["first", "second"]] + ebunch = M_cu.rename( + columns={SRC_COL: VERTEX_PAIR_FIRST_COL, DST_COL: VERTEX_PAIR_SECOND_COL} + ) + ebunch = ebunch[[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]] cg_j = cugraph.jaccard_coefficient(Gnx, ebunch=ebunch) - cg_j_exp = exp_jaccard_coefficient(Gnx, ebunch=ebunch) assert len(nv_js) > len(cg_j) - assert len(nv_js) > len(cg_j_exp) # FIXME: Nx does a full all-pair Jaccard. # cuGraph does a limited 1-hop Jaccard @@ -263,68 +302,58 @@ def test_jaccard_nx(read_csv): @pytest.mark.sg -def test_jaccard_multi_column(read_csv): - - _, M, _ = read_csv +@pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) +@pytest.mark.parametrize("use_weight", [False, True]) +def test_jaccard_multi_column(graph_file, use_weight): + dataset_path = graph_file.get_path() + M = utils.read_csv_for_nx(dataset_path) cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 + cu_M[MULTI_COL_SRC_0_COL] = cudf.Series(M[SRC_COL]) + cu_M[MULTI_COL_DST_0_COL] = cudf.Series(M[DST_COL]) + cu_M[MULTI_COL_SRC_1_COL] = cu_M[MULTI_COL_SRC_0_COL] + 1000 + cu_M[MULTI_COL_DST_1_COL] = cu_M[MULTI_COL_DST_0_COL] + 1000 + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) + G1 = cugraph.Graph() + weight = EDGE_ATT_COL if use_weight else None G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] + cu_M, + source=[MULTI_COL_SRC_0_COL, MULTI_COL_SRC_1_COL], + destination=[MULTI_COL_DST_0_COL, MULTI_COL_DST_1_COL], + weight=weight, ) - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] + vertex_pair = cu_M[ + [ + MULTI_COL_SRC_0_COL, + MULTI_COL_SRC_1_COL, + MULTI_COL_DST_0_COL, + MULTI_COL_DST_1_COL, + ] + ] vertex_pair = vertex_pair[:5] - df_res = cugraph.jaccard(G1, vertex_pair) - df_plc_exp = exp_jaccard(G1, vertex_pair) - - df_plc_exp = df_plc_exp.rename( - columns={ - "0_src": "0_source", - "0_dst": "0_destination", - "1_src": "1_source", - "1_dst": "1_destination", - } - ) - - jaccard_res = df_res["jaccard_coeff"].sort_values().reset_index(drop=True) - jaccard_plc_exp = df_plc_exp["jaccard_coeff"].sort_values().reset_index(drop=True) - assert_series_equal(jaccard_res, jaccard_plc_exp) + df_multi_col_res = cugraph.jaccard(G1, vertex_pair) G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - df_exp = cugraph.jaccard(G2, vertex_pair[["src_0", "dst_0"]]) + G2.from_cudf_edgelist( + cu_M, source=MULTI_COL_SRC_0_COL, destination=MULTI_COL_DST_0_COL, weight=weight + ) + df_single_col_res = cugraph.jaccard( + G2, vertex_pair[[MULTI_COL_SRC_0_COL, MULTI_COL_DST_0_COL]] + ) # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["jaccard_coeff"], expected["jaccard_coeff"]) + actual = df_multi_col_res.sort_values("0_src").reset_index() + expected = df_single_col_res.sort_values(VERTEX_PAIR_FIRST_COL).reset_index() + assert_series_equal(actual[JACCARD_COEFF_COL], expected[JACCARD_COEFF_COL]) @pytest.mark.sg -def test_weighted_exp_jaccard(): +def test_weighted_jaccard(): karate = UNDIRECTED_DATASETS[0] - G = karate.get_graph() - with pytest.raises(ValueError): - exp_jaccard(G) - G = karate.get_graph(ignore_weights=True) - use_weight = True - with pytest.raises(ValueError): - exp_jaccard(G, use_weight=use_weight) - - -@pytest.mark.sg -def test_invalid_datasets_jaccard(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") with pytest.raises(ValueError): - cugraph.jaccard(G) + cugraph.jaccard(G, use_weight=True) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_overlap.py b/python/cugraph/cugraph/tests/link_prediction/test_overlap.py index 586d534cd42..e24deaa61ac 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_overlap.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_overlap.py @@ -20,8 +20,19 @@ import cudf import cugraph from cugraph.testing import utils, UNDIRECTED_DATASETS -from cugraph.experimental import overlap as exp_overlap -from cudf.testing import assert_series_equal, assert_frame_equal +from cudf.testing import assert_series_equal +from cudf.testing.testing import assert_frame_equal + +SRC_COL = "0" +DST_COL = "1" +VERTEX_PAIR_FIRST_COL = "first" +VERTEX_PAIR_SECOND_COL = "second" +OVERLAP_COEFF_COL = "overlap_coeff" +EDGE_ATT_COL = "weight" +MULTI_COL_SRC_0_COL = "src_0" +MULTI_COL_DST_0_COL = "dst_0" +MULTI_COL_SRC_1_COL = "src_1" +MULTI_COL_DST_1_COL = "dst_1" # ============================================================================= @@ -35,7 +46,6 @@ def setup_function(): # Helper functions # ============================================================================= def compare_overlap(cu_coeff, cpu_coeff): - assert len(cu_coeff) == len(cpu_coeff) for i in range(len(cu_coeff)): if np.isnan(cpu_coeff[i]): @@ -47,21 +57,21 @@ def compare_overlap(cu_coeff, cpu_coeff): assert diff < 1.0e-6 -def cugraph_call(benchmark_callable, graph_file, pairs, edgevals=False): +def cugraph_call(benchmark_callable, graph_file, pairs, use_weight=False): # Device data G = graph_file.get_graph( - create_using=cugraph.Graph(directed=False), ignore_weights=not edgevals + create_using=cugraph.Graph(directed=False), ignore_weights=not use_weight ) # cugraph Overlap Call df = benchmark_callable(cugraph.overlap, G, pairs) - df = df.sort_values(by=["first", "second"]).reset_index(drop=True) - if not edgevals: - # experimental overlap currently only supports unweighted graphs - df_exp = exp_overlap(G, pairs) - df_exp = df_exp.sort_values(by=["first", "second"]).reset_index(drop=True) - assert_frame_equal(df, df_exp, check_dtype=False, check_like=True) + df = df.sort_values(by=[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]).reset_index( + drop=True + ) + if use_weight: + res_w_overlap = cugraph.overlap_w(G, vertex_pair=pairs) + assert_frame_equal(res_w_overlap, df, check_dtype=False, check_like=True) - return df["overlap_coeff"].to_numpy() + return df[OVERLAP_COEFF_COL].to_numpy() def intersection(a, b, M): @@ -120,8 +130,10 @@ def read_csv(request): dataset_path = graph_file.get_path() Mnx = utils.read_csv_for_nx(dataset_path) - N = max(max(Mnx["0"]), max(Mnx["1"])) + 1 - M = scipy.sparse.csr_matrix((Mnx.weight, (Mnx["0"], Mnx["1"])), shape=(N, N)) + N = max(max(Mnx[SRC_COL]), max(Mnx[DST_COL])) + 1 + M = scipy.sparse.csr_matrix( + (Mnx.weight, (Mnx[SRC_COL], Mnx[DST_COL])), shape=(N, N) + ) return M, graph_file @@ -135,7 +147,7 @@ def extract_two_hop(read_csv): G = graph_file.get_graph(ignore_weights=True) pairs = ( G.get_two_hop_neighbors() - .sort_values(["first", "second"]) + .sort_values([VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]) .reset_index(drop=True) ) @@ -144,93 +156,91 @@ def extract_two_hop(read_csv): # Test @pytest.mark.sg -def test_overlap(gpubenchmark, read_csv, extract_two_hop): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_overlap(gpubenchmark, read_csv, extract_two_hop, use_weight): M, graph_file = read_csv pairs = extract_two_hop - cu_coeff = cugraph_call(gpubenchmark, graph_file, pairs) - cpu_coeff = cpu_call(M, pairs["first"], pairs["second"]) + cu_coeff = cugraph_call(gpubenchmark, graph_file, pairs, use_weight=use_weight) + cpu_coeff = cpu_call(M, pairs[VERTEX_PAIR_FIRST_COL], pairs[VERTEX_PAIR_SECOND_COL]) compare_overlap(cu_coeff, cpu_coeff) -# Test @pytest.mark.sg -def test_overlap_edge_vals(gpubenchmark, read_csv, extract_two_hop): +@pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) +@pytest.mark.parametrize("use_weight", [False, True]) +def test_directed_graph_check(graph_file, use_weight): + M = utils.read_csv_for_nx(graph_file.get_path()) + cu_M = cudf.DataFrame() + cu_M[SRC_COL] = cudf.Series(M[SRC_COL]) + cu_M[DST_COL] = cudf.Series(M[DST_COL]) + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) - M, graph_file = read_csv - pairs = extract_two_hop + G1 = cugraph.Graph(directed=True) + weight = EDGE_ATT_COL if use_weight else None + G1.from_cudf_edgelist(cu_M, source=SRC_COL, destination=DST_COL, weight=weight) - cu_coeff = cugraph_call(gpubenchmark, graph_file, pairs, edgevals=True) - cpu_coeff = cpu_call(M, pairs["first"], pairs["second"]) + vertex_pair = cu_M[[SRC_COL, DST_COL]] - compare_overlap(cu_coeff, cpu_coeff) + vertex_pair = vertex_pair[:5] + with pytest.raises(ValueError): + cugraph.overlap(G1, vertex_pair, use_weight) @pytest.mark.sg @pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) -def test_overlap_multi_column(graph_file): +@pytest.mark.parametrize("use_weight", [False, True]) +def test_overlap_multi_column(graph_file, use_weight): dataset_path = graph_file.get_path() M = utils.read_csv_for_nx(dataset_path) cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 + cu_M[MULTI_COL_SRC_0_COL] = cudf.Series(M[SRC_COL]) + cu_M[MULTI_COL_DST_0_COL] = cudf.Series(M[DST_COL]) + cu_M[MULTI_COL_SRC_1_COL] = cu_M[MULTI_COL_SRC_0_COL] + 1000 + cu_M[MULTI_COL_DST_1_COL] = cu_M[MULTI_COL_DST_0_COL] + 1000 + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) + G1 = cugraph.Graph() + weight = EDGE_ATT_COL if use_weight else None G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] + cu_M, + source=[MULTI_COL_SRC_0_COL, MULTI_COL_SRC_1_COL], + destination=[MULTI_COL_DST_0_COL, MULTI_COL_DST_1_COL], + weight=weight, ) - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] + vertex_pair = cu_M[ + [ + MULTI_COL_SRC_0_COL, + MULTI_COL_SRC_1_COL, + MULTI_COL_DST_0_COL, + MULTI_COL_DST_1_COL, + ] + ] vertex_pair = vertex_pair[:5] - df_res = cugraph.overlap(G1, vertex_pair) - df_plc_exp = exp_overlap(G1, vertex_pair) - - df_plc_exp = df_plc_exp.rename( - columns={ - "0_src": "0_source", - "0_dst": "0_destination", - "1_src": "1_source", - "1_dst": "1_destination", - } - ) - overlap_res = df_res["overlap_coeff"].sort_values().reset_index(drop=True) - overlap_plc_exp = df_plc_exp["overlap_coeff"].sort_values().reset_index(drop=True) - assert_series_equal(overlap_res, overlap_plc_exp) - + df_multi_col_res = cugraph.overlap(G1, vertex_pair, use_weight=use_weight) G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - df_exp = cugraph.overlap(G2, vertex_pair[["src_0", "dst_0"]]) + G2.from_cudf_edgelist( + cu_M, source=MULTI_COL_SRC_0_COL, destination=MULTI_COL_DST_0_COL, weight=weight + ) + df_single_col_res = cugraph.overlap( + G2, vertex_pair[[MULTI_COL_SRC_0_COL, MULTI_COL_DST_0_COL]] + ) # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["overlap_coeff"], expected["overlap_coeff"]) + actual = df_multi_col_res.sort_values("0_src").reset_index() + expected = df_single_col_res.sort_values(VERTEX_PAIR_FIRST_COL).reset_index() + assert_series_equal(actual[OVERLAP_COEFF_COL], expected[OVERLAP_COEFF_COL]) @pytest.mark.sg -def test_weighted_exp_overlap(): +def test_weighted_overlap(): karate = UNDIRECTED_DATASETS[0] - G = karate.get_graph() - with pytest.raises(ValueError): - exp_overlap(G) - G = karate.get_graph(ignore_weights=True) - use_weight = True - with pytest.raises(ValueError): - exp_overlap(G, use_weight=use_weight) - - -@pytest.mark.sg -def test_invalid_datasets_overlap(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") with pytest.raises(ValueError): - cugraph.overlap(G) + cugraph.overlap(G, use_weight=True) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_sorensen.py b/python/cugraph/cugraph/tests/link_prediction/test_sorensen.py index 3da33a3e853..6b4074fce30 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_sorensen.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_sorensen.py @@ -20,11 +20,19 @@ import cugraph from cugraph.testing import utils, UNDIRECTED_DATASETS from cugraph.datasets import netscience -from cugraph.experimental import sorensen as exp_sorensen -from cudf.testing import assert_series_equal, assert_frame_equal +from cudf.testing import assert_series_equal +from cudf.testing.testing import assert_frame_equal - -print("Networkx version : {} ".format(nx.__version__)) +SRC_COL = "0" +DST_COL = "1" +VERTEX_PAIR_FIRST_COL = "first" +VERTEX_PAIR_SECOND_COL = "second" +SORENSEN_COEFF_COL = "sorensen_coeff" +EDGE_ATT_COL = "weight" +MULTI_COL_SRC_0_COL = "src_0" +MULTI_COL_DST_0_COL = "dst_0" +MULTI_COL_SRC_1_COL = "src_1" +MULTI_COL_DST_1_COL = "dst_1" # ============================================================================= @@ -37,68 +45,89 @@ def setup_function(): # ============================================================================= # Helper functions # ============================================================================= -def compare_sorensen_two_hop(G, Gnx, edgevals=False): +def compare_sorensen_two_hop(G, Gnx, use_weight=False): """ Compute both cugraph and nx sorensen after extracting the two hop neighbors from G and compare both results """ pairs = ( G.get_two_hop_neighbors() - .sort_values(["first", "second"]) + .sort_values([VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]) .reset_index(drop=True) ) - nx_pairs = [] - nx_pairs = list(pairs.to_records(index=False)) - preds = nx.jaccard_coefficient(Gnx, nx_pairs) - nx_coeff = [] - for u, v, p in preds: + + # print(f'G = {G.edgelist.edgelist_df}') + + df = cugraph.sorensen(G, pairs) + df = df.sort_values(by=[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]).reset_index( + drop=True + ) + + if not use_weight: + nx_pairs = list(pairs.to_records(index=False)) + + # print(f'nx_pairs = {len(nx_pairs)}') + + preds = nx.jaccard_coefficient(Gnx, nx_pairs) + # FIXME: Use known correct values of Sorensen for few graphs, # hardcode it and compare to Cugraph Sorensen to get a more robust test # Conversion from Networkx Jaccard to Sorensen # No networkX equivalent - nx_coeff.append((2 * p) / (1 + p)) - df = cugraph.sorensen(G, pairs) - df = df.sort_values(by=["first", "second"]).reset_index(drop=True) - if not edgevals: - # experimental sorensen currently only supports unweighted graphs - df_exp = exp_sorensen(G, pairs) - df_exp = df_exp.sort_values(by=["first", "second"]).reset_index(drop=True) - assert_frame_equal(df, df_exp, check_dtype=False, check_like=True) - assert len(nx_coeff) == len(df) - for i in range(len(df)): - diff = abs(nx_coeff[i] - df["sorensen_coeff"].iloc[i]) - assert diff < 1.0e-6 - - -def cugraph_call(benchmark_callable, graph_file, edgevals=False, input_df=None): + + nx_coeff = list(map(lambda x: (2 * x[2]) / (1 + x[2]), preds)) + + assert len(nx_coeff) == len(df) + for i in range(len(df)): + diff = abs(nx_coeff[i] - df[SORENSEN_COEFF_COL].iloc[i]) + assert diff < 1.0e-6 + else: + # FIXME: compare results against resultset api + res_w_sorensen = cugraph.sorensen_w(G, vertex_pair=pairs) + res_w_sorensen = res_w_sorensen.sort_values( + [VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL] + ).reset_index(drop=True) + assert_frame_equal(res_w_sorensen, df, check_dtype=False, check_like=True) + + +def cugraph_call(benchmark_callable, graph_file, input_df=None, use_weight=False): G = cugraph.Graph() - G = graph_file.get_graph(ignore_weights=not edgevals) + G = graph_file.get_graph(ignore_weights=not use_weight) # If no vertex_pair is passed as input, 'cugraph.sorensen' will # compute the 'sorensen_similarity' with the two_hop_neighbor of the # entire graph while nx compute with the one_hop_neighbor. For better # comparaison, get the one_hop_neighbor of the entire graph for 'cugraph.sorensen' # and pass it as vertex_pair - vertex_pair = input_df.rename(columns={"0": "first", "1": "second"}) - vertex_pair = vertex_pair[["first", "second"]] + if isinstance(input_df, cudf.DataFrame): + vertex_pair = input_df.rename( + columns={SRC_COL: VERTEX_PAIR_FIRST_COL, DST_COL: VERTEX_PAIR_SECOND_COL} + ) + vertex_pair = vertex_pair[[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]] + else: + vertex_pair = cudf.DataFrame( + columns=[VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL], + dtype=G.edgelist.edgelist_df["src"].dtype, + ) # cugraph Sorensen Call df = benchmark_callable(cugraph.sorensen, G, vertex_pair=vertex_pair) - df = df.sort_values(["first", "second"]).reset_index(drop=True) + df = df.sort_values([VERTEX_PAIR_FIRST_COL, VERTEX_PAIR_SECOND_COL]).reset_index( + drop=True + ) return ( - df["first"].to_numpy(), - df["second"].to_numpy(), - df["sorensen_coeff"].to_numpy(), + df[VERTEX_PAIR_FIRST_COL].to_numpy(), + df[VERTEX_PAIR_SECOND_COL].to_numpy(), + df[SORENSEN_COEFF_COL].to_numpy(), ) def networkx_call(M, benchmark_callable=None): - - sources = M["0"] - destinations = M["1"] + sources = M[SRC_COL] + destinations = M[DST_COL] edges = [] for i in range(len(M)): edges.append((sources[i], destinations[i])) @@ -110,7 +139,11 @@ def networkx_call(M, benchmark_callable=None): print("Format conversion ... ") Gnx = nx.from_pandas_edgelist( - M, source="0", target="1", edge_attr="weight", create_using=nx.Graph() + M, + source=SRC_COL, + target=DST_COL, + edge_attr=EDGE_ATT_COL, + create_using=nx.Graph(), ) # Networkx Jaccard Call @@ -149,10 +182,12 @@ def read_csv(request): @pytest.mark.sg -def test_sorensen(gpubenchmark, read_csv): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_sorensen(gpubenchmark, read_csv, use_weight): M_cu, M, graph_file = read_csv - cu_src, cu_dst, cu_coeff = cugraph_call(gpubenchmark, graph_file, input_df=M_cu) + cu_src, cu_dst, cu_coeff = cugraph_call( + gpubenchmark, graph_file, input_df=M_cu, use_weight=use_weight + ) nx_src, nx_dst, nx_coeff = networkx_call(M) # Calculating mismatch @@ -170,20 +205,42 @@ def test_sorensen(gpubenchmark, read_csv): @pytest.mark.sg def test_nx_sorensen_time(gpubenchmark, read_csv): - _, M, _ = read_csv nx_src, nx_dst, nx_coeff = networkx_call(M, gpubenchmark) +@pytest.mark.sg +@pytest.mark.parametrize("use_weight", [False, True]) +def test_directed_graph_check(read_csv, use_weight): + _, M, _ = read_csv + + cu_M = cudf.DataFrame() + cu_M[SRC_COL] = cudf.Series(M[SRC_COL]) + cu_M[DST_COL] = cudf.Series(M[DST_COL]) + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) + + G1 = cugraph.Graph(directed=True) + weight = EDGE_ATT_COL if use_weight else None + G1.from_cudf_edgelist(cu_M, source=SRC_COL, destination=DST_COL, weight=weight) + + vertex_pair = cu_M[[SRC_COL, DST_COL]] + + vertex_pair = vertex_pair[:5] + with pytest.raises(ValueError): + cugraph.sorensen(G1, vertex_pair, use_weight) + + @pytest.mark.sg @pytest.mark.parametrize("graph_file", [netscience]) +@pytest.mark.parametrize("use_weight", [False, True]) @pytest.mark.skip(reason="Skipping because this datasets is unrenumbered") -def test_sorensen_edgevals(gpubenchmark, graph_file): +def test_sorensen_edgevals(gpubenchmark, graph_file, use_weight): dataset_path = netscience.get_path() M = utils.read_csv_for_nx(dataset_path) M_cu = utils.read_csv_file(dataset_path) cu_src, cu_dst, cu_coeff = cugraph_call( - gpubenchmark, netscience, edgevals=True, input_df=M_cu + gpubenchmark, netscience, input_df=M_cu, use_weight=use_weight ) nx_src, nx_dst, nx_coeff = networkx_call(M) @@ -201,92 +258,89 @@ def test_sorensen_edgevals(gpubenchmark, graph_file): @pytest.mark.sg -def test_sorensen_two_hop(read_csv): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_sorensen_two_hop(read_csv, use_weight): _, M, graph_file = read_csv - Gnx = nx.from_pandas_edgelist(M, source="0", target="1", create_using=nx.Graph()) - G = graph_file.get_graph(ignore_weights=True) + Gnx = nx.from_pandas_edgelist( + M, source=SRC_COL, target=DST_COL, create_using=nx.Graph() + ) + G = graph_file.get_graph(ignore_weights=not use_weight) - compare_sorensen_two_hop(G, Gnx) + compare_sorensen_two_hop(G, Gnx, use_weight=use_weight) @pytest.mark.sg -def test_sorensen_two_hop_edge_vals(read_csv): - +@pytest.mark.parametrize("use_weight", [False, True]) +def test_sorensen_two_hop_edge_vals(read_csv, use_weight): _, M, graph_file = read_csv Gnx = nx.from_pandas_edgelist( - M, source="0", target="1", edge_attr="weight", create_using=nx.Graph() + M, + source=SRC_COL, + target=DST_COL, + edge_attr=EDGE_ATT_COL, + create_using=nx.Graph(), ) - G = graph_file.get_graph() + G = graph_file.get_graph(ignore_weights=not use_weight) - compare_sorensen_two_hop(G, Gnx, edgevals=True) + compare_sorensen_two_hop(G, Gnx, use_weight=use_weight) @pytest.mark.sg -def test_sorensen_multi_column(read_csv): - - _, M, _ = read_csv +@pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) +@pytest.mark.parametrize("use_weight", [False, True]) +def test_sorensen_multi_column(graph_file, use_weight): + dataset_path = graph_file.get_path() + M = utils.read_csv_for_nx(dataset_path) cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 + cu_M[MULTI_COL_SRC_0_COL] = cudf.Series(M[SRC_COL]) + cu_M[MULTI_COL_DST_0_COL] = cudf.Series(M[DST_COL]) + cu_M[MULTI_COL_SRC_1_COL] = cu_M[MULTI_COL_SRC_0_COL] + 1000 + cu_M[MULTI_COL_DST_1_COL] = cu_M[MULTI_COL_DST_0_COL] + 1000 + if use_weight: + cu_M[EDGE_ATT_COL] = cudf.Series(M[EDGE_ATT_COL]) + G1 = cugraph.Graph() + weight = EDGE_ATT_COL if use_weight else None G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] + cu_M, + source=[MULTI_COL_SRC_0_COL, MULTI_COL_SRC_1_COL], + destination=[MULTI_COL_DST_0_COL, MULTI_COL_DST_1_COL], + weight=weight, ) - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] + vertex_pair = cu_M[ + [ + MULTI_COL_SRC_0_COL, + MULTI_COL_SRC_1_COL, + MULTI_COL_DST_0_COL, + MULTI_COL_DST_1_COL, + ] + ] vertex_pair = vertex_pair[:5] - df_res = cugraph.sorensen(G1, vertex_pair) - df_plc_exp = exp_sorensen(G1, vertex_pair) - - df_plc_exp = df_plc_exp.rename( - columns={ - "0_src": "0_source", - "0_dst": "0_destination", - "1_src": "1_source", - "1_dst": "1_destination", - } - ) - sorensen_res = df_res["sorensen_coeff"].sort_values().reset_index(drop=True) - sorensen_plc_exp = df_plc_exp["sorensen_coeff"].sort_values().reset_index(drop=True) - assert_series_equal(sorensen_res, sorensen_plc_exp) + df_multi_col_res = cugraph.sorensen(G1, vertex_pair) G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - df_exp = cugraph.sorensen(G2, vertex_pair[["src_0", "dst_0"]]) + G2.from_cudf_edgelist( + cu_M, source=MULTI_COL_SRC_0_COL, destination=MULTI_COL_DST_0_COL, weight=weight + ) + df_single_col_res = cugraph.sorensen( + G2, vertex_pair[[MULTI_COL_SRC_0_COL, MULTI_COL_DST_0_COL]] + ) # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["sorensen_coeff"], expected["sorensen_coeff"]) + actual = df_multi_col_res.sort_values("0_src").reset_index() + expected = df_single_col_res.sort_values(VERTEX_PAIR_FIRST_COL).reset_index() + assert_series_equal(actual[SORENSEN_COEFF_COL], expected[SORENSEN_COEFF_COL]) @pytest.mark.sg -def test_weighted_exp_sorensen(): +def test_weighted_sorensen(): karate = UNDIRECTED_DATASETS[0] - G = karate.get_graph() - with pytest.raises(ValueError): - exp_sorensen(G) - G = karate.get_graph(ignore_weights=True) - use_weight = True - with pytest.raises(ValueError): - exp_sorensen(G, use_weight=use_weight) - - -@pytest.mark.sg -def test_invalid_datasets_sorensen(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") with pytest.raises(ValueError): - cugraph.sorensen(G) + cugraph.sorensen(G, use_weight=True) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_wjaccard.py b/python/cugraph/cugraph/tests/link_prediction/test_wjaccard.py deleted file mode 100644 index 36a21df46b8..00000000000 --- a/python/cugraph/cugraph/tests/link_prediction/test_wjaccard.py +++ /dev/null @@ -1,177 +0,0 @@ -# Copyright (c) 2019-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import gc - -import pytest -import numpy as np -import networkx as nx - -import cudf -import cugraph -from cugraph.testing import utils, UNDIRECTED_DATASETS -from cudf.testing import assert_series_equal - - -print("Networkx version : {} ".format(nx.__version__)) - - -# ============================================================================= -# Pytest Setup / Teardown - called for each test function -# ============================================================================= -def setup_function(): - gc.collect() - - -def cugraph_call(benchmark_callable, graph_file): - # Device data - cu_M = graph_file.get_edgelist() - weight_arr = cudf.Series( - np.ones(max(cu_M["src"].max(), cu_M["dst"].max()) + 1, dtype=np.float32) - ) - weights = cudf.DataFrame() - weights["vertex"] = np.arange(len(weight_arr), dtype=np.int32) - weights["weight"] = weight_arr - - G = graph_file.get_graph(ignore_weights=True) - - # cugraph Jaccard Call - df = benchmark_callable(cugraph.jaccard_w, G, weights) - - df = df.sort_values(["first", "second"]).reset_index(drop=True) - - return df["jaccard_coeff"] - - -def networkx_call(M, benchmark_callable=None): - - sources = M["0"] - destinations = M["1"] - edges = [] - for i in range(len(sources)): - edges.append((sources[i], destinations[i])) - edges.append((destinations[i], sources[i])) - edges = list(dict.fromkeys(edges)) - edges = sorted(edges) - # in NVGRAPH tests we read as CSR and feed as CSC, so here we doing this - # explicitly - print("Format conversion ... ") - - # NetworkX graph - Gnx = nx.from_pandas_edgelist(M, source="0", target="1", create_using=nx.Graph()) - # Networkx Jaccard Call - print("Solving... ") - if benchmark_callable is not None: - preds = benchmark_callable(nx.jaccard_coefficient, Gnx, edges) - else: - preds = nx.jaccard_coefficient(Gnx, edges) - - coeff = [] - for u, v, p in preds: - coeff.append(p) - return coeff - - -# ============================================================================= -# Pytest Fixtures -# ============================================================================= -@pytest.fixture(scope="module", params=UNDIRECTED_DATASETS) -def read_csv(request): - """ - Read csv file for both networkx and cugraph - """ - graph_file = request.param - dataset_path = graph_file.get_path() - M = utils.read_csv_for_nx(dataset_path) - - return M, graph_file - - -@pytest.mark.sg -def test_wjaccard(gpubenchmark, read_csv): - - M, graph_file = read_csv - - cu_coeff = cugraph_call(gpubenchmark, graph_file) - nx_coeff = networkx_call(M) - for i in range(len(cu_coeff)): - diff = abs(nx_coeff[i] - cu_coeff[i]) - assert diff < 1.0e-6 - - -@pytest.mark.sg -def test_nx_wjaccard_time(gpubenchmark, read_csv): - - M, _ = read_csv - networkx_call(M, gpubenchmark) - - -@pytest.mark.sg -def test_wjaccard_multi_column_weights(gpubenchmark, read_csv): - - M, graph_file = read_csv - - cu_coeff = cugraph_call(gpubenchmark, graph_file) - nx_coeff = networkx_call(M) - for i in range(len(cu_coeff)): - diff = abs(nx_coeff[i] - cu_coeff[i]) - assert diff < 1.0e-6 - - -@pytest.mark.sg -def test_wjaccard_multi_column(read_csv): - - M, _ = read_csv - - cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 - G1 = cugraph.Graph() - G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] - ) - - G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] - vertex_pair = vertex_pair[:5] - - weight_arr = cudf.Series(np.ones(G2.number_of_vertices(), dtype=np.float32)) - weights = cudf.DataFrame() - weights["vertex"] = G2.nodes() - weights["vertex_"] = weights["vertex"] + 1000 - weights["weight"] = weight_arr - - df_res = cugraph.jaccard_w(G1, weights, vertex_pair) - - weights = weights[["vertex", "weight"]] - df_exp = cugraph.jaccard_w(G2, weights, vertex_pair[["src_0", "dst_0"]]) - - # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["jaccard_coeff"], expected["jaccard_coeff"]) - - -@pytest.mark.sg -def test_invalid_datasets_jaccard_w(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") - with pytest.raises(ValueError): - cugraph.jaccard_w(G, None) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_woverlap.py b/python/cugraph/cugraph/tests/link_prediction/test_woverlap.py deleted file mode 100644 index 1dffb9fca41..00000000000 --- a/python/cugraph/cugraph/tests/link_prediction/test_woverlap.py +++ /dev/null @@ -1,171 +0,0 @@ -# Copyright (c) 2019-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import gc - -import pytest -import scipy -import numpy as np - -import cudf -import cugraph -from cudf.testing import assert_series_equal -from cugraph.testing import utils, UNDIRECTED_DATASETS - - -# ============================================================================= -# Pytest Setup / Teardown - called for each test function -# ============================================================================= -def setup_function(): - gc.collect() - - -def cugraph_call(benchmark_callable, graph_file, pairs): - # Device data - cu_M = graph_file.get_edgelist() - weights_arr = cudf.Series( - np.ones(max(cu_M["src"].max(), cu_M["dst"].max()) + 1, dtype=np.float32) - ) - weights = cudf.DataFrame() - weights["vertex"] = np.arange(len(weights_arr), dtype=np.int32) - weights["weight"] = weights_arr - - G = graph_file.get_graph(create_using=cugraph.Graph(directed=True)) - - # cugraph Overlap Call - df = benchmark_callable(cugraph.overlap_w, G, weights, pairs) - - df = df.sort_values(by=["first", "second"]) - return df["overlap_coeff"].to_numpy() - - -def intersection(a, b, M): - count = 0 - a_idx = M.indptr[a] - b_idx = M.indptr[b] - - while (a_idx < M.indptr[a + 1]) and (b_idx < M.indptr[b + 1]): - a_vertex = M.indices[a_idx] - b_vertex = M.indices[b_idx] - - if a_vertex == b_vertex: - count += 1 - a_idx += 1 - b_idx += 1 - elif a_vertex < b_vertex: - a_idx += 1 - else: - b_idx += 1 - - return count - - -def degree(a, M): - return M.indptr[a + 1] - M.indptr[a] - - -def overlap(a, b, M): - b_sum = degree(b, M) - if b_sum == 0: - return float("NaN") - - i = intersection(a, b, M) - a_sum = degree(a, M) - total = min(a_sum, b_sum) - return i / total - - -def cpu_call(M, first, second): - result = [] - for i in range(len(first)): - result.append(overlap(first[i], second[i], M)) - return result - - -@pytest.mark.sg -@pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) -def test_woverlap(gpubenchmark, graph_file): - dataset_path = graph_file.get_path() - Mnx = utils.read_csv_for_nx(dataset_path) - N = max(max(Mnx["0"]), max(Mnx["1"])) + 1 - M = scipy.sparse.csr_matrix((Mnx.weight, (Mnx["0"], Mnx["1"])), shape=(N, N)) - - G = graph_file.get_graph(ignore_weights=True) - pairs = ( - G.get_two_hop_neighbors() - .sort_values(["first", "second"]) - .reset_index(drop=True) - ) - - cu_coeff = cugraph_call(gpubenchmark, graph_file, pairs) - cpu_coeff = cpu_call(M, pairs["first"], pairs["second"]) - assert len(cu_coeff) == len(cpu_coeff) - for i in range(len(cu_coeff)): - if np.isnan(cpu_coeff[i]): - assert np.isnan(cu_coeff[i]) - elif np.isnan(cu_coeff[i]): - assert cpu_coeff[i] == cu_coeff[i] - else: - diff = abs(cpu_coeff[i] - cu_coeff[i]) - assert diff < 1.0e-6 - - -@pytest.mark.sg -@pytest.mark.parametrize("graph_file", UNDIRECTED_DATASETS) -def test_woverlap_multi_column(graph_file): - dataset_path = graph_file.get_path() - M = utils.read_csv_for_nx(dataset_path) - - cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 - G1 = cugraph.Graph() - G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] - ) - - G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] - vertex_pair = vertex_pair[:5] - - weight_arr = cudf.Series(np.ones(G2.number_of_vertices(), dtype=np.float32)) - - weights = cudf.DataFrame() - weights["vertex"] = G2.nodes() - weights["vertex_"] = weights["vertex"] + 1000 - weights["weight"] = weight_arr - - df_res = cugraph.overlap_w(G1, weights, vertex_pair) - - weights = weights[["vertex", "weight"]] - df_exp = cugraph.overlap_w(G2, weights, vertex_pair[["src_0", "dst_0"]]) - - # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["overlap_coeff"], expected["overlap_coeff"]) - - -@pytest.mark.sg -def test_invalid_datasets_overlap_w(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") - with pytest.raises(ValueError): - cugraph.overlap_w(G, None) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_wsorensen.py b/python/cugraph/cugraph/tests/link_prediction/test_wsorensen.py deleted file mode 100644 index 8d09b3e25b3..00000000000 --- a/python/cugraph/cugraph/tests/link_prediction/test_wsorensen.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) 2021-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import gc - -import pytest -import numpy as np -import networkx as nx - -import cudf -import cugraph -from cudf.testing import assert_series_equal -from cugraph.testing import utils, UNDIRECTED_DATASETS - - -print("Networkx version : {} ".format(nx.__version__)) - - -# ============================================================================= -# Pytest Setup / Teardown - called for each test function -# ============================================================================= -def setup_function(): - gc.collect() - - -def cugraph_call(benchmark_callable, graph_file): - # Device data - cu_M = graph_file.get_edgelist() - weight_arr = cudf.Series( - np.ones(max(cu_M["src"].max(), cu_M["dst"].max()) + 1, dtype=np.float32) - ) - weights = cudf.DataFrame() - weights["vertex"] = np.arange(len(weight_arr), dtype=np.int32) - weights["weight"] = weight_arr - - G = graph_file.get_graph(ignore_weights=True) - - # cugraph Sorensen Call - df = benchmark_callable(cugraph.sorensen_w, G, weights) - - df = df.sort_values(["first", "second"]).reset_index(drop=True) - - return df["sorensen_coeff"] - - -def networkx_call(M, benchmark_callable=None): - - sources = M["0"] - destinations = M["1"] - edges = [] - for i in range(len(sources)): - edges.append((sources[i], destinations[i])) - edges.append((destinations[i], sources[i])) - edges = list(dict.fromkeys(edges)) - edges = sorted(edges) - # in NVGRAPH tests we read as CSR and feed as CSC, so here we doing this - # explicitly - print("Format conversion ... ") - - # NetworkX graph - Gnx = nx.from_pandas_edgelist(M, source="0", target="1", create_using=nx.Graph()) - # Networkx Jaccard Call - print("Solving... ") - if benchmark_callable is not None: - preds = benchmark_callable(nx.jaccard_coefficient, Gnx, edges) - else: - preds = nx.jaccard_coefficient(Gnx, edges) - coeff = [] - for u, v, p in preds: - # FIXME: Use known correct values of WSorensen for few graphs, - # hardcode it and compare to Cugraph WSorensen - # to get a more robust test - - # Conversion from Networkx Jaccard to Sorensen - coeff.append((2 * p) / (1 + p)) - return coeff - - -# ============================================================================= -# Pytest Fixtures -# ============================================================================= -@pytest.fixture(scope="module", params=UNDIRECTED_DATASETS) -def read_csv(request): - """ - Read csv file for both networkx and cugraph - """ - graph_file = request.param - dataset_path = graph_file.get_path() - M = utils.read_csv_for_nx(dataset_path) - - return M, graph_file - - -@pytest.mark.sg -def test_wsorensen(gpubenchmark, read_csv): - - M, graph_file = read_csv - - cu_coeff = cugraph_call(gpubenchmark, graph_file) - nx_coeff = networkx_call(M) - for i in range(len(cu_coeff)): - diff = abs(nx_coeff[i] - cu_coeff[i]) - assert diff < 1.0e-6 - - -@pytest.mark.sg -def test_nx_wsorensen_time(gpubenchmark, read_csv): - - M, _ = read_csv - networkx_call(M, gpubenchmark) - - -@pytest.mark.sg -def test_wsorensen_multi_column_weights(gpubenchmark, read_csv): - - M, cu_M = read_csv - - cu_coeff = cugraph_call(gpubenchmark, cu_M) - nx_coeff = networkx_call(M) - for i in range(len(cu_coeff)): - diff = abs(nx_coeff[i] - cu_coeff[i]) - assert diff < 1.0e-6 - - -@pytest.mark.sg -def test_wsorensen_multi_column(read_csv): - - M, _ = read_csv - - cu_M = cudf.DataFrame() - cu_M["src_0"] = cudf.Series(M["0"]) - cu_M["dst_0"] = cudf.Series(M["1"]) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 - G1 = cugraph.Graph() - G1.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"] - ) - - G2 = cugraph.Graph() - G2.from_cudf_edgelist(cu_M, source="src_0", destination="dst_0") - - vertex_pair = cu_M[["src_0", "src_1", "dst_0", "dst_1"]] - vertex_pair = vertex_pair[:5] - - weight_arr = cudf.Series(np.ones(G2.number_of_vertices(), dtype=np.float32)) - weights = cudf.DataFrame() - weights["vertex"] = G2.nodes() - weights["vertex_"] = weights["vertex"] + 1000 - weights["weight"] = weight_arr - - df_res = cugraph.sorensen_w(G1, weights, vertex_pair) - - weights = weights[["vertex", "weight"]] - df_exp = cugraph.sorensen_w(G2, weights, vertex_pair[["src_0", "dst_0"]]) - - # Calculating mismatch - actual = df_res.sort_values("0_first").reset_index() - expected = df_exp.sort_values("first").reset_index() - assert_series_equal(actual["sorensen_coeff"], expected["sorensen_coeff"]) - - -@pytest.mark.sg -def test_invalid_datasets_sorensen_w(): - karate = UNDIRECTED_DATASETS[0] - df = karate.get_edgelist() - df = df.add(1) - G = cugraph.Graph(directed=False) - G.from_cudf_edgelist(df, source="src", destination="dst") - with pytest.raises(ValueError): - cugraph.sorensen_w(G, None) diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index 711652bbae6..45f6de2f663 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -87,6 +87,13 @@ from pylibcugraph.generate_rmat_edgelists import generate_rmat_edgelists +from pylibcugraph.jaccard_coefficients import jaccard_coefficients + +from pylibcugraph.overlap_coefficients import overlap_coefficients + +from pylibcugraph.sorensen_coefficients import sorensen_coefficients + + from pylibcugraph import exceptions __version__ = "23.10.00" diff --git a/python/pylibcugraph/pylibcugraph/experimental/__init__.py b/python/pylibcugraph/pylibcugraph/experimental/__init__.py index 1b93f9322af..6194ace5956 100644 --- a/python/pylibcugraph/pylibcugraph/experimental/__init__.py +++ b/python/pylibcugraph/pylibcugraph/experimental/__init__.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -74,18 +74,17 @@ from pylibcugraph.node2vec import node2vec -node2vec = promoted_experimental_warning_wrapper(node2vec) -from pylibcugraph.jaccard_coefficients import EXPERIMENTAL__jaccard_coefficients +# from pylibcugraph.jaccard_coefficients import EXPERIMENTAL__jaccard_coefficients -jaccard_coefficients = experimental_warning_wrapper(EXPERIMENTAL__jaccard_coefficients) +# jaccard_coefficients = experimental_warning_wrapper(EXPERIMENTAL__jaccard_coefficients) -from pylibcugraph.overlap_coefficients import EXPERIMENTAL__overlap_coefficients +# from pylibcugraph.overlap_coefficients import EXPERIMENTAL__overlap_coefficients -overlap_coefficients = experimental_warning_wrapper(EXPERIMENTAL__overlap_coefficients) +# overlap_coefficients = experimental_warning_wrapper(EXPERIMENTAL__overlap_coefficients) -from pylibcugraph.sorensen_coefficients import EXPERIMENTAL__sorensen_coefficients +# from pylibcugraph.sorensen_coefficients import EXPERIMENTAL__sorensen_coefficients -sorensen_coefficients = experimental_warning_wrapper( - EXPERIMENTAL__sorensen_coefficients -) +# sorensen_coefficients = experimental_warning_wrapper( +# EXPERIMENTAL__sorensen_coefficients +# ) diff --git a/python/pylibcugraph/pylibcugraph/jaccard_coefficients.pyx b/python/pylibcugraph/pylibcugraph/jaccard_coefficients.pyx index 805ee821eab..59e94aeb615 100644 --- a/python/pylibcugraph/pylibcugraph/jaccard_coefficients.pyx +++ b/python/pylibcugraph/pylibcugraph/jaccard_coefficients.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,6 +15,8 @@ # cython: language_level = 3 from libc.stdint cimport uintptr_t +from libc.stdio cimport printf +from cython.operator cimport dereference from pylibcugraph._cugraph_c.resource_handle cimport ( bool_t, @@ -57,7 +59,7 @@ from pylibcugraph.utils cimport ( ) -def EXPERIMENTAL__jaccard_coefficients(ResourceHandle resource_handle, +def jaccard_coefficients(ResourceHandle resource_handle, _GPUGraph graph, first, second, @@ -83,8 +85,10 @@ def EXPERIMENTAL__jaccard_coefficients(ResourceHandle resource_handle, second : Destination of the vertex pair. - use_weight : bool, optional (default=False) - Currently not supported + use_weight : bool, optional + If set to True, the compute weighted jaccard_coefficients( + the input graph must be weighted in that case). + Otherwise, computed un-weighted jaccard_coefficients do_expensive_check : bool If True, performs more extensive tests on the inputs to ensure diff --git a/python/pylibcugraph/pylibcugraph/overlap_coefficients.pyx b/python/pylibcugraph/pylibcugraph/overlap_coefficients.pyx index 6af71116469..28360121c64 100644 --- a/python/pylibcugraph/pylibcugraph/overlap_coefficients.pyx +++ b/python/pylibcugraph/pylibcugraph/overlap_coefficients.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -57,7 +57,7 @@ from pylibcugraph.utils cimport ( ) -def EXPERIMENTAL__overlap_coefficients(ResourceHandle resource_handle, +def overlap_coefficients(ResourceHandle resource_handle, _GPUGraph graph, first, second, @@ -84,8 +84,10 @@ def EXPERIMENTAL__overlap_coefficients(ResourceHandle resource_handle, second : Destination of the vertex pair. - use_weight : bool, optional (default=False) - Currently not supported + use_weight : bool, optional + If set to True, the compute weighted jaccard_coefficients( + the input graph must be weighted in that case). + Otherwise, computed un-weighted jaccard_coefficients do_expensive_check : bool If True, performs more extensive tests on the inputs to ensure diff --git a/python/pylibcugraph/pylibcugraph/sorensen_coefficients.pyx b/python/pylibcugraph/pylibcugraph/sorensen_coefficients.pyx index 12647baccb2..983a635012f 100644 --- a/python/pylibcugraph/pylibcugraph/sorensen_coefficients.pyx +++ b/python/pylibcugraph/pylibcugraph/sorensen_coefficients.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -57,7 +57,7 @@ from pylibcugraph.utils cimport ( ) -def EXPERIMENTAL__sorensen_coefficients(ResourceHandle resource_handle, +def sorensen_coefficients(ResourceHandle resource_handle, _GPUGraph graph, first, second, @@ -83,8 +83,10 @@ def EXPERIMENTAL__sorensen_coefficients(ResourceHandle resource_handle, second : Destination of the vertex pair. - use_weight : bool, optional (default=False) - Currently not supported + use_weight : bool, optional + If set to True, the compute weighted jaccard_coefficients( + the input graph must be weighted in that case). + Otherwise, computed un-weighted jaccard_coefficients do_expensive_check : bool If True, performs more extensive tests on the inputs to ensure From db5073da6c69ac3ee44d7130d8799177ec69a0ef Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Thu, 21 Sep 2023 13:52:57 -0500 Subject: [PATCH 006/111] Update image names (#3867) PR updates `rapidsai/ci` references to `rapidsai/ci-conda` Authors: - Jake Awe (https://github.com/AyodeAwe) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3867 --- .github/workflows/build.yaml | 2 +- .github/workflows/pr.yaml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 2d0d58315a0..02b357c7c88 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -62,7 +62,7 @@ jobs: arch: "amd64" branch: ${{ inputs.branch }} build_type: ${{ inputs.build_type || 'branch' }} - container_image: "rapidsai/ci:cuda11.8.0-ubuntu22.04-py3.10" + container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10" date: ${{ inputs.date }} node_type: "gpu-v100-latest-1" run_script: "ci/build_docs.sh" diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 005fe4a0267..d2d24d90fbe 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -63,7 +63,7 @@ jobs: build_type: pull-request node_type: "gpu-v100-latest-1" arch: "amd64" - container_image: "rapidsai/ci:cuda11.8.0-ubuntu22.04-py3.10" + container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10" run_script: "ci/test_notebooks.sh" docs-build: needs: conda-python-build @@ -73,7 +73,7 @@ jobs: build_type: pull-request node_type: "gpu-v100-latest-1" arch: "amd64" - container_image: "rapidsai/ci:cuda11.8.0-ubuntu22.04-py3.10" + container_image: "rapidsai/ci-conda:cuda11.8.0-ubuntu22.04-py3.10" run_script: "ci/build_docs.sh" wheel-build-pylibcugraph: needs: checks From a7047e3f0049597b4da625138107830ff78405e5 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Thu, 21 Sep 2023 17:19:30 -0400 Subject: [PATCH 007/111] adding dining preference dataset (#3866) This dataset is very small, and uses strings as node names. It will be used to test force atlas, and in a new link prediction/similarity notebook. the licensing is contained here. http://networkdata.ics.uci.edu/netdata/html/Dining-table_partners.html Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - ralph (https://github.com/nv-rliu) URL: https://github.com/rapidsai/cugraph/pull/3866 --- python/cugraph/cugraph/datasets/__init__.py | 1 + .../datasets/metadata/dining_prefs.yaml | 23 +++++++++++++++++++ python/cugraph/cugraph/testing/__init__.py | 3 +++ 3 files changed, 27 insertions(+) create mode 100644 python/cugraph/cugraph/datasets/metadata/dining_prefs.yaml diff --git a/python/cugraph/cugraph/datasets/__init__.py b/python/cugraph/cugraph/datasets/__init__.py index 7ba274c5960..65a820f108b 100644 --- a/python/cugraph/cugraph/datasets/__init__.py +++ b/python/cugraph/cugraph/datasets/__init__.py @@ -27,6 +27,7 @@ meta_path = Path(__file__).parent / "metadata" cyber = Dataset(meta_path / "cyber.yaml") +dining_prefs = Dataset(meta_path / "dining_prefs.yaml") dolphins = Dataset(meta_path / "dolphins.yaml") email_Eu_core = Dataset(meta_path / "email_Eu_core.yaml") karate = Dataset(meta_path / "karate.yaml") diff --git a/python/cugraph/cugraph/datasets/metadata/dining_prefs.yaml b/python/cugraph/cugraph/datasets/metadata/dining_prefs.yaml new file mode 100644 index 00000000000..e7ec85d7a1f --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/dining_prefs.yaml @@ -0,0 +1,23 @@ +name: dining_prefs +file_type: .csv +description: Classic social networking dataset describes dining preferences for a dormitory in New York state. +author: J.L. Moreno +refs: + J. L. Moreno (1960). The Sociometry Reader. The Free Press, Glencoe, Illinois, pg.35 +delim: " " +header: None +col_names: + - src + - dst + - wgt +col_types: + - string + - string + - int +has_loop: false +is_directed: false +is_multigraph: false +is_symmetric: true +number_of_edges: 42 +number_of_nodes: 26 +url: https://data.rapids.ai/cugraph/datasets/dining_prefs.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/testing/__init__.py b/python/cugraph/cugraph/testing/__init__.py index bde398aadbd..f5f0bcb06eb 100644 --- a/python/cugraph/cugraph/testing/__init__.py +++ b/python/cugraph/cugraph/testing/__init__.py @@ -23,6 +23,7 @@ ) from cugraph.datasets import ( cyber, + dining_prefs, dolphins, karate, karate_disjoint, @@ -42,6 +43,7 @@ UNDIRECTED_DATASETS = [karate, dolphins] SMALL_DATASETS = [karate, dolphins, polbooks] WEIGHTED_DATASETS = [ + dining_prefs, dolphins, karate, karate_disjoint, @@ -51,6 +53,7 @@ small_tree, ] ALL_DATASETS = [ + dining_prefs, dolphins, karate, karate_disjoint, From 367f36cfd4719fb522f12dbb74cec5b8a1e61aa6 Mon Sep 17 00:00:00 2001 From: Ray Douglass <3107146+raydouglass@users.noreply.github.com> Date: Fri, 22 Sep 2023 10:55:52 -0400 Subject: [PATCH 008/111] Add file to update-version.sh [skip ci] (#3870) Add a new file to `update-version.sh`. Tested locally Authors: - Ray Douglass (https://github.com/raydouglass) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Jake Awe (https://github.com/AyodeAwe) --- ci/release/update-version.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index 2c8735079f0..bd3aa6bc370 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -126,3 +126,5 @@ for FILE in .github/workflows/*.yaml; do sed_runner "s/dask-cuda.git@branch-[0-9][0-9].[0-9][0-9]/dask-cuda.git@branch-${NEXT_SHORT_TAG}/g" "${FILE}" done sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TAG}\"/g" ci/build_docs.sh + +sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" python/nx-cugraph/README.md From 81e27decfa736203e966be185105f2353d86a7d1 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Fri, 22 Sep 2023 10:58:08 -0400 Subject: [PATCH 009/111] v23.12 Updates [skip ci] --- .github/workflows/build.yaml | 20 ++++----- .github/workflows/pr.yaml | 28 ++++++------- .github/workflows/test.yaml | 8 ++-- ci/build_docs.sh | 2 +- .../all_cuda-118_arch-x86_64.yaml | 26 ++++++------ .../all_cuda-120_arch-x86_64.yaml | 26 ++++++------ .../cugraph-service/conda_build_config.yaml | 2 +- conda/recipes/cugraph/conda_build_config.yaml | 2 +- .../pylibcugraph/conda_build_config.yaml | 2 +- cpp/CMakeLists.txt | 2 +- cpp/doxygen/Doxyfile | 2 +- cpp/libcugraph_etl/CMakeLists.txt | 2 +- dependencies.yaml | 42 +++++++++---------- docs/cugraph/source/conf.py | 4 +- fetch_rapids.cmake | 2 +- .../conda/cugraph_dgl_dev_cuda-118.yaml | 4 +- python/cugraph-dgl/cugraph_dgl/__init__.py | 2 +- python/cugraph-dgl/pyproject.toml | 4 +- .../conda/cugraph_pyg_dev_cuda-118.yaml | 4 +- python/cugraph-pyg/cugraph_pyg/__init__.py | 2 +- python/cugraph-pyg/pyproject.toml | 4 +- .../client/cugraph_service_client/__init__.py | 2 +- python/cugraph-service/client/pyproject.toml | 2 +- .../server/cugraph_service_server/__init__.py | 2 +- python/cugraph-service/server/pyproject.toml | 16 +++---- python/cugraph/CMakeLists.txt | 2 +- python/cugraph/cugraph/__init__.py | 2 +- python/cugraph/pyproject.toml | 22 +++++----- python/nx-cugraph/README.md | 2 +- python/nx-cugraph/nx_cugraph/__init__.py | 2 +- python/nx-cugraph/pyproject.toml | 4 +- python/pylibcugraph/CMakeLists.txt | 2 +- python/pylibcugraph/pylibcugraph/__init__.py | 2 +- python/pylibcugraph/pyproject.toml | 12 +++--- 34 files changed, 131 insertions(+), 131 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 02b357c7c88..6ad499a8ce7 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -28,7 +28,7 @@ concurrency: jobs: cpp-build: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -38,7 +38,7 @@ jobs: python-build: needs: [cpp-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -47,7 +47,7 @@ jobs: upload-conda: needs: [cpp-build, python-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -57,7 +57,7 @@ jobs: if: github.ref_type == 'branch' needs: python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: arch: "amd64" branch: ${{ inputs.branch }} @@ -69,7 +69,7 @@ jobs: sha: ${{ inputs.sha }} wheel-build-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -77,12 +77,12 @@ jobs: date: ${{ inputs.date }} script: ci/build_wheel_pylibcugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.10 + extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -92,7 +92,7 @@ jobs: wheel-build-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -100,12 +100,12 @@ jobs: date: ${{ inputs.date }} script: ci/build_wheel_cugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.10 + extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-publish-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index d2d24d90fbe..71ae82a4e8f 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -24,41 +24,41 @@ jobs: - wheel-build-cugraph - wheel-tests-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.12 checks: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@branch-23.12 with: enable_check_generated_files: false conda-cpp-build: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: pull-request node_type: cpu32 conda-cpp-tests: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: pull-request conda-python-build: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: pull-request conda-python-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: pull-request conda-notebook-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -68,7 +68,7 @@ jobs: docs-build: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -78,34 +78,34 @@ jobs: wheel-build-pylibcugraph: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_pylibcugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.10 + extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_pylibcugraph.sh wheel-build-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_cugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.10 + extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-tests-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_cugraph.sh diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 97abca71260..fcfd580aa3f 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -16,7 +16,7 @@ on: jobs: conda-cpp-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -24,7 +24,7 @@ jobs: sha: ${{ inputs.sha }} conda-python-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -32,7 +32,7 @@ jobs: sha: ${{ inputs.sha }} wheel-tests-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -41,7 +41,7 @@ jobs: script: ci/test_wheel_pylibcugraph.sh wheel-tests-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 2941d062d80..7b4cf152b4a 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -37,7 +37,7 @@ rapids-mamba-retry install \ rapids-logger "Install cugraph-dgl" rapids-mamba-retry install "${PYTHON_CHANNEL}/linux-64/cugraph-dgl-*.tar.bz2" -export RAPIDS_VERSION_NUMBER="23.10" +export RAPIDS_VERSION_NUMBER="23.12" export RAPIDS_DOCS_DIR="$(mktemp -d)" rapids-logger "Build CPP docs" diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index c66890f8ae5..4abac5b2de5 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -15,13 +15,13 @@ dependencies: - cmake>=3.26.4 - cuda-version=11.8 - cudatoolkit -- cudf==23.10.* +- cudf==23.12.* - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 - dask-core>=2023.7.1 -- dask-cuda==23.10.* -- dask-cudf==23.10.* +- dask-cuda==23.12.* +- dask-cudf==23.12.* - dask>=2023.7.1 - distributed>=2023.7.1 - doxygen @@ -31,11 +31,11 @@ dependencies: - graphviz - gtest>=1.13.0 - ipython -- libcudf==23.10.* -- libcugraphops==23.10.* -- libraft-headers==23.10.* -- libraft==23.10.* -- librmm==23.10.* +- libcudf==23.12.* +- libcugraphops==23.12.* +- libraft-headers==23.12.* +- libraft==23.12.* +- librmm==23.12.* - nbsphinx - nccl>=2.9.9 - networkx>=2.5.1 @@ -51,18 +51,18 @@ dependencies: - pandas - pre-commit - pydata-sphinx-theme -- pylibcugraphops==23.10.* -- pylibraft==23.10.* +- pylibcugraphops==23.12.* +- pylibraft==23.12.* - pytest - pytest-benchmark - pytest-cov - pytest-mpl - pytest-xdist - python-louvain -- raft-dask==23.10.* +- raft-dask==23.12.* - recommonmark - requests -- rmm==23.10.* +- rmm==23.12.* - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy @@ -71,5 +71,5 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.34.* +- ucx-py==0.35.* name: all_cuda-118_arch-x86_64 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 3afb1415572..51ba25598c8 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -15,13 +15,13 @@ dependencies: - cmake>=3.26.4 - cuda-nvcc - cuda-version=12.0 -- cudf==23.10.* +- cudf==23.12.* - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 - dask-core>=2023.7.1 -- dask-cuda==23.10.* -- dask-cudf==23.10.* +- dask-cuda==23.12.* +- dask-cudf==23.12.* - dask>=2023.7.1 - distributed>=2023.7.1 - doxygen @@ -31,11 +31,11 @@ dependencies: - graphviz - gtest>=1.13.0 - ipython -- libcudf==23.10.* -- libcugraphops==23.10.* -- libraft-headers==23.10.* -- libraft==23.10.* -- librmm==23.10.* +- libcudf==23.12.* +- libcugraphops==23.12.* +- libraft-headers==23.12.* +- libraft==23.12.* +- librmm==23.12.* - nbsphinx - nccl>=2.9.9 - networkx>=2.5.1 @@ -50,18 +50,18 @@ dependencies: - pandas - pre-commit - pydata-sphinx-theme -- pylibcugraphops==23.10.* -- pylibraft==23.10.* +- pylibcugraphops==23.12.* +- pylibraft==23.12.* - pytest - pytest-benchmark - pytest-cov - pytest-mpl - pytest-xdist - python-louvain -- raft-dask==23.10.* +- raft-dask==23.12.* - recommonmark - requests -- rmm==23.10.* +- rmm==23.12.* - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy @@ -70,5 +70,5 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.34.* +- ucx-py==0.35.* name: all_cuda-120_arch-x86_64 diff --git a/conda/recipes/cugraph-service/conda_build_config.yaml b/conda/recipes/cugraph-service/conda_build_config.yaml index 5fe8d372eba..b971a73fd39 100644 --- a/conda/recipes/cugraph-service/conda_build_config.yaml +++ b/conda/recipes/cugraph-service/conda_build_config.yaml @@ -1,2 +1,2 @@ ucx_py_version: - - "0.34.*" + - "0.35.*" diff --git a/conda/recipes/cugraph/conda_build_config.yaml b/conda/recipes/cugraph/conda_build_config.yaml index ba5b46dda1f..c03d515b9f6 100644 --- a/conda/recipes/cugraph/conda_build_config.yaml +++ b/conda/recipes/cugraph/conda_build_config.yaml @@ -17,4 +17,4 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.34.*" + - "0.35.*" diff --git a/conda/recipes/pylibcugraph/conda_build_config.yaml b/conda/recipes/pylibcugraph/conda_build_config.yaml index ba5b46dda1f..c03d515b9f6 100644 --- a/conda/recipes/pylibcugraph/conda_build_config.yaml +++ b/conda/recipes/pylibcugraph/conda_build_config.yaml @@ -17,4 +17,4 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.34.*" + - "0.35.*" diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 0d7bd86075d..cb9dca6349f 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -25,7 +25,7 @@ include(rapids-find) rapids_cuda_init_architectures(CUGRAPH) -project(CUGRAPH VERSION 23.10.00 LANGUAGES C CXX CUDA) +project(CUGRAPH VERSION 23.12.00 LANGUAGES C CXX CUDA) if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0) diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile index eb414925388..482ff988098 100644 --- a/cpp/doxygen/Doxyfile +++ b/cpp/doxygen/Doxyfile @@ -38,7 +38,7 @@ PROJECT_NAME = "libcugraph" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER=23.10 +PROJECT_NUMBER=23.12 # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a diff --git a/cpp/libcugraph_etl/CMakeLists.txt b/cpp/libcugraph_etl/CMakeLists.txt index 18271871087..ac0cb6959e8 100644 --- a/cpp/libcugraph_etl/CMakeLists.txt +++ b/cpp/libcugraph_etl/CMakeLists.txt @@ -25,7 +25,7 @@ include(rapids-find) rapids_cuda_init_architectures(CUGRAPH_ETL) -project(CUGRAPH_ETL VERSION 23.10.00 LANGUAGES C CXX CUDA) +project(CUGRAPH_ETL VERSION 23.12.00 LANGUAGES C CXX CUDA) if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0) diff --git a/dependencies.yaml b/dependencies.yaml index 04ec1b6e957..514c14dceec 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -268,10 +268,10 @@ dependencies: - cxx-compiler - gmock>=1.13.0 - gtest>=1.13.0 - - libcugraphops==23.10.* - - libraft-headers==23.10.* - - libraft==23.10.* - - librmm==23.10.* + - libcugraphops==23.12.* + - libraft-headers==23.12.* + - libraft==23.12.* + - librmm==23.12.* - openmpi # Required for building cpp-mgtests (multi-GPU tests) specific: - output_types: [conda] @@ -316,7 +316,7 @@ dependencies: - sphinx-markdown-tables - sphinx<6 - sphinxcontrib-websupport - - pylibcugraphops==23.10.* + - pylibcugraphops==23.12.* py_version: specific: - output_types: [conda] @@ -343,34 +343,34 @@ dependencies: - output_types: [conda, pyproject] packages: - cython>=3.0.0 - - &pylibraft pylibraft==23.10.* - - &rmm rmm==23.10.* + - &pylibraft pylibraft==23.12.* + - &rmm rmm==23.12.* - scikit-build>=0.13.1 python_build_cugraph: common: - output_types: [conda, pyproject] packages: - - &pylibcugraph pylibcugraph==23.10.* + - &pylibcugraph pylibcugraph==23.12.* python_run_cugraph: common: - output_types: [conda, pyproject] packages: - - &cudf cudf==23.10.* + - &cudf cudf==23.12.* - &dask dask>=2023.7.1 - &distributed distributed>=2023.7.1 - - &dask_cuda dask-cuda==23.10.* - - &dask_cudf dask-cudf==23.10.* + - &dask_cuda dask-cuda==23.12.* + - &dask_cudf dask-cudf==23.12.* - &numba numba>=0.57 - - raft-dask==23.10.* + - raft-dask==23.12.* - *rmm - - &ucx_py ucx-py==0.34.* + - &ucx_py ucx-py==0.35.* - output_types: conda packages: - aiohttp - &cupy cupy>=12.0.0 - &dask-core dask-core>=2023.7.1 - fsspec>=0.6.0 - - libcudf==23.10.* + - libcudf==23.12.* - requests - nccl>=2.9.9 - ucx-proc=*=gpu @@ -407,7 +407,7 @@ dependencies: - &numpy numpy>=1.21 - output_types: [pyproject] packages: - - &cugraph cugraph==23.10.* + - &cugraph cugraph==23.12.* python_run_cugraph_pyg: common: - output_types: [conda, pyproject] @@ -444,7 +444,7 @@ dependencies: packages: - *cupy_pip - *cugraph - - cugraph-service-client==23.10.* + - cugraph-service-client==23.12.* doc: common: - output_types: [conda] @@ -458,7 +458,7 @@ dependencies: - sphinxcontrib-websupport - sphinx-markdown-tables - sphinx-copybutton - - pylibcugraphops==23.10.* + - pylibcugraphops==23.12.* test_cpp: common: - output_types: conda @@ -505,8 +505,8 @@ dependencies: common: - output_types: [conda] packages: - - cugraph==23.10.* - - pylibcugraphops==23.10.* + - cugraph==23.12.* + - pylibcugraphops==23.12.* - pytorch>=2.0 - pytorch-cuda==11.8 - dgl>=1.1.0.cu* @@ -514,8 +514,8 @@ dependencies: common: - output_types: [conda] packages: - - cugraph==23.10.* - - pylibcugraphops==23.10.* + - cugraph==23.12.* + - pylibcugraphops==23.12.* - pytorch==2.0 - pytorch-cuda==11.8 - pyg=2.3.1=*torch_2.0.0*cu118* diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index a96f0fc1e82..58ecca27150 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -76,9 +76,9 @@ # built documents. # # The short X.Y version. -version = '23.10' +version = '23.12' # The full version, including alpha/beta/rc tags. -release = '23.10.00' +release = '23.12.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/fetch_rapids.cmake b/fetch_rapids.cmake index c32dc74da40..2c1dd855cb5 100644 --- a/fetch_rapids.cmake +++ b/fetch_rapids.cmake @@ -12,7 +12,7 @@ # the License. # ============================================================================= if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUGRAPH_RAPIDS.cmake) - file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.10/RAPIDS.cmake + file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.12/RAPIDS.cmake ${CMAKE_CURRENT_BINARY_DIR}/CUGRAPH_RAPIDS.cmake ) endif() diff --git a/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml b/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml index 138d384ebcf..b73ccb0cf9a 100644 --- a/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml +++ b/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml @@ -10,11 +10,11 @@ channels: - conda-forge - nvidia dependencies: -- cugraph==23.10.* +- cugraph==23.12.* - dgl>=1.1.0.cu* - pandas - pre-commit -- pylibcugraphops==23.10.* +- pylibcugraphops==23.12.* - pytest - pytest-benchmark - pytest-cov diff --git a/python/cugraph-dgl/cugraph_dgl/__init__.py b/python/cugraph-dgl/cugraph_dgl/__init__.py index b30cd6c79d9..74be4fdea3f 100644 --- a/python/cugraph-dgl/cugraph_dgl/__init__.py +++ b/python/cugraph-dgl/cugraph_dgl/__init__.py @@ -20,4 +20,4 @@ import cugraph_dgl.dataloading import cugraph_dgl.nn -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/cugraph-dgl/pyproject.toml b/python/cugraph-dgl/pyproject.toml index 50354184133..fa9e1c5abe5 100644 --- a/python/cugraph-dgl/pyproject.toml +++ b/python/cugraph-dgl/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-dgl" -version = "23.10.00" +version = "23.12.00" description = "cugraph extensions for DGL" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -19,7 +19,7 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cugraph==23.10.*", + "cugraph==23.12.*", "numba>=0.57", "numpy>=1.21", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. diff --git a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml index 4e5159e6b45..f98eab430ba 100644 --- a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml +++ b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml @@ -10,11 +10,11 @@ channels: - conda-forge - nvidia dependencies: -- cugraph==23.10.* +- cugraph==23.12.* - pandas - pre-commit - pyg=2.3.1=*torch_2.0.0*cu118* -- pylibcugraphops==23.10.* +- pylibcugraphops==23.12.* - pytest - pytest-benchmark - pytest-cov diff --git a/python/cugraph-pyg/cugraph_pyg/__init__.py b/python/cugraph-pyg/cugraph_pyg/__init__.py index f8187059b86..ecd2f271a00 100644 --- a/python/cugraph-pyg/cugraph_pyg/__init__.py +++ b/python/cugraph-pyg/cugraph_pyg/__init__.py @@ -11,4 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/cugraph-pyg/pyproject.toml b/python/cugraph-pyg/pyproject.toml index 218c09fbd1d..84d30221d55 100644 --- a/python/cugraph-pyg/pyproject.toml +++ b/python/cugraph-pyg/pyproject.toml @@ -12,7 +12,7 @@ testpaths = ["cugraph_pyg/tests"] [project] name = "cugraph_pyg" -version = "23.10.00" +version = "23.12.00" description = "cugraph_pyg - PyG support for cuGraph massive-scale, ultra-fast GPU graph analytics." authors = [ { name = "NVIDIA Corporation" }, @@ -26,7 +26,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", ] dependencies = [ - "cugraph==23.10.*", + "cugraph==23.12.*", "numba>=0.57", "numpy>=1.21", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. diff --git a/python/cugraph-service/client/cugraph_service_client/__init__.py b/python/cugraph-service/client/cugraph_service_client/__init__.py index 229d07b8bc6..a0361abedd3 100644 --- a/python/cugraph-service/client/cugraph_service_client/__init__.py +++ b/python/cugraph-service/client/cugraph_service_client/__init__.py @@ -35,4 +35,4 @@ from cugraph_service_client.client import CugraphServiceClient from cugraph_service_client.remote_graph import RemoteGraph -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/cugraph-service/client/pyproject.toml b/python/cugraph-service/client/pyproject.toml index 3b31a5f2e0a..7f702252f02 100644 --- a/python/cugraph-service/client/pyproject.toml +++ b/python/cugraph-service/client/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-service-client" -version = "23.10.00" +version = "23.12.00" description = "cuGraph Service client" readme = { file = "README.md", content-type = "text/markdown" } authors = [ diff --git a/python/cugraph-service/server/cugraph_service_server/__init__.py b/python/cugraph-service/server/cugraph_service_server/__init__.py index 017f0990f89..87d35005195 100644 --- a/python/cugraph-service/server/cugraph_service_server/__init__.py +++ b/python/cugraph-service/server/cugraph_service_server/__init__.py @@ -61,4 +61,4 @@ def start_server_blocking( server.serve() # blocks until Ctrl-C (kill -2) -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index f25ea6c46e5..3c77cf01c2c 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-service-server" -version = "23.10.00" +version = "23.12.00" description = "cuGraph Service server" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -19,19 +19,19 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.10.*", - "cugraph-service-client==23.10.*", - "cugraph==23.10.*", + "cudf==23.12.*", + "cugraph-service-client==23.12.*", + "cugraph==23.12.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.10.*", - "dask-cudf==23.10.*", + "dask-cuda==23.12.*", + "dask-cudf==23.12.*", "dask>=2023.7.1", "distributed>=2023.7.1", "numba>=0.57", "numpy>=1.21", - "rmm==23.10.*", + "rmm==23.12.*", "thriftpy2", - "ucx-py==0.34.*", + "ucx-py==0.35.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/cugraph/CMakeLists.txt b/python/cugraph/CMakeLists.txt index ecfcb9b219f..4eaf0ce6334 100644 --- a/python/cugraph/CMakeLists.txt +++ b/python/cugraph/CMakeLists.txt @@ -14,7 +14,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR) -set(cugraph_version 23.10.00) +set(cugraph_version 23.12.00) include(../../fetch_rapids.cmake) diff --git a/python/cugraph/cugraph/__init__.py b/python/cugraph/cugraph/__init__.py index 43cb30beceb..f3a335183f3 100644 --- a/python/cugraph/cugraph/__init__.py +++ b/python/cugraph/cugraph/__init__.py @@ -120,4 +120,4 @@ from cugraph import exceptions -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index cadf6879e23..5e17be40e7b 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -6,9 +6,9 @@ requires = [ "cmake>=3.26.4", "cython>=3.0.0", "ninja", - "pylibcugraph==23.10.*", - "pylibraft==23.10.*", - "rmm==23.10.*", + "pylibcugraph==23.12.*", + "pylibraft==23.12.*", + "rmm==23.12.*", "scikit-build>=0.13.1", "setuptools>=61.0.0", "wheel", @@ -20,7 +20,7 @@ testpaths = ["cugraph/tests"] [project] name = "cugraph" -version = "23.10.00" +version = "23.12.00" description = "cuGraph - RAPIDS GPU Graph Analytics" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -29,18 +29,18 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.10.*", + "cudf==23.12.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.10.*", - "dask-cudf==23.10.*", + "dask-cuda==23.12.*", + "dask-cudf==23.12.*", "dask>=2023.7.1", "distributed>=2023.7.1", "fsspec[http]>=0.6.0", "numba>=0.57", - "pylibcugraph==23.10.*", - "raft-dask==23.10.*", - "rmm==23.10.*", - "ucx-py==0.34.*", + "pylibcugraph==23.12.*", + "raft-dask==23.12.*", + "rmm==23.12.*", + "ucx-py==0.35.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index e7cd26218e6..ab267e5a756 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -6,7 +6,7 @@ with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph ### Contribute -Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.10/readme_pages/CONTRIBUTING.md) +Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.12 and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: ``` $ ./build.sh nx-cugraph --pydevelop diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 28066fe2b02..c23542842d4 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -17,4 +17,4 @@ from .classes import * from .convert import * -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 95e9c256e5d..532b5225692 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "nx-cugraph" -version = "23.10.00" +version = "23.12.00" description = "cugraph backend for NetworkX" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -32,7 +32,7 @@ classifiers = [ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", - "pylibcugraph==23.10.*", + "pylibcugraph==23.12.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.optional-dependencies] diff --git a/python/pylibcugraph/CMakeLists.txt b/python/pylibcugraph/CMakeLists.txt index b5b564e6881..057f30ef3ad 100644 --- a/python/pylibcugraph/CMakeLists.txt +++ b/python/pylibcugraph/CMakeLists.txt @@ -14,7 +14,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR) -set(pylibcugraph_version 23.10.00) +set(pylibcugraph_version 23.12.00) include(../../fetch_rapids.cmake) diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index 45f6de2f663..187eabaae0c 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -96,4 +96,4 @@ from pylibcugraph import exceptions -__version__ = "23.10.00" +__version__ = "23.12.00" diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index 806ea65ac6c..f1b439debd2 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -6,8 +6,8 @@ requires = [ "cmake>=3.26.4", "cython>=3.0.0", "ninja", - "pylibraft==23.10.*", - "rmm==23.10.*", + "pylibraft==23.12.*", + "rmm==23.12.*", "scikit-build>=0.13.1", "setuptools>=61.0.0", "wheel", @@ -19,7 +19,7 @@ testpaths = ["pylibcugraph/tests"] [project] name = "pylibcugraph" -version = "23.10.00" +version = "23.12.00" description = "pylibcugraph - Python bindings for the libcugraph cuGraph C/C++/CUDA library" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -28,8 +28,8 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "pylibraft==23.10.*", - "rmm==23.10.*", + "pylibraft==23.12.*", + "rmm==23.12.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", @@ -40,7 +40,7 @@ classifiers = [ [project.optional-dependencies] test = [ - "cudf==23.10.*", + "cudf==23.12.*", "numpy>=1.21", "pandas", "pytest", From f53bb56dc3245f64523aeeb997430c8f49de4624 Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Fri, 22 Sep 2023 12:44:31 -0400 Subject: [PATCH 010/111] Fix torch seed in `cugraph-dgl` and `-pyg` tests for conv layers (#3869) Fixes https://github.com/rapidsai/graph_dl/issues/325 Recently, a few CI runs (ex. [1](https://github.com/rapidsai/cugraph/actions/runs/6254253684/job/16983164330?pr=3828#step:7:5078), [2](https://github.com/rapidsai/cugraph/actions/runs/6224345348/job/16896416094?pr=3843)) failed when comparing results from cugraph-ops-based conv layers against results from upstream frameworks. The tests pass most of the time, but occasionally fail due to a combination of using a strict tolerance and bad numerics (floating point error). This PR fixes the seed used for generating random feature tensors so that CI behaves consistently across different runs. Authors: - Tingyu Wang (https://github.com/tingyu66) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/3869 --- python/cugraph-dgl/tests/nn/test_gatconv.py | 2 ++ python/cugraph-dgl/tests/nn/test_gatv2conv.py | 2 ++ python/cugraph-dgl/tests/nn/test_relgraphconv.py | 15 +++++++++++---- python/cugraph-dgl/tests/nn/test_sageconv.py | 1 + .../cugraph-dgl/tests/nn/test_transformerconv.py | 1 + .../cugraph_pyg/tests/nn/test_gat_conv.py | 1 + .../cugraph_pyg/tests/nn/test_gatv2_conv.py | 1 + .../cugraph_pyg/tests/nn/test_rgcn_conv.py | 1 + .../cugraph_pyg/tests/nn/test_sage_conv.py | 1 + .../cugraph_pyg/tests/nn/test_transformer_conv.py | 1 + 10 files changed, 22 insertions(+), 4 deletions(-) diff --git a/python/cugraph-dgl/tests/nn/test_gatconv.py b/python/cugraph-dgl/tests/nn/test_gatconv.py index ef3047dc2cd..ce145b2bc87 100644 --- a/python/cugraph-dgl/tests/nn/test_gatconv.py +++ b/python/cugraph-dgl/tests/nn/test_gatconv.py @@ -35,6 +35,7 @@ def test_gatconv_equality( ): from dgl.nn.pytorch import GATConv + torch.manual_seed(12345) g = create_graph1().to("cuda") if idtype_int: @@ -121,6 +122,7 @@ def test_gatconv_equality( def test_gatconv_edge_feats( bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats ): + torch.manual_seed(12345) g = create_graph1().to("cuda") if to_block: diff --git a/python/cugraph-dgl/tests/nn/test_gatv2conv.py b/python/cugraph-dgl/tests/nn/test_gatv2conv.py index cc46a6e4b39..52003edacca 100644 --- a/python/cugraph-dgl/tests/nn/test_gatv2conv.py +++ b/python/cugraph-dgl/tests/nn/test_gatv2conv.py @@ -35,6 +35,7 @@ def test_gatv2conv_equality( ): from dgl.nn.pytorch import GATv2Conv + torch.manual_seed(12345) g = create_graph1().to("cuda") if idtype_int: @@ -109,6 +110,7 @@ def test_gatv2conv_equality( def test_gatv2conv_edge_feats( bias, bipartite, concat, max_in_degree, num_heads, to_block, use_edge_feats ): + torch.manual_seed(12345) g = create_graph1().to("cuda") if to_block: diff --git a/python/cugraph-dgl/tests/nn/test_relgraphconv.py b/python/cugraph-dgl/tests/nn/test_relgraphconv.py index 901f9ba1433..bdaa89e57f2 100644 --- a/python/cugraph-dgl/tests/nn/test_relgraphconv.py +++ b/python/cugraph-dgl/tests/nn/test_relgraphconv.py @@ -41,6 +41,7 @@ def test_relgraphconv_equality( ): from dgl.nn.pytorch import RelGraphConv + torch.manual_seed(12345) in_feat, out_feat, num_rels = 10, 2, 3 args = (in_feat, out_feat, num_rels) kwargs = { @@ -75,12 +76,18 @@ def test_relgraphconv_equality( size=size, src_ids=indices, cdst_ids=offsets, values=etypes, formats="csc" ) - torch.manual_seed(0) conv1 = RelGraphConv(*args, **kwargs).cuda() + conv2 = CuGraphRelGraphConv(*args, **kwargs, apply_norm=False).cuda() - torch.manual_seed(0) - kwargs["apply_norm"] = False - conv2 = CuGraphRelGraphConv(*args, **kwargs).cuda() + with torch.no_grad(): + if self_loop: + conv2.W.data[:-1] = conv1.linear_r.W.data + conv2.W.data[-1] = conv1.loop_weight.data + else: + conv2.W.data = conv1.linear_r.W.data.detach().clone() + + if regularizer is not None: + conv2.coeff.data = conv1.linear_r.coeff.data.detach().clone() out1 = conv1(g, feat, g.edata[dgl.ETYPE]) diff --git a/python/cugraph-dgl/tests/nn/test_sageconv.py b/python/cugraph-dgl/tests/nn/test_sageconv.py index e2acf9e6596..b5d0a44b868 100644 --- a/python/cugraph-dgl/tests/nn/test_sageconv.py +++ b/python/cugraph-dgl/tests/nn/test_sageconv.py @@ -35,6 +35,7 @@ def test_sageconv_equality( ): from dgl.nn.pytorch import SAGEConv + torch.manual_seed(12345) kwargs = {"aggregator_type": aggr, "bias": bias} g = create_graph1().to("cuda") diff --git a/python/cugraph-dgl/tests/nn/test_transformerconv.py b/python/cugraph-dgl/tests/nn/test_transformerconv.py index b2b69cb35ab..5ac4fd7bea7 100644 --- a/python/cugraph-dgl/tests/nn/test_transformerconv.py +++ b/python/cugraph-dgl/tests/nn/test_transformerconv.py @@ -41,6 +41,7 @@ def test_transformerconv( use_edge_feats, sparse_format, ): + torch.manual_seed(12345) device = "cuda" g = create_graph1().to(device) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gat_conv.py b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gat_conv.py index 21c43bad38c..62bebb9211d 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gat_conv.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gat_conv.py @@ -32,6 +32,7 @@ def test_gat_conv_equality( import torch from torch_geometric.nn import GATConv + torch.manual_seed(12345) edge_index, size = request.getfixturevalue(graph) edge_index = edge_index.cuda() diff --git a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gatv2_conv.py b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gatv2_conv.py index 6b11e87154a..a4794628410 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gatv2_conv.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_gatv2_conv.py @@ -28,6 +28,7 @@ def test_gatv2_conv_equality(bipartite, concat, heads, use_edge_attr, graph, req import torch from torch_geometric.nn import GATv2Conv + torch.manual_seed(12345) edge_index, size = request.getfixturevalue(graph) edge_index = edge_index.cuda() diff --git a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_rgcn_conv.py b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_rgcn_conv.py index 233c6aa2836..ded4f300c0c 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_rgcn_conv.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_rgcn_conv.py @@ -31,6 +31,7 @@ def test_rgcn_conv_equality( import torch from torch_geometric.nn import FastRGCNConv as RGCNConv + torch.manual_seed(12345) in_channels, out_channels, num_relations = (4, 2, 3) kwargs = dict(aggr=aggr, bias=bias, num_bases=num_bases, root_weight=root_weight) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_sage_conv.py b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_sage_conv.py index 7f73cddbdbb..b2977d1d175 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_sage_conv.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_sage_conv.py @@ -32,6 +32,7 @@ def test_sage_conv_equality( import torch from torch_geometric.nn import SAGEConv + torch.manual_seed(12345) edge_index, size = request.getfixturevalue(graph) edge_index = edge_index.cuda() csc = CuGraphSAGEConv.to_csc(edge_index, size) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_transformer_conv.py b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_transformer_conv.py index 7dba1a6d515..fbdb244898b 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/nn/test_transformer_conv.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/nn/test_transformer_conv.py @@ -27,6 +27,7 @@ def test_transformer_conv_equality(bipartite, concat, heads, graph, request): import torch from torch_geometric.nn import TransformerConv + torch.manual_seed(12345) edge_index, size = request.getfixturevalue(graph) edge_index = edge_index.cuda() csc = CuGraphTransformerConv.to_csc(edge_index, size) From fe17abc6da469d810ea512d1d887407032613405 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 25 Sep 2023 10:48:40 -0400 Subject: [PATCH 011/111] cuGraph-PyG Loader Improvements (#3795) Consolidates various speed improvements tested while running performance benchmarks. Avoids copying batch data, removes redundant data loading code, simplifies and improves de-offsetting, even though that is now being bypassed entirely for homogeneous graphs. Removes extra host to device copy. Properly flips the src/dst columns in the returned `HeteroData` minibatch objects, avoid exposing this to the end user. I've confirmed this cuts the MFG time by a factor of 4. Closes #3807 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Vibhu Jawa (https://github.com/VibhuJawa) - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3795 --- .../cugraph_pyg/data/cugraph_store.py | 218 ++++++++++---- .../cugraph_pyg/loader/cugraph_node_loader.py | 107 +++++-- .../cugraph-pyg/cugraph_pyg/loader/filter.py | 57 ---- .../cugraph_pyg/sampler/cugraph_sampler.py | 281 +++++++++++------- .../tests/mg/test_mg_cugraph_loader.py | 4 +- .../tests/mg/test_mg_cugraph_sampler.py | 28 +- .../tests/mg/test_mg_cugraph_store.py | 6 +- .../cugraph_pyg/tests/test_cugraph_loader.py | 158 ++++++---- .../cugraph_pyg/tests/test_cugraph_sampler.py | 28 +- .../cugraph_pyg/tests/test_cugraph_store.py | 2 +- 10 files changed, 548 insertions(+), 341 deletions(-) delete mode 100644 python/cugraph-pyg/cugraph_pyg/loader/filter.py diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 8d5d2fd4894..e0d318adbe0 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -25,6 +25,7 @@ import pandas import cudf import cugraph +import warnings from cugraph.utilities.utils import import_optional, MissingModule @@ -211,7 +212,9 @@ def __init__( F: cugraph.gnn.FeatureStore, G: Union[Dict[str, Tuple[TensorType]], Dict[str, int]], num_nodes_dict: Dict[str, int], + *, multi_gpu: bool = False, + order: str = "CSC", ): """ Constructs a new CuGraphStore from the provided @@ -256,11 +259,20 @@ def __init__( multi_gpu: bool (Optional, default = False) Whether the store should be backed by a multi-GPU graph. Requires dask to have been set up. + + order: str (Optional ["CSR", "CSC"], default = CSC) + The order to use for sampling. Should nearly always be CSC + unless there is a specific expectation of "reverse" sampling. + It is also not uncommon to use CSR order for correctness + testing, which some cuGraph-PyG tests do. """ if None in G: raise ValueError("Unspecified edge types not allowed in PyG") + if order != "CSR" and order != "CSC": + raise ValueError("invalid valid for order") + self.__vertex_dtype = torch.int64 self._tensor_attr_cls = CuGraphTensorAttr @@ -289,6 +301,7 @@ def __init__( self.__features = F self.__graph = None self.__is_graph_owner = False + self.__order = order if construct_graph: if multi_gpu: @@ -297,7 +310,9 @@ def __init__( ) if self.__graph is None: - self.__graph = self.__construct_graph(G, multi_gpu=multi_gpu) + self.__graph = self.__construct_graph( + G, multi_gpu=multi_gpu, order=order + ) self.__is_graph_owner = True self.__subgraphs = {} @@ -347,6 +362,7 @@ def __construct_graph( self, edge_info: Dict[Tuple[str, str, str], List[TensorType]], multi_gpu: bool = False, + order: str = "CSC", ) -> cugraph.MultiGraph: """ This function takes edge information and uses it to construct @@ -363,6 +379,14 @@ def __construct_graph( multi_gpu: bool (Optional, default=False) Whether to construct a single-GPU or multi-GPU cugraph Graph. Defaults to a single-GPU graph. + + order: str (CSC or CSR) + Essentially whether to reverse edges so that the cuGraph + sampling algorithm operates on the CSC matrix instead of + the CSR matrix. Should nearly always be CSC unless there + is a specific expectation of reverse sampling, or correctness + testing is being performed. + Returns ------- A newly-constructed directed cugraph.MultiGraph object. @@ -371,6 +395,9 @@ def __construct_graph( # Ensure the original dict is not modified. edge_info_cg = {} + if order != "CSR" and order != "CSC": + raise ValueError("Order must be either CSC (default) or CSR!") + # Iterate over the keys in sorted order so that the created # numerical types correspond to the lexicographic order # of the keys, which is critical to converting the numeric @@ -430,20 +457,43 @@ def __construct_graph( df = pandas.DataFrame( { - "src": pandas.Series(na_src), - "dst": pandas.Series(na_dst), + "src": pandas.Series(na_dst) + if order == "CSC" + else pandas.Series(na_src), + "dst": pandas.Series(na_src) + if order == "CSC" + else pandas.Series(na_dst), "etp": pandas.Series(na_etp), } ) + vertex_dtype = df.src.dtype if multi_gpu: nworkers = len(distributed.get_client().scheduler_info()["workers"]) - df = dd.from_pandas(df, npartitions=nworkers).persist() - df = df.map_partitions(cudf.DataFrame.from_pandas) - else: - df = cudf.from_pandas(df) + df = dd.from_pandas(df, npartitions=nworkers if len(df) > 32 else 1) + + # Ensure the dataframe is constructed on each partition + # instead of adding additional synchronization head from potential + # host to device copies. + def get_empty_df(): + return cudf.DataFrame( + { + "src": cudf.Series([], dtype=vertex_dtype), + "dst": cudf.Series([], dtype=vertex_dtype), + "etp": cudf.Series([], dtype="int32"), + } + ) - df = df.reset_index(drop=True) + # Have to check for empty partitions and handle them appropriately + df = df.persist() + df = df.map_partitions( + lambda f: cudf.DataFrame.from_pandas(f) + if len(f) > 0 + else get_empty_df(), + meta=get_empty_df(), + ).reset_index(drop=True) + else: + df = cudf.from_pandas(df).reset_index(drop=True) graph = cugraph.MultiGraph(directed=True) if multi_gpu: @@ -468,6 +518,10 @@ def __construct_graph( def _edge_types_to_attrs(self) -> dict: return dict(self.__edge_types_to_attrs) + @property + def order(self) -> str: + return self.__order + @property def node_types(self) -> List[NodeType]: return list(self.__vertex_type_offsets["type"]) @@ -557,6 +611,7 @@ def _get_edge_index(self, attr: CuGraphEdgeAttr) -> Tuple[TensorType, TensorType raise ValueError("Graph is not in memory, cannot access edge index!") if attr.layout != EdgeLayout.COO: + # TODO support returning CSR/CSC (Issue #3802) raise TypeError("Only COO direct access is supported!") # Currently, graph creation enforces that input vertex ids are always of @@ -566,12 +621,14 @@ def _get_edge_index(self, attr: CuGraphEdgeAttr) -> Tuple[TensorType, TensorType # This may change in the future if/when renumbering or the graph # creation process is refactored. # See Issue #3201 for more details. + # Also note src/dst are flipped so that cuGraph sampling is done in + # CSC format rather than CSR format. if self._is_delayed: - src_col_name = self.__graph.renumber_map.renumbered_src_col_name - dst_col_name = self.__graph.renumber_map.renumbered_dst_col_name + dst_col_name = self.__graph.renumber_map.renumbered_src_col_name + src_col_name = self.__graph.renumber_map.renumbered_dst_col_name else: - src_col_name = self.__graph.srcCol - dst_col_name = self.__graph.dstCol + dst_col_name = self.__graph.srcCol + src_col_name = self.__graph.dstCol # If there is only one edge type (homogeneous graph) then # bypass the edge filters for a significant speed improvement. @@ -785,29 +842,73 @@ def _get_renumbered_edge_groups_from_sample( """ row_dict = {} col_dict = {} - if len(self.__edge_types_to_attrs) == 1: + # If there is only 1 edge type (includes heterogeneous graphs) + if len(self.edge_types) == 1: t_pyg_type = list(self.__edge_types_to_attrs.values())[0].edge_type src_type, _, dst_type = t_pyg_type - dst_id_table = noi_index[dst_type] - dst_id_map = ( - cudf.Series(cupy.asarray(dst_id_table), name="dst") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("dst") - ) - dst = dst_id_map["new_id"].loc[sampling_results.destinations] - col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") - - src_id_table = noi_index[src_type] - src_id_map = ( - cudf.Series(cupy.asarray(src_id_table), name="src") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("src") - ) - src = src_id_map["new_id"].loc[sampling_results.sources] - row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") + # If there is only 1 node type (homogeneous) + # This should only occur if the cuGraph loader was + # not used. This logic is deprecated. + if len(self.node_types) == 1: + warnings.warn( + "Renumbering after sampling for homogeneous graphs is deprecated.", + FutureWarning, + ) + + # Create a dataframe mapping old ids to new ids. + vtype = src_type + id_table = noi_index[vtype] + id_map = cudf.Series( + cupy.arange(id_table.shape[0], dtype="int32"), + name="new_id", + index=cupy.asarray(id_table), + ).sort_index() + + # Renumber the sources using binary search + # Step 1: get the index of the new id + ix_r = torch.searchsorted( + torch.as_tensor(id_map.index.values, device="cuda"), + torch.as_tensor(sampling_results.sources.values, device="cuda"), + ) + # Step 2: Go from id indices to actual ids + row_dict[t_pyg_type] = torch.as_tensor(id_map.values, device="cuda")[ + ix_r + ] + + # Renumber the destinations using binary search + # Step 1: get the index of the new id + ix_c = torch.searchsorted( + torch.as_tensor(id_map.index.values, device="cuda"), + torch.as_tensor( + sampling_results.destinations.values, device="cuda" + ), + ) + # Step 2: Go from id indices to actual ids + col_dict[t_pyg_type] = torch.as_tensor(id_map.values, device="cuda")[ + ix_c + ] + else: + # Handle the heterogeneous case where there is only 1 edge type + dst_id_table = noi_index[dst_type] + dst_id_map = cudf.DataFrame( + { + "dst": cupy.asarray(dst_id_table), + "new_id": cupy.arange(dst_id_table.shape[0]), + } + ).set_index("dst") + dst = dst_id_map["new_id"].loc[sampling_results.destinations] + col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") + + src_id_table = noi_index[src_type] + src_id_map = cudf.DataFrame( + { + "src": cupy.asarray(src_id_table), + "new_id": cupy.arange(src_id_table.shape[0]), + } + ).set_index("src") + src = src_id_map["new_id"].loc[sampling_results.sources] + row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") else: # This will retrieve the single string representation. @@ -822,36 +923,18 @@ def _get_renumbered_edge_groups_from_sample( for pyg_can_edge_type_str, ix in eoi_types.items(): pyg_can_edge_type = tuple(pyg_can_edge_type_str.split("__")) - src_type, _, dst_type = pyg_can_edge_type - - # Get the de-offsetted sources - sources = torch.as_tensor( - sampling_results.sources.iloc[ix].values, device="cuda" - ) - sources_ix = torch.searchsorted( - self.__vertex_type_offsets["stop"], sources - ) - sources -= self.__vertex_type_offsets["start"][sources_ix] - # Create the row entry for this type - src_id_table = noi_index[src_type] - src_id_map = ( - cudf.Series(cupy.asarray(src_id_table), name="src") - .reset_index() - .rename(columns={"index": "new_id"}) - .set_index("src") - ) - src = src_id_map["new_id"].loc[cupy.asarray(sources)] - row_dict[pyg_can_edge_type] = torch.as_tensor(src.values, device="cuda") + if self.__order == "CSR": + src_type, _, dst_type = pyg_can_edge_type + else: # CSC + dst_type, _, src_type = pyg_can_edge_type # Get the de-offsetted destinations + dst_num_type = self._numeric_vertex_type_from_name(dst_type) destinations = torch.as_tensor( sampling_results.destinations.iloc[ix].values, device="cuda" ) - destinations_ix = torch.searchsorted( - self.__vertex_type_offsets["stop"], destinations - ) - destinations -= self.__vertex_type_offsets["start"][destinations_ix] + destinations -= self.__vertex_type_offsets["start"][dst_num_type] # Create the col entry for this type dst_id_table = noi_index[dst_type] @@ -864,6 +947,24 @@ def _get_renumbered_edge_groups_from_sample( dst = dst_id_map["new_id"].loc[cupy.asarray(destinations)] col_dict[pyg_can_edge_type] = torch.as_tensor(dst.values, device="cuda") + # Get the de-offsetted sources + src_num_type = self._numeric_vertex_type_from_name(src_type) + sources = torch.as_tensor( + sampling_results.sources.iloc[ix].values, device="cuda" + ) + sources -= self.__vertex_type_offsets["start"][src_num_type] + + # Create the row entry for this type + src_id_table = noi_index[src_type] + src_id_map = ( + cudf.Series(cupy.asarray(src_id_table), name="src") + .reset_index() + .rename(columns={"index": "new_id"}) + .set_index("src") + ) + src = src_id_map["new_id"].loc[cupy.asarray(sources)] + row_dict[pyg_can_edge_type] = torch.as_tensor(src.values, device="cuda") + return row_dict, col_dict def put_tensor(self, tensor, attr) -> None: @@ -959,9 +1060,7 @@ def _get_tensor(self, attr: CuGraphTensorAttr) -> TensorType: t = t[-1] if isinstance(t, np.ndarray): - t = torch.as_tensor(t, device="cuda") - else: - t = t.cuda() + t = torch.as_tensor(t, device="cpu") return t @@ -979,7 +1078,6 @@ def _get_tensor(self, attr: CuGraphTensorAttr) -> TensorType: t = torch.concatenate([t, u]) - t = t.cuda() return t def _multi_get_tensor(self, attrs: List[CuGraphTensorAttr]) -> List[TensorType]: diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 8d79685965f..cf7eb330d67 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -23,12 +23,15 @@ from cugraph.utilities.utils import import_optional, MissingModule from cugraph_pyg.data import CuGraphStore -from cugraph_pyg.loader.filter import _filter_cugraph_store -from cugraph_pyg.sampler.cugraph_sampler import _sampler_output_from_sampling_results +from cugraph_pyg.sampler.cugraph_sampler import ( + _sampler_output_from_sampling_results_heterogeneous, + _sampler_output_from_sampling_results_homogeneous, +) from typing import Union, Tuple, Sequence, List, Dict torch_geometric = import_optional("torch_geometric") +torch = import_optional("torch") InputNodes = ( Sequence if isinstance(torch_geometric, MissingModule) @@ -253,55 +256,97 @@ def __next__(self): raw_sample_data = cudf.read_parquet(parquet_path) if "map" in raw_sample_data.columns: - self.__renumber_map = raw_sample_data["map"] + num_batches = end_inclusive - self.__start_inclusive + 1 + + map_end = raw_sample_data["map"].iloc[num_batches] + + map = torch.as_tensor( + raw_sample_data["map"].iloc[0:map_end], device="cuda" + ) raw_sample_data.drop("map", axis=1, inplace=True) + + self.__renumber_map_offsets = map[0 : num_batches + 1] - map[0] + self.__renumber_map = map[num_batches + 1 :] + else: self.__renumber_map = None self.__data = raw_sample_data[list(columns.keys())].astype(columns) self.__data.dropna(inplace=True) + if ( + len(self.__graph_store.edge_types) == 1 + and len(self.__graph_store.node_types) == 1 + ): + group_cols = ["batch_id", "hop_id"] + self.__data_index = self.__data.groupby(group_cols, as_index=True).agg( + {"sources": "max", "destinations": "max"} + ) + self.__data_index.rename( + columns={"sources": "src_max", "destinations": "dst_max"}, + inplace=True, + ) + self.__data_index = self.__data_index.to_dict(orient="index") + # Pull the next set of sampling results out of the dataframe in memory f = self.__data["batch_id"] == self.__next_batch if self.__renumber_map is not None: i = self.__next_batch - self.__start_inclusive - ix = self.__renumber_map.iloc[[i, i + 1]] - ix_start, ix_end = ix.iloc[0], ix.iloc[1] - current_renumber_map = self.__renumber_map.iloc[ix_start:ix_end] - if len(current_renumber_map) != ix_end - ix_start: - raise ValueError("invalid renumber map") - else: - current_renumber_map = None - sampler_output = _sampler_output_from_sampling_results( - self.__data[f], current_renumber_map, self.__graph_store - ) + # this should avoid d2h copy + current_renumber_map = self.__renumber_map[ + self.__renumber_map_offsets[i] : self.__renumber_map_offsets[i + 1] + ] - # Get ready for next iteration - self.__next_batch += 1 + else: + current_renumber_map = None # Get and return the sampled subgraph - if isinstance(torch_geometric, MissingModule): - noi_index, row_dict, col_dict, edge_dict = sampler_output["out"] - return _filter_cugraph_store( - self.__feature_store, + if ( + len(self.__graph_store.edge_types) == 1 + and len(self.__graph_store.node_types) == 1 + ): + sampler_output = _sampler_output_from_sampling_results_homogeneous( + self.__data[f], + current_renumber_map, self.__graph_store, - noi_index, - row_dict, - col_dict, - edge_dict, + self.__data_index, + self.__next_batch, ) else: - out = torch_geometric.loader.utils.filter_custom_store( - self.__feature_store, - self.__graph_store, - sampler_output.node, - sampler_output.row, - sampler_output.col, - sampler_output.edge, + sampler_output = _sampler_output_from_sampling_results_heterogeneous( + self.__data[f], current_renumber_map, self.__graph_store ) - return out + # Get ready for next iteration + self.__next_batch += 1 + + # Create a PyG HeteroData object, loading the required features + out = torch_geometric.loader.utils.filter_custom_store( + self.__feature_store, + self.__graph_store, + sampler_output.node, + sampler_output.row, + sampler_output.col, + sampler_output.edge, + ) + + # Account for CSR format in cuGraph vs. CSC format in PyG + if self.__graph_store.order == "CSC": + for node_type in out.edge_index_dict: + out[node_type].edge_index[0], out[node_type].edge_index[1] = ( + out[node_type].edge_index[1], + out[node_type].edge_index[0], + ) + + out.set_value_dict("num_sampled_nodes", sampler_output.num_sampled_nodes) + out.set_value_dict("num_sampled_edges", sampler_output.num_sampled_edges) + + return out + + @property + def _starting_batch_id(self): + return self.__starting_batch_id def __iter__(self): return self diff --git a/python/cugraph-pyg/cugraph_pyg/loader/filter.py b/python/cugraph-pyg/cugraph_pyg/loader/filter.py deleted file mode 100644 index f519ba7cfc9..00000000000 --- a/python/cugraph-pyg/cugraph_pyg/loader/filter.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) 2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import cupy - -from cugraph_pyg.data import CuGraphStore - -from typing import ( - Dict, - Sequence, -) - - -def _filter_cugraph_store( - feature_store: CuGraphStore, - graph_store: CuGraphStore, - node_dict: Dict[str, Sequence], - row_dict: Dict[str, Sequence], - col_dict: Dict[str, Sequence], - edge_dict: Dict[str, Sequence], -) -> dict: - """ - Primarily for testing without torch and torch_geometric. - Returns a dictionary containing the sampled subgraph. - """ - data = {} - - for attr in graph_store.get_all_edge_attrs(): - key = attr.edge_type - if key in row_dict and key in col_dict: - edge_index = cupy.stack([row_dict[key], col_dict[key]]) - data[attr.edge_type] = {} - data[attr.edge_type]["edge_index"] = edge_index - - # Filter node storage: - required_attrs = [] - for attr in feature_store.get_all_tensor_attrs(): - if attr.group_name in node_dict: - attr.index = node_dict[attr.group_name] - required_attrs.append(attr) - data[attr.group_name] = {} - data["num_nodes"] = attr.index.size - tensors = feature_store.multi_get_tensor(required_attrs) - for i, attr in enumerate(required_attrs): - data[attr.group_name][attr.attr_name] = tensors[i] - - return data diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index d4f600006be..6e8c4322418 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -12,26 +12,21 @@ # limitations under the License. -from typing import Sequence +from typing import Sequence, Dict, Tuple from cugraph_pyg.data import CuGraphStore -from cugraph.utilities.utils import import_optional, MissingModule +from cugraph.utilities.utils import import_optional import cudf dask_cudf = import_optional("dask_cudf") torch_geometric = import_optional("torch_geometric") torch = import_optional("torch") +HeteroSamplerOutput = torch_geometric.sampler.base.HeteroSamplerOutput -HeteroSamplerOutput = ( - None - if isinstance(torch_geometric, MissingModule) - else torch_geometric.sampler.base.HeteroSamplerOutput -) - -def _count_unique_nodes( +def _get_unique_nodes( sampling_results: cudf.DataFrame, graph_store: CuGraphStore, node_type: str, @@ -54,8 +49,8 @@ def _count_unique_nodes( Returns ------- - int - The number of unique nodes of the given node type. + cudf.Series + The unique nodes of the given node type. """ if node_position == "src": edge_index = "sources" @@ -78,12 +73,111 @@ def _count_unique_nodes( sampling_results_node = sampling_results[f] else: - return 0 + return cudf.Series([], dtype="int64") - return sampling_results_node[edge_index].nunique() + return sampling_results_node[edge_index] -def _sampler_output_from_sampling_results( +def _sampler_output_from_sampling_results_homogeneous( + sampling_results: cudf.DataFrame, + renumber_map: torch.Tensor, + graph_store: CuGraphStore, + data_index: Dict[Tuple[int, int], Dict[str, int]], + batch_id: int, + metadata: Sequence = None, +) -> HeteroSamplerOutput: + """ + Parameters + ---------- + sampling_results: cudf.DataFrame + The dataframe containing sampling results. + renumber_map: torch.Tensor + The tensor containing the renumber map, or None if there + is no renumber map. + graph_store: CuGraphStore + The graph store containing the structure of the sampled graph. + data_index: Dict[Tuple[int, int], Dict[str, int]] + Dictionary where keys are the batch id and hop id, + and values are dictionaries containing the max src + and max dst node ids for the batch and hop. + batch_id: int + The current batch id, whose samples are being retrieved + from the sampling results and data index. + metadata: Tensor + The metadata for the sampled batch. + + Returns + ------- + HeteroSamplerOutput + """ + + if len(graph_store.edge_types) > 1 or len(graph_store.node_types) > 1: + raise ValueError("Graph is heterogeneous") + + hops = torch.arange( + sampling_results.hop_id.iloc[len(sampling_results) - 1] + 1, device="cuda" + ) + hops = torch.searchsorted( + torch.as_tensor(sampling_results.hop_id, device="cuda"), hops + ) + + node_type = graph_store.node_types[0] + edge_type = graph_store.edge_types[0] + + num_nodes_per_hop_dict = {node_type: torch.zeros(len(hops) + 1, dtype=torch.int64)} + num_edges_per_hop_dict = {edge_type: torch.zeros(len(hops), dtype=torch.int64)} + + if renumber_map is None: + raise ValueError("Renumbered input is expected for homogeneous graphs") + + noi_index = {node_type: torch.as_tensor(renumber_map, device="cuda")} + + row_dict = { + edge_type: torch.as_tensor(sampling_results.sources, device="cuda"), + } + + col_dict = { + edge_type: torch.as_tensor(sampling_results.destinations, device="cuda"), + } + + num_nodes_per_hop_dict[node_type][0] = data_index[batch_id, 0]["src_max"] + 1 + for hop in range(len(hops)): + hop_ix_start = hops[hop] + hop_ix_end = hops[hop + 1] if hop < len(hops) - 1 else len(sampling_results) + + if num_nodes_per_hop_dict[node_type][hop] > 0: + max_id_hop = data_index[batch_id, hop]["dst_max"] + max_id_prev_hop = ( + data_index[batch_id, hop - 1]["dst_max"] + if hop > 0 + else data_index[batch_id, 0]["src_max"] + ) + + if max_id_hop > max_id_prev_hop: + num_nodes_per_hop_dict[node_type][hop + 1] = ( + max_id_hop - max_id_prev_hop + ) + else: + num_nodes_per_hop_dict[node_type][hop + 1] = 0 + # will default to 0 if the previous hop was 0, since this is a PyG requirement + + num_edges_per_hop_dict[edge_type][hop] = hop_ix_end - hop_ix_start + + if HeteroSamplerOutput is None: + raise ImportError("Error importing from pyg") + + return HeteroSamplerOutput( + node=noi_index, + row=row_dict, + col=col_dict, + edge=None, + num_sampled_nodes=num_nodes_per_hop_dict, + num_sampled_edges=num_edges_per_hop_dict, + metadata=metadata, + ) + + +def _sampler_output_from_sampling_results_heterogeneous( sampling_results: cudf.DataFrame, renumber_map: cudf.Series, graph_store: CuGraphStore, @@ -109,7 +203,7 @@ def _sampler_output_from_sampling_results( hops = torch.arange(sampling_results.hop_id.max() + 1, device="cuda") hops = torch.searchsorted( - torch.as_tensor(sampling_results.hop_id.values, device="cuda"), hops + torch.as_tensor(sampling_results.hop_id, device="cuda"), hops ) num_nodes_per_hop_dict = {} @@ -119,13 +213,11 @@ def _sampler_output_from_sampling_results( sampling_results_hop_0 = sampling_results.iloc[ 0 : (hops[1] if len(hops) > 1 else len(sampling_results)) ] + for node_type in graph_store.node_types: - if len(graph_store.node_types) == 1: - num_unique_nodes = sampling_results_hop_0.sources.nunique() - else: - num_unique_nodes = _count_unique_nodes( - sampling_results_hop_0, graph_store, node_type, "src" - ) + num_unique_nodes = _get_unique_nodes( + sampling_results_hop_0, graph_store, node_type, "src" + ).nunique() if num_unique_nodes > 0: num_nodes_per_hop_dict[node_type] = torch.zeros( @@ -134,112 +226,87 @@ def _sampler_output_from_sampling_results( num_nodes_per_hop_dict[node_type][0] = num_unique_nodes if renumber_map is not None: - if len(graph_store.node_types) > 1 or len(graph_store.edge_types) > 1: - raise ValueError( - "Precomputing the renumber map is currently " - "unsupported for heterogeneous graphs." - ) + raise ValueError( + "Precomputing the renumber map is currently " + "unsupported for heterogeneous graphs." + ) - node_type = graph_store.node_types[0] - if not isinstance(node_type, str): - raise ValueError("Node types must be strings") - noi_index = {node_type: torch.as_tensor(renumber_map.values, device="cuda")} - - edge_type = graph_store.edge_types[0] - if ( - not isinstance(edge_type, tuple) - or not isinstance(edge_type[0], str) - or len(edge_type) != 3 - ): - raise ValueError("Edge types must be 3-tuples of strings") - if edge_type[0] != node_type or edge_type[2] != node_type: - raise ValueError("Edge src/dst type must match for homogeneous graphs") - row_dict = { - edge_type: torch.as_tensor(sampling_results.sources.values, device="cuda"), - } - col_dict = { - edge_type: torch.as_tensor( - sampling_results.destinations.values, device="cuda" + # Calculate nodes of interest based on unique nodes in order of appearance + # Use hop 0 sources since those are the only ones not included in destinations + # Use torch.concat based on benchmark performance (vs. cudf.concat) + + if sampling_results_hop_0 is None: + sampling_results_hop_0 = sampling_results.iloc[ + 0 : (hops[1] if len(hops) > 1 else len(sampling_results)) + ] + + nodes_of_interest = ( + cudf.Series( + torch.concat( + [ + torch.as_tensor(sampling_results_hop_0.sources, device="cuda"), + torch.as_tensor(sampling_results.destinations, device="cuda"), + ] ), - } - else: - # Calculate nodes of interest based on unique nodes in order of appearance - # Use hop 0 sources since those are the only ones not included in destinations - # Use torch.concat based on benchmark performance (vs. cudf.concat) - nodes_of_interest = ( - cudf.Series( - torch.concat( - [ - torch.as_tensor( - sampling_results_hop_0.sources.values, device="cuda" - ), - torch.as_tensor( - sampling_results.destinations.values, device="cuda" - ), - ] - ), - name="nodes_of_interest", - ) - .drop_duplicates() - .sort_index() + name="nodes_of_interest", ) - del sampling_results_hop_0 + .drop_duplicates() + .sort_index() + ) - # Get the grouped node index (for creating the renumbered grouped edge index) - noi_index = graph_store._get_vertex_groups_from_sample( - torch.as_tensor(nodes_of_interest.values, device="cuda") - ) - del nodes_of_interest + # Get the grouped node index (for creating the renumbered grouped edge index) + noi_index = graph_store._get_vertex_groups_from_sample( + torch.as_tensor(nodes_of_interest, device="cuda") + ) + del nodes_of_interest - # Get the new edge index (by type as expected for HeteroData) - # FIXME handle edge ids/types after the C++ updates - row_dict, col_dict = graph_store._get_renumbered_edge_groups_from_sample( - sampling_results, noi_index - ) + # Get the new edge index (by type as expected for HeteroData) + # FIXME handle edge ids/types after the C++ updates + row_dict, col_dict = graph_store._get_renumbered_edge_groups_from_sample( + sampling_results, noi_index + ) for hop in range(len(hops)): hop_ix_start = hops[hop] hop_ix_end = hops[hop + 1] if hop < len(hops) - 1 else len(sampling_results) - sampling_results_hop = sampling_results.iloc[hop_ix_start:hop_ix_end] + sampling_results_to_hop = sampling_results.iloc[0:hop_ix_end] for node_type in graph_store.node_types: - if len(graph_store.node_types) == 1: - num_unique_nodes = sampling_results_hop.destinations.nunique() - else: - num_unique_nodes = _count_unique_nodes( - sampling_results_hop, graph_store, node_type, "dst" - ) + unique_nodes_hop = _get_unique_nodes( + sampling_results_to_hop, graph_store, node_type, "dst" + ) + + unique_nodes_0 = _get_unique_nodes( + sampling_results_hop_0, graph_store, node_type, "src" + ) + + num_unique_nodes = cudf.concat([unique_nodes_0, unique_nodes_hop]).nunique() if num_unique_nodes > 0: if node_type not in num_nodes_per_hop_dict: num_nodes_per_hop_dict[node_type] = torch.zeros( len(hops) + 1, dtype=torch.int64 ) - num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes + num_nodes_per_hop_dict[node_type][hop + 1] = num_unique_nodes - int( + num_nodes_per_hop_dict[node_type][: hop + 1].sum(0) + ) - if len(graph_store.edge_types) == 1: - edge_type = graph_store.edge_types[0] - if edge_type not in num_edges_per_hop_dict: - num_edges_per_hop_dict[edge_type] = torch.zeros( + numeric_etypes, counts = torch.unique( + torch.as_tensor( + sampling_results.iloc[hop_ix_start:hop_ix_end].edge_type, + device="cuda", + ), + return_counts=True, + ) + numeric_etypes = list(numeric_etypes) + counts = list(counts) + for num_etype, count in zip(numeric_etypes, counts): + can_etype = graph_store.numeric_edge_type_to_canonical(num_etype) + if can_etype not in num_edges_per_hop_dict: + num_edges_per_hop_dict[can_etype] = torch.zeros( len(hops), dtype=torch.int64 ) - num_edges_per_hop_dict[graph_store.edge_types[0]][hop] = len( - sampling_results_hop - ) - else: - numeric_etypes, counts = torch.unique( - torch.as_tensor(sampling_results_hop.edge_type.values, device="cuda"), - return_counts=True, - ) - numeric_etypes = list(numeric_etypes) - counts = list(counts) - for num_etype, count in zip(numeric_etypes, counts): - can_etype = graph_store.numeric_edge_type_to_canonical(num_etype) - if can_etype not in num_edges_per_hop_dict: - num_edges_per_hop_dict[can_etype] = torch.zeros( - len(hops), dtype=torch.int64 - ) - num_edges_per_hop_dict[can_etype][hop] = count + num_edges_per_hop_dict[can_etype][hop] = count if HeteroSamplerOutput is None: raise ImportError("Error importing from pyg") diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py index e29f3aea512..55aebf305da 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py @@ -24,7 +24,7 @@ @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_basic(dask_client, karate_gnn): F, G, N = karate_gnn - cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) + cugraph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") loader = CuGraphNeighborLoader( (cugraph_store, cugraph_store), torch.arange(N["type0"] + N["type1"], dtype=torch.int64), @@ -52,7 +52,7 @@ def test_cugraph_loader_basic(dask_client, karate_gnn): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_hetero(dask_client, karate_gnn): F, G, N = karate_gnn - cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) + cugraph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") loader = CuGraphNeighborLoader( (cugraph_store, cugraph_store), input_nodes=("type1", torch.tensor([0, 1, 2, 5], device="cuda")), diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py index 550852a3303..a1a72a44d0c 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py @@ -17,7 +17,9 @@ import pytest from cugraph_pyg.data import CuGraphStore -from cugraph_pyg.sampler.cugraph_sampler import _sampler_output_from_sampling_results +from cugraph_pyg.sampler.cugraph_sampler import ( + _sampler_output_from_sampling_results_heterogeneous, +) from cugraph.gnn import FeatureStore @@ -31,7 +33,7 @@ @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_neighbor_sample(dask_client, basic_graph_1): F, G, N = basic_graph_1 - cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) + cugraph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") batches = cudf.DataFrame( { @@ -56,7 +58,7 @@ def test_neighbor_sample(dask_client, basic_graph_1): .sort_values(by=["sources", "destinations"]) ) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, renumber_map=None, graph_store=cugraph_store, @@ -84,7 +86,7 @@ def test_neighbor_sample(dask_client, basic_graph_1): # check the hop dictionaries assert len(out.num_sampled_nodes) == 1 - assert out.num_sampled_nodes["vt1"].tolist() == [4, 4] + assert out.num_sampled_nodes["vt1"].tolist() == [4, 1] assert len(out.num_sampled_edges) == 1 assert out.num_sampled_edges[("vt1", "pig", "vt1")].tolist() == [6] @@ -95,7 +97,7 @@ def test_neighbor_sample(dask_client, basic_graph_1): @pytest.mark.skip(reason="broken") def test_neighbor_sample_multi_vertex(dask_client, multi_edge_multi_vertex_graph_1): F, G, N = multi_edge_multi_vertex_graph_1 - cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) + cugraph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") batches = cudf.DataFrame( { @@ -119,7 +121,7 @@ def test_neighbor_sample_multi_vertex(dask_client, multi_edge_multi_vertex_graph .compute() ) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, renumber_map=None, graph_store=cugraph_store, @@ -144,8 +146,8 @@ def test_neighbor_sample_multi_vertex(dask_client, multi_edge_multi_vertex_graph # check the hop dictionaries assert len(out.num_sampled_nodes) == 2 - assert out.num_sampled_nodes["black"].tolist() == [2, 2] - assert out.num_sampled_nodes["brown"].tolist() == [3, 2] + assert out.num_sampled_nodes["black"].tolist() == [2, 0] + assert out.num_sampled_nodes["brown"].tolist() == [3, 0] assert len(out.num_sampled_edges) == 5 assert out.num_sampled_edges[("brown", "horse", "brown")].tolist() == [2] @@ -186,7 +188,7 @@ def test_neighbor_sample_mock_sampling_results(dask_client): torch.tensor([3.2, 2.1], dtype=torch.float32), type_name="A", feat_name="prop1" ) - graph_store = CuGraphStore(F, G, N, multi_gpu=True) + graph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( @@ -198,7 +200,7 @@ def test_neighbor_sample_mock_sampling_results(dask_client): } ) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( mock_sampling_results, None, graph_store, None ) @@ -218,9 +220,9 @@ def test_neighbor_sample_mock_sampling_results(dask_client): assert out.col[("B", "ba", "A")].tolist() == [1, 1] assert len(out.num_sampled_nodes) == 3 - assert out.num_sampled_nodes["A"].tolist() == [2, 0, 1, 0, 1] - assert out.num_sampled_nodes["B"].tolist() == [0, 2, 0, 1, 0] - assert out.num_sampled_nodes["C"].tolist() == [0, 0, 2, 0, 2] + assert out.num_sampled_nodes["A"].tolist() == [2, 0, 0, 0, 0] + assert out.num_sampled_nodes["B"].tolist() == [0, 2, 0, 0, 0] + assert out.num_sampled_nodes["C"].tolist() == [0, 0, 2, 0, 1] assert len(out.num_sampled_edges) == 3 assert out.num_sampled_edges[("A", "ab", "B")].tolist() == [3, 0, 1, 0] diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py index a5a59623710..43b1e5da5a0 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py @@ -117,8 +117,8 @@ def test_get_edge_index(graph, edge_index_type, dask_client): G[et][1] = cudf.Series(G[et][1]) elif edge_index_type == "dask-cudf": for et in list(G.keys()): - G[et][0] = dask_cudf.from_cudf(cudf.Series(G[et][0]), npartitions=2) - G[et][1] = dask_cudf.from_cudf(cudf.Series(G[et][1]), npartitions=2) + G[et][0] = dask_cudf.from_cudf(cudf.Series(G[et][0]), npartitions=1) + G[et][1] = dask_cudf.from_cudf(cudf.Series(G[et][1]), npartitions=1) cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) @@ -215,7 +215,7 @@ def test_renumber_vertices_multi_edge_multi_vertex( def test_renumber_edges(abc_graph, dask_client): F, G, N = abc_graph - graph_store = CuGraphStore(F, G, N, multi_gpu=True) + graph_store = CuGraphStore(F, G, N, multi_gpu=True, order="CSR") # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 620f1a5eb85..48a21cb7fd6 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -26,12 +26,14 @@ from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") +torch_geometric = import_optional("torch_geometric") +trim_to_layer = import_optional("torch_geometric.utils.trim_to_layer") @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_basic(karate_gnn): F, G, N = karate_gnn - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") loader = CuGraphNeighborLoader( (cugraph_store, cugraph_store), torch.arange(N["type0"] + N["type1"], dtype=torch.int64), @@ -57,7 +59,7 @@ def test_cugraph_loader_basic(karate_gnn): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_hetero(karate_gnn): F, G, N = karate_gnn - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") loader = CuGraphNeighborLoader( (cugraph_store, cugraph_store), input_nodes=("type1", torch.tensor([0, 1, 2, 5], device="cuda")), @@ -82,23 +84,29 @@ def test_cugraph_loader_hetero(karate_gnn): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_from_disk(): + m = [2, 9, 99, 82, 9, 3, 18, 1, 12] + n = torch.arange(1, 1 + len(m), dtype=torch.int32) + x = torch.zeros(256, dtype=torch.int32) + x[torch.tensor(m, dtype=torch.int32)] = n F = FeatureStore() - F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), "t0", "x") + F.add_data(x, "t0", "x") - G = {("t0", "knows", "t0"): 7} - N = {"t0": 7} + G = {("t0", "knows", "t0"): 9080} + N = {"t0": 256} - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6], - "destinations": [6, 4, 3, 2, 2, 1, 5], - "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype="int32"), - "edge_id": [5, 10, 15, 20, 25, 30, 35], - "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype="int32"), + "sources": [0, 1, 2, 3, 4, 5, 6, 6], + "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), } ) + map = cudf.Series(m, name="map") + bogus_samples = bogus_samples.join(map, how="outer").sort_index() tempdir = tempfile.TemporaryDirectory() for s in range(256): @@ -115,32 +123,49 @@ def test_cugraph_loader_from_disk(): for sample in loader: num_samples += 1 assert sample["t0"]["num_nodes"] == 7 - # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] - assert sample["t0"]["x"].tolist() == [1, 2, 3, 7, 5, 4, 6] - assert list(sample[("t0", "knows", "t0")]["edge_index"].shape) == [2, 7] + # correct vertex order is [0, 1, 2, 5, 4, 3, 6]; x = [1, 2, 3, 6, 5, 4, 7] + assert sample["t0"]["x"].tolist() == [3, 4, 5, 6, 7, 8, 9] + + edge_index = sample[("t0", "knows", "t0")]["edge_index"] + assert list(edge_index.shape) == [2, 8] + + assert ( + edge_index[0].tolist() + == bogus_samples.sources.dropna().values_host.tolist() + ) + assert ( + edge_index[1].tolist() + == bogus_samples.destinations.dropna().values_host.tolist() + ) assert num_samples == 256 @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_from_disk_subset(): + m = [2, 9, 99, 82, 9, 3, 18, 1, 12] + n = torch.arange(1, 1 + len(m), dtype=torch.int32) + x = torch.zeros(256, dtype=torch.int32) + x[torch.tensor(m, dtype=torch.int32)] = n F = FeatureStore() - F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), "t0", "x") + F.add_data(x, "t0", "x") - G = {("t0", "knows", "t0"): 7} - N = {"t0": 7} + G = {("t0", "knows", "t0"): 9080} + N = {"t0": 256} - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6], - "destinations": [6, 4, 3, 2, 2, 1, 5], - "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype="int32"), - "edge_id": [5, 10, 15, 20, 25, 30, 35], - "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype="int32"), + "sources": [0, 1, 2, 3, 4, 5, 6, 6], + "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), } ) + map = cudf.Series(m, name="map") + bogus_samples = bogus_samples.join(map, how="outer").sort_index() tempdir = tempfile.TemporaryDirectory() for s in range(256): @@ -159,33 +184,45 @@ def test_cugraph_loader_from_disk_subset(): num_samples += 1 assert sample["t0"]["num_nodes"] == 7 # correct vertex order is [0, 1, 2, 6, 4, 3, 5]; x = [1, 2, 3, 7, 5, 4, 6] - assert sample["t0"]["x"].tolist() == [1, 2, 3, 7, 5, 4, 6] - assert list(sample[("t0", "knows", "t0")]["edge_index"].shape) == [2, 7] + assert sample["t0"]["x"].tolist() == [3, 4, 5, 6, 7, 8, 9] + + edge_index = sample[("t0", "knows", "t0")]["edge_index"] + assert list(edge_index.shape) == [2, 8] + + assert ( + edge_index[0].tolist() + == bogus_samples.sources.dropna().values_host.tolist() + ) + assert ( + edge_index[1].tolist() + == bogus_samples.destinations.dropna().values_host.tolist() + ) assert num_samples == 100 @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") -def test_cugraph_loader_from_disk_subset_renumbered(): +def test_cugraph_loader_e2e_coo(): + m = [2, 9, 99, 82, 9, 3, 18, 1, 12] + x = torch.randint(3000, (256, 256)).to(torch.float32) F = FeatureStore() - F.add_data(torch.tensor([1, 2, 3, 4, 5, 6, 7]), "t0", "x") + F.add_data(x, "t0", "x") - G = {("t0", "knows", "t0"): 7} - N = {"t0": 7} + G = {("t0", "knows", "t0"): 9999} + N = {"t0": 256} - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6], - "destinations": [6, 4, 3, 2, 2, 1, 5], - "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0], dtype="int32"), - "edge_id": [5, 10, 15, 20, 25, 30, 35], - "hop_id": cudf.Series([0, 0, 0, 1, 1, 2, 2], dtype="int32"), + "sources": [0, 1, 2, 3, 4, 5, 6, 6], + "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], + "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), } ) - - map = cudf.Series([2, 9, 0, 2, 1, 3, 4, 6, 5], name="map") + map = cudf.Series(m, name="map") bogus_samples = bogus_samples.join(map, how="outer").sort_index() tempdir = tempfile.TemporaryDirectory() @@ -200,22 +237,35 @@ def test_cugraph_loader_from_disk_subset_renumbered(): input_files=list(os.listdir(tempdir.name))[100:200], ) - num_samples = 0 - for sample in loader: - num_samples += 1 - assert sample["t0"]["num_nodes"] == 7 - # correct vertex order is [0, 2, 1, 3, 4, 6, 5]; x = [1, 3, 2, 4, 5, 7, 6] - assert sample["t0"]["x"].tolist() == [1, 3, 2, 4, 5, 7, 6] + convs = [ + torch_geometric.nn.SAGEConv(256, 64, aggr="mean").cuda(), + torch_geometric.nn.SAGEConv(64, 8, aggr="mean").cuda(), + torch_geometric.nn.SAGEConv(8, 1, aggr="mean").cuda(), + ] - edge_index = sample[("t0", "knows", "t0")]["edge_index"] - assert list(edge_index.shape) == [2, 7] - assert ( - edge_index[0].tolist() - == bogus_samples.sources.dropna().values_host.tolist() - ) - assert ( - edge_index[1].tolist() - == bogus_samples.destinations.dropna().values_host.tolist() - ) + trim = trim_to_layer.TrimToLayer() + relu = torch.nn.functional.relu + dropout = torch.nn.functional.dropout - assert num_samples == 100 + for hetero_data in loader: + ei = hetero_data["t0", "knows", "t0"]["edge_index"] + x = hetero_data["t0"]["x"].cuda() + num_sampled_nodes = hetero_data["t0"]["num_sampled_nodes"] + num_sampled_edges = hetero_data["t0", "knows", "t0"]["num_sampled_edges"] + + print(num_sampled_nodes, num_sampled_edges) + + for i in range(len(convs)): + x, ei, _ = trim(i, num_sampled_nodes, num_sampled_edges, x, ei, None) + + s = x.shape[0] + + x = convs[i](x, ei, size=(s, s)) + x = relu(x) + x = dropout(x, p=0.5) + print(x.shape) + + print(x.shape) + x = x.narrow(dim=0, start=0, length=x.shape[0] - num_sampled_nodes[1]) + + assert list(x.shape) == [3, 1] diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py index 08a8625b33b..84f62e80c9d 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py @@ -17,7 +17,9 @@ import pytest from cugraph_pyg.data import CuGraphStore -from cugraph_pyg.sampler.cugraph_sampler import _sampler_output_from_sampling_results +from cugraph_pyg.sampler.cugraph_sampler import ( + _sampler_output_from_sampling_results_heterogeneous, +) from cugraph.utilities.utils import import_optional, MissingModule from cugraph import uniform_neighbor_sample @@ -29,7 +31,7 @@ @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_neighbor_sample(basic_graph_1): F, G, N = basic_graph_1 - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") batches = cudf.DataFrame( { @@ -49,7 +51,7 @@ def test_neighbor_sample(basic_graph_1): return_offsets=False, ).sort_values(by=["sources", "destinations"]) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, renumber_map=None, graph_store=cugraph_store, @@ -77,7 +79,7 @@ def test_neighbor_sample(basic_graph_1): # check the hop dictionaries assert len(out.num_sampled_nodes) == 1 - assert out.num_sampled_nodes["vt1"].tolist() == [4, 4] + assert out.num_sampled_nodes["vt1"].tolist() == [4, 1] assert len(out.num_sampled_edges) == 1 assert out.num_sampled_edges[("vt1", "pig", "vt1")].tolist() == [6] @@ -87,7 +89,7 @@ def test_neighbor_sample(basic_graph_1): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_neighbor_sample_multi_vertex(multi_edge_multi_vertex_graph_1): F, G, N = multi_edge_multi_vertex_graph_1 - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSR") batches = cudf.DataFrame( { @@ -107,7 +109,7 @@ def test_neighbor_sample_multi_vertex(multi_edge_multi_vertex_graph_1): with_batch_ids=True, ).sort_values(by=["sources", "destinations"]) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, renumber_map=None, graph_store=cugraph_store, @@ -132,8 +134,8 @@ def test_neighbor_sample_multi_vertex(multi_edge_multi_vertex_graph_1): # check the hop dictionaries assert len(out.num_sampled_nodes) == 2 - assert out.num_sampled_nodes["black"].tolist() == [2, 2] - assert out.num_sampled_nodes["brown"].tolist() == [3, 2] + assert out.num_sampled_nodes["black"].tolist() == [2, 0] + assert out.num_sampled_nodes["brown"].tolist() == [3, 0] assert len(out.num_sampled_edges) == 5 assert out.num_sampled_edges[("brown", "horse", "brown")].tolist() == [2] @@ -147,7 +149,7 @@ def test_neighbor_sample_multi_vertex(multi_edge_multi_vertex_graph_1): def test_neighbor_sample_mock_sampling_results(abc_graph): F, G, N = abc_graph - graph_store = CuGraphStore(F, G, N) + graph_store = CuGraphStore(F, G, N, order="CSR") # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( @@ -159,7 +161,7 @@ def test_neighbor_sample_mock_sampling_results(abc_graph): } ) - out = _sampler_output_from_sampling_results( + out = _sampler_output_from_sampling_results_heterogeneous( mock_sampling_results, None, graph_store, None ) @@ -179,9 +181,9 @@ def test_neighbor_sample_mock_sampling_results(abc_graph): assert out.col[("B", "ba", "A")].tolist() == [1, 1] assert len(out.num_sampled_nodes) == 3 - assert out.num_sampled_nodes["A"].tolist() == [2, 0, 1, 0, 1] - assert out.num_sampled_nodes["B"].tolist() == [0, 2, 0, 1, 0] - assert out.num_sampled_nodes["C"].tolist() == [0, 0, 2, 0, 2] + assert out.num_sampled_nodes["A"].tolist() == [2, 0, 0, 0, 0] + assert out.num_sampled_nodes["B"].tolist() == [0, 2, 0, 0, 0] + assert out.num_sampled_nodes["C"].tolist() == [0, 0, 2, 0, 1] assert len(out.num_sampled_edges) == 3 assert out.num_sampled_edges[("A", "ab", "B")].tolist() == [3, 0, 1, 0] diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py index 289dd69a829..e815b813050 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py @@ -199,7 +199,7 @@ def test_renumber_vertices_multi_edge_multi_vertex(multi_edge_multi_vertex_graph def test_renumber_edges(abc_graph): F, G, N = abc_graph - graph_store = CuGraphStore(F, G, N) + graph_store = CuGraphStore(F, G, N, order="CSR") # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( From 9c96b2613f029eb9616c19aefb38a689c1267bae Mon Sep 17 00:00:00 2001 From: Vibhu Jawa Date: Mon, 25 Sep 2023 07:49:19 -0700 Subject: [PATCH 012/111] Update dgl benchmarks (#3775) This PR adds upstream DGL benchmarks , I will expand this to add `cugraph-dgl` soon. image CC: @tingyu66 , @BradReesWork Authors: - Vibhu Jawa (https://github.com/VibhuJawa) Approvers: - Brad Rees (https://github.com/BradReesWork) - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/3775 --- .../cugraph-dgl/scale-benchmarks/__init__.py | 0 .../scale-benchmarks/dgl_benchmark.py | 152 ++++++++++++++++++ .../scale-benchmarks/load_graph_feats.py | 123 ++++++++++++++ .../cugraph-dgl/scale-benchmarks/model.py | 110 +++++++++++++ 4 files changed, 385 insertions(+) create mode 100644 benchmarks/cugraph-dgl/scale-benchmarks/__init__.py create mode 100644 benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py create mode 100644 benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py create mode 100644 benchmarks/cugraph-dgl/scale-benchmarks/model.py diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/__init__.py b/benchmarks/cugraph-dgl/scale-benchmarks/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py b/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py new file mode 100644 index 00000000000..3762226d570 --- /dev/null +++ b/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py @@ -0,0 +1,152 @@ +# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import time +import dgl +from dgl.dataloading import MultiLayerNeighborSampler, DataLoader +import pandas as pd +import torch +from model import run_1_epoch +from argparse import ArgumentParser +from load_graph_feats import load_edges_from_disk, load_node_labels, load_node_features + +class DataLoaderArgs: + def __init__(self, args): + self.dataset_path = args.dataset_path + self.replication_factors = [int(x) for x in args.replication_factors.split(",")] + self.fanouts = [[int(y) for y in x.split("_")] for x in args.fanouts.split(",")] + self.batch_sizes = [int(x) for x in args.batch_sizes.split(",")] + self.use_uva = not args.do_not_use_uva + + + +def create_dataloader(g, train_idx, batch_size, fanouts, use_uva): + print("Creating dataloader", flush=True) + st = time.time() + if use_uva: + train_idx = {k: v.to("cuda") for k, v in train_idx.items()} + sampler = MultiLayerNeighborSampler(fanouts=fanouts) + dataloader = DataLoader( + g, + train_idx, + sampler, + num_workers=0, + batch_size=batch_size, + use_uva=use_uva, + shuffle=False, + drop_last=False, + ) + et = time.time() + print(f"Time to create dataloader = {et - st:.2f} seconds", flush=True) + return dataloader + + + +def create_dgl_graph_from_disk(dataset_path, replication_factor=1): + """ + Create a DGL graph from a dataset on disk. + Args: + dataset_path: Path to the dataset on disk. + replication_factor: Number of times to replicate the edges. + Returns: + DGLGraph: DGLGraph with the loaded dataset. + """ + with open(os.path.join(dataset_path, "meta.json"), "r") as f: + input_meta = json.load(f) + + parquet_path = os.path.join(dataset_path, "parquet") + graph_data = load_edges_from_disk( + parquet_path, replication_factor, input_meta + ) + label_data = load_node_labels(dataset_path, replication_factor, input_meta) + if replication_factor <8 : + feat_data = load_node_features(dataset_path, replication_factor, node_type='paper') + else: + feat_data = None + print("labels and features loaded ", flush=True) + + g = dgl.heterograph(graph_data) + + return g, label_data, feat_data + + +def main(args): + print(f"Running dgl dataloading benchmark with the following parameters:\n" + f"Dataset path = {args.dataset_path}\n" + f"Replication factors = {args.replication_factors}\n" + f"Fanouts = {args.fanouts}\n" + f"Batch sizes = {args.batch_sizes}\n" + f"Use UVA = {args.use_uva}\n" + f"{'=' * 30}") + + time_ls = [] + for replication_factor in args.replication_factors: + start_time = time.time() + g, label_data, feat_data = create_dgl_graph_from_disk(args.dataset_path, replication_factor) + elapsed_time = time.time() - start_time + + print(f"Replication factor = {replication_factor}\n" + f"G has {g.num_edges():,} edges and took {elapsed_time:.2f} seconds to load", flush=True) + + train_idx = {"paper": label_data["paper"]["train_idx"]} + y = label_data["paper"]["y"] + r_time_ls = e2e_benchmark(g, feat_data, y, train_idx, args.fanouts, args.batch_sizes, use_uva=args.use_uva) + [x.update({"replication_factor": replication_factor}) for x in r_time_ls] + [x.update({"num_edges": g.num_edges()}) for x in r_time_ls] + time_ls.extend(r_time_ls) + + print(f"Benchmark completed for replication factor = {replication_factor}\n{'=' * 30}", flush=True) + + df = pd.DataFrame(time_ls) + df.to_csv("dgl_e2e_benchmark.csv", index=False) + print(f"Benchmark completed for all replication factors\n{'=' * 30}", flush=True) + + +def e2e_benchmark(g, feat, y, train_idx, fanouts, batch_sizes, use_uva): + """ + Run the e2e_benchmark + Args: + g: DGLGraph + feat: Tensor containing the features. + y: Tensor containing the labels. + train_idx: Tensor containing the training indices. + fanouts: List of fanouts to use for the dataloader. + batch_sizes: List of batch sizes to use for the dataloader. + use_uva: Whether to use unified virtual address space. + model_backend: Backend of model to use. + """ + time_ls = [] + for fanout in fanouts: + for batch_size in batch_sizes: + dataloader = create_dataloader(g, train_idx, batch_size, fanout, use_uva) + time_d = run_1_epoch(dataloader, feat, y, fanout, batch_size, model_backend='dgl') + time_ls.append(time_d) + print("="*30) + return time_ls + + + +def parse_arguments(): + parser = ArgumentParser() + parser.add_argument("--dataset_path", type=str, default="/raid/vjawa/ogbn_papers100M") + parser.add_argument("--replication_factors", type=str, default="2") + parser.add_argument("--fanouts", type=str, default="10_10_10") + parser.add_argument("--batch_sizes", type=str, default="512,1024,8192,16384") + parser.add_argument("--do_not_use_uva", action="store_true") + return parser.parse_args() + +if __name__ == "__main__": + arguments = parse_arguments() + main(DataLoaderArgs(arguments)) diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py b/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py new file mode 100644 index 00000000000..4f0f81c70e1 --- /dev/null +++ b/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py @@ -0,0 +1,123 @@ +# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import pandas as pd +import torch +import os + + +def load_edges_from_disk(parquet_path, replication_factor, input_meta): + """ + Load the edges from disk into a graph data dictionary. + Args: + parquet_path: Path to the parquet directory. + replication_factor: Number of times to replicate the edges. + input_meta: Input meta data. + Returns: + dict: Dictionary of edge types to a tuple of (src, dst) + """ + graph_data = {} + + for edge_type in input_meta["num_edges"].keys(): + print(f"Loading edge index for edge type {edge_type} for replication factor = {replication_factor}") + + canonical_edge_type = tuple(edge_type.split("__")) + edge_index = pd.read_parquet(os.path.join(parquet_path, edge_type, "edge_index.parquet")) + edge_index = { + "src": torch.from_numpy(edge_index.src.values), + "dst": torch.from_numpy(edge_index.dst.values), + } + + if replication_factor > 1: + src_list, dst_list = replicate_edges(edge_index, canonical_edge_type, replication_factor, input_meta) + edge_index["src"] = torch.cat(src_list).contiguous() + edge_index["dst"] = torch.cat(dst_list).contiguous() + + graph_data[canonical_edge_type] = edge_index["src"], edge_index["dst"] + + print("Read Edge Data") + return graph_data + + +def replicate_edges(edge_index, canonical_edge_type, replication_factor, input_meta): + src_list = [edge_index["src"]] + dst_list = [edge_index["dst"]] + + for r in range(1, replication_factor): + new_src = edge_index["src"] + (r * input_meta["num_nodes"][canonical_edge_type[0]]) + new_dst = edge_index["dst"] + (r * input_meta["num_nodes"][canonical_edge_type[2]]) + src_list.append(new_src) + dst_list.append(new_dst) + + return src_list, dst_list + + + + +def load_node_labels(dataset_path, replication_factor, input_meta): + num_nodes_dict = {node_type: t * replication_factor for node_type, t in input_meta["num_nodes"].items()} + node_data = {} + + for node_type in input_meta["num_nodes"].keys(): + node_data[node_type] = {} + label_path = os.path.join(dataset_path, "parquet", node_type, "node_label.parquet") + + if os.path.exists(label_path): + node_data[node_type] = process_node_label(label_path, node_type, replication_factor, num_nodes_dict, input_meta) + + else: + node_data[node_type]["num_nodes"] = num_nodes_dict[node_type] + + print("Loaded node labels", flush=True) + return node_data + +def process_node_label(label_path, node_type, replication_factor, num_nodes_dict, input_meta): + node_label = pd.read_parquet(label_path) + + if replication_factor > 1: + node_label = replicate_node_label(node_label, node_type, replication_factor, input_meta) + + node_label_tensor = torch.full((num_nodes_dict[node_type],), -1, dtype=torch.float32) + node_label_tensor[torch.as_tensor(node_label.node.values)] = torch.as_tensor(node_label.label.values) + + del node_label + + return { + "train_idx": (node_label_tensor > -1).contiguous().nonzero().view(-1), + "y": node_label_tensor.contiguous().long() + } + + +def replicate_node_label(node_label, node_type, replication_factor, input_meta): + base_num_nodes = input_meta["num_nodes"][node_type] + + replicated_df = pd.DataFrame({ + "node": pd.concat([node_label.node + (r * base_num_nodes) for r in range(1, replication_factor)]), + "label": pd.concat([node_label.label for _ in range(1, replication_factor)]) + }) + + return pd.concat([node_label, replicated_df]).reset_index(drop=True) + + +def load_node_features(dataset_path, replication_factor, node_type): + print("Loading node features", flush=True) + node_type_path = os.path.join(dataset_path, "npy", node_type) + if replication_factor == 1: + fname = os.path.join(node_type_path, "node_feat.npy") + else: + fname = os.path.join(node_type_path, f"node_feat_{replication_factor}x.npy") + + feat = torch.from_numpy(np.load(fname)) + print("Loaded node features", flush=True) + return feat diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/model.py b/benchmarks/cugraph-dgl/scale-benchmarks/model.py new file mode 100644 index 00000000000..506e3bd5227 --- /dev/null +++ b/benchmarks/cugraph-dgl/scale-benchmarks/model.py @@ -0,0 +1,110 @@ +# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +import time + + +class GNN(torch.nn.Module): + def __init__(self, in_channels, hidden_channels, out_channels, num_layers, model_backend='dgl'): + if model_backend == 'dgl': + from dgl.nn import SAGEConv + else: + from cugraph_dgl.nn import SAGEConv + + super(GNN, self).__init__() + self.convs = torch.nn.ModuleList() + for _ in range(num_layers - 1): + self.convs.append(SAGEConv(in_channels, hidden_channels, aggregator_type='mean')) + in_channels = hidden_channels + self.convs.append(SAGEConv(hidden_channels, out_channels, aggregator_type='mean')) + + def forward(self, blocks, x): + for i, conv in enumerate(self.convs): + x = conv(blocks[i], x) + if i != len(self.convs) - 1: + x = F.relu(x) + return x + + +def create_model(feat_size, num_classes, num_layers, model_backend='dgl'): + model = GNN(feat_size, 64, num_classes, num_layers, model_backend=model_backend) + model = model.to('cuda') + model.train() + return model + +def train_model(model, dataloader, opt, feat, y): + times = {key: 0 for key in ['mfg_creation', 'feature', 'm_fwd', 'm_bkwd']} + epoch_st = time.time() + mfg_st = time.time() + for input_nodes, output_nodes, blocks in dataloader: + times['mfg_creation'] += time.time() - mfg_st + if feat is not None: + fst = time.time() + input_nodes = input_nodes.to('cpu') + input_feat = feat[input_nodes] + input_feat = input_feat.to('cuda') + if isinstance(output_nodes, dict): + output_nodes = output_nodes['paper'] + output_nodes = output_nodes.to(y.device) + y_batch = y[output_nodes].to('cuda') + times['feature'] += time.time() - fst + + m_fwd_st = time.time() + y_hat = model(blocks, input_feat) + times['m_fwd'] += time.time() - m_fwd_st + + m_bkwd_st = time.time() + loss = F.cross_entropy(y_hat, y_batch) + opt.zero_grad() + loss.backward() + opt.step() + times['m_bkwd'] += time.time() - m_bkwd_st + mfg_st = time.time() + + print(f"Epoch time = {time.time() - epoch_st:.2f} seconds") + + return times + +def analyze_time(dataloader, times, epoch_time, fanout, batch_size): + num_batches = len(dataloader) + time_d = { + "fanout": fanout, + "batch_size": batch_size, + "epoch_time": epoch_time, + "epoch_time_per_batch": epoch_time / num_batches, + "num_batches": num_batches, + } + for key, value in times.items(): + time_d[f"{key}_time_per_epoch"] = value + time_d[f"{key}_time_per_batch"] = value / num_batches + + print(f"Time analysis for fanout = {fanout}, batch_size = {batch_size}") + for k in time_d.keys(): + if 'time_per_epoch' in str(k): + print(f"{k} = {time_d[k]:.2f} seconds") + return time_d + +def run_1_epoch(dataloader, feat, y, fanout, batch_size, model_backend): + if feat is not None: + model = create_model(feat.shape[1], 172, len(fanout), model_backend=model_backend) + opt = torch.optim.Adam(model.parameters(), lr=0.01) + else: + model = None + opt = None + epoch_st = time.time() + times = train_model(model, dataloader, opt, feat, y) + epoch_time = time.time() - epoch_st + time_d = analyze_time(dataloader, times, epoch_time, fanout, batch_size) + return time_d From c11eff23926dd483d23444a4757629e8ed069683 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Mon, 25 Sep 2023 21:59:29 -0400 Subject: [PATCH 013/111] similarity notebook to compare link prediction algos (#3868) New notebook to compare link prediction Dependent on dining_prefs being added to datasets api in PR #3866 Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3868 --- .../link_prediction/similarity_combined.ipynb | 217 ++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 notebooks/algorithms/link_prediction/similarity_combined.ipynb diff --git a/notebooks/algorithms/link_prediction/similarity_combined.ipynb b/notebooks/algorithms/link_prediction/similarity_combined.ipynb new file mode 100644 index 00000000000..cd80ee34002 --- /dev/null +++ b/notebooks/algorithms/link_prediction/similarity_combined.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Similarity Compared\n", + "----\n", + "\n", + "In this notebook, we will compute vertex similarity scores using the various cuGraph algorithms. We will then compare the similarities scores in tables.\n", + "\n", + "| Author Credit | Date | Update | cuGraph Version | Test Hardware |\n", + "| --------------|------------|------------------|-----------------|-----------------------|\n", + "| Don Acosta | 09/25/2023 | created | 23.10 nightly | AMPERE A6000 CUDA 11.7|\n", + "\n", + "\n", + "**Note: On large graphs these algorithms can take prohibitive time or memory. The notebook will show how to run on defined pairs instead.**\n", + "\n", + "The Similarity algorithms in cuGraph use different methods to compare pairs of vertices. All of them use the intersection of the set of adjacent nodes for the set overlap. However each of the three algorithms differ on the denominator to determine the similarity coefficients. All three are normalized between zero and one. where zero is no overlap at all and one means identical adjacencies.\n", + "\n", + "__Jaccard Similarity__
\n", + "The [Jaccard similarity](https://en.wikipedia.org/wiki/Jaccard_index) measure was developed by botonist, Paul Jaccard who used the measure to compare plant species. His work popularized the measure's use in in other fields as well.\n", + "\n", + "It can be expressed as:
\n", + "$\\text{Jaccard similarity} = \\frac{|A \\cap B|}{|A \\cup B|}$\n", + "\n", + "__Overlap Similarity__
\n", + "The [Overlap Similarity](https://en.wikipedia.org/wiki/Overlap_coefficient) is also known as the Szymkiewicz–Simpson coefficient. It is often used to compare binary and categorical data in the fields of Genome analysis, recommender systems and anomaly detection. It differs from the Jaccard measure above in that it uses the size of the smaller of the two set sizes as the denominator.\n", + "\n", + "It can be expressed as\n", + "\n", + "$oc(A,B)=\\frac{|A|\\cap|B|}{min(|A|,|B|)}$\n", + "\n", + "__Sørensen-Dice Coefficient__
\n", + "The [Sørensen coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient#) is known as the Sørensen-Dice coefficient. It was independently developed for use by botonists Lee Raymond Dice and Thorvald Sørensen. Although originating in the field of Botony, the coefficient is now used in computer vision, Natural Language Processing(NLP) and Data Mining among other fields.\n", + "It differs from Jaccard and Overlap in that the calculation doubles the intersection size and divides it by the sum of the two set sizes.\n", + "\n", + "It can be expressed as\n", + "\n", + "Sørensen coefficient = $\\left(2 * |A \\cap B| \\right) \\over \\left(|A| + |B| \\right)$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "# Now for the code !" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the required dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cugraph\n", + "from cugraph.datasets import dining_prefs\n", + "# only needed to display results in a table \n", + "from IPython.display import display_html " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function that calls all the cuGraph similarity/link prediction algorithms " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_similarity(G,pairs=None):\n", + " _jdf = cugraph.jaccard(G,pairs)\n", + " _jdf2 = _jdf[ (_jdf['first'] != _jdf['second'] ) ]\n", + " _odf = cugraph.overlap(G,pairs)\n", + " _odf2 = _odf[ (_odf['first'] != _odf['second'] ) ]\n", + " _sdf = cugraph.sorensen_coefficient(G,pairs)\n", + " _sdf2 = _sdf[ (_sdf['first'] != _sdf['second'] ) ]\n", + " return _jdf2, _odf2, _sdf2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Function to put all the results in a convenient table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print function\n", + "def print_similarity(jdf,odf,sdf,num_records=5):\n", + "\n", + " js_top = jdf.sort_values(by='jaccard_coeff', ascending=False).head(num_records).to_pandas()\n", + " os_top = odf.sort_values(by='overlap_coeff', ascending=False).head(num_records).to_pandas()\n", + " ss_top = sdf.sort_values(by='sorensen_coeff', ascending=False).head(num_records).to_pandas()\n", + " \n", + " df1_styler = js_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Jaccard').hide(axis='index')\n", + " df2_styler = os_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Overlap').hide(axis='index')\n", + " df3_styler = ss_top.style.set_table_attributes(\"style='display:inline'\").set_caption('Sørensen').hide(axis='index')\n", + "\n", + " display_html(df1_styler._repr_html_()+df2_styler._repr_html_()+df3_styler._repr_html_(), raw=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the graph from the Dining preferences data set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "G = dining_prefs.get_graph(download=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the three similarity Algorithms and print out the five links with the highest scores." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "jdf, odf, sdf = compute_similarity(G)\n", + "print_similarity(jdf,odf,sdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now find the the complete set of two-hop neigbors and compare them instead of just using the existing one-hop edges. In a larger graph, this will run considerably faster since the default " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# this cugraph algorithm pulls a set containing every pair of vertices\n", + "# that are within 2-hops of each other\n", + "two_hops_pairs = G.get_two_hop_neighbors()\n", + "\n", + "jdf_hops, odf_hops, sdf_hops = compute_similarity(G,pairs=two_hops_pairs)\n", + "print_similarity(jdf_hops,odf_hops,sdf_hops)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### It's that easy with cuGraph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "Copyright (c) 2023, NVIDIA CORPORATION.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0\n", + "\n", + "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cugraph_0802", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b199bf01a2dbb3a8bc89198c6d35fd5a0444e213 Mon Sep 17 00:00:00 2001 From: Vibhu Jawa Date: Tue, 26 Sep 2023 05:33:13 -0700 Subject: [PATCH 014/111] [REVIEW] Add Pure DGL Dataloading benchmark (#3660) This PR adds the DGL data loading benchmark: Arguments supported: - dataset_path: path to the dataset - replication_factors: replication factors for number of edges - fanouts: fanouts - batch_sizes: batch sizes ```bash python3 dgl_dataloading.py --dataset_path "/datasets/abarghi/ogbn_papers100M" \ --replication_factors "1,2,4" \ --fanouts "25_25,10_10_10,5_10_20" \ --batch_sizes "512,1024" ``` This produces the following results on a V100: | Fanout | Batch Size | Data Loading Time per Epoch | Data Loading Time per Batch | Number of Edges | Number of Batches | Replication Factor | |--------|------------|-----------------------------|-----------------------------|-----------------|-------------------|--------------------| | [25, 25] | 512 | 9.48 | 0.0031 | 1615685872 | 3022 | 1 | | [25, 25] | 1024 | 6.39 | 0.0042 | 1615685872 | 1511 | 1 | | [10, 10, 10] | 512 | 15.91 | 0.0053 | 1615685872 | 3022 | 1 | | [10, 10, 10] | 1024 | 11.64 | 0.0077 | 1615685872 | 1511 | 1 | | [5, 10, 20] | 512 | 17.73 | 0.0059 | 1615685872 | 3022 | 1 | | [5, 10, 20] | 1024 | 13.52 | 0.0089 | 1615685872 | 1511 | 1 | | [25, 25] | 512 | 19.44 | 0.0032 | 3231371744 | 6043 | 2 | | [25, 25] | 1024 | 12.98 | 0.0043 | 3231371744 | 3022 | 2 | | [10, 10, 10] | 512 | 32.88 | 0.0054 | 3231371744 | 6043 | 2 | | [10, 10, 10] | 1024 | 24.35 | 0.0081 | 3231371744 | 3022 | 2 | | [5, 10, 20] | 512 | 38.35 | 0.0063 | 3231371744 | 6043 | 2 | | [5, 10, 20] | 1024 | 28.93 | 0.0096 | 3231371744 | 3022 | 2 | | [25, 25] | 512 | 37.31 | 0.0031 | 6462743488 | 12085 | 4 | | [25, 25] | 1024 | 25.15 | 0.0042 | 6462743488 | 6043 | 4 | | [10, 10, 10] | 512 | 64.29 | 0.0053 | 6462743488 | 12085 | 4 | | [10, 10, 10] | 1024 | 47.13 | 0.0078 | 6462743488 | 6043 | 4 | | [5, 10, 20] | 512 | 72.90 | 0.0060 | 6462743488 | 12085 | 4 | | [5, 10, 20] | 1024 | 56.70 | 0.0094 | 6462743488 | 6043 | 4 | | [25, 25] | 512 | 80.99 | 0.0034 | 12925486976 | 24169 | 8 | | [25, 25] | 1024 | 50.89 | 0.0042 | 12925486976 | 12085 | 8 | | [10, 10, 10] | 512 | 129.49 | 0.0054 | 12925486976 | 24169 | 8 | | [10, 10, 10] | 1024 | 93.66 | 0.0078 | 12925486976 | 12085 | 8 | | [5, 10, 20] | 512 | 143.45 | 0.0059 | 12925486976 | 24169 | 8 | | [5, 10, 20] | 1024 | 110.22 | 0.0091 | 12925486976 | 12085 | 8 | Authors: - Vibhu Jawa (https://github.com/VibhuJawa) - Alex Barghi (https://github.com/alexbarghi-nv) - Brad Rees (https://github.com/BradReesWork) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Tingyu Wang (https://github.com/tingyu66) URL: https://github.com/rapidsai/cugraph/pull/3660 --- .../dgl_benchmark.py | 291 ++++++++++++++++++ 1 file changed, 291 insertions(+) create mode 100644 benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py diff --git a/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py b/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py new file mode 100644 index 00000000000..0a52703c546 --- /dev/null +++ b/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py @@ -0,0 +1,291 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import dgl +import torch +import pandas as pd +import os +import time +import json +import random +import numpy as np +from argparse import ArgumentParser + + +def load_edges_from_disk(parquet_path, replication_factor, input_meta): + """ + Load the edges from disk into a graph data dictionary. + Args: + parquet_path: Path to the parquet directory. + replication_factor: Number of times to replicate the edges. + input_meta: Input meta data. + Returns: + dict: Dictionary of edge types to a tuple of (src, dst) + """ + graph_data = {} + for edge_type in input_meta["num_edges"].keys(): + print( + f"Loading edge index for edge type {edge_type}" + f"for replication factor = {replication_factor}" + ) + can_edge_type = tuple(edge_type.split("__")) + # TODO: Rename `edge_index` to a better name + ei = pd.read_parquet( + os.path.join(parquet_path, edge_type, "edge_index.parquet") + ) + ei = { + "src": torch.from_numpy(ei.src.values), + "dst": torch.from_numpy(ei.dst.values), + } + if replication_factor > 1: + src_ls = [ei["src"]] + dst_ls = [ei["dst"]] + for r in range(1, replication_factor): + new_src = ei["src"] + ( + r * input_meta["num_nodes"][can_edge_type[0]] + ) + src_ls.append(new_src) + new_dst = ei["dst"] + ( + r * input_meta["num_nodes"][can_edge_type[2]] + ) + dst_ls.append(new_dst) + + ei["src"] = torch.cat(src_ls).contiguous() + ei["dst"] = torch.cat(dst_ls).contiguous() + graph_data[can_edge_type] = ei["src"], ei["dst"] + print("Graph Data compiled") + return graph_data + + +def load_node_labels(dataset_path, replication_factor, input_meta): + num_nodes_dict = { + node_type: t * replication_factor + for node_type, t in input_meta["num_nodes"].items() + } + node_data = {} + for node_type in input_meta["num_nodes"].keys(): + node_data[node_type] = {} + label_path = os.path.join( + dataset_path, "parquet", node_type, "node_label.parquet" + ) + if os.path.exists(label_path): + node_label = pd.read_parquet(label_path) + if replication_factor > 1: + base_num_nodes = input_meta["num_nodes"][node_type] + dfr = pd.DataFrame( + { + "node": pd.concat( + [ + node_label.node + (r * base_num_nodes) + for r in range(1, replication_factor) + ] + ), + "label": pd.concat( + [ + node_label.label + for r in range(1, replication_factor) + ] + ), + } + ) + node_label = pd.concat([node_label, dfr]).reset_index( + drop=True + ) + + node_label_tensor = torch.full( + (num_nodes_dict[node_type],), -1, dtype=torch.float32 + ) + node_label_tensor[ + torch.as_tensor(node_label.node.values) + ] = torch.as_tensor(node_label.label.values) + + del node_label + node_data[node_type]["train_idx"] = ( + (node_label_tensor > -1).contiguous().nonzero().view(-1) + ) + node_data[node_type]["y"] = node_label_tensor.contiguous() + else: + node_data[node_type]["num_nodes"] = num_nodes_dict[node_type] + return node_data + + +def create_dgl_graph_from_disk(dataset_path, replication_factor=1): + """ + Create a DGL graph from a dataset on disk. + Args: + dataset_path: Path to the dataset on disk. + replication_factor: Number of times to replicate the edges. + Returns: + DGLGraph: DGLGraph with the loaded dataset. + """ + with open(os.path.join(dataset_path, "meta.json"), "r") as f: + input_meta = json.load(f) + + parquet_path = os.path.join(dataset_path, "parquet") + graph_data = load_edges_from_disk( + parquet_path, replication_factor, input_meta + ) + node_data = load_node_labels(dataset_path, replication_factor, input_meta) + g = dgl.heterograph(graph_data) + + return g, node_data + + +def create_dataloader(g, train_idx, batch_size, fanouts, use_uva): + """ + Create a DGL dataloader from a DGL graph. + Args: + g: DGLGraph to create the dataloader from. + train_idx: Tensor containing the training indices. + batch_size: Batch size to use for the dataloader. + fanouts: List of fanouts to use for the dataloader. + use_uva: Whether to use unified virtual address space. + Returns: + DGLGraph: DGLGraph with the loaded dataset. + """ + + print("Creating dataloader", flush=True) + st = time.time() + if use_uva: + train_idx = {k: v.to("cuda") for k, v in train_idx.items()} + sampler = dgl.dataloading.MultiLayerNeighborSampler(fanouts=fanouts) + dataloader = dgl.dataloading.DataLoader( + g, + train_idx, + sampler, + num_workers=0, + batch_size=batch_size, + use_uva=use_uva, + shuffle=False, + drop_last=False, + ) + et = time.time() + print(f"Time to create dataloader = {et - st:.2f} seconds") + return dataloader + + +def dataloading_benchmark(g, train_idx, fanouts, batch_sizes, use_uva): + """ + Run the dataloading benchmark. + Args: + g: DGLGraph + train_idx: Tensor containing the training indices. + fanouts: List of fanouts to use for the dataloader. + batch_sizes: List of batch sizes to use for the dataloader. + use_uva: Whether to use unified virtual address space. + """ + time_ls = [] + for fanout in fanouts: + for batch_size in batch_sizes: + dataloader = create_dataloader( + g, + train_idx, + batch_size=batch_size, + fanouts=fanout, + use_uva=use_uva, + ) + dataloading_st = time.time() + for input_nodes, output_nodes, blocks in dataloader: + pass + dataloading_et = time.time() + dataloading_time = dataloading_et - dataloading_st + time_d = { + "fanout": fanout, + "batch_size": batch_size, + "dataloading_time_per_epoch": dataloading_time, + "dataloading_time_per_batch": dataloading_time / len(dataloader), + "num_edges": g.num_edges(), + "num_batches": len(dataloader), + } + time_ls.append(time_d) + + print("Dataloading completed") + print(f"Fanout = {fanout}, batch_size = {batch_size}") + print( + f"Time taken {dataloading_time:.2f} ", + f"seconds for num batches {len(dataloader)}", + flush=True, + ) + print("==============================================") + return time_ls + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument( + "--dataset_path", type=str, default="/datasets/abarghi/ogbn_papers100M" + ) + parser.add_argument("--replication_factors", type=str, default="1,2,4,8") + parser.add_argument( + "--fanouts", type=str, default="25_25,10_10_10,5_10_20" + ) + parser.add_argument("--batch_sizes", type=str, default="512,1024") + parser.add_argument("--do_not_use_uva", action="store_true") + parser.add_argument("--seed", type=int, default=42) + args = parser.parse_args() + + if args.do_not_use_uva: + use_uva = False + else: + use_uva = True + set_seed(args.seed) + replication_factors = [int(x) for x in args.replication_factors.split(",")] + fanouts = [[int(y) for y in x.split("_")] for x in args.fanouts.split(",")] + batch_sizes = [int(x) for x in args.batch_sizes.split(",")] + + print("Running dgl dataloading benchmark with the following parameters:") + print(f"Dataset path = {args.dataset_path}") + print(f"Replication factors = {replication_factors}") + print(f"Fanouts = {fanouts}") + print(f"Batch sizes = {batch_sizes}") + print(f"Use UVA = {use_uva}") + print("==============================================") + + time_ls = [] + for replication_factor in replication_factors: + st = time.time() + g, node_data = create_dgl_graph_from_disk( + dataset_path=args.dataset_path, + replication_factor=replication_factor, + ) + et = time.time() + print(f"Replication factor = {replication_factor}") + print( + f"G has {g.num_edges()} edges and took", + f" {et - st:.2f} seconds to load" + ) + train_idx = {"paper": node_data["paper"]["train_idx"]} + r_time_ls = dataloading_benchmark( + g, train_idx, fanouts, batch_sizes, use_uva=use_uva + ) + print( + "Benchmark completed for replication factor = ", replication_factor + ) + print("==============================================") + # Add replication factor to the time list + [ + x.update({"replication_factor": replication_factor}) + for x in r_time_ls + ] + time_ls.extend(r_time_ls) + + df = pd.DataFrame(time_ls) + df.to_csv("dgl_dataloading_benchmark.csv", index=False) + print("Benchmark completed for all replication factors") + print("==============================================") From 8b02e241617df8bac33d0fd69b03046d2ddaf2d9 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Tue, 26 Sep 2023 15:43:02 +0200 Subject: [PATCH 015/111] Enable temporarily disabled MG tests (#3837) Enable TEMPORARILY disable single-GPU "MG" tests And Skip deleting copied Dataframe while creating distributed graph from cudf edge-lists. Ideally we would like to merger this PR once the [issue 3790](https://github.com/rapidsai/cugraph/issues/3790) is closed, but me might need to merger it if the issue is not resolved before the next release. Authors: - Naim (https://github.com/naimnv) Approvers: - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3837 --- ci/test_python.sh | 6 +----- ci/test_wheel.sh | 4 +--- .../graph_implementation/simpleDistributedGraph.py | 5 ++++- python/cugraph/cugraph/tests/traversal/test_bfs_mg.py | 5 ++++- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index 14886909fc9..7b0077991ae 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -63,10 +63,6 @@ pytest \ tests popd -# FIXME: TEMPORARILY disable single-GPU "MG" testing until -# https://github.com/rapidsai/cugraph/issues/3790 is closed -# When closed, replace -k "not _mg" with -# -k "not test_property_graph_mg" \ rapids-logger "pytest cugraph" pushd python/cugraph/cugraph export DASK_WORKER_DEVICES="0" @@ -79,7 +75,7 @@ pytest \ --cov=cugraph \ --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cugraph-coverage.xml" \ --cov-report=term \ - -k "not _mg" \ + -k "not test_property_graph_mg" \ tests popd diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index b62635d08b4..146186ae2e7 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -18,7 +18,5 @@ arch=$(uname -m) if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then python ./ci/wheel_smoke_test_${package_name}.py else - # FIXME: TEMPORARILY disable single-GPU "MG" testing until - # https://github.com/rapidsai/cugraph/issues/3790 is closed - RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest -k "not _mg" ./python/${package_name}/${package_name}/tests + RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${package_name}/tests fi diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index 0586d0d853c..01885c2d1c3 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -14,6 +14,7 @@ import gc from typing import Union import warnings +import random import cudf import cupy as cp @@ -182,7 +183,9 @@ def __from_edgelist( # Repartition to 2 partitions per GPU for memory efficient process input_ddf = input_ddf.repartition(npartitions=len(workers) * 2) # FIXME: Make a copy of the input ddf before implicitly altering it. - input_ddf = input_ddf.map_partitions(lambda df: df.copy()) + input_ddf = input_ddf.map_partitions( + lambda df: df.copy(), token="custom-" + str(random.random()) + ) # The dataframe will be symmetrized iff the graph is undirected # otherwise, the inital dataframe will be returned if edge_attr is not None: diff --git a/python/cugraph/cugraph/tests/traversal/test_bfs_mg.py b/python/cugraph/cugraph/tests/traversal/test_bfs_mg.py index 8ffbecea4fc..5eafc231141 100644 --- a/python/cugraph/cugraph/tests/traversal/test_bfs_mg.py +++ b/python/cugraph/cugraph/tests/traversal/test_bfs_mg.py @@ -12,6 +12,7 @@ # limitations under the License. import gc +import random import pytest @@ -61,7 +62,9 @@ def modify_dataset(df): return cudf.concat([df, temp_df]) meta = ddf._meta - ddf = ddf.map_partitions(modify_dataset, meta=meta) + ddf = ddf.map_partitions( + modify_dataset, meta=meta, token="custom-" + str(random.random()) + ) df = cudf.read_csv( input_data_path, From a9f4297223593f8df211599277519e206c597630 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Tue, 26 Sep 2023 08:57:51 -0500 Subject: [PATCH 016/111] Enable weights for MG similarity algorithms (#3879) This is a follow up PR to #3828 which enabled weighted for the python SG similarity algorithms. This PR also updates the tests, docstrings and remove experimental calls Authors: - Joseph Nke (https://github.com/jnke2016) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/3879 --- .../cugraph/dask/link_prediction/jaccard.py | 10 +--- .../cugraph/dask/link_prediction/overlap.py | 10 +--- .../cugraph/dask/link_prediction/sorensen.py | 10 +--- .../tests/link_prediction/test_jaccard_mg.py | 59 ++++++------------- .../tests/link_prediction/test_overlap_mg.py | 59 ++++++------------- .../tests/link_prediction/test_sorensen_mg.py | 59 ++++++------------- 6 files changed, 60 insertions(+), 147 deletions(-) diff --git a/python/cugraph/cugraph/dask/link_prediction/jaccard.py b/python/cugraph/cugraph/dask/link_prediction/jaccard.py index 218e6206fc3..5362c7a9e1e 100644 --- a/python/cugraph/cugraph/dask/link_prediction/jaccard.py +++ b/python/cugraph/cugraph/dask/link_prediction/jaccard.py @@ -118,7 +118,9 @@ def jaccard(input_graph, vertex_pair=None, use_weight=False): adjacent vertices in the graph. use_weight : bool, optional (default=False) - Currently not supported + Flag to indicate whether to compute weighted jaccard (if use_weight==True) + or un-weighted jaccard (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. Returns ------- @@ -144,12 +146,6 @@ def jaccard(input_graph, vertex_pair=None, use_weight=False): vertex_pair_col_name = vertex_pair.columns - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if input_graph.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - if isinstance(vertex_pair, (dask_cudf.DataFrame, cudf.DataFrame)): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) diff --git a/python/cugraph/cugraph/dask/link_prediction/overlap.py b/python/cugraph/cugraph/dask/link_prediction/overlap.py index 5540be28fd1..4bda05e3c95 100644 --- a/python/cugraph/cugraph/dask/link_prediction/overlap.py +++ b/python/cugraph/cugraph/dask/link_prediction/overlap.py @@ -96,7 +96,9 @@ def overlap(input_graph, vertex_pair=None, use_weight=False): adjacent vertices in the graph. use_weight : bool, optional (default=False) - Currently not supported + Flag to indicate whether to compute weighted overlap (if use_weight==True) + or un-weighted overlap (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. Returns ------- @@ -122,12 +124,6 @@ def overlap(input_graph, vertex_pair=None, use_weight=False): vertex_pair_col_name = vertex_pair.columns - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if input_graph.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - if isinstance(vertex_pair, (dask_cudf.DataFrame, cudf.DataFrame)): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) diff --git a/python/cugraph/cugraph/dask/link_prediction/sorensen.py b/python/cugraph/cugraph/dask/link_prediction/sorensen.py index 24295ac330c..163b0d0dc16 100644 --- a/python/cugraph/cugraph/dask/link_prediction/sorensen.py +++ b/python/cugraph/cugraph/dask/link_prediction/sorensen.py @@ -92,7 +92,9 @@ def sorensen(input_graph, vertex_pair=None, use_weight=False): adjacent vertices in the graph. use_weight : bool, optional (default=False) - Currently not supported + Flag to indicate whether to compute weighted sorensen (if use_weight==True) + or un-weighted sorensen (if use_weight==False). + 'input_graph' must be weighted if 'use_weight=True'. Returns ------- @@ -118,12 +120,6 @@ def sorensen(input_graph, vertex_pair=None, use_weight=False): vertex_pair_col_name = vertex_pair.columns - if use_weight: - raise ValueError("'use_weight' is currently not supported.") - - if input_graph.is_weighted(): - raise ValueError("Weighted graphs are currently not supported.") - if isinstance(vertex_pair, (dask_cudf.DataFrame, cudf.DataFrame)): vertex_pair = renumber_vertex_pair(input_graph, vertex_pair) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_jaccard_mg.py b/python/cugraph/cugraph/tests/link_prediction/test_jaccard_mg.py index b56a6baae2b..ee739c9f236 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_jaccard_mg.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_jaccard_mg.py @@ -34,6 +34,7 @@ def setup_function(): IS_DIRECTED = [False] HAS_VERTEX_PAIR = [True, False] +IS_WEIGHTED = [True, False] # ============================================================================= @@ -48,6 +49,7 @@ def setup_function(): (datasets, "graph_file"), (IS_DIRECTED, "directed"), (HAS_VERTEX_PAIR, "has_vertex_pair"), + (IS_WEIGHTED, "is_weighted"), ) @@ -57,7 +59,9 @@ def input_combo(request): Simply return the current combination of params as a dictionary for use in tests or other parameterized fixtures. """ - parameters = dict(zip(("graph_file", "directed", "has_vertex_pair"), request.param)) + parameters = dict( + zip(("graph_file", "directed", "has_vertex_pair", "is_weighted"), request.param) + ) return parameters @@ -72,7 +76,10 @@ def input_expected_output(input_combo): input_data_path = input_combo["graph_file"] directed = input_combo["directed"] has_vertex_pair = input_combo["has_vertex_pair"] - G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed) + is_weighted = input_combo["is_weighted"] + G = utils.generate_cugraph_graph_from_file( + input_data_path, directed=directed, edgevals=is_weighted + ) if has_vertex_pair: # Sample random vertices from the graph and compute the two_hop_neighbors # with those seeds @@ -84,7 +91,9 @@ def input_expected_output(input_combo): vertex_pair = None input_combo["vertex_pair"] = vertex_pair - sg_cugraph_jaccard = cugraph.experimental.jaccard(G, input_combo["vertex_pair"]) + sg_cugraph_jaccard = cugraph.jaccard( + G, input_combo["vertex_pair"], use_weight=is_weighted + ) # Save the results back to the input_combo dictionary to prevent redundant # cuGraph runs. Other tests using the input_combo fixture will look for # them, and if not present they will have to re-run the same cuGraph call. @@ -104,6 +113,7 @@ def input_expected_output(input_combo): ddf, source="src", destination="dst", + edge_attr="value" if is_weighted else None, renumber=True, store_transposed=True, ) @@ -122,8 +132,11 @@ def input_expected_output(input_combo): def test_dask_mg_jaccard(dask_client, benchmark, input_expected_output): dg = input_expected_output["MGGraph"] + use_weight = input_expected_output["is_weighted"] - result_jaccard = benchmark(dcg.jaccard, dg, input_expected_output["vertex_pair"]) + result_jaccard = benchmark( + dcg.jaccard, dg, input_expected_output["vertex_pair"], use_weight=use_weight + ) result_jaccard = ( result_jaccard.compute() @@ -151,41 +164,3 @@ def test_dask_mg_jaccard(dask_client, benchmark, input_expected_output): assert len(jaccard_coeff_diffs1) == 0 assert len(jaccard_coeff_diffs2) == 0 - - -@pytest.mark.mg -def test_dask_mg_weighted_jaccard(dask_client): - input_data_path = datasets[0] - chunksize = dcg.get_chunksize(input_data_path) - ddf = dask_cudf.read_csv( - input_data_path, - chunksize=chunksize, - delimiter=" ", - names=["src", "dst", "value"], - dtype=["int32", "int32", "float32"], - ) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - renumber=True, - store_transposed=True, - ) - with pytest.raises(ValueError): - dcg.jaccard(dg) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - store_transposed=True, - ) - - use_weight = True - with pytest.raises(ValueError): - dcg.jaccard(dg, use_weight=use_weight) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_overlap_mg.py b/python/cugraph/cugraph/tests/link_prediction/test_overlap_mg.py index ce4bf619f47..87407d7b59c 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_overlap_mg.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_overlap_mg.py @@ -34,6 +34,7 @@ def setup_function(): IS_DIRECTED = [False] HAS_VERTEX_PAIR = [True, False] +IS_WEIGHTED = [True, False] # ============================================================================= @@ -48,6 +49,7 @@ def setup_function(): (datasets, "graph_file"), (IS_DIRECTED, "directed"), (HAS_VERTEX_PAIR, "has_vertex_pair"), + (IS_WEIGHTED, "is_weighted"), ) @@ -57,7 +59,9 @@ def input_combo(request): Simply return the current combination of params as a dictionary for use in tests or other parameterized fixtures. """ - parameters = dict(zip(("graph_file", "directed", "has_vertex_pair"), request.param)) + parameters = dict( + zip(("graph_file", "directed", "has_vertex_pair", "is_weighted"), request.param) + ) return parameters @@ -72,7 +76,10 @@ def input_expected_output(input_combo): input_data_path = input_combo["graph_file"] directed = input_combo["directed"] has_vertex_pair = input_combo["has_vertex_pair"] - G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed) + is_weighted = input_combo["is_weighted"] + G = utils.generate_cugraph_graph_from_file( + input_data_path, directed=directed, edgevals=is_weighted + ) if has_vertex_pair: # Sample random vertices from the graph and compute the two_hop_neighbors # with those seeds @@ -84,7 +91,9 @@ def input_expected_output(input_combo): vertex_pair = None input_combo["vertex_pair"] = vertex_pair - sg_cugraph_overlap = cugraph.experimental.overlap(G, input_combo["vertex_pair"]) + sg_cugraph_overlap = cugraph.overlap( + G, input_combo["vertex_pair"], use_weight=is_weighted + ) # Save the results back to the input_combo dictionary to prevent redundant # cuGraph runs. Other tests using the input_combo fixture will look for # them, and if not present they will have to re-run the same cuGraph call. @@ -104,6 +113,7 @@ def input_expected_output(input_combo): ddf, source="src", destination="dst", + edge_attr="value" if is_weighted else None, renumber=True, store_transposed=True, ) @@ -125,8 +135,11 @@ def input_expected_output(input_combo): def test_dask_mg_overlap(dask_client, benchmark, input_expected_output): dg = input_expected_output["MGGraph"] + use_weight = input_expected_output["is_weighted"] - result_overlap = benchmark(dcg.overlap, dg, input_expected_output["vertex_pair"]) + result_overlap = benchmark( + dcg.overlap, dg, input_expected_output["vertex_pair"], use_weight=use_weight + ) result_overlap = ( result_overlap.compute() @@ -154,41 +167,3 @@ def test_dask_mg_overlap(dask_client, benchmark, input_expected_output): assert len(overlap_coeff_diffs1) == 0 assert len(overlap_coeff_diffs2) == 0 - - -@pytest.mark.mg -def test_dask_mg_weighted_overlap(): - input_data_path = datasets[0] - chunksize = dcg.get_chunksize(input_data_path) - ddf = dask_cudf.read_csv( - input_data_path, - chunksize=chunksize, - delimiter=" ", - names=["src", "dst", "value"], - dtype=["int32", "int32", "float32"], - ) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - renumber=True, - store_transposed=True, - ) - with pytest.raises(ValueError): - dcg.overlap(dg) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - store_transposed=True, - ) - - use_weight = True - with pytest.raises(ValueError): - dcg.overlap(dg, use_weight=use_weight) diff --git a/python/cugraph/cugraph/tests/link_prediction/test_sorensen_mg.py b/python/cugraph/cugraph/tests/link_prediction/test_sorensen_mg.py index af6b60771a0..66832d08427 100644 --- a/python/cugraph/cugraph/tests/link_prediction/test_sorensen_mg.py +++ b/python/cugraph/cugraph/tests/link_prediction/test_sorensen_mg.py @@ -35,6 +35,7 @@ def setup_function(): IS_DIRECTED = [False] HAS_VERTEX_PAIR = [True, False] +IS_WEIGHTED = [True, False] # ============================================================================= @@ -49,6 +50,7 @@ def setup_function(): (datasets, "graph_file"), (IS_DIRECTED, "directed"), (HAS_VERTEX_PAIR, "has_vertex_pair"), + (IS_WEIGHTED, "is_weighted"), ) @@ -58,7 +60,9 @@ def input_combo(request): Simply return the current combination of params as a dictionary for use in tests or other parameterized fixtures. """ - parameters = dict(zip(("graph_file", "directed", "has_vertex_pair"), request.param)) + parameters = dict( + zip(("graph_file", "directed", "has_vertex_pair", "is_weighted"), request.param) + ) return parameters @@ -73,7 +77,10 @@ def input_expected_output(input_combo): input_data_path = input_combo["graph_file"] directed = input_combo["directed"] has_vertex_pair = input_combo["has_vertex_pair"] - G = utils.generate_cugraph_graph_from_file(input_data_path, directed=directed) + is_weighted = input_combo["is_weighted"] + G = utils.generate_cugraph_graph_from_file( + input_data_path, directed=directed, edgevals=is_weighted + ) if has_vertex_pair: # Sample random vertices from the graph and compute the two_hop_neighbors # with those seeds @@ -85,7 +92,9 @@ def input_expected_output(input_combo): vertex_pair = None input_combo["vertex_pair"] = vertex_pair - sg_cugraph_sorensen = cugraph.experimental.sorensen(G, input_combo["vertex_pair"]) + sg_cugraph_sorensen = cugraph.sorensen( + G, input_combo["vertex_pair"], use_weight=is_weighted + ) # Save the results back to the input_combo dictionary to prevent redundant # cuGraph runs. Other tests using the input_combo fixture will look for # them, and if not present they will have to re-run the same cuGraph call. @@ -105,6 +114,7 @@ def input_expected_output(input_combo): ddf, source="src", destination="dst", + edge_attr="value" if is_weighted else None, renumber=True, store_transposed=True, ) @@ -124,8 +134,11 @@ def input_expected_output(input_combo): def test_dask_mg_sorensen(dask_client, benchmark, input_expected_output): dg = input_expected_output["MGGraph"] + use_weight = input_expected_output["is_weighted"] - result_sorensen = benchmark(dcg.sorensen, dg, input_expected_output["vertex_pair"]) + result_sorensen = benchmark( + dcg.sorensen, dg, input_expected_output["vertex_pair"], use_weight=use_weight + ) result_sorensen = ( result_sorensen.compute() @@ -153,41 +166,3 @@ def test_dask_mg_sorensen(dask_client, benchmark, input_expected_output): assert len(sorensen_coeff_diffs1) == 0 assert len(sorensen_coeff_diffs2) == 0 - - -@pytest.mark.mg -def test_dask_mg_weighted_sorensen(dask_client): - input_data_path = datasets[0] - chunksize = dcg.get_chunksize(input_data_path) - ddf = dask_cudf.read_csv( - input_data_path, - chunksize=chunksize, - delimiter=" ", - names=["src", "dst", "value"], - dtype=["int32", "int32", "float32"], - ) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - renumber=True, - store_transposed=True, - ) - with pytest.raises(ValueError): - dcg.sorensen(dg) - - dg = cugraph.Graph(directed=False) - dg.from_dask_cudf_edgelist( - ddf, - source="src", - destination="dst", - edge_attr="value", - store_transposed=True, - ) - - use_weight = True - with pytest.raises(ValueError): - dcg.sorensen(dg, use_weight=use_weight) From 5c34d3dd340c76678d8f2667057c6b0ce2f1f480 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Tue, 26 Sep 2023 11:57:30 -0400 Subject: [PATCH 017/111] Update Allocator Selection in cuGraph-DGL Example (#3877) Closes #3847 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Vibhu Jawa (https://github.com/VibhuJawa) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3877 --- .../cugraph-dgl/examples/graphsage/node-classification.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/python/cugraph-dgl/examples/graphsage/node-classification.py b/python/cugraph-dgl/examples/graphsage/node-classification.py index 24df73ada75..320890b0312 100644 --- a/python/cugraph-dgl/examples/graphsage/node-classification.py +++ b/python/cugraph-dgl/examples/graphsage/node-classification.py @@ -39,14 +39,16 @@ def set_allocators(): + import rmm import cudf import cupy - import rmm + from rmm.allocators.torch import rmm_torch_allocator + from rmm.allocators.cupy import rmm_cupy_allocator mr = rmm.mr.CudaAsyncMemoryResource() rmm.mr.set_current_device_resource(mr) - torch.cuda.memory.change_current_allocator(rmm.rmm_torch_allocator) - cupy.cuda.set_allocator(rmm.allocators.cupy.rmm_cupy_allocator) + torch.cuda.memory.change_current_allocator(rmm_torch_allocator) + cupy.cuda.set_allocator(rmm_cupy_allocator) cudf.set_option("spill", True) From 5d2f5486899bdd2d71c00b12fb26afbdd60100d1 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Tue, 26 Sep 2023 20:59:55 -0500 Subject: [PATCH 018/111] Updates to build and test `nx-cugraph` wheel as part of CI and nightly workflows (#3852) closes rapidsai/graph_dl#302 * Updates GHA yaml files to build and test a `nx-cugraph` wheel * Adds CI scripts for building and testing the `nx-cugraph` wheel * Adds a smoketest script for `nx-cugraph` * Relevant code cleanup: removes unnecessary dataset download from cugraph wheel testing Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Ray Douglass (https://github.com/raydouglass) - Vyas Ramasubramani (https://github.com/vyasr) - Bradley Dice (https://github.com/bdice) URL: https://github.com/rapidsai/cugraph/pull/3852 --- .github/workflows/build.yaml | 20 ++++++++++++++++ .github/workflows/pr.yaml | 16 +++++++++++++ .github/workflows/test.yaml | 9 ++++++++ ci/build_wheel.sh | 12 ++++++---- ci/build_wheel_nx-cugraph.sh | 6 +++++ ci/test_wheel.sh | 9 +++++--- ci/test_wheel_cugraph.sh | 8 ------- ci/test_wheel_nx-cugraph.sh | 6 +++++ ci/wheel_smoke_test_nx-cugraph.py | 38 +++++++++++++++++++++++++++++++ 9 files changed, 109 insertions(+), 15 deletions(-) create mode 100755 ci/build_wheel_nx-cugraph.sh create mode 100755 ci/test_wheel_nx-cugraph.sh create mode 100644 ci/wheel_smoke_test_nx-cugraph.py diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index 02b357c7c88..c01a6fcb94a 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -112,3 +112,23 @@ jobs: sha: ${{ inputs.sha }} date: ${{ inputs.date }} package-name: cugraph + wheel-build-nx-cugraph: + needs: wheel-publish-pylibcugraph + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + with: + build_type: ${{ inputs.build_type || 'branch' }} + branch: ${{ inputs.branch }} + sha: ${{ inputs.sha }} + date: ${{ inputs.date }} + script: ci/build_wheel_nx-cugraph.sh + wheel-publish-nx-cugraph: + needs: wheel-build-nx-cugraph + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.10 + with: + build_type: ${{ inputs.build_type || 'branch' }} + branch: ${{ inputs.branch }} + sha: ${{ inputs.sha }} + date: ${{ inputs.date }} + package-name: nx-cugraph diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index d2d24d90fbe..d49ae7f8d3d 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -23,6 +23,8 @@ jobs: - wheel-tests-pylibcugraph - wheel-build-cugraph - wheel-tests-cugraph + - wheel-build-nx-cugraph + - wheel-tests-nx-cugraph secrets: inherit uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.10 checks: @@ -109,3 +111,17 @@ jobs: with: build_type: pull-request script: ci/test_wheel_cugraph.sh + wheel-build-nx-cugraph: + needs: wheel-tests-pylibcugraph + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + with: + build_type: pull-request + script: ci/build_wheel_nx-cugraph.sh + wheel-tests-nx-cugraph: + needs: wheel-build-nx-cugraph + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + with: + build_type: pull-request + script: ci/test_wheel_nx-cugraph.sh diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 97abca71260..dc9ed60b29e 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -48,3 +48,12 @@ jobs: date: ${{ inputs.date }} sha: ${{ inputs.sha }} script: ci/test_wheel_cugraph.sh + wheel-tests-nx-cugraph: + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + with: + build_type: nightly + branch: ${{ inputs.branch }} + date: ${{ inputs.date }} + sha: ${{ inputs.sha }} + script: ci/test_wheel_nx-cugraph.sh diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh index 3798d561126..821aa25c1b9 100755 --- a/ci/build_wheel.sh +++ b/ci/build_wheel.sh @@ -49,7 +49,11 @@ cd "${package_dir}" python -m pip wheel . -w dist -vvv --no-deps --disable-pip-version-check -mkdir -p final_dist -python -m auditwheel repair -w final_dist dist/* - -RAPIDS_PY_WHEEL_NAME="${package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 final_dist +# pure-python packages should not have auditwheel run on them. +if [[ ${package_name} == "nx-cugraph" ]]; then + RAPIDS_PY_WHEEL_NAME="${package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 dist +else + mkdir -p final_dist + python -m auditwheel repair -w final_dist dist/* + RAPIDS_PY_WHEEL_NAME="${package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-upload-wheels-to-s3 final_dist +fi diff --git a/ci/build_wheel_nx-cugraph.sh b/ci/build_wheel_nx-cugraph.sh new file mode 100755 index 00000000000..4481de1283d --- /dev/null +++ b/ci/build_wheel_nx-cugraph.sh @@ -0,0 +1,6 @@ +#!/bin/bash +# Copyright (c) 2023, NVIDIA CORPORATION. + +set -euo pipefail + +./ci/build_wheel.sh nx-cugraph python/nx-cugraph diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index 146186ae2e7..3ac3549f143 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -6,17 +6,20 @@ set -eoxu pipefail package_name=$1 package_dir=$2 +python_package_name=$(echo ${package_name}|sed 's/-/_/g') + mkdir -p ./dist RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})" -# echo to expand wildcard before adding `[extra]` requires for pip +# use 'ls' to expand wildcard before adding `[extra]` requires for pip RAPIDS_PY_WHEEL_NAME="${package_name}_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./dist -python -m pip install $(echo ./dist/${package_name}*.whl)[test] +# pip creates wheels using python package names +python -m pip install $(ls ./dist/${python_package_name}*.whl)[test] # Run smoke tests for aarch64 pull requests arch=$(uname -m) if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then python ./ci/wheel_smoke_test_${package_name}.py else - RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${package_name}/tests + RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${python_package_name}/tests fi diff --git a/ci/test_wheel_cugraph.sh b/ci/test_wheel_cugraph.sh index 4d511ac2a0f..f9e2aa6d8da 100755 --- a/ci/test_wheel_cugraph.sh +++ b/ci/test_wheel_cugraph.sh @@ -11,12 +11,4 @@ python -m pip install --no-deps ./local-pylibcugraph-dep/pylibcugraph*.whl # Always install latest dask for testing python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main -# Only download test data for x86 -arch=$(uname -m) -if [[ "${arch}" == "x86_64" ]]; then - pushd ./datasets - bash ./get_test_data.sh - popd -fi - ./ci/test_wheel.sh cugraph python/cugraph diff --git a/ci/test_wheel_nx-cugraph.sh b/ci/test_wheel_nx-cugraph.sh new file mode 100755 index 00000000000..53d40960fc3 --- /dev/null +++ b/ci/test_wheel_nx-cugraph.sh @@ -0,0 +1,6 @@ +#!/bin/bash +# Copyright (c) 2023, NVIDIA CORPORATION. + +set -eoxu pipefail + +./ci/test_wheel.sh nx-cugraph python/nx-cugraph diff --git a/ci/wheel_smoke_test_nx-cugraph.py b/ci/wheel_smoke_test_nx-cugraph.py new file mode 100644 index 00000000000..10d26e3aac7 --- /dev/null +++ b/ci/wheel_smoke_test_nx-cugraph.py @@ -0,0 +1,38 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import networkx as nx +import nx_cugraph as nxcg + + +if __name__ == "__main__": + G = nx.Graph() + G.add_edges_from([(0, 1), (1, 2), (2, 3)]) + + nx_result = nx.betweenness_centrality(G) + # nx_cugraph is intended to be called via the NetworkX dispatcher, like + # this: + # nxcu_result = nx.betweenness_centrality(G, backend="cugraph") + # + # but here it is being called directly since the NetworkX version that + # supports the "backend" kwarg may not be available in the testing env. + nxcu_result = nxcg.betweenness_centrality(G) + + nx_nodes, nxcu_nodes = nx_result.keys(), nxcu_result.keys() + assert nxcu_nodes == nx_nodes + for node_id in nx_nodes: + nx_bc, nxcu_bc = nx_result[node_id], nxcu_result[node_id] + assert math.isclose(nx_bc, nxcu_bc, rel_tol=1e-6), \ + f"bc for {node_id=} exceeds tolerance: {nx_bc=}, {nxcu_bc=}" From 8b87915c8c5e068caef04be4eeeb9cf7ae27b488 Mon Sep 17 00:00:00 2001 From: Paul Taylor <178183+trxcllnt@users.noreply.github.com> Date: Wed, 27 Sep 2023 12:08:12 -0700 Subject: [PATCH 019/111] Add cuGraph devcontainers (#3838) This PR adds some [devcontainers](https://containers.dev/) to help simplify building the cuGraph C++ and Python libraries. It also adds an optional job to the `pr.yaml` to [build the cuGraph libs in each devcontainer](https://github.com/trxcllnt/cugraph/blob/fea/devcontainers/.github/workflows/pr.yaml#L113-L119), so the build caches are populated for devs by CI. A devcontainer can be launched by clicking the "Reopen in Container" button that VSCode shows when opening the repo (or by using the "Rebuild and Reopen in Container" command from the command palette): ![image](https://user-images.githubusercontent.com/178183/221771999-97ab29d5-e718-4e5f-b32f-2cdd51bba25c.png) Clicking this button will cause VSCode to prompt the user to select one of these devcontainer variants: ![image](https://github.com/rapidsai/rmm/assets/178183/68d4b264-4fc2-4008-92b6-cb4bdd19b29f) On startup, the devcontainer creates or updates the conda/pip environment using `cugraph/dependencies.yaml`. The envs/package caches are cached on the host via volume mounts, which are described in more detail in [`.devcontainer/README.md`](https://github.com/trxcllnt/cugraph/blob/fea/devcontainers/.devcontainer/README.md). The container includes convenience functions to clean, configure, and build the various cuGraph components: ```shell $ clean-cugraph-cpp # only cleans the C++ build dir $ clean-cugraph-python # only cleans the Python build dir $ clean-cugraph # cleans both C++ and Python build dirs $ configure-cugraph-cpp # only configures cugraph C++ lib $ build-cugraph-cpp # only builds cugraph C++ lib $ build-cugraph-python # only builds cugraph Python lib $ build-cugraph # builds both C++ and Python libs ``` * The C++ build script is a small wrapper around `cmake -S ~/cugraph/cpp -B ~/cugraph/cpp/build` and `cmake --build ~/cugraph/cpp/build` * The Python build script is a small wrapper around `pip install --editable ~/cugraph/cpp` Unlike `build.sh`, these convenience scripts *don't* install the libraries after building them. Instead, they automatically inject the correct arguments to build the C++ libraries from source and use their build dirs as package roots: ```shell $ cmake -S ~/cugraph/cpp -B ~/cugraph/cpp/build $ CMAKE_ARGS="-Dcugraph_ROOT=~/cugraph/cpp/build" \ # <-- this argument is automatic pip install -e ~/cugraph/cpp ``` Authors: - Paul Taylor (https://github.com/trxcllnt) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/3838 --- .devcontainer/Dockerfile | 33 +++ .devcontainer/README.md | 34 +++ .../cuda11.8-conda/devcontainer.json | 37 +++ .devcontainer/cuda11.8-pip/devcontainer.json | 37 +++ .../cuda12.0-conda/devcontainer.json | 37 +++ .devcontainer/cuda12.0-pip/devcontainer.json | 37 +++ .github/workflows/pr.yaml | 10 + .gitignore | 4 + ci/release/update-version.sh | 7 + .../all_cuda-118_arch-x86_64.yaml | 2 + .../all_cuda-120_arch-x86_64.yaml | 2 + cpp/.clangd | 65 +++++ dependencies.yaml | 263 +++++++++++++++--- 13 files changed, 527 insertions(+), 41 deletions(-) create mode 100644 .devcontainer/Dockerfile create mode 100644 .devcontainer/README.md create mode 100644 .devcontainer/cuda11.8-conda/devcontainer.json create mode 100644 .devcontainer/cuda11.8-pip/devcontainer.json create mode 100644 .devcontainer/cuda12.0-conda/devcontainer.json create mode 100644 .devcontainer/cuda12.0-pip/devcontainer.json create mode 100644 cpp/.clangd diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile new file mode 100644 index 00000000000..3d0ac075be3 --- /dev/null +++ b/.devcontainer/Dockerfile @@ -0,0 +1,33 @@ +# syntax=docker/dockerfile:1.5 + +ARG BASE +ARG PYTHON_PACKAGE_MANAGER=conda + +FROM ${BASE} as pip-base + +ENV DEFAULT_VIRTUAL_ENV=rapids + +FROM ${BASE} as conda-base + +ENV DEFAULT_CONDA_ENV=rapids + +FROM ${PYTHON_PACKAGE_MANAGER}-base + +ARG CUDA +ENV CUDAARCHS="RAPIDS" +ENV CUDA_VERSION="${CUDA_VERSION:-${CUDA}}" + +ARG PYTHON_PACKAGE_MANAGER +ENV PYTHON_PACKAGE_MANAGER="${PYTHON_PACKAGE_MANAGER}" + +ENV PYTHONSAFEPATH="1" +ENV PYTHONUNBUFFERED="1" +ENV PYTHONDONTWRITEBYTECODE="1" + +ENV SCCACHE_REGION="us-east-2" +ENV SCCACHE_BUCKET="rapids-sccache-devs" +ENV VAULT_HOST="https://vault.ops.k8s.rapids.ai" +ENV HISTFILE="/home/coder/.cache/._bash_history" + +# cugraph_pyg's setup.py needs this defined when building in a conda env +ENV CUDA_HOME="${CUDA_HOME:-/home/coder/.conda/envs/$DEFAULT_CONDA_ENV}" diff --git a/.devcontainer/README.md b/.devcontainer/README.md new file mode 100644 index 00000000000..e645c51de8b --- /dev/null +++ b/.devcontainer/README.md @@ -0,0 +1,34 @@ +# cuGraph Development Containers + +This directory contains [devcontainer configurations](https://containers.dev/implementors/json_reference/) for using VSCode to [develop in a container](https://code.visualstudio.com/docs/devcontainers/containers) via the `Remote Containers` [extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) or [GitHub Codespaces](https://github.com/codespaces). + +This container is a turnkey development environment for building and testing the cuGraph C++ and Python libraries. + +## Table of Contents + +* [Prerequisites](#prerequisites) +* [Host bind mounts](#host-bind-mounts) +* [Launch a Dev Container](#launch-a-dev-container) +## Prerequisites + +* [VSCode](https://code.visualstudio.com/download) +* [VSCode Remote Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) + +## Host bind mounts + +By default, the following directories are bind-mounted into the devcontainer: + +* `${repo}:/home/coder/cugraph` +* `${repo}/../.aws:/home/coder/.aws` +* `${repo}/../.local:/home/coder/.local` +* `${repo}/../.cache:/home/coder/.cache` +* `${repo}/../.conda:/home/coder/.conda` +* `${repo}/../.config:/home/coder/.config` + +This ensures caches, configurations, dependencies, and your commits are persisted on the host across container runs. + +## Launch a Dev Container + +To launch a devcontainer from VSCode, open the cuGraph repo and select the "Reopen in Container" button in the bottom right:
+ +Alternatively, open the VSCode command palette (typically `cmd/ctrl + shift + P`) and run the "Rebuild and Reopen in Container" command. diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json new file mode 100644 index 00000000000..cf4ba5aa114 --- /dev/null +++ b/.devcontainer/cuda11.8-conda/devcontainer.json @@ -0,0 +1,37 @@ +{ + "build": { + "context": "${localWorkspaceFolder}/.devcontainer", + "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile", + "args": { + "CUDA": "11.8", + "PYTHON_PACKAGE_MANAGER": "conda", + "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04" + } + }, + "hostRequirements": {"gpu": "optional"}, + "features": { + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + }, + "overrideFeatureInstallOrder": [ + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" + ], + "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda11.8-envs}"], + "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"], + "workspaceFolder": "/home/coder", + "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cugraph,type=bind,consistency=consistent", + "mounts": [ + "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda11.8-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent" + ], + "customizations": { + "vscode": { + "extensions": [ + "ms-python.flake8", + "nvidia.nsight-vscode-edition" + ] + } + } +} diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json new file mode 100644 index 00000000000..e86a38abbde --- /dev/null +++ b/.devcontainer/cuda11.8-pip/devcontainer.json @@ -0,0 +1,37 @@ +{ + "build": { + "context": "${localWorkspaceFolder}/.devcontainer", + "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile", + "args": { + "CUDA": "11.8", + "PYTHON_PACKAGE_MANAGER": "pip", + "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda11.8-ubuntu22.04" + } + }, + "hostRequirements": {"gpu": "optional"}, + "features": { + "ghcr.io/rapidsai/devcontainers/features/ucx:23.10": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + }, + "overrideFeatureInstallOrder": [ + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" + ], + "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs}"], + "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"], + "workspaceFolder": "/home/coder", + "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cugraph,type=bind,consistency=consistent", + "mounts": [ + "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda11.8-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent" + ], + "customizations": { + "vscode": { + "extensions": [ + "ms-python.flake8", + "nvidia.nsight-vscode-edition" + ] + } + } +} diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.0-conda/devcontainer.json new file mode 100644 index 00000000000..863eeea48ff --- /dev/null +++ b/.devcontainer/cuda12.0-conda/devcontainer.json @@ -0,0 +1,37 @@ +{ + "build": { + "context": "${localWorkspaceFolder}/.devcontainer", + "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile", + "args": { + "CUDA": "12.0", + "PYTHON_PACKAGE_MANAGER": "conda", + "BASE": "rapidsai/devcontainers:23.10-cpp-mambaforge-ubuntu22.04" + } + }, + "hostRequirements": {"gpu": "optional"}, + "features": { + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + }, + "overrideFeatureInstallOrder": [ + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" + ], + "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config,conda/pkgs,conda/${localWorkspaceFolderBasename}-cuda12.0-envs}"], + "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"], + "workspaceFolder": "/home/coder", + "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cugraph,type=bind,consistency=consistent", + "mounts": [ + "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.conda/pkgs,target=/home/coder/.conda/pkgs,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.conda/${localWorkspaceFolderBasename}-cuda12.0-envs,target=/home/coder/.conda/envs,type=bind,consistency=consistent" + ], + "customizations": { + "vscode": { + "extensions": [ + "ms-python.flake8", + "nvidia.nsight-vscode-edition" + ] + } + } +} diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json new file mode 100644 index 00000000000..c7612771fd3 --- /dev/null +++ b/.devcontainer/cuda12.0-pip/devcontainer.json @@ -0,0 +1,37 @@ +{ + "build": { + "context": "${localWorkspaceFolder}/.devcontainer", + "dockerfile": "${localWorkspaceFolder}/.devcontainer/Dockerfile", + "args": { + "CUDA": "12.0", + "PYTHON_PACKAGE_MANAGER": "pip", + "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda12.0-ubuntu22.04" + } + }, + "hostRequirements": {"gpu": "optional"}, + "features": { + "ghcr.io/rapidsai/devcontainers/features/ucx:23.10": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + }, + "overrideFeatureInstallOrder": [ + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" + ], + "initializeCommand": ["/bin/bash", "-c", "mkdir -m 0755 -p ${localWorkspaceFolder}/../.{aws,cache,config/pip,local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs}"], + "postAttachCommand": ["/bin/bash", "-c", "if [ ${CODESPACES:-false} = 'true' ]; then . devcontainer-utils-post-attach-command; . rapids-post-attach-command; fi"], + "workspaceFolder": "/home/coder", + "workspaceMount": "source=${localWorkspaceFolder},target=/home/coder/cugraph,type=bind,consistency=consistent", + "mounts": [ + "source=${localWorkspaceFolder}/../.aws,target=/home/coder/.aws,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.cache,target=/home/coder/.cache,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.config,target=/home/coder/.config,type=bind,consistency=consistent", + "source=${localWorkspaceFolder}/../.local/share/${localWorkspaceFolderBasename}-cuda12.0-venvs,target=/home/coder/.local/share/venvs,type=bind,consistency=consistent" + ], + "customizations": { + "vscode": { + "extensions": [ + "ms-python.flake8", + "nvidia.nsight-vscode-edition" + ] + } + } +} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index d49ae7f8d3d..7b267d7edf3 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -25,6 +25,7 @@ jobs: - wheel-tests-cugraph - wheel-build-nx-cugraph - wheel-tests-nx-cugraph + - devcontainer secrets: inherit uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.10 checks: @@ -125,3 +126,12 @@ jobs: with: build_type: pull-request script: ci/test_wheel_nx-cugraph.sh + devcontainer: + secrets: inherit + uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.10 + with: + extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY + build_command: | + sccache -z; + build-all --verbose; + sccache -s; diff --git a/.gitignore b/.gitignore index 3fda9f8a037..c6bcf6965d7 100644 --- a/.gitignore +++ b/.gitignore @@ -97,3 +97,7 @@ python/cugraph/cugraph/tests/dask-worker-space docs/cugraph/source/api_docs/api/* _html _text + +# clang tooling +compile_commands.json +.clangd/ diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index bd3aa6bc370..f3892fbd3c4 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -128,3 +128,10 @@ done sed_runner "s/RAPIDS_VERSION_NUMBER=\".*/RAPIDS_VERSION_NUMBER=\"${NEXT_SHORT_TAG}\"/g" ci/build_docs.sh sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" python/nx-cugraph/README.md + +# .devcontainer files +find .devcontainer/ -type f -name devcontainer.json -print0 | while IFS= read -r -d '' filename; do + sed_runner "s@rapidsai/devcontainers:[0-9.]*@rapidsai/devcontainers:${NEXT_SHORT_TAG}@g" "${filename}" + sed_runner "s@rapidsai/devcontainers/features/ucx:[0-9.]*@rapidsai/devcontainers/features/ucx:${NEXT_SHORT_TAG_PEP440}@" "${filename}" + sed_runner "s@rapidsai/devcontainers/features/rapids-build-utils:[0-9.]*@rapidsai/devcontainers/features/rapids-build-utils:${NEXT_SHORT_TAG_PEP440}@" "${filename}" +done diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index c66890f8ae5..86de24c991d 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -66,10 +66,12 @@ dependencies: - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy +- setuptools>=61.0.0 - sphinx-copybutton - sphinx-markdown-tables - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.34.* +- wheel name: all_cuda-118_arch-x86_64 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 3afb1415572..1054f75ba54 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -65,10 +65,12 @@ dependencies: - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy +- setuptools>=61.0.0 - sphinx-copybutton - sphinx-markdown-tables - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.34.* +- wheel name: all_cuda-120_arch-x86_64 diff --git a/cpp/.clangd b/cpp/.clangd new file mode 100644 index 00000000000..7c4fe036ddf --- /dev/null +++ b/cpp/.clangd @@ -0,0 +1,65 @@ +# https://clangd.llvm.org/config + +# Apply a config conditionally to all C files +If: + PathMatch: .*\.(c|h)$ + +--- + +# Apply a config conditionally to all C++ files +If: + PathMatch: .*\.(c|h)pp + +--- + +# Apply a config conditionally to all CUDA files +If: + PathMatch: .*\.cuh? +CompileFlags: + Add: + - "-x" + - "cuda" + # No error on unknown CUDA versions + - "-Wno-unknown-cuda-version" + # Allow variadic CUDA functions + - "-Xclang=-fcuda-allow-variadic-functions" +Diagnostics: + Suppress: + - "variadic_device_fn" + - "attributes_not_allowed" + +--- + +# Tweak the clangd parse settings for all files +CompileFlags: + Add: + # report all errors + - "-ferror-limit=0" + - "-fmacro-backtrace-limit=0" + - "-ftemplate-backtrace-limit=0" + # Skip the CUDA version check + - "--no-cuda-version-check" + Remove: + # remove gcc's -fcoroutines + - -fcoroutines + # remove nvc++ flags unknown to clang + - "-gpu=*" + - "-stdpar*" + # remove nvcc flags unknown to clang + - "-arch*" + - "-gencode*" + - "--generate-code*" + - "-ccbin*" + - "-t=*" + - "--threads*" + - "-Xptxas*" + - "-Xcudafe*" + - "-Xfatbin*" + - "-Xcompiler*" + - "--diag-suppress*" + - "--diag_suppress*" + - "--compiler-options*" + - "--expt-extended-lambda" + - "--expt-relaxed-constexpr" + - "-forward-unknown-to-host-compiler" + - "-Werror=cross-execution-space-call" diff --git a/dependencies.yaml b/dependencies.yaml index 04ec1b6e957..a162ac01354 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -11,9 +11,15 @@ files: - cpp_build - cudatoolkit - docs + - python_build_wheel - python_build_cythonize + - depends_on_rmm + - depends_on_cudf + - depends_on_dask_cudf + - depends_on_pylibraft + - depends_on_raft_dask + - depends_on_cupy - python_run_cugraph - - python_run_pylibcugraph - python_run_nx_cugraph - python_run_cugraph_dgl - python_run_cugraph_pyg @@ -50,6 +56,7 @@ files: output: none includes: - cudatoolkit + - depends_on_cudf - py_version - test_python_common - test_python_cugraph @@ -62,14 +69,22 @@ files: includes: - common_build - python_build_wheel + - depends_on_rmm + - depends_on_pylibraft + - depends_on_pylibcugraph - python_build_cythonize - - python_build_cugraph py_run_cugraph: output: pyproject pyproject_dir: python/cugraph extras: table: project includes: + - depends_on_rmm + - depends_on_cudf + - depends_on_dask_cudf + - depends_on_raft_dask + - depends_on_pylibcugraph + - depends_on_cupy - python_run_cugraph py_test_cugraph: output: pyproject @@ -88,6 +103,8 @@ files: includes: - common_build - python_build_wheel + - depends_on_rmm + - depends_on_pylibraft - python_build_cythonize py_run_pylibcugraph: output: pyproject @@ -95,7 +112,8 @@ files: extras: table: project includes: - - python_run_pylibcugraph + - depends_on_rmm + - depends_on_pylibraft py_test_pylibcugraph: output: pyproject pyproject_dir: python/pylibcugraph @@ -103,6 +121,7 @@ files: table: project.optional-dependencies key: test includes: + - depends_on_cudf - test_python_common - test_python_pylibcugraph py_build_nx_cugraph: @@ -118,6 +137,8 @@ files: extras: table: project includes: + - depends_on_pylibcugraph + - depends_on_cupy - python_run_nx_cugraph py_test_nx_cugraph: output: pyproject @@ -183,6 +204,10 @@ files: extras: table: project includes: + - depends_on_rmm + - depends_on_cudf + - depends_on_dask_cudf + - depends_on_cupy - python_run_cugraph_service_server py_test_cugraph_service_server: output: pyproject @@ -334,41 +359,29 @@ dependencies: - python>=3.9,<3.11 python_build_wheel: common: - - output_types: [conda, pyproject] + - output_types: [conda, pyproject, requirements] packages: - - wheel - setuptools>=61.0.0 + - wheel python_build_cythonize: common: - - output_types: [conda, pyproject] + - output_types: [conda, pyproject, requirements] packages: - cython>=3.0.0 - - &pylibraft pylibraft==23.10.* - - &rmm rmm==23.10.* - scikit-build>=0.13.1 - python_build_cugraph: - common: - - output_types: [conda, pyproject] - packages: - - &pylibcugraph pylibcugraph==23.10.* python_run_cugraph: common: - output_types: [conda, pyproject] packages: - - &cudf cudf==23.10.* - &dask dask>=2023.7.1 - &distributed distributed>=2023.7.1 - &dask_cuda dask-cuda==23.10.* - - &dask_cudf dask-cudf==23.10.* - &numba numba>=0.57 - - raft-dask==23.10.* - - *rmm - &ucx_py ucx-py==0.34.* - output_types: conda packages: - aiohttp - - &cupy cupy>=12.0.0 - - &dask-core dask-core>=2023.7.1 + - &dask-core_conda dask-core>=2023.7.1 - fsspec>=0.6.0 - libcudf==23.10.* - requests @@ -376,29 +389,14 @@ dependencies: - ucx-proc=*=gpu - output_types: pyproject packages: - - &cupy_pip cupy-cuda11x>=12.0.0 # cudf uses fsspec but is protocol independent. cugraph # dataset APIs require [http] extras for use with cudf. - fsspec[http]>=0.6.0 - - *pylibcugraph - python_run_pylibcugraph: - common: - - output_types: [conda, pyproject] - packages: - - *pylibraft - - *rmm python_run_nx_cugraph: common: - output_types: [conda, pyproject] packages: - networkx>=3.0 - - output_types: conda - packages: - - *cupy - - output_types: pyproject - packages: - - *cupy_pip - - *pylibcugraph python_run_cugraph_dgl: common: - output_types: [conda, pyproject] @@ -426,23 +424,18 @@ dependencies: common: - output_types: [conda, pyproject] packages: - - *cudf - *dask - *dask_cuda - - *dask_cudf - *distributed - *numba - *numpy - - *rmm - *thrift - *ucx_py - output_types: conda packages: - - *cupy - - *dask-core + - *dask-core_conda - output_types: pyproject packages: - - *cupy_pip - *cugraph - cugraph-service-client==23.10.* doc: @@ -492,7 +485,6 @@ dependencies: common: - output_types: [conda, pyproject] packages: - - *cudf - *numpy test_python_nx_cugraph: common: @@ -519,3 +511,192 @@ dependencies: - pytorch==2.0 - pytorch-cuda==11.8 - pyg=2.3.1=*torch_2.0.0*cu118* + + depends_on_rmm: + common: + - output_types: conda + packages: + - &rmm_conda rmm==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &rmm_packages_pip_cu12 + - rmm-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *rmm_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *rmm_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &rmm_packages_pip_cu11 + - rmm-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *rmm_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *rmm_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *rmm_packages_pip_cu11} + - {matrix: null, packages: [*rmm_conda]} + + depends_on_cudf: + common: + - output_types: conda + packages: + - &cudf_conda cudf==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &cudf_packages_pip_cu12 + - cudf-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *cudf_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *cudf_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &cudf_packages_pip_cu11 + - cudf-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *cudf_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *cudf_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *cudf_packages_pip_cu11} + - {matrix: null, packages: [*cudf_conda]} + + depends_on_dask_cudf: + common: + - output_types: conda + packages: + - &dask_cudf_conda dask-cudf==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &dask_cudf_packages_pip_cu12 + - dask-cudf-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *dask_cudf_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *dask_cudf_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &dask_cudf_packages_pip_cu11 + - dask-cudf-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *dask_cudf_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *dask_cudf_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *dask_cudf_packages_pip_cu11} + - {matrix: null, packages: [*dask_cudf_conda]} + + depends_on_pylibraft: + common: + - output_types: conda + packages: + - &pylibraft_conda pylibraft==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &pylibraft_packages_pip_cu12 + - pylibraft-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *pylibraft_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *pylibraft_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &pylibraft_packages_pip_cu11 + - pylibraft-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *pylibraft_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *pylibraft_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *pylibraft_packages_pip_cu11} + - {matrix: null, packages: [*pylibraft_conda]} + + depends_on_raft_dask: + common: + - output_types: conda + packages: + - &raft_dask_conda raft-dask==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &raft_dask_packages_pip_cu12 + - raft-dask-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *raft_dask_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *raft_dask_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &raft_dask_packages_pip_cu11 + - raft-dask-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *raft_dask_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *raft_dask_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *raft_dask_packages_pip_cu11} + - {matrix: null, packages: [*raft_dask_conda]} + + depends_on_pylibcugraph: + common: + - output_types: conda + packages: + - &pylibcugraph_conda pylibcugraph==23.10.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &pylibcugraph_packages_pip_cu12 + - pylibcugraph-cu12==23.10.* + - {matrix: {cuda: "12.1"}, packages: *pylibcugraph_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *pylibcugraph_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &pylibcugraph_packages_pip_cu11 + - pylibcugraph-cu11==23.10.* + - {matrix: {cuda: "11.5"}, packages: *pylibcugraph_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *pylibcugraph_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *pylibcugraph_packages_pip_cu11} + - {matrix: null, packages: [*pylibcugraph_conda]} + + depends_on_cupy: + common: + - output_types: conda + packages: + - cupy>=12.0.0 + specific: + - output_types: [requirements, pyproject] + matrices: + # All CUDA 12 + x86_64 versions + - matrix: {cuda: "12.2", arch: x86_64} + packages: &cupy_packages_cu12_x86_64 + - cupy-cuda12x>=12.0.0 + - {matrix: {cuda: "12.1", arch: x86_64}, packages: *cupy_packages_cu12_x86_64} + - {matrix: {cuda: "12.0", arch: x86_64}, packages: *cupy_packages_cu12_x86_64} + + # All CUDA 12 + aarch64 versions + - matrix: {cuda: "12.2", arch: aarch64} + packages: &cupy_packages_cu12_aarch64 + - cupy-cuda12x -f https://pip.cupy.dev/aarch64 # TODO: Verify that this works. + - {matrix: {cuda: "12.1", arch: aarch64}, packages: *cupy_packages_cu12_aarch64} + - {matrix: {cuda: "12.0", arch: aarch64}, packages: *cupy_packages_cu12_aarch64} + + # All CUDA 11 + x86_64 versions + - matrix: {cuda: "11.8", arch: x86_64} + packages: &cupy_packages_cu11_x86_64 + - cupy-cuda11x>=12.0.0 + - {matrix: {cuda: "11.5", arch: x86_64}, packages: *cupy_packages_cu11_x86_64} + - {matrix: {cuda: "11.4", arch: x86_64}, packages: *cupy_packages_cu11_x86_64} + - {matrix: {cuda: "11.2", arch: x86_64}, packages: *cupy_packages_cu11_x86_64} + + # All CUDA 11 + aarch64 versions + - matrix: {cuda: "11.8", arch: aarch64} + packages: &cupy_packages_cu11_aarch64 + - cupy-cuda11x -f https://pip.cupy.dev/aarch64 # TODO: Verify that this works. + - {matrix: {cuda: "11.5", arch: aarch64}, packages: *cupy_packages_cu11_aarch64} + - {matrix: {cuda: "11.4", arch: aarch64}, packages: *cupy_packages_cu11_aarch64} + - {matrix: {cuda: "11.2", arch: aarch64}, packages: *cupy_packages_cu11_aarch64} + - {matrix: null, packages: [cupy-cuda11x>=12.0.0]} From 84207c34ee3a5a02853762f85b40cdaeb5afdee9 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Thu, 28 Sep 2023 10:38:29 -0400 Subject: [PATCH 020/111] Integrate C++ Renumbering and Compression (#3841) - [x] C API - [x] PLC - [x] Python API - [x] Bulk Sampling API - [x] Documentation for Python SG - [x] Documentation for Python MG - [x] Documentation for Bulk Sampler - [x] Resolve the C++ empty batch issue with new check - [x] Add FutureWarnings for all temporary flags - [x] Remove all print statements and pytest tags - [x] Verify cuGraph-PyG and cuGraph-DGL tests Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Seunghwa Kang (https://github.com/seunghwak) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Tingyu Wang (https://github.com/tingyu66) - Seunghwa Kang (https://github.com/seunghwak) - Joseph Nke (https://github.com/jnke2016) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3841 --- cpp/include/cugraph/sampling_functions.hpp | 2 +- cpp/include/cugraph_c/sampling_algorithms.h | 127 ++++---- cpp/src/c_api/uniform_neighbor_sampling.cpp | 291 ++++++++++++----- .../sampling_post_processing_impl.cuh | 4 +- cpp/tests/c_api/create_graph_test.c | 26 +- .../c_api/mg_uniform_neighbor_sample_test.c | 193 +++++++---- .../c_api/uniform_neighbor_sample_test.c | 267 ++++------------ .../dask/sampling/uniform_neighbor_sample.py | 300 +++++++++--------- .../cugraph/gnn/data_loading/bulk_sampler.py | 1 + .../gnn/data_loading/bulk_sampler_io.py | 219 ++++++++++++- .../cugraph/sampling/sampling_utilities.py | 198 ++++++++++++ .../sampling/uniform_neighbor_sample.py | 197 ++++++------ .../tests/sampling/test_bulk_sampler.py | 52 ++- .../tests/sampling/test_bulk_sampler_io.py | 69 +++- .../tests/sampling/test_bulk_sampler_io_mg.py | 14 +- .../tests/sampling/test_bulk_sampler_mg.py | 58 +++- .../sampling/test_uniform_neighbor_sample.py | 207 +++++++++++- .../test_uniform_neighbor_sample_mg.py | 244 +++++++++++++- .../pylibcugraph/_cugraph_c/algorithms.pxd | 48 ++- .../_cugraph_c/sampling_algorithms.pxd | 17 - .../internal_types/sampling_result.pyx | 91 +++++- .../tests/test_uniform_neighbor_sample.py | 4 +- .../pylibcugraph/uniform_neighbor_sample.pyx | 112 ++++++- 23 files changed, 2021 insertions(+), 720 deletions(-) create mode 100644 python/cugraph/cugraph/sampling/sampling_utilities.py diff --git a/cpp/include/cugraph/sampling_functions.hpp b/cpp/include/cugraph/sampling_functions.hpp index e42ef9bfcf3..75cf8f91f92 100644 --- a/cpp/include/cugraph/sampling_functions.hpp +++ b/cpp/include/cugraph/sampling_functions.hpp @@ -103,7 +103,7 @@ namespace cugraph { * std::get<1>(*edgelist_label_offsets) if @p edgelist_label_offsets.has_value() is true and 1 * otherwise and # hops = std::get<1>(*edgelist_hops) if edgelist_hops.has_value() is true and 1 * otherwise, valid only if at least one of @p edgelist_label_offsets.has_value() or @p - * edgelist_hops.has_value() is rue), renumber_map to query original vertices (size = # unique + * edgelist_hops.has_value() is true), renumber_map to query original vertices (size = # unique * vertices or aggregate # unique vertices for every label), and label offsets to the renumber_map * (size = std::get<1>(*edgelist_label_offsets) + 1, valid only if @p * edgelist_label_offsets.has_value() is true). diff --git a/cpp/include/cugraph_c/sampling_algorithms.h b/cpp/include/cugraph_c/sampling_algorithms.h index 37124d100dd..92fe50ef622 100644 --- a/cpp/include/cugraph_c/sampling_algorithms.h +++ b/cpp/include/cugraph_c/sampling_algorithms.h @@ -205,6 +205,21 @@ typedef enum cugraph_prior_sources_behavior_t { but exclude any vertex that has already been used as a source */ } cugraph_prior_sources_behavior_t; +/** + * @brief Selects the type of compression to use for the output samples. + */ +typedef enum cugraph_compression_type_t { + COO = 0, /** Outputs in COO format. Default. */ + CSR, /** Compresses in CSR format. This means the row (src) column + is compressed into a row pointer. */ + CSC, /** Compresses in CSC format. This means the col (dst) column + is compressed into a column pointer. */ + DCSR, /** Compresses in DCSR format. This outputs an additional index + that avoids empty entries in the row pointer. */ + DCSC /** Compresses in DCSC format. This outputs an additional index + that avoid empty entries in the col pointer. */ +} cugraph_compression_type_t; + /** * @brief Create sampling options object * @@ -225,6 +240,14 @@ cugraph_error_code_t cugraph_sampling_options_create(cugraph_sampling_options_t* */ void cugraph_sampling_set_renumber_results(cugraph_sampling_options_t* options, bool_t value); +/** + * @brief Set whether to compress per-hop (True) or globally (False) + * + * @param options - opaque pointer to the sampling options + * @param value - Boolean value to assign to the option + */ +void cugraph_sampling_set_compress_per_hop(cugraph_sampling_options_t* options, bool_t value); + /** * @brief Set flag to sample with_replacement * @@ -241,6 +264,15 @@ void cugraph_sampling_set_with_replacement(cugraph_sampling_options_t* options, */ void cugraph_sampling_set_return_hops(cugraph_sampling_options_t* options, bool_t value); +/** + * @brief Set compression type + * + * @param options - opaque pointer to the sampling options + * @param value - Enum defining the compresion type + */ +void cugraph_sampling_set_compression_type(cugraph_sampling_options_t* options, + cugraph_compression_type_t value); + /** * @brief Set prior sources behavior * @@ -265,62 +297,6 @@ void cugraph_sampling_set_dedupe_sources(cugraph_sampling_options_t* options, bo */ void cugraph_sampling_options_free(cugraph_sampling_options_t* options); -/** - * @brief Uniform Neighborhood Sampling - * @deprecated This call should be replaced with cugraph_uniform_neighbor_sample - * - * Returns a sample of the neighborhood around specified start vertices. Optionally, each - * start vertex can be associated with a label, allowing the caller to specify multiple batches - * of sampling requests in the same function call - which should improve GPU utilization. - * - * If label is NULL then all start vertices will be considered part of the same batch and the - * return value will not have a label column. - * - * @param [in] handle Handle for accessing resources - * @param [in] graph Pointer to graph. NOTE: Graph might be modified if the storage - * needs to be transposed - * @param [in] start_vertices Device array of start vertices for the sampling - * @param [in] start_vertex_labels Device array of start vertex labels for the sampling. The - * labels associated with each start vertex will be included in the output associated with results - * that were derived from that start vertex. We only support label of type INT32. If label is - * NULL, the return data will not be labeled. - * @param [in] label_list Device array of the labels included in @p start_vertex_labels. If - * @p label_to_comm_rank is not specified this parameter is ignored. If specified, label_list - * must be sorted in ascending order. - * @param [in] label_to_comm_rank Device array identifying which comm rank the output for a - * particular label should be shuffled in the output. If not specifed the data is not organized in - * output. If specified then the all data from @p label_list[i] will be shuffled to rank @p - * label_to_comm_rank[i]. If not specified then the output data will not be shuffled between ranks. - * @param [in] fanout Host array defining the fan out at each step in the sampling algorithm. - * We only support fanout values of type INT32 - * @param [in/out] rng_state State of the random number generator, updated with each call - * @param [in] with_replacement - * Boolean value. If true selection of edges is done with - * replacement. If false selection is done without replacement. - * @param [in] return_hops Boolean value. If true include the hop number in the result, - * If false the hop number will not be included in result. - * @param [in] do_expensive_check - * A flag to run expensive checks for input arguments (if set to true) - * @param [in] result Output from the uniform_neighbor_sample call - * @param [out] error Pointer to an error object storing details of any error. Will - * be populated if error code is not CUGRAPH_SUCCESS - * @return error code - */ -cugraph_error_code_t cugraph_uniform_neighbor_sample_with_edge_properties( - const cugraph_resource_handle_t* handle, - cugraph_graph_t* graph, - const cugraph_type_erased_device_array_view_t* start_vertices, - const cugraph_type_erased_device_array_view_t* start_vertex_labels, - const cugraph_type_erased_device_array_view_t* label_list, - const cugraph_type_erased_device_array_view_t* label_to_comm_rank, - const cugraph_type_erased_host_array_view_t* fan_out, - cugraph_rng_state_t* rng_state, - bool_t with_replacement, - bool_t return_hops, - bool_t do_expensive_check, - cugraph_sample_result_t** result, - cugraph_error_t** error); - /** * @brief Uniform Neighborhood Sampling * @@ -374,6 +350,7 @@ cugraph_error_code_t cugraph_uniform_neighbor_sample( cugraph_error_t** error); /** + * @deprecated This call should be replaced with cugraph_sample_result_get_majors * @brief Get the source vertices from the sampling algorithm result * * @param [in] result The result from a sampling algorithm @@ -383,6 +360,7 @@ cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_sources( const cugraph_sample_result_t* result); /** + * @deprecated This call should be replaced with cugraph_sample_result_get_minors * @brief Get the destination vertices from the sampling algorithm result * * @param [in] result The result from a sampling algorithm @@ -391,6 +369,33 @@ cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_sources( cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_destinations( const cugraph_sample_result_t* result); +/** + * @brief Get the major vertices from the sampling algorithm result + * + * @param [in] result The result from a sampling algorithm + * @return type erased array pointing to the major vertices in device memory + */ +cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_majors( + const cugraph_sample_result_t* result); + +/** + * @brief Get the minor vertices from the sampling algorithm result + * + * @param [in] result The result from a sampling algorithm + * @return type erased array pointing to the minor vertices in device memory + */ +cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_minors( + const cugraph_sample_result_t* result); + +/** + * @brief Get the major offsets from the sampling algorithm result + * + * @param [in] result The result from a sampling algorithm + * @return type erased array pointing to the major offsets in device memory + */ +cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_major_offsets( + const cugraph_sample_result_t* result); + /** * @brief Get the start labels from the sampling algorithm result * @@ -436,6 +441,15 @@ cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_edge_weight( cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_hop( const cugraph_sample_result_t* result); +/** + * @brief Get the label-hop offsets from the sampling algorithm result + * + * @param [in] result The result from a sampling algorithm + * @return type erased array pointing to the label-hop offsets + */ +cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_label_hop_offsets( + const cugraph_sample_result_t* result); + /** * @brief Get the index from the sampling algorithm result * @@ -446,6 +460,7 @@ cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_index( const cugraph_sample_result_t* result); /** + * @deprecated This call should be replaced with cugraph_sample_get_get_label_hop_offsets * @brief Get the result offsets from the sampling algorithm result * * @param [in] result The result from a sampling algorithm diff --git a/cpp/src/c_api/uniform_neighbor_sampling.cpp b/cpp/src/c_api/uniform_neighbor_sampling.cpp index f146c331d8c..1a53c899109 100644 --- a/cpp/src/c_api/uniform_neighbor_sampling.cpp +++ b/cpp/src/c_api/uniform_neighbor_sampling.cpp @@ -38,17 +38,20 @@ struct cugraph_sampling_options_t { prior_sources_behavior_t prior_sources_behavior_{prior_sources_behavior_t::DEFAULT}; bool_t dedupe_sources_{FALSE}; bool_t renumber_results_{FALSE}; + cugraph_compression_type_t compression_type_{cugraph_compression_type_t::COO}; + bool_t compress_per_hop_{FALSE}; }; struct cugraph_sample_result_t { - cugraph_type_erased_device_array_t* src_{nullptr}; - cugraph_type_erased_device_array_t* dst_{nullptr}; + cugraph_type_erased_device_array_t* major_offsets_{nullptr}; + cugraph_type_erased_device_array_t* majors_{nullptr}; + cugraph_type_erased_device_array_t* minors_{nullptr}; cugraph_type_erased_device_array_t* edge_id_{nullptr}; cugraph_type_erased_device_array_t* edge_type_{nullptr}; cugraph_type_erased_device_array_t* wgt_{nullptr}; cugraph_type_erased_device_array_t* hop_{nullptr}; + cugraph_type_erased_device_array_t* label_hop_offsets_{nullptr}; cugraph_type_erased_device_array_t* label_{nullptr}; - cugraph_type_erased_device_array_t* offsets_{nullptr}; cugraph_type_erased_device_array_t* renumber_map_{nullptr}; cugraph_type_erased_device_array_t* renumber_map_offsets_{nullptr}; }; @@ -186,6 +189,8 @@ struct uniform_neighbor_sampling_functor : public cugraph::c_api::abstract_funct graph_view.local_vertex_partition_range_last(), do_expensive_check_); + bool has_labels = start_vertex_labels_ != nullptr; + auto&& [src, dst, wgt, edge_id, edge_type, hop, edge_label, offsets] = cugraph::uniform_neighbor_sample( handle_, @@ -229,25 +234,130 @@ struct uniform_neighbor_sampling_functor : public cugraph::c_api::abstract_funct vertex_partition_lasts, do_expensive_check_); + std::optional> majors{std::nullopt}; + rmm::device_uvector minors(0, handle_.get_stream()); + std::optional> major_offsets{std::nullopt}; + + std::optional> label_hop_offsets{std::nullopt}; + std::optional> renumber_map{std::nullopt}; std::optional> renumber_map_offsets{std::nullopt}; + bool src_is_major = (options_.compression_type_ == cugraph_compression_type_t::CSR) || + (options_.compression_type_ == cugraph_compression_type_t::DCSR) || + (options_.compression_type_ == cugraph_compression_type_t::COO); + if (options_.renumber_results_) { - std::tie(src, dst, renumber_map, renumber_map_offsets) = cugraph::renumber_sampled_edgelist( - handle_, - std::move(src), - std::move(dst), - hop ? std::make_optional(raft::device_span{hop->data(), hop->size()}) - : std::nullopt, - std::make_optional(std::make_tuple( - raft::device_span{edge_label->data(), edge_label->size()}, - raft::device_span{offsets->data(), offsets->size()})), - do_expensive_check_); + if (options_.compression_type_ == cugraph_compression_type_t::COO) { + // COO + + rmm::device_uvector output_majors(0, handle_.get_stream()); + rmm::device_uvector output_renumber_map(0, handle_.get_stream()); + std::tie(output_majors, + minors, + wgt, + edge_id, + edge_type, + label_hop_offsets, + output_renumber_map, + renumber_map_offsets) = + cugraph::renumber_and_sort_sampled_edgelist( + handle_, + std::move(src), + std::move(dst), + wgt ? std::move(wgt) : std::nullopt, + edge_id ? std::move(edge_id) : std::nullopt, + edge_type ? std::move(edge_type) : std::nullopt, + hop ? std::make_optional(std::make_tuple(std::move(*hop), fan_out_->size_)) + : std::nullopt, + offsets ? std::make_optional(std::make_tuple( + raft::device_span{offsets->data(), offsets->size()}, + edge_label->size())) + : std::nullopt, + src_is_major, + do_expensive_check_); + + majors.emplace(std::move(output_majors)); + renumber_map.emplace(std::move(output_renumber_map)); + } else { + // (D)CSC, (D)CSR + + bool doubly_compress = (options_.compression_type_ == cugraph_compression_type_t::DCSR) || + (options_.compression_type_ == cugraph_compression_type_t::DCSC); + + rmm::device_uvector output_major_offsets(0, handle_.get_stream()); + rmm::device_uvector output_renumber_map(0, handle_.get_stream()); + std::tie(majors, + output_major_offsets, + minors, + wgt, + edge_id, + edge_type, + label_hop_offsets, + output_renumber_map, + renumber_map_offsets) = + cugraph::renumber_and_compress_sampled_edgelist( + handle_, + std::move(src), + std::move(dst), + wgt ? std::move(wgt) : std::nullopt, + edge_id ? std::move(edge_id) : std::nullopt, + edge_type ? std::move(edge_type) : std::nullopt, + hop ? std::make_optional(std::make_tuple(std::move(*hop), fan_out_->size_)) + : std::nullopt, + offsets ? std::make_optional(std::make_tuple( + raft::device_span{offsets->data(), offsets->size()}, + edge_label->size())) + : std::nullopt, + src_is_major, + options_.compress_per_hop_, + doubly_compress, + do_expensive_check_); + + renumber_map.emplace(std::move(output_renumber_map)); + major_offsets.emplace(std::move(output_major_offsets)); + } + + // These are now represented by label_hop_offsets + hop.reset(); + offsets.reset(); + } else { + if (options_.compression_type_ != cugraph_compression_type_t::COO) { + CUGRAPH_FAIL("Can only use COO format if not renumbering"); + } + + std::tie(src, dst, wgt, edge_id, edge_type, label_hop_offsets) = + cugraph::sort_sampled_edgelist( + handle_, + std::move(src), + std::move(dst), + wgt ? std::move(wgt) : std::nullopt, + edge_id ? std::move(edge_id) : std::nullopt, + edge_type ? std::move(edge_type) : std::nullopt, + hop ? std::make_optional(std::make_tuple(std::move(*hop), fan_out_->size_)) + : std::nullopt, + offsets ? std::make_optional(std::make_tuple( + raft::device_span{offsets->data(), offsets->size()}, + edge_label->size())) + : std::nullopt, + src_is_major, + do_expensive_check_); + + majors.emplace(std::move(src)); + minors = std::move(dst); + + hop.reset(); + offsets.reset(); } result_ = new cugraph::c_api::cugraph_sample_result_t{ - new cugraph::c_api::cugraph_type_erased_device_array_t(src, graph_->vertex_type_), - new cugraph::c_api::cugraph_type_erased_device_array_t(dst, graph_->vertex_type_), + (major_offsets) + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*major_offsets, SIZE_T) + : nullptr, + (majors) + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*majors, graph_->vertex_type_) + : nullptr, + new cugraph::c_api::cugraph_type_erased_device_array_t(minors, graph_->vertex_type_), (edge_id) ? new cugraph::c_api::cugraph_type_erased_device_array_t(*edge_id, graph_->edge_type_) : nullptr, @@ -256,12 +366,14 @@ struct uniform_neighbor_sampling_functor : public cugraph::c_api::abstract_funct : nullptr, (wgt) ? new cugraph::c_api::cugraph_type_erased_device_array_t(*wgt, graph_->weight_type_) : nullptr, - (hop) ? new cugraph::c_api::cugraph_type_erased_device_array_t(*hop, INT32) : nullptr, + (hop) ? new cugraph::c_api::cugraph_type_erased_device_array_t(*hop, INT32) + : nullptr, // FIXME get rid of this + (label_hop_offsets) + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*label_hop_offsets, SIZE_T) + : nullptr, (edge_label) ? new cugraph::c_api::cugraph_type_erased_device_array_t(edge_label.value(), INT32) : nullptr, - (offsets) ? new cugraph::c_api::cugraph_type_erased_device_array_t(offsets.value(), SIZE_T) - : nullptr, (renumber_map) ? new cugraph::c_api::cugraph_type_erased_device_array_t( renumber_map.value(), graph_->vertex_type_) : nullptr, @@ -295,6 +407,13 @@ extern "C" void cugraph_sampling_set_renumber_results(cugraph_sampling_options_t internal_pointer->renumber_results_ = value; } +extern "C" void cugraph_sampling_set_compress_per_hop(cugraph_sampling_options_t* options, + bool_t value) +{ + auto internal_pointer = reinterpret_cast(options); + internal_pointer->compress_per_hop_ = value; +} + extern "C" void cugraph_sampling_set_with_replacement(cugraph_sampling_options_t* options, bool_t value) { @@ -308,6 +427,20 @@ extern "C" void cugraph_sampling_set_return_hops(cugraph_sampling_options_t* opt internal_pointer->return_hops_ = value; } +extern "C" void cugraph_sampling_set_compression_type(cugraph_sampling_options_t* options, + cugraph_compression_type_t value) +{ + auto internal_pointer = reinterpret_cast(options); + switch (value) { + case COO: internal_pointer->compression_type_ = cugraph_compression_type_t::COO; break; + case CSR: internal_pointer->compression_type_ = cugraph_compression_type_t::CSR; break; + case CSC: internal_pointer->compression_type_ = cugraph_compression_type_t::CSC; break; + case DCSR: internal_pointer->compression_type_ = cugraph_compression_type_t::DCSR; break; + case DCSC: internal_pointer->compression_type_ = cugraph_compression_type_t::DCSC; break; + default: CUGRAPH_FAIL("Invalid compression type"); + } +} + extern "C" void cugraph_sampling_set_prior_sources_behavior(cugraph_sampling_options_t* options, cugraph_prior_sources_behavior_t value) { @@ -341,15 +474,45 @@ extern "C" void cugraph_sampling_options_free(cugraph_sampling_options_t* option extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_sources( const cugraph_sample_result_t* result) { - auto internal_pointer = reinterpret_cast(result); - return reinterpret_cast(internal_pointer->src_->view()); + // Deprecated. + return cugraph_sample_result_get_majors(result); } extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_destinations( const cugraph_sample_result_t* result) +{ + // Deprecated. + return cugraph_sample_result_get_minors(result); +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_majors( + const cugraph_sample_result_t* result) +{ + auto internal_pointer = reinterpret_cast(result); + return (internal_pointer->majors_ != nullptr) + ? reinterpret_cast( + internal_pointer->majors_->view()) + + : NULL; +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_major_offsets( + const cugraph_sample_result_t* result) +{ + auto internal_pointer = reinterpret_cast(result); + return (internal_pointer->major_offsets_ != nullptr) + ? reinterpret_cast( + internal_pointer->major_offsets_->view()) + + : NULL; +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_minors( + const cugraph_sample_result_t* result) { auto internal_pointer = reinterpret_cast(result); - return reinterpret_cast(internal_pointer->dst_->view()); + return reinterpret_cast( + internal_pointer->minors_->view()); } extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_start_labels( @@ -402,6 +565,16 @@ extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_ho : NULL; } +extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_label_hop_offsets( + const cugraph_sample_result_t* result) +{ + auto internal_pointer = reinterpret_cast(result); + return internal_pointer->label_hop_offsets_ != nullptr + ? reinterpret_cast( + internal_pointer->label_hop_offsets_->view()) + : NULL; +} + extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_index( const cugraph_sample_result_t* result) { @@ -413,9 +586,8 @@ extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_in extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_offsets( const cugraph_sample_result_t* result) { - auto internal_pointer = reinterpret_cast(result); - return reinterpret_cast( - internal_pointer->offsets_->view()); + // Deprecated. + return cugraph_sample_result_get_label_hop_offsets(result); } extern "C" cugraph_type_erased_device_array_view_t* cugraph_sample_result_get_renumber_map( @@ -532,6 +704,7 @@ extern "C" cugraph_error_code_t cugraph_test_uniform_neighborhood_sample_result_ // create new cugraph_sample_result_t *result = reinterpret_cast(new cugraph::c_api::cugraph_sample_result_t{ + nullptr, reinterpret_cast( new_device_srcs.release()), reinterpret_cast( @@ -675,78 +848,20 @@ extern "C" cugraph_error_code_t cugraph_test_sample_result_create( extern "C" void cugraph_sample_result_free(cugraph_sample_result_t* result) { auto internal_pointer = reinterpret_cast(result); - delete internal_pointer->src_; - delete internal_pointer->dst_; + delete internal_pointer->major_offsets_; + delete internal_pointer->majors_; + delete internal_pointer->minors_; delete internal_pointer->edge_id_; delete internal_pointer->edge_type_; delete internal_pointer->wgt_; delete internal_pointer->hop_; + delete internal_pointer->label_hop_offsets_; delete internal_pointer->label_; + delete internal_pointer->renumber_map_; + delete internal_pointer->renumber_map_offsets_; delete internal_pointer; } -extern "C" cugraph_error_code_t cugraph_uniform_neighbor_sample_with_edge_properties( - const cugraph_resource_handle_t* handle, - cugraph_graph_t* graph, - const cugraph_type_erased_device_array_view_t* start_vertices, - const cugraph_type_erased_device_array_view_t* start_vertex_labels, - const cugraph_type_erased_device_array_view_t* label_list, - const cugraph_type_erased_device_array_view_t* label_to_comm_rank, - const cugraph_type_erased_host_array_view_t* fan_out, - cugraph_rng_state_t* rng_state, - bool_t with_replacement, - bool_t return_hops, - bool_t do_expensive_check, - cugraph_sample_result_t** result, - cugraph_error_t** error) -{ - CAPI_EXPECTS((start_vertex_labels == nullptr) || - (reinterpret_cast( - start_vertex_labels) - ->type_ == INT32), - CUGRAPH_INVALID_INPUT, - "start_vertex_labels should be of type int", - *error); - - CAPI_EXPECTS((label_to_comm_rank == nullptr) || (start_vertex_labels != nullptr), - CUGRAPH_INVALID_INPUT, - "cannot specify label_to_comm_rank unless start_vertex_labels is also specified", - *error); - - CAPI_EXPECTS((label_to_comm_rank == nullptr) || (label_list != nullptr), - CUGRAPH_INVALID_INPUT, - "cannot specify label_to_comm_rank unless label_list is also specified", - *error); - - CAPI_EXPECTS(reinterpret_cast(graph)->vertex_type_ == - reinterpret_cast( - start_vertices) - ->type_, - CUGRAPH_INVALID_INPUT, - "vertex type of graph and start_vertices must match", - *error); - - CAPI_EXPECTS( - reinterpret_cast(fan_out) - ->type_ == INT32, - CUGRAPH_INVALID_INPUT, - "fan_out should be of type int", - *error); - - uniform_neighbor_sampling_functor functor{ - handle, - graph, - start_vertices, - start_vertex_labels, - label_list, - label_to_comm_rank, - fan_out, - rng_state, - cugraph::c_api::cugraph_sampling_options_t{with_replacement, return_hops}, - do_expensive_check}; - return cugraph::c_api::run_algorithm(graph, functor, result, error); -} - cugraph_error_code_t cugraph_uniform_neighbor_sample( const cugraph_resource_handle_t* handle, cugraph_graph_t* graph, diff --git a/cpp/src/sampling/sampling_post_processing_impl.cuh b/cpp/src/sampling/sampling_post_processing_impl.cuh index 0c397d91b20..77d4f2d865f 100644 --- a/cpp/src/sampling/sampling_post_processing_impl.cuh +++ b/cpp/src/sampling/sampling_post_processing_impl.cuh @@ -166,9 +166,7 @@ void check_input_edges( std::numeric_limits::max()), "Invalid input arguments: current implementation assumes that the number of " "unique labels is no larger than std::numeric_limits::max()."); - CUGRAPH_EXPECTS(!edgelist_label_offsets || std::get<1>(*edgelist_label_offsets) > 0, - "Invlaid input arguments: there should be 1 or more labels if " - "edgelist_label_offsets.has_value() is true."); + CUGRAPH_EXPECTS( !edgelist_label_offsets.has_value() || (std::get<0>(*edgelist_label_offsets).size() == std::get<1>(*edgelist_label_offsets) + 1), diff --git a/cpp/tests/c_api/create_graph_test.c b/cpp/tests/c_api/create_graph_test.c index eef49458f2b..736db761ebd 100644 --- a/cpp/tests/c_api/create_graph_test.c +++ b/cpp/tests/c_api/create_graph_test.c @@ -142,6 +142,14 @@ int test_create_sg_graph_csr() vertex_t h_start[] = {0, 1, 2, 3, 4, 5}; weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; + bool_t with_replacement = FALSE; + bool_t return_hops = TRUE; + cugraph_prior_sources_behavior_t prior_sources_behavior = DEFAULT; + bool_t dedupe_sources = FALSE; + bool_t renumber_results = FALSE; + cugraph_compression_type_t compression = COO; + bool_t compress_per_hop = FALSE; + cugraph_resource_handle_t* handle = NULL; cugraph_graph_t* graph = NULL; cugraph_graph_properties_t properties; @@ -238,8 +246,21 @@ int test_create_sg_graph_csr() ret_code = cugraph_rng_state_create(handle, 0, &rng_state, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "rng_state create failed."); - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties( - handle, graph, d_start_view, NULL, NULL, NULL, h_fan_out_view, rng_state, FALSE, FALSE, FALSE, &result, &ret_error); + cugraph_sampling_options_t *sampling_options; + + ret_code = cugraph_sampling_options_create(&sampling_options, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "sampling_options create failed."); + + cugraph_sampling_set_with_replacement(sampling_options, with_replacement); + cugraph_sampling_set_return_hops(sampling_options, return_hops); + cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior); + cugraph_sampling_set_dedupe_sources(sampling_options, dedupe_sources); + cugraph_sampling_set_renumber_results(sampling_options, renumber_results); + cugraph_sampling_set_compression_type(sampling_options, compression); + cugraph_sampling_set_compress_per_hop(sampling_options, compress_per_hop); + + ret_code = cugraph_uniform_neighbor_sample( + handle, graph, d_start_view, NULL, NULL, NULL, h_fan_out_view, rng_state, sampling_options, FALSE, &result, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, "uniform_neighbor_sample failed."); @@ -289,6 +310,7 @@ int test_create_sg_graph_csr() cugraph_free_resource_handle(handle); cugraph_error_free(ret_error); + cugraph_sampling_options_free(sampling_options); return test_ret_value; } diff --git a/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c b/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c index f8241bd8a5f..86a0a92eb01 100644 --- a/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c +++ b/cpp/tests/c_api/mg_uniform_neighbor_sample_test.c @@ -213,11 +213,6 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "gatherv_fill failed."); } - if (return_hops) { - ret_code = cugraph_test_device_gatherv_fill(handle, result_hops, h_result_hops); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "gatherv_fill failed."); - } - if (d_start_labels != NULL) { size_t sz = cugraph_type_erased_device_array_view_size(result_offsets); @@ -452,6 +447,7 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) size_t num_vertices = 5; size_t fan_out_size = 2; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 1, 2, 3, 4, 3, 4, 2, 0, 1, 0, 2}; vertex_t dst[] = {1, 2, 4, 2, 3, 4, 1, 1, 2, 3, 4, 4}; @@ -462,7 +458,6 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) int32_t batch[] = {0, 1}; int fan_out[] = {2, 2}; - bool_t with_replacement = TRUE; bool_t store_transposed = FALSE; int test_ret_value = 0; @@ -472,6 +467,14 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) cugraph_graph_t* graph = NULL; cugraph_sample_result_t* result = NULL; + bool_t with_replacement = FALSE; + bool_t return_hops = TRUE; + cugraph_prior_sources_behavior_t prior_sources_behavior = DEFAULT; + bool_t dedupe_sources = FALSE; + bool_t renumber_results = FALSE; + cugraph_compression_type_t compression = COO; + bool_t compress_per_hop = FALSE; + cugraph_type_erased_device_array_t* d_start = NULL; cugraph_type_erased_device_array_t* d_label = NULL; cugraph_type_erased_device_array_view_t* d_start_view = NULL; @@ -512,19 +515,31 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) h_fan_out_view = cugraph_type_erased_host_array_view_create(fan_out, fan_out_size, INT32); - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, - graph, - d_start_view, - d_label_view, - NULL, - NULL, - h_fan_out_view, - rng_state, - with_replacement, - TRUE, - FALSE, - &result, - &ret_error); + cugraph_sampling_options_t *sampling_options; + + ret_code = cugraph_sampling_options_create(&sampling_options, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "sampling_options create failed."); + + cugraph_sampling_set_with_replacement(sampling_options, with_replacement); + cugraph_sampling_set_return_hops(sampling_options, return_hops); + cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior); + cugraph_sampling_set_dedupe_sources(sampling_options, dedupe_sources); + cugraph_sampling_set_renumber_results(sampling_options, renumber_results); + cugraph_sampling_set_compression_type(sampling_options, compression); + cugraph_sampling_set_compress_per_hop(sampling_options, compress_per_hop); + + ret_code = cugraph_uniform_neighbor_sample(handle, + graph, + d_start_view, + d_label_view, + NULL, + NULL, + h_fan_out_view, + rng_state, + sampling_options, + FALSE, + &result, + &ret_error); #ifdef NO_CUGRAPH_OPS TEST_ASSERT( @@ -540,6 +555,7 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) cugraph_type_erased_device_array_view_t* result_weight; cugraph_type_erased_device_array_view_t* result_labels; cugraph_type_erased_device_array_view_t* result_hops; + cugraph_type_erased_device_array_view_t* result_offsets; result_src = cugraph_sample_result_get_sources(result); result_dst = cugraph_sample_result_get_destinations(result); @@ -548,8 +564,10 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) result_weight = cugraph_sample_result_get_edge_weight(result); result_labels = cugraph_sample_result_get_start_labels(result); result_hops = cugraph_sample_result_get_hop(result); + result_offsets = cugraph_sample_result_get_offsets(result); size_t result_size = cugraph_type_erased_device_array_view_size(result_src); + size_t offsets_size = cugraph_type_erased_device_array_view_size(result_offsets); vertex_t h_srcs[result_size]; vertex_t h_dsts[result_size]; @@ -558,6 +576,7 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) weight_t h_wgt[result_size]; int h_labels[result_size]; int h_hop[result_size]; + int h_offsets[offsets_size]; ret_code = cugraph_type_erased_device_array_view_copy_to_host( handle, (byte_t*)h_srcs, result_src, &ret_error); @@ -584,9 +603,24 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_hop, result_hops, &ret_error); + handle, (byte_t*)h_offsets, result_offsets, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + for(int k = 0; k < offsets_size-1; k += fan_out_size) { + for(int h = 0; h < fan_out_size; ++h) { + int hop_start = h_offsets[k+h]; + int hop_end = h_offsets[k+h+1]; + for(int i = hop_start; i < hop_end; ++i) { + h_hop[i] = h; + } + } + } + + for(int k = 0; k < num_start_labels+1; ++k) { + h_offsets[k] = h_offsets[k*fan_out_size]; + } + offsets_size = num_start_labels + 1; + // NOTE: The C++ tester does a more thorough validation. For our purposes // here we will do a simpler validation, merely checking that all edges // are actually part of the graph @@ -611,6 +645,7 @@ int test_uniform_neighbor_from_alex(const cugraph_resource_handle_t* handle) cugraph_type_erased_host_array_view_free(h_fan_out_view); cugraph_mg_graph_free(graph); cugraph_error_free(ret_error); + cugraph_sampling_options_free(sampling_options); return test_ret_value; } @@ -661,6 +696,15 @@ int test_uniform_neighbor_sample_alex_bug(const cugraph_resource_handle_t* handl size_t expected_size[] = { 3, 2, 1, 1, 1, 1, 1, 1 }; + + bool_t with_replacement = FALSE; + bool_t return_hops = TRUE; + cugraph_prior_sources_behavior_t prior_sources_behavior = CARRY_OVER; + bool_t dedupe_sources = TRUE; + bool_t renumber_results = FALSE; + cugraph_compression_type_t compression = COO; + bool_t compress_per_hop = FALSE; + // Create graph int test_ret_value = 0; cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; @@ -747,19 +791,30 @@ int test_uniform_neighbor_sample_alex_bug(const cugraph_resource_handle_t* handl h_fan_out_view = cugraph_type_erased_host_array_view_create(fan_out, fan_out_size, INT32); - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, - graph, - d_start_view, - d_start_labels_view, - d_label_list_view, - d_label_to_output_comm_rank_view, - h_fan_out_view, - rng_state, - FALSE, - TRUE, - FALSE, - &result, - &ret_error); + cugraph_sampling_options_t* sampling_options; + ret_code = cugraph_sampling_options_create(&sampling_options, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "sampling_options create failed."); + + cugraph_sampling_set_with_replacement(sampling_options, with_replacement); + cugraph_sampling_set_return_hops(sampling_options, return_hops); + cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior); + cugraph_sampling_set_dedupe_sources(sampling_options, dedupe_sources); + cugraph_sampling_set_renumber_results(sampling_options, renumber_results); + cugraph_sampling_set_compression_type(sampling_options, compression); + cugraph_sampling_set_compress_per_hop(sampling_options, compress_per_hop); + + ret_code = cugraph_uniform_neighbor_sample(handle, + graph, + d_start_view, + d_start_labels_view, + d_label_list_view, + d_label_to_output_comm_rank_view, + h_fan_out_view, + rng_state, + sampling_options, + FALSE, + &result, + &ret_error); #ifdef NO_CUGRAPH_OPS TEST_ASSERT( @@ -900,6 +955,14 @@ int test_uniform_neighbor_sample_sort_by_hop(const cugraph_resource_handle_t* ha size_t expected_size[] = { 3, 2, 1, 1, 1, 1, 1, 1 }; + bool_t with_replacement = FALSE; + bool_t return_hops = TRUE; + cugraph_prior_sources_behavior_t prior_sources_behavior = CARRY_OVER; + bool_t dedupe_sources = TRUE; + bool_t renumber_results = FALSE; + cugraph_compression_type_t compression = COO; + bool_t compress_per_hop = FALSE; + // Create graph int test_ret_value = 0; cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; @@ -986,19 +1049,30 @@ int test_uniform_neighbor_sample_sort_by_hop(const cugraph_resource_handle_t* ha h_fan_out_view = cugraph_type_erased_host_array_view_create(fan_out, fan_out_size, INT32); - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, - graph, - d_start_view, - d_start_labels_view, - d_label_list_view, - d_label_to_output_comm_rank_view, - h_fan_out_view, - rng_state, - FALSE, - TRUE, - FALSE, - &result, - &ret_error); + cugraph_sampling_options_t* sampling_options; + ret_code = cugraph_sampling_options_create(&sampling_options, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "sampling_options create failed."); + + cugraph_sampling_set_with_replacement(sampling_options, with_replacement); + cugraph_sampling_set_return_hops(sampling_options, return_hops); + cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior); + cugraph_sampling_set_dedupe_sources(sampling_options, dedupe_sources); + cugraph_sampling_set_renumber_results(sampling_options, renumber_results); + cugraph_sampling_set_compression_type(sampling_options, compression); + cugraph_sampling_set_compress_per_hop(sampling_options, compress_per_hop); + + ret_code = cugraph_uniform_neighbor_sample(handle, + graph, + d_start_view, + d_start_labels_view, + d_label_list_view, + d_label_to_output_comm_rank_view, + h_fan_out_view, + rng_state, + sampling_options, + FALSE, + &result, + &ret_error); #ifdef NO_CUGRAPH_OPS TEST_ASSERT( @@ -1047,14 +1121,27 @@ int test_uniform_neighbor_sample_sort_by_hop(const cugraph_resource_handle_t* ha handle, (byte_t*)h_weight, result_weights, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_hops, result_hops, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( handle, (byte_t*)h_result_offsets, result_offsets, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + for(int k = 0; k < result_offsets_size-1; k += fan_out_size) { + for(int h = 0; h < fan_out_size; ++h) { + int hop_start = h_result_offsets[k+h]; + int hop_end = h_result_offsets[k+h+1]; + for(int i = hop_start; i < hop_end; ++i) { + h_hops[i] = h; + } + } + } + + size_t num_local_labels = (result_offsets_size - 1) / fan_out_size; + + for(int k = 0; k < num_local_labels+1; ++k) { + h_result_offsets[k] = h_result_offsets[k*fan_out_size]; + } + result_offsets_size = num_local_labels + 1; + // NOTE: The C++ tester does a more thorough validation. For our purposes // here we will do a simpler validation, merely checking that all edges // are actually part of the graph @@ -1223,9 +1310,9 @@ int main(int argc, char** argv) result |= RUN_MG_TEST(test_uniform_neighbor_from_alex, handle); //result |= RUN_MG_TEST(test_uniform_neighbor_sample_alex_bug, handle); result |= RUN_MG_TEST(test_uniform_neighbor_sample_sort_by_hop, handle); - result |= RUN_MG_TEST(test_uniform_neighbor_sample_dedupe_sources, handle); - result |= RUN_MG_TEST(test_uniform_neighbor_sample_unique_sources, handle); - result |= RUN_MG_TEST(test_uniform_neighbor_sample_carry_over_sources, handle); + //result |= RUN_MG_TEST(test_uniform_neighbor_sample_dedupe_sources, handle); + //result |= RUN_MG_TEST(test_uniform_neighbor_sample_unique_sources, handle); + //result |= RUN_MG_TEST(test_uniform_neighbor_sample_carry_over_sources, handle); cugraph_free_resource_handle(handle); free_mg_raft_handle(raft_handle); diff --git a/cpp/tests/c_api/uniform_neighbor_sample_test.c b/cpp/tests/c_api/uniform_neighbor_sample_test.c index a2c1e230485..92f3821e3cc 100644 --- a/cpp/tests/c_api/uniform_neighbor_sample_test.c +++ b/cpp/tests/c_api/uniform_neighbor_sample_test.c @@ -53,6 +53,7 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle vertex_t *h_start, int *h_start_labels, size_t num_start_vertices, + size_t num_start_labels, int *fan_out, size_t fan_out_size, bool_t with_replacement, @@ -192,7 +193,7 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle int32_t h_result_edge_types[result_size]; int32_t h_result_hops[result_size]; size_t h_result_offsets[result_offsets_size]; - int h_result_labels[result_offsets_size-1]; + int h_result_labels[num_start_labels]; vertex_t h_renumber_map[renumber_map_size]; size_t h_renumber_map_offsets[result_offsets_size]; @@ -216,9 +217,7 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle handle, (byte_t*)h_result_edge_types, result_edge_types, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_result_hops, result_hops, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + TEST_ASSERT(test_ret_value, result_hops == NULL, "hops was not empty"); ret_code = cugraph_type_erased_device_array_view_copy_to_host( handle, (byte_t*)h_result_offsets, result_offsets, &ret_error); @@ -228,6 +227,21 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle handle, (byte_t*)h_result_labels, result_labels, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + for(int k = 0; k < result_offsets_size-1; k += fan_out_size) { + for(int h = 0; h < fan_out_size; ++h) { + int hop_start = h_result_offsets[k+h]; + int hop_end = h_result_offsets[k+h+1]; + for(int i = hop_start; i < hop_end; ++i) { + h_result_hops[i] = h; + } + } + } + + for(int k = 0; k < num_start_labels+1; ++k) { + h_result_offsets[k] = h_result_offsets[k*fan_out_size]; + } + result_offsets_size = num_start_labels + 1; + if (renumber_results) { ret_code = cugraph_type_erased_device_array_view_copy_to_host( handle, (byte_t*)h_renumber_map, result_renumber_map, &ret_error); @@ -348,6 +362,7 @@ int generic_uniform_neighbor_sample_test(const cugraph_resource_handle_t* handle for (size_t i = h_result_offsets[label_id]; (i < h_result_offsets[label_id+1]) && (test_ret_value == 0) ; ++i) { if (h_result_hops[i] == hop) { + bool found = false; for (size_t j = 0 ; (!found) && (j < sources_size) ; ++j) { found = renumber_results ? (h_renumber_map[h_renumber_map_offsets[label_id] + h_result_srcs[i]] == check_sources[j]) @@ -516,183 +531,6 @@ int create_test_graph_with_edge_ids(const cugraph_resource_handle_t* p_handle, return test_ret_value; } -int test_uniform_neighbor_sample_with_properties(const cugraph_resource_handle_t* handle) -{ - data_type_id_t vertex_tid = INT32; - data_type_id_t edge_tid = INT32; - data_type_id_t weight_tid = FLOAT32; - data_type_id_t edge_id_tid = INT32; - data_type_id_t edge_type_tid = INT32; - - size_t num_edges = 8; - size_t num_vertices = 6; - size_t fan_out_size = 1; - size_t num_starts = 1; - - vertex_t src[] = {0, 1, 1, 2, 2, 2, 3, 4}; - vertex_t dst[] = {1, 3, 4, 0, 1, 3, 5, 5}; - edge_t edge_ids[] = {0, 1, 2, 3, 4, 5, 6, 7}; - weight_t weight[] = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}; - int32_t edge_types[] = {7, 6, 5, 4, 3, 2, 1, 0}; - vertex_t start[] = {2}; - int fan_out[] = {-1}; - - // Create graph - int test_ret_value = 0; - cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; - cugraph_error_t* ret_error = NULL; - cugraph_graph_t* graph = NULL; - cugraph_sample_result_t* result = NULL; - - ret_code = create_sg_test_graph(handle, - vertex_tid, - edge_tid, - src, - dst, - weight_tid, - weight, - edge_type_tid, - edge_types, - edge_id_tid, - edge_ids, - num_edges, - FALSE, - TRUE, - FALSE, - FALSE, - &graph, - &ret_error); - - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); - - cugraph_type_erased_device_array_t* d_start = NULL; - cugraph_type_erased_device_array_view_t* d_start_view = NULL; - cugraph_type_erased_host_array_view_t* h_fan_out_view = NULL; - - ret_code = - cugraph_type_erased_device_array_create(handle, num_starts, INT32, &d_start, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "d_start create failed."); - - d_start_view = cugraph_type_erased_device_array_view(d_start); - - ret_code = cugraph_type_erased_device_array_view_copy_from_host( - handle, d_start_view, (byte_t*)start, &ret_error); - - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "start copy_from_host failed."); - - h_fan_out_view = cugraph_type_erased_host_array_view_create(fan_out, 1, INT32); - - cugraph_rng_state_t *rng_state; - ret_code = cugraph_rng_state_create(handle, 0, &rng_state, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "rng_state create failed."); - - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, - graph, - d_start_view, - NULL, - NULL, - NULL, - h_fan_out_view, - rng_state, - FALSE, - TRUE, - FALSE, - &result, - &ret_error); - -#ifdef NO_CUGRAPH_OPS - TEST_ASSERT( - test_ret_value, ret_code != CUGRAPH_SUCCESS, "uniform_neighbor_sample should have failed") -#else - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "uniform_neighbor_sample failed."); - - cugraph_type_erased_device_array_view_t* result_srcs; - cugraph_type_erased_device_array_view_t* result_dsts; - cugraph_type_erased_device_array_view_t* result_edge_id; - cugraph_type_erased_device_array_view_t* result_weights; - cugraph_type_erased_device_array_view_t* result_edge_types; - cugraph_type_erased_device_array_view_t* result_hops; - - result_srcs = cugraph_sample_result_get_sources(result); - result_dsts = cugraph_sample_result_get_destinations(result); - result_edge_id = cugraph_sample_result_get_edge_id(result); - result_weights = cugraph_sample_result_get_edge_weight(result); - result_edge_types = cugraph_sample_result_get_edge_type(result); - result_hops = cugraph_sample_result_get_hop(result); - - size_t result_size = cugraph_type_erased_device_array_view_size(result_srcs); - - vertex_t h_srcs[result_size]; - vertex_t h_dsts[result_size]; - edge_t h_edge_id[result_size]; - weight_t h_weight[result_size]; - int32_t h_edge_types[result_size]; - int32_t h_hops[result_size]; - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_srcs, result_srcs, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_dsts, result_dsts, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_edge_id, result_edge_id, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_weight, result_weights, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_edge_types, result_edge_types, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_hops, result_hops, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - - // NOTE: The C++ tester does a more thorough validation. For our purposes - // here we will do a simpler validation, merely checking that all edges - // are actually part of the graph - weight_t M_w[num_vertices][num_vertices]; - edge_t M_edge_id[num_vertices][num_vertices]; - int32_t M_edge_type[num_vertices][num_vertices]; - - for (int i = 0; i < num_vertices; ++i) - for (int j = 0; j < num_vertices; ++j) { - M_w[i][j] = 0.0; - M_edge_id[i][j] = -1; - M_edge_type[i][j] = -1; - } - - for (int i = 0; i < num_edges; ++i) { - M_w[src[i]][dst[i]] = weight[i]; - M_edge_id[src[i]][dst[i]] = edge_ids[i]; - M_edge_type[src[i]][dst[i]] = edge_types[i]; - } - - for (int i = 0; (i < result_size) && (test_ret_value == 0); ++i) { - TEST_ASSERT(test_ret_value, - M_w[h_srcs[i]][h_dsts[i]] == h_weight[i], - "uniform_neighbor_sample got edge that doesn't exist"); - TEST_ASSERT(test_ret_value, - M_edge_id[h_srcs[i]][h_dsts[i]] == h_edge_id[i], - "uniform_neighbor_sample got edge that doesn't exist"); - TEST_ASSERT(test_ret_value, - M_edge_type[h_srcs[i]][h_dsts[i]] == h_edge_types[i], - "uniform_neighbor_sample got edge that doesn't exist"); - } - - cugraph_sample_result_free(result); -#endif - - cugraph_sg_graph_free(graph); - cugraph_error_free(ret_error); -} - int test_uniform_neighbor_sample_with_labels(const cugraph_resource_handle_t* handle) { data_type_id_t vertex_tid = INT32; @@ -722,6 +560,14 @@ int test_uniform_neighbor_sample_with_labels(const cugraph_resource_handle_t* ha cugraph_graph_t* graph = NULL; cugraph_sample_result_t* result = NULL; + bool_t with_replacement = TRUE; + bool_t return_hops = TRUE; + cugraph_prior_sources_behavior_t prior_sources_behavior = DEFAULT; + bool_t dedupe_sources = FALSE; + bool_t renumber_results = FALSE; + cugraph_compression_type_t compression = COO; + bool_t compress_per_hop = FALSE; + ret_code = create_sg_test_graph(handle, vertex_tid, edge_tid, @@ -775,19 +621,31 @@ int test_uniform_neighbor_sample_with_labels(const cugraph_resource_handle_t* ha ret_code = cugraph_rng_state_create(handle, 0, &rng_state, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "rng_state create failed."); - ret_code = cugraph_uniform_neighbor_sample_with_edge_properties(handle, - graph, - d_start_view, - d_start_labels_view, - NULL, - NULL, - h_fan_out_view, - rng_state, - FALSE, - TRUE, - FALSE, - &result, - &ret_error); + cugraph_sampling_options_t *sampling_options; + + ret_code = cugraph_sampling_options_create(&sampling_options, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "sampling_options create failed."); + + cugraph_sampling_set_with_replacement(sampling_options, with_replacement); + cugraph_sampling_set_return_hops(sampling_options, return_hops); + cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior); + cugraph_sampling_set_dedupe_sources(sampling_options, dedupe_sources); + cugraph_sampling_set_renumber_results(sampling_options, renumber_results); + cugraph_sampling_set_compression_type(sampling_options, compression); + cugraph_sampling_set_compress_per_hop(sampling_options, compress_per_hop); + + ret_code = cugraph_uniform_neighbor_sample(handle, + graph, + d_start_view, + d_start_labels_view, + NULL, + NULL, + h_fan_out_view, + rng_state, + sampling_options, + FALSE, + &result, + &ret_error); #ifdef NO_CUGRAPH_OPS TEST_ASSERT( @@ -843,9 +701,7 @@ int test_uniform_neighbor_sample_with_labels(const cugraph_resource_handle_t* ha handle, (byte_t*)h_edge_types, result_edge_types, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); - ret_code = cugraph_type_erased_device_array_view_copy_to_host( - handle, (byte_t*)h_hops, result_hops, &ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + TEST_ASSERT(test_ret_value, result_hops == NULL, "hops was not empty"); ret_code = cugraph_type_erased_device_array_view_copy_to_host( handle, (byte_t*)h_result_offsets, result_offsets, &ret_error); @@ -884,6 +740,7 @@ int test_uniform_neighbor_sample_with_labels(const cugraph_resource_handle_t* ha } cugraph_sample_result_free(result); + cugraph_sampling_options_free(sampling_options); #endif cugraph_sg_graph_free(graph); @@ -902,6 +759,7 @@ int test_uniform_neighbor_sample_clean(const cugraph_resource_handle_t* handle) size_t num_vertices = 6; size_t fan_out_size = 3; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t dst[] = {1, 3, 3, 4, 0, 1, 3, 5, 5}; @@ -923,7 +781,7 @@ int test_uniform_neighbor_sample_clean(const cugraph_resource_handle_t* handle) bool_t renumber_results = FALSE; return generic_uniform_neighbor_sample_test(handle, src, dst, weight, edge_ids, edge_types, num_vertices, num_edges, - start, start_labels, num_starts, + start, start_labels, num_starts, num_start_labels, fan_out, fan_out_size, with_replacement, return_hops, prior_sources_behavior, dedupe_sources, renumber_results); } @@ -940,6 +798,7 @@ int test_uniform_neighbor_sample_dedupe_sources(const cugraph_resource_handle_t* size_t num_vertices = 6; size_t fan_out_size = 3; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t dst[] = {1, 3, 3, 4, 0, 1, 3, 5, 5}; @@ -961,7 +820,7 @@ int test_uniform_neighbor_sample_dedupe_sources(const cugraph_resource_handle_t* bool_t renumber_results = FALSE; return generic_uniform_neighbor_sample_test(handle, src, dst, weight, edge_ids, edge_types, num_vertices, num_edges, - start, start_labels, num_starts, + start, start_labels, num_starts, num_start_labels, fan_out, fan_out_size, with_replacement, return_hops, prior_sources_behavior, dedupe_sources, renumber_results); } @@ -978,6 +837,7 @@ int test_uniform_neighbor_sample_unique_sources(const cugraph_resource_handle_t* size_t num_vertices = 6; size_t fan_out_size = 3; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t dst[] = {1, 2, 3, 4, 0, 1, 3, 5, 5}; @@ -999,7 +859,7 @@ int test_uniform_neighbor_sample_unique_sources(const cugraph_resource_handle_t* bool_t renumber_results = FALSE; return generic_uniform_neighbor_sample_test(handle, src, dst, weight, edge_ids, edge_types, num_vertices, num_edges, - start, start_labels, num_starts, + start, start_labels, num_starts, num_start_labels, fan_out, fan_out_size, with_replacement, return_hops, prior_sources_behavior, dedupe_sources, renumber_results); } @@ -1016,6 +876,7 @@ int test_uniform_neighbor_sample_carry_over_sources(const cugraph_resource_handl size_t num_vertices = 6; size_t fan_out_size = 3; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t dst[] = {1, 2, 3, 4, 0, 1, 3, 5, 5}; @@ -1037,7 +898,7 @@ int test_uniform_neighbor_sample_carry_over_sources(const cugraph_resource_handl bool_t renumber_results = FALSE; return generic_uniform_neighbor_sample_test(handle, src, dst, weight, edge_ids, edge_types, num_vertices, num_edges, - start, start_labels, num_starts, + start, start_labels, num_starts, num_start_labels, fan_out, fan_out_size, with_replacement, return_hops, prior_sources_behavior, dedupe_sources, renumber_results); } @@ -1054,6 +915,7 @@ int test_uniform_neighbor_sample_renumber_results(const cugraph_resource_handle_ size_t num_vertices = 6; size_t fan_out_size = 3; size_t num_starts = 2; + size_t num_start_labels = 2; vertex_t src[] = {0, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t dst[] = {1, 2, 3, 4, 0, 1, 3, 5, 5}; @@ -1075,7 +937,7 @@ int test_uniform_neighbor_sample_renumber_results(const cugraph_resource_handle_ bool_t renumber_results = TRUE; return generic_uniform_neighbor_sample_test(handle, src, dst, weight, edge_ids, edge_types, num_vertices, num_edges, - start, start_labels, num_starts, + start, start_labels, num_starts, num_start_labels, fan_out, fan_out_size, with_replacement, return_hops, prior_sources_behavior, dedupe_sources, renumber_results); } @@ -1087,7 +949,6 @@ int main(int argc, char** argv) handle = cugraph_create_resource_handle(NULL); int result = 0; - result |= RUN_TEST_NEW(test_uniform_neighbor_sample_with_properties, handle); result |= RUN_TEST_NEW(test_uniform_neighbor_sample_with_labels, handle); result |= RUN_TEST_NEW(test_uniform_neighbor_sample_clean, handle); result |= RUN_TEST_NEW(test_uniform_neighbor_sample_dedupe_sources, handle); diff --git a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py index 9e50169b4a7..03746561817 100644 --- a/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/dask/sampling/uniform_neighbor_sample.py @@ -42,6 +42,7 @@ if TYPE_CHECKING: from cugraph import Graph + src_n = "sources" dst_n = "destinations" indices_n = "indices" @@ -71,8 +72,21 @@ def create_empty_df(indices_t, weight_t): def create_empty_df_with_edge_props( - indices_t, weight_t, return_offsets=False, renumber=False + indices_t, + weight_t, + return_offsets=False, + renumber=False, + use_legacy_names=True, + include_hop_column=True, + compression="COO", ): + if compression != "COO": + majors_name = "major_offsets" + else: + majors_name = src_n if use_legacy_names else "majors" + + minors_name = dst_n if use_legacy_names else "minors" + if renumber: empty_df_renumber = cudf.DataFrame( { @@ -84,14 +98,17 @@ def create_empty_df_with_edge_props( if return_offsets: df = cudf.DataFrame( { - src_n: numpy.empty(shape=0, dtype=indices_t), - dst_n: numpy.empty(shape=0, dtype=indices_t), + majors_name: numpy.empty(shape=0, dtype=indices_t), + minors_name: numpy.empty(shape=0, dtype=indices_t), weight_n: numpy.empty(shape=0, dtype=weight_t), edge_id_n: numpy.empty(shape=0, dtype=indices_t), edge_type_n: numpy.empty(shape=0, dtype="int32"), - hop_id_n: numpy.empty(shape=0, dtype="int32"), } ) + + if include_hop_column: + df[hop_id_n] = numpy.empty(shape=0, dtype="int32") + empty_df_offsets = cudf.DataFrame( { offsets_n: numpy.empty(shape=0, dtype="int32"), @@ -106,13 +123,13 @@ def create_empty_df_with_edge_props( else: df = cudf.DataFrame( { - src_n: numpy.empty(shape=0, dtype=indices_t), - dst_n: numpy.empty(shape=0, dtype=indices_t), + majors_name: numpy.empty(shape=0, dtype=indices_t), + minors_name: numpy.empty(shape=0, dtype=indices_t), weight_n: numpy.empty(shape=0, dtype=weight_t), edge_id_n: numpy.empty(shape=0, dtype=indices_t), edge_type_n: numpy.empty(shape=0, dtype="int32"), - hop_id_n: numpy.empty(shape=0, dtype="int32"), batch_id_n: numpy.empty(shape=0, dtype="int32"), + hop_id_n: numpy.empty(shape=0, dtype="int32"), } ) if renumber: @@ -121,102 +138,6 @@ def create_empty_df_with_edge_props( return df -def convert_to_cudf( - cp_arrays, weight_t, with_edge_properties, return_offsets=False, renumber=False -): - """ - Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper - """ - df = cudf.DataFrame() - - if with_edge_properties: - if renumber: - ( - sources, - destinations, - weights, - edge_ids, - edge_types, - batch_ids, - offsets, - hop_ids, - renumber_map, - renumber_map_offsets, - ) = cp_arrays - else: - ( - sources, - destinations, - weights, - edge_ids, - edge_types, - batch_ids, - offsets, - hop_ids, - ) = cp_arrays - - df[src_n] = sources - df[dst_n] = destinations - df[weight_n] = weights - df[edge_id_n] = edge_ids - df[edge_type_n] = edge_types - df[hop_id_n] = hop_ids - - return_dfs = [df] - - if return_offsets: - offsets_df = cudf.DataFrame( - { - batch_id_n: batch_ids, - offsets_n: offsets[:-1], - } - ) - - if renumber: - offsets_df[map_offsets_n] = renumber_map_offsets[:-1] - - return_dfs.append(offsets_df) - else: - batch_ids_b = batch_ids - if len(batch_ids_b) > 0: - batch_ids_b = cudf.Series(batch_ids_b).repeat(cp.diff(offsets)) - batch_ids_b.reset_index(drop=True, inplace=True) - - df[batch_id_n] = batch_ids_b - - if renumber: - renumber_df = cudf.DataFrame( - { - "map": renumber_map, - } - ) - - if not return_offsets: - batch_ids_r = cudf.Series(batch_ids).repeat( - cp.diff(renumber_map_offsets) - ) - batch_ids_r.reset_index(drop=True, inplace=True) - renumber_df["batch_id"] = batch_ids_r - - return_dfs.append(renumber_df) - - return tuple(return_dfs) - else: - cupy_sources, cupy_destinations, cupy_indices = cp_arrays - - df[src_n] = cupy_sources - df[dst_n] = cupy_destinations - df[indices_n] = cupy_indices - - if cupy_indices is not None: - if weight_t == "int32": - df.indices = df.indices.astype("int32") - elif weight_t == "int64": - df.indices = df.indices.astype("int64") - - return (df,) - - def __get_label_to_output_comm_rank(min_batch_id, max_batch_id, n_workers): num_batches = max_batch_id - min_batch_id + 1 num_batches = int(num_batches) @@ -246,6 +167,10 @@ def _call_plc_uniform_neighbor_sample( prior_sources_behavior=None, deduplicate_sources=False, renumber=False, + use_legacy_names=True, + include_hop_column=True, + compress_per_hop=False, + compression="COO", ): st_x = st_x[0] start_list_x = st_x[start_col_name] @@ -259,7 +184,7 @@ def _call_plc_uniform_neighbor_sample( min_batch_id, max_batch_id, n_workers ) - cp_arrays = pylibcugraph_uniform_neighbor_sample( + cupy_array_dict = pylibcugraph_uniform_neighbor_sample( resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), input_graph=mg_graph_x, start_list=start_list_x, @@ -275,13 +200,25 @@ def _call_plc_uniform_neighbor_sample( deduplicate_sources=deduplicate_sources, return_hops=return_hops, renumber=renumber, + compression=compression, + compress_per_hop=compress_per_hop, + return_dict=True, + ) + + # have to import here due to circular import issue + from cugraph.sampling.sampling_utilities import ( + sampling_results_from_cupy_array_dict, ) - return convert_to_cudf( - cp_arrays, + + return sampling_results_from_cupy_array_dict( + cupy_array_dict, weight_t, - with_edge_properties, + len(fanout_vals), + with_edge_properties=with_edge_properties, return_offsets=return_offsets, renumber=renumber, + use_legacy_names=use_legacy_names, + include_hop_column=include_hop_column, ) @@ -304,6 +241,10 @@ def _mg_call_plc_uniform_neighbor_sample( prior_sources_behavior=None, deduplicate_sources=False, renumber=False, + use_legacy_names=True, + include_hop_column=True, + compress_per_hop=False, + compression="COO", ): n_workers = None if keep_batches_together: @@ -335,6 +276,10 @@ def _mg_call_plc_uniform_neighbor_sample( prior_sources_behavior=prior_sources_behavior, deduplicate_sources=deduplicate_sources, renumber=renumber, + use_legacy_names=use_legacy_names, # remove in 23.12 + include_hop_column=include_hop_column, # remove in 23.12 + compress_per_hop=compress_per_hop, + compression=compression, allow_other_workers=False, pure=False, ) @@ -348,6 +293,9 @@ def _mg_call_plc_uniform_neighbor_sample( weight_t, return_offsets=return_offsets, renumber=renumber, + use_legacy_names=use_legacy_names, + compression=compression, + include_hop_column=include_hop_column, ) if with_edge_properties else create_empty_df(indices_t, weight_t) @@ -397,6 +345,7 @@ def uniform_neighbor_sample( input_graph: Graph, start_list: Sequence, fanout_vals: List[int], + *, with_replacement: bool = True, with_edge_properties: bool = False, # deprecated with_batch_ids: bool = False, @@ -406,9 +355,13 @@ def uniform_neighbor_sample( random_state: int = None, return_offsets: bool = False, return_hops: bool = True, + include_hop_column: bool = True, # deprecated prior_sources_behavior: str = None, deduplicate_sources: bool = False, renumber: bool = False, + use_legacy_names=True, # deprecated + compress_per_hop=False, + compression="COO", _multiple_clients: bool = False, ) -> Union[dask_cudf.DataFrame, Tuple[dask_cudf.DataFrame, dask_cudf.DataFrame]]: """ @@ -463,6 +416,12 @@ def uniform_neighbor_sample( corresponding to the hop where the edge appeared. Defaults to True. + include_hop_column: bool, optional (default=True) + Deprecated. Defaults to True. + If True, will include the hop column even if + return_offsets is True. This option will + be removed in release 23.12. + prior_sources_behavior: str (Optional) Options are "carryover", and "exclude". Default will leave the source list as-is. @@ -481,6 +440,21 @@ def uniform_neighbor_sample( will return the renumber map and renumber map offsets as an additional dataframe. + use_legacy_names: bool, optional (default=True) + Whether to use the legacy column names (sources, destinations). + If True, will use "sources" and "destinations" as the column names. + If False, will use "majors" and "minors" as the column names. + Deprecated. Will be removed in release 23.12 in favor of always + using the new names "majors" and "minors". + + compress_per_hop: bool, optional (default=False) + Whether to compress globally (default), or to produce a separate + compressed edgelist per hop. + + compression: str, optional (default=COO) + Sets the compression type for the output minibatches. + Valid options are COO (default), CSR, CSC, DCSR, and DCSC. + _multiple_clients: bool, optional (default=False) internal flag to ensure sampling works with multiple dask clients set to True to prevent hangs in multi-client environment @@ -548,12 +522,46 @@ def uniform_neighbor_sample( Contains the batch offsets for the renumber maps """ + if compression not in ["COO", "CSR", "CSC", "DCSR", "DCSC"]: + raise ValueError("compression must be one of COO, CSR, CSC, DCSR, or DCSC") + if with_edge_properties: warning_msg = ( "The with_edge_properties flag is deprecated" " and will be removed in the next release." ) - warnings.warn(warning_msg, DeprecationWarning) + warnings.warn(warning_msg, FutureWarning) + + if ( + (compression != "COO") + and (not compress_per_hop) + and prior_sources_behavior != "exclude" + ): + raise ValueError( + "hop-agnostic compression is only supported with" + " the exclude prior sources behavior due to limitations " + "of the libcugraph C++ API" + ) + + if compress_per_hop and prior_sources_behavior != "carryover": + raise ValueError( + "Compressing the edgelist per hop is only supported " + "with the carryover prior sources behavior due to limitations" + " of the libcugraph C++ API" + ) + + if include_hop_column: + warning_msg = ( + "The include_hop_column flag is deprecated and will be" + " removed in the next release in favor of always " + "excluding the hop column when return_offsets is True" + ) + warnings.warn(warning_msg, FutureWarning) + + if compression != "COO": + raise ValueError( + "Including the hop id column is only supported with COO compression." + ) if isinstance(start_list, int): start_list = [start_list] @@ -643,6 +651,31 @@ def uniform_neighbor_sample( ddf = persist_dask_df_equal_parts_per_worker(ddf, client) ddf = get_persisted_df_worker_map(ddf, client) + sample_call_kwargs = { + "client": client, + "session_id": session_id, + "input_graph": input_graph, + "ddf": ddf, + "keep_batches_together": keep_batches_together, + "min_batch_id": min_batch_id, + "max_batch_id": max_batch_id, + "fanout_vals": fanout_vals, + "with_replacement": with_replacement, + "weight_t": weight_t, + "indices_t": indices_t, + "with_edge_properties": with_edge_properties, + "random_state": random_state, + "return_offsets": return_offsets, + "return_hops": return_hops, + "prior_sources_behavior": prior_sources_behavior, + "deduplicate_sources": deduplicate_sources, + "renumber": renumber, + "use_legacy_names": use_legacy_names, + "include_hop_column": include_hop_column, + "compress_per_hop": compress_per_hop, + "compression": compression, + } + if _multiple_clients: # Distributed centralized lock to allow # two disconnected processes (clients) to coordinate a lock @@ -650,26 +683,7 @@ def uniform_neighbor_sample( lock = Lock("plc_graph_access") if lock.acquire(timeout=100): try: - ddf = _mg_call_plc_uniform_neighbor_sample( - client=client, - session_id=session_id, - input_graph=input_graph, - ddf=ddf, - keep_batches_together=keep_batches_together, - min_batch_id=min_batch_id, - max_batch_id=max_batch_id, - fanout_vals=fanout_vals, - with_replacement=with_replacement, - weight_t=weight_t, - indices_t=indices_t, - with_edge_properties=with_edge_properties, - random_state=random_state, - return_offsets=return_offsets, - return_hops=return_hops, - prior_sources_behavior=prior_sources_behavior, - deduplicate_sources=deduplicate_sources, - renumber=renumber, - ) + ddf = _mg_call_plc_uniform_neighbor_sample(**sample_call_kwargs) finally: lock.release() else: @@ -677,26 +691,7 @@ def uniform_neighbor_sample( "Failed to acquire lock(plc_graph_access) while trying to sampling" ) else: - ddf = _mg_call_plc_uniform_neighbor_sample( - client=client, - session_id=session_id, - input_graph=input_graph, - ddf=ddf, - keep_batches_together=keep_batches_together, - min_batch_id=min_batch_id, - max_batch_id=max_batch_id, - fanout_vals=fanout_vals, - with_replacement=with_replacement, - weight_t=weight_t, - indices_t=indices_t, - with_edge_properties=with_edge_properties, - random_state=random_state, - return_offsets=return_offsets, - return_hops=return_hops, - prior_sources_behavior=prior_sources_behavior, - deduplicate_sources=deduplicate_sources, - renumber=renumber, - ) + ddf = _mg_call_plc_uniform_neighbor_sample(**sample_call_kwargs) if return_offsets: if renumber: @@ -708,9 +703,12 @@ def uniform_neighbor_sample( ddf, renumber_df = ddf if input_graph.renumbered and not renumber: - ddf = input_graph.unrenumber(ddf, "sources", preserve_order=True) - ddf = input_graph.unrenumber(ddf, "destinations", preserve_order=True) - + if use_legacy_names: + ddf = input_graph.unrenumber(ddf, "sources", preserve_order=True) + ddf = input_graph.unrenumber(ddf, "destinations", preserve_order=True) + else: + ddf = input_graph.unrenumber(ddf, "majors", preserve_order=True) + ddf = input_graph.unrenumber(ddf, "minors", preserve_order=True) if return_offsets: if renumber: return ddf, offsets_df, renumber_df diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py index 92caba6dbaf..dbfcb124ce5 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler.py @@ -269,6 +269,7 @@ def flush(self) -> None: with_edge_properties=True, return_offsets=True, renumber=self.__renumber, + # use_legacy_names=False, ) if self.__renumber: diff --git a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py index e9e5be26fc3..7e67eab83c9 100644 --- a/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py +++ b/python/cugraph/cugraph/gnn/data_loading/bulk_sampler_io.py @@ -15,10 +15,24 @@ import cudf import cupy -from typing import Union, Optional +from math import ceil +from pandas import isna -def _write_samples_to_parquet( +from typing import Union, Optional, List + + +def create_df_from_disjoint_series(series_list: List[cudf.Series]): + series_list.sort(key=lambda s: len(s), reverse=True) + + df = cudf.DataFrame() + for s in series_list: + df[s.name] = s + + return df + + +def _write_samples_to_parquet_csr( results: cudf.DataFrame, offsets: cudf.DataFrame, renumber_map: cudf.DataFrame, @@ -27,7 +41,184 @@ def _write_samples_to_parquet( partition_info: Optional[Union[dict, str]] = None, ) -> cudf.Series: """ - Writes the samples to parquet. + Writes CSR/CSC compressed samples to parquet. + + Batches that are empty are discarded, and the remaining non-empty + batches are renumbered to be contiguous starting from the first + batch id. This means that the output batch ids may not match + the input batch ids. + + results: cudf.DataFrame + The results dataframe containing the sampled minibatches. + offsets: cudf.DataFrame + The offsets dataframe indicating the start/end of each minibatch + in the reuslts dataframe. + renumber_map: cudf.DataFrame + The renumber map containing the mapping of renumbered vertex ids + to original vertex ids. + batches_per_partition: int + The maximum number of minibatches allowed per written parquet partition. + output_path: str + The output path (where parquet files should be written to). + partition_info: Union[dict, str] + Either a dictionary containing partition data from dask, the string 'sg' + indicating that this is a single GPU write, or None indicating that this + function should perform a no-op (required by dask). + + Returns an empty cudf series. + """ + # Required by dask; need to skip dummy partitions. + if partition_info is None or len(results) == 0: + return cudf.Series(dtype="int64") + if partition_info != "sg" and (not isinstance(partition_info, dict)): + raise ValueError("Invalid value of partition_info") + + # Additional check to skip dummy partitions required for CSR format. + if isna(offsets.batch_id.iloc[0]): + return cudf.Series(dtype="int64") + + # Output: + # major_offsets - CSR/CSC row/col pointers + # minors - CSR/CSC col/row indices + # edge id - edge ids (same shape as minors) + # edge type - edge types (same shape as minors) + # weight - edge weight (same shape as minors) + # renumber map - the original vertex ids + # renumber map offsets - start/end of the map for each batch + # (only 1 per batch b/c of framework + # stipulations making this legal) + # label-hop offsets - indicate the start/end of each hop + # for each batch + + batch_ids = offsets.batch_id + label_hop_offsets = offsets.offsets + renumber_map_offsets = offsets.renumber_map_offsets + del offsets + + batch_ids.dropna(inplace=True) + label_hop_offsets.dropna(inplace=True) + renumber_map_offsets.dropna(inplace=True) + + major_offsets_array = results.major_offsets + results.drop(columns="major_offsets", inplace=True) + major_offsets_array.dropna(inplace=True) + major_offsets_array = major_offsets_array.values + + minors_array = results.minors + results.drop(columns="minors", inplace=True) + minors_array.dropna(inplace=True) + minors_array = minors_array.values + + weight_array = results.weight + results.drop(columns="weight", inplace=True) + weight_array.dropna(inplace=True) + weight_array = ( + cupy.array([], dtype="float32") if weight_array.empty else weight_array.values + ) + + edge_id_array = results.edge_id + results.drop(columns="edge_id", inplace=True) + edge_id_array.dropna(inplace=True) + edge_id_array = ( + cupy.array([], dtype="int64") if edge_id_array.empty else edge_id_array.values + ) + + edge_type_array = results.edge_type + results.drop(columns="edge_type", inplace=True) + edge_type_array.dropna(inplace=True) + edge_type_array = ( + cupy.array([], dtype="int32") + if edge_type_array.empty + else edge_type_array.values + ) + + del results + + offsets_length = len(label_hop_offsets) - 1 + if offsets_length % len(batch_ids) != 0: + raise ValueError("Invalid hop offsets") + fanout_length = int(offsets_length / len(batch_ids)) + + for p in range(0, int(ceil(len(batch_ids) / batches_per_partition))): + partition_start = p * (batches_per_partition) + partition_end = (p + 1) * (batches_per_partition) + + label_hop_offsets_current_partition = label_hop_offsets.iloc[ + partition_start * fanout_length : partition_end * fanout_length + 1 + ].reset_index(drop=True) + label_hop_offsets_current_partition.name = "label_hop_offsets" + + batch_ids_current_partition = batch_ids.iloc[partition_start:partition_end] + + ( + major_offsets_start, + major_offsets_end, + ) = label_hop_offsets_current_partition.iloc[ + [0, -1] + ].values # legal since offsets has the 1 extra offset + results_start, results_end = major_offsets_array[ + [major_offsets_start, major_offsets_end] + ] # avoid d2h copy + + # no need to use end batch id, just ensure the batch is labeled correctly + start_batch_id = batch_ids_current_partition.iloc[0] + # end_batch_id = batch_ids_current_partition.iloc[-1] + + # create the renumber map offsets + renumber_map_offsets_current_partition = renumber_map_offsets.iloc[ + partition_start : partition_end + 1 + ].reset_index(drop=True) + renumber_map_offsets_current_partition.name = "renumber_map_offsets" + + ( + renumber_map_start, + renumber_map_end, + ) = renumber_map_offsets_current_partition.iloc[ + [0, -1] + ].values # avoid d2h copy + + results_current_partition = create_df_from_disjoint_series( + [ + cudf.Series(minors_array[results_start:results_end], name="minors"), + cudf.Series( + renumber_map.map.values[renumber_map_start:renumber_map_end], + name="map", + ), + label_hop_offsets_current_partition, + cudf.Series( + major_offsets_array[major_offsets_start : major_offsets_end + 1], + name="major_offsets", + ), + cudf.Series(weight_array[results_start:results_end], name="weight"), + cudf.Series(edge_id_array[results_start:results_end], name="edge_id"), + cudf.Series( + edge_type_array[results_start:results_end], name="edge_type" + ), + renumber_map_offsets_current_partition, + ] + ) + + end_batch_id = start_batch_id + len(batch_ids_current_partition) - 1 + filename = f"batch={start_batch_id}-{end_batch_id}.parquet" + full_output_path = os.path.join(output_path, filename) + + results_current_partition.to_parquet( + full_output_path, compression=None, index=False, force_nullable_schema=True + ) + + return cudf.Series(dtype="int64") + + +def _write_samples_to_parquet_coo( + results: cudf.DataFrame, + offsets: cudf.DataFrame, + renumber_map: cudf.DataFrame, + batches_per_partition: int, + output_path: str, + partition_info: Optional[Union[dict, str]] = None, +) -> cudf.Series: + """ + Writes COO compressed samples to parquet. Batches that are empty are discarded, and the remaining non-empty batches are renumbered to be contiguous starting from the first @@ -60,8 +251,10 @@ def _write_samples_to_parquet( if partition_info != "sg" and (not isinstance(partition_info, dict)): raise ValueError("Invalid value of partition_info") + offsets = offsets[:-1] + # Offsets is always in order, so the last batch id is always the highest - max_batch_id = offsets.batch_id.iloc[len(offsets) - 1] + max_batch_id = offsets.batch_id.iloc[-1] results.dropna(axis=1, how="all", inplace=True) results["hop_id"] = results["hop_id"].astype("uint8") @@ -182,9 +375,23 @@ def write_samples( output_path: str The output path (where parquet files should be written to). """ + + if ("majors" in results.columns) and ("minors" in results.columns): + write_fn = _write_samples_to_parquet_coo + + # TODO these names will be deprecated in release 23.12 + elif ("sources" in results.columns) and ("destinations" in results.columns): + write_fn = _write_samples_to_parquet_coo + + elif "major_offsets" in results.columns and "minors" in results.columns: + write_fn = _write_samples_to_parquet_csr + + else: + raise ValueError("invalid columns") + if hasattr(results, "compute"): results.map_partitions( - _write_samples_to_parquet, + write_fn, offsets, renumber_map, batches_per_partition, @@ -194,7 +401,7 @@ def write_samples( ).compute() else: - _write_samples_to_parquet( + write_fn( results, offsets, renumber_map, diff --git a/python/cugraph/cugraph/sampling/sampling_utilities.py b/python/cugraph/cugraph/sampling/sampling_utilities.py new file mode 100644 index 00000000000..50c315129dc --- /dev/null +++ b/python/cugraph/cugraph/sampling/sampling_utilities.py @@ -0,0 +1,198 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import cupy +import cudf + +import warnings + + +def sampling_results_from_cupy_array_dict( + cupy_array_dict, + weight_t, + num_hops, + with_edge_properties=False, + return_offsets=False, + renumber=False, + use_legacy_names=True, + include_hop_column=True, +): + """ + Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper + """ + results_df = cudf.DataFrame() + + if use_legacy_names: + major_col_name = "sources" + minor_col_name = "destinations" + warning_msg = ( + "The legacy column names (sources, destinations)" + " will no longer be supported for uniform_neighbor_sample" + " in release 23.12. The use_legacy_names=False option will" + " become the only option, and (majors, minors) will be the" + " only supported column names." + ) + warnings.warn(warning_msg, FutureWarning) + else: + major_col_name = "majors" + minor_col_name = "minors" + + if with_edge_properties: + majors = cupy_array_dict["majors"] + if majors is not None: + results_df["majors"] = majors + + results_df_cols = [ + "minors", + "weight", + "edge_id", + "edge_type", + ] + + for col in results_df_cols: + array = cupy_array_dict[col] + # The length of each of these arrays should be the same + results_df[col] = array + + results_df.rename( + columns={"majors": major_col_name, "minors": minor_col_name}, inplace=True + ) + + label_hop_offsets = cupy_array_dict["label_hop_offsets"] + batch_ids = cupy_array_dict["batch_id"] + + if renumber: + renumber_df = cudf.DataFrame( + { + "map": cupy_array_dict["renumber_map"], + } + ) + + if not return_offsets: + if len(batch_ids) > 0: + batch_ids_r = cudf.Series(batch_ids).repeat( + cupy.diff(cupy_array_dict["renumber_map_offsets"]) + ) + batch_ids_r.reset_index(drop=True, inplace=True) + renumber_df["batch_id"] = batch_ids_r + else: + renumber_df["batch_id"] = None + + if return_offsets: + batches_series = cudf.Series( + batch_ids, + name="batch_id", + ) + if include_hop_column: + # TODO remove this logic in release 23.12 + offsets_df = cudf.Series( + label_hop_offsets[cupy.arange(len(batch_ids) + 1) * num_hops], + name="offsets", + ).to_frame() + else: + offsets_df = cudf.Series( + label_hop_offsets, + name="offsets", + ).to_frame() + + if len(batches_series) > len(offsets_df): + # this is extremely rare so the inefficiency is ok + offsets_df = offsets_df.join(batches_series, how="outer").sort_index() + else: + offsets_df["batch_id"] = batches_series + + if renumber: + renumber_offset_series = cudf.Series( + cupy_array_dict["renumber_map_offsets"], name="renumber_map_offsets" + ) + + if len(renumber_offset_series) > len(offsets_df): + # this is extremely rare so the inefficiency is ok + offsets_df = offsets_df.join( + renumber_offset_series, how="outer" + ).sort_index() + else: + offsets_df["renumber_map_offsets"] = renumber_offset_series + + else: + if len(batch_ids) > 0: + batch_ids_r = cudf.Series(cupy.repeat(batch_ids, num_hops)) + batch_ids_r = cudf.Series(batch_ids_r).repeat( + cupy.diff(label_hop_offsets) + ) + batch_ids_r.reset_index(drop=True, inplace=True) + + results_df["batch_id"] = batch_ids_r + else: + results_df["batch_id"] = None + + # TODO remove this logic in release 23.12, hops will always returned as offsets + if include_hop_column: + if len(batch_ids) > 0: + hop_ids_r = cudf.Series(cupy.arange(num_hops)) + hop_ids_r = cudf.concat([hop_ids_r] * len(batch_ids), ignore_index=True) + + # generate the hop column + hop_ids_r = ( + cudf.Series(hop_ids_r, name="hop_id") + .repeat(cupy.diff(label_hop_offsets)) + .reset_index(drop=True) + ) + else: + hop_ids_r = cudf.Series(name="hop_id", dtype="int32") + + results_df = results_df.join(hop_ids_r, how="outer").sort_index() + + if major_col_name not in results_df: + if use_legacy_names: + raise ValueError("Can't use legacy names with major offsets") + + major_offsets_series = cudf.Series( + cupy_array_dict["major_offsets"], name="major_offsets" + ) + if len(major_offsets_series) > len(results_df): + # this is extremely rare so the inefficiency is ok + results_df = results_df.join( + major_offsets_series, how="outer" + ).sort_index() + else: + results_df["major_offsets"] = major_offsets_series + + else: + # TODO this is deprecated, remove it in 23.12 + + results_df[major_col_name] = cupy_array_dict["sources"] + results_df[minor_col_name] = cupy_array_dict["destinations"] + indices = cupy_array_dict["indices"] + + if indices is None: + results_df["indices"] = None + else: + results_df["indices"] = indices + if weight_t == "int32": + results_df["indices"] = indices.astype("int32") + elif weight_t == "int64": + results_df["indices"] = indices.astype("int64") + else: + results_df["indices"] = indices + + if return_offsets: + if renumber: + return results_df, offsets_df, renumber_df + else: + return results_df, offsets_df + + if renumber: + return results_df, renumber_df + + return (results_df,) diff --git a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py index 219854bb002..1832585c0ab 100644 --- a/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/sampling/uniform_neighbor_sample.py @@ -16,6 +16,8 @@ from pylibcugraph import ResourceHandle from pylibcugraph import uniform_neighbor_sample as pylibcugraph_uniform_neighbor_sample +from cugraph.sampling.sampling_utilities import sampling_results_from_cupy_array_dict + import numpy import cudf @@ -58,15 +60,20 @@ def uniform_neighbor_sample( G: Graph, start_list: Sequence, fanout_vals: List[int], + *, with_replacement: bool = True, with_edge_properties: bool = False, # deprecated with_batch_ids: bool = False, random_state: int = None, return_offsets: bool = False, return_hops: bool = True, + include_hop_column: bool = True, # deprecated prior_sources_behavior: str = None, deduplicate_sources: bool = False, renumber: bool = False, + use_legacy_names: bool = True, # deprecated + compress_per_hop: bool = False, + compression: str = "COO", ) -> Union[cudf.DataFrame, Tuple[cudf.DataFrame, cudf.DataFrame]]: """ Does neighborhood sampling, which samples nodes from a graph based on the @@ -111,6 +118,12 @@ def uniform_neighbor_sample( corresponding to the hop where the edge appeared. Defaults to True. + include_hop_column: bool, optional (default=True) + Deprecated. Defaults to True. + If True, will include the hop column even if + return_offsets is True. This option will + be removed in release 23.12. + prior_sources_behavior: str, optional (default=None) Options are "carryover", and "exclude". Default will leave the source list as-is. @@ -129,6 +142,21 @@ def uniform_neighbor_sample( will return the renumber map and renumber map offsets as an additional dataframe. + use_legacy_names: bool, optional (default=True) + Whether to use the legacy column names (sources, destinations). + If True, will use "sources" and "destinations" as the column names. + If False, will use "majors" and "minors" as the column names. + Deprecated. Will be removed in release 23.12 in favor of always + using the new names "majors" and "minors". + + compress_per_hop: bool, optional (default=False) + Whether to compress globally (default), or to produce a separate + compressed edgelist per hop. + + compression: str, optional (default=COO) + Sets the compression type for the output minibatches. + Valid options are COO (default), CSR, CSC, DCSR, and DCSC. + Returns ------- result : cudf.DataFrame or Tuple[cudf.DataFrame, cudf.DataFrame] @@ -193,12 +221,62 @@ def uniform_neighbor_sample( Contains the batch offsets for the renumber maps """ + if use_legacy_names: + major_col_name = "sources" + minor_col_name = "destinations" + warning_msg = ( + "The legacy column names (sources, destinations)" + " will no longer be supported for uniform_neighbor_sample" + " in release 23.12. The use_legacy_names=False option will" + " become the only option, and (majors, minors) will be the" + " only supported column names." + ) + warnings.warn(warning_msg, FutureWarning) + else: + major_col_name = "majors" + minor_col_name = "minors" + + if compression not in ["COO", "CSR", "CSC", "DCSR", "DCSC"]: + raise ValueError("compression must be one of COO, CSR, CSC, DCSR, or DCSC") + + if ( + (compression != "COO") + and (not compress_per_hop) + and prior_sources_behavior != "exclude" + ): + raise ValueError( + "hop-agnostic compression is only supported with" + " the exclude prior sources behavior due to limitations " + "of the libcugraph C++ API" + ) + + if compress_per_hop and prior_sources_behavior != "carryover": + raise ValueError( + "Compressing the edgelist per hop is only supported " + "with the carryover prior sources behavior due to limitations" + " of the libcugraph C++ API" + ) + + if include_hop_column: + warning_msg = ( + "The include_hop_column flag is deprecated and will be" + " removed in the next release in favor of always " + "excluding the hop column when return_offsets is True" + ) + warnings.warn(warning_msg, FutureWarning) + + if compression != "COO": + raise ValueError( + "Including the hop id column is only supported with COO compression." + ) + if with_edge_properties: warning_msg = ( "The with_edge_properties flag is deprecated" - " and will be removed in the next release." + " and will be removed in the next release in favor" + " of returning all properties in the graph" ) - warnings.warn(warning_msg, DeprecationWarning) + warnings.warn(warning_msg, FutureWarning) if isinstance(start_list, int): start_list = [start_list] @@ -255,7 +333,7 @@ def uniform_neighbor_sample( start_list = G.lookup_internal_vertex_id(start_list, columns) start_list = start_list.rename(columns={columns[0]: start_col_name}) - sampling_result = pylibcugraph_uniform_neighbor_sample( + sampling_result_array_dict = pylibcugraph_uniform_neighbor_sample( resource_handle=ResourceHandle(), input_graph=G._plc_graph, start_list=start_list[start_col_name], @@ -271,104 +349,27 @@ def uniform_neighbor_sample( deduplicate_sources=deduplicate_sources, return_hops=return_hops, renumber=renumber, + compression=compression, + compress_per_hop=compress_per_hop, + return_dict=True, ) - df = cudf.DataFrame() - - if with_edge_properties: - # TODO use a dictionary at PLC w/o breaking users - if renumber: - ( - sources, - destinations, - weights, - edge_ids, - edge_types, - batch_ids, - offsets, - hop_ids, - renumber_map, - renumber_map_offsets, - ) = sampling_result - else: - ( - sources, - destinations, - weights, - edge_ids, - edge_types, - batch_ids, - offsets, - hop_ids, - ) = sampling_result - - df["sources"] = sources - df["destinations"] = destinations - df["weight"] = weights - df["edge_id"] = edge_ids - df["edge_type"] = edge_types - df["hop_id"] = hop_ids - - if renumber: - renumber_df = cudf.DataFrame( - { - "map": renumber_map, - } - ) - - if not return_offsets: - batch_ids_r = cudf.Series(batch_ids).repeat( - cp.diff(renumber_map_offsets) - ) - batch_ids_r.reset_index(drop=True, inplace=True) - renumber_df["batch_id"] = batch_ids_r - - if return_offsets: - offsets_df = cudf.DataFrame( - { - "batch_id": batch_ids, - "offsets": offsets[:-1], - } - ) - - if renumber: - offsets_df["renumber_map_offsets"] = renumber_map_offsets[:-1] - - else: - if len(batch_ids) > 0: - batch_ids = cudf.Series(batch_ids).repeat(cp.diff(offsets)) - batch_ids.reset_index(drop=True, inplace=True) - - df["batch_id"] = batch_ids - - else: - sources, destinations, indices = sampling_result - - df["sources"] = sources - df["destinations"] = destinations - - if indices is None: - df["indices"] = None - else: - df["indices"] = indices - if weight_t == "int32": - df["indices"] = indices.astype("int32") - elif weight_t == "int64": - df["indices"] = indices.astype("int64") - else: - df["indices"] = indices + dfs = sampling_results_from_cupy_array_dict( + sampling_result_array_dict, + weight_t, + len(fanout_vals), + with_edge_properties=with_edge_properties, + return_offsets=return_offsets, + renumber=renumber, + use_legacy_names=use_legacy_names, + include_hop_column=include_hop_column, + ) if G.renumbered and not renumber: - df = G.unrenumber(df, "sources", preserve_order=True) - df = G.unrenumber(df, "destinations", preserve_order=True) - - if return_offsets: - if renumber: - return df, offsets_df, renumber_df - else: - return df, offsets_df + dfs[0] = G.unrenumber(dfs[0], major_col_name, preserve_order=True) + dfs[0] = G.unrenumber(dfs[0], minor_col_name, preserve_order=True) - if renumber: - return df, renumber_df + if len(dfs) > 1: + return dfs - return df + return dfs[0] diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py index 5ea79e0893a..a945881394b 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler.py @@ -16,7 +16,7 @@ import cudf import cupy import cugraph -from cugraph.datasets import karate +from cugraph.datasets import karate, email_Eu_core from cugraph.experimental.gnn import BulkSampler from cugraph.utilities.utils import create_directory_with_overwrite @@ -297,3 +297,53 @@ def test_bulk_sampler_empty_batches(scratch_dir): assert df.batch_id.max() == 1 shutil.rmtree(samples_path) + + +@pytest.mark.sg +def test_bulk_sampler_csr(scratch_dir): + el = email_Eu_core.get_edgelist() + + G = cugraph.Graph(directed=True) + G.from_cudf_edgelist(el, source="src", destination="dst") + + samples_path = os.path.join(scratch_dir, "test_bulk_sampler_csr") + create_directory_with_overwrite(samples_path) + + bs = BulkSampler( + batch_size=7, + output_path=samples_path, + graph=G, + fanout_vals=[5, 4, 3], + with_replacement=False, + batches_per_partition=7, + renumber=True, + use_legacy_names=False, + compression="CSR", + compress_per_hop=False, + prior_sources_behavior="exclude", + include_hop_column=False, + ) + + seeds = G.select_random_vertices(62, 1000) + batch_ids = cudf.Series( + cupy.repeat(cupy.arange(int(1000 / 7) + 1, dtype="int32"), 7)[:1000] + ).sort_values() + + batch_df = cudf.DataFrame( + { + "seed": seeds, + "batch": batch_ids, + } + ) + + bs.add_batches(batch_df, start_col_name="seed", batch_col_name="batch") + bs.flush() + + assert len(os.listdir(samples_path)) == 21 + + for file in os.listdir(samples_path): + df = cudf.read_parquet(os.path.join(samples_path, file)) + + assert df.major_offsets.dropna().iloc[-1] - df.major_offsets.iloc[0] == len(df) + + shutil.rmtree(samples_path) diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py index f71c16a8368..5eafe89ea83 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io.py @@ -16,6 +16,7 @@ import pytest +import cupy import cudf from cugraph.gnn.data_loading.bulk_sampler_io import write_samples from cugraph.utilities.utils import create_directory_with_overwrite @@ -34,7 +35,9 @@ def test_bulk_sampler_io(scratch_dir): } ) - offsets = cudf.DataFrame({"offsets": [0, 8], "batch_id": [0, 1]}) + assert len(results) == 12 + + offsets = cudf.DataFrame({"offsets": [0, 8, 12], "batch_id": [0, 1, None]}) samples_path = os.path.join(scratch_dir, "test_bulk_sampler_io") create_directory_with_overwrite(samples_path) @@ -138,8 +141,12 @@ def test_bulk_sampler_io_empty_batch(scratch_dir): } ) + assert len(results) == 20 + # some batches are missing - offsets = cudf.DataFrame({"offsets": [0, 8, 12, 16], "batch_id": [0, 3, 4, 10]}) + offsets = cudf.DataFrame( + {"offsets": [0, 8, 12, 16, 20], "batch_id": [0, 3, 4, 10, None]} + ) samples_path = os.path.join(scratch_dir, "test_bulk_sampler_io_empty_batch") create_directory_with_overwrite(samples_path) @@ -157,3 +164,61 @@ def test_bulk_sampler_io_empty_batch(scratch_dir): df1 = cudf.read_parquet(os.path.join(samples_path, "batch=4-5.parquet")) assert df1.batch_id.min() == 4 assert df1.batch_id.max() == 5 + + shutil.rmtree(samples_path) + + +@pytest.mark.sg +def test_bulk_sampler_io_mock_csr(scratch_dir): + major_offsets_array = cudf.Series([0, 5, 10, 15]) + minors_array = cudf.Series([1, 2, 3, 4, 8, 9, 1, 3, 4, 5, 3, 0, 4, 9, 1]) + edge_ids = cudf.Series(cupy.arange(len(minors_array))) + + # 2 hops + label_hop_offsets = cudf.Series([0, 1, 3]) + + # map + renumber_map = cudf.Series(cupy.arange(10)) + renumber_map_offsets = cudf.Series([0, 10]) + + results_df = cudf.DataFrame() + results_df["minors"] = minors_array + results_df["major_offsets"] = major_offsets_array + results_df["edge_id"] = edge_ids + results_df["edge_type"] = None + results_df["weight"] = None + + offsets_df = cudf.DataFrame() + offsets_df["offsets"] = label_hop_offsets + offsets_df["renumber_map_offsets"] = renumber_map_offsets + offsets_df["batch_id"] = cudf.Series([0]) + + renumber_df = cudf.DataFrame() + renumber_df["map"] = renumber_map + + samples_path = os.path.join(scratch_dir, "test_bulk_sampler_io_mock_csr") + create_directory_with_overwrite(samples_path) + + write_samples(results_df, offsets_df, renumber_df, 1, samples_path) + + result = cudf.read_parquet(os.path.join(samples_path, "batch=0-0.parquet")) + + assert ( + result.minors.dropna().values_host.tolist() == minors_array.values_host.tolist() + ) + assert ( + result.major_offsets.dropna().values_host.tolist() + == major_offsets_array.values_host.tolist() + ) + assert result.edge_id.dropna().values_host.tolist() == edge_ids.values_host.tolist() + assert ( + result.renumber_map_offsets.dropna().values_host.tolist() + == renumber_map_offsets.values_host.tolist() + ) + assert result.map.dropna().values_host.tolist() == renumber_map.values_host.tolist() + assert ( + result.label_hop_offsets.dropna().values_host.tolist() + == label_hop_offsets.values_host.tolist() + ) + + shutil.rmtree(samples_path) diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py index 41f68c08e5c..638cccbdcaa 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_io_mg.py @@ -38,8 +38,12 @@ def test_bulk_sampler_io(scratch_dir): divisions=[0, 8, 11] ) - offsets = cudf.DataFrame({"offsets": [0, 0], "batch_id": [0, 1]}) - offsets = dask_cudf.from_cudf(offsets, npartitions=2) + assert len(results) == 12 + + offsets = cudf.DataFrame({"offsets": [0, 8, 0, 4], "batch_id": [0, None, 1, None]}) + offsets = dask_cudf.from_cudf(offsets, npartitions=1).repartition( + divisions=[0, 2, 3] + ) samples_path = os.path.join(scratch_dir, "mg_test_bulk_sampler_io") create_directory_with_overwrite(samples_path) @@ -149,9 +153,11 @@ def test_bulk_sampler_io_empty_batch(scratch_dir): ) # some batches are missing - offsets = cudf.DataFrame({"offsets": [0, 8, 0, 4], "batch_id": [0, 3, 4, 10]}) + offsets = cudf.DataFrame( + {"offsets": [0, 8, 12, 0, 4, 8], "batch_id": [0, 3, None, 4, 10, None]} + ) offsets = dask_cudf.from_cudf(offsets, npartitions=1).repartition( - divisions=[0, 2, 3] + divisions=[0, 3, 5] ) samples_path = os.path.join(scratch_dir, "mg_test_bulk_sampler_io_empty_batch") diff --git a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py index eded435f897..aee81e5ffed 100644 --- a/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_bulk_sampler_mg.py @@ -21,7 +21,7 @@ import cupy import cugraph import dask_cudf -from cugraph.datasets import karate +from cugraph.datasets import karate, email_Eu_core from cugraph.experimental import BulkSampler from cugraph.utilities.utils import create_directory_with_overwrite @@ -247,3 +247,59 @@ def test_bulk_sampler_empty_batches(dask_client, scratch_dir): assert df.batch_id.max() == 1 shutil.rmtree(samples_path) + + +@pytest.mark.mg +@pytest.mark.parametrize("mg_input", [True, False]) +def test_bulk_sampler_csr(dask_client, scratch_dir, mg_input): + nworkers = len(dask_client.scheduler_info()["workers"]) + el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=nworkers * 2) + + G = cugraph.Graph(directed=True) + G.from_dask_cudf_edgelist(el, source="src", destination="dst") + + samples_path = os.path.join(scratch_dir, "mg_test_bulk_sampler_csr") + create_directory_with_overwrite(samples_path) + + bs = BulkSampler( + batch_size=7, + output_path=samples_path, + graph=G, + fanout_vals=[5, 4, 3], + with_replacement=False, + batches_per_partition=7, + renumber=True, + use_legacy_names=False, + compression="CSR", + compress_per_hop=True, + prior_sources_behavior="carryover", + deduplicate_sources=True, + include_hop_column=False, + ) + + seeds = G.select_random_vertices(62, 1000) + batch_ids = cudf.Series( + cupy.repeat(cupy.arange(int(1000 / 7) + 1, dtype="int32"), 7)[:1000] + ).sort_values() + + batch_df = cudf.DataFrame( + { + "seed": seeds.compute().values, + "batch": batch_ids, + } + ) + + if mg_input: + batch_df = dask_cudf.from_cudf(batch_df, npartitions=2) + + bs.add_batches(batch_df, start_col_name="seed", batch_col_name="batch") + bs.flush() + + assert len(os.listdir(samples_path)) == 21 + + for file in os.listdir(samples_path): + df = cudf.read_parquet(os.path.join(samples_path, file)) + + assert df.major_offsets.dropna().iloc[-1] - df.major_offsets.iloc[0] == len(df) + + shutil.rmtree(samples_path) diff --git a/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample.py b/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample.py index 62599291d04..206898088ab 100644 --- a/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample.py +++ b/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample.py @@ -15,6 +15,7 @@ import pytest +import cupy import cudf import cugraph from cugraph import uniform_neighbor_sample @@ -151,7 +152,7 @@ def test_uniform_neighbor_sample_simple(input_combo): G, input_combo["start_list"], input_combo["fanout_vals"], - input_combo["with_replacement"], + with_replacement=input_combo["with_replacement"], ) print(input_df) @@ -254,7 +255,9 @@ def test_uniform_neighbor_sample_tree(directed): start_list = cudf.Series([0, 0], dtype="int32") fanout_vals = [4, 1, 3] with_replacement = True - result_nbr = uniform_neighbor_sample(G, start_list, fanout_vals, with_replacement) + result_nbr = uniform_neighbor_sample( + G, start_list, fanout_vals, with_replacement=with_replacement + ) result_nbr = result_nbr.drop_duplicates() @@ -288,7 +291,7 @@ def test_uniform_neighbor_sample_unweighted(simple_unweighted_input_expected_out test_data["Graph"], test_data["start_list"].astype("int64"), test_data["fanout_vals"], - test_data["with_replacement"], + with_replacement=test_data["with_replacement"], ) actual_src = sampling_results.sources @@ -303,7 +306,8 @@ def test_uniform_neighbor_sample_unweighted(simple_unweighted_input_expected_out @pytest.mark.sg @pytest.mark.cugraph_ops @pytest.mark.parametrize("return_offsets", [True, False]) -def test_uniform_neighbor_sample_edge_properties(return_offsets): +@pytest.mark.parametrize("include_hop_column", [True, False]) +def test_uniform_neighbor_sample_edge_properties(return_offsets, include_hop_column): edgelist_df = cudf.DataFrame( { "src": cudf.Series([0, 1, 2, 3, 4, 3, 4, 2, 0, 1, 0, 2], dtype="int32"), @@ -337,6 +341,7 @@ def test_uniform_neighbor_sample_edge_properties(return_offsets): with_edge_properties=True, with_batch_ids=True, return_offsets=return_offsets, + include_hop_column=include_hop_column, ) if return_offsets: sampling_results, sampling_offsets = sampling_results @@ -359,11 +364,29 @@ def test_uniform_neighbor_sample_edge_properties(return_offsets): == sampling_results["destinations"].values_host.tolist() ) - assert sampling_results["hop_id"].values_host.tolist() == ([0, 0, 1, 1, 1, 1] * 2) + if include_hop_column: + assert sampling_results["hop_id"].values_host.tolist() == ( + [0, 0, 1, 1, 1, 1] * 2 + ) + else: + assert "hop_id" not in sampling_results if return_offsets: - assert sampling_offsets["batch_id"].values_host.tolist() == [0, 1] - assert sampling_offsets["offsets"].values_host.tolist() == [0, 6] + assert sampling_offsets["batch_id"].dropna().values_host.tolist() == [0, 1] + if include_hop_column: + assert sampling_offsets["offsets"].dropna().values_host.tolist() == [ + 0, + 6, + 12, + ] + else: + assert sampling_offsets["offsets"].dropna().values_host.tolist() == [ + 0, + 2, + 6, + 8, + 12, + ] else: assert sampling_results["batch_id"].values_host.tolist() == ([0] * 6 + [1] * 6) @@ -778,6 +801,176 @@ def test_uniform_neighbor_sample_renumber(hops): assert (renumber_map.batch_id == 0).all() +@pytest.mark.sg +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +def test_uniform_neighbor_sample_offset_renumber(hops): + el = email_Eu_core.get_edgelist() + + G = cugraph.Graph(directed=True) + G.from_cudf_edgelist(el, source="src", destination="dst") + + seeds = G.select_random_vertices(62, int(0.0001 * len(el))) + + ( + sampling_results_unrenumbered, + offsets_unrenumbered, + ) = cugraph.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + renumber=False, + return_offsets=True, + random_state=62, + ) + + ( + sampling_results_renumbered, + offsets_renumbered, + renumber_map, + ) = cugraph.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + renumber=True, + return_offsets=True, + random_state=62, + ) + + sources_hop_0 = sampling_results_unrenumbered[ + sampling_results_unrenumbered.hop_id == 0 + ].sources + for hop in range(len(hops)): + destinations_hop = sampling_results_unrenumbered[ + sampling_results_unrenumbered.hop_id <= hop + ].destinations + expected_renumber_map = cudf.concat([sources_hop_0, destinations_hop]).unique() + + assert sorted(expected_renumber_map.values_host.tolist()) == sorted( + renumber_map.map[0 : len(expected_renumber_map)].values_host.tolist() + ) + + renumber_map_offsets = offsets_renumbered.renumber_map_offsets.dropna() + assert len(renumber_map_offsets) == 2 + assert renumber_map_offsets.iloc[0] == 0 + assert renumber_map_offsets.iloc[-1] == len(renumber_map) + + assert len(offsets_renumbered) == 2 + + +@pytest.mark.sg +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +@pytest.mark.parametrize("seed", [62, 66, 68]) +def test_uniform_neighbor_sample_csr_csc_global(hops, seed): + el = email_Eu_core.get_edgelist() + + G = cugraph.Graph(directed=True) + G.from_cudf_edgelist(el, source="src", destination="dst") + + seeds = G.select_random_vertices(seed, int(0.0001 * len(el))) + + sampling_results, offsets, renumber_map = cugraph.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + # carryover not valid because C++ sorts on (hop,src) + prior_sources_behavior="exclude", + renumber=True, + return_offsets=True, + random_state=seed, + use_legacy_names=False, + compress_per_hop=False, + compression="CSR", + include_hop_column=False, + ) + + major_offsets = sampling_results["major_offsets"].dropna().values + majors = cudf.Series(cupy.arange(len(major_offsets) - 1)) + majors = majors.repeat(cupy.diff(major_offsets)) + + minors = sampling_results["minors"].dropna() + assert len(majors) == len(minors) + + majors = renumber_map.map.iloc[majors] + minors = renumber_map.map.iloc[minors] + + for i in range(len(majors)): + assert 1 == len(el[(el.src == majors.iloc[i]) & (el.dst == minors.iloc[i])]) + + +@pytest.mark.sg +@pytest.mark.parametrize("seed", [62, 66, 68]) +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +def test_uniform_neighbor_sample_csr_csc_local(hops, seed): + el = email_Eu_core.get_edgelist(download=True) + + G = cugraph.Graph(directed=True) + G.from_cudf_edgelist(el, source="src", destination="dst") + + seeds = cudf.Series( + [49, 71], dtype="int32" + ) # hardcoded to ensure out-degree is high enough + + sampling_results, offsets, renumber_map = cugraph.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + prior_sources_behavior="carryover", + renumber=True, + return_offsets=True, + random_state=seed, + use_legacy_names=False, + compress_per_hop=True, + compression="CSR", + include_hop_column=False, + ) + + for hop in range(len(hops)): + major_offsets = sampling_results["major_offsets"].iloc[ + offsets.offsets.iloc[hop] : (offsets.offsets.iloc[hop + 1] + 1) + ] + + minors = sampling_results["minors"].iloc[ + major_offsets.iloc[0] : major_offsets.iloc[-1] + ] + + majors = cudf.Series(cupy.arange(len(major_offsets) - 1)) + majors = majors.repeat(cupy.diff(major_offsets)) + + majors = renumber_map.map.iloc[majors] + minors = renumber_map.map.iloc[minors] + + for i in range(len(majors)): + assert 1 == len(el[(el.src == majors.iloc[i]) & (el.dst == minors.iloc[i])]) + + +@pytest.mark.sg +@pytest.mark.skip(reason="needs to be written!") +def test_uniform_neighbor_sample_dcsr_dcsc_global(): + raise NotImplementedError + + +@pytest.mark.sg +@pytest.mark.skip(reason="needs to be written!") +def test_uniform_neighbor_sample_dcsr_dcsc_local(): + raise NotImplementedError + + @pytest.mark.sg @pytest.mark.skip(reason="needs to be written!") def test_multi_client_sampling(): diff --git a/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample_mg.py b/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample_mg.py index 9d87c097287..460a25cbd14 100644 --- a/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample_mg.py +++ b/python/cugraph/cugraph/tests/sampling/test_uniform_neighbor_sample_mg.py @@ -17,6 +17,7 @@ import pytest +import pandas import cupy import cudf import cugraph @@ -138,7 +139,7 @@ def test_mg_uniform_neighbor_sample_simple(dask_client, input_combo): dg, input_combo["start_list"], input_combo["fanout_vals"], - input_combo["with_replacement"], + with_replacement=input_combo["with_replacement"], ) # multi edges are dropped to easily verify that each edge in the @@ -228,7 +229,9 @@ def test_mg_uniform_neighbor_sample_tree(dask_client, directed): start_list = cudf.Series([0, 0], dtype="int32") fanout_vals = [4, 1, 3] with_replacement = True - result_nbr = uniform_neighbor_sample(G, start_list, fanout_vals, with_replacement) + result_nbr = uniform_neighbor_sample( + G, start_list, fanout_vals, with_replacement=with_replacement + ) result_nbr = result_nbr.drop_duplicates() @@ -283,7 +286,7 @@ def test_mg_uniform_neighbor_sample_unweighted(dask_client): with_replacement = True sampling_results = uniform_neighbor_sample( - G, start_list, fanout_vals, with_replacement + G, start_list, fanout_vals, with_replacement=with_replacement ) expected_src = [0, 0] @@ -380,13 +383,17 @@ def test_uniform_neighbor_sample_edge_properties(dask_client, return_offsets): dfp = sampling_results.get_partition(i).compute() if len(dfp) > 0: offsets_p = sampling_offsets.get_partition(i).compute() + print(offsets_p) assert len(offsets_p) > 0 if offsets_p.batch_id.iloc[0] == 1: batches_found[1] += 1 - assert offsets_p.batch_id.values_host.tolist() == [1] - assert offsets_p.offsets.values_host.tolist() == [0] + assert offsets_p.batch_id.dropna().values_host.tolist() == [1] + assert offsets_p.offsets.dropna().values_host.tolist() == [ + 0, + len(dfp), + ] assert sorted(dfp.sources.values_host.tolist()) == ( [1, 1, 3, 3, 4, 4] @@ -397,8 +404,11 @@ def test_uniform_neighbor_sample_edge_properties(dask_client, return_offsets): elif offsets_p.batch_id.iloc[0] == 0: batches_found[0] += 1 - assert offsets_p.batch_id.values_host.tolist() == [0] - assert offsets_p.offsets.values_host.tolist() == [0] + assert offsets_p.batch_id.dropna().values_host.tolist() == [0] + assert offsets_p.offsets.dropna().values_host.tolist() == [ + 0, + len(dfp), + ] assert sorted(dfp.sources.values_host.tolist()) == ( [0, 0, 0, 1, 1, 2, 2, 2, 4, 4] @@ -703,7 +713,6 @@ def test_uniform_neighbor_sample_batched(dask_client, dataset, input_df, max_bat source="src", destination="dst", edge_attr=["wgt", "eid", "etp"], - legacy_renum_only=True, ) input_vertices = dask_cudf.concat([df.src, df.dst]).unique().compute() @@ -960,7 +969,6 @@ def test_uniform_neighbor_sample_deduplicate_sources_email_eu_core(dask_client): @pytest.mark.mg @pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) -@pytest.mark.tags("runme") def test_uniform_neighbor_sample_renumber(dask_client, hops): # FIXME This test is not very good because there is a lot of # non-deterministic behavior that still exists despite passing @@ -1005,6 +1013,224 @@ def test_uniform_neighbor_sample_renumber(dask_client, hops): ) +@pytest.mark.mg +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +def test_uniform_neighbor_sample_offset_renumber(dask_client, hops): + el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=4) + + G = cugraph.Graph(directed=True) + G.from_dask_cudf_edgelist(el, source="src", destination="dst") + + seeds = G.select_random_vertices(62, int(0.0001 * len(el))) + + ( + sampling_results_unrenumbered, + offsets_unrenumbered, + ) = cugraph.dask.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + renumber=False, + return_offsets=True, + random_state=62, + ) + sampling_results_unrenumbered = sampling_results_unrenumbered.compute() + offsets_unrenumbered = offsets_unrenumbered.compute() + + ( + sampling_results_renumbered, + offsets_renumbered, + renumber_map, + ) = cugraph.dask.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + renumber=True, + keep_batches_together=True, + min_batch_id=0, + max_batch_id=0, + return_offsets=True, + random_state=62, + ) + + # can't use compute() since empty batches still get a partition + n_workers = len(dask_client.scheduler_info()["workers"]) + for p in range(n_workers): + partition = offsets_renumbered.get_partition(p).compute() + if not pandas.isna(partition.batch_id.iloc[0]): + break + + sampling_results_renumbered = sampling_results_renumbered.get_partition(p).compute() + offsets_renumbered = offsets_renumbered.get_partition(p).compute() + renumber_map = renumber_map.get_partition(p).compute() + + sources_hop_0 = sampling_results_unrenumbered[ + sampling_results_unrenumbered.hop_id == 0 + ].sources + for hop in range(len(hops)): + destinations_hop = sampling_results_unrenumbered[ + sampling_results_unrenumbered.hop_id <= hop + ].destinations + expected_renumber_map = cudf.concat([sources_hop_0, destinations_hop]).unique() + + assert sorted(expected_renumber_map.values_host.tolist()) == sorted( + renumber_map.map[0 : len(expected_renumber_map)].values_host.tolist() + ) + + renumber_map_offsets = offsets_renumbered.renumber_map_offsets.dropna() + assert len(renumber_map_offsets) == 2 + assert renumber_map_offsets.iloc[0] == 0 + assert renumber_map_offsets.iloc[-1] == len(renumber_map) + + assert len(offsets_renumbered) == 2 + + +@pytest.mark.mg +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +@pytest.mark.parametrize("seed", [62, 66, 68]) +def test_uniform_neighbor_sample_csr_csc_global(dask_client, hops, seed): + el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=4) + + G = cugraph.Graph(directed=True) + G.from_dask_cudf_edgelist(el, source="src", destination="dst") + + seeds = G.select_random_vertices(seed, int(0.0001 * len(el))) + + sampling_results, offsets, renumber_map = cugraph.dask.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + # carryover not valid because C++ sorts on (hop,src) + prior_sources_behavior="exclude", + renumber=True, + return_offsets=True, + random_state=seed, + use_legacy_names=False, + compress_per_hop=False, + compression="CSR", + include_hop_column=False, + keep_batches_together=True, + min_batch_id=0, + max_batch_id=0, + ) + + # can't use compute() since empty batches still get a partition + n_workers = len(dask_client.scheduler_info()["workers"]) + for p in range(n_workers): + partition = offsets.get_partition(p).compute() + if not pandas.isna(partition.batch_id.iloc[0]): + break + + sampling_results = sampling_results.get_partition(p).compute() + offsets = offsets.get_partition(p).compute() + renumber_map = renumber_map.get_partition(p).compute() + + major_offsets = sampling_results["major_offsets"].dropna().values + majors = cudf.Series(cupy.arange(len(major_offsets) - 1)) + majors = majors.repeat(cupy.diff(major_offsets)) + + minors = sampling_results["minors"].dropna() + assert len(majors) == len(minors) + + majors = renumber_map.map.iloc[majors] + minors = renumber_map.map.iloc[minors] + + for i in range(len(majors)): + assert 1 == len(el[(el.src == majors.iloc[i]) & (el.dst == minors.iloc[i])]) + + +@pytest.mark.mg +@pytest.mark.parametrize("seed", [62, 66, 68]) +@pytest.mark.parametrize("hops", [[5], [5, 5], [5, 5, 5]]) +def test_uniform_neighbor_sample_csr_csc_local(dask_client, hops, seed): + el = dask_cudf.from_cudf(email_Eu_core.get_edgelist(), npartitions=4) + + G = cugraph.Graph(directed=True) + G.from_dask_cudf_edgelist(el, source="src", destination="dst") + + seeds = dask_cudf.from_cudf( + cudf.Series([49, 71], dtype="int32"), npartitions=1 + ) # hardcoded to ensure out-degree is high enough + + sampling_results, offsets, renumber_map = cugraph.dask.uniform_neighbor_sample( + G, + seeds, + hops, + with_replacement=False, + with_edge_properties=True, + with_batch_ids=False, + deduplicate_sources=True, + prior_sources_behavior="carryover", + renumber=True, + return_offsets=True, + random_state=seed, + use_legacy_names=False, + compress_per_hop=True, + compression="CSR", + include_hop_column=False, + keep_batches_together=True, + min_batch_id=0, + max_batch_id=0, + ) + + # can't use compute() since empty batches still get a partition + n_workers = len(dask_client.scheduler_info()["workers"]) + for p in range(n_workers): + partition = offsets.get_partition(p).compute() + + if not pandas.isna(partition.batch_id.iloc[0]): + break + + sampling_results = sampling_results.get_partition(p).compute() + offsets = offsets.get_partition(p).compute() + renumber_map = renumber_map.get_partition(p).compute() + + print(sampling_results) + print(offsets) + + for hop in range(len(hops)): + major_offsets = sampling_results["major_offsets"].iloc[ + offsets.offsets.iloc[hop] : (offsets.offsets.iloc[hop + 1] + 1) + ] + + minors = sampling_results["minors"].iloc[ + major_offsets.iloc[0] : major_offsets.iloc[-1] + ] + + majors = cudf.Series(cupy.arange(len(major_offsets) - 1)) + majors = majors.repeat(cupy.diff(major_offsets)) + + majors = renumber_map.map.iloc[majors] + minors = renumber_map.map.iloc[minors] + + for i in range(len(majors)): + assert 1 == len(el[(el.src == majors.iloc[i]) & (el.dst == minors.iloc[i])]) + + +@pytest.mark.mg +@pytest.mark.skip(reason="needs to be written!") +def test_uniform_neighbor_sample_dcsr_dcsc_global(): + raise NotImplementedError + + +@pytest.mark.mg +@pytest.mark.skip(reason="needs to be written!") +def test_uniform_neighbor_sample_dcsr_dcsc_local(): + raise NotImplementedError + + # ============================================================================= # Benchmarks # ============================================================================= diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/algorithms.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/algorithms.pxd index ffb458b409c..29c6d79e08d 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/algorithms.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/algorithms.pxd @@ -176,15 +176,32 @@ cdef extern from "cugraph_c/algorithms.h": const cugraph_sample_result_t* result ) + # Deprecated, use cugraph_sample_result_get_majors cdef cugraph_type_erased_device_array_view_t* \ cugraph_sample_result_get_sources( const cugraph_sample_result_t* result ) + # Deprecated, use cugraph_sample_result_get_minors cdef cugraph_type_erased_device_array_view_t* \ cugraph_sample_result_get_destinations( const cugraph_sample_result_t* result ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_sample_result_get_majors( + const cugraph_sample_result_t* result + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_sample_result_get_minors( + const cugraph_sample_result_t* result + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_sample_result_get_major_offsets( + const cugraph_sample_result_t* result + ) cdef cugraph_type_erased_device_array_view_t* \ cugraph_sample_result_get_index( @@ -211,11 +228,17 @@ cdef extern from "cugraph_c/algorithms.h": const cugraph_sample_result_t* result ) + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_sample_result_get_label_hop_offsets( + const cugraph_sample_result_t* result + ) + cdef cugraph_type_erased_device_array_view_t* \ cugraph_sample_result_get_start_labels( const cugraph_sample_result_t* result ) + # Deprecated cdef cugraph_type_erased_device_array_view_t* \ cugraph_sample_result_get_offsets( const cugraph_sample_result_t* result @@ -246,10 +269,17 @@ cdef extern from "cugraph_c/algorithms.h": pass ctypedef enum cugraph_prior_sources_behavior_t: - DEFAULT + DEFAULT=0 CARRY_OVER EXCLUDE + ctypedef enum cugraph_compression_type_t: + COO=0 + CSR + CSC + DCSR + DCSC + cdef cugraph_error_code_t \ cugraph_sampling_options_create( cugraph_sampling_options_t** options, @@ -277,7 +307,7 @@ cdef extern from "cugraph_c/algorithms.h": cdef void \ cugraph_sampling_set_prior_sources_behavior( cugraph_sampling_options_t* options, - cugraph_prior_sources_behavior_t value + cugraph_prior_sources_behavior_t value, ) cdef void \ @@ -286,10 +316,22 @@ cdef extern from "cugraph_c/algorithms.h": bool_t value, ) + cdef void \ + cugraph_sampling_set_compress_per_hop( + cugraph_sampling_options_t* options, + bool_t value, + ) + + cdef void \ + cugraph_sampling_set_compression_type( + cugraph_sampling_options_t* options, + cugraph_compression_type_t value, + ) + cdef void \ cugraph_sampling_options_free( cugraph_sampling_options_t* options, - ) + ) # uniform random walks cdef cugraph_error_code_t \ diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/sampling_algorithms.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/sampling_algorithms.pxd index 91cc11d6b1c..c32b57f8621 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/sampling_algorithms.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/sampling_algorithms.pxd @@ -43,23 +43,6 @@ from pylibcugraph._cugraph_c.array cimport ( cdef extern from "cugraph_c/sampling_algorithms.h": ########################################################################### - # deprecated, should migrate to cugraph_uniform_neighbor_sample - cdef cugraph_error_code_t cugraph_uniform_neighbor_sample_with_edge_properties( - const cugraph_resource_handle_t* handle, - cugraph_graph_t* graph, - const cugraph_type_erased_device_array_view_t* start_vertices, - const cugraph_type_erased_device_array_view_t* start_vertex_labels, - const cugraph_type_erased_device_array_view_t* label_list, - const cugraph_type_erased_device_array_view_t* label_to_comm_rank, - const cugraph_type_erased_host_array_view_t* fan_out, - cugraph_rng_state_t* rng_state, - bool_t with_replacement, - bool_t return_hops, - bool_t do_expensive_check, - cugraph_sample_result_t** result, - cugraph_error_t** error - ) - cdef cugraph_error_code_t cugraph_uniform_neighbor_sample( const cugraph_resource_handle_t* handle, cugraph_graph_t* graph, diff --git a/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx b/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx index d11f6994298..9f98b4f37b0 100644 --- a/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx +++ b/python/pylibcugraph/pylibcugraph/internal_types/sampling_result.pyx @@ -20,14 +20,18 @@ from pylibcugraph._cugraph_c.array cimport ( ) from pylibcugraph._cugraph_c.algorithms cimport ( cugraph_sample_result_t, - cugraph_sample_result_get_sources, - cugraph_sample_result_get_destinations, + cugraph_sample_result_get_major_offsets, + cugraph_sample_result_get_majors, + cugraph_sample_result_get_minors, + cugraph_sample_result_get_label_hop_offsets, + cugraph_sample_result_get_sources, # deprecated + cugraph_sample_result_get_destinations, # deprecated cugraph_sample_result_get_edge_weight, cugraph_sample_result_get_edge_id, cugraph_sample_result_get_edge_type, - cugraph_sample_result_get_hop, + cugraph_sample_result_get_hop, # deprecated cugraph_sample_result_get_start_labels, - cugraph_sample_result_get_offsets, + cugraph_sample_result_get_offsets, # deprecated cugraph_sample_result_get_renumber_map, cugraph_sample_result_get_renumber_map_offsets, cugraph_sample_result_free, @@ -60,23 +64,71 @@ cdef class SamplingResult: cdef set_ptr(self, cugraph_sample_result_t* sample_result_ptr): self.c_sample_result_ptr = sample_result_ptr + def get_major_offsets(self): + if self.c_sample_result_ptr is NULL: + raise ValueError("pointer not set, must call set_ptr() with a " + "non-NULL value first.") + + cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( + cugraph_sample_result_get_major_offsets(self.c_sample_result_ptr) + ) + if device_array_view_ptr is NULL: + return None + + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, + self) + + def get_majors(self): + if self.c_sample_result_ptr is NULL: + raise ValueError("pointer not set, must call set_ptr() with a " + "non-NULL value first.") + cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( + cugraph_sample_result_get_majors(self.c_sample_result_ptr) + ) + if device_array_view_ptr is NULL: + return None + + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, + self) + + def get_minors(self): + if self.c_sample_result_ptr is NULL: + raise ValueError("pointer not set, must call set_ptr() with a " + "non-NULL value first.") + cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( + cugraph_sample_result_get_minors(self.c_sample_result_ptr) + ) + if device_array_view_ptr is NULL: + return None + + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, + self) + def get_sources(self): + # Deprecated if self.c_sample_result_ptr is NULL: raise ValueError("pointer not set, must call set_ptr() with a " "non-NULL value first.") cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_sources(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) def get_destinations(self): + # Deprecated if self.c_sample_result_ptr is NULL: raise ValueError("pointer not set, must call set_ptr() with a " "non-NULL value first.") cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_destinations(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) @@ -95,6 +147,7 @@ cdef class SamplingResult: self) def get_indices(self): + # Deprecated return self.get_edge_weights() def get_edge_ids(self): @@ -132,9 +185,26 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_start_labels(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) + def get_label_hop_offsets(self): + if self.c_sample_result_ptr is NULL: + raise ValueError("pointer not set, must call set_ptr() with a " + "non-NULL value first.") + cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( + cugraph_sample_result_get_label_hop_offsets(self.c_sample_result_ptr) + ) + if device_array_view_ptr is NULL: + return None + + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, + self) + + # Deprecated def get_offsets(self): if self.c_sample_result_ptr is NULL: raise ValueError("pointer not set, must call set_ptr() with a " @@ -142,9 +212,13 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_offsets(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) + # Deprecated def get_hop_ids(self): if self.c_sample_result_ptr is NULL: raise ValueError("pointer not set, must call set_ptr() with a " @@ -152,6 +226,9 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_hop(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) @@ -162,6 +239,9 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_renumber_map(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) @@ -172,5 +252,8 @@ cdef class SamplingResult: cdef cugraph_type_erased_device_array_view_t* device_array_view_ptr = ( cugraph_sample_result_get_renumber_map_offsets(self.c_sample_result_ptr) ) + if device_array_view_ptr is NULL: + return None + return create_cupy_array_view_for_device_ptr(device_array_view_ptr, self) \ No newline at end of file diff --git a/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py b/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py index 74aa6830d24..ac04635edcf 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py @@ -266,7 +266,7 @@ def test_neighborhood_sampling_large_sg_graph(gpubenchmark): def test_sample_result(): """ - Ensure the SampleResult class returns zero-opy cupy arrays and properly + Ensure the SampleResult class returns zero-copy cupy arrays and properly frees device memory when all references to it are gone and it's garbage collected. """ @@ -304,6 +304,8 @@ def test_sample_result(): assert isinstance(destinations, cp.ndarray) assert isinstance(indices, cp.ndarray) + print("sources:", destinations) + # Delete the SampleResult instance. This *should not* free the device # memory yet since the variables sources, destinations, and indices are # keeping the refcount >0. diff --git a/python/pylibcugraph/pylibcugraph/uniform_neighbor_sample.pyx b/python/pylibcugraph/pylibcugraph/uniform_neighbor_sample.pyx index bc2aa9205f1..ce6493c38f5 100644 --- a/python/pylibcugraph/pylibcugraph/uniform_neighbor_sample.pyx +++ b/python/pylibcugraph/pylibcugraph/uniform_neighbor_sample.pyx @@ -38,6 +38,7 @@ from pylibcugraph._cugraph_c.graph cimport ( from pylibcugraph._cugraph_c.algorithms cimport ( cugraph_sample_result_t, cugraph_prior_sources_behavior_t, + cugraph_compression_type_t, cugraph_sampling_options_t, cugraph_sampling_options_create, cugraph_sampling_options_free, @@ -46,7 +47,8 @@ from pylibcugraph._cugraph_c.algorithms cimport ( cugraph_sampling_set_prior_sources_behavior, cugraph_sampling_set_dedupe_sources, cugraph_sampling_set_renumber_results, - + cugraph_sampling_set_compress_per_hop, + cugraph_sampling_set_compression_type, ) from pylibcugraph._cugraph_c.sampling_algorithms cimport ( cugraph_uniform_neighbor_sample, @@ -73,6 +75,7 @@ from pylibcugraph._cugraph_c.random cimport ( from pylibcugraph.random cimport ( CuGraphRandomState ) +import warnings # TODO accept cupy/numpy random state in addition to raw seed. def uniform_neighbor_sample(ResourceHandle resource_handle, @@ -90,7 +93,10 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, deduplicate_sources=False, return_hops=False, renumber=False, - random_state=None): + compression='COO', + compress_per_hop=False, + random_state=None, + return_dict=False,): """ Does neighborhood sampling, which samples nodes from a graph based on the current node's neighbors, with a corresponding fanout value at each hop. @@ -153,11 +159,27 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, If True, will renumber the sources and destinations on a per-batch basis and return the renumber map and batch offsets in additional to the standard returns. + + compression: str (Optional) + Options: COO (default), CSR, CSC, DCSR, DCSR + Sets the compression format for the returned samples. + + compress_per_hop: bool (Optional) + If False (default), will create a compressed edgelist for the + entire batch. + If True, will create a separate compressed edgelist per hop within + a batch. random_state: int (Optional) Random state to use when generating samples. Optional argument, defaults to a hash of process id, time, and hostname. (See pylibcugraph.random.CuGraphRandomState) + + return_dict: bool (Optional) + Whether to return a dictionary instead of a tuple. + Optional argument, defaults to False, returning a tuple. + This argument will eventually be deprecated in favor + of always returning a dictionary. Returns ------- @@ -173,13 +195,16 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, the renumber map for each batch starts). """ - cdef cugraph_resource_handle_t* c_resource_handle_ptr = \ + cdef cugraph_resource_handle_t* c_resource_handle_ptr = ( resource_handle.c_resource_handle_ptr + ) + cdef cugraph_graph_t* c_graph_ptr = input_graph.c_graph_ptr cdef bool_t c_deduplicate_sources = deduplicate_sources cdef bool_t c_return_hops = return_hops cdef bool_t c_renumber = renumber + cdef bool_t c_compress_per_hop = compress_per_hop assert_CAI_type(start_list, "start_list") assert_CAI_type(batch_id_list, "batch_id_list", True) @@ -269,6 +294,23 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, f'Invalid option {prior_sources_behavior}' ' for prior sources behavior' ) + + cdef cugraph_compression_type_t compression_behavior_e + if compression is None or compression == 'COO': + compression_behavior_e = cugraph_compression_type_t.COO + elif compression == 'CSR': + compression_behavior_e = cugraph_compression_type_t.CSR + elif compression == 'CSC': + compression_behavior_e = cugraph_compression_type_t.CSC + elif compression == 'DCSR': + compression_behavior_e = cugraph_compression_type_t.DCSR + elif compression == 'DCSC': + compression_behavior_e = cugraph_compression_type_t.DCSC + else: + raise ValueError( + f'Invalid option {compression}' + ' for compression type' + ) cdef cugraph_sampling_options_t* sampling_options error_code = cugraph_sampling_options_create(&sampling_options, &error_ptr) @@ -279,6 +321,8 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, cugraph_sampling_set_dedupe_sources(sampling_options, c_deduplicate_sources) cugraph_sampling_set_prior_sources_behavior(sampling_options, prior_sources_behavior_e) cugraph_sampling_set_renumber_results(sampling_options, c_renumber) + cugraph_sampling_set_compression_type(sampling_options, compression_behavior_e) + cugraph_sampling_set_compress_per_hop(sampling_options, c_compress_per_hop) error_code = cugraph_uniform_neighbor_sample( c_resource_handle_ptr, @@ -311,26 +355,74 @@ def uniform_neighbor_sample(ResourceHandle resource_handle, # Get cupy "views" of the individual arrays to return. These each increment # the refcount on the SamplingResult instance which will keep the data alive # until all references are removed and the GC runs. + # TODO Return everything that isn't null in release 23.12 if with_edge_properties: - cupy_sources = result.get_sources() - cupy_destinations = result.get_destinations() + cupy_majors = result.get_majors() + cupy_major_offsets = result.get_major_offsets() + cupy_minors = result.get_minors() cupy_edge_weights = result.get_edge_weights() cupy_edge_ids = result.get_edge_ids() cupy_edge_types = result.get_edge_types() cupy_batch_ids = result.get_batch_ids() - cupy_offsets = result.get_offsets() - cupy_hop_ids = result.get_hop_ids() + cupy_label_hop_offsets = result.get_label_hop_offsets() if renumber: cupy_renumber_map = result.get_renumber_map() cupy_renumber_map_offsets = result.get_renumber_map_offsets() - return (cupy_sources, cupy_destinations, cupy_edge_weights, cupy_edge_ids, cupy_edge_types, cupy_batch_ids, cupy_offsets, cupy_hop_ids, cupy_renumber_map, cupy_renumber_map_offsets) + # TODO drop the placeholder for hop ids in release 23.12 + if return_dict: + return { + 'major_offsets': cupy_major_offsets, + 'majors': cupy_majors, + 'minors': cupy_minors, + 'weight': cupy_edge_weights, + 'edge_id': cupy_edge_ids, + 'edge_type': cupy_edge_types, + 'batch_id': cupy_batch_ids, + 'label_hop_offsets': cupy_label_hop_offsets, + 'hop_id': None, + 'renumber_map': cupy_renumber_map, + 'renumber_map_offsets': cupy_renumber_map_offsets + } + else: + cupy_majors = cupy_major_offsets if cupy_majors is None else cupy_majors + return (cupy_majors, cupy_minors, cupy_edge_weights, cupy_edge_ids, cupy_edge_types, cupy_batch_ids, cupy_label_hop_offsets, None, cupy_renumber_map, cupy_renumber_map_offsets) else: - return (cupy_sources, cupy_destinations, cupy_edge_weights, cupy_edge_ids, cupy_edge_types, cupy_batch_ids, cupy_offsets, cupy_hop_ids) + cupy_hop_ids = result.get_hop_ids() # FIXME remove this + if return_dict: + return { + 'major_offsets': cupy_major_offsets, + 'majors': cupy_majors, + 'minors': cupy_minors, + 'weight': cupy_edge_weights, + 'edge_id': cupy_edge_ids, + 'edge_type': cupy_edge_types, + 'batch_id': cupy_batch_ids, + 'label_hop_offsets': cupy_label_hop_offsets, + 'hop_id': cupy_hop_ids, + } + else: + cupy_majors = cupy_major_offsets if cupy_majors is None else cupy_majors + return (cupy_majors, cupy_minors, cupy_edge_weights, cupy_edge_ids, cupy_edge_types, cupy_batch_ids, cupy_label_hop_offsets, cupy_hop_ids) else: + # TODO this is deprecated, remove it in release 23.12 + warnings.warn( + "Calling uniform_neighbor_sample with the 'with_edge_properties' argument is deprecated." + " Starting in release 23.12, this argument will be removed in favor of behaving like the " + "with_edge_properties=True option, returning whatever properties are in the graph.", + FutureWarning, + ) + cupy_sources = result.get_sources() cupy_destinations = result.get_destinations() cupy_indices = result.get_indices() - return (cupy_sources, cupy_destinations, cupy_indices) + if return_dict: + return { + 'sources': cupy_sources, + 'destinations': cupy_destinations, + 'indices': cupy_indices + } + else: + return (cupy_sources, cupy_destinations, cupy_indices) From f0d633322e66be5e1521b2c91d94a6c96ed699bc Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Thu, 28 Sep 2023 13:09:17 -0500 Subject: [PATCH 021/111] Add entry point to tell NetworkX about nx-cugraph without importing it. (#3848) This allows NetworkX docstrings to be updated (among other things). This will have a companion PR in NetworkX. We still need to determine (and agree) on the dict returned by this entry point, and NetworkX doesn't need to use everything I have here. We should probably add a string for `"description"` that gives a very short description of the backend, and maybe `"url"` or `"homepage"` or whatever so online docs can have links. Here's how to use the entry point (Python >= 3.10) after installing it: ```python In [1]: from importlib.metadata import entry_points In [2]: items = entry_points(group="networkx.plugin_info") In [3]: [plugin] = items In [4]: plugin.load()() Out[4]: {'backend_name': 'cugraph', 'project': 'nx-cugraph', 'package': 'nx_cugraph', 'functions': {'betweenness_centrality', 'edge_betweenness_centrality', 'louvain_communities'}, 'extra_docstrings': {'betweenness_centrality': '`weight` parameter is not yet supported.', 'edge_betweenness_centrality': '`weight` parameter is not yet supported.', 'louvain_communities': '`threshold` and `seed` parameters are currently ignored.'}, 'extra_parameters': {'louvain_communities': {'max_level': 'Upper limit of the number of macro-iterations.'}}} ``` CC @rlratzel @betochimas Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3848 --- ci/release/update-version.sh | 1 + python/nx-cugraph/.flake8 | 1 + python/nx-cugraph/Makefile | 10 +++ python/nx-cugraph/_nx_cugraph/__init__.py | 88 ++++++++++++++++++ python/nx-cugraph/_nx_cugraph/core.py | 90 +++++++++++++++++++ python/nx-cugraph/lint.yaml | 19 ++-- python/nx-cugraph/nx_cugraph/__init__.py | 16 +++- .../algorithms/centrality/betweenness.py | 6 +- .../algorithms/community/louvain.py | 17 ++-- python/nx-cugraph/nx_cugraph/interface.py | 5 +- .../nx_cugraph/tests/test_match_api.py | 5 +- .../nx-cugraph/nx_cugraph/utils/decorators.py | 41 +++++++-- python/nx-cugraph/nx_cugraph/utils/misc.py | 6 +- python/nx-cugraph/pyproject.toml | 10 +++ 14 files changed, 278 insertions(+), 37 deletions(-) create mode 100644 python/nx-cugraph/_nx_cugraph/__init__.py create mode 100644 python/nx-cugraph/_nx_cugraph/core.py diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index f3892fbd3c4..adf3273e311 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -62,6 +62,7 @@ sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugr sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph-service/server/cugraph_service_server/__init__.py sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/pylibcugraph/pylibcugraph/__init__.py sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/nx_cugraph/__init__.py +sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/_nx_cugraph/__init__.py # Python pyproject.toml updates sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph/pyproject.toml diff --git a/python/nx-cugraph/.flake8 b/python/nx-cugraph/.flake8 index 3a2e3fb8617..c5874e54f7e 100644 --- a/python/nx-cugraph/.flake8 +++ b/python/nx-cugraph/.flake8 @@ -11,3 +11,4 @@ extend-ignore = per-file-ignores = nx_cugraph/tests/*.py:T201, __init__.py:F401,F403, + _nx_cugraph/__init__.py:E501, diff --git a/python/nx-cugraph/Makefile b/python/nx-cugraph/Makefile index c9caf147d53..6e1b98ee6e9 100644 --- a/python/nx-cugraph/Makefile +++ b/python/nx-cugraph/Makefile @@ -1,7 +1,17 @@ # Copyright (c) 2023, NVIDIA CORPORATION. SHELL= /bin/bash +.PHONY: all +all: plugin-info lint + +.PHONY: lint lint: git ls-files | xargs pre-commit run --config lint.yaml --files + +.PHONY: lint-update lint-update: pre-commit autoupdate --config lint.yaml + +.PHONY: plugin-info +plugin-info: + python _nx_cugraph/__init__.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py new file mode 100644 index 00000000000..9b3332106ec --- /dev/null +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -0,0 +1,88 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tell NetworkX about the cugraph backend. This file can update itself: + +$ make plugin-info # Recommended method for development + +or + +$ python _nx_cugraph/__init__.py +""" + +# Entries between BEGIN and END are automatically generated +_info = { + "backend_name": "cugraph", + "project": "nx-cugraph", + "package": "nx_cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-23.10/python/nx-cugraph", + "short_summary": "GPU-accelerated backend.", + # "description": "TODO", + "functions": { + # BEGIN: functions + "betweenness_centrality", + "edge_betweenness_centrality", + "louvain_communities", + # END: functions + }, + "extra_docstrings": { + # BEGIN: extra_docstrings + "betweenness_centrality": "`weight` parameter is not yet supported.", + "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "louvain_communities": "`threshold` and `seed` parameters are currently ignored.", + # END: extra_docstrings + }, + "extra_parameters": { + # BEGIN: extra_parameters + "louvain_communities": { + "max_level : int, optional": "Upper limit of the number of macro-iterations.", + }, + # END: extra_parameters + }, +} + + +def get_info(): + """Target of ``networkx.plugin_info`` entry point. + + This tells NetworkX about the cugraph backend without importing nx_cugraph. + """ + # Convert to e.g. `{"functions": {"myfunc": {"extra_docstring": ...}}}` + d = _info.copy() + info_keys = { + "extra_docstrings": "extra_docstring", + "extra_parameters": "extra_parameters", + } + d["functions"] = { + func: { + new_key: vals[func] + for old_key, new_key in info_keys.items() + if func in (vals := d[old_key]) + } + for func in d["functions"] + } + for key in info_keys: + del d[key] + return d + + +__version__ = "23.10.00" + +if __name__ == "__main__": + from pathlib import Path + + from _nx_cugraph.core import main + + filepath = Path(__file__) + text = main(filepath) + with filepath.open("w") as f: + f.write(text) diff --git a/python/nx-cugraph/_nx_cugraph/core.py b/python/nx-cugraph/_nx_cugraph/core.py new file mode 100644 index 00000000000..72f9203897e --- /dev/null +++ b/python/nx-cugraph/_nx_cugraph/core.py @@ -0,0 +1,90 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Utilities to help keep _nx_cugraph up to date.""" + + +def get_functions(): + from nx_cugraph.interface import BackendInterface + from nx_cugraph.utils import networkx_algorithm + + return { + key: val + for key, val in vars(BackendInterface).items() + if isinstance(val, networkx_algorithm) + } + + +def get_extra_docstrings(functions=None): + if functions is None: + functions = get_functions() + return {key: val.extra_doc for key, val in functions.items() if val.extra_doc} + + +def get_extra_parameters(functions=None): + if functions is None: + functions = get_functions() + return {key: val.extra_params for key, val in functions.items() if val.extra_params} + + +def update_text(text, lines_to_add, target, indent=" " * 8): + begin = f"# BEGIN: {target}\n" + end = f"# END: {target}\n" + start = text.index(begin) + stop = text.index(end) + to_add = "\n".join([f"{indent}{line}" for line in lines_to_add]) + return f"{text[:start]}{begin}{to_add}\n{indent}{text[stop:]}" + + +def dict_to_lines(d, *, indent=""): + for key in sorted(d): + val = d[key] + if "\n" not in val: + yield f"{indent}{key!r}: {val!r}," + else: + yield f"{indent}{key!r}: (" + *lines, last_line = val.split("\n") + for line in lines: + line += "\n" + yield f" {indent}{line!r}" + yield f" {indent}{last_line!r}" + yield f"{indent})," + + +def main(filepath): + from pathlib import Path + + filepath = Path(filepath) + with filepath.open() as f: + orig_text = f.read() + text = orig_text + + # Update functions + functions = get_functions() + to_add = [f'"{name}",' for name in sorted(functions)] + text = update_text(text, to_add, "functions") + + # Update extra_docstrings + extra_docstrings = get_extra_docstrings(functions) + to_add = list(dict_to_lines(extra_docstrings)) + text = update_text(text, to_add, "extra_docstrings") + + # Update extra_parameters + extra_parameters = get_extra_parameters(functions) + to_add = [] + for name in sorted(extra_parameters): + params = extra_parameters[name] + to_add.append(f"{name!r}: {{") + to_add.extend(dict_to_lines(params, indent=" " * 4)) + to_add.append("},") + text = update_text(text, to_add, "extra_parameters") + return text diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index dba061bd6b5..6a462a6af79 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -31,7 +31,7 @@ repos: - id: validate-pyproject name: Validate pyproject.toml - repo: https://github.com/PyCQA/autoflake - rev: v2.2.0 + rev: v2.2.1 hooks: - id: autoflake args: [--in-place] @@ -40,17 +40,17 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v3.10.1 + rev: v3.13.0 hooks: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.7.0 + rev: 23.9.1 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.286 + rev: v0.0.291 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -58,11 +58,12 @@ repos: rev: 6.1.0 hooks: - id: flake8 + args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501'] # Why is this necessary? additional_dependencies: &flake8_dependencies - # These versions need updated manually - - flake8==6.1.0 - - flake8-bugbear==23.7.10 - - flake8-simplify==0.20.0 + # These versions need updated manually + - flake8==6.1.0 + - flake8-bugbear==23.9.16 + - flake8-simplify==0.20.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 hooks: @@ -76,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.286 + rev: v0.0.291 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 28066fe2b02..4a0e95a109f 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -12,9 +12,21 @@ # limitations under the License. from networkx.exception import * -from . import algorithms, classes, convert, utils -from .algorithms import * +from . import utils + +from . import classes from .classes import * + +from . import convert from .convert import * +# from . import convert_matrix +# from .convert_matrix import * + +# from . import generators +# from .generators import * + +from . import algorithms +from .algorithms import * + __version__ = "23.10.00" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index b777919f86f..104ac87414c 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -13,7 +13,7 @@ import pylibcugraph as plc from nx_cugraph.convert import _to_graph -from nx_cugraph.utils import _handle_seed, networkx_algorithm +from nx_cugraph.utils import _seed_to_int, networkx_algorithm __all__ = ["betweenness_centrality", "edge_betweenness_centrality"] @@ -22,11 +22,12 @@ def betweenness_centrality( G, k=None, normalized=True, weight=None, endpoints=False, seed=None ): + """`weight` parameter is not yet supported.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" ) - seed = _handle_seed(seed) + seed = _seed_to_int(seed) G = _to_graph(G, weight) node_ids, values = plc.betweenness_centrality( resource_handle=plc.ResourceHandle(), @@ -47,6 +48,7 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None): @networkx_algorithm def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None): + """`weight` parameter is not yet supported.""" if weight is not None: raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index ca5f05c2014..a183b59fe1d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -17,7 +17,7 @@ from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import ( _groupby, - _handle_seed, + _seed_to_int, networkx_algorithm, not_implemented_for, ) @@ -26,16 +26,17 @@ @not_implemented_for("directed") -@networkx_algorithm(extra_params="max_level") +@networkx_algorithm( + extra_params={ + "max_level : int, optional": "Upper limit of the number of macro-iterations." + } +) def louvain_communities( G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None ): - """`threshold` and `seed` parameters are currently ignored. - - Extra parameter: `max_level` controls the maximum number of levels of the algorithm. - """ + """`threshold` and `seed` parameters are currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. - seed = _handle_seed(seed) # Unused, but ensure it's valid for future compatibility + seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) if G.row_indices.size == 0: # TODO: PLC doesn't handle empty graphs gracefully! @@ -46,8 +47,8 @@ def louvain_communities( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(), max_level=max_level, # TODO: add this parameter to NetworkX + threshold=threshold, resolution=resolution, - # threshold=threshold, # TODO: add this parameter to PLC do_expensive_check=False, ) groups = _groupby(clusters, vertices) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index cc750cd2d5b..2ad23acd940 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -62,9 +62,7 @@ def key(testpath): # Reasons for xfailing no_weights = "weighted implementation not currently supported" no_multigraph = "multigraphs not currently supported" - louvain_different = ( - "Louvain may be different due to RNG or unsupported threshold parameter" - ) + louvain_different = "Louvain may be different due to RNG" xfail = {} @@ -176,7 +174,6 @@ def key(testpath): ): louvain_different, key("test_louvain.py:test_none_weight_param"): louvain_different, key("test_louvain.py:test_multigraph"): louvain_different, - key("test_louvain.py:test_threshold"): louvain_different, } ) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index 64d3704dd65..ecfda1397db 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -45,11 +45,14 @@ def test_match_signature_and_names(): assert orig_sig == func_sig else: # Ignore extra parameters added to nx-cugraph algorithm + # The key of func.extra_params may be like "max_level : int, optional", + # but we only want "max_level" here. + extra_params = {name.split(" ")[0] for name in func.extra_params} assert orig_sig == func_sig.replace( parameters=[ p for name, p in func_sig.parameters.items() - if name not in func.extra_params + if name not in extra_params ] ) if func.can_run is not nxcg.utils.decorators._default_can_run: diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index 3dbdb07e87f..0f15d236ecd 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -10,13 +10,21 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + from functools import partial, update_wrapper -from networkx.utils.decorators import not_implemented_for +from networkx.utils.decorators import nodes_or_number, not_implemented_for from nx_cugraph.interface import BackendInterface -__all__ = ["not_implemented_for", "networkx_algorithm"] +try: + from networkx.utils.backends import _registered_algorithms +except ModuleNotFoundError: + from networkx.classes.backends import _registered_algorithms + + +__all__ = ["not_implemented_for", "nodes_or_number", "networkx_algorithm"] def networkx_class(api): @@ -28,7 +36,17 @@ def inner(func): class networkx_algorithm: - def __new__(cls, func=None, *, name=None, extra_params=None): + name: str + extra_doc: str | None + extra_params: dict[str, str] | None + + def __new__( + cls, + func=None, + *, + name: str | None = None, + extra_params: dict[str, str] | str | None = None, + ): if func is None: return partial(networkx_algorithm, name=name, extra_params=extra_params) instance = object.__new__(cls) @@ -37,13 +55,20 @@ def __new__(cls, func=None, *, name=None, extra_params=None): instance.__defaults__ = func.__defaults__ instance.__kwdefaults__ = func.__kwdefaults__ instance.name = func.__name__ if name is None else name - # TODO: should extra_params be a dict[str, str] that describes the parameters? if extra_params is None: - instance.extra_params = None + pass elif isinstance(extra_params, str): - instance.extra_params = {extra_params} - else: - instance.extra_params = set(extra_params) + extra_params = {extra_params: ""} + elif not isinstance(extra_params, dict): + raise TypeError( + f"extra_params must be dict, str, or None; got {type(extra_params)}" + ) + instance.extra_params = extra_params + # The docstring on our function is added to the NetworkX docstring. + instance.extra_doc = func.__doc__ + # Copy __doc__ from NetworkX + if instance.name in _registered_algorithms: + instance.__doc__ = _registered_algorithms[instance.name].__doc__ instance.can_run = _default_can_run setattr(BackendInterface, instance.name, instance) # Set methods so they are in __dict__ diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 64c0be066f2..72e4094b8b7 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -18,7 +18,7 @@ import cupy as cp -__all__ = ["_groupby", "_handle_seed"] +__all__ = ["_groupby", "_seed_to_int"] def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: @@ -51,8 +51,8 @@ def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: return rv -def _handle_seed(seed: int | Random | None) -> int: - """Handle seed argument and ensure it is what pylibcugraph needs: an int.""" +def _seed_to_int(seed: int | Random | None) -> int: + """Handle any valid seed argument and convert it to an int if necessary.""" if seed is None: return if isinstance(seed, Random): diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 95e9c256e5d..db3b3a22545 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -54,6 +54,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [project.entry-points."networkx.plugins"] cugraph = "nx_cugraph.interface:BackendInterface" +[project.entry-points."networkx.plugin_info"] +cugraph = "_nx_cugraph:get_info" + [tool.setuptools] license-files = ["LICENSE"] @@ -61,6 +64,8 @@ license-files = ["LICENSE"] include = [ "nx_cugraph*", "nx_cugraph.*", + "_nx_cugraph*", + "_nx_cugraph.*", ] [tool.black] @@ -75,6 +80,7 @@ float_to_top = true default_section = "THIRDPARTY" known_first_party = "nx_cugraph" line_length = 88 +extend_skip_glob = ["nx_cugraph/__init__.py"] [tool.pytest.ini_options] minversion = "6.0" @@ -128,6 +134,9 @@ exclude_lines = [ # https://github.com/charliermarsh/ruff/ line-length = 88 target-version = "py39" +unfixable = [ + "F841", # unused-variable (Note: can leave useless expression) +] select = [ "ALL", ] @@ -203,6 +212,7 @@ ignore = [ "__init__.py" = ["F401"] # Allow unused imports (w/o defining `__all__`) # Allow assert, print, RNG, and no docstring "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] +"_nx_cugraph/__init__.py" = ["E501"] [tool.ruff.flake8-annotations] mypy-init-return = true From f57119bf8d322a2eba902a5498ae194832d8d732 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Thu, 28 Sep 2023 13:55:59 -0500 Subject: [PATCH 022/111] Temporarily disable the deletion of the dask dataframe (#3814) temporarily disable the deletion of the dask dataframe Authors: - Joseph Nke (https://github.com/jnke2016) - Naim (https://github.com/naimnv) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3814 --- .../graph_implementation/simpleDistributedGraph.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index 01885c2d1c3..fa94fa67625 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -14,7 +14,6 @@ import gc from typing import Union import warnings -import random import cudf import cupy as cp @@ -182,10 +181,7 @@ def __from_edgelist( workers = _client.scheduler_info()["workers"] # Repartition to 2 partitions per GPU for memory efficient process input_ddf = input_ddf.repartition(npartitions=len(workers) * 2) - # FIXME: Make a copy of the input ddf before implicitly altering it. - input_ddf = input_ddf.map_partitions( - lambda df: df.copy(), token="custom-" + str(random.random()) - ) + input_ddf = input_ddf.map_partitions(lambda df: df.copy()) # The dataframe will be symmetrized iff the graph is undirected # otherwise, the inital dataframe will be returned if edge_attr is not None: @@ -337,7 +333,7 @@ def __from_edgelist( ) for w, edata in ddf.items() } - del ddf + # FIXME: For now, don't delete the copied dataframe to avoid crash self._plc_graph = { w: _client.compute(delayed_task, workers=w, allow_other_workers=False) for w, delayed_task in delayed_tasks_d.items() @@ -1196,7 +1192,5 @@ def _get_column_from_ls_dfs(lst_df, col_name): if len_df == 0: return lst_df[0][col_name] output_col = cudf.concat([df[col_name] for df in lst_df], ignore_index=True) - for df in lst_df: - df.drop(columns=[col_name], inplace=True) - gc.collect() + # FIXME: For now, don't delete the copied dataframe to avoid cras return output_col From 91fbcca659ea1b29e4658913ca4d7f8381584df7 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Thu, 28 Sep 2023 14:03:26 -0500 Subject: [PATCH 023/111] Updates the source build docs to include libcugraphops as a build prerequisite (#3893) closes #3722 Updates the source build docs to include `libcugraphops` as a build prerequisite. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3893 --- docs/cugraph/source/installation/source_build.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index 7782591f1ce..f5ee0741da6 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -6,10 +6,10 @@ The cuGraph package include both a C/C++ CUDA portion and a python portion. Bot ## Prerequisites -__Compiler__: -* `gcc` version 9.3+ -* `nvcc` version 11.0+ -* `cmake` version 3.20.1+ +__Compiler:__ +* `gcc` version 9.3+ +* `nvcc` version 11.0+ +* `cmake` version 3.20.1+ __CUDA:__ * CUDA 11.0+ @@ -18,6 +18,11 @@ __CUDA:__ You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). +__Packages:__ +* `cmake` version 3.20.1+ +* `libcugraphops` (version matching source branch version, eg. `23.10`) + +You can obtain `libcugraphops` using `conda`/`mamba` from the `nvidia` channel, or using `pip` with the `--extra-index-url=https://pypi.nvidia.com` option. See the [RAPIDS docs](https://docs.rapids.ai/install#environment) for more details. ## Building cuGraph To install cuGraph from source, ensure the dependencies are met. From b24121fc0f76f29cfab878875ff9a953b49cc6cd Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Thu, 28 Sep 2023 15:53:49 -0500 Subject: [PATCH 024/111] Pin `dask` and `distributed` for `23.10` release (#3896) This PR pins `dask` and `distributed` to `2023.9.2` for `23.10` release. xref: https://github.com/rapidsai/cudf/pull/14225 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Ray Douglass (https://github.com/raydouglass) - Peter Andreas Entschev (https://github.com/pentschev) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3896 --- ci/test_wheel_cugraph.sh | 2 +- conda/environments/all_cuda-118_arch-x86_64.yaml | 6 +++--- conda/environments/all_cuda-120_arch-x86_64.yaml | 6 +++--- conda/recipes/cugraph-pyg/meta.yaml | 2 +- conda/recipes/cugraph-service/meta.yaml | 2 +- conda/recipes/cugraph/meta.yaml | 6 +++--- dependencies.yaml | 6 +++--- python/cugraph-service/server/pyproject.toml | 4 ++-- python/cugraph/pyproject.toml | 4 ++-- 9 files changed, 19 insertions(+), 19 deletions(-) diff --git a/ci/test_wheel_cugraph.sh b/ci/test_wheel_cugraph.sh index f9e2aa6d8da..ac18459128a 100755 --- a/ci/test_wheel_cugraph.sh +++ b/ci/test_wheel_cugraph.sh @@ -9,6 +9,6 @@ RAPIDS_PY_WHEEL_NAME="pylibcugraph_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-whe python -m pip install --no-deps ./local-pylibcugraph-dep/pylibcugraph*.whl # Always install latest dask for testing -python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main +python -m pip install git+https://github.com/dask/dask.git@2023.9.2 git+https://github.com/dask/distributed.git@2023.9.2 ./ci/test_wheel.sh cugraph python/cugraph diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 86de24c991d..952ec9317e2 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -19,11 +19,11 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core>=2023.7.1 +- dask-core==2023.9.2 - dask-cuda==23.10.* - dask-cudf==23.10.* -- dask>=2023.7.1 -- distributed>=2023.7.1 +- dask==2023.9.2 +- distributed==2023.9.2 - doxygen - fsspec>=0.6.0 - gcc_linux-64=11.* diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 1054f75ba54..38936c78c38 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -19,11 +19,11 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core>=2023.7.1 +- dask-core==2023.9.2 - dask-cuda==23.10.* - dask-cudf==23.10.* -- dask>=2023.7.1 -- distributed>=2023.7.1 +- dask==2023.9.2 +- distributed==2023.9.2 - doxygen - fsspec>=0.6.0 - gcc_linux-64=11.* diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index 2d7ed2f4cda..1dc5a75c41b 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -26,7 +26,7 @@ requirements: - python - scikit-build >=0.13.1 run: - - distributed >=2023.7.1 + - distributed ==2023.9.2 - numba >=0.57 - numpy >=1.21 - python diff --git a/conda/recipes/cugraph-service/meta.yaml b/conda/recipes/cugraph-service/meta.yaml index f3229c27364..2daf0438351 100644 --- a/conda/recipes/cugraph-service/meta.yaml +++ b/conda/recipes/cugraph-service/meta.yaml @@ -59,7 +59,7 @@ outputs: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - distributed >=2023.7.1 + - distributed ==2023.9.2 - numba >=0.57 - numpy >=1.21 - python diff --git a/conda/recipes/cugraph/meta.yaml b/conda/recipes/cugraph/meta.yaml index ad5965ad20c..f9bf54a2ef4 100644 --- a/conda/recipes/cugraph/meta.yaml +++ b/conda/recipes/cugraph/meta.yaml @@ -76,9 +76,9 @@ requirements: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - dask >=2023.7.1 - - dask-core >=2023.7.1 - - distributed >=2023.7.1 + - dask ==2023.9.2 + - dask-core ==2023.9.2 + - distributed ==2023.9.2 - fsspec>=0.6.0 - libcugraph ={{ version }} - pylibcugraph ={{ version }} diff --git a/dependencies.yaml b/dependencies.yaml index a162ac01354..f74ed13115b 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -373,15 +373,15 @@ dependencies: common: - output_types: [conda, pyproject] packages: - - &dask dask>=2023.7.1 - - &distributed distributed>=2023.7.1 + - &dask dask==2023.9.2 + - &distributed distributed==2023.9.2 - &dask_cuda dask-cuda==23.10.* - &numba numba>=0.57 - &ucx_py ucx-py==0.34.* - output_types: conda packages: - aiohttp - - &dask-core_conda dask-core>=2023.7.1 + - &dask-core_conda dask-core==2023.9.2 - fsspec>=0.6.0 - libcudf==23.10.* - requests diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index f25ea6c46e5..8787cb838be 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -25,8 +25,8 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "dask-cuda==23.10.*", "dask-cudf==23.10.*", - "dask>=2023.7.1", - "distributed>=2023.7.1", + "dask==2023.9.2", + "distributed==2023.9.2", "numba>=0.57", "numpy>=1.21", "rmm==23.10.*", diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index cadf6879e23..1835ac8bb49 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -33,8 +33,8 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "dask-cuda==23.10.*", "dask-cudf==23.10.*", - "dask>=2023.7.1", - "distributed>=2023.7.1", + "dask==2023.9.2", + "distributed==2023.9.2", "fsspec[http]>=0.6.0", "numba>=0.57", "pylibcugraph==23.10.*", From 6e5e0667b19106b61c98107b0cc58ff2ef7d4202 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Fri, 29 Sep 2023 03:44:59 +0200 Subject: [PATCH 025/111] Adds logic to handle isolated vertices at python layer (#3886) - Adds logic to handle isolated vertices - Clamp downs max level to a sane number closes #3804 Authors: - Naim (https://github.com/naimnv) Approvers: - Joseph Nke (https://github.com/jnke2016) - Brad Rees (https://github.com/BradReesWork) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3886 --- python/cugraph/cugraph/community/louvain.py | 91 +++++++++++++++---- .../cugraph/tests/community/test_louvain.py | 16 ++++ python/cugraph/cugraph/utilities/utils.py | 4 +- 3 files changed, 91 insertions(+), 20 deletions(-) diff --git a/python/cugraph/cugraph/community/louvain.py b/python/cugraph/cugraph/community/louvain.py index 7f9742c8f09..0bedd427824 100644 --- a/python/cugraph/cugraph/community/louvain.py +++ b/python/cugraph/cugraph/community/louvain.py @@ -11,7 +11,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +from typing import Union, Tuple +from cugraph.structure import Graph from cugraph.utilities import ( + is_nx_graph_type, ensure_cugraph_obj_for_nx, df_score_to_dictionary, ) @@ -21,9 +24,26 @@ from pylibcugraph import louvain as pylibcugraph_louvain from pylibcugraph import ResourceHandle +from cugraph.utilities.utils import import_optional + +# FIXME: the networkx.Graph type used in type annotations is specified +# using a string literal to avoid depending on and importing networkx. +# Instead, networkx is imported optionally, which may cause a problem +# for a type checker if run in an environment where networkx is not installed. +networkx = import_optional("networkx") + +VERTEX_COL_NAME = "vertex" +CLUSTER_ID_COL_NAME = "partition" + # FIXME: max_level should default to 100 once max_iter is removed -def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): +def louvain( + G: Union[Graph, "networkx.Graph"], + max_level: Union[int, None] = None, + max_iter: Union[int, None] = None, + resolution: float = 1.0, + threshold: float = 1e-7, +) -> Tuple[Union[cudf.DataFrame, dict], float]: """ Compute the modularity optimizing partition of the input graph using the Louvain method @@ -48,6 +68,9 @@ def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): than the specified number of levels. No error occurs when the algorithm terminates early in this manner. + If max_level > 500, it will be set to 500 and a warning is emitted + in order to prevent excessive runtime. + max_iter : integer, optional (default=None) This parameter is deprecated in favor of max_level. Previously it was used to control the maximum number of levels of the Louvain @@ -68,18 +91,21 @@ def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): Returns ------- - parts : cudf.DataFrame - GPU data frame of size V containing two columns the vertex id and the - partition id it is assigned to. + result: cudf.DataFrame or dict + If input graph G is of type cugraph.Graph, a GPU dataframe + with two columns. + + result[VERTEX_COL_NAME] : cudf.Series + Contains the vertex identifiers + result[CLUSTER_ID_COL_NAME] : cudf.Series + Contains the partition assigned to the vertices - df['vertex'] : cudf.Series - Contains the vertex identifiers - df['partition'] : cudf.Series - Contains the partition assigned to the vertices + If input graph G is of type networkx.Graph, a dict + Dictionary of vertices and their partition ids. modularity_score : float - a floating point number containing the global modularity score of the - partitioning. + A floating point number containing the global modularity score + of the partitioning. Examples -------- @@ -89,6 +115,17 @@ def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): """ + # FIXME: Onece the graph construction calls support isolated vertices through + # the C API (the C++ interface already supports this) then there will be + # no need to compute isolated vertices here. + + isolated_vertices = list() + if is_nx_graph_type(type(G)): + isolated_vertices = [v for v in range(G.number_of_nodes()) if G.degree[v] == 0] + else: + # FIXME: Gather isolated vertices of G + pass + G, isNx = ensure_cugraph_obj_for_nx(G) if G.is_directed(): @@ -112,7 +149,12 @@ def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): if max_level is None: max_level = 100 - vertex, partition, mod_score = pylibcugraph_louvain( + if max_level > 500: + w_msg = "max_level is set too high, clamping it down to 500." + warnings.warn(w_msg) + max_level = 500 + + vertex, partition, modularity_score = pylibcugraph_louvain( resource_handle=ResourceHandle(), graph=G._plc_graph, max_level=max_level, @@ -121,14 +163,27 @@ def louvain(G, max_level=None, max_iter=None, resolution=1.0, threshold=1e-7): do_expensive_check=False, ) - df = cudf.DataFrame() - df["vertex"] = vertex - df["partition"] = partition + result = cudf.DataFrame() + result[VERTEX_COL_NAME] = vertex + result[CLUSTER_ID_COL_NAME] = partition + + if len(isolated_vertices) > 0: + unique_cids = result[CLUSTER_ID_COL_NAME].unique() + max_cluster_id = -1 if len(result) == 0 else unique_cids.max() + + isolated_vtx_and_cids = cudf.DataFrame() + isolated_vtx_and_cids[VERTEX_COL_NAME] = isolated_vertices + isolated_vtx_and_cids[CLUSTER_ID_COL_NAME] = [ + (max_cluster_id + i + 1) for i in range(len(isolated_vertices)) + ] + result = cudf.concat( + [result, isolated_vtx_and_cids], ignore_index=True, sort=False + ) - if G.renumbered: - df = G.unrenumber(df, "vertex") + if G.renumbered and len(G.input_df) > 0: + result = G.unrenumber(result, VERTEX_COL_NAME) if isNx is True: - df = df_score_to_dictionary(df, "partition") + result = df_score_to_dictionary(result, CLUSTER_ID_COL_NAME) - return df, mod_score + return result, modularity_score diff --git a/python/cugraph/cugraph/tests/community/test_louvain.py b/python/cugraph/cugraph/tests/community/test_louvain.py index 183be071a44..5441998fb46 100644 --- a/python/cugraph/cugraph/tests/community/test_louvain.py +++ b/python/cugraph/cugraph/tests/community/test_louvain.py @@ -142,3 +142,19 @@ def test_louvain_csr_graph(is_weighted): assert len(parition_diffs) == 0 assert mod_csr == mod_coo + + +@pytest.mark.sg +def test_louvain_nx_graph_with_isolated_nodes(): + # Cluster IDs are expected to unique if all nodes are isolated + G = nx.Graph() + G.add_nodes_from(range(5)) + result, _ = cugraph.louvain(G) + assert set(result.keys()) == set(G.nodes) + assert len(set(result.values())) == G.number_of_nodes() + + # A graph with 5 nodes, where 3 of the nodes are isolated + G.add_edge(1, 2) + result, _ = cugraph.louvain(G) + assert set(result.keys()) == set(G.nodes) + assert len(set(result.values())) == G.number_of_nodes() - 1 diff --git a/python/cugraph/cugraph/utilities/utils.py b/python/cugraph/cugraph/utilities/utils.py index e68b5dd4880..7a54a0bf2cf 100644 --- a/python/cugraph/cugraph/utilities/utils.py +++ b/python/cugraph/cugraph/utilities/utils.py @@ -364,8 +364,8 @@ def is_matrix_type(m): return is_cp_matrix_type(m) or is_sp_matrix_type(m) -def is_nx_graph_type(g): - return g in __nx_graph_types +def is_nx_graph_type(graph_type): + return graph_type in __nx_graph_types def is_cugraph_graph_type(g): From eed12230fb41da701ab9ea302642765d81024bc8 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Fri, 29 Sep 2023 11:35:12 -0500 Subject: [PATCH 026/111] Refactor legacy k truss (#3843) This PR refactor legacy k truss by leveraging the `C API` instead of the `legacy:COO` graph. This also stands as a preliminary work to later provide a new implementation of `k_truss` matching the `C API`. Closes #3753 Closes #3754 Authors: - Joseph Nke (https://github.com/jnke2016) - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Brad Rees (https://github.com/BradReesWork) - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/3843 --- cpp/CMakeLists.txt | 1 + cpp/include/cugraph/algorithms.hpp | 43 ++-- .../cugraph/utilities/graph_traits.hpp | 22 +- cpp/include/cugraph_c/community_algorithms.h | 21 ++ cpp/src/c_api/legacy_k_truss.cpp | 150 +++++++++++ cpp/src/community/legacy/ktruss.cu | 127 ++++++---- cpp/tests/CMakeLists.txt | 1 + cpp/tests/c_api/legacy_k_truss_test.c | 236 ++++++++++++++++++ python/cugraph/CMakeLists.txt | 1 - .../cugraph/cugraph/community/CMakeLists.txt | 25 -- .../cugraph/community/ktruss_subgraph.pxd | 26 -- .../cugraph/community/ktruss_subgraph.py | 54 +++- .../community/ktruss_subgraph_wrapper.pyx | 43 ---- .../pylibcugraph/pylibcugraph/CMakeLists.txt | 1 + python/pylibcugraph/pylibcugraph/__init__.py | 2 + .../_cugraph_c/community_algorithms.pxd | 10 + .../pylibcugraph/k_truss_subgraph.pyx | 163 ++++++++++++ 17 files changed, 759 insertions(+), 167 deletions(-) create mode 100644 cpp/src/c_api/legacy_k_truss.cpp create mode 100644 cpp/tests/c_api/legacy_k_truss_test.c delete mode 100644 python/cugraph/cugraph/community/CMakeLists.txt delete mode 100644 python/cugraph/cugraph/community/ktruss_subgraph.pxd delete mode 100644 python/cugraph/cugraph/community/ktruss_subgraph_wrapper.pyx create mode 100644 python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 0d7bd86075d..d9f87d7dd72 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -435,6 +435,7 @@ add_library(cugraph_c src/c_api/labeling_result.cpp src/c_api/weakly_connected_components.cpp src/c_api/strongly_connected_components.cpp + src/c_api/legacy_k_truss.cpp ) add_library(cugraph::cugraph_c ALIAS cugraph_c) diff --git a/cpp/include/cugraph/algorithms.hpp b/cpp/include/cugraph/algorithms.hpp index b624ec5c0e0..78846bc5766 100644 --- a/cpp/include/cugraph/algorithms.hpp +++ b/cpp/include/cugraph/algorithms.hpp @@ -430,34 +430,39 @@ void connected_components(legacy::GraphCSRView const& graph, VT* labels); /** - * @brief Compute k truss for a graph + * @brief Compute k truss for a graph ** temporary * * K Truss is the maximal subgraph of a graph which contains at least three * vertices where every edge is incident to at least k-2 triangles. * - * Note that current implementation does not support a weighted graph. + * This version is a temporary solution to clean up python integration through the C API. * - * @throws cugraph::logic_error with a custom message when an error - * occurs. + * This version is only supported SG. * - * @tparam VT Type of vertex identifiers. Supported value : int (signed, - * 32-bit) - * @tparam ET Type of edge identifiers. Supported value : int (signed, - * 32-bit) - * @tparam WT Type of edge weights. Supported values : float or double. + * @throws cugraph::logic_error with a custom message when an error + * occurs. * - * @param[in] graph cuGraph graph descriptor, should contain the connectivity - * information as a COO - * @param[in] k The order of the truss - * @param[in] mr Memory resource used to allocate the returned graph - * @return Unique pointer to K Truss subgraph in COO format + * @tparam vertex_t Type of vertex identifiers. Supported value : int (signed, 32-bit) + * @tparam weight_t Type of edge weights. Supported values : float or double. * + * @param[in] handle Library handle (RAFT). + * @param[in] src Source vertices from COO + * @param[in] dst Destination vertices from COO + * @param[in] wgt Optional edge weights from COO + * @param[in] k The order of the truss + * @return Tuple containing extracted src, dst and optional weights for the + * subgraph */ -template -std::unique_ptr> k_truss_subgraph( - legacy::GraphCOOView const& graph, - int k, - rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource()); +template +std::tuple, + rmm::device_uvector, + std::optional>> +k_truss_subgraph(raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + std::optional> wgt, + size_t number_of_vertices, + int k); // FIXME: Internally distances is of int (signed 32-bit) data type, but current // template uses data from VT, ET, WT from the legacy::GraphCSR View even if weights diff --git a/cpp/include/cugraph/utilities/graph_traits.hpp b/cpp/include/cugraph/utilities/graph_traits.hpp index 7385630c011..e2737305aed 100644 --- a/cpp/include/cugraph/utilities/graph_traits.hpp +++ b/cpp/include/cugraph/utilities/graph_traits.hpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021, NVIDIA CORPORATION. + * Copyright (c) 2021-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -47,6 +47,16 @@ struct is_vertex_edge_combo { (sizeof(vertex_t) <= sizeof(edge_t)); }; +// meta-function that constrains +// vertex_t and edge_t template param candidates to only int32_t: +// +template +struct is_vertex_edge_combo_legacy { + static constexpr bool value = is_one_of::value && + is_one_of::value && + (sizeof(vertex_t) <= sizeof(edge_t)); +}; + // meta-function that constrains // all 3 template param candidates: // @@ -56,4 +66,14 @@ struct is_candidate { is_vertex_edge_combo::value && is_one_of::value; }; +// meta-function that constrains +// all 3 template param candidates where vertex_t and edge_t +// are restricted to int32_t: +// +template +struct is_candidate_legacy { + static constexpr bool value = is_vertex_edge_combo_legacy::value && + is_one_of::value; +}; + } // namespace cugraph diff --git a/cpp/include/cugraph_c/community_algorithms.h b/cpp/include/cugraph_c/community_algorithms.h index e938c77cccd..8f1015f8632 100644 --- a/cpp/include/cugraph_c/community_algorithms.h +++ b/cpp/include/cugraph_c/community_algorithms.h @@ -227,6 +227,27 @@ cugraph_error_code_t cugraph_extract_ego( cugraph_induced_subgraph_result_t** result, cugraph_error_t** error); +/** + * @brief Extract k truss for a graph + * + * @param [in] handle Handle for accessing resources + * @param [in] graph Pointer to graph. NOTE: Graph might be modified if the storage + * needs to be transposed + * @param [in] k The order of the truss + * @param [in] do_expensive_check + * A flag to run expensive checks for input arguments (if set to true) + * @param [out] result Opaque object containing the extracted subgraph + * @param [out] error Pointer to an error object storing details of any error. Will + * be populated if error code is not CUGRAPH_SUCCESS + * @return error code + */ +cugraph_error_code_t cugraph_k_truss_subgraph(const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + size_t k, + bool_t do_expensive_check, + cugraph_induced_subgraph_result_t** result, + cugraph_error_t** error); + /** * @brief Opaque clustering output */ diff --git a/cpp/src/c_api/legacy_k_truss.cpp b/cpp/src/c_api/legacy_k_truss.cpp new file mode 100644 index 00000000000..90e0894783a --- /dev/null +++ b/cpp/src/c_api/legacy_k_truss.cpp @@ -0,0 +1,150 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#include + +namespace { + +struct k_truss_functor : public cugraph::c_api::abstract_functor { + raft::handle_t const& handle_; + cugraph::c_api::cugraph_graph_t* graph_; + size_t k_; + bool do_expensive_check_; + cugraph::c_api::cugraph_induced_subgraph_result_t* result_{}; + + k_truss_functor(::cugraph_resource_handle_t const* handle, + ::cugraph_graph_t* graph, + size_t k, + bool do_expensive_check) + : abstract_functor(), + handle_(*reinterpret_cast(handle)->handle_), + graph_(reinterpret_cast(graph)), + k_(k), + do_expensive_check_(do_expensive_check) + { + } + + template + void operator()() + { + if constexpr (!cugraph::is_candidate_legacy::value) { + unsupported(); + } else if constexpr (multi_gpu) { + unsupported(); + } else { + // k_truss expects store_transposed == false + if constexpr (store_transposed) { + error_code_ = cugraph::c_api:: + transpose_storage( + handle_, graph_, error_.get()); + if (error_code_ != CUGRAPH_SUCCESS) return; + } + + auto graph = + reinterpret_cast*>(graph_->graph_); + + auto edge_weights = reinterpret_cast< + cugraph::edge_property_t, + weight_t>*>(graph_->edge_weights_); + + auto number_map = reinterpret_cast*>(graph_->number_map_); + + auto graph_view = graph->view(); + rmm::device_uvector src(0, handle_.get_stream()); + rmm::device_uvector dst(0, handle_.get_stream()); + std::optional> wgt{std::nullopt}; + + std::tie(src, dst, wgt, std::ignore) = cugraph::decompress_to_edgelist( + handle_, + graph_view, + edge_weights ? std::make_optional(edge_weights->view()) : std::nullopt, + std::optional>{std::nullopt}, + std::optional>(std::nullopt), + do_expensive_check_); + + auto [result_src, result_dst, result_wgt] = cugraph::k_truss_subgraph( + handle_, + raft::device_span(src.data(), src.size()), + raft::device_span(dst.data(), dst.size()), + wgt ? std::make_optional(raft::device_span(wgt->data(), wgt->size())) + : std::nullopt, + graph_view.number_of_vertices(), + k_); + + cugraph::unrenumber_int_vertices( + handle_, + result_src.data(), + result_src.size(), + number_map->data(), + graph_view.vertex_partition_range_lasts(), + do_expensive_check_); + + cugraph::unrenumber_int_vertices( + handle_, + result_dst.data(), + result_dst.size(), + number_map->data(), + graph_view.vertex_partition_range_lasts(), + do_expensive_check_); + + rmm::device_uvector edge_offsets(2, handle_.get_stream()); + std::vector h_edge_offsets{{0, result_src.size()}}; + raft::update_device( + edge_offsets.data(), h_edge_offsets.data(), h_edge_offsets.size(), handle_.get_stream()); + + result_ = new cugraph::c_api::cugraph_induced_subgraph_result_t{ + new cugraph::c_api::cugraph_type_erased_device_array_t(result_src, graph_->vertex_type_), + new cugraph::c_api::cugraph_type_erased_device_array_t(result_dst, graph_->vertex_type_), + wgt ? new cugraph::c_api::cugraph_type_erased_device_array_t(*result_wgt, + graph_->weight_type_) + : NULL, + new cugraph::c_api::cugraph_type_erased_device_array_t(edge_offsets, + cugraph_data_type_id_t::SIZE_T)}; + } + } +}; + +} // namespace + +extern "C" cugraph_error_code_t cugraph_k_truss_subgraph(const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + size_t k, + bool_t do_expensive_check, + cugraph_induced_subgraph_result_t** result, + cugraph_error_t** error) +{ + k_truss_functor functor(handle, graph, k, do_expensive_check); + + return cugraph::c_api::run_algorithm(graph, functor, result, error); +} diff --git a/cpp/src/community/legacy/ktruss.cu b/cpp/src/community/legacy/ktruss.cu index 74a871adb01..403593128c1 100644 --- a/cpp/src/community/legacy/ktruss.cu +++ b/cpp/src/community/legacy/ktruss.cu @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2021, NVIDIA CORPORATION. + * Copyright (c) 2019-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -34,18 +34,24 @@ namespace cugraph { namespace detail { -template -std::unique_ptr> ktruss_subgraph_impl( - legacy::GraphCOOView const& graph, int k, rmm::mr::device_memory_resource* mr) +template +std::tuple, rmm::device_uvector> ktruss_subgraph_impl( + raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + size_t number_of_vertices, + int k) { - using HornetGraph = hornet::gpu::Hornet; - using UpdatePtr = hornet::BatchUpdatePtr; - using Update = hornet::gpu::BatchUpdate; - cudaStream_t stream{nullptr}; - UpdatePtr ptr(graph.number_of_edges, graph.src_indices, graph.dst_indices); + using HornetGraph = hornet::gpu::Hornet; + using UpdatePtr = hornet::BatchUpdatePtr; + using Update = hornet::gpu::BatchUpdate; + + HornetGraph hnt(number_of_vertices + 1); + + // NOTE: Should a constant pointer be passed for @src and @dst + UpdatePtr ptr(static_cast(src.size()), src.data(), dst.data()); Update batch(ptr); - HornetGraph hnt(graph.number_of_vertices + 1); hnt.insert(batch); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to initialize graph"); @@ -67,32 +73,42 @@ std::unique_ptr> ktruss_subgraph_impl( kt.runForK(k); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to run"); - auto out_graph = std::make_unique>( - graph.number_of_vertices, kt.getGraphEdgeCount(), graph.has_data(), stream, mr); + rmm::device_uvector result_src(kt.getGraphEdgeCount(), handle.get_stream()); + rmm::device_uvector result_dst(kt.getGraphEdgeCount(), handle.get_stream()); - kt.copyGraph(out_graph->src_indices(), out_graph->dst_indices()); + kt.copyGraph(result_src.data(), result_dst.data()); kt.release(); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to release"); - return out_graph; + return std::make_tuple(std::move(result_src), std::move(result_dst)); } -template -std::unique_ptr> weighted_ktruss_subgraph_impl( - legacy::GraphCOOView const& graph, int k, rmm::mr::device_memory_resource* mr) + +template +std::tuple, + rmm::device_uvector, + std::optional>> +weighted_ktruss_subgraph_impl(raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + std::optional> wgt, + size_t number_of_vertices, + int k) { - using HornetGraph = hornet::gpu::Hornet>; - using UpdatePtr = hornet::BatchUpdatePtr, hornet::DeviceType::DEVICE>; - using Update = hornet::gpu::BatchUpdate>; - cudaStream_t stream{nullptr}; - UpdatePtr ptr(graph.number_of_edges, graph.src_indices, graph.dst_indices, graph.edge_data); + using HornetGraph = hornet::gpu::Hornet>; + using UpdatePtr = + hornet::BatchUpdatePtr, hornet::DeviceType::DEVICE>; + using Update = hornet::gpu::BatchUpdate>; + + HornetGraph hnt(number_of_vertices + 1); + + UpdatePtr ptr(static_cast(src.size()), src.data(), dst.data(), wgt->data()); Update batch(ptr); - HornetGraph hnt(graph.number_of_vertices + 1); hnt.insert(batch); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to initialize graph"); - KTrussWeighted kt(hnt); + KTrussWeighted kt(hnt); kt.init(); kt.reset(); @@ -110,41 +126,60 @@ std::unique_ptr> weighted_ktruss_subgraph_impl( kt.runForK(k); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to run"); - auto out_graph = std::make_unique>( - graph.number_of_vertices, kt.getGraphEdgeCount(), graph.has_data(), stream, mr); + rmm::device_uvector result_src(kt.getGraphEdgeCount(), handle.get_stream()); + rmm::device_uvector result_dst(kt.getGraphEdgeCount(), handle.get_stream()); + std::optional> result_wgt{std::nullopt}; - kt.copyGraph(out_graph->src_indices(), out_graph->dst_indices(), out_graph->edge_data()); + result_wgt = rmm::device_uvector(kt.getGraphEdgeCount(), handle.get_stream()); + kt.copyGraph(result_src.data(), result_dst.data(), result_wgt->data()); kt.release(); CUGRAPH_EXPECTS(cudaPeekAtLastError() == cudaSuccess, "KTruss : Failed to release"); - return out_graph; + return std::make_tuple(std::move(result_src), std::move(result_dst), std::move(result_wgt)); } } // namespace detail -template -std::unique_ptr> k_truss_subgraph( - legacy::GraphCOOView const& graph, int k, rmm::mr::device_memory_resource* mr) +template +std::tuple, + rmm::device_uvector, + std::optional>> +k_truss_subgraph(raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + std::optional> wgt, + size_t number_of_vertices, + int k) { - CUGRAPH_EXPECTS(graph.src_indices != nullptr, "Graph source indices cannot be a nullptr"); - CUGRAPH_EXPECTS(graph.dst_indices != nullptr, "Graph destination indices cannot be a nullptr"); - - if (graph.edge_data == nullptr) { - return detail::ktruss_subgraph_impl(graph, k, mr); + if (wgt.has_value()) { + return detail::weighted_ktruss_subgraph_impl(handle, src, dst, wgt, number_of_vertices, k); } else { - return detail::weighted_ktruss_subgraph_impl(graph, k, mr); + auto [result_src, result_dst] = + detail::ktruss_subgraph_impl(handle, src, dst, number_of_vertices, k); + std::optional> result_wgt{std::nullopt}; + return std::make_tuple(std::move(result_src), std::move(result_dst), std::move(result_wgt)); } } -template std::unique_ptr> -k_truss_subgraph(legacy::GraphCOOView const&, - int, - rmm::mr::device_memory_resource*); - -template std::unique_ptr> -k_truss_subgraph(legacy::GraphCOOView const&, - int, - rmm::mr::device_memory_resource*); +template std::tuple, + rmm::device_uvector, + std::optional>> +k_truss_subgraph(raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + std::optional> wgt, + size_t number_of_vertices, + int k); + +template std::tuple, + rmm::device_uvector, + std::optional>> +k_truss_subgraph(raft::handle_t const& handle, + raft::device_span src, + raft::device_span dst, + std::optional> wgt, + size_t number_of_vertices, + int k); } // namespace cugraph diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index f08606df8ea..2a4bb8ab2a5 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -741,5 +741,6 @@ ConfigureCTest(CAPI_K_CORE_TEST c_api/k_core_test.c) ConfigureCTest(CAPI_INDUCED_SUBGRAPH_TEST c_api/induced_subgraph_test.c) ConfigureCTest(CAPI_EGONET_TEST c_api/egonet_test.c) ConfigureCTest(CAPI_TWO_HOP_NEIGHBORS_TEST c_api/two_hop_neighbors_test.c) +ConfigureCTest(CAPI_LEGACY_K_TRUSS_TEST c_api/legacy_k_truss_test.c) rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing_c DESTINATION bin/gtests/libcugraph_c) diff --git a/cpp/tests/c_api/legacy_k_truss_test.c b/cpp/tests/c_api/legacy_k_truss_test.c new file mode 100644 index 00000000000..bc85f568688 --- /dev/null +++ b/cpp/tests/c_api/legacy_k_truss_test.c @@ -0,0 +1,236 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "c_test_utils.h" /* RUN_TEST */ + +#include +#include + +#include + +typedef int32_t vertex_t; +typedef int32_t edge_t; +typedef float weight_t; + +int generic_k_truss_test(vertex_t* h_src, + vertex_t* h_dst, + weight_t* h_wgt, + vertex_t* h_expected_src, + vertex_t* h_expected_dst, + weight_t* h_expected_wgt, + size_t* h_expected_offsets, + size_t num_vertices, + size_t num_edges, + size_t k, + size_t num_expected_offsets, + size_t num_expected_edges, + bool_t store_transposed) +{ + int test_ret_value = 0; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + data_type_id_t edge_id_tid = INT32; + data_type_id_t edge_type_tid = INT32; + + cugraph_resource_handle_t* resource_handle = NULL; + cugraph_graph_t* graph = NULL; + cugraph_type_erased_device_array_t* seeds = NULL; + cugraph_type_erased_device_array_view_t* seeds_view = NULL; + cugraph_induced_subgraph_result_t* result = NULL; + + resource_handle = cugraph_create_resource_handle(NULL); + TEST_ASSERT(test_ret_value, resource_handle != NULL, "resource handle creation failed."); + + ret_code = create_sg_test_graph( + resource_handle, + vertex_tid, + edge_tid, + h_src, + h_dst, + weight_tid, + h_wgt, + edge_type_tid, + NULL, + edge_id_tid, + NULL, + num_edges, + store_transposed, + FALSE, + TRUE, + FALSE, + &graph, + &ret_error); + + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + ret_code = + cugraph_k_truss_subgraph(resource_handle, graph, k, FALSE, &result, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, "cugraph_k_truss_subgraph failed."); + + if (test_ret_value == 0) { + cugraph_type_erased_device_array_view_t* src; + cugraph_type_erased_device_array_view_t* dst; + cugraph_type_erased_device_array_view_t* wgt; + cugraph_type_erased_device_array_view_t* offsets; + + src = cugraph_induced_subgraph_get_sources(result); + dst = cugraph_induced_subgraph_get_destinations(result); + wgt = cugraph_induced_subgraph_get_edge_weights(result); + offsets = cugraph_induced_subgraph_get_subgraph_offsets(result); + + size_t num_result_edges = cugraph_type_erased_device_array_view_size(src); + size_t num_result_offsets = cugraph_type_erased_device_array_view_size(offsets); + + vertex_t h_result_src[num_result_edges]; + vertex_t h_result_dst[num_result_edges]; + weight_t h_result_wgt[num_result_edges]; + size_t h_result_offsets[num_result_offsets]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_result_src, src, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_result_dst, dst, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + if (wgt != NULL){ + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_result_wgt, wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } + + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + resource_handle, (byte_t*)h_result_offsets, offsets, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + TEST_ASSERT(test_ret_value, num_result_edges == num_expected_edges, "results not the same size"); + + for (size_t i = 0; (i < num_expected_offsets) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + h_expected_offsets[i] == h_result_offsets[i], + "graph offsets should match"); + } + + for (size_t i = 0; (i < num_expected_edges) && (test_ret_value == 0); ++i) { + bool_t found = FALSE; + for (size_t j = 0; (j < num_expected_edges) && !found; ++j) { + if ((h_expected_src[i] == h_result_src[j]) && (h_expected_dst[i] == h_result_dst[j])) + if (wgt != NULL){ + found = (nearlyEqual(h_expected_wgt[i], h_result_wgt[j], 0.001)); + } + else{ + found = TRUE; + } + } + TEST_ASSERT(test_ret_value, found, "extracted an edge that doesn't match"); + } + + cugraph_type_erased_device_array_view_free(src); + cugraph_type_erased_device_array_view_free(dst); + cugraph_type_erased_device_array_view_free(wgt); + cugraph_type_erased_device_array_view_free(offsets); + cugraph_induced_subgraph_result_free(result); + } + + cugraph_sg_graph_free(graph); + cugraph_free_resource_handle(resource_handle); + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_k_truss() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t k = 3; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; + weight_t h_wgt[] = { + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; + + vertex_t h_result_src[] = {0, 0, 1, 1, 1, 2, 2, 2, 3, 3}; + vertex_t h_result_dst[] = {1, 2, 0, 2, 3, 0, 1, 3, 1, 2}; + weight_t h_result_wgt[] = {0.1, 5.1, 0.1, 3.1, 2.1, 5.1, 3.1, 4.1, 2.1, 4.1}; + size_t h_result_offsets[] = {0, 10}; + size_t num_expected_edges = 10; + size_t num_expected_offsets = 2; + + return generic_k_truss_test(h_src, + h_dst, + h_wgt, + h_result_src, + h_result_dst, + h_result_wgt, + h_result_offsets, + num_vertices, + num_edges, + k, + num_expected_offsets, + num_expected_edges, + FALSE); +} + +int test_k_truss_no_weights() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t k = 3; + + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; + + vertex_t h_result_src[] = {0, 0, 1, 1, 1, 2, 2, 2, 3, 3}; + vertex_t h_result_dst[] = {1, 2, 0, 2, 3, 0, 1, 3, 1, 2}; + size_t h_result_offsets[] = {0, 10}; + size_t num_expected_edges = 10; + size_t num_expected_offsets = 2; + + return generic_k_truss_test(h_src, + h_dst, + NULL, + h_result_src, + h_result_dst, + NULL, + h_result_offsets, + num_vertices, + num_edges, + k, + num_expected_offsets, + num_expected_edges, + FALSE); +} + + +/******************************************************************************/ + +int main(int argc, char** argv) +{ + int result = 0; + result |= RUN_TEST(test_k_truss); + result |= RUN_TEST(test_k_truss_no_weights); + return result; +} diff --git a/python/cugraph/CMakeLists.txt b/python/cugraph/CMakeLists.txt index ecfcb9b219f..64db9571dc9 100644 --- a/python/cugraph/CMakeLists.txt +++ b/python/cugraph/CMakeLists.txt @@ -82,7 +82,6 @@ endif() rapids_cython_init() -add_subdirectory(cugraph/community) add_subdirectory(cugraph/components) add_subdirectory(cugraph/dask/comms) add_subdirectory(cugraph/dask/structure) diff --git a/python/cugraph/cugraph/community/CMakeLists.txt b/python/cugraph/cugraph/community/CMakeLists.txt deleted file mode 100644 index 185f6accbab..00000000000 --- a/python/cugraph/cugraph/community/CMakeLists.txt +++ /dev/null @@ -1,25 +0,0 @@ -# ============================================================================= -# Copyright (c) 2022-2023, NVIDIA CORPORATION. -# -# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except -# in compliance with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software distributed under the License -# is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express -# or implied. See the License for the specific language governing permissions and limitations under -# the License. -# ============================================================================= - -set(cython_sources - ktruss_subgraph_wrapper.pyx -) - -set(linked_libraries cugraph::cugraph) -rapids_cython_create_modules( - CXX - SOURCE_FILES "${cython_sources}" - LINKED_LIBRARIES "${linked_libraries}" MODULE_PREFIX community_ - ASSOCIATED_TARGETS cugraph -) diff --git a/python/cugraph/cugraph/community/ktruss_subgraph.pxd b/python/cugraph/cugraph/community/ktruss_subgraph.pxd deleted file mode 100644 index d993c31c375..00000000000 --- a/python/cugraph/cugraph/community/ktruss_subgraph.pxd +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) 2019-2021, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.structure.graph_primtypes cimport * - - -cdef extern from "cugraph/algorithms.hpp" namespace "cugraph": - - cdef unique_ptr[GraphCOO[VT,ET,WT]] k_truss_subgraph[VT,ET,WT]( - const GraphCOOView[VT,ET,WT] &graph, - int k) except + diff --git a/python/cugraph/cugraph/community/ktruss_subgraph.py b/python/cugraph/cugraph/community/ktruss_subgraph.py index 0ebbe633317..15a10007610 100644 --- a/python/cugraph/cugraph/community/ktruss_subgraph.py +++ b/python/cugraph/cugraph/community/ktruss_subgraph.py @@ -11,14 +11,27 @@ # See the License for the specific language governing permissions and # limitations under the License. -from cugraph.community import ktruss_subgraph_wrapper from cugraph.structure.graph_classes import Graph +from typing import Union from cugraph.utilities import ( ensure_cugraph_obj_for_nx, cugraph_to_nx, ) +from pylibcugraph import k_truss_subgraph as pylibcugraph_k_truss_subgraph +from pylibcugraph import ResourceHandle +import warnings + from numba import cuda +import cudf +from cugraph.utilities.utils import import_optional + +# FIXME: the networkx.Graph type used in the type annotation for +# ktruss_subgraph() is specified using a string literal to avoid depending on +# and importing networkx. Instead, networkx is imported optionally, which may +# cause a problem for a type checker if run in an environment where networkx is +# not installed. +networkx = import_optional("networkx") # FIXME: special case for ktruss on CUDA 11.4: an 11.4 bug causes ktruss to @@ -39,7 +52,9 @@ def _ensure_compatible_cuda_version(): ) -def k_truss(G, k): +def k_truss( + G: Union[Graph, "networkx.Graph"], k: int +) -> Union[Graph, "networkx.Graph"]: """ Returns the K-Truss subgraph of a graph for a specific k. @@ -90,7 +105,11 @@ def k_truss(G, k): # FIXME: merge this function with k_truss -def ktruss_subgraph(G, k, use_weights=True): +def ktruss_subgraph( + G: Union[Graph, "networkx.Graph"], + k: int, + use_weights=True, # deprecated +) -> Graph: """ Returns the K-Truss subgraph of a graph for a specific k. @@ -103,7 +122,7 @@ def ktruss_subgraph(G, k, use_weights=True): finding the maximal k-clique is known to be NP-Hard. In contrast, finding a k-truss is computationally tractable as its - key building block, namely triangle counting counting, can be executed + key building block, namely triangle counting, can be executed in polnymomial time.Typically, it takes many iterations of triangle counting to find the k-truss of a graph. Yet these iterations operate on a weakly monotonically shrinking graph. @@ -141,7 +160,10 @@ def ktruss_subgraph(G, k, use_weights=True): The desired k to be used for extracting the k-truss subgraph. use_weights : bool, optional (default=True) - whether the output should contain the edge weights if G has them + Whether the output should contain the edge weights if G has them. + + Deprecated: If 'weights' were passed at the graph creation, they will + be used. Returns ------- @@ -162,7 +184,27 @@ def ktruss_subgraph(G, k, use_weights=True): if G.is_directed(): raise ValueError("input graph must be undirected") - subgraph_df = ktruss_subgraph_wrapper.ktruss_subgraph(G, k, use_weights) + if use_weights: + warning_msg = ( + "The use_weights flag is deprecated " + "and will be removed in the next release. if weights " + "were passed at the graph creation, they will be used." + ) + warnings.warn(warning_msg, FutureWarning) + + sources, destinations, edge_weights, _ = pylibcugraph_k_truss_subgraph( + resource_handle=ResourceHandle(), + graph=G._plc_graph, + k=k, + do_expensive_check=True, + ) + + subgraph_df = cudf.DataFrame() + subgraph_df["src"] = sources + subgraph_df["dst"] = destinations + if edge_weights is not None: + subgraph_df["weight"] = edge_weights + if G.renumbered: subgraph_df = G.unrenumber(subgraph_df, "src") subgraph_df = G.unrenumber(subgraph_df, "dst") diff --git a/python/cugraph/cugraph/community/ktruss_subgraph_wrapper.pyx b/python/cugraph/cugraph/community/ktruss_subgraph_wrapper.pyx deleted file mode 100644 index 8b705e8a7b4..00000000000 --- a/python/cugraph/cugraph/community/ktruss_subgraph_wrapper.pyx +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) 2019-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# cython: profile=False -# distutils: language = c++ -# cython: embedsignature = True -# cython: language_level = 3 - -from cugraph.community.ktruss_subgraph cimport * -from cugraph.structure.graph_primtypes cimport * -from cugraph.structure import graph_primtypes_wrapper -import numpy as np - - -def ktruss_subgraph_float(input_graph, k, use_weights): - cdef GraphCOOViewFloat in_graph = get_coo_float_graph_view(input_graph, use_weights) - return coo_to_df(move(k_truss_subgraph[int,int,float](in_graph, k))) - - -def ktruss_subgraph_double(input_graph, k, use_weights): - cdef GraphCOOViewDouble in_graph = get_coo_double_graph_view(input_graph, use_weights) - return coo_to_df(move(k_truss_subgraph[int,int,double](in_graph, k))) - - -def ktruss_subgraph(input_graph, k, use_weights): - [input_graph.edgelist.edgelist_df['src'], - input_graph.edgelist.edgelist_df['dst']] = graph_primtypes_wrapper.datatype_cast([input_graph.edgelist.edgelist_df['src'], - input_graph.edgelist.edgelist_df['dst']], - [np.int32]) - if graph_primtypes_wrapper.weight_type(input_graph) == np.float64 and use_weights: - return ktruss_subgraph_double(input_graph, k, use_weights) - else: - return ktruss_subgraph_float(input_graph, k, use_weights) diff --git a/python/pylibcugraph/pylibcugraph/CMakeLists.txt b/python/pylibcugraph/pylibcugraph/CMakeLists.txt index 2f7e63b5c55..6618c50122c 100644 --- a/python/pylibcugraph/pylibcugraph/CMakeLists.txt +++ b/python/pylibcugraph/pylibcugraph/CMakeLists.txt @@ -35,6 +35,7 @@ set(cython_sources hits.pyx induced_subgraph.pyx k_core.pyx + k_truss_subgraph.pyx jaccard_coefficients.pyx sorensen_coefficients.pyx overlap_coefficients.pyx diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index 45f6de2f663..bd47c7da184 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -87,6 +87,8 @@ from pylibcugraph.generate_rmat_edgelists import generate_rmat_edgelists +from pylibcugraph.k_truss_subgraph import k_truss_subgraph + from pylibcugraph.jaccard_coefficients import jaccard_coefficients from pylibcugraph.overlap_coefficients import overlap_coefficients diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/community_algorithms.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/community_algorithms.pxd index 64944e8773f..3c273b7d3fa 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/community_algorithms.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/community_algorithms.pxd @@ -253,3 +253,13 @@ cdef extern from "cugraph_c/community_algorithms.h": cugraph_error_t** error ) + ########################################################################### + # K truss + cdef cugraph_error_code_t \ + cugraph_k_truss_subgraph( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + size_t k, + bool_t do_expensive_check, + cugraph_induced_subgraph_result_t** result, + cugraph_error_t** error) diff --git a/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx b/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx new file mode 100644 index 00000000000..cc91e76dd55 --- /dev/null +++ b/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx @@ -0,0 +1,163 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Have cython use python 3 syntax +# cython: language_level = 3 + + +from pylibcugraph._cugraph_c.resource_handle cimport ( + bool_t, + cugraph_resource_handle_t, +) +from pylibcugraph._cugraph_c.error cimport ( + cugraph_error_code_t, + cugraph_error_t, +) +from pylibcugraph._cugraph_c.array cimport ( + cugraph_type_erased_device_array_view_t, +) +from pylibcugraph._cugraph_c.graph cimport ( + cugraph_graph_t, +) +from pylibcugraph._cugraph_c.graph_functions cimport ( + cugraph_induced_subgraph_result_t, + cugraph_induced_subgraph_get_sources, + cugraph_induced_subgraph_get_destinations, + cugraph_induced_subgraph_get_edge_weights, + cugraph_induced_subgraph_get_subgraph_offsets, + cugraph_induced_subgraph_result_free, +) +from pylibcugraph._cugraph_c.community_algorithms cimport ( + cugraph_k_truss_subgraph, +) +from pylibcugraph.resource_handle cimport ( + ResourceHandle, +) +from pylibcugraph.graphs cimport ( + _GPUGraph, +) +from pylibcugraph.utils cimport ( + assert_success, + copy_to_cupy_array, +) + + +def k_truss_subgraph(ResourceHandle resource_handle, + _GPUGraph graph, + size_t k, + bool_t do_expensive_check): + """ + Extract k truss of a graph for a specific k. + + Parameters + ---------- + resource_handle : ResourceHandle + Handle to the underlying device resources needed for referencing data + and running algorithms. + + graph : SGGraph + The input graph. + + k: size_t + The desired k to be used for extracting the k-truss subgraph. + + do_expensive_check : bool_t + If True, performs more extensive tests on the inputs to ensure + validitity, at the expense of increased run time. + + Returns + ------- + A tuple of device arrays containing the sources, destinations, + edge_weights and edge_offsets. + + Examples + -------- + >>> import pylibcugraph, cupy, numpy + >>> srcs = cupy.asarray([0, 1, 1, 3, 1, 4, 2, 0, 2, 1, 2, + ... 3, 3, 4, 3, 5, 4, 5], dtype=numpy.int32) + >>> dsts = cupy.asarray([1, 0, 3, 1, 4, 1, 0, 2, 1, 2, 3, + ... 2, 4, 3, 5, 3, 5, 4], dtype=numpy.int32) + >>> weights = cupy.asarray( + ... [0.1, 0.1, 2.1, 2.1, 1.1, 1.1, 7.2, 7.2, 2.1, 2.1, + ... 1.1, 1.1, 7.2, 7.2, 3.2, 3.2, 6.1, 6.1] + ... ,dtype=numpy.float32) + >>> k = 2 + >>> resource_handle = pylibcugraph.ResourceHandle() + >>> graph_props = pylibcugraph.GraphProperties( + ... is_symmetric=True, is_multigraph=False) + >>> G = pylibcugraph.SGGraph( + ... resource_handle, graph_props, srcs, dsts, weights, + ... store_transposed=False, renumber=False, do_expensive_check=False) + >>> (sources, destinations, edge_weights, subgraph_offsets) = + ... pylibcugraph.k_truss_subgraph(resource_handle, G, k, False) + >>> sources + [0 0 1 1 1 1 2 2 2 3 3 3 3 4 4 4 5 5] + >>> destinations + [1 2 0 2 3 4 0 1 3 1 2 4 5 1 3 5 3 4] + >>> edge_weights + [0.1 7.2 0.1 2.1 2.1 1.1 7.2 2.1 1.1 2.1 1.1 7.2 3.2 1.1 7.2 6.1 3.2 6.1] + >>> subgraph_offsets + [0 18] + + """ + cdef cugraph_resource_handle_t* c_resource_handle_ptr = \ + resource_handle.c_resource_handle_ptr + cdef cugraph_graph_t* c_graph_ptr = graph.c_graph_ptr + cdef cugraph_induced_subgraph_result_t* result_ptr + cdef cugraph_error_code_t error_code + cdef cugraph_error_t* error_ptr + + error_code = cugraph_k_truss_subgraph(c_resource_handle_ptr, + c_graph_ptr, + k, + do_expensive_check, + &result_ptr, + &error_ptr) + assert_success(error_code, error_ptr, "cugraph_k_truss_subgraph") + + # Extract individual device array pointers from result and copy to cupy + # arrays for returning. + cdef cugraph_type_erased_device_array_view_t* sources_ptr = \ + cugraph_induced_subgraph_get_sources(result_ptr) + cdef cugraph_type_erased_device_array_view_t* destinations_ptr = \ + cugraph_induced_subgraph_get_destinations(result_ptr) + cdef cugraph_type_erased_device_array_view_t* edge_weights_ptr = \ + cugraph_induced_subgraph_get_edge_weights(result_ptr) + cdef cugraph_type_erased_device_array_view_t* subgraph_offsets_ptr = \ + cugraph_induced_subgraph_get_subgraph_offsets(result_ptr) + + + # FIXME: Get ownership of the result data instead of performing a copy + # for perfomance improvement + cupy_sources = copy_to_cupy_array( + c_resource_handle_ptr, sources_ptr) + + cupy_destinations = copy_to_cupy_array( + c_resource_handle_ptr, destinations_ptr) + + if edge_weights_ptr is not NULL: + cupy_edge_weights = copy_to_cupy_array( + c_resource_handle_ptr, edge_weights_ptr) + else: + cupy_edge_weights = None + + # FIXME: Should we keep the offsets array or just drop it from the final + # solution? + cupy_subgraph_offsets = copy_to_cupy_array( + c_resource_handle_ptr, subgraph_offsets_ptr) + + + # Free pointer + cugraph_induced_subgraph_result_free(result_ptr) + + return (cupy_sources, cupy_destinations, cupy_edge_weights, cupy_subgraph_offsets) From 9dd01d82c17b6ada9c6701a9f3238e10053ce8a6 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Fri, 29 Sep 2023 15:17:22 -0400 Subject: [PATCH 027/111] fixes force atlas to allow string as vertex names (#3891) fixes force atlas to allow string as vertex names and removes need for mtx formated datasets. resolves #3610 Authors: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - ralph (https://github.com/nv-rliu) URL: https://github.com/rapidsai/cugraph/pull/3891 --- .../cugraph/layout/force_atlas2_wrapper.pyx | 8 +- .../cugraph/tests/layout/test_force_atlas2.py | 145 ++++++++---------- 2 files changed, 67 insertions(+), 86 deletions(-) diff --git a/python/cugraph/cugraph/layout/force_atlas2_wrapper.pyx b/python/cugraph/cugraph/layout/force_atlas2_wrapper.pyx index 4258be3ef71..5a2784e2363 100644 --- a/python/cugraph/cugraph/layout/force_atlas2_wrapper.pyx +++ b/python/cugraph/cugraph/layout/force_atlas2_wrapper.pyx @@ -56,9 +56,11 @@ def force_atlas2(input_graph, if not input_graph.edgelist: input_graph.view_edge_list() - # FIXME: This implementation assumes that the number of vertices - # is the max vertex ID + 1 which is not always the case. - num_verts = input_graph.nodes().max() + 1 + # this code allows handling of renumbered graphs + if input_graph.is_renumbered(): + num_verts = input_graph.renumber_map.df_internal_to_external['id'].max()+1 + else: + num_verts = input_graph.nodes().max() + 1 num_edges = len(input_graph.edgelist.edgelist_df['src']) cdef GraphCOOView[int,int,float] graph_float diff --git a/python/cugraph/cugraph/tests/layout/test_force_atlas2.py b/python/cugraph/cugraph/tests/layout/test_force_atlas2.py index 495a2d945c0..6b1fd6bcc4e 100644 --- a/python/cugraph/cugraph/tests/layout/test_force_atlas2.py +++ b/python/cugraph/cugraph/tests/layout/test_force_atlas2.py @@ -13,13 +13,46 @@ import time import pytest -import scipy.io -from sklearn.manifold import trustworthiness -import cudf import cugraph +from cugraph.structure import number_map from cugraph.internals import GraphBasedDimRedCallback -from cugraph.datasets import karate, polbooks, dolphins, netscience +from sklearn.manifold import trustworthiness +import scipy.io +from cugraph.datasets import ( + karate, + polbooks, + dolphins, + netscience, + dining_prefs, +) + +# FIXME Removed the multi column positional due to it being non-deterministic +# need to replace this coverage. Issue 3890 in cuGraph repo was created. + +# This method renumbers a dataframe so it can be tested using Trustworthiness. +# it converts a dataframe with string vertex ids to a renumbered int one. + + +def renumbered_edgelist(df): + renumbered_df, num_map = number_map.NumberMap.renumber(df, "src", "dst") + new_df = renumbered_df[["renumbered_src", "renumbered_dst", "wgt"]] + column_names = {"renumbered_src": "src", "renumbered_dst": "dst"} + new_df = new_df.rename(columns=column_names) + return new_df + + +# This method converts a dataframe to a sparce matrix that is required by +# scipy Trustworthiness to verify the layout +def get_coo_array(edgelist): + coo = edgelist + x = max(coo["src"].max(), coo["dst"].max()) + 1 + row = coo["src"].to_numpy() + col = coo["dst"].to_numpy() + data = coo["wgt"].to_numpy() + M = scipy.sparse.coo_array((data, (row, col)), shape=(x, x)) + + return M def cugraph_call( @@ -37,11 +70,15 @@ def cugraph_call( strong_gravity_mode, gravity, callback=None, + renumber=False, ): - G = cugraph.Graph() + if cu_M["src"] is not int or cu_M["dst"] is not int: + renumber = True + else: + renumber = False G.from_cudf_edgelist( - cu_M, source="src", destination="dst", edge_attr="wgt", renumber=False + cu_M, source="src", destination="dst", edge_attr="wgt", renumber=renumber ) t1 = time.time() @@ -66,7 +103,19 @@ def cugraph_call( return pos -DATASETS = [(karate, 0.70), (polbooks, 0.75), (dolphins, 0.66), (netscience, 0.66)] +DATASETS = [ + (karate, 0.70), + (polbooks, 0.75), + (dolphins, 0.66), + (netscience, 0.66), + (dining_prefs, 0.50), +] + +DATASETS2 = [ + (polbooks, 0.75), + (dolphins, 0.66), + (netscience, 0.66), +] MAX_ITERATIONS = [500] @@ -95,8 +144,7 @@ def on_train_end(self, positions): @pytest.mark.parametrize("max_iter", MAX_ITERATIONS) @pytest.mark.parametrize("barnes_hut_optimize", BARNES_HUT_OPTIMIZE) def test_force_atlas2(graph_file, score, max_iter, barnes_hut_optimize): - cu_M = graph_file.get_edgelist() - dataset_path = graph_file.get_path() + cu_M = graph_file.get_edgelist(download=True) test_callback = TestCallback() cu_pos = cugraph_call( cu_M, @@ -126,9 +174,11 @@ def test_force_atlas2(graph_file, score, max_iter, barnes_hut_optimize): iterations on a given graph. """ - matrix_file = dataset_path.with_suffix(".mtx") - M = scipy.io.mmread(matrix_file) - M = M.toarray() + if "string" in graph_file.metadata["col_types"]: + df = renumbered_edgelist(graph_file.get_edgelist(download=True)) + M = get_coo_array(df) + else: + M = get_coo_array(graph_file.get_edgelist(download=True)) cu_trust = trustworthiness(M, cu_pos[["x", "y"]].to_pandas()) print(cu_trust, score) assert cu_trust > score @@ -138,74 +188,3 @@ def test_force_atlas2(graph_file, score, max_iter, barnes_hut_optimize): assert test_callback.on_epoch_end_called_count == max_iter # verify `on_train_end` was only called once assert test_callback.on_train_end_called_count == 1 - - -# FIXME: this test occasionally fails - skipping to prevent CI failures but -# need to revisit ASAP -@pytest.mark.sg -@pytest.mark.skip(reason="non-deterministric - needs fixing!") -@pytest.mark.parametrize("graph_file, score", DATASETS[:-1]) -@pytest.mark.parametrize("max_iter", MAX_ITERATIONS) -@pytest.mark.parametrize("barnes_hut_optimize", BARNES_HUT_OPTIMIZE) -def test_force_atlas2_multi_column_pos_list( - graph_file, score, max_iter, barnes_hut_optimize -): - cu_M = graph_file.get_edgelist() - dataset_path = graph_file.get_path() - test_callback = TestCallback() - pos = cugraph_call( - cu_M, - max_iter=max_iter, - pos_list=None, - outbound_attraction_distribution=True, - lin_log_mode=False, - prevent_overlapping=False, - edge_weight_influence=1.0, - jitter_tolerance=1.0, - barnes_hut_optimize=False, - barnes_hut_theta=0.5, - scaling_ratio=2.0, - strong_gravity_mode=False, - gravity=1.0, - callback=test_callback, - ) - - cu_M.rename(columns={"0": "src_0", "1": "dst_0"}, inplace=True) - cu_M["src_1"] = cu_M["src_0"] + 1000 - cu_M["dst_1"] = cu_M["dst_0"] + 1000 - - G = cugraph.Graph() - G.from_cudf_edgelist( - cu_M, source=["src_0", "src_1"], destination=["dst_0", "dst_1"], edge_attr="2" - ) - - pos_list = cudf.DataFrame() - pos_list["vertex_0"] = pos["vertex"] - pos_list["vertex_1"] = pos_list["vertex_0"] + 1000 - pos_list["x"] = pos["x"] - pos_list["y"] = pos["y"] - - cu_pos = cugraph.force_atlas2( - G, - max_iter=max_iter, - pos_list=pos_list, - outbound_attraction_distribution=True, - lin_log_mode=False, - prevent_overlapping=False, - edge_weight_influence=1.0, - jitter_tolerance=1.0, - barnes_hut_optimize=False, - barnes_hut_theta=0.5, - scaling_ratio=2.0, - strong_gravity_mode=False, - gravity=1.0, - callback=test_callback, - ) - - cu_pos = cu_pos.sort_values("0_vertex") - matrix_file = dataset_path.with_suffix(".mtx") - M = scipy.io.mmread(matrix_file) - M = M.todense() - cu_trust = trustworthiness(M, cu_pos[["x", "y"]].to_pandas()) - print(cu_trust, score) - assert cu_trust > score From c00c3f1b9b64dbda3173b8f343de37f62d70ad94 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Sat, 30 Sep 2023 19:35:44 -0400 Subject: [PATCH 028/111] WholeGraph Feature Store for cuGraph-PyG and cuGraph-DGL (#3874) Created based on code from @dongxuy04 Adds support for `WholeGraph` `WholeMemory` in the cuGraph `FeatureStore` class. This enables both DGL and PyG to take advantage of distributed feature store functionality. Adds `pylibwholegraph` as a testing dependency so the feature store can be tested. Adds appropriate SG and MG tests. Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Ray Douglass (https://github.com/raydouglass) - Brad Rees (https://github.com/BradReesWork) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3874 --- ci/release/update-version.sh | 4 + .../all_cuda-118_arch-x86_64.yaml | 1 + .../all_cuda-120_arch-x86_64.yaml | 1 + dependencies.yaml | 3 + .../gnn/feature_storage/feat_storage.py | 147 ++++++++++++++++-- .../tests/data_store/test_gnn_feat_storage.py | 1 - .../test_gnn_feat_storage_wholegraph.py | 85 ++++++++++ 7 files changed, 226 insertions(+), 16 deletions(-) create mode 100644 python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index adf3273e311..aaeaa715434 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -82,6 +82,9 @@ NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; prin DEPENDENCIES=( cudf cugraph + cugraph-dgl + cugraph-pyg + cugraph-service-server cugraph-service-client cuxfilter dask-cuda @@ -93,6 +96,7 @@ DEPENDENCIES=( librmm pylibcugraph pylibcugraphops + pylibwholegraph pylibraft pyraft raft-dask diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 952ec9317e2..87179ef892e 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -53,6 +53,7 @@ dependencies: - pydata-sphinx-theme - pylibcugraphops==23.10.* - pylibraft==23.10.* +- pylibwholegraph==23.10.* - pytest - pytest-benchmark - pytest-cov diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 38936c78c38..d54dc0abf51 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -52,6 +52,7 @@ dependencies: - pydata-sphinx-theme - pylibcugraphops==23.10.* - pylibraft==23.10.* +- pylibwholegraph==23.10.* - pytest - pytest-benchmark - pytest-cov diff --git a/dependencies.yaml b/dependencies.yaml index f74ed13115b..292fcf0baed 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -481,6 +481,9 @@ dependencies: - *numpy - python-louvain - scikit-learn>=0.23.1 + - output_types: [conda] + packages: + - pylibwholegraph==23.10.* test_python_pylibcugraph: common: - output_types: [conda, pyproject] diff --git a/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py b/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py index e3fdeb7f150..77a53882fc4 100644 --- a/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py +++ b/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py @@ -17,23 +17,77 @@ import cupy as cp import numpy as np import pandas as pd -from cugraph.utilities.utils import import_optional +from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") +wgth = import_optional("pylibwholegraph.torch") class FeatureStore: - """The feature-store class used to store feature data for GNNS""" + """The feature-store class used to store feature data for GNNs""" + + def __init__( + self, + backend: str = "numpy", + wg_comm: object = None, + wg_type: str = None, + wg_location: str = None, + ): + """ + Constructs a new FeatureStore object + + Parameters: + ---------- + backend: str ('numpy', 'torch', 'wholegraph') + Optional (default='numpy') + The name of the backend to use. + + wg_comm: WholeMemoryCommunicator + Optional (default=automatic) + Only used with the 'wholegraph' backend. + The communicator to use to store features in WholeGraph. + + wg_type: str ('distributed', 'continuous', 'chunked') + Optional (default='distributed') + Only used with the 'wholegraph' backend. + The memory format (distributed, continuous, or chunked) of + this FeatureStore. For more information see the WholeGraph + documentation. + + wg_location: str ('cpu', 'cuda') + Optional (default='cuda') + Only used with the 'wholegraph' backend. + Where the data is stored (cpu or cuda). + Defaults to storing on the GPU (cuda). + """ - def __init__(self, backend="numpy"): self.fd = defaultdict(dict) - if backend not in ["numpy", "torch"]: + if backend not in ["numpy", "torch", "wholegraph"]: raise ValueError( - f"backend {backend} not supported. Supported backends are numpy, torch" + f"backend {backend} not supported. " + "Supported backends are numpy, torch, wholegraph" ) self.backend = backend - def add_data(self, feat_obj: Sequence, type_name: str, feat_name: str) -> None: + self.__wg_comm = None + self.__wg_type = None + self.__wg_location = None + + if backend == "wholegraph": + self.__wg_comm = ( + wg_comm if wg_comm is not None else wgth.get_local_node_communicator() + ) + self.__wg_type = wg_type if wg_type is not None else "distributed" + self.__wg_location = wg_location if wg_location is not None else "cuda" + + if self.__wg_type not in ["distributed", "chunked", "continuous"]: + raise ValueError(f"invalid memory format {self.__wg_type}") + if (self.__wg_location != "cuda") and (self.__wg_location != "cpu"): + raise ValueError(f"invalid location {self.__wg_location}") + + def add_data( + self, feat_obj: Sequence, type_name: str, feat_name: str, **kwargs + ) -> None: """ Add the feature data to the feature_storage class Parameters: @@ -49,9 +103,31 @@ def add_data(self, feat_obj: Sequence, type_name: str, feat_name: str) -> None: None """ self.fd[feat_name][type_name] = self._cast_feat_obj_to_backend( - feat_obj, self.backend + feat_obj, + self.backend, + wg_comm=self.__wg_comm, + wg_type=self.__wg_type, + wg_location=self.__wg_location, + **kwargs, ) + def add_data_no_cast(self, feat_obj, type_name: str, feat_name: str) -> None: + """ + Direct add the feature data to the feature_storage class with no cast + Parameters: + ---------- + feat_obj : array_like object + The feature object to save in feature store + type_name : str + The node-type/edge-type of the feature + feat_name: str + The name of the feature being stored + Returns: + ------- + None + """ + self.fd[feat_name][type_name] = feat_obj + def get_data( self, indices: Union[np.ndarray, torch.Tensor], @@ -87,26 +163,67 @@ def get_data( f" feature: {list(self.fd[feat_name].keys())}" ) - return self.fd[feat_name][type_name][indices] + feat = self.fd[feat_name][type_name] + if not isinstance(wgth, MissingModule) and isinstance( + feat, wgth.WholeMemoryEmbedding + ): + indices_tensor = ( + indices + if isinstance(indices, torch.Tensor) + else torch.as_tensor(indices, device="cuda") + ) + return feat.gather(indices_tensor) + else: + return feat[indices] def get_feature_list(self) -> list[str]: return {feat_name: feats.keys() for feat_name, feats in self.fd.items()} @staticmethod - def _cast_feat_obj_to_backend(feat_obj, backend: str): + def _cast_feat_obj_to_backend(feat_obj, backend: str, **kwargs): if backend == "numpy": if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): - return _cast_to_numpy_ar(feat_obj.values) + return _cast_to_numpy_ar(feat_obj.values, **kwargs) else: - return _cast_to_numpy_ar(feat_obj) + return _cast_to_numpy_ar(feat_obj, **kwargs) elif backend == "torch": if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): - return _cast_to_torch_tensor(feat_obj.values) + return _cast_to_torch_tensor(feat_obj.values, **kwargs) else: - return _cast_to_torch_tensor(feat_obj) + return _cast_to_torch_tensor(feat_obj, **kwargs) + elif backend == "wholegraph": + return _get_wg_embedding(feat_obj, **kwargs) + +def _get_wg_embedding(feat_obj, wg_comm=None, wg_type=None, wg_location=None, **kwargs): + wg_comm_obj = wg_comm or wgth.get_local_node_communicator() + wg_type_str = wg_type or "distributed" + wg_location_str = wg_location or "cuda" -def _cast_to_torch_tensor(ar): + if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): + th_tensor = _cast_to_torch_tensor(feat_obj.values) + else: + th_tensor = _cast_to_torch_tensor(feat_obj) + wg_embedding = wgth.create_embedding( + wg_comm_obj, + wg_type_str, + wg_location_str, + th_tensor.dtype, + th_tensor.shape, + ) + ( + local_wg_tensor, + local_ld_offset, + ) = wg_embedding.get_embedding_tensor().get_local_tensor() + local_th_tensor = th_tensor[ + local_ld_offset : local_ld_offset + local_wg_tensor.shape[0] + ] + local_wg_tensor.copy_(local_th_tensor) + wg_comm_obj.barrier() + return wg_embedding + + +def _cast_to_torch_tensor(ar, **kwargs): if isinstance(ar, cp.ndarray): ar = torch.as_tensor(ar, device="cuda") elif isinstance(ar, np.ndarray): @@ -116,7 +233,7 @@ def _cast_to_torch_tensor(ar): return ar -def _cast_to_numpy_ar(ar): +def _cast_to_numpy_ar(ar, **kwargs): if isinstance(ar, cp.ndarray): ar = ar.get() elif type(ar).__name__ == "Tensor": diff --git a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py index 2d1537d11e3..2b0ec4b11d0 100644 --- a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py +++ b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py @@ -10,7 +10,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# Import FeatureStore class import pytest import numpy as np diff --git a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py new file mode 100644 index 00000000000..1892e8a85a6 --- /dev/null +++ b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py @@ -0,0 +1,85 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import numpy as np + +from cugraph.gnn import FeatureStore + +from cugraph.utilities.utils import import_optional, MissingModule + +pylibwholegraph = import_optional("pylibwholegraph") +wmb = import_optional("pylibwholegraph.binding.wholememory_binding") +torch = import_optional("torch") + + +def runtest(world_rank: int, world_size: int): + from pylibwholegraph.torch.initialize import init_torch_env_and_create_wm_comm + + wm_comm, _ = init_torch_env_and_create_wm_comm( + world_rank, + world_size, + world_rank, + world_size, + ) + wm_comm = wm_comm.wmb_comm + + generator = np.random.default_rng(62) + arr = ( + generator.integers(low=0, high=100, size=100_000) + .reshape(10_000, -1) + .astype("float64") + ) + + fs = FeatureStore(backend="wholegraph") + fs.add_data(arr, "type2", "feat1") + wm_comm.barrier() + + indices_to_fetch = np.random.randint(low=0, high=len(arr), size=1024) + output_fs = fs.get_data(indices_to_fetch, type_name="type2", feat_name="feat1") + assert isinstance(output_fs, torch.Tensor) + assert output_fs.is_cuda + expected = arr[indices_to_fetch] + np.testing.assert_array_equal(output_fs.cpu().numpy(), expected) + + wmb.finalize() + + +@pytest.mark.sg +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(pylibwholegraph, MissingModule), reason="wholegraph not available" +) +def test_feature_storage_wholegraph_backend(): + from pylibwholegraph.utils.multiprocess import multiprocess_run + + gpu_count = wmb.fork_get_gpu_count() + print("gpu count:", gpu_count) + assert gpu_count > 0 + + multiprocess_run(1, runtest) + + +@pytest.mark.mg +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(pylibwholegraph, MissingModule), reason="wholegraph not available" +) +def test_feature_storage_wholegraph_backend_mg(): + from pylibwholegraph.utils.multiprocess import multiprocess_run + + gpu_count = wmb.fork_get_gpu_count() + print("gpu count:", gpu_count) + assert gpu_count > 0 + + multiprocess_run(gpu_count, runtest) From a8638355d4e74351e58706c4f747dcc63d23bd81 Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Tue, 3 Oct 2023 13:45:14 -0400 Subject: [PATCH 029/111] Integrate renumbering and compression to `cugraph-dgl` to accelerate MFG creation (#3887) Allow cugraph-dgl dataloader to consume sampled outputs from BulkSampler in CSC format. Authors: - Tingyu Wang (https://github.com/tingyu66) - Seunghwa Kang (https://github.com/seunghwak) - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3887 --- .../cugraph_dgl/dataloading/__init__.py | 2 +- .../cugraph_dgl/dataloading/dataloader.py | 49 ++++-- .../cugraph_dgl/dataloading/dataset.py | 37 +++-- .../dataloading/utils/sampling_helpers.py | 155 +++++++++++++++++- .../cugraph-dgl/cugraph_dgl/nn/conv/base.py | 7 + python/cugraph-dgl/tests/test_utils.py | 28 ++++ 6 files changed, 250 insertions(+), 28 deletions(-) diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py b/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py index 6cabea198f6..2fd7d29bd49 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py @@ -13,7 +13,7 @@ from cugraph_dgl.dataloading.dataset import ( HomogenousBulkSamplerDataset, - HetrogenousBulkSamplerDataset, + HeterogenousBulkSamplerDataset, ) from cugraph_dgl.dataloading.neighbor_sampler import NeighborSampler from cugraph_dgl.dataloading.dataloader import DataLoader diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py index 0480f61807a..f154b096256 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py @@ -21,7 +21,7 @@ from dask.distributed import default_client, Event from cugraph_dgl.dataloading import ( HomogenousBulkSamplerDataset, - HetrogenousBulkSamplerDataset, + HeterogenousBulkSamplerDataset, ) from cugraph_dgl.dataloading.utils.extract_graph_helpers import ( create_cugraph_graph_from_edges_dict, @@ -47,19 +47,20 @@ def __init__( graph_sampler: cugraph_dgl.dataloading.NeighborSampler, sampling_output_dir: str, batches_per_partition: int = 50, - seeds_per_call: int = 400_000, + seeds_per_call: int = 200_000, device: torch.device = None, use_ddp: bool = False, ddp_seed: int = 0, batch_size: int = 1024, drop_last: bool = False, shuffle: bool = False, + sparse_format: str = "coo", **kwargs, ): """ Constructor for CuGraphStorage: ------------------------------- - graph : CuGraphStorage + graph : CuGraphStorage The graph. indices : Tensor or dict[ntype, Tensor] The set of indices. It can either be a tensor of @@ -89,7 +90,12 @@ def __init__( The seed for shuffling the dataset in :class:`torch.utils.data.distributed.DistributedSampler`. Only effective when :attr:`use_ddp` is True. - batch_size: int, + batch_size: int + Batch size. + sparse_format: str, default = "coo" + The sparse format of the emitted sampled graphs. Choose between "csc" + and "coo". When using "csc", the graphs are of type + cugraph_dgl.nn.SparseGraph. kwargs : dict Key-word arguments to be passed to the parent PyTorch :py:class:`torch.utils.data.DataLoader` class. Common arguments are: @@ -123,6 +129,12 @@ def __init__( ... for input_nodes, output_nodes, blocks in dataloader: ... """ + if sparse_format not in ["coo", "csc"]: + raise ValueError( + f"sparse_format must be one of 'coo', 'csc', " + f"but got {sparse_format}." + ) + self.sparse_format = sparse_format self.ddp_seed = ddp_seed self.use_ddp = use_ddp @@ -156,11 +168,12 @@ def __init__( self.cugraph_dgl_dataset = HomogenousBulkSamplerDataset( total_number_of_nodes=graph.total_number_of_nodes, edge_dir=self.graph_sampler.edge_dir, + sparse_format=sparse_format, ) else: etype_id_to_etype_str_dict = {v: k for k, v in graph._etype_id_dict.items()} - self.cugraph_dgl_dataset = HetrogenousBulkSamplerDataset( + self.cugraph_dgl_dataset = HeterogenousBulkSamplerDataset( num_nodes_dict=graph.num_nodes_dict, etype_id_dict=etype_id_to_etype_str_dict, etype_offset_dict=graph._etype_offset_d, @@ -210,14 +223,23 @@ def __iter__(self): output_dir = os.path.join( self._sampling_output_dir, "epoch_" + str(self.epoch_number) ) + kwargs = {} if isinstance(self.cugraph_dgl_dataset, HomogenousBulkSamplerDataset): - deduplicate_sources = True - prior_sources_behavior = "carryover" - renumber = True + kwargs["deduplicate_sources"] = True + kwargs["prior_sources_behavior"] = "carryover" + kwargs["renumber"] = True + + if self.sparse_format == "csc": + kwargs["compression"] = "CSR" + kwargs["compress_per_hop"] = True + # The following kwargs will be deprecated in uniform sampler. + kwargs["use_legacy_names"] = False + kwargs["include_hop_column"] = False + else: - deduplicate_sources = False - prior_sources_behavior = None - renumber = False + kwargs["deduplicate_sources"] = False + kwargs["prior_sources_behavior"] = None + kwargs["renumber"] = False bs = BulkSampler( output_path=output_dir, @@ -227,10 +249,9 @@ def __iter__(self): seeds_per_call=self._seeds_per_call, fanout_vals=self.graph_sampler._reversed_fanout_vals, with_replacement=self.graph_sampler.replace, - deduplicate_sources=deduplicate_sources, - prior_sources_behavior=prior_sources_behavior, - renumber=renumber, + **kwargs, ) + if self.shuffle: self.tensorized_indices_ds.shuffle() diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py index e0d51bcf4cf..815fd30d8eb 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py @@ -19,6 +19,7 @@ from cugraph_dgl.dataloading.utils.sampling_helpers import ( create_homogeneous_sampled_graphs_from_dataframe, create_heterogeneous_sampled_graphs_from_dataframe, + create_homogeneous_sampled_graphs_from_dataframe_csc, ) @@ -33,17 +34,19 @@ def __init__( total_number_of_nodes: int, edge_dir: str, return_type: str = "dgl.Block", + sparse_format: str = "coo", ): if return_type not in ["dgl.Block", "cugraph_dgl.nn.SparseGraph"]: raise ValueError( - "return_type must be either 'dgl.Block' or \ - 'cugraph_dgl.nn.SparseGraph' " + "return_type must be either 'dgl.Block' or " + "'cugraph_dgl.nn.SparseGraph'." ) # TODO: Deprecate `total_number_of_nodes` # as it is no longer needed # in the next release self.total_number_of_nodes = total_number_of_nodes self.edge_dir = edge_dir + self.sparse_format = sparse_format self._current_batch_fn = None self._input_files = None self._return_type = return_type @@ -60,10 +63,20 @@ def __getitem__(self, idx: int): fn, batch_offset = self._batch_to_fn_d[idx] if fn != self._current_batch_fn: - df = _load_sampled_file(dataset_obj=self, fn=fn) - self._current_batches = create_homogeneous_sampled_graphs_from_dataframe( - sampled_df=df, edge_dir=self.edge_dir, return_type=self._return_type - ) + if self.sparse_format == "csc": + df = _load_sampled_file(dataset_obj=self, fn=fn, skip_rename=True) + self._current_batches = ( + create_homogeneous_sampled_graphs_from_dataframe_csc(df) + ) + else: + df = _load_sampled_file(dataset_obj=self, fn=fn) + self._current_batches = ( + create_homogeneous_sampled_graphs_from_dataframe( + sampled_df=df, + edge_dir=self.edge_dir, + return_type=self._return_type, + ) + ) current_offset = idx - batch_offset return self._current_batches[current_offset] @@ -87,7 +100,7 @@ def set_input_files( ) -class HetrogenousBulkSamplerDataset(torch.utils.data.Dataset): +class HeterogenousBulkSamplerDataset(torch.utils.data.Dataset): def __init__( self, num_nodes_dict: Dict[str, int], @@ -141,18 +154,18 @@ def set_input_files( ---------- input_directory: str input_directory which contains all the files that will be - loaded by HetrogenousBulkSamplerDataset + loaded by HeterogenousBulkSamplerDataset input_file_paths: List[str] - File names that will be loaded by the HetrogenousBulkSamplerDataset + File names that will be loaded by the HeterogenousBulkSamplerDataset """ _set_input_files( self, input_directory=input_directory, input_file_paths=input_file_paths ) -def _load_sampled_file(dataset_obj, fn): +def _load_sampled_file(dataset_obj, fn, skip_rename=False): df = cudf.read_parquet(os.path.join(fn)) - if dataset_obj.edge_dir == "in": + if dataset_obj.edge_dir == "in" and not skip_rename: df.rename( columns={"sources": "destinations", "destinations": "sources"}, inplace=True, @@ -181,7 +194,7 @@ def get_batch_to_fn_d(files): def _set_input_files( - dataset_obj: Union[HomogenousBulkSamplerDataset, HetrogenousBulkSamplerDataset], + dataset_obj: Union[HomogenousBulkSamplerDataset, HeterogenousBulkSamplerDataset], input_directory: Optional[str] = None, input_file_paths: Optional[List[str]] = None, ) -> None: diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py index bdac3b1a323..a4f64668348 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py @@ -11,10 +11,12 @@ # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations -from typing import Tuple, Dict, Optional +from typing import List, Tuple, Dict, Optional from collections import defaultdict import cudf +import cupy from cugraph.utilities.utils import import_optional +from cugraph_dgl.nn import SparseGraph dgl = import_optional("dgl") torch = import_optional("torch") @@ -401,3 +403,154 @@ def create_heterogenous_dgl_block_from_tensors_dict( block = dgl.to_block(sampled_graph, dst_nodes=seed_nodes, src_nodes=src_d) block.edata[dgl.EID] = sampled_graph.edata[dgl.EID] return block + + +def _process_sampled_df_csc( + df: cudf.DataFrame, + reverse_hop_id: bool = True, +) -> Tuple[ + Dict[int, Dict[int, Dict[str, torch.Tensor]]], + List[torch.Tensor], + List[List[int, int]], +]: + """ + Convert a dataframe generated by BulkSampler to a dictionary of tensors, to + facilitate MFG creation. The sampled graphs in the dataframe use CSC-format. + + Parameters + ---------- + df: cudf.DataFrame + The output from BulkSampler compressed in CSC format. The dataframe + should be generated with `compression="CSR"` in BulkSampler, + since the sampling routine treats seed nodes as sources. + + reverse_hop_id: bool (default=True) + Reverse hop id. + + Returns + ------- + tensors_dict: dict + A nested dictionary keyed by batch id and hop id. + `tensor_dict[batch_id][hop_id]` holds "minors" and "major_offsets" + values for CSC MFGs. + + renumber_map_list: list + List of renumbering maps for looking up global indices of nodes. One + map for each batch. + + mfg_sizes: list + List of the number of nodes in each message passing layer. For the + k-th hop, mfg_sizes[k] and mfg_sizes[k+1] is the number of sources and + destinations, respectively. + """ + # dropna + major_offsets = df.major_offsets.dropna().values + label_hop_offsets = df.label_hop_offsets.dropna().values + renumber_map_offsets = df.renumber_map_offsets.dropna().values + renumber_map = df.map.dropna().values + minors = df.minors.dropna().values + + n_batches = renumber_map_offsets.size - 1 + n_hops = int((label_hop_offsets.size - 1) / n_batches) + + # make global offsets local + major_offsets -= major_offsets[0] + label_hop_offsets -= label_hop_offsets[0] + renumber_map_offsets -= renumber_map_offsets[0] + + # get the sizes of each adjacency matrix (for MFGs) + mfg_sizes = (label_hop_offsets[1:] - label_hop_offsets[:-1]).reshape( + (n_batches, n_hops) + ) + n_nodes = renumber_map_offsets[1:] - renumber_map_offsets[:-1] + mfg_sizes = cupy.hstack((mfg_sizes, n_nodes.reshape(n_batches, -1))) + if reverse_hop_id: + mfg_sizes = mfg_sizes[:, ::-1] + + tensors_dict = {} + renumber_map_list = [] + for batch_id in range(n_batches): + batch_dict = {} + + for hop_id in range(n_hops): + hop_dict = {} + idx = batch_id * n_hops + hop_id # idx in label_hop_offsets + major_offsets_start = label_hop_offsets[idx].item() + major_offsets_end = label_hop_offsets[idx + 1].item() + minors_start = major_offsets[major_offsets_start].item() + minors_end = major_offsets[major_offsets_end].item() + # Note: minors and major_offsets from BulkSampler are of type int32 + # and int64 respectively. Since pylibcugraphops binding code doesn't + # support distinct node and edge index type, we simply casting both + # to int32 for now. + hop_dict["minors"] = torch.as_tensor( + minors[minors_start:minors_end], device="cuda" + ).int() + hop_dict["major_offsets"] = torch.as_tensor( + major_offsets[major_offsets_start : major_offsets_end + 1] + - major_offsets[major_offsets_start], + device="cuda", + ).int() + if reverse_hop_id: + batch_dict[n_hops - 1 - hop_id] = hop_dict + else: + batch_dict[hop_id] = hop_dict + + tensors_dict[batch_id] = batch_dict + + renumber_map_list.append( + torch.as_tensor( + renumber_map[ + renumber_map_offsets[batch_id] : renumber_map_offsets[batch_id + 1] + ], + device="cuda", + ) + ) + + return tensors_dict, renumber_map_list, mfg_sizes.tolist() + + +def _create_homogeneous_sparse_graphs_from_csc( + tensors_dict: Dict[int, Dict[int, Dict[str, torch.Tensor]]], + renumber_map_list: List[torch.Tensor], + mfg_sizes: List[int, int], +) -> List[List[torch.Tensor, torch.Tensor, List[SparseGraph]]]: + """Create mini-batches of MFGs. The input arguments are the outputs of + the function `_process_sampled_df_csc`. + + Returns + ------- + output: list + A list of mini-batches. Each mini-batch is a list that consists of + `input_nodes` tensor, `output_nodes` tensor and a list of MFGs. + """ + n_batches, n_hops = len(mfg_sizes), len(mfg_sizes[0]) - 1 + output = [] + for b_id in range(n_batches): + output_batch = [] + output_batch.append(renumber_map_list[b_id]) + output_batch.append(renumber_map_list[b_id][: mfg_sizes[b_id][-1]]) + mfgs = [ + SparseGraph( + size=(mfg_sizes[b_id][h_id], mfg_sizes[b_id][h_id + 1]), + src_ids=tensors_dict[b_id][h_id]["minors"], + cdst_ids=tensors_dict[b_id][h_id]["major_offsets"], + formats=["csc"], + reduce_memory=True, + ) + for h_id in range(n_hops) + ] + + output_batch.append(mfgs) + + output.append(output_batch) + + return output + + +def create_homogeneous_sampled_graphs_from_dataframe_csc(sampled_df: cudf.DataFrame): + """Public API to create mini-batches of MFGs using a dataframe output by + BulkSampler, where the sampled graph is compressed in CSC format.""" + return _create_homogeneous_sparse_graphs_from_csc( + *(_process_sampled_df_csc(sampled_df)) + ) diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py index 307eb33078e..ddd95a76366 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py @@ -248,6 +248,13 @@ def csr(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: value = value[self._perm_csc2csr] return csrc_ids, dst_ids, value + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(num_src_nodes={self._num_src_nodes}, " + f"num_dst_nodes={self._num_dst_nodes}, " + f"num_edges={self._src_ids.size(0)}, formats={self._formats})" + ) + class BaseConv(torch.nn.Module): r"""An abstract base class for cugraph-ops nn module.""" diff --git a/python/cugraph-dgl/tests/test_utils.py b/python/cugraph-dgl/tests/test_utils.py index 740db59ce7f..4be66758b43 100644 --- a/python/cugraph-dgl/tests/test_utils.py +++ b/python/cugraph-dgl/tests/test_utils.py @@ -22,6 +22,7 @@ create_homogeneous_sampled_graphs_from_dataframe, _get_source_destination_range, _create_homogeneous_cugraph_dgl_nn_sparse_graph, + create_homogeneous_sampled_graphs_from_dataframe_csc, ) from cugraph.utilities.utils import import_optional @@ -50,6 +51,23 @@ def get_dummy_sampled_df(): return df +def get_dummy_sampled_df_csc(): + df_dict = dict( + minors=np.array( + [1, 1, 2, 1, 0, 3, 1, 3, 2, 3, 2, 4, 0, 1, 1, 0, 3, 2], dtype=np.int32 + ), + major_offsets=np.arange(19, dtype=np.int64), + map=np.array( + [26, 29, 33, 22, 23, 32, 18, 29, 33, 33, 8, 30, 32], dtype=np.int32 + ), + renumber_map_offsets=np.array([0, 4, 9, 13], dtype=np.int64), + label_hop_offsets=np.array([0, 1, 3, 6, 7, 9, 13, 14, 16, 18], dtype=np.int64), + ) + + # convert values to Series so that NaNs are padded automatically + return cudf.DataFrame({k: cudf.Series(v) for k, v in df_dict.items()}) + + def test_get_renumber_map(): sampled_df = get_dummy_sampled_df() @@ -176,3 +194,13 @@ def test__create_homogeneous_cugraph_dgl_nn_sparse_graph(): assert sparse_graph.num_src_nodes() == 2 assert sparse_graph.num_dst_nodes() == seednodes_range + 1 assert isinstance(sparse_graph, cugraph_dgl.nn.SparseGraph) + + +def test_create_homogeneous_sampled_graphs_from_dataframe_csc(): + df = get_dummy_sampled_df_csc() + batches = create_homogeneous_sampled_graphs_from_dataframe_csc(df) + + assert len(batches) == 3 + assert torch.equal(batches[0][0], torch.IntTensor([26, 29, 33, 22]).cuda()) + assert torch.equal(batches[1][0], torch.IntTensor([23, 32, 18, 29, 33]).cuda()) + assert torch.equal(batches[2][0], torch.IntTensor([33, 8, 30, 32]).cuda()) From 5ce3ee1b11db62647337514a200845bbb392d351 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 3 Oct 2023 16:50:10 -0500 Subject: [PATCH 030/111] nx-cugraph: handle louvain with isolated nodes (#3897) This handles isolated nodes in `louvain_communities` similar to what is done in #3886. This is expected to be a temporary fix until pylibcugraph can handle isolated nodes. As a bonus, I added `isolates` algorithm :tada: CC @naimnv @rlratzel Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3897 --- python/nx-cugraph/_nx_cugraph/__init__.py | 7 ++- python/nx-cugraph/lint.yaml | 2 +- .../nx_cugraph/algorithms/__init__.py | 1 + .../algorithms/community/louvain.py | 28 +++++++-- .../nx_cugraph/algorithms/isolate.py | 63 +++++++++++++++++++ .../nx-cugraph/nx_cugraph/classes/digraph.py | 2 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 6 +- python/nx-cugraph/nx_cugraph/convert.py | 2 +- .../nx_cugraph/tests/test_community.py | 53 ++++++++++++++++ 9 files changed, 151 insertions(+), 13 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/isolate.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_community.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 9b3332106ec..ebd13daded0 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -31,20 +31,23 @@ # BEGIN: functions "betweenness_centrality", "edge_betweenness_centrality", + "is_isolate", + "isolates", "louvain_communities", + "number_of_isolates", # END: functions }, "extra_docstrings": { # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", - "louvain_communities": "`threshold` and `seed` parameters are currently ignored.", + "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, "extra_parameters": { # BEGIN: extra_parameters "louvain_communities": { - "max_level : int, optional": "Upper limit of the number of macro-iterations.", + "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, # END: extra_parameters }, diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 6a462a6af79..338ca7b230e 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -63,7 +63,7 @@ repos: # These versions need updated manually - flake8==6.1.0 - flake8-bugbear==23.9.16 - - flake8-simplify==0.20.0 + - flake8-simplify==0.21.0 - repo: https://github.com/asottile/yesqa rev: v1.5.0 hooks: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 3a585452d6d..dfd9adfc61a 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -12,3 +12,4 @@ # limitations under the License. from . import centrality, community from .centrality import * +from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index a183b59fe1d..dc209870c89 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -10,7 +10,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import sys +import warnings import pylibcugraph as plc @@ -22,19 +22,23 @@ not_implemented_for, ) +from ..isolate import _isolates + __all__ = ["louvain_communities"] @not_implemented_for("directed") @networkx_algorithm( extra_params={ - "max_level : int, optional": "Upper limit of the number of macro-iterations." + "max_level : int, optional": ( + "Upper limit of the number of macro-iterations (max: 500)." + ) } ) def louvain_communities( G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None ): - """`threshold` and `seed` parameters are currently ignored.""" + """`seed` parameter is currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) @@ -42,7 +46,14 @@ def louvain_communities( # TODO: PLC doesn't handle empty graphs gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] if max_level is None: - max_level = sys.maxsize + max_level = 500 + elif max_level > 500: + warnings.warn( + f"max_level is set too high (={max_level}), setting it to 500.", + UserWarning, + stacklevel=2, + ) + max_level = 500 vertices, clusters, modularity = plc.louvain( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(), @@ -52,7 +63,14 @@ def louvain_communities( do_expensive_check=False, ) groups = _groupby(clusters, vertices) - return [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] + rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] + # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + isolates = _isolates(G) + if isolates.size > 0: + isolates = isolates[isolates > vertices.max()] + if isolates.size > 0: + rv.extend({node} for node in G._nodearray_to_list(isolates)) + return rv @louvain_communities._can_run diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py new file mode 100644 index 00000000000..774627e84f6 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -0,0 +1,63 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from typing import TYPE_CHECKING + +import cupy as cp + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import networkx_algorithm + +if TYPE_CHECKING: # pragma: no cover + from nx_cugraph.typing import IndexValue + +__all__ = ["is_isolate", "isolates", "number_of_isolates"] + + +@networkx_algorithm +def is_isolate(G, n): + G = _to_graph(G) + index = n if G.key_to_id is None else G.key_to_id[n] + return not ( + (G.row_indices == index).any().tolist() + or G.is_directed() + and (G.col_indices == index).any().tolist() + ) + + +def _mark_isolates(G) -> cp.ndarray[bool]: + """Return a boolean mask array indicating indices of isolated nodes.""" + mark_isolates = cp.ones(len(G), bool) + mark_isolates[G.row_indices] = False + if G.is_directed(): + mark_isolates[G.col_indices] = False + return mark_isolates + + +def _isolates(G) -> cp.ndarray[IndexValue]: + """Like isolates, but return an array of indices instead of an iterator of nodes.""" + G = _to_graph(G) + return cp.nonzero(_mark_isolates(G))[0] + + +@networkx_algorithm +def isolates(G): + G = _to_graph(G) + return G._nodeiter_to_iter(iter(_isolates(G).tolist())) + + +@networkx_algorithm +def number_of_isolates(G): + G = _to_graph(G) + return _mark_isolates(G).sum().tolist() diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 0aaf88fd793..72a1bff21a9 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -20,7 +20,7 @@ from .graph import Graph -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from nx_cugraph.typing import NodeKey __all__ = ["DiGraph"] diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 1432f68c752..ded4cc3943f 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -23,7 +23,7 @@ import nx_cugraph as nxcg -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from collections.abc import Iterable, Iterator from nx_cugraph.typing import ( @@ -245,9 +245,9 @@ def from_dcsc( def __new__(cls, incoming_graph_data=None, **attr) -> Graph: if incoming_graph_data is None: new_graph = cls.from_coo(0, cp.empty(0, np.int32), cp.empty(0, np.int32)) - elif incoming_graph_data.__class__ is new_graph.__class__: + elif incoming_graph_data.__class__ is cls: new_graph = incoming_graph_data.copy() - elif incoming_graph_data.__class__ is new_graph.to_networkx_class(): + elif incoming_graph_data.__class__ is cls.to_networkx_class(): new_graph = nxcg.from_networkx(incoming_graph_data, preserve_all_attrs=True) else: raise NotImplementedError diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index 9be8cac7877..1240ea71db7 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -24,7 +24,7 @@ import nx_cugraph as nxcg -if TYPE_CHECKING: +if TYPE_CHECKING: # pragma: no cover from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue __all__ = [ diff --git a/python/nx-cugraph/nx_cugraph/tests/test_community.py b/python/nx-cugraph/nx_cugraph/tests/test_community.py new file mode 100644 index 00000000000..126f45c14ae --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_community.py @@ -0,0 +1,53 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +def test_louvain_isolated_nodes(): + is_nx_30_or_31 = hasattr(nx.classes, "backends") + + def check(left, right): + assert len(left) == len(right) + assert set(map(frozenset, left)) == set(map(frozenset, right)) + + # Empty graph (no nodes) + G = nx.Graph() + if is_nx_30_or_31: + with pytest.raises(ZeroDivisionError): + nx.community.louvain_communities(G) + else: + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Graph with no edges + G.add_nodes_from(range(5)) + if is_nx_30_or_31: + with pytest.raises(ZeroDivisionError): + nx.community.louvain_communities(G) + else: + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Graph with isolated nodes + G.add_edge(1, 2) + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) + # Another one + G.add_edge(4, 4) + nx_result = nx.community.louvain_communities(G) + cg_result = nxcg.community.louvain_communities(G) + check(nx_result, cg_result) From 26af14ebad6a6b1f115779d90d3c0a68f0d380ee Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Wed, 4 Oct 2023 11:44:01 -0400 Subject: [PATCH 031/111] cuGraph-PyG MFG Creation and Conversion (#3873) Integrates the new CSR bulk sampler output, allowing reading of batches without having to call CSC conversion or count the numbers of vertices and edges in each batch. Should result in major performance improvements, especially for small batches. Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Seunghwa Kang (https://github.com/seunghwak) - Brad Rees (https://github.com/BradReesWork) Approvers: - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) - Tingyu Wang (https://github.com/tingyu66) URL: https://github.com/rapidsai/cugraph/pull/3873 --- ci/test_python.sh | 7 +- .../cugraph_pyg/data/cugraph_store.py | 38 ++-- .../cugraph_pyg/loader/cugraph_node_loader.py | 199 +++++++++++++---- .../cugraph_pyg/sampler/cugraph_sampler.py | 130 +++++++++++- .../tests/mg/test_mg_cugraph_sampler.py | 10 +- .../tests/mg/test_mg_cugraph_store.py | 4 +- .../cugraph_pyg/tests/test_cugraph_loader.py | 200 ++++++++++++++++-- .../cugraph_pyg/tests/test_cugraph_sampler.py | 10 +- .../cugraph_pyg/tests/test_cugraph_store.py | 4 +- 9 files changed, 497 insertions(+), 105 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index 7b0077991ae..825d5b242d5 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -200,8 +200,11 @@ if [[ "${RAPIDS_CUDA_VERSION}" == "11.8.0" ]]; then --channel pytorch \ --channel nvidia \ 'pyg=2.3' \ - 'pytorch>=2.0' \ - 'pytorch-cuda>=11.8' + 'pytorch=2.0.0' \ + 'pytorch-cuda=11.8' + + # Install pyg dependencies (which requires pip) + pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html rapids-mamba-retry install \ --channel "${CPP_CHANNEL}" \ diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index e0d318adbe0..fd2172e6ade 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -819,8 +819,8 @@ def _get_renumbered_edge_groups_from_sample( before this one to get the noi_index. Example Input: Series({ - 'sources': [0, 5, 11, 3], - 'destinations': [8, 2, 3, 5]}, + 'majors': [0, 5, 11, 3], + 'minors': [8, 2, 3, 5]}, 'edge_type': [1, 3, 5, 14] }), { @@ -865,24 +865,22 @@ def _get_renumbered_edge_groups_from_sample( index=cupy.asarray(id_table), ).sort_index() - # Renumber the sources using binary search + # Renumber the majors using binary search # Step 1: get the index of the new id ix_r = torch.searchsorted( torch.as_tensor(id_map.index.values, device="cuda"), - torch.as_tensor(sampling_results.sources.values, device="cuda"), + torch.as_tensor(sampling_results.majors.values, device="cuda"), ) # Step 2: Go from id indices to actual ids row_dict[t_pyg_type] = torch.as_tensor(id_map.values, device="cuda")[ ix_r ] - # Renumber the destinations using binary search + # Renumber the minors using binary search # Step 1: get the index of the new id ix_c = torch.searchsorted( torch.as_tensor(id_map.index.values, device="cuda"), - torch.as_tensor( - sampling_results.destinations.values, device="cuda" - ), + torch.as_tensor(sampling_results.minors.values, device="cuda"), ) # Step 2: Go from id indices to actual ids col_dict[t_pyg_type] = torch.as_tensor(id_map.values, device="cuda")[ @@ -897,7 +895,7 @@ def _get_renumbered_edge_groups_from_sample( "new_id": cupy.arange(dst_id_table.shape[0]), } ).set_index("dst") - dst = dst_id_map["new_id"].loc[sampling_results.destinations] + dst = dst_id_map["new_id"].loc[sampling_results.minors] col_dict[t_pyg_type] = torch.as_tensor(dst.values, device="cuda") src_id_table = noi_index[src_type] @@ -907,7 +905,7 @@ def _get_renumbered_edge_groups_from_sample( "new_id": cupy.arange(src_id_table.shape[0]), } ).set_index("src") - src = src_id_map["new_id"].loc[sampling_results.sources] + src = src_id_map["new_id"].loc[sampling_results.majors] row_dict[t_pyg_type] = torch.as_tensor(src.values, device="cuda") else: @@ -929,12 +927,12 @@ def _get_renumbered_edge_groups_from_sample( else: # CSC dst_type, _, src_type = pyg_can_edge_type - # Get the de-offsetted destinations + # Get the de-offsetted minors dst_num_type = self._numeric_vertex_type_from_name(dst_type) - destinations = torch.as_tensor( - sampling_results.destinations.iloc[ix].values, device="cuda" + minors = torch.as_tensor( + sampling_results.minors.iloc[ix].values, device="cuda" ) - destinations -= self.__vertex_type_offsets["start"][dst_num_type] + minors -= self.__vertex_type_offsets["start"][dst_num_type] # Create the col entry for this type dst_id_table = noi_index[dst_type] @@ -944,15 +942,15 @@ def _get_renumbered_edge_groups_from_sample( .rename(columns={"index": "new_id"}) .set_index("dst") ) - dst = dst_id_map["new_id"].loc[cupy.asarray(destinations)] + dst = dst_id_map["new_id"].loc[cupy.asarray(minors)] col_dict[pyg_can_edge_type] = torch.as_tensor(dst.values, device="cuda") - # Get the de-offsetted sources + # Get the de-offsetted majors src_num_type = self._numeric_vertex_type_from_name(src_type) - sources = torch.as_tensor( - sampling_results.sources.iloc[ix].values, device="cuda" + majors = torch.as_tensor( + sampling_results.majors.iloc[ix].values, device="cuda" ) - sources -= self.__vertex_type_offsets["start"][src_num_type] + majors -= self.__vertex_type_offsets["start"][src_num_type] # Create the row entry for this type src_id_table = noi_index[src_type] @@ -962,7 +960,7 @@ def _get_renumbered_edge_groups_from_sample( .rename(columns={"index": "new_id"}) .set_index("src") ) - src = src_id_map["new_id"].loc[cupy.asarray(sources)] + src = src_id_map["new_id"].loc[cupy.asarray(majors)] row_dict[pyg_can_edge_type] = torch.as_tensor(src.values, device="cuda") return row_dict, col_dict diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index cf7eb330d67..8552e7412e0 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -25,7 +25,9 @@ from cugraph_pyg.data import CuGraphStore from cugraph_pyg.sampler.cugraph_sampler import ( _sampler_output_from_sampling_results_heterogeneous, - _sampler_output_from_sampling_results_homogeneous, + _sampler_output_from_sampling_results_homogeneous_csr, + _sampler_output_from_sampling_results_homogeneous_coo, + filter_cugraph_store_csc, ) from typing import Union, Tuple, Sequence, List, Dict @@ -58,6 +60,7 @@ def __init__( # Sampler args num_neighbors: Union[List[int], Dict[Tuple[str, str, str], List[int]]] = None, replace: bool = True, + compression: str = "COO", # Other kwargs for the BulkSampler **kwargs, ): @@ -128,6 +131,10 @@ def __init__( self.__batches_per_partition = batches_per_partition self.__starting_batch_id = starting_batch_id + self._total_read_time = 0.0 + self._total_convert_time = 0.0 + self._total_feature_time = 0.0 + if input_nodes is None: # Will be loading from disk self.__num_batches = input_nodes @@ -174,6 +181,10 @@ def __init__( with_replacement=replace, batches_per_partition=self.__batches_per_partition, renumber=renumber, + use_legacy_names=False, + deduplicate_sources=True, + prior_sources_behavior="exclude", + include_hop_column=(compression == "COO"), **kwargs, ) @@ -211,6 +222,10 @@ def __init__( self.__input_files = iter(os.listdir(self.__directory.name)) def __next__(self): + from time import perf_counter + + start_time_read_data = perf_counter() + # Load the next set of sampling results if necessary if self.__next_batch >= self.__end_exclusive: if self.__directory is None: @@ -245,51 +260,98 @@ def __next__(self): fname, ) - columns = { - "sources": "int64", - "destinations": "int64", - # 'edge_id':'int64', - "edge_type": "int32", - "batch_id": "int32", - "hop_id": "int32", - } - raw_sample_data = cudf.read_parquet(parquet_path) + if "map" in raw_sample_data.columns: - num_batches = end_inclusive - self.__start_inclusive + 1 + if "renumber_map_offsets" not in raw_sample_data.columns: + num_batches = end_inclusive - self.__start_inclusive + 1 - map_end = raw_sample_data["map"].iloc[num_batches] + map_end = raw_sample_data["map"].iloc[num_batches] - map = torch.as_tensor( - raw_sample_data["map"].iloc[0:map_end], device="cuda" - ) - raw_sample_data.drop("map", axis=1, inplace=True) + map = torch.as_tensor( + raw_sample_data["map"].iloc[0:map_end], device="cuda" + ) + raw_sample_data.drop("map", axis=1, inplace=True) - self.__renumber_map_offsets = map[0 : num_batches + 1] - map[0] - self.__renumber_map = map[num_batches + 1 :] + self.__renumber_map_offsets = map[0 : num_batches + 1] - map[0] + self.__renumber_map = map[num_batches + 1 :] + else: + self.__renumber_map = raw_sample_data["map"] + self.__renumber_map_offsets = raw_sample_data[ + "renumber_map_offsets" + ] + raw_sample_data.drop( + columns=["map", "renumber_map_offsets"], inplace=True + ) + + self.__renumber_map.dropna(inplace=True) + self.__renumber_map = torch.as_tensor( + self.__renumber_map, device="cuda" + ) + + self.__renumber_map_offsets.dropna(inplace=True) + self.__renumber_map_offsets = torch.as_tensor( + self.__renumber_map_offsets, device="cuda" + ) else: self.__renumber_map = None - self.__data = raw_sample_data[list(columns.keys())].astype(columns) - self.__data.dropna(inplace=True) + self.__data = raw_sample_data + self.__coo = "majors" in self.__data.columns + if self.__coo: + self.__data.dropna(inplace=True) if ( len(self.__graph_store.edge_types) == 1 and len(self.__graph_store.node_types) == 1 ): - group_cols = ["batch_id", "hop_id"] - self.__data_index = self.__data.groupby(group_cols, as_index=True).agg( - {"sources": "max", "destinations": "max"} - ) - self.__data_index.rename( - columns={"sources": "src_max", "destinations": "dst_max"}, - inplace=True, - ) - self.__data_index = self.__data_index.to_dict(orient="index") + if self.__coo: + group_cols = ["batch_id", "hop_id"] + self.__data_index = self.__data.groupby( + group_cols, as_index=True + ).agg({"majors": "max", "minors": "max"}) + self.__data_index.rename( + columns={"majors": "src_max", "minors": "dst_max"}, + inplace=True, + ) + self.__data_index = self.__data_index.to_dict(orient="index") + else: + self.__data_index = None + + self.__label_hop_offsets = self.__data["label_hop_offsets"] + self.__data.drop(columns=["label_hop_offsets"], inplace=True) + self.__label_hop_offsets.dropna(inplace=True) + self.__label_hop_offsets = torch.as_tensor( + self.__label_hop_offsets, device="cuda" + ) + self.__label_hop_offsets -= self.__label_hop_offsets[0].clone() + + self.__major_offsets = self.__data["major_offsets"] + self.__data.drop(columns="major_offsets", inplace=True) + self.__major_offsets.dropna(inplace=True) + self.__major_offsets = torch.as_tensor( + self.__major_offsets, device="cuda" + ) + self.__major_offsets -= self.__major_offsets[0].clone() + + self.__minors = self.__data["minors"] + self.__data.drop(columns="minors", inplace=True) + self.__minors.dropna(inplace=True) + self.__minors = torch.as_tensor(self.__minors, device="cuda") + + num_batches = self.__end_exclusive - self.__start_inclusive + offsets_len = len(self.__label_hop_offsets) - 1 + if offsets_len % num_batches != 0: + raise ValueError("invalid label-hop offsets") + self.__fanout_length = int(offsets_len / num_batches) + + end_time_read_data = perf_counter() + self._total_read_time += end_time_read_data - start_time_read_data # Pull the next set of sampling results out of the dataframe in memory - f = self.__data["batch_id"] == self.__next_batch + if self.__coo: + f = self.__data["batch_id"] == self.__next_batch if self.__renumber_map is not None: i = self.__next_batch - self.__start_inclusive @@ -301,18 +363,43 @@ def __next__(self): else: current_renumber_map = None + start_time_convert = perf_counter() # Get and return the sampled subgraph if ( len(self.__graph_store.edge_types) == 1 and len(self.__graph_store.node_types) == 1 ): - sampler_output = _sampler_output_from_sampling_results_homogeneous( - self.__data[f], - current_renumber_map, - self.__graph_store, - self.__data_index, - self.__next_batch, - ) + if self.__coo: + sampler_output = _sampler_output_from_sampling_results_homogeneous_coo( + self.__data[f], + current_renumber_map, + self.__graph_store, + self.__data_index, + self.__next_batch, + ) + else: + i = (self.__next_batch - self.__start_inclusive) * self.__fanout_length + current_label_hop_offsets = self.__label_hop_offsets[ + i : i + self.__fanout_length + 1 + ] + + current_major_offsets = self.__major_offsets[ + current_label_hop_offsets[0] : (current_label_hop_offsets[-1] + 1) + ] + + current_minors = self.__minors[ + current_major_offsets[0] : current_major_offsets[-1] + ] + + sampler_output = _sampler_output_from_sampling_results_homogeneous_csr( + current_major_offsets, + current_minors, + current_renumber_map, + self.__graph_store, + current_label_hop_offsets, + self.__data_index, + self.__next_batch, + ) else: sampler_output = _sampler_output_from_sampling_results_heterogeneous( self.__data[f], current_renumber_map, self.__graph_store @@ -321,18 +408,35 @@ def __next__(self): # Get ready for next iteration self.__next_batch += 1 + end_time_convert = perf_counter() + self._total_convert_time += end_time_convert - start_time_convert + + start_time_feature = perf_counter() # Create a PyG HeteroData object, loading the required features - out = torch_geometric.loader.utils.filter_custom_store( - self.__feature_store, - self.__graph_store, - sampler_output.node, - sampler_output.row, - sampler_output.col, - sampler_output.edge, - ) + if self.__coo: + out = torch_geometric.loader.utils.filter_custom_store( + self.__feature_store, + self.__graph_store, + sampler_output.node, + sampler_output.row, + sampler_output.col, + sampler_output.edge, + ) + else: + if self.__graph_store.order == "CSR": + raise ValueError("CSR format incompatible with CSC output") + + out = filter_cugraph_store_csc( + self.__feature_store, + self.__graph_store, + sampler_output.node, + sampler_output.row, + sampler_output.col, + sampler_output.edge, + ) # Account for CSR format in cuGraph vs. CSC format in PyG - if self.__graph_store.order == "CSC": + if self.__coo and self.__graph_store.order == "CSC": for node_type in out.edge_index_dict: out[node_type].edge_index[0], out[node_type].edge_index[1] = ( out[node_type].edge_index[1], @@ -342,6 +446,9 @@ def __next__(self): out.set_value_dict("num_sampled_nodes", sampler_output.num_sampled_nodes) out.set_value_dict("num_sampled_edges", sampler_output.num_sampled_edges) + end_time_feature = perf_counter() + self._total_feature_time = end_time_feature - start_time_feature + return out @property diff --git a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py index 6e8c4322418..300ca9beb5a 100644 --- a/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/sampler/cugraph_sampler.py @@ -53,10 +53,10 @@ def _get_unique_nodes( The unique nodes of the given node type. """ if node_position == "src": - edge_index = "sources" + edge_index = "majors" edge_sel = 0 elif node_position == "dst": - edge_index = "destinations" + edge_index = "minors" edge_sel = -1 else: raise ValueError(f"Illegal value {node_position} for node_position") @@ -78,7 +78,7 @@ def _get_unique_nodes( return sampling_results_node[edge_index] -def _sampler_output_from_sampling_results_homogeneous( +def _sampler_output_from_sampling_results_homogeneous_coo( sampling_results: cudf.DataFrame, renumber_map: torch.Tensor, graph_store: CuGraphStore, @@ -133,11 +133,11 @@ def _sampler_output_from_sampling_results_homogeneous( noi_index = {node_type: torch.as_tensor(renumber_map, device="cuda")} row_dict = { - edge_type: torch.as_tensor(sampling_results.sources, device="cuda"), + edge_type: torch.as_tensor(sampling_results.majors, device="cuda"), } col_dict = { - edge_type: torch.as_tensor(sampling_results.destinations, device="cuda"), + edge_type: torch.as_tensor(sampling_results.minors, device="cuda"), } num_nodes_per_hop_dict[node_type][0] = data_index[batch_id, 0]["src_max"] + 1 @@ -177,6 +177,88 @@ def _sampler_output_from_sampling_results_homogeneous( ) +def _sampler_output_from_sampling_results_homogeneous_csr( + major_offsets: torch.Tensor, + minors: torch.Tensor, + renumber_map: torch.Tensor, + graph_store: CuGraphStore, + label_hop_offsets: torch.Tensor, + batch_id: int, + metadata: Sequence = None, +) -> HeteroSamplerOutput: + """ + Parameters + ---------- + major_offsets: torch.Tensor + The major offsets for the CSC/CSR matrix ("row pointer") + minors: torch.Tensor + The minors for the CSC/CSR matrix ("col index") + renumber_map: torch.Tensor + The tensor containing the renumber map. + Required. + graph_store: CuGraphStore + The graph store containing the structure of the sampled graph. + label_hop_offsets: torch.Tensor + The tensor containing the label-hop offsets. + batch_id: int + The current batch id, whose samples are being retrieved + from the sampling results and data index. + metadata: Tensor + The metadata for the sampled batch. + + Returns + ------- + HeteroSamplerOutput + """ + + if len(graph_store.edge_types) > 1 or len(graph_store.node_types) > 1: + raise ValueError("Graph is heterogeneous") + + if renumber_map is None: + raise ValueError("Renumbered input is expected for homogeneous graphs") + + node_type = graph_store.node_types[0] + edge_type = graph_store.edge_types[0] + + major_offsets = major_offsets.clone() - major_offsets[0] + label_hop_offsets = label_hop_offsets.clone() - label_hop_offsets[0] + + num_edges_per_hop_dict = {edge_type: major_offsets[label_hop_offsets].diff().cpu()} + + label_hop_offsets = label_hop_offsets.cpu() + num_nodes_per_hop_dict = { + node_type: torch.concat( + [ + label_hop_offsets.diff(), + (renumber_map.shape[0] - label_hop_offsets[-1]).reshape((1,)), + ] + ).cpu() + } + + noi_index = {node_type: torch.as_tensor(renumber_map, device="cuda")} + + col_dict = { + edge_type: major_offsets, + } + + row_dict = { + edge_type: minors, + } + + if HeteroSamplerOutput is None: + raise ImportError("Error importing from pyg") + + return HeteroSamplerOutput( + node=noi_index, + row=row_dict, + col=col_dict, + edge=None, + num_sampled_nodes=num_nodes_per_hop_dict, + num_sampled_edges=num_edges_per_hop_dict, + metadata=metadata, + ) + + def _sampler_output_from_sampling_results_heterogeneous( sampling_results: cudf.DataFrame, renumber_map: cudf.Series, @@ -244,8 +326,8 @@ def _sampler_output_from_sampling_results_heterogeneous( cudf.Series( torch.concat( [ - torch.as_tensor(sampling_results_hop_0.sources, device="cuda"), - torch.as_tensor(sampling_results.destinations, device="cuda"), + torch.as_tensor(sampling_results_hop_0.majors, device="cuda"), + torch.as_tensor(sampling_results.minors, device="cuda"), ] ), name="nodes_of_interest", @@ -320,3 +402,37 @@ def _sampler_output_from_sampling_results_heterogeneous( num_sampled_edges=num_edges_per_hop_dict, metadata=metadata, ) + + +def filter_cugraph_store_csc( + feature_store: torch_geometric.data.FeatureStore, + graph_store: torch_geometric.data.GraphStore, + node_dict: Dict[str, torch.Tensor], + row_dict: Dict[str, torch.Tensor], + col_dict: Dict[str, torch.Tensor], + edge_dict: Dict[str, Tuple[torch.Tensor]], +) -> torch_geometric.data.HeteroData: + data = torch_geometric.data.HeteroData() + + for attr in graph_store.get_all_edge_attrs(): + key = attr.edge_type + if key in row_dict and key in col_dict: + data.put_edge_index( + (row_dict[key], col_dict[key]), + edge_type=key, + layout="csc", + is_sorted=True, + ) + + required_attrs = [] + for attr in feature_store.get_all_tensor_attrs(): + if attr.group_name in node_dict: + attr.index = node_dict[attr.group_name] + required_attrs.append(attr) + data[attr.group_name].num_nodes = attr.index.size(0) + + tensors = feature_store.multi_get_tensor(required_attrs) + for i, attr in enumerate(required_attrs): + data[attr.group_name][attr.attr_name] = tensors[i] + + return data diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py index a1a72a44d0c..80a2d0a6c79 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_sampler.py @@ -53,9 +53,10 @@ def test_neighbor_sample(dask_client, basic_graph_1): random_state=62, return_offsets=False, return_hops=True, + use_legacy_names=False, ) .compute() - .sort_values(by=["sources", "destinations"]) + .sort_values(by=["majors", "minors"]) ) out = _sampler_output_from_sampling_results_heterogeneous( @@ -116,8 +117,9 @@ def test_neighbor_sample_multi_vertex(dask_client, multi_edge_multi_vertex_graph random_state=62, return_offsets=False, with_batch_ids=True, + use_legacy_names=False, ) - .sort_values(by=["sources", "destinations"]) + .sort_values(by=["majors", "minors"]) .compute() ) @@ -193,8 +195,8 @@ def test_neighbor_sample_mock_sampling_results(dask_client): # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( { - "sources": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), - "destinations": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), + "majors": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), + "minors": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 3, 3, 3], dtype="int32"), "edge_type": cudf.Series([0, 0, 0, 2, 1, 2, 0, 1, 2, 2], dtype="int32"), } diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py index 43b1e5da5a0..ed7f70034e2 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py @@ -220,8 +220,8 @@ def test_renumber_edges(abc_graph, dask_client): # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( { - "sources": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), - "destinations": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), + "majors": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), + "minors": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 3, 3, 3], dtype="int32"), "edge_type": cudf.Series([0, 0, 0, 2, 1, 2, 0, 1, 2, 2], dtype="int32"), } diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 48a21cb7fd6..03274948158 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -22,6 +22,8 @@ from cugraph_pyg.loader import CuGraphNeighborLoader from cugraph_pyg.loader import BulkSampleLoader from cugraph_pyg.data import CuGraphStore +from cugraph_pyg.nn import SAGEConv as CuGraphSAGEConv + from cugraph.gnn import FeatureStore from cugraph.utilities.utils import import_optional, MissingModule @@ -98,8 +100,8 @@ def test_cugraph_loader_from_disk(): bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6, 6], - "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "majors": [0, 1, 2, 3, 4, 5, 6, 6], + "minors": [5, 4, 3, 2, 2, 6, 5, 2], "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), @@ -130,12 +132,10 @@ def test_cugraph_loader_from_disk(): assert list(edge_index.shape) == [2, 8] assert ( - edge_index[0].tolist() - == bogus_samples.sources.dropna().values_host.tolist() + edge_index[0].tolist() == bogus_samples.majors.dropna().values_host.tolist() ) assert ( - edge_index[1].tolist() - == bogus_samples.destinations.dropna().values_host.tolist() + edge_index[1].tolist() == bogus_samples.minors.dropna().values_host.tolist() ) assert num_samples == 256 @@ -157,8 +157,8 @@ def test_cugraph_loader_from_disk_subset(): bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6, 6], - "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "majors": [0, 1, 2, 3, 4, 5, 6, 6], + "minors": [5, 4, 3, 2, 2, 6, 5, 2], "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), @@ -190,13 +190,77 @@ def test_cugraph_loader_from_disk_subset(): assert list(edge_index.shape) == [2, 8] assert ( - edge_index[0].tolist() - == bogus_samples.sources.dropna().values_host.tolist() + edge_index[0].tolist() == bogus_samples.majors.dropna().values_host.tolist() + ) + assert ( + edge_index[1].tolist() == bogus_samples.minors.dropna().values_host.tolist() ) + + assert num_samples == 100 + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_from_disk_subset_csr(): + m = [2, 9, 99, 82, 11, 13] + n = torch.arange(1, 1 + len(m), dtype=torch.int32) + x = torch.zeros(256, dtype=torch.int32) + x[torch.tensor(m, dtype=torch.int32)] = n + F = FeatureStore() + F.add_data(x, "t0", "x") + + G = {("t0", "knows", "t0"): 9080} + N = {"t0": 256} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame( + { + "major_offsets": [0, 3, 5, 7, 8, None, None, None], + "minors": [1, 2, 3, 0, 3, 4, 5, 1], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], + "label_hop_offsets": cudf.Series( + [0, 1, 4, None, None, None, None, None], dtype="int32" + ), + "renumber_map_offsets": cudf.Series([0, 6], dtype="int32"), + } + ) + map = cudf.Series(m, name="map") + bogus_samples["map"] = map + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + # offset the offsets + bogus_samples["batch_id"] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f"batch={s}-{s}.parquet")) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + input_files=list(os.listdir(tempdir.name))[100:200], + ) + + num_samples = 0 + for sample in loader: + num_samples += 1 + assert sample["t0"]["num_nodes"] == 6 + + assert sample["t0"]["x"].tolist() == [1, 2, 3, 4, 5, 6] + + edge_index = sample[("t0", "knows", "t0")]["adj_t"] + assert edge_index.size(0) == 4 + assert edge_index.size(1) == 6 + + colptr, row, _ = edge_index.csr() + assert ( - edge_index[1].tolist() - == bogus_samples.destinations.dropna().values_host.tolist() + colptr.tolist() == bogus_samples.major_offsets.dropna().values_host.tolist() ) + assert row.tolist() == bogus_samples.minors.dropna().values_host.tolist() + + assert sample["t0"]["num_sampled_nodes"].tolist() == [1, 3, 2] + assert sample["t0", "knows", "t0"]["num_sampled_edges"].tolist() == [3, 5] assert num_samples == 100 @@ -215,8 +279,8 @@ def test_cugraph_loader_e2e_coo(): bogus_samples = cudf.DataFrame( { - "sources": [0, 1, 2, 3, 4, 5, 6, 6], - "destinations": [5, 4, 3, 2, 2, 6, 5, 2], + "majors": [0, 1, 2, 3, 4, 5, 6, 6], + "minors": [5, 4, 3, 2, 2, 6, 5, 2], "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 2], dtype="int32"), @@ -253,8 +317,6 @@ def test_cugraph_loader_e2e_coo(): num_sampled_nodes = hetero_data["t0"]["num_sampled_nodes"] num_sampled_edges = hetero_data["t0", "knows", "t0"]["num_sampled_edges"] - print(num_sampled_nodes, num_sampled_edges) - for i in range(len(convs)): x, ei, _ = trim(i, num_sampled_nodes, num_sampled_edges, x, ei, None) @@ -263,9 +325,111 @@ def test_cugraph_loader_e2e_coo(): x = convs[i](x, ei, size=(s, s)) x = relu(x) x = dropout(x, p=0.5) - print(x.shape) - print(x.shape) x = x.narrow(dim=0, start=0, length=x.shape[0] - num_sampled_nodes[1]) assert list(x.shape) == [3, 1] + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.parametrize("framework", ["pyg", "cugraph-ops"]) +def test_cugraph_loader_e2e_csc(framework): + m = [2, 9, 99, 82, 9, 3, 18, 1, 12] + x = torch.randint(3000, (256, 256)).to(torch.float32) + F = FeatureStore() + F.add_data(x, "t0", "x") + + G = {("t0", "knows", "t0"): 9999} + N = {"t0": 256} + + cugraph_store = CuGraphStore(F, G, N) + + bogus_samples = cudf.DataFrame( + { + "major_offsets": [0, 3, 5, 7, 8, None, None, None], + "minors": [1, 2, 3, 0, 3, 4, 5, 1], + "edge_type": cudf.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="int32"), + "edge_id": [5, 10, 15, 20, 25, 30, 35, 40], + "label_hop_offsets": cudf.Series( + [0, 1, 4, None, None, None, None, None], dtype="int32" + ), + "renumber_map_offsets": cudf.Series([0, 6], dtype="int32"), + } + ) + map = cudf.Series(m, name="map") + bogus_samples = bogus_samples.join(map, how="outer").sort_index() + + tempdir = tempfile.TemporaryDirectory() + for s in range(256): + bogus_samples["batch_id"] = cupy.int32(s) + bogus_samples.to_parquet(os.path.join(tempdir.name, f"batch={s}-{s}.parquet")) + + loader = BulkSampleLoader( + feature_store=cugraph_store, + graph_store=cugraph_store, + directory=tempdir, + input_files=list(os.listdir(tempdir.name))[100:200], + ) + + if framework == "pyg": + convs = [ + torch_geometric.nn.SAGEConv(256, 64, aggr="mean").cuda(), + torch_geometric.nn.SAGEConv(64, 1, aggr="mean").cuda(), + ] + else: + convs = [ + CuGraphSAGEConv(256, 64, aggr="mean").cuda(), + CuGraphSAGEConv(64, 1, aggr="mean").cuda(), + ] + + trim = trim_to_layer.TrimToLayer() + relu = torch.nn.functional.relu + dropout = torch.nn.functional.dropout + + for hetero_data in loader: + x = hetero_data["t0"]["x"].cuda() + + if framework == "pyg": + ei = hetero_data["t0", "knows", "t0"]["adj_t"].coo() + ei = torch.stack((ei[0], ei[1])) + else: + ei = hetero_data["t0", "knows", "t0"]["adj_t"].csr() + ei = [ei[1], ei[0], x.shape[0]] + + num_sampled_nodes = hetero_data["t0"]["num_sampled_nodes"] + num_sampled_edges = hetero_data["t0", "knows", "t0"]["num_sampled_edges"] + + s = x.shape[0] + for i in range(len(convs)): + if framework == "pyg": + x, ei, _ = trim(i, num_sampled_nodes, num_sampled_edges, x, ei, None) + else: + if i > 0: + x = x.narrow( + dim=0, + start=0, + length=s - num_sampled_nodes[-i], + ) + + ei[0] = ei[0].narrow( + dim=0, + start=0, + length=ei[0].size(0) - num_sampled_edges[-i], + ) + ei[1] = ei[1].narrow( + dim=0, start=0, length=ei[1].size(0) - num_sampled_nodes[-i] + ) + ei[2] = x.size(0) + + s = x.shape[0] + + if framework == "pyg": + x = convs[i](x, ei, size=(s, s)) + else: + x = convs[i](x, ei) + x = relu(x) + x = dropout(x, p=0.5) + + x = x.narrow(dim=0, start=0, length=s - num_sampled_nodes[1]) + + assert list(x.shape) == [1, 1] diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py index 84f62e80c9d..e703d477b70 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_sampler.py @@ -49,7 +49,8 @@ def test_neighbor_sample(basic_graph_1): with_batch_ids=True, random_state=62, return_offsets=False, - ).sort_values(by=["sources", "destinations"]) + use_legacy_names=False, + ).sort_values(by=["majors", "minors"]) out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, @@ -107,7 +108,8 @@ def test_neighbor_sample_multi_vertex(multi_edge_multi_vertex_graph_1): random_state=62, return_offsets=False, with_batch_ids=True, - ).sort_values(by=["sources", "destinations"]) + use_legacy_names=False, + ).sort_values(by=["majors", "minors"]) out = _sampler_output_from_sampling_results_heterogeneous( sampling_results=sampling_results, @@ -154,8 +156,8 @@ def test_neighbor_sample_mock_sampling_results(abc_graph): # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( { - "sources": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), - "destinations": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), + "majors": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), + "minors": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 3, 3, 3], dtype="int32"), "edge_type": cudf.Series([0, 0, 0, 2, 1, 2, 0, 1, 2, 2], dtype="int32"), } diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py index e815b813050..da3043760d4 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py @@ -204,8 +204,8 @@ def test_renumber_edges(abc_graph): # let 0, 1 be the start vertices, fanout = [2, 1, 2, 3] mock_sampling_results = cudf.DataFrame( { - "sources": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), - "destinations": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), + "majors": cudf.Series([0, 0, 1, 2, 3, 3, 1, 3, 3, 3], dtype="int64"), + "minors": cudf.Series([2, 3, 3, 8, 1, 7, 3, 1, 5, 7], dtype="int64"), "hop_id": cudf.Series([0, 0, 0, 1, 1, 1, 2, 3, 3, 3], dtype="int32"), "edge_type": cudf.Series([0, 0, 0, 2, 1, 2, 0, 1, 2, 2], dtype="int32"), } From 061ada449b7b11fd39789aca59a65a663f631a3e Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Wed, 4 Oct 2023 14:41:24 -0500 Subject: [PATCH 032/111] Increase dask-related timeouts for CI testing (#3907) This PR increases the minimum timeout when waiting for the workers to complete their tasks. Authors: - Joseph Nke (https://github.com/jnke2016) Approvers: - Brad Rees (https://github.com/BradReesWork) - Vibhu Jawa (https://github.com/VibhuJawa) - Rick Ratzel (https://github.com/rlratzel) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/3907 --- ci/test_python.sh | 5 ++++- ci/test_wheel.sh | 6 +++++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index 825d5b242d5..df0f34377a3 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -65,7 +65,10 @@ popd rapids-logger "pytest cugraph" pushd python/cugraph/cugraph -export DASK_WORKER_DEVICES="0" +DASK_WORKER_DEVICES="0" \ +DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ +DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ +DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ pytest \ -v \ --benchmark-disable \ diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index 3ac3549f143..d6ec67cd9e9 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -21,5 +21,9 @@ arch=$(uname -m) if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then python ./ci/wheel_smoke_test_${package_name}.py else - RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${python_package_name}/tests + RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets \ + DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ + DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ + DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ + python -m pytest ./python/${package_name}/${python_package_name}/tests fi From b3d2452071da4983714cbd490d652cc1e13a7381 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Wed, 4 Oct 2023 15:53:25 -0500 Subject: [PATCH 033/111] CUDA 12.0 ARM conda support. (#3903) This PR builds conda packages using CUDA 12 on ARM. Closes #3864. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Ray Douglass (https://github.com/raydouglass) --- .github/workflows/build.yaml | 20 ++++++++++---------- .github/workflows/pr.yaml | 30 +++++++++++++++--------------- .github/workflows/test.yaml | 10 +++++----- 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index fb8fc6bea9d..b02b134b337 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -28,7 +28,7 @@ concurrency: jobs: cpp-build: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -38,7 +38,7 @@ jobs: python-build: needs: [cpp-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -47,7 +47,7 @@ jobs: upload-conda: needs: [cpp-build, python-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -57,7 +57,7 @@ jobs: if: github.ref_type == 'branch' needs: python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm with: arch: "amd64" branch: ${{ inputs.branch }} @@ -69,7 +69,7 @@ jobs: sha: ${{ inputs.sha }} wheel-build-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -82,7 +82,7 @@ jobs: wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -92,7 +92,7 @@ jobs: wheel-build-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -105,7 +105,7 @@ jobs: wheel-publish-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -115,7 +115,7 @@ jobs: wheel-build-nx-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -125,7 +125,7 @@ jobs: wheel-publish-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 4e9b0ce1774..cd6c5457a93 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -27,41 +27,41 @@ jobs: - wheel-tests-nx-cugraph - devcontainer secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@cuda-120-arm checks: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@cuda-120-arm with: enable_check_generated_files: false conda-cpp-build: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@cuda-120-arm with: build_type: pull-request node_type: cpu32 conda-cpp-tests: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-120-arm with: build_type: pull-request conda-python-build: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@cuda-120-arm with: build_type: pull-request conda-python-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@cuda-120-arm with: build_type: pull-request conda-notebook-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -71,7 +71,7 @@ jobs: docs-build: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -81,7 +81,7 @@ jobs: wheel-build-pylibcugraph: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: pull-request script: ci/build_wheel_pylibcugraph.sh @@ -91,14 +91,14 @@ jobs: wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: pull-request script: ci/test_wheel_pylibcugraph.sh wheel-build-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: pull-request script: ci/build_wheel_cugraph.sh @@ -108,27 +108,27 @@ jobs: wheel-tests-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: pull-request script: ci/test_wheel_cugraph.sh wheel-build-nx-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm with: build_type: pull-request script: ci/build_wheel_nx-cugraph.sh wheel-tests-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: pull-request script: ci/test_wheel_nx-cugraph.sh devcontainer: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-120-arm with: extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY build_command: | diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index ed359d4f4ef..dc40eae5f0d 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -16,7 +16,7 @@ on: jobs: conda-cpp-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-120-arm with: build_type: nightly branch: ${{ inputs.branch }} @@ -24,7 +24,7 @@ jobs: sha: ${{ inputs.sha }} conda-python-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@cuda-120-arm with: build_type: nightly branch: ${{ inputs.branch }} @@ -32,7 +32,7 @@ jobs: sha: ${{ inputs.sha }} wheel-tests-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: nightly branch: ${{ inputs.branch }} @@ -41,7 +41,7 @@ jobs: script: ci/test_wheel_pylibcugraph.sh wheel-tests-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: nightly branch: ${{ inputs.branch }} @@ -50,7 +50,7 @@ jobs: script: ci/test_wheel_cugraph.sh wheel-tests-nx-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.10 + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm with: build_type: nightly branch: ${{ inputs.branch }} From d03cb0fd33e072756f6d87fbe009b561ff48bafb Mon Sep 17 00:00:00 2001 From: Vibhu Jawa Date: Thu, 5 Oct 2023 09:31:01 -0700 Subject: [PATCH 034/111] Remove `dask_cudf` dataframe for the `_make_plc_graph` while creating `cugraph.Graph` (#3895) This PR attempts to fix https://github.com/rapidsai/cugraph/issues/3790 Please note that I have not being able to cause failure locally so it is really hard for me to know if it actually fixes anything or not . MRE being used to test locally: https://gist.github.com/VibhuJawa/4b1ec24022b6e2dd7879cd2e8d3fab67 CC: @jnke2016 , @rlratzel , CC: @rjzamora , Please let me know what i can do better here. Authors: - Vibhu Jawa (https://github.com/VibhuJawa) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/3895 --- .../cugraph/cugraph/dask/common/part_utils.py | 62 ++++++++++++++++--- .../simpleDistributedGraph.py | 24 ++++--- 2 files changed, 71 insertions(+), 15 deletions(-) diff --git a/python/cugraph/cugraph/dask/common/part_utils.py b/python/cugraph/cugraph/dask/common/part_utils.py index 7c0aad6c3ee..25311902b29 100644 --- a/python/cugraph/cugraph/dask/common/part_utils.py +++ b/python/cugraph/cugraph/dask/common/part_utils.py @@ -99,19 +99,65 @@ def _chunk_lst(ls, num_parts): return [ls[i::num_parts] for i in range(num_parts)] -def persist_dask_df_equal_parts_per_worker(dask_df, client): +def persist_dask_df_equal_parts_per_worker( + dask_df, client, return_type="dask_cudf.DataFrame" +): + """ + Persist dask_df with equal parts per worker + Args: + dask_df: dask_cudf.DataFrame + client: dask.distributed.Client + return_type: str, "dask_cudf.DataFrame" or "dict" + Returns: + persisted_keys: dict of {worker: [persisted_keys]} + """ + if return_type not in ["dask_cudf.DataFrame", "dict"]: + raise ValueError("return_type must be either 'dask_cudf.DataFrame' or 'dict'") + ddf_keys = dask_df.to_delayed() workers = client.scheduler_info()["workers"].keys() ddf_keys_ls = _chunk_lst(ddf_keys, len(workers)) - persisted_keys = [] + persisted_keys_d = {} for w, ddf_k in zip(workers, ddf_keys_ls): - persisted_keys.extend( - client.persist(ddf_k, workers=w, allow_other_workers=False) + persisted_keys_d[w] = client.compute( + ddf_k, workers=w, allow_other_workers=False, pure=False ) - dask_df = dask_cudf.from_delayed(persisted_keys, meta=dask_df._meta).persist() - wait(dask_df) - client.rebalance(dask_df) - return dask_df + + persisted_keys_ls = [ + item for sublist in persisted_keys_d.values() for item in sublist + ] + wait(persisted_keys_ls) + if return_type == "dask_cudf.DataFrame": + dask_df = dask_cudf.from_delayed( + persisted_keys_ls, meta=dask_df._meta + ).persist() + wait(dask_df) + return dask_df + + return persisted_keys_d + + +def get_length_of_parts(persisted_keys_d, client): + """ + Get the length of each partition + Args: + persisted_keys_d: dict of {worker: [persisted_keys]} + client: dask.distributed.Client + Returns: + length_of_parts: dict of {worker: [length_of_parts]} + """ + length_of_parts = {} + for w, p_keys in persisted_keys_d.items(): + length_of_parts[w] = [ + client.submit( + len, p_key, pure=False, workers=[w], allow_other_workers=False + ) + for p_key in p_keys + ] + + for w, len_futures in length_of_parts.items(): + length_of_parts[w] = client.gather(len_futures) + return length_of_parts async def _extract_partitions( diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index fa94fa67625..935d0c597d4 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -36,6 +36,7 @@ from cugraph.structure.symmetrize import symmetrize from cugraph.dask.common.part_utils import ( get_persisted_df_worker_map, + get_length_of_parts, persist_dask_df_equal_parts_per_worker, ) from cugraph.dask import get_n_workers @@ -318,9 +319,14 @@ def __from_edgelist( is_symmetric=not self.properties.directed, ) ddf = ddf.repartition(npartitions=len(workers) * 2) - ddf = persist_dask_df_equal_parts_per_worker(ddf, _client) - num_edges = len(ddf) - ddf = get_persisted_df_worker_map(ddf, _client) + persisted_keys_d = persist_dask_df_equal_parts_per_worker( + ddf, _client, return_type="dict" + ) + del ddf + length_of_parts = get_length_of_parts(persisted_keys_d, _client) + num_edges = sum( + [item for sublist in length_of_parts.values() for item in sublist] + ) delayed_tasks_d = { w: delayed(simpleDistributedGraphImpl._make_plc_graph)( Comms.get_session_id(), @@ -331,14 +337,16 @@ def __from_edgelist( store_transposed, num_edges, ) - for w, edata in ddf.items() + for w, edata in persisted_keys_d.items() } - # FIXME: For now, don't delete the copied dataframe to avoid crash self._plc_graph = { - w: _client.compute(delayed_task, workers=w, allow_other_workers=False) + w: _client.compute( + delayed_task, workers=w, allow_other_workers=False, pure=False + ) for w, delayed_task in delayed_tasks_d.items() } wait(list(self._plc_graph.values())) + del persisted_keys_d del delayed_tasks_d _client.run(gc.collect) @@ -1192,5 +1200,7 @@ def _get_column_from_ls_dfs(lst_df, col_name): if len_df == 0: return lst_df[0][col_name] output_col = cudf.concat([df[col_name] for df in lst_df], ignore_index=True) - # FIXME: For now, don't delete the copied dataframe to avoid cras + for df in lst_df: + df.drop(columns=[col_name], inplace=True) + gc.collect() return output_col From 2c1626ff84ac0c56fb2e686c3c9e0202ee97e1b9 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Fri, 6 Oct 2023 12:18:24 -0500 Subject: [PATCH 035/111] Adds benchmarks for `nx-cugraph` (#3854) closes rapidsai/graph_dl#299 This PR adds new benchmarks for `nx-cugraph` which can be used to compare performance for NetworkX with and without the `cugraph` backend. These benchmarks depend on `pytest`, `pytest-benchmark`, `networkx>=3.0`, `cugraph` (only for the Dataset APIs), and `nx-cugraph` Some results can be seen here: ![image](https://github.com/rapidsai/cugraph/assets/3039903/d595eb3f-2ae6-4b2e-a0b1-a0f0bcb050a8) ![image](https://github.com/rapidsai/cugraph/assets/3039903/3ca367b4-14b8-4673-91d2-f83a947163e7) ### Other changes: * `black` is now run on all python sources under `benchmarks`, which resulted in several format-only changes. * Several other updates to versions, etc. were applied to various .yaml files in order to resolve CI errors. * Merged in changes from [jnke2016/branch-23.10_increase-timeout](https://github.com/rapidsai/cugraph/pull/3854/commits/cbdbd8a6d22903f251c64b1b0321ba41ec03fcb3) to get changes needed to fix dask CI failure. Authors: - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) - AJ Schmidt (https://github.com/ajschmidt8) - Naim (https://github.com/naimnv) - Seunghwa Kang (https://github.com/seunghwak) - https://github.com/lmeyerov - Erik Welch (https://github.com/eriknw) - Alex Barghi (https://github.com/alexbarghi-nv) - Vyas Ramasubramani (https://github.com/vyasr) - Divye Gala (https://github.com/divyegala) - Tingyu Wang (https://github.com/tingyu66) - Chuck Hastings (https://github.com/ChuckHastings) - ralph (https://github.com/nv-rliu) - Paul Taylor (https://github.com/trxcllnt) Approvers: - Erik Welch (https://github.com/eriknw) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3854 --- .../cuda11.8-conda/devcontainer.json | 4 +- .devcontainer/cuda11.8-pip/devcontainer.json | 6 +- .../cuda12.0-conda/devcontainer.json | 4 +- .devcontainer/cuda12.0-pip/devcontainer.json | 6 +- .github/workflows/pr.yaml | 2 +- .pre-commit-config.yaml | 2 +- ...nch_cugraph_dgl_uniform_neighbor_sample.py | 51 +- .../dgl_benchmark.py | 41 +- .../python-script/ogbn_mag_benchmark.py | 89 +-- .../scale-benchmarks/dgl_benchmark.py | 71 ++- .../scale-benchmarks/load_graph_feats.py | 75 ++- .../cugraph-dgl/scale-benchmarks/model.py | 54 +- .../pytest-based/bench_cgs_client_scaling.py | 22 +- .../cugraph/pytest-based/bench_algos.py | 185 +++--- benchmarks/cugraph/pytest-based/conftest.py | 65 ++- benchmarks/cugraph/standalone/benchmark.py | 60 +- .../bulk_sampling/cugraph_bulk_sampling.py | 552 ++++++++++-------- .../cugraph/standalone/cugraph_dask_funcs.py | 67 ++- .../cugraph/standalone/cugraph_funcs.py | 58 +- .../standalone/cugraph_graph_creation.py | 2 +- benchmarks/cugraph/standalone/main.py | 200 ++++--- .../cugraph/standalone/pylibcugraph_bench.py | 47 +- benchmarks/cugraph/standalone/reporting.py | 12 +- benchmarks/dgl/create_dataset.py | 10 +- benchmarks/dgl/pytest-based/dgl_benchmark.py | 114 ++-- .../nx-cugraph/pytest-based/bench_algos.py | 212 +++++++ benchmarks/pytest.ini | 4 +- .../test_client_bandwidth.py | 37 +- .../test_cugraph_sampling.py | 35 +- .../python/cugraph_benchmarking/params.py | 158 +++-- ci/release/update-version.sh | 4 + ci/test_python.sh | 5 +- ci/test_wheel.sh | 6 +- .../all_cuda-118_arch-x86_64.yaml | 1 + .../all_cuda-120_arch-x86_64.yaml | 1 + dependencies.yaml | 84 +-- .../cugraph_dgl/dataloading/__init__.py | 2 +- .../cugraph_dgl/dataloading/dataloader.py | 49 +- .../cugraph_dgl/dataloading/dataset.py | 37 +- .../dataloading/utils/sampling_helpers.py | 155 ++++- .../cugraph-dgl/cugraph_dgl/nn/conv/base.py | 7 + python/cugraph-dgl/tests/test_utils.py | 28 + .../gnn/feature_storage/feat_storage.py | 147 ++++- .../tests/data_store/test_gnn_feat_storage.py | 1 - .../test_gnn_feat_storage_wholegraph.py | 85 +++ python/nx-cugraph/_nx_cugraph/__init__.py | 2 +- 46 files changed, 1955 insertions(+), 904 deletions(-) create mode 100644 benchmarks/nx-cugraph/pytest-based/bench_algos.py create mode 100644 python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json index cf4ba5aa114..8cfe0e35990 100644 --- a/.devcontainer/cuda11.8-conda/devcontainer.json +++ b/.devcontainer/cuda11.8-conda/devcontainer.json @@ -5,12 +5,12 @@ "args": { "CUDA": "11.8", "PYTHON_PACKAGE_MANAGER": "conda", - "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04" + "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json index e86a38abbde..fd3f8d22f89 100644 --- a/.devcontainer/cuda11.8-pip/devcontainer.json +++ b/.devcontainer/cuda11.8-pip/devcontainer.json @@ -5,13 +5,13 @@ "args": { "CUDA": "11.8", "PYTHON_PACKAGE_MANAGER": "pip", - "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda11.8-ubuntu22.04" + "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda11.8-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.10": {"version": "1.14.1"}, - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.0-conda/devcontainer.json index 863eeea48ff..bc4a2cb6fb4 100644 --- a/.devcontainer/cuda12.0-conda/devcontainer.json +++ b/.devcontainer/cuda12.0-conda/devcontainer.json @@ -5,12 +5,12 @@ "args": { "CUDA": "12.0", "PYTHON_PACKAGE_MANAGER": "conda", - "BASE": "rapidsai/devcontainers:23.10-cpp-mambaforge-ubuntu22.04" + "BASE": "rapidsai/devcontainers:23.12-cpp-mambaforge-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json index c7612771fd3..abff840ebdd 100644 --- a/.devcontainer/cuda12.0-pip/devcontainer.json +++ b/.devcontainer/cuda12.0-pip/devcontainer.json @@ -5,13 +5,13 @@ "args": { "CUDA": "12.0", "PYTHON_PACKAGE_MANAGER": "pip", - "BASE": "rapidsai/devcontainers:23.10-cpp-llvm16-cuda12.0-ubuntu22.04" + "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda12.0-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.10": {"version": "1.14.1"}, - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.10": {} + "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index cd6c5457a93..e8834e58ace 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -133,5 +133,5 @@ jobs: extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY build_command: | sccache -z; - build-all --verbose; + build-all --verbose -j$(nproc --ignore=1); sccache -s; diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 865d06b20e4..bab39557c99 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: - id: black language_version: python3 args: [--target-version=py38] - files: ^python/ + files: ^(python/.*|benchmarks/.*)$ - repo: https://github.com/PyCQA/flake8 rev: 6.0.0 hooks: diff --git a/benchmarks/cugraph-dgl/pytest-based/bench_cugraph_dgl_uniform_neighbor_sample.py b/benchmarks/cugraph-dgl/pytest-based/bench_cugraph_dgl_uniform_neighbor_sample.py index eeee163b0af..dc961223cb1 100644 --- a/benchmarks/cugraph-dgl/pytest-based/bench_cugraph_dgl_uniform_neighbor_sample.py +++ b/benchmarks/cugraph-dgl/pytest-based/bench_cugraph_dgl_uniform_neighbor_sample.py @@ -21,6 +21,7 @@ import pytest import numpy as np import cupy as cp + # Facing issues with rapids-pytest-benchmark plugin # pytest-benchmark. import pytest_benchmark @@ -33,6 +34,7 @@ import dgl import torch import rmm + _seed = 42 @@ -71,21 +73,21 @@ def create_graph(graph_data): else: raise TypeError(f"graph_data can only be str or dict, got {type(graph_data)}") - num_nodes = max(edgelist_df['src'].max(), - edgelist_df['dst'].max())+1 + num_nodes = max(edgelist_df["src"].max(), edgelist_df["dst"].max()) + 1 - num_nodes_dict = {'_N':num_nodes} + num_nodes_dict = {"_N": num_nodes} gs = CuGraphStorage(num_nodes_dict=num_nodes_dict, single_gpu=True) - gs.add_edge_data(edgelist_df, - # reverse to make same graph as cugraph - node_col_names=['dst', 'src'], - canonical_etype=['_N', 'connects', '_N']) + gs.add_edge_data( + edgelist_df, + # reverse to make same graph as cugraph + node_col_names=["dst", "src"], + canonical_etype=["_N", "connects", "_N"], + ) return gs - def create_mg_graph(graph_data): """ Create a graph instance based on the data to be loaded/generated. @@ -93,7 +95,9 @@ def create_mg_graph(graph_data): # range starts at 1 to let let 0 be used by benchmark/client process visible_devices = os.getenv("DASK_WORKER_DEVICES", "1,2,3,4") - cluster = LocalCUDACluster(protocol='ucx', rmm_pool_size='25GB', CUDA_VISIBLE_DEVICES=visible_devices) + cluster = LocalCUDACluster( + protocol="ucx", rmm_pool_size="25GB", CUDA_VISIBLE_DEVICES=visible_devices + ) client = Client(cluster) Comms.initialize(p2p=True) rmm.reinitialize(pool_allocator=True) @@ -126,25 +130,23 @@ def create_mg_graph(graph_data): else: raise TypeError(f"graph_data can only be str or dict, got {type(graph_data)}") - num_nodes = max(edgelist_df['src'].max().compute(), - edgelist_df['dst'].max().compute()) + num_nodes = max( + edgelist_df["src"].max().compute(), edgelist_df["dst"].max().compute() + ) # running into issues with smaller partitions - edgelist_df = edgelist_df.repartition(npartitions=edgelist_df.npartitions*2) + edgelist_df = edgelist_df.repartition(npartitions=edgelist_df.npartitions * 2) - num_nodes_dict = {'_N':num_nodes} + num_nodes_dict = {"_N": num_nodes} - gs = CuGraphStorage(num_nodes_dict=num_nodes_dict, single_gpu=False) - gs.add_edge_data(edgelist_df, - node_col_names=['dst', 'src'], - canonical_etype=['_N', 'C', '_N']) + gs = CuGraphStorage(num_nodes_dict=num_nodes_dict, single_gpu=False) + gs.add_edge_data( + edgelist_df, node_col_names=["dst", "src"], canonical_etype=["_N", "C", "_N"] + ) return (gs, client, cluster) - -def get_uniform_neighbor_sample_args( - G, seed, batch_size, fanout, with_replacement -): +def get_uniform_neighbor_sample_args(G, seed, batch_size, fanout, with_replacement): """ Return a dictionary containing the args for uniform_neighbor_sample based on the graph and desired args passed in. For example, if a large start list @@ -165,7 +167,7 @@ def get_uniform_neighbor_sample_args( else: num_start_verts = batch_size - srcs = G.graphstore.gdata.get_edge_data()['_SRC_'] + srcs = G.graphstore.gdata.get_edge_data()["_SRC_"] start_list = srcs.head(num_start_verts) assert len(start_list) == num_start_verts @@ -205,7 +207,6 @@ def graph_objs(request): dask_cluster.close() - ################################################################################ # Benchmarks @pytest.mark.parametrize("batch_size", params.batch_sizes.values()) @@ -223,7 +224,7 @@ def bench_cugraph_dgl_uniform_neighbor_sample( # Reverse to match cugraph # DGL does from dst to src - fanout_val = uns_args['fanout'] + fanout_val = uns_args["fanout"] fanout_val.reverse() sampler = dgl.dataloading.NeighborSampler(uns_args["fanout"]) sampler_f = sampler.sample_blocks @@ -231,7 +232,7 @@ def bench_cugraph_dgl_uniform_neighbor_sample( # Warmup _ = sampler_f(g=G, seed_nodes=uns_args["seed_nodes"]) # print(f"\n{uns_args}") - result_seed_nodes, output_nodes, blocks = benchmark( + result_seed_nodes, output_nodes, blocks = benchmark( sampler_f, g=G, seed_nodes=uns_args["seed_nodes"], diff --git a/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py b/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py index 0a52703c546..fc7543a91e0 100644 --- a/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py +++ b/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py @@ -52,13 +52,9 @@ def load_edges_from_disk(parquet_path, replication_factor, input_meta): src_ls = [ei["src"]] dst_ls = [ei["dst"]] for r in range(1, replication_factor): - new_src = ei["src"] + ( - r * input_meta["num_nodes"][can_edge_type[0]] - ) + new_src = ei["src"] + (r * input_meta["num_nodes"][can_edge_type[0]]) src_ls.append(new_src) - new_dst = ei["dst"] + ( - r * input_meta["num_nodes"][can_edge_type[2]] - ) + new_dst = ei["dst"] + (r * input_meta["num_nodes"][can_edge_type[2]]) dst_ls.append(new_dst) ei["src"] = torch.cat(src_ls).contiguous() @@ -92,16 +88,11 @@ def load_node_labels(dataset_path, replication_factor, input_meta): ] ), "label": pd.concat( - [ - node_label.label - for r in range(1, replication_factor) - ] + [node_label.label for r in range(1, replication_factor)] ), } ) - node_label = pd.concat([node_label, dfr]).reset_index( - drop=True - ) + node_label = pd.concat([node_label, dfr]).reset_index(drop=True) node_label_tensor = torch.full( (num_nodes_dict[node_type],), -1, dtype=torch.float32 @@ -133,9 +124,7 @@ def create_dgl_graph_from_disk(dataset_path, replication_factor=1): input_meta = json.load(f) parquet_path = os.path.join(dataset_path, "parquet") - graph_data = load_edges_from_disk( - parquet_path, replication_factor, input_meta - ) + graph_data = load_edges_from_disk(parquet_path, replication_factor, input_meta) node_data = load_node_labels(dataset_path, replication_factor, input_meta) g = dgl.heterograph(graph_data) @@ -154,7 +143,7 @@ def create_dataloader(g, train_idx, batch_size, fanouts, use_uva): Returns: DGLGraph: DGLGraph with the loaded dataset. """ - + print("Creating dataloader", flush=True) st = time.time() if use_uva: @@ -220,21 +209,21 @@ def dataloading_benchmark(g, train_idx, fanouts, batch_sizes, use_uva): print("==============================================") return time_ls + def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) + if __name__ == "__main__": parser = ArgumentParser() parser.add_argument( "--dataset_path", type=str, default="/datasets/abarghi/ogbn_papers100M" ) parser.add_argument("--replication_factors", type=str, default="1,2,4,8") - parser.add_argument( - "--fanouts", type=str, default="25_25,10_10_10,5_10_20" - ) + parser.add_argument("--fanouts", type=str, default="25_25,10_10_10,5_10_20") parser.add_argument("--batch_sizes", type=str, default="512,1024") parser.add_argument("--do_not_use_uva", action="store_true") parser.add_argument("--seed", type=int, default=42) @@ -267,22 +256,16 @@ def set_seed(seed): et = time.time() print(f"Replication factor = {replication_factor}") print( - f"G has {g.num_edges()} edges and took", - f" {et - st:.2f} seconds to load" + f"G has {g.num_edges()} edges and took", f" {et - st:.2f} seconds to load" ) train_idx = {"paper": node_data["paper"]["train_idx"]} r_time_ls = dataloading_benchmark( g, train_idx, fanouts, batch_sizes, use_uva=use_uva ) - print( - "Benchmark completed for replication factor = ", replication_factor - ) + print("Benchmark completed for replication factor = ", replication_factor) print("==============================================") # Add replication factor to the time list - [ - x.update({"replication_factor": replication_factor}) - for x in r_time_ls - ] + [x.update({"replication_factor": replication_factor}) for x in r_time_ls] time_ls.extend(r_time_ls) df = pd.DataFrame(time_ls) diff --git a/benchmarks/cugraph-dgl/python-script/ogbn_mag_benchmark.py b/benchmarks/cugraph-dgl/python-script/ogbn_mag_benchmark.py index 7c8b352d7af..539fe333b1e 100644 --- a/benchmarks/cugraph-dgl/python-script/ogbn_mag_benchmark.py +++ b/benchmarks/cugraph-dgl/python-script/ogbn_mag_benchmark.py @@ -16,15 +16,17 @@ import torch.nn.functional as F import time import argparse + ## DGL Specific Import from cugraph_dgl import c + def load_dgl_graph(): from ogb.nodeproppred import DglNodePropPredDataset - dataset = DglNodePropPredDataset(name="ogbn-mag", root='/datasets/vjawa/gnn/') + dataset = DglNodePropPredDataset(name="ogbn-mag", root="/datasets/vjawa/gnn/") split_idx = dataset.get_idx_split() - g, labels = dataset[0] + g, labels = dataset[0] # Uncomment for obgn-mag labels = labels["paper"].flatten() # labels = labels @@ -33,26 +35,30 @@ def load_dgl_graph(): return g, labels, dataset.num_classes, split_idx -def sampling_func(g, seed_nodes,labels, train_loader): +def sampling_func(g, seed_nodes, labels, train_loader): category = "paper" for input_nodes, seeds, blocks in train_loader: seeds = seeds[category] - feat = blocks[0].srcdata['feat']['paper'] + feat = blocks[0].srcdata["feat"]["paper"] return None if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Benchmark Sampling') - parser.add_argument('--batch_size', type=int, default=100_000) - parser.add_argument('--use_cugraph',dest='use_cugraph', action='store_true', default=True) - parser.add_argument('--use_dgl_upstream', dest='use_cugraph', action='store_false') - parser.add_argument('--single_gpu', dest='single_gpu', action='store_true', default=True) - parser.add_argument('--multi_gpu', dest='single_gpu', action='store_false') - parser.add_argument('--n_gpus', type=int, default=1) - + parser = argparse.ArgumentParser(description="Benchmark Sampling") + parser.add_argument("--batch_size", type=int, default=100_000) + parser.add_argument( + "--use_cugraph", dest="use_cugraph", action="store_true", default=True + ) + parser.add_argument("--use_dgl_upstream", dest="use_cugraph", action="store_false") + parser.add_argument( + "--single_gpu", dest="single_gpu", action="store_true", default=True + ) + parser.add_argument("--multi_gpu", dest="single_gpu", action="store_false") + parser.add_argument("--n_gpus", type=int, default=1) + args = parser.parse_args() print(args, flush=True) - + single_gpu = args.single_gpu use_cugraph = args.use_cugraph batch_size = args.batch_size @@ -60,42 +66,49 @@ def sampling_func(g, seed_nodes,labels, train_loader): if single_gpu: import rmm - rmm.reinitialize(pool_allocator=True,initial_pool_size=5e+9, maximum_pool_size=22e+9) + + rmm.reinitialize( + pool_allocator=True, initial_pool_size=5e9, maximum_pool_size=22e9 + ) else: #### Dask Cluster from dask_cuda import LocalCUDACluster from cugraph.dask.comms import comms as Comms from dask.distributed import Client - #Change according to your GPUS - #Client at GPU-0 - #Workers at specifed GPUS - #UCX seems to be freezing :-0 on DGX - cuda_visible_devices = ','.join([str(i) for i in range(1,n_gpus+1)]) - cluster = LocalCUDACluster(CUDA_VISIBLE_DEVICES=cuda_visible_devices, protocol='tcp', rmm_pool_size='12 GB', - jit_unspill=True) + # Change according to your GPUS + # Client at GPU-0 + # Workers at specifed GPUS + # UCX seems to be freezing :-0 on DGX + cuda_visible_devices = ",".join([str(i) for i in range(1, n_gpus + 1)]) + cluster = LocalCUDACluster( + CUDA_VISIBLE_DEVICES=cuda_visible_devices, + protocol="tcp", + rmm_pool_size="12 GB", + jit_unspill=True, + ) client = Client(cluster) Comms.initialize(p2p=True) - - - device = 'cuda' + + device = "cuda" g, labels, num_classes, split_idx = load_dgl_graph() g = g.to(device) - + if use_cugraph: if not single_gpu: g = g.int() g = cugraph_storage_from_heterograph(g, single_gpu=single_gpu) - - - indx_type = g.idtype - subset_split_idx = {'train': {k: v.to(device).to(g.idtype) for k,v in split_idx['train'].items()}, - 'valid' : {k: v.to(device).to(g.idtype) for k,v in split_idx['valid'].items()}, - 'test' : {k: v.to(device).to(g.idtype) for k,v in split_idx['test'].items()}, - } + indx_type = g.idtype + subset_split_idx = { + "train": {k: v.to(device).to(g.idtype) for k, v in split_idx["train"].items()}, + "valid": {k: v.to(device).to(g.idtype) for k, v in split_idx["valid"].items()}, + "test": {k: v.to(device).to(g.idtype) for k, v in split_idx["test"].items()}, + } - sampler = dgl.dataloading.MultiLayerNeighborSampler([20,25], prefetch_node_feats={'paper':['feat']}) + sampler = dgl.dataloading.MultiLayerNeighborSampler( + [20, 25], prefetch_node_feats={"paper": ["feat"]} + ) train_loader = dgl.dataloading.DataLoader( g, subset_split_idx["train"], @@ -107,10 +120,10 @@ def sampling_func(g, seed_nodes,labels, train_loader): ) ### Warmup RUN - sampling_func(g, subset_split_idx['train'],labels, train_loader) - + sampling_func(g, subset_split_idx["train"], labels, train_loader) + ### Benchmarking RUN st = time.time() - sampling_func(g, subset_split_idx['train'],labels, train_loader) - et = time.time() - print(f"Sampling time taken = {et-st} s") \ No newline at end of file + sampling_func(g, subset_split_idx["train"], labels, train_loader) + et = time.time() + print(f"Sampling time taken = {et-st} s") diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py b/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py index 3762226d570..3e0b8e3e0cc 100644 --- a/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py +++ b/benchmarks/cugraph-dgl/scale-benchmarks/dgl_benchmark.py @@ -22,6 +22,7 @@ from argparse import ArgumentParser from load_graph_feats import load_edges_from_disk, load_node_labels, load_node_features + class DataLoaderArgs: def __init__(self, args): self.dataset_path = args.dataset_path @@ -31,7 +32,6 @@ def __init__(self, args): self.use_uva = not args.do_not_use_uva - def create_dataloader(g, train_idx, batch_size, fanouts, use_uva): print("Creating dataloader", flush=True) st = time.time() @@ -53,7 +53,6 @@ def create_dataloader(g, train_idx, batch_size, fanouts, use_uva): return dataloader - def create_dgl_graph_from_disk(dataset_path, replication_factor=1): """ Create a DGL graph from a dataset on disk. @@ -67,14 +66,14 @@ def create_dgl_graph_from_disk(dataset_path, replication_factor=1): input_meta = json.load(f) parquet_path = os.path.join(dataset_path, "parquet") - graph_data = load_edges_from_disk( - parquet_path, replication_factor, input_meta - ) + graph_data = load_edges_from_disk(parquet_path, replication_factor, input_meta) label_data = load_node_labels(dataset_path, replication_factor, input_meta) - if replication_factor <8 : - feat_data = load_node_features(dataset_path, replication_factor, node_type='paper') + if replication_factor < 8: + feat_data = load_node_features( + dataset_path, replication_factor, node_type="paper" + ) else: - feat_data = None + feat_data = None print("labels and features loaded ", flush=True) g = dgl.heterograph(graph_data) @@ -83,31 +82,49 @@ def create_dgl_graph_from_disk(dataset_path, replication_factor=1): def main(args): - print(f"Running dgl dataloading benchmark with the following parameters:\n" - f"Dataset path = {args.dataset_path}\n" - f"Replication factors = {args.replication_factors}\n" - f"Fanouts = {args.fanouts}\n" - f"Batch sizes = {args.batch_sizes}\n" - f"Use UVA = {args.use_uva}\n" - f"{'=' * 30}") + print( + f"Running dgl dataloading benchmark with the following parameters:\n" + f"Dataset path = {args.dataset_path}\n" + f"Replication factors = {args.replication_factors}\n" + f"Fanouts = {args.fanouts}\n" + f"Batch sizes = {args.batch_sizes}\n" + f"Use UVA = {args.use_uva}\n" + f"{'=' * 30}" + ) time_ls = [] for replication_factor in args.replication_factors: start_time = time.time() - g, label_data, feat_data = create_dgl_graph_from_disk(args.dataset_path, replication_factor) + g, label_data, feat_data = create_dgl_graph_from_disk( + args.dataset_path, replication_factor + ) elapsed_time = time.time() - start_time - print(f"Replication factor = {replication_factor}\n" - f"G has {g.num_edges():,} edges and took {elapsed_time:.2f} seconds to load", flush=True) + print( + f"Replication factor = {replication_factor}\n" + f"G has {g.num_edges():,} edges and took {elapsed_time:.2f} seconds to load", + flush=True, + ) train_idx = {"paper": label_data["paper"]["train_idx"]} y = label_data["paper"]["y"] - r_time_ls = e2e_benchmark(g, feat_data, y, train_idx, args.fanouts, args.batch_sizes, use_uva=args.use_uva) + r_time_ls = e2e_benchmark( + g, + feat_data, + y, + train_idx, + args.fanouts, + args.batch_sizes, + use_uva=args.use_uva, + ) [x.update({"replication_factor": replication_factor}) for x in r_time_ls] - [x.update({"num_edges": g.num_edges()}) for x in r_time_ls] + [x.update({"num_edges": g.num_edges()}) for x in r_time_ls] time_ls.extend(r_time_ls) - print(f"Benchmark completed for replication factor = {replication_factor}\n{'=' * 30}", flush=True) + print( + f"Benchmark completed for replication factor = {replication_factor}\n{'=' * 30}", + flush=True, + ) df = pd.DataFrame(time_ls) df.to_csv("dgl_e2e_benchmark.csv", index=False) @@ -131,22 +148,26 @@ def e2e_benchmark(g, feat, y, train_idx, fanouts, batch_sizes, use_uva): for fanout in fanouts: for batch_size in batch_sizes: dataloader = create_dataloader(g, train_idx, batch_size, fanout, use_uva) - time_d = run_1_epoch(dataloader, feat, y, fanout, batch_size, model_backend='dgl') + time_d = run_1_epoch( + dataloader, feat, y, fanout, batch_size, model_backend="dgl" + ) time_ls.append(time_d) - print("="*30) + print("=" * 30) return time_ls - def parse_arguments(): parser = ArgumentParser() - parser.add_argument("--dataset_path", type=str, default="/raid/vjawa/ogbn_papers100M") + parser.add_argument( + "--dataset_path", type=str, default="/raid/vjawa/ogbn_papers100M" + ) parser.add_argument("--replication_factors", type=str, default="2") parser.add_argument("--fanouts", type=str, default="10_10_10") parser.add_argument("--batch_sizes", type=str, default="512,1024,8192,16384") parser.add_argument("--do_not_use_uva", action="store_true") return parser.parse_args() + if __name__ == "__main__": arguments = parse_arguments() main(DataLoaderArgs(arguments)) diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py b/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py index 4f0f81c70e1..f38d4432d46 100644 --- a/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py +++ b/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py @@ -30,17 +30,23 @@ def load_edges_from_disk(parquet_path, replication_factor, input_meta): graph_data = {} for edge_type in input_meta["num_edges"].keys(): - print(f"Loading edge index for edge type {edge_type} for replication factor = {replication_factor}") + print( + f"Loading edge index for edge type {edge_type} for replication factor = {replication_factor}" + ) canonical_edge_type = tuple(edge_type.split("__")) - edge_index = pd.read_parquet(os.path.join(parquet_path, edge_type, "edge_index.parquet")) + edge_index = pd.read_parquet( + os.path.join(parquet_path, edge_type, "edge_index.parquet") + ) edge_index = { "src": torch.from_numpy(edge_index.src.values), "dst": torch.from_numpy(edge_index.dst.values), } if replication_factor > 1: - src_list, dst_list = replicate_edges(edge_index, canonical_edge_type, replication_factor, input_meta) + src_list, dst_list = replicate_edges( + edge_index, canonical_edge_type, replication_factor, input_meta + ) edge_index["src"] = torch.cat(src_list).contiguous() edge_index["dst"] = torch.cat(dst_list).contiguous() @@ -55,26 +61,35 @@ def replicate_edges(edge_index, canonical_edge_type, replication_factor, input_m dst_list = [edge_index["dst"]] for r in range(1, replication_factor): - new_src = edge_index["src"] + (r * input_meta["num_nodes"][canonical_edge_type[0]]) - new_dst = edge_index["dst"] + (r * input_meta["num_nodes"][canonical_edge_type[2]]) + new_src = edge_index["src"] + ( + r * input_meta["num_nodes"][canonical_edge_type[0]] + ) + new_dst = edge_index["dst"] + ( + r * input_meta["num_nodes"][canonical_edge_type[2]] + ) src_list.append(new_src) dst_list.append(new_dst) return src_list, dst_list - - def load_node_labels(dataset_path, replication_factor, input_meta): - num_nodes_dict = {node_type: t * replication_factor for node_type, t in input_meta["num_nodes"].items()} + num_nodes_dict = { + node_type: t * replication_factor + for node_type, t in input_meta["num_nodes"].items() + } node_data = {} for node_type in input_meta["num_nodes"].keys(): node_data[node_type] = {} - label_path = os.path.join(dataset_path, "parquet", node_type, "node_label.parquet") + label_path = os.path.join( + dataset_path, "parquet", node_type, "node_label.parquet" + ) if os.path.exists(label_path): - node_data[node_type] = process_node_label(label_path, node_type, replication_factor, num_nodes_dict, input_meta) + node_data[node_type] = process_node_label( + label_path, node_type, replication_factor, num_nodes_dict, input_meta + ) else: node_data[node_type]["num_nodes"] = num_nodes_dict[node_type] @@ -82,30 +97,48 @@ def load_node_labels(dataset_path, replication_factor, input_meta): print("Loaded node labels", flush=True) return node_data -def process_node_label(label_path, node_type, replication_factor, num_nodes_dict, input_meta): + +def process_node_label( + label_path, node_type, replication_factor, num_nodes_dict, input_meta +): node_label = pd.read_parquet(label_path) if replication_factor > 1: - node_label = replicate_node_label(node_label, node_type, replication_factor, input_meta) + node_label = replicate_node_label( + node_label, node_type, replication_factor, input_meta + ) - node_label_tensor = torch.full((num_nodes_dict[node_type],), -1, dtype=torch.float32) - node_label_tensor[torch.as_tensor(node_label.node.values)] = torch.as_tensor(node_label.label.values) + node_label_tensor = torch.full( + (num_nodes_dict[node_type],), -1, dtype=torch.float32 + ) + node_label_tensor[torch.as_tensor(node_label.node.values)] = torch.as_tensor( + node_label.label.values + ) del node_label return { "train_idx": (node_label_tensor > -1).contiguous().nonzero().view(-1), - "y": node_label_tensor.contiguous().long() + "y": node_label_tensor.contiguous().long(), } def replicate_node_label(node_label, node_type, replication_factor, input_meta): base_num_nodes = input_meta["num_nodes"][node_type] - replicated_df = pd.DataFrame({ - "node": pd.concat([node_label.node + (r * base_num_nodes) for r in range(1, replication_factor)]), - "label": pd.concat([node_label.label for _ in range(1, replication_factor)]) - }) + replicated_df = pd.DataFrame( + { + "node": pd.concat( + [ + node_label.node + (r * base_num_nodes) + for r in range(1, replication_factor) + ] + ), + "label": pd.concat( + [node_label.label for _ in range(1, replication_factor)] + ), + } + ) return pd.concat([node_label, replicated_df]).reset_index(drop=True) @@ -114,10 +147,10 @@ def load_node_features(dataset_path, replication_factor, node_type): print("Loading node features", flush=True) node_type_path = os.path.join(dataset_path, "npy", node_type) if replication_factor == 1: - fname = os.path.join(node_type_path, "node_feat.npy") + fname = os.path.join(node_type_path, "node_feat.npy") else: fname = os.path.join(node_type_path, f"node_feat_{replication_factor}x.npy") - + feat = torch.from_numpy(np.load(fname)) print("Loaded node features", flush=True) return feat diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/model.py b/benchmarks/cugraph-dgl/scale-benchmarks/model.py index 506e3bd5227..08ae0e8b1ee 100644 --- a/benchmarks/cugraph-dgl/scale-benchmarks/model.py +++ b/benchmarks/cugraph-dgl/scale-benchmarks/model.py @@ -17,8 +17,15 @@ class GNN(torch.nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, num_layers, model_backend='dgl'): - if model_backend == 'dgl': + def __init__( + self, + in_channels, + hidden_channels, + out_channels, + num_layers, + model_backend="dgl", + ): + if model_backend == "dgl": from dgl.nn import SAGEConv else: from cugraph_dgl.nn import SAGEConv @@ -26,9 +33,13 @@ def __init__(self, in_channels, hidden_channels, out_channels, num_layers, model super(GNN, self).__init__() self.convs = torch.nn.ModuleList() for _ in range(num_layers - 1): - self.convs.append(SAGEConv(in_channels, hidden_channels, aggregator_type='mean')) + self.convs.append( + SAGEConv(in_channels, hidden_channels, aggregator_type="mean") + ) in_channels = hidden_channels - self.convs.append(SAGEConv(hidden_channels, out_channels, aggregator_type='mean')) + self.convs.append( + SAGEConv(hidden_channels, out_channels, aggregator_type="mean") + ) def forward(self, blocks, x): for i, conv in enumerate(self.convs): @@ -38,45 +49,47 @@ def forward(self, blocks, x): return x -def create_model(feat_size, num_classes, num_layers, model_backend='dgl'): +def create_model(feat_size, num_classes, num_layers, model_backend="dgl"): model = GNN(feat_size, 64, num_classes, num_layers, model_backend=model_backend) - model = model.to('cuda') + model = model.to("cuda") model.train() return model + def train_model(model, dataloader, opt, feat, y): - times = {key: 0 for key in ['mfg_creation', 'feature', 'm_fwd', 'm_bkwd']} + times = {key: 0 for key in ["mfg_creation", "feature", "m_fwd", "m_bkwd"]} epoch_st = time.time() mfg_st = time.time() for input_nodes, output_nodes, blocks in dataloader: - times['mfg_creation'] += time.time() - mfg_st + times["mfg_creation"] += time.time() - mfg_st if feat is not None: fst = time.time() - input_nodes = input_nodes.to('cpu') + input_nodes = input_nodes.to("cpu") input_feat = feat[input_nodes] - input_feat = input_feat.to('cuda') + input_feat = input_feat.to("cuda") if isinstance(output_nodes, dict): - output_nodes = output_nodes['paper'] + output_nodes = output_nodes["paper"] output_nodes = output_nodes.to(y.device) - y_batch = y[output_nodes].to('cuda') - times['feature'] += time.time() - fst + y_batch = y[output_nodes].to("cuda") + times["feature"] += time.time() - fst m_fwd_st = time.time() y_hat = model(blocks, input_feat) - times['m_fwd'] += time.time() - m_fwd_st - + times["m_fwd"] += time.time() - m_fwd_st + m_bkwd_st = time.time() loss = F.cross_entropy(y_hat, y_batch) opt.zero_grad() loss.backward() opt.step() - times['m_bkwd'] += time.time() - m_bkwd_st + times["m_bkwd"] += time.time() - m_bkwd_st mfg_st = time.time() print(f"Epoch time = {time.time() - epoch_st:.2f} seconds") - + return times + def analyze_time(dataloader, times, epoch_time, fanout, batch_size): num_batches = len(dataloader) time_d = { @@ -92,13 +105,16 @@ def analyze_time(dataloader, times, epoch_time, fanout, batch_size): print(f"Time analysis for fanout = {fanout}, batch_size = {batch_size}") for k in time_d.keys(): - if 'time_per_epoch' in str(k): + if "time_per_epoch" in str(k): print(f"{k} = {time_d[k]:.2f} seconds") return time_d + def run_1_epoch(dataloader, feat, y, fanout, batch_size, model_backend): if feat is not None: - model = create_model(feat.shape[1], 172, len(fanout), model_backend=model_backend) + model = create_model( + feat.shape[1], 172, len(fanout), model_backend=model_backend + ) opt = torch.optim.Adam(model.parameters(), lr=0.01) else: model = None diff --git a/benchmarks/cugraph-service/pytest-based/bench_cgs_client_scaling.py b/benchmarks/cugraph-service/pytest-based/bench_cgs_client_scaling.py index 9871ce64a31..541b3e23490 100644 --- a/benchmarks/cugraph-service/pytest-based/bench_cgs_client_scaling.py +++ b/benchmarks/cugraph-service/pytest-based/bench_cgs_client_scaling.py @@ -46,11 +46,8 @@ def running_server_for_sampling_with_graph(request): control returns to this fixture, the graph is deleted. If the fixture started a server subprocess, then it is terminated as well. """ - (client, server_process) = ( - utils.ensure_running_server_for_sampling(host=_host, - port=_port, - dask_scheduler_file=None, - start_local_cuda_cluster=True) + (client, server_process) = utils.ensure_running_server_for_sampling( + host=_host, port=_port, dask_scheduler_file=None, start_local_cuda_cluster=True ) num_edges = (2**_graph_scale) * _edge_factor gid = client.call_graph_creation_extension( @@ -80,9 +77,7 @@ def create_sampling_client(host, port, graph_id): """ client = CugraphServiceClient(host, port) fanout_vals = [10, 25] - start_list = client.call_extension( - "gen_vertex_list", graph_id, _batch_size - ) + start_list = client.call_extension("gen_vertex_list", graph_id, _batch_size) def sampling_function(result_device): return client.uniform_neighbor_sample( @@ -108,9 +103,11 @@ def sampling_function(result_device): # Use the benchmark fixture once it can be run in a way where each run is timed # with other running clients. + @pytest.mark.parametrize("num_clients", params.num_clients.values()) -def bench_cgs_client_scaling_individual_time(running_server_for_sampling_with_graph, - num_clients): +def bench_cgs_client_scaling_individual_time( + running_server_for_sampling_with_graph, num_clients +): graph_id = running_server_for_sampling_with_graph @@ -133,8 +130,9 @@ def bench_cgs_client_scaling_individual_time(running_server_for_sampling_with_gr @pytest.mark.parametrize("num_clients", params.num_clients.values()) -def bench_cgs_client_scaling_total_time(running_server_for_sampling_with_graph, - num_clients): +def bench_cgs_client_scaling_total_time( + running_server_for_sampling_with_graph, num_clients +): graph_id = running_server_for_sampling_with_graph diff --git a/benchmarks/cugraph/pytest-based/bench_algos.py b/benchmarks/cugraph/pytest-based/bench_algos.py index d7fcb7812e4..69d6700ca3d 100644 --- a/benchmarks/cugraph/pytest-based/bench_algos.py +++ b/benchmarks/cugraph/pytest-based/bench_algos.py @@ -14,13 +14,16 @@ import pytest import numpy as np import pytest_benchmark + # FIXME: Remove this when rapids_pytest_benchmark.gpubenchmark is available # everywhere try: from rapids_pytest_benchmark import setFixtureParamNames except ImportError: - print("\n\nWARNING: rapids_pytest_benchmark is not installed, " - "falling back to pytest_benchmark fixtures.\n") + print( + "\n\nWARNING: rapids_pytest_benchmark is not installed, " + "falling back to pytest_benchmark fixtures.\n" + ) # if rapids_pytest_benchmark is not available, just perfrom time-only # benchmarking and replace the util functions with nops @@ -29,6 +32,7 @@ def setFixtureParamNames(*args, **kwargs): pass + import rmm import dask_cudf from pylibcugraph.testing import gen_fixture_params_product @@ -65,7 +69,7 @@ def get_edgelist(self, fetch=False): if self._edgelist is None: self._edgelist = rmat( self._scale, - (2**self._scale)*self._edgefactor, + (2**self._scale) * self._edgefactor, 0.57, # from Graph500 0.19, # from Graph500 0.19, # from Graph500 @@ -73,22 +77,25 @@ def get_edgelist(self, fetch=False): clip_and_flip=False, scramble_vertex_ids=True, create_using=None, # return edgelist instead of Graph instance - mg=self.mg + mg=self.mg, ) rng = np.random.default_rng(seed) if self.mg: self._edgelist["weight"] = self._edgelist.map_partitions( - lambda df: rng.random(size=len(df))) + lambda df: rng.random(size=len(df)) + ) else: self._edgelist["weight"] = rng.random(size=len(self._edgelist)) return self._edgelist - def get_graph(self, - fetch=False, - create_using=cugraph.Graph, - ignore_weights=False, - store_transposed=False): + def get_graph( + self, + fetch=False, + create_using=cugraph.Graph, + ignore_weights=False, + store_transposed=False, + ): if isinstance(create_using, cugraph.Graph): # what about BFS if trnaposed is True attrs = {"directed": create_using.is_directed()} @@ -99,17 +106,21 @@ def get_graph(self, edge_attr = None if ignore_weights else "weight" df = self.get_edgelist() if isinstance(df, dask_cudf.DataFrame): - G.from_dask_cudf_edgelist(df, - source="src", - destination="dst", - edge_attr=edge_attr, - store_transposed=store_transposed) + G.from_dask_cudf_edgelist( + df, + source="src", + destination="dst", + edge_attr=edge_attr, + store_transposed=store_transposed, + ) else: - G.from_cudf_edgelist(df, - source="src", - destination="dst", - edge_attr=edge_attr, - store_transposed=store_transposed) + G.from_cudf_edgelist( + df, + source="src", + destination="dst", + edge_attr=edge_attr, + store_transposed=store_transposed, + ) return G def get_path(self): @@ -125,26 +136,27 @@ def unload(self): _rmat_scale = getattr(pytest, "_rmat_scale", 20) # ~1M vertices _rmat_edgefactor = getattr(pytest, "_rmat_edgefactor", 16) # ~17M edges -rmat_sg_dataset = pytest.param(RmatDataset(scale=_rmat_scale, - edgefactor=_rmat_edgefactor, - mg=False), - marks=[pytest.mark.rmat_data, - pytest.mark.sg, - ]) -rmat_mg_dataset = pytest.param(RmatDataset(scale=_rmat_scale, - edgefactor=_rmat_edgefactor, - mg=True), - marks=[pytest.mark.rmat_data, - pytest.mark.mg, - ]) +rmat_sg_dataset = pytest.param( + RmatDataset(scale=_rmat_scale, edgefactor=_rmat_edgefactor, mg=False), + marks=[ + pytest.mark.rmat_data, + pytest.mark.sg, + ], +) +rmat_mg_dataset = pytest.param( + RmatDataset(scale=_rmat_scale, edgefactor=_rmat_edgefactor, mg=True), + marks=[ + pytest.mark.rmat_data, + pytest.mark.mg, + ], +) rmm_fixture_params = gen_fixture_params_product( - (managed_memory, "mm"), - (pool_allocator, "pa")) + (managed_memory, "mm"), (pool_allocator, "pa") +) dataset_fixture_params = gen_fixture_params_product( - (directed_datasets + - undirected_datasets + - [rmat_sg_dataset, rmat_mg_dataset], "ds")) + (directed_datasets + undirected_datasets + [rmat_sg_dataset, rmat_mg_dataset], "ds") +) # Record the current RMM settings so reinitialize() will be called only when a # change is needed (RMM defaults both values to False). The --allow-rmm-reinit @@ -153,8 +165,7 @@ def unload(self): # (see conftest.py for details). # The defaults for managed_mem (False) and pool_alloc (True) are set in # conftest.py -RMM_SETTINGS = {"managed_mem": False, - "pool_alloc": False} +RMM_SETTINGS = {"managed_mem": False, "pool_alloc": False} # FIXME: this only changes the RMM config in a SG environment. The dask config # that applies to RMM in an MG environment is not changed by this! @@ -163,16 +174,16 @@ def reinitRMM(managed_mem, pool_alloc): Reinitializes RMM to the value of managed_mem and pool_alloc, but only if those values are different that the current configuration. """ - if (managed_mem != RMM_SETTINGS["managed_mem"]) or \ - (pool_alloc != RMM_SETTINGS["pool_alloc"]): + if (managed_mem != RMM_SETTINGS["managed_mem"]) or ( + pool_alloc != RMM_SETTINGS["pool_alloc"] + ): rmm.reinitialize( managed_memory=managed_mem, pool_allocator=pool_alloc, - initial_pool_size=2 << 27 + initial_pool_size=2 << 27, ) - RMM_SETTINGS.update(managed_mem=managed_mem, - pool_alloc=pool_alloc) + RMM_SETTINGS.update(managed_mem=managed_mem, pool_alloc=pool_alloc) ############################################################################### @@ -185,8 +196,8 @@ def reinitRMM(managed_mem, pool_alloc): # For benchmarks, the operations performed in fixtures are not measured as part # of the benchmark. -@pytest.fixture(scope="module", - params=rmm_fixture_params) + +@pytest.fixture(scope="module", params=rmm_fixture_params) def rmm_config(request): # Since parameterized fixtures do not assign param names to param values, # manually call the helper to do so. Ensure the order of the name list @@ -196,8 +207,7 @@ def rmm_config(request): reinitRMM(request.param[0], request.param[1]) -@pytest.fixture(scope="module", - params=dataset_fixture_params) +@pytest.fixture(scope="module", params=dataset_fixture_params) def dataset(request, rmm_config): """ @@ -261,27 +271,29 @@ def is_graph_distributed(graph): ############################################################################### # Benchmarks def bench_create_graph(gpubenchmark, edgelist): - gpubenchmark(cugraph.from_cudf_edgelist, - edgelist, - source="src", destination="dst", - create_using=cugraph.structure.graph_classes.Graph, - renumber=False) + gpubenchmark( + cugraph.from_cudf_edgelist, + edgelist, + source="src", + destination="dst", + create_using=cugraph.structure.graph_classes.Graph, + renumber=False, + ) # Creating directed Graphs on small datasets runs in micro-seconds, which # results in thousands of rounds before the default threshold is met, so lower # the max_time for this benchmark. -@pytest.mark.benchmark( - warmup=True, - warmup_iterations=10, - max_time=0.005 -) +@pytest.mark.benchmark(warmup=True, warmup_iterations=10, max_time=0.005) def bench_create_digraph(gpubenchmark, edgelist): - gpubenchmark(cugraph.from_cudf_edgelist, - edgelist, - source="src", destination="dst", - create_using=cugraph.Graph(directed=True), - renumber=False) + gpubenchmark( + cugraph.from_cudf_edgelist, + edgelist, + source="src", + destination="dst", + create_using=cugraph.Graph(directed=True), + renumber=False, + ) def bench_renumber(gpubenchmark, edgelist): @@ -289,8 +301,11 @@ def bench_renumber(gpubenchmark, edgelist): def bench_pagerank(gpubenchmark, transposed_graph): - pagerank = dask_cugraph.pagerank if is_graph_distributed(transposed_graph) \ - else cugraph.pagerank + pagerank = ( + dask_cugraph.pagerank + if is_graph_distributed(transposed_graph) + else cugraph.pagerank + ) gpubenchmark(pagerank, transposed_graph) @@ -319,7 +334,8 @@ def bench_jaccard(gpubenchmark, unweighted_graph): @pytest.mark.skipif( - is_device_version_less_than((7, 0)), reason="Not supported on Pascal") + is_device_version_less_than((7, 0)), reason="Not supported on Pascal" +) def bench_louvain(gpubenchmark, graph): louvain = dask_cugraph.louvain if is_graph_distributed(graph) else cugraph.louvain gpubenchmark(louvain, graph) @@ -342,8 +358,11 @@ def bench_overlap(gpubenchmark, unweighted_graph): def bench_triangle_count(gpubenchmark, graph): - tc = dask_cugraph.triangle_count if is_graph_distributed(graph) \ - else cugraph.triangle_count + tc = ( + dask_cugraph.triangle_count + if is_graph_distributed(graph) + else cugraph.triangle_count + ) gpubenchmark(tc, graph) @@ -353,12 +372,13 @@ def bench_spectralBalancedCutClustering(gpubenchmark, graph): gpubenchmark(cugraph.spectralBalancedCutClustering, graph, 2) -@pytest.mark.skip(reason="Need to guarantee graph has weights, " - "not doing that yet") +@pytest.mark.skip(reason="Need to guarantee graph has weights, " "not doing that yet") def bench_spectralModularityMaximizationClustering(gpubenchmark, graph): - smmc = dask_cugraph.spectralModularityMaximizationClustering \ - if is_graph_distributed(graph) \ - else cugraph.spectralModularityMaximizationClustering + smmc = ( + dask_cugraph.spectralModularityMaximizationClustering + if is_graph_distributed(graph) + else cugraph.spectralModularityMaximizationClustering + ) gpubenchmark(smmc, graph, 2) @@ -373,8 +393,11 @@ def bench_graph_degrees(gpubenchmark, graph): def bench_betweenness_centrality(gpubenchmark, graph): - bc = dask_cugraph.betweenness_centrality if is_graph_distributed(graph) \ - else cugraph.betweenness_centrality + bc = ( + dask_cugraph.betweenness_centrality + if is_graph_distributed(graph) + else cugraph.betweenness_centrality + ) gpubenchmark(bc, graph, k=10, random_state=123) @@ -385,8 +408,11 @@ def bench_edge_betweenness_centrality(gpubenchmark, graph): def bench_uniform_neighbor_sample(gpubenchmark, graph): - uns = dask_cugraph.uniform_neighbor_sample if is_graph_distributed(graph) \ - else cugraph.uniform_neighbor_sample + uns = ( + dask_cugraph.uniform_neighbor_sample + if is_graph_distributed(graph) + else cugraph.uniform_neighbor_sample + ) seed = 42 # FIXME: may need to provide number_of_vertices separately @@ -405,8 +431,9 @@ def bench_uniform_neighbor_sample(gpubenchmark, graph): def bench_egonet(gpubenchmark, graph): - egonet = dask_cugraph.ego_graph if is_graph_distributed(graph) \ - else cugraph.ego_graph + egonet = ( + dask_cugraph.ego_graph if is_graph_distributed(graph) else cugraph.ego_graph + ) n = 1 radius = 2 gpubenchmark(egonet, graph, n, radius=radius) diff --git a/benchmarks/cugraph/pytest-based/conftest.py b/benchmarks/cugraph/pytest-based/conftest.py index fd029471869..57329366a13 100644 --- a/benchmarks/cugraph/pytest-based/conftest.py +++ b/benchmarks/cugraph/pytest-based/conftest.py @@ -15,31 +15,37 @@ def pytest_addoption(parser): - parser.addoption("--allow-rmm-reinit", - action="store_true", - default=False, - help="Allow RMM to be reinitialized, possibly multiple times within " - "the same process, in order to run benchmarks with different managed " - "memory and pool allocator options. This is not the default behavior " - "since it does not represent a typical use case, and support for " - "this may be limited. Instead, consider multiple pytest runs that " - "use a fixed set of RMM settings.") - parser.addoption("--rmat-scale", - action="store", - type=int, - default=20, - metavar="scale", - help="For use when using synthetic graph data generated using RMAT. " - "This results in a graph with 2^scale vertices. Default is " - "%(default)s.") - parser.addoption("--rmat-edgefactor", - action="store", - type=int, - default=16, - metavar="edgefactor", - help="For use when using synthetic graph data generated using RMAT. " - "This results in a graph with (2^scale)*edgefactor edges. Default " - "is %(default)s.") + parser.addoption( + "--allow-rmm-reinit", + action="store_true", + default=False, + help="Allow RMM to be reinitialized, possibly multiple times within " + "the same process, in order to run benchmarks with different managed " + "memory and pool allocator options. This is not the default behavior " + "since it does not represent a typical use case, and support for " + "this may be limited. Instead, consider multiple pytest runs that " + "use a fixed set of RMM settings.", + ) + parser.addoption( + "--rmat-scale", + action="store", + type=int, + default=20, + metavar="scale", + help="For use when using synthetic graph data generated using RMAT. " + "This results in a graph with 2^scale vertices. Default is " + "%(default)s.", + ) + parser.addoption( + "--rmat-edgefactor", + action="store", + type=int, + default=16, + metavar="edgefactor", + help="For use when using synthetic graph data generated using RMAT. " + "This results in a graph with (2^scale)*edgefactor edges. Default " + "is %(default)s.", + ) def pytest_sessionstart(session): @@ -57,10 +63,11 @@ def pytest_sessionstart(session): if session.config.getoption("allow_rmm_reinit") is False: currentMarkexpr = session.config.getoption("markexpr") - if ("managedmem" in currentMarkexpr) or \ - ("poolallocator" in currentMarkexpr): - raise RuntimeError("managedmem and poolallocator markers cannot " - "be used without --allow-rmm-reinit.") + if ("managedmem" in currentMarkexpr) or ("poolallocator" in currentMarkexpr): + raise RuntimeError( + "managedmem and poolallocator markers cannot " + "be used without --allow-rmm-reinit." + ) newMarkexpr = "managedmem_off and poolallocator_on" if currentMarkexpr: diff --git a/benchmarks/cugraph/standalone/benchmark.py b/benchmarks/cugraph/standalone/benchmark.py index e02194648a6..74b73d9d8ff 100644 --- a/benchmarks/cugraph/standalone/benchmark.py +++ b/benchmarks/cugraph/standalone/benchmark.py @@ -21,6 +21,7 @@ class BenchmarkedResult: Class to hold results (the return value of the callable being benchmarked and meta-data about the benchmarked function) of a benchmarked function run. """ + def __init__(self, name, retval, runtime, params=None): self.name = name self.retval = retval @@ -45,16 +46,18 @@ def benchmark(func): functions to benchmark. """ benchmark_name = getattr(func, "benchmark_name", func.__name__) + @wraps(func) def benchmark_wrapper(*func_args, **func_kwargs): t1 = time.perf_counter() retval = func(*func_args, **func_kwargs) t2 = time.perf_counter() - return BenchmarkedResult(name=benchmark_name, - retval=retval, - runtime=(t2-t1), - params=func_kwargs, - ) + return BenchmarkedResult( + name=benchmark_name, + retval=retval, + runtime=(t2 - t1), + params=func_kwargs, + ) # Assign the name to the returned callable as well for use in debug prints, # etc. @@ -68,17 +71,21 @@ class BenchmarkRun: method, and results are saved as BenchmarkedResult instances in the results list member. """ - def __init__(self, - input_dataframe, - construct_graph_func, - algo_func_param_list, - algo_validator_list=None - ): + + def __init__( + self, + input_dataframe, + construct_graph_func, + algo_func_param_list, + algo_validator_list=None, + ): self.input_dataframe = input_dataframe if type(construct_graph_func) is tuple: - (construct_graph_func, - self.construct_graph_func_args) = construct_graph_func + ( + construct_graph_func, + self.construct_graph_func_args, + ) = construct_graph_func else: self.construct_graph_func_args = None @@ -89,15 +96,15 @@ def __init__(self, # add starting node to algos: BFS and SSSP # FIXME: Refactor BenchmarkRun __init__ because all the work # done below should be done elsewhere - for i, algo in enumerate (algo_func_param_list): + for i, algo in enumerate(algo_func_param_list): if benchmark(algo).name in ["bfs", "sssp", "uniform_neighbor_sample"]: - param={} - param["start"]=self.input_dataframe['src'].head()[0] + param = {} + param["start"] = self.input_dataframe["src"].head()[0] if benchmark(algo).name in ["uniform_neighbor_sample"]: start = [param.pop("start")] param["start_list"] = start param["fanout_vals"] = [1] - algo_func_param_list[i]=(algo,)+(param,) + algo_func_param_list[i] = (algo,) + (param,) self.algos = [] for item in algo_func_param_list: @@ -110,13 +117,11 @@ def __init__(self, self.validators = algo_validator_list or [None] * len(self.algos) self.results = [] - @staticmethod def __log(s, end="\n"): print(s, end=end) sys.stdout.flush() - def run(self): """ Run and time the graph construction step, then run and time each algo. @@ -124,8 +129,9 @@ def run(self): self.results = [] self.__log(f"running {self.construct_graph.name}...", end="") - result = self.construct_graph(self.input_dataframe, - *self.construct_graph_func_args) + result = self.construct_graph( + self.input_dataframe, *self.construct_graph_func_args + ) self.__log("done.") G = result.retval self.results.append(result) @@ -141,22 +147,21 @@ def run(self): if self.construct_graph.name == "from_dask_cudf_edgelist": # compute out_degree before renumbering because out_degree # has transpose=False - degree_max = G.degree()['degree'].max().compute() + degree_max = G.degree()["degree"].max().compute() katz_alpha = 1 / (degree_max) self.algos[i][1]["alpha"] = katz_alpha elif self.construct_graph.name == "from_cudf_edgelist": - degree_max = G.degree()['degree'].max() + degree_max = G.degree()["degree"].max() katz_alpha = 1 / (degree_max) self.algos[i][1]["alpha"] = katz_alpha if hasattr(G, "compute_renumber_edge_list"): - G.compute_renumber_edge_list( - transposed=True) + G.compute_renumber_edge_list(transposed=True) else: # FIXME: Pagerank still follows the old path. Update this once it # follows the pylibcugraph/C path if hasattr(G, "compute_renumber_edge_list"): G.compute_renumber_edge_list(transposed=True) - else: #set transpose=False when renumbering + else: # set transpose=False when renumbering self.__log("running compute_renumber_edge_list...", end="") if hasattr(G, "compute_renumber_edge_list"): if self.algos[i][0].name in ["wcc", "louvain"]: @@ -164,8 +169,7 @@ def run(self): # Update this once it follows the pylibcugraph/C path G.compute_renumber_edge_list(transposed=False) else: - G.compute_renumber_edge_list( - transposed=False) + G.compute_renumber_edge_list(transposed=False) self.__log("done.") # FIXME: need to handle individual algo args for ((algo, params), validator) in zip(self.algos, self.validators): diff --git a/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py index d2a3716da8a..a8c0658767d 100644 --- a/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py @@ -66,19 +66,19 @@ def construct_graph(dask_dataframe): Returns: G: cugraph.Graph """ - assert dask_dataframe['src'].dtype == 'int64' - assert dask_dataframe['dst'].dtype == 'int64' + assert dask_dataframe["src"].dtype == "int64" + assert dask_dataframe["dst"].dtype == "int64" - if 'etp' in dask_dataframe.columns: - assert dask_dataframe['etp'].dtype == 'int32' + if "etp" in dask_dataframe.columns: + assert dask_dataframe["etp"].dtype == "int32" G = cugraph.MultiGraph(directed=True) G.from_dask_cudf_edgelist( dask_dataframe, - source="src", + source="src", destination="dst", - edge_type='etp' if 'etp' in dask_dataframe.columns else None, - renumber=False + edge_type="etp" if "etp" in dask_dataframe.columns else None, + renumber=False, ) return G @@ -86,66 +86,87 @@ def construct_graph(dask_dataframe): def symmetrize_ddf(dask_dataframe): source_col, dest_col = symmetrize( dask_dataframe, - 'src', - 'dst', + "src", + "dst", multi=True, symmetrize=True, ) new_ddf = source_col.to_frame() - new_ddf['dst'] = dest_col + new_ddf["dst"] = dest_col return new_ddf + def renumber_ddf(dask_df, persist=False): - vertices = dask_cudf.concat([dask_df['src'], dask_df['dst']]).unique().reset_index(drop=True) + vertices = ( + dask_cudf.concat([dask_df["src"], dask_df["dst"]]) + .unique() + .reset_index(drop=True) + ) if persist: vertices = vertices.persist() - - vertices.name = 'v' - vertices = vertices.reset_index().set_index('v').rename(columns={'index': 'm'}) + + vertices.name = "v" + vertices = vertices.reset_index().set_index("v").rename(columns={"index": "m"}) if persist: vertices = vertices.persist() - src = dask_df.merge(vertices, left_on='src', right_on='v', how='left').m.rename('src') - dst = dask_df.merge(vertices, left_on='dst', right_on='v', how='left').m.rename('dst') + src = dask_df.merge(vertices, left_on="src", right_on="v", how="left").m.rename( + "src" + ) + dst = dask_df.merge(vertices, left_on="dst", right_on="v", how="left").m.rename( + "dst" + ) df = src.to_frame() - df['dst'] = dst + df["dst"] = dst return df.reset_index(drop=True) -def _make_batch_ids(bdf: cudf.DataFrame, batch_size: int, num_workers: int, partition_info: Optional[Union[dict, str]] = None): + +def _make_batch_ids( + bdf: cudf.DataFrame, + batch_size: int, + num_workers: int, + partition_info: Optional[Union[dict, str]] = None, +): # Required by dask; need to skip dummy partitions. if partition_info is None: - return cudf.DataFrame({ - 'batch': cudf.Series(dtype='int32'), - 'start': cudf.Series(dtype='int64') - }) - - partition = partition_info['number'] + return cudf.DataFrame( + {"batch": cudf.Series(dtype="int32"), "start": cudf.Series(dtype="int64")} + ) + + partition = partition_info["number"] if partition is None: - raise ValueError('division is absent') + raise ValueError("division is absent") num_batches = int(ceil(len(bdf) / batch_size)) - + batch_ids = cupy.repeat( - cupy.arange(num_batches * partition, num_batches * (partition + 1), dtype='int32'), - batch_size - )[:len(bdf)] + cupy.arange( + num_batches * partition, num_batches * (partition + 1), dtype="int32" + ), + batch_size, + )[: len(bdf)] bdf = bdf.reset_index(drop=True) - bdf['batch'] = cudf.Series(batch_ids) + bdf["batch"] = cudf.Series(batch_ids) return bdf -def _replicate_df(df: cudf.DataFrame, replication_factor: int, col_item_counts:Dict[str, int], partition_info: Optional[Union[dict, str]] = None): +def _replicate_df( + df: cudf.DataFrame, + replication_factor: int, + col_item_counts: Dict[str, int], + partition_info: Optional[Union[dict, str]] = None, +): # Required by dask; need to skip dummy partitions. if partition_info is None: - return cudf.DataFrame({ - col: cudf.Series(dtype=df[col].dtype) for col in col_item_counts.keys() - }) - + return cudf.DataFrame( + {col: cudf.Series(dtype=df[col].dtype) for col in col_item_counts.keys()} + ) + original_df = df.copy() if replication_factor > 1: @@ -153,14 +174,24 @@ def _replicate_df(df: cudf.DataFrame, replication_factor: int, col_item_counts:D df_replicated = original_df for col, offset in col_item_counts.items(): df_replicated[col] += offset * r - + df = cudf.concat([df, df_replicated], ignore_index=True) - + return df @get_allocation_counts_dask_lazy(return_allocations=True, logging=True) -def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_call=200000, batches_per_partition=100, fanout=[5, 5, 5], persist=False): +def sample_graph( + G, + label_df, + output_path, + seed=42, + batch_size=500, + seeds_per_call=200000, + batches_per_partition=100, + fanout=[5, 5, 5], + persist=False, +): cupy.random.seed(seed) sampler = BulkSampler( @@ -172,35 +203,35 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal random_state=seed, seeds_per_call=seeds_per_call, batches_per_partition=batches_per_partition, - log_level = logging.INFO + log_level=logging.INFO, ) - n_workers = len(default_client().scheduler_info()['workers']) + n_workers = len(default_client().scheduler_info()["workers"]) - meta = cudf.DataFrame({ - 'node': cudf.Series(dtype='int64'), - 'batch': cudf.Series(dtype='int32') - }) + meta = cudf.DataFrame( + {"node": cudf.Series(dtype="int64"), "batch": cudf.Series(dtype="int32")} + ) + + batch_df = label_df.map_partitions( + _make_batch_ids, batch_size, n_workers, meta=meta + ) + # batch_df = batch_df.sort_values(by='node') - batch_df = label_df.map_partitions(_make_batch_ids, batch_size, n_workers, meta=meta) - #batch_df = batch_df.sort_values(by='node') - # should always persist the batch dataframe or performance may be suboptimal batch_df = batch_df.persist() del label_df - print('created batches') - + print("created batches") start_time = perf_counter() - sampler.add_batches(batch_df, start_col_name='node', batch_col_name='batch') + sampler.add_batches(batch_df, start_col_name="node", batch_col_name="batch") sampler.flush() end_time = perf_counter() - print('flushed all batches') - return (end_time - start_time) + print("flushed all batches") + return end_time - start_time -def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor:int=1): +def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor: int = 1): # cuGraph-PyG assigns offsets based on lexicographic order node_offsets = {} node_offsets_replicated = {} @@ -212,10 +243,19 @@ def assign_offsets_pyg(node_counts: Dict[str, int], replication_factor:int=1): count += node_counts[node_type] count_replicated += node_counts[node_type] * replication_factor - + return node_offsets, node_offsets_replicated, count_replicated -def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels=256, reverse_edges=False, persist=False, add_edge_types=False): + +def generate_rmat_dataset( + dataset, + seed=62, + labeled_percentage=0.01, + num_labels=256, + reverse_edges=False, + persist=False, + add_edge_types=False, +): """ Generates an rmat dataset. Currently does not support heterogeneous datasets. @@ -227,17 +267,20 @@ def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels= reverse_edges: Whether to reverse the edges in the edgelist (should be True for DGL, False, for PyG) """ - dataset = dataset.split('_') + dataset = dataset.split("_") scale = int(dataset[1]) edgefactor = int(dataset[2]) dask_edgelist_df = generate_edgelist_rmat( - scale=scale, edgefactor=edgefactor, seed=seed, unweighted=True, mg=True, + scale=scale, + edgefactor=edgefactor, + seed=seed, + unweighted=True, + mg=True, ) dask_edgelist_df = dask_edgelist_df.astype("int64") dask_edgelist_df = dask_edgelist_df.reset_index(drop=True) - dask_edgelist_df = renumber_ddf(dask_edgelist_df).persist() if persist: dask_edgelist_df = dask_edgelist_df.persist() @@ -247,96 +290,126 @@ def generate_rmat_dataset(dataset, seed=62, labeled_percentage=0.01, num_labels= dask_edgelist_df = dask_edgelist_df.persist() if add_edge_types: - dask_edgelist_df['etp'] = cupy.int32(0) # doesn't matter what the value is, really - + dask_edgelist_df["etp"] = cupy.int32( + 0 + ) # doesn't matter what the value is, really + # generator = np.random.default_rng(seed=seed) - num_labeled_nodes = int(2**(scale+1) * labeled_percentage) - label_df = pd.DataFrame({ - 'node': np.arange(num_labeled_nodes), - # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') - }) - - n_workers = len(default_client().scheduler_info()['workers']) - dask_label_df = ddf.from_pandas(label_df, npartitions=n_workers*2) + num_labeled_nodes = int(2 ** (scale + 1) * labeled_percentage) + label_df = pd.DataFrame( + { + "node": np.arange(num_labeled_nodes), + # 'label': generator.integers(0, num_labels - 1, num_labeled_nodes).astype('float32') + } + ) + + n_workers = len(default_client().scheduler_info()["workers"]) + dask_label_df = ddf.from_pandas(label_df, npartitions=n_workers * 2) del label_df gc.collect() dask_label_df = dask_cudf.from_dask_dataframe(dask_label_df) - node_offsets = {'paper': 0} - edge_offsets = {('paper','cites','paper'):0} - total_num_nodes = int(dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]).nunique().compute()) + node_offsets = {"paper": 0} + edge_offsets = {("paper", "cites", "paper"): 0} + total_num_nodes = int( + dask_cudf.concat([dask_edgelist_df.src, dask_edgelist_df.dst]) + .nunique() + .compute() + ) if reverse_edges: - dask_edgelist_df = dask_edgelist_df.rename(columns={'src':'dst', 'dst':'src'}) + dask_edgelist_df = dask_edgelist_df.rename(columns={"src": "dst", "dst": "src"}) return dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes -def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_factor=1, persist=False, add_edge_types=False): +def load_disk_dataset( + dataset, + dataset_dir=".", + reverse_edges=True, + replication_factor=1, + persist=False, + add_edge_types=False, +): from pathlib import Path + path = Path(dataset_dir) / dataset - parquet_path = path / 'parquet' + parquet_path = path / "parquet" n_workers = get_n_workers() - with open(os.path.join(path, 'meta.json')) as meta_file: + with open(os.path.join(path, "meta.json")) as meta_file: meta = json.load(meta_file) - - node_offsets, node_offsets_replicated, total_num_nodes = \ - assign_offsets_pyg(meta['num_nodes'], replication_factor=replication_factor) + + node_offsets, node_offsets_replicated, total_num_nodes = assign_offsets_pyg( + meta["num_nodes"], replication_factor=replication_factor + ) edge_index_dict = {} - for edge_type in meta['num_edges'].keys(): - print(f'Loading edge index for edge type {edge_type}') + for edge_type in meta["num_edges"].keys(): + print(f"Loading edge index for edge type {edge_type}") - can_edge_type = tuple(edge_type.split('__')) + can_edge_type = tuple(edge_type.split("__")) edge_index_dict[can_edge_type] = dask_cudf.read_parquet( - Path(parquet_path) / edge_type / 'edge_index.parquet' - ).repartition(n_workers*2) + Path(parquet_path) / edge_type / "edge_index.parquet" + ).repartition(n_workers * 2) - edge_index_dict[can_edge_type]['src'] += node_offsets_replicated[can_edge_type[0]] - edge_index_dict[can_edge_type]['dst'] += node_offsets_replicated[can_edge_type[-1]] + edge_index_dict[can_edge_type]["src"] += node_offsets_replicated[ + can_edge_type[0] + ] + edge_index_dict[can_edge_type]["dst"] += node_offsets_replicated[ + can_edge_type[-1] + ] edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type] if persist: edge_index_dict = edge_index_dict.persist() if replication_factor > 1: - edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].map_partitions( + edge_index_dict[can_edge_type] = edge_index_dict[ + can_edge_type + ].map_partitions( _replicate_df, replication_factor, { - 'src': meta['num_nodes'][can_edge_type[0]], - 'dst': meta['num_nodes'][can_edge_type[2]], + "src": meta["num_nodes"][can_edge_type[0]], + "dst": meta["num_nodes"][can_edge_type[2]], }, - meta=cudf.DataFrame({'src':cudf.Series(dtype='int64'), 'dst':cudf.Series(dtype='int64')}) + meta=cudf.DataFrame( + { + "src": cudf.Series(dtype="int64"), + "dst": cudf.Series(dtype="int64"), + } + ), ) - + if persist: - edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].persist() - + edge_index_dict[can_edge_type] = edge_index_dict[ + can_edge_type + ].persist() + gc.collect() if reverse_edges: - edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].rename(columns={'src':'dst','dst':'src'}) - + edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].rename( + columns={"src": "dst", "dst": "src"} + ) + if persist: edge_index_dict[can_edge_type] = edge_index_dict[can_edge_type].persist() - + # Assign numeric edge type ids based on lexicographic order edge_offsets = {} edge_count = 0 for num_edge_type, can_edge_type in enumerate(sorted(edge_index_dict.keys())): if add_edge_types: - edge_index_dict[can_edge_type]['etp'] = cupy.int32(num_edge_type) + edge_index_dict[can_edge_type]["etp"] = cupy.int32(num_edge_type) edge_offsets[can_edge_type] = edge_count edge_count += len(edge_index_dict[can_edge_type]) - - all_edges_df = dask_cudf.concat( - list(edge_index_dict.values()) - ) - + + all_edges_df = dask_cudf.concat(list(edge_index_dict.values())) + if persist: all_edges_df = all_edges_df.persist() @@ -345,55 +418,65 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_ node_labels = {} for node_type, offset in node_offsets_replicated.items(): - print(f'Loading node labels for node type {node_type} (offset={offset})') - node_label_path = os.path.join(os.path.join(parquet_path, node_type), 'node_label.parquet') + print(f"Loading node labels for node type {node_type} (offset={offset})") + node_label_path = os.path.join( + os.path.join(parquet_path, node_type), "node_label.parquet" + ) if os.path.exists(node_label_path): - node_labels[node_type] = dask_cudf.read_parquet(node_label_path).repartition(n_workers).drop('label',axis=1).persist() - node_labels[node_type]['node'] += offset + node_labels[node_type] = ( + dask_cudf.read_parquet(node_label_path) + .repartition(n_workers) + .drop("label", axis=1) + .persist() + ) + node_labels[node_type]["node"] += offset node_labels[node_type] = node_labels[node_type].persist() if replication_factor > 1: node_labels[node_type] = node_labels[node_type].map_partitions( _replicate_df, replication_factor, - { - 'node': meta['num_nodes'][node_type] - }, - meta=cudf.DataFrame({'node':cudf.Series(dtype='int64')}) + {"node": meta["num_nodes"][node_type]}, + meta=cudf.DataFrame({"node": cudf.Series(dtype="int64")}), ) - + if persist: node_labels[node_type] = node_labels[node_type].persist() gc.collect() - - node_labels_df = dask_cudf.concat( - list(node_labels.values()) - ) - + + node_labels_df = dask_cudf.concat(list(node_labels.values())) + if persist: node_labels_df = node_labels_df.persist() del node_labels gc.collect() - return all_edges_df, node_labels_df, node_offsets_replicated, edge_offsets, total_num_nodes - + return ( + all_edges_df, + node_labels_df, + node_offsets_replicated, + edge_offsets, + total_num_nodes, + ) + def benchmark_cugraph_bulk_sampling( - dataset, - output_path, - seed, - batch_size, - seeds_per_call, - fanout, - reverse_edges=True, - dataset_dir='.', - replication_factor=1, - num_labels=256, - labeled_percentage=0.001, - persist=False, - add_edge_types=False): + dataset, + output_path, + seed, + batch_size, + seeds_per_call, + fanout, + reverse_edges=True, + dataset_dir=".", + replication_factor=1, + num_labels=256, + labeled_percentage=0.001, + persist=False, + add_edge_types=False, +): """ Entry point for the benchmark. @@ -430,47 +513,55 @@ def benchmark_cugraph_bulk_sampling( Defaults to False. """ print(dataset) - if dataset[0:4] == 'rmat': - dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ - generate_rmat_dataset( - dataset, - reverse_edges=reverse_edges, - seed=seed, - labeled_percentage=labeled_percentage, - num_labels=num_labels, - persist=persist, - add_edge_types=add_edge_types - ) + if dataset[0:4] == "rmat": + ( + dask_edgelist_df, + dask_label_df, + node_offsets, + edge_offsets, + total_num_nodes, + ) = generate_rmat_dataset( + dataset, + reverse_edges=reverse_edges, + seed=seed, + labeled_percentage=labeled_percentage, + num_labels=num_labels, + persist=persist, + add_edge_types=add_edge_types, + ) else: - dask_edgelist_df, dask_label_df, node_offsets, edge_offsets, total_num_nodes = \ - load_disk_dataset( - dataset, - dataset_dir=dataset_dir, - reverse_edges=reverse_edges, - replication_factor=replication_factor, - persist=persist, - add_edge_types=add_edge_types - ) + ( + dask_edgelist_df, + dask_label_df, + node_offsets, + edge_offsets, + total_num_nodes, + ) = load_disk_dataset( + dataset, + dataset_dir=dataset_dir, + reverse_edges=reverse_edges, + replication_factor=replication_factor, + persist=persist, + add_edge_types=add_edge_types, + ) num_input_edges = len(dask_edgelist_df) - print( - f"Number of input edges = {num_input_edges:,}" - ) + print(f"Number of input edges = {num_input_edges:,}") - G = construct_graph( - dask_edgelist_df - ) + G = construct_graph(dask_edgelist_df) del dask_edgelist_df - print('constructed graph') + print("constructed graph") input_memory = G.edgelist.edgelist_df.memory_usage().sum().compute() - print(f'input memory: {input_memory}') + print(f"input memory: {input_memory}") - output_subdir = os.path.join(output_path, f'{dataset}[{replication_factor}]_b{batch_size}_f{fanout}') + output_subdir = os.path.join( + output_path, f"{dataset}[{replication_factor}]_b{batch_size}_f{fanout}" + ) os.makedirs(output_subdir) - output_sample_path = os.path.join(output_subdir, 'samples') + output_sample_path = os.path.join(output_subdir, "samples") os.makedirs(output_sample_path) batches_per_partition = 200_000 // batch_size @@ -487,30 +578,26 @@ def benchmark_cugraph_bulk_sampling( ) output_meta = { - 'dataset': dataset, - 'dataset_dir': dataset_dir, - 'seed': seed, - 'node_offsets': node_offsets, - 'edge_offsets': {'__'.join(k): v for k, v in edge_offsets.items()}, - 'total_num_nodes': total_num_nodes, - 'total_num_edges': num_input_edges, - 'batch_size': batch_size, - 'seeds_per_call': seeds_per_call, - 'batches_per_partition': batches_per_partition, - 'fanout': fanout, - 'replication_factor': replication_factor, - 'num_sampling_gpus': len(G._plc_graph), - 'execution_time': execution_time, + "dataset": dataset, + "dataset_dir": dataset_dir, + "seed": seed, + "node_offsets": node_offsets, + "edge_offsets": {"__".join(k): v for k, v in edge_offsets.items()}, + "total_num_nodes": total_num_nodes, + "total_num_edges": num_input_edges, + "batch_size": batch_size, + "seeds_per_call": seeds_per_call, + "batches_per_partition": batches_per_partition, + "fanout": fanout, + "replication_factor": replication_factor, + "num_sampling_gpus": len(G._plc_graph), + "execution_time": execution_time, } - with open(os.path.join(output_subdir, 'output_meta.json'), 'w') as f: - json.dump( - output_meta, - f, - indent='\t' - ) + with open(os.path.join(output_subdir, "output_meta.json"), "w") as f: + json.dump(output_meta, f, indent="\t") - print('allocation counts b:') + print("allocation counts b:") print(allocation_counts.values()) ( @@ -558,91 +645,87 @@ def get_memory_statistics(allocation_counts, input_memory): def get_args(): parser = argparse.ArgumentParser() - + parser.add_argument( - '--output_root', + "--output_root", type=str, - help='The output root directory. File/folder names are auto-generated.', + help="The output root directory. File/folder names are auto-generated.", required=True, ) parser.add_argument( - '--dataset_root', + "--dataset_root", type=str, - help='The dataset root directory containing ogb datasets.', + help="The dataset root directory containing ogb datasets.", required=True, ) parser.add_argument( - '--datasets', + "--datasets", type=str, help=( - 'Comma separated list of datasets; can specify ogb or rmat (i.e. ogb_papers100M[2],rmat_22_16).' - ' For ogb datasets, can provide replication factor using brackets.' + "Comma separated list of datasets; can specify ogb or rmat (i.e. ogb_papers100M[2],rmat_22_16)." + " For ogb datasets, can provide replication factor using brackets." ), required=True, ) parser.add_argument( - '--fanouts', + "--fanouts", type=str, - help='Comma separated list of fanouts (i.e. 10_25,5_5_5)', + help="Comma separated list of fanouts (i.e. 10_25,5_5_5)", required=False, - default='10_25', + default="10_25", ) parser.add_argument( - '--batch_sizes', + "--batch_sizes", type=str, - help='Comma separated list of batch sizes (i.e. 500,1000)', + help="Comma separated list of batch sizes (i.e. 500,1000)", required=False, - default='512,1024' + default="512,1024", ) parser.add_argument( - '--seeds_per_call_opts', + "--seeds_per_call_opts", type=str, - help='Comma separated list of seeds per call (i.e. 1000000,2000000)', + help="Comma separated list of seeds per call (i.e. 1000000,2000000)", required=False, - default='524288', + default="524288", ) - + parser.add_argument( - '--reverse_edges', - action='store_true', - help='Whether to reverse the edges for DGL (defaults to False). Should be True for DGL, False for PyG.', + "--reverse_edges", + action="store_true", + help="Whether to reverse the edges for DGL (defaults to False). Should be True for DGL, False for PyG.", required=False, default=False, ) parser.add_argument( - '--dask_worker_devices', + "--dask_worker_devices", type=str, - help='Comma separated list of dask worker devices', + help="Comma separated list of dask worker devices", required=False, - default="0" + default="0", ) parser.add_argument( - '--random_seed', - type=int, - help='Random seed', - required=False, - default=62 + "--random_seed", type=int, help="Random seed", required=False, default=62 ) parser.add_argument( - '--persist', - action='store_true', - help='Will add additional persist() calls to speed up ETL. Does not affect sampling runtime.', + "--persist", + action="store_true", + help="Will add additional persist() calls to speed up ETL. Does not affect sampling runtime.", required=False, default=False, ) parser.add_argument( - '--add_edge_types', - action='store_true', - help='Adds edge types to the edgelist. Required for PyG if not providing edge ids.', + "--add_edge_types", + action="store_true", + help="Adds edge types to the edgelist. Required for PyG if not providing edge ids.", required=False, default=False, ) @@ -655,18 +738,25 @@ def get_args(): logging.basicConfig() args = get_args() - fanouts = [[int(f) for f in fanout.split('_')] for fanout in args.fanouts.split(',')] - datasets = args.datasets.split(',') - batch_sizes = [int(b) for b in args.batch_sizes.split(',')] - seeds_per_call_opts = [int(s) for s in args.seeds_per_call_opts.split(',')] - dask_worker_devices = [int(d) for d in args.dask_worker_devices.split(',')] - - client, cluster = start_dask_client(dask_worker_devices=dask_worker_devices, jit_unspill=False, rmm_pool_size=28e9, rmm_async=True) + fanouts = [ + [int(f) for f in fanout.split("_")] for fanout in args.fanouts.split(",") + ] + datasets = args.datasets.split(",") + batch_sizes = [int(b) for b in args.batch_sizes.split(",")] + seeds_per_call_opts = [int(s) for s in args.seeds_per_call_opts.split(",")] + dask_worker_devices = [int(d) for d in args.dask_worker_devices.split(",")] + + client, cluster = start_dask_client( + dask_worker_devices=dask_worker_devices, + jit_unspill=False, + rmm_pool_size=28e9, + rmm_async=True, + ) enable_spilling() stats_ls = [] client.run(enable_spilling) for dataset in datasets: - if re.match(r'([A-z]|[0-9])+\[[0-9]+\]', dataset): + if re.match(r"([A-z]|[0-9])+\[[0-9]+\]", dataset): replication_factor = int(dataset[-2]) dataset = dataset[:-3] else: @@ -675,10 +765,10 @@ def get_args(): for fanout in fanouts: for batch_size in batch_sizes: for seeds_per_call in seeds_per_call_opts: - print(f'dataset: {dataset}') - print(f'batch size: {batch_size}') - print(f'fanout: {fanout}') - print(f'seeds_per_call: {seeds_per_call}') + print(f"dataset: {dataset}") + print(f"batch size: {batch_size}") + print(f"fanout: {fanout}") + print(f"seeds_per_call: {seeds_per_call}") try: stats_d = {} @@ -706,7 +796,9 @@ def get_args(): stats_d["batch_size"] = batch_size stats_d["fanout"] = fanout stats_d["seeds_per_call"] = seeds_per_call - stats_d["input_memory_per_worker"] = sizeof_fmt(input_memory_per_worker) + stats_d["input_memory_per_worker"] = sizeof_fmt( + input_memory_per_worker + ) stats_d["peak_allocation_across_workers"] = sizeof_fmt( peak_allocation_across_workers ) @@ -714,7 +806,7 @@ def get_args(): stats_d["output_to_peak_ratio"] = output_to_peak_ratio stats_ls.append(stats_d) except Exception as e: - warnings.warn('An Exception Occurred!') + warnings.warn("An Exception Occurred!") print(e) traceback.print_exc() restart_client(client) diff --git a/benchmarks/cugraph/standalone/cugraph_dask_funcs.py b/benchmarks/cugraph/standalone/cugraph_dask_funcs.py index c6aa4a06100..e8f2c3a62bb 100644 --- a/benchmarks/cugraph/standalone/cugraph_dask_funcs.py +++ b/benchmarks/cugraph/standalone/cugraph_dask_funcs.py @@ -26,8 +26,6 @@ from cugraph.testing.mg_utils import generate_edgelist - - def read_csv(input_csv_file, scale): """ Returns a dask_cudf DataFrame from reading input_csv_file. @@ -41,15 +39,16 @@ def read_csv(input_csv_file, scale): """ vertex_t = "int32" if scale <= 32 else "int64" dtypes = [vertex_t, vertex_t, "float32"] - names=["src", "dst", "weight"], + names = (["src", "dst", "weight"],) chunksize = cugraph.dask.get_chunksize(input_csv_file) - return dask_cudf.read_csv(input_csv_file, - chunksize=chunksize, - delimiter=" ", - #names=names, - dtype=dtypes, - header=None, + return dask_cudf.read_csv( + input_csv_file, + chunksize=chunksize, + delimiter=" ", + # names=names, + dtype=dtypes, + header=None, ) @@ -59,6 +58,7 @@ def read_csv(input_csv_file, scale): # The "benchmark_name" attr is used by the benchmark infra for reporting and is # set to assign more meaningful names to be displayed in reports. + def construct_graph(dask_dataframe, symmetric=False): """ dask_dataframe contains weighted and undirected edges with self @@ -72,28 +72,27 @@ def construct_graph(dask_dataframe, symmetric=False): G = cugraph.Graph(directed=True) if len(dask_dataframe.columns) > 2: - if symmetric: #symmetrize dask dataframe - dask_dataframe = symmetrize_ddf( - dask_dataframe, 'src', 'dst', 'weight') + if symmetric: # symmetrize dask dataframe + dask_dataframe = symmetrize_ddf(dask_dataframe, "src", "dst", "weight") G.from_dask_cudf_edgelist( - dask_dataframe, source="src", destination="dst", edge_attr="weight") - #G.from_dask_cudf_edgelist( + dask_dataframe, source="src", destination="dst", edge_attr="weight" + ) + # G.from_dask_cudf_edgelist( # dask_dataframe, source="0", destination="1", edge_attr="2") else: - if symmetric: #symmetrize dask dataframe - dask_dataframe = symmetrize_ddf(dask_dataframe, 'src', 'dst') - G.from_dask_cudf_edgelist( - dask_dataframe, source="src", destination="dst") + if symmetric: # symmetrize dask dataframe + dask_dataframe = symmetrize_ddf(dask_dataframe, "src", "dst") + G.from_dask_cudf_edgelist(dask_dataframe, source="src", destination="dst") return G + construct_graph.benchmark_name = "from_dask_cudf_edgelist" def bfs(G, start): - return cugraph.dask.bfs( - G, start=start, return_distances=True, check_start=False) + return cugraph.dask.bfs(G, start=start, return_distances=True, check_start=False) def sssp(G, start): @@ -116,39 +115,49 @@ def katz(G, alpha=None): print(alpha) return cugraph.dask.katz_centrality(G, alpha) + def hits(G): return cugraph.dask.hits(G) + def uniform_neighbor_sample(G, start_list=None, fanout_vals=None): # convert list to cudf.Series - start_list = cudf.Series(start_list, dtype="int32") + start_list = cudf.Series(start_list, dtype="int32") return cugraph.dask.uniform_neighbor_sample( - G, start_list=start_list, fanout_vals=fanout_vals) + G, start_list=start_list, fanout_vals=fanout_vals + ) + def triangle_count(G): return cugraph.dask.triangle_count(G) + def eigenvector_centrality(G): return cugraph.dask.eigenvector_centrality(G) + ################################################################################ # Session-wide setup and teardown + def setup(dask_scheduler_file=None, rmm_pool_size=None): if dask_scheduler_file: cluster = None # Env var UCX_MAX_RNDV_RAILS=1 must be set too. - initialize(enable_tcp_over_ucx=True, - enable_nvlink=True, - enable_infiniband=False, - enable_rdmacm=False, - #net_devices="mlx5_0:1", - ) + initialize( + enable_tcp_over_ucx=True, + enable_nvlink=True, + enable_infiniband=False, + enable_rdmacm=False, + # net_devices="mlx5_0:1", + ) client = Client(scheduler_file=dask_scheduler_file) else: tempdir_object = tempfile.TemporaryDirectory() - cluster = LocalCUDACluster(local_directory=tempdir_object.name, rmm_pool_size=rmm_pool_size) + cluster = LocalCUDACluster( + local_directory=tempdir_object.name, rmm_pool_size=rmm_pool_size + ) client = Client(cluster) # add the obj to the client so it doesn't get deleted until # the 'client' obj gets cleaned up diff --git a/benchmarks/cugraph/standalone/cugraph_funcs.py b/benchmarks/cugraph/standalone/cugraph_funcs.py index ba33471a54f..d53471fa828 100644 --- a/benchmarks/cugraph/standalone/cugraph_funcs.py +++ b/benchmarks/cugraph/standalone/cugraph_funcs.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022, NVIDIA CORPORATION. +# Copyright (c) 2021-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -18,11 +18,12 @@ import cudf -def generate_edgelist(scale, - edgefactor, - seed=None, - unweighted=False, - ): +def generate_edgelist( + scale, + edgefactor, + seed=None, + unweighted=False, +): """ Returns a cudf DataFrame created using the R-MAT graph generator. @@ -43,7 +44,7 @@ def generate_edgelist(scale, """ df = rmat( scale, - (2**scale)*edgefactor, + (2**scale) * edgefactor, 0.57, # from Graph500 0.19, # from Graph500 0.19, # from Graph500 @@ -51,7 +52,7 @@ def generate_edgelist(scale, clip_and_flip=False, scramble_vertex_ids=True, create_using=None, # return edgelist instead of Graph instance - mg=False + mg=False, ) if not unweighted: rng = np.random.default_rng(seed) @@ -72,16 +73,17 @@ def read_csv(input_csv_file, scale): """ vertex_t = "int32" if scale <= 32 else "int64" dtypes = [vertex_t, vertex_t, "float32"] - names=["src", "dst", "weight"], + names = (["src", "dst", "weight"],) chunksize = cugraph.dask.get_chunksize(input_csv_file) - return cudf.read_csv(input_csv_file, - chunksize=chunksize, - delimiter=" ", - #names=names, - dtype=dtypes, - header=None, - ) + return cudf.read_csv( + input_csv_file, + chunksize=chunksize, + delimiter=" ", + # names=names, + dtype=dtypes, + header=None, + ) ################################################################################ @@ -90,6 +92,7 @@ def read_csv(input_csv_file, scale): # The "benchmark_name" attr is used by the benchmark infra for reporting and is # set to assign more meaningful names to be displayed in reports. + def construct_graph(dataframe, symmetric=False): """ dataframe contains weighted and undirected edges with self loops. Multiple @@ -103,15 +106,17 @@ def construct_graph(dataframe, symmetric=False): if len(dataframe.columns) > 2: G.from_cudf_edgelist( - dataframe, source="src", destination="dst", edge_attr="weight") - #G.from_cudf_edgelist( + dataframe, source="src", destination="dst", edge_attr="weight" + ) + # G.from_cudf_edgelist( # dataframe, source="0", destination="1", edge_attr="2") else: - G.from_cudf_edgelist( - dataframe, source="src", destination="dst") - #G.from_cudf_edgelist( + G.from_cudf_edgelist(dataframe, source="src", destination="dst") + # G.from_cudf_edgelist( # dataframe, source="0", destination="1") return G + + construct_graph.benchmark_name = "from_cudf_edgelist" @@ -138,24 +143,31 @@ def pagerank(G): def katz(G, alpha=None): return cugraph.katz_centrality(G, alpha) + def hits(G): return cugraph.hits(G) + def uniform_neighbor_sample(G, start_list=None, fanout_vals=None): # convert list to cudf.Series - start_list = cudf.Series(start_list, dtype="int32") + start_list = cudf.Series(start_list, dtype="int32") return cugraph.uniform_neighbor_sample( - G, start_list=start_list, fanout_vals=fanout_vals) + G, start_list=start_list, fanout_vals=fanout_vals + ) + def triangle_count(G): return cugraph.triangle_count(G) + def eigenvector_centrality(G): return cugraph.eigenvector_centrality(G) + ################################################################################ # Session-wide setup and teardown + def setup(*args, **kwargs): return tuple() diff --git a/benchmarks/cugraph/standalone/cugraph_graph_creation.py b/benchmarks/cugraph/standalone/cugraph_graph_creation.py index 1edf67bba44..70be0f8d470 100644 --- a/benchmarks/cugraph/standalone/cugraph_graph_creation.py +++ b/benchmarks/cugraph/standalone/cugraph_graph_creation.py @@ -210,7 +210,7 @@ def get_memory_statistics(allocation_counts, input_memory): print("-" * 40 + f"renumber completed" + "-" * 40) stats_df = pd.DataFrame( - stats_ls, + stats_ls, columns=[ "scale", "num_input_edges", diff --git a/benchmarks/cugraph/standalone/main.py b/benchmarks/cugraph/standalone/main.py index 206be6e9362..07e8eefde1d 100644 --- a/benchmarks/cugraph/standalone/main.py +++ b/benchmarks/cugraph/standalone/main.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022, NVIDIA CORPORATION. +# Copyright (c) 2021-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -15,9 +15,10 @@ from cugraph.dask.common.mg_utils import get_visible_devices -from reporting import (generate_console_report, - update_csv_report, - ) +from reporting import ( + generate_console_report, + update_csv_report, +) import cugraph_funcs import cugraph_dask_funcs @@ -27,43 +28,45 @@ from pathlib import Path -def store_results_json(benchmark_dir=None, - algo_name=None, - algo_time=None, - n_gpus=None, - scale=None): +def store_results_json( + benchmark_dir=None, algo_name=None, algo_time=None, n_gpus=None, scale=None +): """ Store all benchmark results in json files """ benchmark_result = {} - benchmark_result['funcName'] = algo_name - benchmark_result['result'] = algo_time - benchmark_result['argNameValuePairs'] = [('scale', scale), ('ngpus', n_gpus)] + benchmark_result["funcName"] = algo_name + benchmark_result["result"] = algo_time + benchmark_result["argNameValuePairs"] = [("scale", scale), ("ngpus", n_gpus)] json_object = json.dumps(benchmark_result, indent=4) benchmark_dir_path = Path(benchmark_dir) - with open(f"{benchmark_dir_path}/benchmark_result_scale_{scale}_ngpus_{n_gpus}_{algo_name}.json", "w") as outfile: + with open( + f"{benchmark_dir_path}/benchmark_result_scale_{scale}_ngpus_{n_gpus}_{algo_name}.json", + "w", + ) as outfile: outfile.write(json_object) - def log(s, end="\n"): print(s, end=end) sys.stdout.flush() -def run(algos, - scale=None, - csv_graph_file=None, - csv_results_file=None, - unweighted=False, - symmetric=False, - edgefactor=None, - benchmark_dir=None, - dask_scheduler_file=None, - rmm_pool_size=None): +def run( + algos, + scale=None, + csv_graph_file=None, + csv_results_file=None, + unweighted=False, + symmetric=False, + edgefactor=None, + benchmark_dir=None, + dask_scheduler_file=None, + rmm_pool_size=None, +): """ Run the nightly benchmark on cugraph. Return True on success, False on failure. @@ -80,24 +83,24 @@ def run(algos, # Setup the benchmarks to run based on algos specified, or all. # Values are either callables, or tuples of (callable, args) pairs. - benchmarks = {"bfs": funcs.bfs, - "sssp": funcs.sssp, - "louvain": funcs.louvain, - "pagerank": funcs.pagerank, - "wcc": funcs.wcc, - "katz": funcs.katz, - "wcc": funcs.wcc, - "hits": funcs.hits, - "uniform_neighbor_sample": funcs.uniform_neighbor_sample, - "triangle_count": funcs.triangle_count, - "eigenvector_centrality": funcs.eigenvector_centrality, - } + benchmarks = { + "bfs": funcs.bfs, + "sssp": funcs.sssp, + "louvain": funcs.louvain, + "pagerank": funcs.pagerank, + "wcc": funcs.wcc, + "katz": funcs.katz, + "wcc": funcs.wcc, + "hits": funcs.hits, + "uniform_neighbor_sample": funcs.uniform_neighbor_sample, + "triangle_count": funcs.triangle_count, + "eigenvector_centrality": funcs.eigenvector_centrality, + } if algos: invalid_benchmarks = set(algos) - set(benchmarks.keys()) if invalid_benchmarks: - raise ValueError("Invalid benchmark(s) specified " - f"{invalid_benchmarks}") + raise ValueError("Invalid benchmark(s) specified " f"{invalid_benchmarks}") benchmarks_to_run = [benchmarks[b] for b in algos] else: benchmarks_to_run = list(benchmarks.values()) @@ -110,7 +113,7 @@ def run(algos, # If the number of GPUs is None, This is a MNMG run # Extract the number of gpus from the client if n_gpus is None: - n_gpus = len(setup_objs[0].scheduler_info()['workers']) + n_gpus = len(setup_objs[0].scheduler_info()["workers"]) log("done.") try: @@ -120,18 +123,18 @@ def run(algos, log("done.") elif scale: log("running generate_edgelist (RMAT)...", end="") - df = funcs.generate_edgelist(scale, - edgefactor=edgefactor, - seed=seed, - unweighted=unweighted) + df = funcs.generate_edgelist( + scale, edgefactor=edgefactor, seed=seed, unweighted=unweighted + ) log("done.") else: raise ValueError("Must specify either scale or csv_graph_file") - benchmark = BenchmarkRun(df, - (funcs.construct_graph, (symmetric,)), - benchmarks_to_run, - ) + benchmark = BenchmarkRun( + df, + (funcs.construct_graph, (symmetric,)), + benchmarks_to_run, + ) success = benchmark.run() algo_name = benchmark.results[1].name @@ -140,7 +143,7 @@ def run(algos, # Generate json files containing the benchmark results if benchmark_dir is not None: store_results_json(benchmark_dir, algo_name, algo_time, n_gpus, scale) - + # Report results print(generate_console_report(benchmark.results)) if csv_results_file: @@ -163,42 +166,75 @@ def run(algos, import argparse ap = argparse.ArgumentParser() - ap.add_argument("--scale", type=int, default=None, - help="scale factor for the graph edgelist generator " - "(num_verts=2**SCALE).") - ap.add_argument("--csv", type=str, default=None, - help="path to CSV file to read instead of generating a " - "graph edgelist.") - ap.add_argument("--unweighted", default=False, action="store_true", - help="Generate a graph without weights.") - ap.add_argument("--algo", action="append", - help="Algo to benchmark. May be specified multiple times. " - "Default is all algos.") - ap.add_argument("--dask-scheduler-file", type=str, default=None, - help="Dask scheduler file for multi-node configuration.") - ap.add_argument("--symmetric-graph", default=False, action="store_true", - help="Generate a symmetric (undirected) Graph instead of " - "a DiGraph.") - ap.add_argument("--edgefactor", type=int, default=16, - help="edge factor for the graph edgelist generator " - "(num_edges=num_verts*EDGEFACTOR).") - ap.add_argument("--benchmark-dir", type=str, default=None, - help="directory to store the results in json files") - ap.add_argument("--rmm-pool-size", type=str, default=None, - help="RMM pool size to initialize each worker with") - + ap.add_argument( + "--scale", + type=int, + default=None, + help="scale factor for the graph edgelist generator " "(num_verts=2**SCALE).", + ) + ap.add_argument( + "--csv", + type=str, + default=None, + help="path to CSV file to read instead of generating a " "graph edgelist.", + ) + ap.add_argument( + "--unweighted", + default=False, + action="store_true", + help="Generate a graph without weights.", + ) + ap.add_argument( + "--algo", + action="append", + help="Algo to benchmark. May be specified multiple times. " + "Default is all algos.", + ) + ap.add_argument( + "--dask-scheduler-file", + type=str, + default=None, + help="Dask scheduler file for multi-node configuration.", + ) + ap.add_argument( + "--symmetric-graph", + default=False, + action="store_true", + help="Generate a symmetric (undirected) Graph instead of " "a DiGraph.", + ) + ap.add_argument( + "--edgefactor", + type=int, + default=16, + help="edge factor for the graph edgelist generator " + "(num_edges=num_verts*EDGEFACTOR).", + ) + ap.add_argument( + "--benchmark-dir", + type=str, + default=None, + help="directory to store the results in json files", + ) + ap.add_argument( + "--rmm-pool-size", + type=str, + default=None, + help="RMM pool size to initialize each worker with", + ) args = ap.parse_args() - exitcode = run(algos=args.algo, - scale=args.scale, - csv_graph_file=args.csv, - csv_results_file="out.csv", - unweighted=args.unweighted, - symmetric=args.symmetric_graph, - edgefactor=args.edgefactor, - benchmark_dir=args.benchmark_dir, - dask_scheduler_file=args.dask_scheduler_file, - rmm_pool_size=args.rmm_pool_size) + exitcode = run( + algos=args.algo, + scale=args.scale, + csv_graph_file=args.csv, + csv_results_file="out.csv", + unweighted=args.unweighted, + symmetric=args.symmetric_graph, + edgefactor=args.edgefactor, + benchmark_dir=args.benchmark_dir, + dask_scheduler_file=args.dask_scheduler_file, + rmm_pool_size=args.rmm_pool_size, + ) sys.exit(exitcode) diff --git a/benchmarks/cugraph/standalone/pylibcugraph_bench.py b/benchmarks/cugraph/standalone/pylibcugraph_bench.py index eff3e2e8b7d..b958be8a4b8 100644 --- a/benchmarks/cugraph/standalone/pylibcugraph_bench.py +++ b/benchmarks/cugraph/standalone/pylibcugraph_bench.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -31,8 +31,9 @@ st = time.time() G2 = cugraph.Graph(directed=True) print(f"cugraph Graph create time: {time.time()-st}") -G2.from_cudf_edgelist(edgelist_df, source="src", destination="dst", - edge_attr="weight", renumber=True) +G2.from_cudf_edgelist( + edgelist_df, source="src", destination="dst", edge_attr="weight", renumber=True +) st = time.time() result = cugraph.pagerank(G2, alpha=0.85, tol=1.0e-6, max_iter=500) print(f"cugraph time: {time.time()-st}") @@ -42,27 +43,49 @@ resource_handle = pylibcugraph.experimental.ResourceHandle() graph_props = pylibcugraph.experimental.GraphProperties( - is_symmetric=False, is_multigraph=False) + is_symmetric=False, is_multigraph=False +) st = time.time() G = pylibcugraph.experimental.SGGraph( - resource_handle, graph_props, srcs, dsts, weights, - store_transposed=True, renumber=True, do_expensive_check=False) + resource_handle, + graph_props, + srcs, + dsts, + weights, + store_transposed=True, + renumber=True, + do_expensive_check=False, +) print(f"pylibcugraph Graph create time: {time.time()-st}") st = time.time() (vertices, pageranks) = pylibcugraph.experimental.pagerank( - resource_handle, G, None, alpha=0.85, epsilon=1.0e-6, max_iterations=500, - has_initial_guess=False, do_expensive_check=True) + resource_handle, + G, + None, + alpha=0.85, + epsilon=1.0e-6, + max_iterations=500, + has_initial_guess=False, + do_expensive_check=True, +) print(f"pylibcugraph time: {time.time()-st} (expensive check)") st = time.time() (vertices, pageranks) = pylibcugraph.experimental.pagerank( - resource_handle, G, None, alpha=0.85, epsilon=1.0e-6, max_iterations=500, - has_initial_guess=False, do_expensive_check=False) + resource_handle, + G, + None, + alpha=0.85, + epsilon=1.0e-6, + max_iterations=500, + has_initial_guess=False, + do_expensive_check=False, +) print(f"pylibcugraph time: {time.time()-st}") ######## print() vert_to_check = 4800348 -p = result['pagerank'][result['vertex'] == vert_to_check] +p = result["pagerank"][result["vertex"] == vert_to_check] print(f"cugraph pagerank for vert: {vert_to_check}: {p.iloc[0]}") host_verts = vertices.tolist() @@ -70,7 +93,7 @@ print(f"pylibcugraph pagerank for vert: {vert_to_check}: {pageranks[index]}") vert_to_check = 268434647 -p = result['pagerank'][result['vertex'] == vert_to_check] +p = result["pagerank"][result["vertex"] == vert_to_check] print(f"cugraph pagerank for vert: {vert_to_check}: {p.iloc[0]}") index = host_verts.index(vert_to_check) diff --git a/benchmarks/cugraph/standalone/reporting.py b/benchmarks/cugraph/standalone/reporting.py index afc04241384..e486ccf2c44 100644 --- a/benchmarks/cugraph/standalone/reporting.py +++ b/benchmarks/cugraph/standalone/reporting.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022, NVIDIA CORPORATION. +# Copyright (c) 2021-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -56,6 +56,7 @@ def generate_console_report(benchmark_result_list): return retstring + def update_csv_report(csv_results_file, benchmark_result_list, ngpus): """ Update (or create if DNE) csv_results_file as a CSV file containing the @@ -93,9 +94,12 @@ def update_csv_report(csv_results_file, benchmark_result_list, ngpus): # Add any new algos that were not present in the existing CSV. If there was # no existing CSV, then this is all the algos ran in this run. for name in new_names - names_from_csv: - rows.append({"name": name, - ngpus_key: new_times[name], - }) + rows.append( + { + "name": name, + ngpus_key: new_times[name], + } + ) with open(csv_results_file, "w") as csv_file: field_names = sorted(all_fields) diff --git a/benchmarks/dgl/create_dataset.py b/benchmarks/dgl/create_dataset.py index c47d9068844..f002f79831c 100644 --- a/benchmarks/dgl/create_dataset.py +++ b/benchmarks/dgl/create_dataset.py @@ -15,6 +15,7 @@ import rmm from cugraph.generators import rmat + def create_dataset(scale, edgefactor, folder_path): _seed = 42 num_edges = (2**scale) * edgefactor @@ -31,14 +32,15 @@ def create_dataset(scale, edgefactor, folder_path): create_using=None, # None == return edgelist mg=False, ) - filepath = os.path.join(folder_path, f'rmat_scale_{scale}_edgefactor_{edgefactor}.parquet') + filepath = os.path.join( + folder_path, f"rmat_scale_{scale}_edgefactor_{edgefactor}.parquet" + ) edgelist_df.to_parquet(filepath) - -folder_path = os.path.join(os.getcwd(), 'datasets') +folder_path = os.path.join(os.getcwd(), "datasets") os.makedirs(folder_path, exist_ok=True) rmm.reinitialize(managed_memory=True) -for scale in [24,25,26]: +for scale in [24, 25, 26]: edgefactor = 16 create_dataset(scale=scale, edgefactor=edgefactor, folder_path=folder_path) diff --git a/benchmarks/dgl/pytest-based/dgl_benchmark.py b/benchmarks/dgl/pytest-based/dgl_benchmark.py index 93e50736359..456fa8fedc6 100644 --- a/benchmarks/dgl/pytest-based/dgl_benchmark.py +++ b/benchmarks/dgl/pytest-based/dgl_benchmark.py @@ -18,39 +18,59 @@ import torch import dgl -def get_edgelist(scale, edgefactor, dataset_dir = '../datasets'): - fp = os.path.join(dataset_dir, f'rmat_scale_{scale}_edgefactor_{edgefactor}.parquet') + +def get_edgelist(scale, edgefactor, dataset_dir="../datasets"): + fp = os.path.join( + dataset_dir, f"rmat_scale_{scale}_edgefactor_{edgefactor}.parquet" + ) return pd.read_parquet(fp) - + + def create_dgl_graph_from_df(df): - src_tensor = torch.as_tensor(df['src'].values) - dst_tensor = torch.as_tensor(df['dst'].values) + src_tensor = torch.as_tensor(df["src"].values) + dst_tensor = torch.as_tensor(df["dst"].values) # Reverse edges to match cuGraph behavior - g = dgl.graph(data = (dst_tensor, src_tensor)) + g = dgl.graph(data=(dst_tensor, src_tensor)) return g - -@pytest.mark.parametrize("scale_edge_factor", [[24,16],[25,16]]) -@pytest.mark.parametrize("batch_size", [100, 500, 1_000, 2_500, 5_000, 10_000, 20_000, 30_000, 40_000, 50_000, 60_000, 70_000, 80_000, 90_000, 100_000]) +@pytest.mark.parametrize("scale_edge_factor", [[24, 16], [25, 16]]) +@pytest.mark.parametrize( + "batch_size", + [ + 100, + 500, + 1_000, + 2_500, + 5_000, + 10_000, + 20_000, + 30_000, + 40_000, + 50_000, + 60_000, + 70_000, + 80_000, + 90_000, + 100_000, + ], +) @pytest.mark.parametrize("fanout", [[10, 25]]) def bench_dgl_pure_gpu(benchmark, scale_edge_factor, batch_size, fanout): df = get_edgelist(scale_edge_factor[0], scale_edge_factor[1]) - g = create_dgl_graph_from_df(df).to('cuda') - assert g.device.type =='cuda' - seed_nodes = torch.as_tensor(df['dst'][:batch_size]) - seed_nodes = seed_nodes.to('cuda') - assert len(seed_nodes)==batch_size + g = create_dgl_graph_from_df(df).to("cuda") + assert g.device.type == "cuda" + seed_nodes = torch.as_tensor(df["dst"][:batch_size]) + seed_nodes = seed_nodes.to("cuda") + assert len(seed_nodes) == batch_size ### Reverse because dgl sampler samples from destination to source fanout.reverse() sampler = dgl.dataloading.MultiLayerNeighborSampler(fanout) - + input_nodes, output_nodes, blocks = benchmark( - sampler.sample, - g, - seed_nodes=seed_nodes + sampler.sample, g, seed_nodes=seed_nodes ) - assert len(output_nodes)==batch_size + assert len(output_nodes) == batch_size return @@ -61,33 +81,55 @@ def dgl_graph_26_16(): return g, df -@pytest.mark.parametrize("batch_size", [100, 500, 1_000, 2_500, 5_000, 10_000, 20_000, 30_000, 40_000, 50_000, 60_000, 70_000, 80_000, 90_000, 100_000]) +@pytest.mark.parametrize( + "batch_size", + [ + 100, + 500, + 1_000, + 2_500, + 5_000, + 10_000, + 20_000, + 30_000, + 40_000, + 50_000, + 60_000, + 70_000, + 80_000, + 90_000, + 100_000, + ], +) @pytest.mark.parametrize("fanout", [[10, 25]]) def bench_dgl_uva(benchmark, dgl_graph_26_16, batch_size, fanout): - g,df = dgl_graph_26_16 - assert g.device.type =='cpu' - seed_nodes = torch.as_tensor(df['dst'][:30_000_000]) - seed_nodes = seed_nodes.to('cuda') + g, df = dgl_graph_26_16 + assert g.device.type == "cpu" + seed_nodes = torch.as_tensor(df["dst"][:30_000_000]) + seed_nodes = seed_nodes.to("cuda") ### Reverse because dgl sampler samples from destination to source fanout.reverse() sampler = dgl.dataloading.MultiLayerNeighborSampler(fanout) dataloader = dgl.dataloading.DataLoader( - g, - seed_nodes, # train_nid must be on GPU. - sampler, - device=torch.device('cuda:0'), # The device argument must be GPU. - num_workers=0, # Number of workers must be 0. - use_uva=True, - batch_size=batch_size, - drop_last=False, - shuffle=False) + g, + seed_nodes, # train_nid must be on GPU. + sampler, + device=torch.device("cuda:0"), # The device argument must be GPU. + num_workers=0, # Number of workers must be 0. + use_uva=True, + batch_size=batch_size, + drop_last=False, + shuffle=False, + ) def uva_benchmark(dataloader_it): input_nodes, output_nodes, blocks = next(dataloader_it) - return input_nodes, output_nodes, blocks + return input_nodes, output_nodes, blocks # added iterations and rounds to prevent dataloader going over num batches dataloader_it = iter(dataloader) - input_nodes, output_nodes, blocks = benchmark.pedantic(uva_benchmark,kwargs = {'dataloader_it': dataloader_it}, iterations=10, rounds=10) - assert len(output_nodes)==batch_size + input_nodes, output_nodes, blocks = benchmark.pedantic( + uva_benchmark, kwargs={"dataloader_it": dataloader_it}, iterations=10, rounds=10 + ) + assert len(output_nodes) == batch_size return diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py new file mode 100644 index 00000000000..971c3ff1032 --- /dev/null +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -0,0 +1,212 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import networkx as nx +import pandas as pd +import pytest +from cugraph import datasets + +# FIXME: promote these to cugraph.datasets so the following steps aren't +# necessary +# +# These datasets can be downloaded using the script in the 'datasets' dir: +# +# cd /datasets +# ./get_test_data.sh --benchmark +# +# Then set the following env var so the dataset utils can find their location: +# +# export RAPIDS_DATASET_ROOT_DIR=/datasets +# +from cugraph_benchmarking.params import ( + hollywood, + europe_osm, + cit_patents, + soc_livejournal, +) + +# Attempt to import the NetworkX dispatching module, which is only needed when +# testing with NX <3.2 in order to dynamically switch backends. NX >=3.2 allows +# the backend to be specified directly in the API call. +try: + from networkx.classes import backends # NX <3.2 +except ImportError: + backends = None + + +################################################################################ +# Fixtures and helpers +backend_params = ["cugraph", None] + +dataset_params = [ + pytest.param(datasets.karate, marks=[pytest.mark.small, pytest.mark.undirected]), + pytest.param(datasets.netscience, marks=[pytest.mark.small, pytest.mark.directed]), + pytest.param( + datasets.email_Eu_core, marks=[pytest.mark.small, pytest.mark.directed] + ), + pytest.param(cit_patents, marks=[pytest.mark.medium, pytest.mark.directed]), + pytest.param(hollywood, marks=[pytest.mark.medium, pytest.mark.undirected]), + pytest.param(europe_osm, marks=[pytest.mark.medium, pytest.mark.undirected]), + pytest.param(soc_livejournal, marks=[pytest.mark.large, pytest.mark.directed]), +] + + +def nx_graph_from_dataset(dataset_obj): + """ + Read the dataset specified by the dataset_obj and create and return a + nx.Graph or nx.DiGraph instance based on the dataset is_directed metadata. + """ + create_using = nx.DiGraph if dataset_obj.metadata["is_directed"] else nx.Graph + names = dataset_obj.metadata["col_names"] + dtypes = dataset_obj.metadata["col_types"] + if isinstance(dataset_obj.metadata["header"], int): + header = dataset_obj.metadata["header"] + else: + header = None + + pandas_edgelist = pd.read_csv( + dataset_obj.get_path(), + delimiter=dataset_obj.metadata["delim"], + names=names, + dtype=dict(zip(names, dtypes)), + header=header, + ) + G = nx.from_pandas_edgelist( + pandas_edgelist, source=names[0], target=names[1], create_using=create_using + ) + return G + + +# Test IDs are generated using the lambda assigned to the ids arg to provide an +# easier-to-read name from the Dataset obj string repr. +# See: https://docs.pytest.org/en/stable/reference/reference.html#pytest-fixture +@pytest.fixture(scope="module", params=dataset_params, ids=lambda ds: f"ds={str(ds)}") +def graph_obj(request): + """ + Returns a NX Graph or DiGraph obj from the dataset instance parameter. + """ + dataset = request.param + return nx_graph_from_dataset(dataset) + + +def get_legacy_backend_selector(backend_name): + """ + Returns a callable that wraps an algo function with either the default + dispatch decorator, or the "testing" decorator which unconditionally + dispatches. + This is only supported for NetworkX <3.2 + """ + backends.plugin_name = "cugraph" + orig_dispatch = backends._dispatch + testing_dispatch = backends.test_override_dispatch + + # Testing with the networkx <3.2 dispatch mechanism is based on decorating + # networkx APIs. The decorator is either one that only uses a backend if + # the input graph type is for that backend (the default decorator), or the + # "testing" decorator, which unconditionally converts a graph type to the + # type needed by the backend then calls the backend. If the cugraph backend + # is specified, create a callable that decorates the benchmarked function + # with the testing decorator. + # + # Because both the default and testing decorators assume they are only + # applied once and do bookkeeping to ensure algos are not registered + # multiple times, the callable also clears bookkeeping so the decorators + # can be reapplied multiple times. This is obviously a hack and networkx + # >=3.2 makes this use case properly supported. + if backend_name == "cugraph": + + def wrapper(*args, **kwargs): + backends._registered_algorithms = {} + return testing_dispatch(*args, **kwargs) + + else: + + def wrapper(*args, **kwargs): + backends._registered_algorithms = {} + return orig_dispatch(*args, **kwargs) + + return wrapper + + +def get_backend_selector(backend_name): + """ + Returns a callable that wraps an algo function in order to set the + "backend" kwarg on it. + This is only supported for NetworkX >= 3.2 + """ + + def get_callable_for_func(func): + def wrapper(*args, **kwargs): + kwargs["backend"] = backend_name + return func(*args, **kwargs) + + return wrapper + + return get_callable_for_func + + +@pytest.fixture( + scope="module", params=backend_params, ids=lambda backend: f"backend={backend}" +) +def backend_selector(request): + """ + Returns a callable that takes a function algo and wraps it in another + function that calls the algo using the appropriate backend. + """ + backend_name = request.param + if backends is not None: + return get_legacy_backend_selector(backend_name) + else: + return get_backend_selector(backend_name) + + +################################################################################ +# Benchmarks +normalized_params = [True, False] +k_params = [10, 100] + + +@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") +@pytest.mark.parametrize("k", k_params, ids=lambda k: f"{k=}") +def bench_betweenness_centrality(benchmark, graph_obj, backend_selector, normalized, k): + result = benchmark( + backend_selector(nx.betweenness_centrality), + graph_obj, + weight=None, + normalized=normalized, + k=k, + ) + assert type(result) is dict + + +@pytest.mark.parametrize("normalized", normalized_params, ids=lambda norm: f"{norm=}") +def bench_edge_betweenness_centrality( + benchmark, graph_obj, backend_selector, normalized +): + result = benchmark( + backend_selector(nx.edge_betweenness_centrality), + graph_obj, + weight=None, + normalized=normalized, + ) + assert type(result) is dict + + +def bench_louvain_communities(benchmark, graph_obj, backend_selector): + # The cugraph backend for louvain_communities only supports undirected graphs + if isinstance(graph_obj, nx.DiGraph): + G = graph_obj.to_undirected() + else: + G = graph_obj + result = benchmark(backend_selector(nx.community.louvain_communities), G) + assert type(result) is list diff --git a/benchmarks/pytest.ini b/benchmarks/pytest.ini index 6af3aab27fe..b3d8a8bb36c 100644 --- a/benchmarks/pytest.ini +++ b/benchmarks/pytest.ini @@ -14,8 +14,10 @@ markers = managedmem_off: RMM managed memory disabled poolallocator_on: RMM pool allocator enabled poolallocator_off: RMM pool allocator disabled - small: small datasets tiny: tiny datasets + small: small datasets + medium: medium datasets + large: large datasets directed: directed datasets undirected: undirected datasets matrix_types: inputs are matrices diff --git a/benchmarks/shared/build_cugraph_ucx/test_client_bandwidth.py b/benchmarks/shared/build_cugraph_ucx/test_client_bandwidth.py index 681e304546a..3d52fb46421 100644 --- a/benchmarks/shared/build_cugraph_ucx/test_client_bandwidth.py +++ b/benchmarks/shared/build_cugraph_ucx/test_client_bandwidth.py @@ -20,51 +20,65 @@ import rmm from time import perf_counter_ns + def benchmark_func(func, n_times=10): def wrap_func(*args, **kwargs): time_ls = [] # ignore 1st run # and return other runs - for _ in range(0,n_times+1): + for _ in range(0, n_times + 1): t1 = perf_counter_ns() result = func(*args, **kwargs) t2 = perf_counter_ns() - time_ls.append(t2-t1) + time_ls.append(t2 - t1) return result, time_ls[1:] + return wrap_func + def create_dataframe(client): n_rows = 25_000_000 - df = cudf.DataFrame({'src':cp.arange(0,n_rows,dtype=cp.int32), 'dst':cp.arange(0,n_rows, dtype=cp.int32), 'eids':cp.ones(n_rows, cp.int32)}) - ddf = dask_cudf.from_cudf(df,npartitions= len(client.scheduler_info()['workers'])).persist() + df = cudf.DataFrame( + { + "src": cp.arange(0, n_rows, dtype=cp.int32), + "dst": cp.arange(0, n_rows, dtype=cp.int32), + "eids": cp.ones(n_rows, cp.int32), + } + ) + ddf = dask_cudf.from_cudf( + df, npartitions=len(client.scheduler_info()["workers"]) + ).persist() client.rebalance(ddf) del df _ = wait(ddf) return ddf + @benchmark_func def get_n_rows(ddf, n): - if n==-1: - df = ddf.compute() + if n == -1: + df = ddf.compute() else: df = ddf.head(n) return df + def run_bandwidth_test(ddf, n): df, time_ls = get_n_rows(ddf, n) time_ar = np.asarray(time_ls) time_mean = time_ar.mean() size_bytes = df.memory_usage().sum() - size_gb = round(size_bytes/(pow(1024,3)), 2) + size_gb = round(size_bytes / (pow(1024, 3)), 2) print(f"Getting {len(df):,} rows of size {size_gb} took = {time_mean*1e-6} ms") - time_mean_s = time_mean*1e-9 + time_mean_s = time_mean * 1e-9 print(f"Bandwidth = {round(size_gb/time_mean_s, 4)} gb/s") return - if __name__ == "__main__": - cluster = LocalCUDACluster(protocol='ucx',rmm_pool_size='15GB', CUDA_VISIBLE_DEVICES='1,2,3') + cluster = LocalCUDACluster( + protocol="ucx", rmm_pool_size="15GB", CUDA_VISIBLE_DEVICES="1,2,3" + ) client = Client(cluster) rmm.reinitialize(pool_allocator=True) @@ -74,7 +88,6 @@ def run_bandwidth_test(ddf, n): run_bandwidth_test(ddf, 4_000_000) run_bandwidth_test(ddf, -1) - print("--"*20+"Completed Test"+"--"*20, flush=True) + print("--" * 20 + "Completed Test" + "--" * 20, flush=True) client.shutdown() cluster.close() - diff --git a/benchmarks/shared/build_cugraph_ucx/test_cugraph_sampling.py b/benchmarks/shared/build_cugraph_ucx/test_cugraph_sampling.py index 110ad80838a..2903e1607df 100644 --- a/benchmarks/shared/build_cugraph_ucx/test_cugraph_sampling.py +++ b/benchmarks/shared/build_cugraph_ucx/test_cugraph_sampling.py @@ -23,19 +23,22 @@ _seed = 42 + def benchmark_func(func, n_times=10): def wrap_func(*args, **kwargs): time_ls = [] # ignore 1st run # and return other runs - for _ in range(0,n_times+1): + for _ in range(0, n_times + 1): t1 = perf_counter_ns() result = func(*args, **kwargs) t2 = perf_counter_ns() - time_ls.append(t2-t1) + time_ls.append(t2 - t1) return result, time_ls[1:] + return wrap_func + def create_mg_graph(graph_data): """ Create a graph instance based on the data to be loaded/generated. @@ -67,41 +70,47 @@ def create_mg_graph(graph_data): ) return G + @benchmark_func def sample_graph(G, start_list): - output_ddf = uniform_neighbor_sample_mg(G,start_list=start_list, fanout_vals=[10,25]) + output_ddf = uniform_neighbor_sample_mg( + G, start_list=start_list, fanout_vals=[10, 25] + ) df = output_ddf.compute() return df + def run_sampling_test(ddf, start_list): df, time_ls = sample_graph(ddf, start_list) time_ar = np.asarray(time_ls) time_mean = time_ar.mean() print(f"Sampling {len(start_list):,} took = {time_mean*1e-6} ms") return - if __name__ == "__main__": - cluster = LocalCUDACluster(protocol='ucx',rmm_pool_size='15GB', CUDA_VISIBLE_DEVICES='1,2,3,4,5,6,7,8') + cluster = LocalCUDACluster( + protocol="ucx", rmm_pool_size="15GB", CUDA_VISIBLE_DEVICES="1,2,3,4,5,6,7,8" + ) client = Client(cluster) Comms.initialize(p2p=True) rmm.reinitialize(pool_allocator=True) - graph_data = {"scale": 26, - "edgefactor": 8 , - } - + graph_data = { + "scale": 26, + "edgefactor": 8, + } + g = create_mg_graph(graph_data) for num_start_verts in [1_000, 10_000, 100_000]: start_list = g.input_df["src"].head(num_start_verts) - assert len(start_list)==num_start_verts + assert len(start_list) == num_start_verts run_sampling_test(g, start_list) - - print("--"*20+"Completed Test"+"--"*20, flush=True) + + print("--" * 20 + "Completed Test" + "--" * 20, flush=True) Comms.destroy() client.shutdown() - cluster.close() \ No newline at end of file + cluster.close() diff --git a/benchmarks/shared/python/cugraph_benchmarking/params.py b/benchmarks/shared/python/cugraph_benchmarking/params.py index ee63b8768a6..d82cfd26117 100644 --- a/benchmarks/shared/python/cugraph_benchmarking/params.py +++ b/benchmarks/shared/python/cugraph_benchmarking/params.py @@ -15,9 +15,11 @@ from pylibcugraph.testing.utils import gen_fixture_params from cugraph.testing import RAPIDS_DATASET_ROOT_DIR_PATH -from cugraph.experimental.datasets import ( +from cugraph.datasets import ( Dataset, karate, + netscience, + email_Eu_core, ) # Create Dataset objects from .csv files. @@ -26,67 +28,104 @@ hollywood = Dataset( csv_file=RAPIDS_DATASET_ROOT_DIR_PATH / "csv/undirected/hollywood.csv", csv_col_names=["src", "dst"], - csv_col_dtypes=["int32", "int32"]) + csv_col_dtypes=["int32", "int32"], +) +hollywood.metadata["is_directed"] = False europe_osm = Dataset( csv_file=RAPIDS_DATASET_ROOT_DIR_PATH / "csv/undirected/europe_osm.csv", csv_col_names=["src", "dst"], - csv_col_dtypes=["int32", "int32"]) + csv_col_dtypes=["int32", "int32"], +) +europe_osm.metadata["is_directed"] = False cit_patents = Dataset( csv_file=RAPIDS_DATASET_ROOT_DIR_PATH / "csv/directed/cit-Patents.csv", csv_col_names=["src", "dst"], - csv_col_dtypes=["int32", "int32"]) + csv_col_dtypes=["int32", "int32"], +) +cit_patents.metadata["is_directed"] = True soc_livejournal = Dataset( csv_file=RAPIDS_DATASET_ROOT_DIR_PATH / "csv/directed/soc-LiveJournal1.csv", csv_col_names=["src", "dst"], - csv_col_dtypes=["int32", "int32"]) + csv_col_dtypes=["int32", "int32"], +) +soc_livejournal.metadata["is_directed"] = True # Assume all "file_data" (.csv file on disk) datasets are too small to be useful for MG. undirected_datasets = [ - pytest.param(karate, - marks=[pytest.mark.tiny, - pytest.mark.undirected, - pytest.mark.file_data, - pytest.mark.sg, - ]), - pytest.param(hollywood, - marks=[pytest.mark.small, - pytest.mark.undirected, - pytest.mark.file_data, - pytest.mark.sg, - ]), - pytest.param(europe_osm, - marks=[pytest.mark.undirected, - pytest.mark.file_data, - pytest.mark.sg, - ]), + pytest.param( + karate, + marks=[ + pytest.mark.tiny, + pytest.mark.undirected, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), + pytest.param( + hollywood, + marks=[ + pytest.mark.small, + pytest.mark.undirected, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), + pytest.param( + europe_osm, + marks=[ + pytest.mark.undirected, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), ] directed_datasets = [ - pytest.param(cit_patents, - marks=[pytest.mark.small, - pytest.mark.directed, - pytest.mark.file_data, - pytest.mark.sg, - ]), - pytest.param(soc_livejournal, - marks=[pytest.mark.directed, - pytest.mark.file_data, - pytest.mark.sg, - ]), + pytest.param( + netscience, + marks=[ + pytest.mark.small, + pytest.mark.directed, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), + pytest.param( + email_Eu_core, + marks=[ + pytest.mark.small, + pytest.mark.directed, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), + pytest.param( + cit_patents, + marks=[ + pytest.mark.small, + pytest.mark.directed, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), + pytest.param( + soc_livejournal, + marks=[ + pytest.mark.directed, + pytest.mark.file_data, + pytest.mark.sg, + ], + ), ] managed_memory = [ - pytest.param(True, - marks=[pytest.mark.managedmem_on]), - pytest.param(False, - marks=[pytest.mark.managedmem_off]), + pytest.param(True, marks=[pytest.mark.managedmem_on]), + pytest.param(False, marks=[pytest.mark.managedmem_off]), ] pool_allocator = [ - pytest.param(True, - marks=[pytest.mark.poolallocator_on]), - pytest.param(False, - marks=[pytest.mark.poolallocator_off]), + pytest.param(True, marks=[pytest.mark.poolallocator_on]), + pytest.param(False, marks=[pytest.mark.poolallocator_off]), ] sg = pytest.param( @@ -117,17 +156,31 @@ for scale in _rmat_scales: for edgefactor in _rmat_edgefactors: rmat[f"{scale}_{edgefactor}"] = pytest.param( - {"scale": scale, - "edgefactor": edgefactor, - }, + { + "scale": scale, + "edgefactor": edgefactor, + }, id=f"dataset=rmat_{scale}_{edgefactor}", ) # sampling algos length of start list -_batch_sizes = [100, 500, 1000, 2500, 5000, - 10000, 20000, 30000, 40000, - 50000, 60000, 70000, 80000, - 90000, 100000] +_batch_sizes = [ + 100, + 500, + 1000, + 2500, + 5000, + 10000, + 20000, + 30000, + 40000, + 50000, + 60000, + 70000, + 80000, + 90000, + 100000, +] batch_sizes = {} for bs in _batch_sizes: batch_sizes[bs] = pytest.param( @@ -152,11 +205,10 @@ _num_clients = [2, 4, 8, 16, 32] num_clients = {} for nc in _num_clients: - num_clients[nc] = ( - pytest.param(nc, - id=f"num_clients={nc}", - marks=[getattr(pytest.mark, f"num_clients_{nc}")], - ) + num_clients[nc] = pytest.param( + nc, + id=f"num_clients={nc}", + marks=[getattr(pytest.mark, f"num_clients_{nc}")], ) # Parameters for Graph generation fixture diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index adf3273e311..aaeaa715434 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -82,6 +82,9 @@ NEXT_SHORT_TAG_PEP440=$(python -c "from setuptools.extern import packaging; prin DEPENDENCIES=( cudf cugraph + cugraph-dgl + cugraph-pyg + cugraph-service-server cugraph-service-client cuxfilter dask-cuda @@ -93,6 +96,7 @@ DEPENDENCIES=( librmm pylibcugraph pylibcugraphops + pylibwholegraph pylibraft pyraft raft-dask diff --git a/ci/test_python.sh b/ci/test_python.sh index 7b0077991ae..5183dc37a49 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -65,7 +65,10 @@ popd rapids-logger "pytest cugraph" pushd python/cugraph/cugraph -export DASK_WORKER_DEVICES="0" +DASK_WORKER_DEVICES="0" \ +DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ +DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ +DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ pytest \ -v \ --benchmark-disable \ diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index 3ac3549f143..d6ec67cd9e9 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -21,5 +21,9 @@ arch=$(uname -m) if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then python ./ci/wheel_smoke_test_${package_name}.py else - RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets python -m pytest ./python/${package_name}/${python_package_name}/tests + RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets \ + DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ + DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ + DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ + python -m pytest ./python/${package_name}/${python_package_name}/tests fi diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index c970d0f21cf..35330db5815 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -53,6 +53,7 @@ dependencies: - pydata-sphinx-theme - pylibcugraphops==23.12.* - pylibraft==23.12.* +- pylibwholegraph==23.12.* - pytest - pytest-benchmark - pytest-cov diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index d2b26312936..84967412dc5 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -52,6 +52,7 @@ dependencies: - pydata-sphinx-theme - pylibcugraphops==23.12.* - pylibraft==23.12.* +- pylibwholegraph==23.12.* - pytest - pytest-benchmark - pytest-cov diff --git a/dependencies.yaml b/dependencies.yaml index 4a57835e8ba..f5067ddae70 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -18,6 +18,7 @@ files: - depends_on_dask_cudf - depends_on_pylibraft - depends_on_raft_dask + - depends_on_pylibcugraphops - depends_on_cupy - python_run_cugraph - python_run_nx_cugraph @@ -39,6 +40,7 @@ files: - cudatoolkit - docs - py_version + - depends_on_pylibcugraphops test_cpp: output: none includes: @@ -225,6 +227,7 @@ files: conda_dir: python/cugraph-dgl/conda includes: - checks + - depends_on_pylibcugraphops - cugraph_dgl_dev - test_python_common cugraph_pyg_dev: @@ -234,6 +237,7 @@ files: conda_dir: python/cugraph-pyg/conda includes: - checks + - depends_on_pylibcugraphops - cugraph_pyg_dev - test_python_common channels: @@ -341,7 +345,6 @@ dependencies: - sphinx-markdown-tables - sphinx<6 - sphinxcontrib-websupport - - &pylibcugraphops pylibcugraphops==23.12.* py_version: specific: - output_types: [conda] @@ -368,26 +371,15 @@ dependencies: - output_types: [conda, pyproject, requirements] packages: - cython>=3.0.0 - - &pylibraft pylibraft==23.12.* - - &rmm rmm==23.12.* - scikit-build>=0.13.1 - python_build_cugraph: - common: - - output_types: [conda, pyproject] - packages: - - &pylibcugraph pylibcugraph==23.12.* python_run_cugraph: common: - output_types: [conda, pyproject] packages: - - &cudf cudf==23.12.* - &dask dask>=2023.7.1 - &distributed distributed>=2023.7.1 - &dask_cuda dask-cuda==23.12.* - - &dask_cudf dask-cudf==23.12.* - &numba numba>=0.57 - - raft-dask==23.12.* - - *rmm - &ucx_py ucx-py==0.35.* - output_types: conda packages: @@ -449,20 +441,6 @@ dependencies: packages: - *cugraph - cugraph-service-client==23.12.* - doc: - common: - - output_types: [conda] - packages: - - doxygen - - nbsphinx - - numpydoc - - pydata-sphinx-theme - - recommonmark - - sphinx - - sphinxcontrib-websupport - - sphinx-markdown-tables - - sphinx-copybutton - - *pylibcugraphops test_cpp: common: - output_types: conda @@ -492,6 +470,9 @@ dependencies: - *numpy - python-louvain - scikit-learn>=0.23.1 + - output_types: [conda] + packages: + - pylibwholegraph==23.12.* test_python_pylibcugraph: common: - output_types: [conda, pyproject] @@ -509,7 +490,6 @@ dependencies: - output_types: [conda] packages: - cugraph==23.12.* - - *pylibcugraphops - pytorch>=2.0 - pytorch-cuda==11.8 - dgl>=1.1.0.cu* @@ -518,7 +498,6 @@ dependencies: - output_types: [conda] packages: - cugraph==23.12.* - - *pylibcugraphops - pytorch==2.0 - pytorch-cuda==11.8 - pyg=2.3.1=*torch_2.0.0*cu118* @@ -527,7 +506,7 @@ dependencies: common: - output_types: conda packages: - - *rmm + - &rmm_conda rmm==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -546,13 +525,13 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *rmm_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *rmm_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *rmm_packages_pip_cu11} - - {matrix: null, packages: [*rmm]} + - {matrix: null, packages: [*rmm_conda]} depends_on_cudf: common: - output_types: conda packages: - - *cudf + - &cudf_conda cudf==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -571,13 +550,13 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *cudf_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *cudf_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *cudf_packages_pip_cu11} - - {matrix: null, packages: [*cudf]} + - {matrix: null, packages: [*cudf_conda]} depends_on_dask_cudf: common: - output_types: conda packages: - - *dask_cudf + - &dask_cudf_conda dask-cudf==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -596,13 +575,13 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *dask_cudf_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *dask_cudf_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *dask_cudf_packages_pip_cu11} - - {matrix: null, packages: [*dask_cudf]} + - {matrix: null, packages: [*dask_cudf_conda]} depends_on_pylibraft: common: - output_types: conda packages: - - *pylibraft + - &pylibraft_conda pylibraft==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -621,13 +600,13 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *pylibraft_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *pylibraft_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *pylibraft_packages_pip_cu11} - - {matrix: null, packages: [*pylibraft]} + - {matrix: null, packages: [*pylibraft_conda]} depends_on_raft_dask: common: - output_types: conda packages: - - &raft_dask raft-dask==23.12.* + - &raft_dask_conda raft-dask==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -646,13 +625,13 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *raft_dask_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *raft_dask_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *raft_dask_packages_pip_cu11} - - {matrix: null, packages: [*raft_dask]} + - {matrix: null, packages: [*raft_dask_conda]} depends_on_pylibcugraph: common: - output_types: conda packages: - - *pylibcugraph + - &pylibcugraph_conda pylibcugraph==23.12.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -671,7 +650,32 @@ dependencies: - {matrix: {cuda: "11.5"}, packages: *pylibcugraph_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *pylibcugraph_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *pylibcugraph_packages_pip_cu11} - - {matrix: null, packages: [*pylibcugraph]} + - {matrix: null, packages: [*pylibcugraph_conda]} + + depends_on_pylibcugraphops: + common: + - output_types: conda + packages: + - &pylibcugraphops_conda pylibcugraphops==23.12.* + - output_types: requirements + packages: + # pip recognizes the index as a global option for the requirements.txt file + - --extra-index-url=https://pypi.nvidia.com + specific: + - output_types: [requirements, pyproject] + matrices: + - matrix: {cuda: "12.2"} + packages: &pylibcugraphops_packages_pip_cu12 + - pylibcugraphops-cu12==23.12.* + - {matrix: {cuda: "12.1"}, packages: *pylibcugraphops_packages_pip_cu12} + - {matrix: {cuda: "12.0"}, packages: *pylibcugraphops_packages_pip_cu12} + - matrix: {cuda: "11.8"} + packages: &pylibcugraphops_packages_pip_cu11 + - pylibcugraphops-cu11==23.12.* + - {matrix: {cuda: "11.5"}, packages: *pylibcugraphops_packages_pip_cu11} + - {matrix: {cuda: "11.4"}, packages: *pylibcugraphops_packages_pip_cu11} + - {matrix: {cuda: "11.2"}, packages: *pylibcugraphops_packages_pip_cu11} + - {matrix: null, packages: [*pylibcugraphops_conda]} depends_on_cupy: common: diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py b/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py index 6cabea198f6..2fd7d29bd49 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/__init__.py @@ -13,7 +13,7 @@ from cugraph_dgl.dataloading.dataset import ( HomogenousBulkSamplerDataset, - HetrogenousBulkSamplerDataset, + HeterogenousBulkSamplerDataset, ) from cugraph_dgl.dataloading.neighbor_sampler import NeighborSampler from cugraph_dgl.dataloading.dataloader import DataLoader diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py index 0480f61807a..f154b096256 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataloader.py @@ -21,7 +21,7 @@ from dask.distributed import default_client, Event from cugraph_dgl.dataloading import ( HomogenousBulkSamplerDataset, - HetrogenousBulkSamplerDataset, + HeterogenousBulkSamplerDataset, ) from cugraph_dgl.dataloading.utils.extract_graph_helpers import ( create_cugraph_graph_from_edges_dict, @@ -47,19 +47,20 @@ def __init__( graph_sampler: cugraph_dgl.dataloading.NeighborSampler, sampling_output_dir: str, batches_per_partition: int = 50, - seeds_per_call: int = 400_000, + seeds_per_call: int = 200_000, device: torch.device = None, use_ddp: bool = False, ddp_seed: int = 0, batch_size: int = 1024, drop_last: bool = False, shuffle: bool = False, + sparse_format: str = "coo", **kwargs, ): """ Constructor for CuGraphStorage: ------------------------------- - graph : CuGraphStorage + graph : CuGraphStorage The graph. indices : Tensor or dict[ntype, Tensor] The set of indices. It can either be a tensor of @@ -89,7 +90,12 @@ def __init__( The seed for shuffling the dataset in :class:`torch.utils.data.distributed.DistributedSampler`. Only effective when :attr:`use_ddp` is True. - batch_size: int, + batch_size: int + Batch size. + sparse_format: str, default = "coo" + The sparse format of the emitted sampled graphs. Choose between "csc" + and "coo". When using "csc", the graphs are of type + cugraph_dgl.nn.SparseGraph. kwargs : dict Key-word arguments to be passed to the parent PyTorch :py:class:`torch.utils.data.DataLoader` class. Common arguments are: @@ -123,6 +129,12 @@ def __init__( ... for input_nodes, output_nodes, blocks in dataloader: ... """ + if sparse_format not in ["coo", "csc"]: + raise ValueError( + f"sparse_format must be one of 'coo', 'csc', " + f"but got {sparse_format}." + ) + self.sparse_format = sparse_format self.ddp_seed = ddp_seed self.use_ddp = use_ddp @@ -156,11 +168,12 @@ def __init__( self.cugraph_dgl_dataset = HomogenousBulkSamplerDataset( total_number_of_nodes=graph.total_number_of_nodes, edge_dir=self.graph_sampler.edge_dir, + sparse_format=sparse_format, ) else: etype_id_to_etype_str_dict = {v: k for k, v in graph._etype_id_dict.items()} - self.cugraph_dgl_dataset = HetrogenousBulkSamplerDataset( + self.cugraph_dgl_dataset = HeterogenousBulkSamplerDataset( num_nodes_dict=graph.num_nodes_dict, etype_id_dict=etype_id_to_etype_str_dict, etype_offset_dict=graph._etype_offset_d, @@ -210,14 +223,23 @@ def __iter__(self): output_dir = os.path.join( self._sampling_output_dir, "epoch_" + str(self.epoch_number) ) + kwargs = {} if isinstance(self.cugraph_dgl_dataset, HomogenousBulkSamplerDataset): - deduplicate_sources = True - prior_sources_behavior = "carryover" - renumber = True + kwargs["deduplicate_sources"] = True + kwargs["prior_sources_behavior"] = "carryover" + kwargs["renumber"] = True + + if self.sparse_format == "csc": + kwargs["compression"] = "CSR" + kwargs["compress_per_hop"] = True + # The following kwargs will be deprecated in uniform sampler. + kwargs["use_legacy_names"] = False + kwargs["include_hop_column"] = False + else: - deduplicate_sources = False - prior_sources_behavior = None - renumber = False + kwargs["deduplicate_sources"] = False + kwargs["prior_sources_behavior"] = None + kwargs["renumber"] = False bs = BulkSampler( output_path=output_dir, @@ -227,10 +249,9 @@ def __iter__(self): seeds_per_call=self._seeds_per_call, fanout_vals=self.graph_sampler._reversed_fanout_vals, with_replacement=self.graph_sampler.replace, - deduplicate_sources=deduplicate_sources, - prior_sources_behavior=prior_sources_behavior, - renumber=renumber, + **kwargs, ) + if self.shuffle: self.tensorized_indices_ds.shuffle() diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py b/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py index e0d51bcf4cf..815fd30d8eb 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/dataset.py @@ -19,6 +19,7 @@ from cugraph_dgl.dataloading.utils.sampling_helpers import ( create_homogeneous_sampled_graphs_from_dataframe, create_heterogeneous_sampled_graphs_from_dataframe, + create_homogeneous_sampled_graphs_from_dataframe_csc, ) @@ -33,17 +34,19 @@ def __init__( total_number_of_nodes: int, edge_dir: str, return_type: str = "dgl.Block", + sparse_format: str = "coo", ): if return_type not in ["dgl.Block", "cugraph_dgl.nn.SparseGraph"]: raise ValueError( - "return_type must be either 'dgl.Block' or \ - 'cugraph_dgl.nn.SparseGraph' " + "return_type must be either 'dgl.Block' or " + "'cugraph_dgl.nn.SparseGraph'." ) # TODO: Deprecate `total_number_of_nodes` # as it is no longer needed # in the next release self.total_number_of_nodes = total_number_of_nodes self.edge_dir = edge_dir + self.sparse_format = sparse_format self._current_batch_fn = None self._input_files = None self._return_type = return_type @@ -60,10 +63,20 @@ def __getitem__(self, idx: int): fn, batch_offset = self._batch_to_fn_d[idx] if fn != self._current_batch_fn: - df = _load_sampled_file(dataset_obj=self, fn=fn) - self._current_batches = create_homogeneous_sampled_graphs_from_dataframe( - sampled_df=df, edge_dir=self.edge_dir, return_type=self._return_type - ) + if self.sparse_format == "csc": + df = _load_sampled_file(dataset_obj=self, fn=fn, skip_rename=True) + self._current_batches = ( + create_homogeneous_sampled_graphs_from_dataframe_csc(df) + ) + else: + df = _load_sampled_file(dataset_obj=self, fn=fn) + self._current_batches = ( + create_homogeneous_sampled_graphs_from_dataframe( + sampled_df=df, + edge_dir=self.edge_dir, + return_type=self._return_type, + ) + ) current_offset = idx - batch_offset return self._current_batches[current_offset] @@ -87,7 +100,7 @@ def set_input_files( ) -class HetrogenousBulkSamplerDataset(torch.utils.data.Dataset): +class HeterogenousBulkSamplerDataset(torch.utils.data.Dataset): def __init__( self, num_nodes_dict: Dict[str, int], @@ -141,18 +154,18 @@ def set_input_files( ---------- input_directory: str input_directory which contains all the files that will be - loaded by HetrogenousBulkSamplerDataset + loaded by HeterogenousBulkSamplerDataset input_file_paths: List[str] - File names that will be loaded by the HetrogenousBulkSamplerDataset + File names that will be loaded by the HeterogenousBulkSamplerDataset """ _set_input_files( self, input_directory=input_directory, input_file_paths=input_file_paths ) -def _load_sampled_file(dataset_obj, fn): +def _load_sampled_file(dataset_obj, fn, skip_rename=False): df = cudf.read_parquet(os.path.join(fn)) - if dataset_obj.edge_dir == "in": + if dataset_obj.edge_dir == "in" and not skip_rename: df.rename( columns={"sources": "destinations", "destinations": "sources"}, inplace=True, @@ -181,7 +194,7 @@ def get_batch_to_fn_d(files): def _set_input_files( - dataset_obj: Union[HomogenousBulkSamplerDataset, HetrogenousBulkSamplerDataset], + dataset_obj: Union[HomogenousBulkSamplerDataset, HeterogenousBulkSamplerDataset], input_directory: Optional[str] = None, input_file_paths: Optional[List[str]] = None, ) -> None: diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py index bdac3b1a323..a4f64668348 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py @@ -11,10 +11,12 @@ # See the License for the specific language governing permissions and # limitations under the License. from __future__ import annotations -from typing import Tuple, Dict, Optional +from typing import List, Tuple, Dict, Optional from collections import defaultdict import cudf +import cupy from cugraph.utilities.utils import import_optional +from cugraph_dgl.nn import SparseGraph dgl = import_optional("dgl") torch = import_optional("torch") @@ -401,3 +403,154 @@ def create_heterogenous_dgl_block_from_tensors_dict( block = dgl.to_block(sampled_graph, dst_nodes=seed_nodes, src_nodes=src_d) block.edata[dgl.EID] = sampled_graph.edata[dgl.EID] return block + + +def _process_sampled_df_csc( + df: cudf.DataFrame, + reverse_hop_id: bool = True, +) -> Tuple[ + Dict[int, Dict[int, Dict[str, torch.Tensor]]], + List[torch.Tensor], + List[List[int, int]], +]: + """ + Convert a dataframe generated by BulkSampler to a dictionary of tensors, to + facilitate MFG creation. The sampled graphs in the dataframe use CSC-format. + + Parameters + ---------- + df: cudf.DataFrame + The output from BulkSampler compressed in CSC format. The dataframe + should be generated with `compression="CSR"` in BulkSampler, + since the sampling routine treats seed nodes as sources. + + reverse_hop_id: bool (default=True) + Reverse hop id. + + Returns + ------- + tensors_dict: dict + A nested dictionary keyed by batch id and hop id. + `tensor_dict[batch_id][hop_id]` holds "minors" and "major_offsets" + values for CSC MFGs. + + renumber_map_list: list + List of renumbering maps for looking up global indices of nodes. One + map for each batch. + + mfg_sizes: list + List of the number of nodes in each message passing layer. For the + k-th hop, mfg_sizes[k] and mfg_sizes[k+1] is the number of sources and + destinations, respectively. + """ + # dropna + major_offsets = df.major_offsets.dropna().values + label_hop_offsets = df.label_hop_offsets.dropna().values + renumber_map_offsets = df.renumber_map_offsets.dropna().values + renumber_map = df.map.dropna().values + minors = df.minors.dropna().values + + n_batches = renumber_map_offsets.size - 1 + n_hops = int((label_hop_offsets.size - 1) / n_batches) + + # make global offsets local + major_offsets -= major_offsets[0] + label_hop_offsets -= label_hop_offsets[0] + renumber_map_offsets -= renumber_map_offsets[0] + + # get the sizes of each adjacency matrix (for MFGs) + mfg_sizes = (label_hop_offsets[1:] - label_hop_offsets[:-1]).reshape( + (n_batches, n_hops) + ) + n_nodes = renumber_map_offsets[1:] - renumber_map_offsets[:-1] + mfg_sizes = cupy.hstack((mfg_sizes, n_nodes.reshape(n_batches, -1))) + if reverse_hop_id: + mfg_sizes = mfg_sizes[:, ::-1] + + tensors_dict = {} + renumber_map_list = [] + for batch_id in range(n_batches): + batch_dict = {} + + for hop_id in range(n_hops): + hop_dict = {} + idx = batch_id * n_hops + hop_id # idx in label_hop_offsets + major_offsets_start = label_hop_offsets[idx].item() + major_offsets_end = label_hop_offsets[idx + 1].item() + minors_start = major_offsets[major_offsets_start].item() + minors_end = major_offsets[major_offsets_end].item() + # Note: minors and major_offsets from BulkSampler are of type int32 + # and int64 respectively. Since pylibcugraphops binding code doesn't + # support distinct node and edge index type, we simply casting both + # to int32 for now. + hop_dict["minors"] = torch.as_tensor( + minors[minors_start:minors_end], device="cuda" + ).int() + hop_dict["major_offsets"] = torch.as_tensor( + major_offsets[major_offsets_start : major_offsets_end + 1] + - major_offsets[major_offsets_start], + device="cuda", + ).int() + if reverse_hop_id: + batch_dict[n_hops - 1 - hop_id] = hop_dict + else: + batch_dict[hop_id] = hop_dict + + tensors_dict[batch_id] = batch_dict + + renumber_map_list.append( + torch.as_tensor( + renumber_map[ + renumber_map_offsets[batch_id] : renumber_map_offsets[batch_id + 1] + ], + device="cuda", + ) + ) + + return tensors_dict, renumber_map_list, mfg_sizes.tolist() + + +def _create_homogeneous_sparse_graphs_from_csc( + tensors_dict: Dict[int, Dict[int, Dict[str, torch.Tensor]]], + renumber_map_list: List[torch.Tensor], + mfg_sizes: List[int, int], +) -> List[List[torch.Tensor, torch.Tensor, List[SparseGraph]]]: + """Create mini-batches of MFGs. The input arguments are the outputs of + the function `_process_sampled_df_csc`. + + Returns + ------- + output: list + A list of mini-batches. Each mini-batch is a list that consists of + `input_nodes` tensor, `output_nodes` tensor and a list of MFGs. + """ + n_batches, n_hops = len(mfg_sizes), len(mfg_sizes[0]) - 1 + output = [] + for b_id in range(n_batches): + output_batch = [] + output_batch.append(renumber_map_list[b_id]) + output_batch.append(renumber_map_list[b_id][: mfg_sizes[b_id][-1]]) + mfgs = [ + SparseGraph( + size=(mfg_sizes[b_id][h_id], mfg_sizes[b_id][h_id + 1]), + src_ids=tensors_dict[b_id][h_id]["minors"], + cdst_ids=tensors_dict[b_id][h_id]["major_offsets"], + formats=["csc"], + reduce_memory=True, + ) + for h_id in range(n_hops) + ] + + output_batch.append(mfgs) + + output.append(output_batch) + + return output + + +def create_homogeneous_sampled_graphs_from_dataframe_csc(sampled_df: cudf.DataFrame): + """Public API to create mini-batches of MFGs using a dataframe output by + BulkSampler, where the sampled graph is compressed in CSC format.""" + return _create_homogeneous_sparse_graphs_from_csc( + *(_process_sampled_df_csc(sampled_df)) + ) diff --git a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py index 307eb33078e..ddd95a76366 100644 --- a/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py +++ b/python/cugraph-dgl/cugraph_dgl/nn/conv/base.py @@ -248,6 +248,13 @@ def csr(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: value = value[self._perm_csc2csr] return csrc_ids, dst_ids, value + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}(num_src_nodes={self._num_src_nodes}, " + f"num_dst_nodes={self._num_dst_nodes}, " + f"num_edges={self._src_ids.size(0)}, formats={self._formats})" + ) + class BaseConv(torch.nn.Module): r"""An abstract base class for cugraph-ops nn module.""" diff --git a/python/cugraph-dgl/tests/test_utils.py b/python/cugraph-dgl/tests/test_utils.py index 740db59ce7f..4be66758b43 100644 --- a/python/cugraph-dgl/tests/test_utils.py +++ b/python/cugraph-dgl/tests/test_utils.py @@ -22,6 +22,7 @@ create_homogeneous_sampled_graphs_from_dataframe, _get_source_destination_range, _create_homogeneous_cugraph_dgl_nn_sparse_graph, + create_homogeneous_sampled_graphs_from_dataframe_csc, ) from cugraph.utilities.utils import import_optional @@ -50,6 +51,23 @@ def get_dummy_sampled_df(): return df +def get_dummy_sampled_df_csc(): + df_dict = dict( + minors=np.array( + [1, 1, 2, 1, 0, 3, 1, 3, 2, 3, 2, 4, 0, 1, 1, 0, 3, 2], dtype=np.int32 + ), + major_offsets=np.arange(19, dtype=np.int64), + map=np.array( + [26, 29, 33, 22, 23, 32, 18, 29, 33, 33, 8, 30, 32], dtype=np.int32 + ), + renumber_map_offsets=np.array([0, 4, 9, 13], dtype=np.int64), + label_hop_offsets=np.array([0, 1, 3, 6, 7, 9, 13, 14, 16, 18], dtype=np.int64), + ) + + # convert values to Series so that NaNs are padded automatically + return cudf.DataFrame({k: cudf.Series(v) for k, v in df_dict.items()}) + + def test_get_renumber_map(): sampled_df = get_dummy_sampled_df() @@ -176,3 +194,13 @@ def test__create_homogeneous_cugraph_dgl_nn_sparse_graph(): assert sparse_graph.num_src_nodes() == 2 assert sparse_graph.num_dst_nodes() == seednodes_range + 1 assert isinstance(sparse_graph, cugraph_dgl.nn.SparseGraph) + + +def test_create_homogeneous_sampled_graphs_from_dataframe_csc(): + df = get_dummy_sampled_df_csc() + batches = create_homogeneous_sampled_graphs_from_dataframe_csc(df) + + assert len(batches) == 3 + assert torch.equal(batches[0][0], torch.IntTensor([26, 29, 33, 22]).cuda()) + assert torch.equal(batches[1][0], torch.IntTensor([23, 32, 18, 29, 33]).cuda()) + assert torch.equal(batches[2][0], torch.IntTensor([33, 8, 30, 32]).cuda()) diff --git a/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py b/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py index e3fdeb7f150..77a53882fc4 100644 --- a/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py +++ b/python/cugraph/cugraph/gnn/feature_storage/feat_storage.py @@ -17,23 +17,77 @@ import cupy as cp import numpy as np import pandas as pd -from cugraph.utilities.utils import import_optional +from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") +wgth = import_optional("pylibwholegraph.torch") class FeatureStore: - """The feature-store class used to store feature data for GNNS""" + """The feature-store class used to store feature data for GNNs""" + + def __init__( + self, + backend: str = "numpy", + wg_comm: object = None, + wg_type: str = None, + wg_location: str = None, + ): + """ + Constructs a new FeatureStore object + + Parameters: + ---------- + backend: str ('numpy', 'torch', 'wholegraph') + Optional (default='numpy') + The name of the backend to use. + + wg_comm: WholeMemoryCommunicator + Optional (default=automatic) + Only used with the 'wholegraph' backend. + The communicator to use to store features in WholeGraph. + + wg_type: str ('distributed', 'continuous', 'chunked') + Optional (default='distributed') + Only used with the 'wholegraph' backend. + The memory format (distributed, continuous, or chunked) of + this FeatureStore. For more information see the WholeGraph + documentation. + + wg_location: str ('cpu', 'cuda') + Optional (default='cuda') + Only used with the 'wholegraph' backend. + Where the data is stored (cpu or cuda). + Defaults to storing on the GPU (cuda). + """ - def __init__(self, backend="numpy"): self.fd = defaultdict(dict) - if backend not in ["numpy", "torch"]: + if backend not in ["numpy", "torch", "wholegraph"]: raise ValueError( - f"backend {backend} not supported. Supported backends are numpy, torch" + f"backend {backend} not supported. " + "Supported backends are numpy, torch, wholegraph" ) self.backend = backend - def add_data(self, feat_obj: Sequence, type_name: str, feat_name: str) -> None: + self.__wg_comm = None + self.__wg_type = None + self.__wg_location = None + + if backend == "wholegraph": + self.__wg_comm = ( + wg_comm if wg_comm is not None else wgth.get_local_node_communicator() + ) + self.__wg_type = wg_type if wg_type is not None else "distributed" + self.__wg_location = wg_location if wg_location is not None else "cuda" + + if self.__wg_type not in ["distributed", "chunked", "continuous"]: + raise ValueError(f"invalid memory format {self.__wg_type}") + if (self.__wg_location != "cuda") and (self.__wg_location != "cpu"): + raise ValueError(f"invalid location {self.__wg_location}") + + def add_data( + self, feat_obj: Sequence, type_name: str, feat_name: str, **kwargs + ) -> None: """ Add the feature data to the feature_storage class Parameters: @@ -49,9 +103,31 @@ def add_data(self, feat_obj: Sequence, type_name: str, feat_name: str) -> None: None """ self.fd[feat_name][type_name] = self._cast_feat_obj_to_backend( - feat_obj, self.backend + feat_obj, + self.backend, + wg_comm=self.__wg_comm, + wg_type=self.__wg_type, + wg_location=self.__wg_location, + **kwargs, ) + def add_data_no_cast(self, feat_obj, type_name: str, feat_name: str) -> None: + """ + Direct add the feature data to the feature_storage class with no cast + Parameters: + ---------- + feat_obj : array_like object + The feature object to save in feature store + type_name : str + The node-type/edge-type of the feature + feat_name: str + The name of the feature being stored + Returns: + ------- + None + """ + self.fd[feat_name][type_name] = feat_obj + def get_data( self, indices: Union[np.ndarray, torch.Tensor], @@ -87,26 +163,67 @@ def get_data( f" feature: {list(self.fd[feat_name].keys())}" ) - return self.fd[feat_name][type_name][indices] + feat = self.fd[feat_name][type_name] + if not isinstance(wgth, MissingModule) and isinstance( + feat, wgth.WholeMemoryEmbedding + ): + indices_tensor = ( + indices + if isinstance(indices, torch.Tensor) + else torch.as_tensor(indices, device="cuda") + ) + return feat.gather(indices_tensor) + else: + return feat[indices] def get_feature_list(self) -> list[str]: return {feat_name: feats.keys() for feat_name, feats in self.fd.items()} @staticmethod - def _cast_feat_obj_to_backend(feat_obj, backend: str): + def _cast_feat_obj_to_backend(feat_obj, backend: str, **kwargs): if backend == "numpy": if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): - return _cast_to_numpy_ar(feat_obj.values) + return _cast_to_numpy_ar(feat_obj.values, **kwargs) else: - return _cast_to_numpy_ar(feat_obj) + return _cast_to_numpy_ar(feat_obj, **kwargs) elif backend == "torch": if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): - return _cast_to_torch_tensor(feat_obj.values) + return _cast_to_torch_tensor(feat_obj.values, **kwargs) else: - return _cast_to_torch_tensor(feat_obj) + return _cast_to_torch_tensor(feat_obj, **kwargs) + elif backend == "wholegraph": + return _get_wg_embedding(feat_obj, **kwargs) + +def _get_wg_embedding(feat_obj, wg_comm=None, wg_type=None, wg_location=None, **kwargs): + wg_comm_obj = wg_comm or wgth.get_local_node_communicator() + wg_type_str = wg_type or "distributed" + wg_location_str = wg_location or "cuda" -def _cast_to_torch_tensor(ar): + if isinstance(feat_obj, (cudf.DataFrame, pd.DataFrame)): + th_tensor = _cast_to_torch_tensor(feat_obj.values) + else: + th_tensor = _cast_to_torch_tensor(feat_obj) + wg_embedding = wgth.create_embedding( + wg_comm_obj, + wg_type_str, + wg_location_str, + th_tensor.dtype, + th_tensor.shape, + ) + ( + local_wg_tensor, + local_ld_offset, + ) = wg_embedding.get_embedding_tensor().get_local_tensor() + local_th_tensor = th_tensor[ + local_ld_offset : local_ld_offset + local_wg_tensor.shape[0] + ] + local_wg_tensor.copy_(local_th_tensor) + wg_comm_obj.barrier() + return wg_embedding + + +def _cast_to_torch_tensor(ar, **kwargs): if isinstance(ar, cp.ndarray): ar = torch.as_tensor(ar, device="cuda") elif isinstance(ar, np.ndarray): @@ -116,7 +233,7 @@ def _cast_to_torch_tensor(ar): return ar -def _cast_to_numpy_ar(ar): +def _cast_to_numpy_ar(ar, **kwargs): if isinstance(ar, cp.ndarray): ar = ar.get() elif type(ar).__name__ == "Tensor": diff --git a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py index 2d1537d11e3..2b0ec4b11d0 100644 --- a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py +++ b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage.py @@ -10,7 +10,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -# Import FeatureStore class import pytest import numpy as np diff --git a/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py new file mode 100644 index 00000000000..1892e8a85a6 --- /dev/null +++ b/python/cugraph/cugraph/tests/data_store/test_gnn_feat_storage_wholegraph.py @@ -0,0 +1,85 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import pytest +import numpy as np + +from cugraph.gnn import FeatureStore + +from cugraph.utilities.utils import import_optional, MissingModule + +pylibwholegraph = import_optional("pylibwholegraph") +wmb = import_optional("pylibwholegraph.binding.wholememory_binding") +torch = import_optional("torch") + + +def runtest(world_rank: int, world_size: int): + from pylibwholegraph.torch.initialize import init_torch_env_and_create_wm_comm + + wm_comm, _ = init_torch_env_and_create_wm_comm( + world_rank, + world_size, + world_rank, + world_size, + ) + wm_comm = wm_comm.wmb_comm + + generator = np.random.default_rng(62) + arr = ( + generator.integers(low=0, high=100, size=100_000) + .reshape(10_000, -1) + .astype("float64") + ) + + fs = FeatureStore(backend="wholegraph") + fs.add_data(arr, "type2", "feat1") + wm_comm.barrier() + + indices_to_fetch = np.random.randint(low=0, high=len(arr), size=1024) + output_fs = fs.get_data(indices_to_fetch, type_name="type2", feat_name="feat1") + assert isinstance(output_fs, torch.Tensor) + assert output_fs.is_cuda + expected = arr[indices_to_fetch] + np.testing.assert_array_equal(output_fs.cpu().numpy(), expected) + + wmb.finalize() + + +@pytest.mark.sg +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(pylibwholegraph, MissingModule), reason="wholegraph not available" +) +def test_feature_storage_wholegraph_backend(): + from pylibwholegraph.utils.multiprocess import multiprocess_run + + gpu_count = wmb.fork_get_gpu_count() + print("gpu count:", gpu_count) + assert gpu_count > 0 + + multiprocess_run(1, runtest) + + +@pytest.mark.mg +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(pylibwholegraph, MissingModule), reason="wholegraph not available" +) +def test_feature_storage_wholegraph_backend_mg(): + from pylibwholegraph.utils.multiprocess import multiprocess_run + + gpu_count = wmb.fork_get_gpu_count() + print("gpu count:", gpu_count) + assert gpu_count > 0 + + multiprocess_run(gpu_count, runtest) diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 9b3332106ec..ba7fe5e91d7 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -75,7 +75,7 @@ def get_info(): return d -__version__ = "23.10.00" +__version__ = "23.12.00" if __name__ == "__main__": from pathlib import Path From 40a5f8e1dd868b6a9faf9f70dfcd6a70fa78e461 Mon Sep 17 00:00:00 2001 From: Ray Douglass <3107146+raydouglass@users.noreply.github.com> Date: Fri, 6 Oct 2023 16:05:30 -0400 Subject: [PATCH 036/111] Add wget to test_notebook dependencies (#3918) Running certain notebooks requires downloading datasets via https://github.com/rapidsai/cugraph/blob/branch-23.10/notebooks/cugraph_benchmarks/dataPrep.sh which uses `wget`, so this PR ensures the `test_notebooks` environment created in `rapidsai/docker` will have `wget` installed. Authors: - Ray Douglass (https://github.com/raydouglass) Approvers: - Jake Awe (https://github.com/AyodeAwe) --- conda/environments/all_cuda-118_arch-x86_64.yaml | 1 + conda/environments/all_cuda-120_arch-x86_64.yaml | 1 + dependencies.yaml | 3 +++ 3 files changed, 5 insertions(+) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 87179ef892e..dea52887f23 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -74,5 +74,6 @@ dependencies: - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.34.* +- wget - wheel name: all_cuda-118_arch-x86_64 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index d54dc0abf51..2d55f73c5d1 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -73,5 +73,6 @@ dependencies: - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.34.* +- wget - wheel name: all_cuda-120_arch-x86_64 diff --git a/dependencies.yaml b/dependencies.yaml index 292fcf0baed..f330361ba88 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -463,6 +463,9 @@ dependencies: packages: - ipython - notebook>=0.5.0 + - output_types: [conda] + packages: + - wget test_python_common: common: - output_types: [conda, pyproject] From c02b295d040f209db025e04b29d146fc00fb5ee8 Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Tue, 10 Oct 2023 12:53:01 -0700 Subject: [PATCH 037/111] update Dask worker min timeout --- ci/test_python.sh | 2 +- ci/test_wheel.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index df0f34377a3..c7687396ff0 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -68,7 +68,7 @@ pushd python/cugraph/cugraph DASK_WORKER_DEVICES="0" \ DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ -DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ +DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT="1000s" \ pytest \ -v \ --benchmark-disable \ diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index d6ec67cd9e9..c4adb638437 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -24,6 +24,6 @@ else RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets \ DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ - DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT=20 \ + DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT="1000s" \ python -m pytest ./python/${package_name}/${python_package_name}/tests fi From 81e33bdf3a31d1087bfb33e465862315f252ac4f Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Wed, 11 Oct 2023 08:16:36 -0500 Subject: [PATCH 038/111] Link wholegrah and cugraphops XML docs (#3906) Configures `breathe` and links the uploaded `wholegraph` and `cugraph-ops` XML docs artifacts. Authors: - Jake Awe (https://github.com/AyodeAwe) Approvers: - Brad Rees (https://github.com/BradReesWork) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3906 --- ci/build_docs.sh | 7 +++++++ conda/environments/all_cuda-118_arch-x86_64.yaml | 1 + conda/environments/all_cuda-120_arch-x86_64.yaml | 1 + dependencies.yaml | 1 + docs/cugraph/source/conf.py | 9 ++++++++- 5 files changed, 18 insertions(+), 1 deletion(-) diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 7b4cf152b4a..e774a6f9871 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -40,6 +40,13 @@ rapids-mamba-retry install "${PYTHON_CHANNEL}/linux-64/cugraph-dgl-*.tar.bz2" export RAPIDS_VERSION_NUMBER="23.12" export RAPIDS_DOCS_DIR="$(mktemp -d)" +for PROJECT in libcugraphops libwholegraph; do + rapids-logger "Download ${PROJECT} xml_tar" + TMP_DIR=$(mktemp -d) + export XML_DIR_${PROJECT^^}="$TMP_DIR" + aws s3 cp --only-show-errors "s3://rapidsai-docs/${PROJECT}/xml_tar/${RAPIDS_VERSION_NUMBER}/xml.tar.gz" - | tar xzf - -C "${TMP_DIR}" +done + rapids-logger "Build CPP docs" pushd cpp/doxygen doxygen Doxyfile diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 35330db5815..42a218fcf66 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -11,6 +11,7 @@ channels: - nvidia dependencies: - aiohttp +- breathe - c-compiler - cmake>=3.26.4 - cuda-version=11.8 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 84967412dc5..0a1774b769f 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -11,6 +11,7 @@ channels: - nvidia dependencies: - aiohttp +- breathe - c-compiler - cmake>=3.26.4 - cuda-nvcc diff --git a/dependencies.yaml b/dependencies.yaml index f5067ddae70..7b63f6dc536 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -334,6 +334,7 @@ dependencies: common: - output_types: [conda] packages: + - breathe - doxygen - graphviz - ipython diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 58ecca27150..470086b4faa 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -37,6 +37,7 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ + "breathe", "sphinx.ext.intersphinx", "sphinx.ext.autodoc", "sphinx.ext.autosummary", @@ -205,4 +206,10 @@ def setup(app): # The following is used by sphinx.ext.linkcode to provide links to github linkcode_resolve = make_linkcode_resolve( "https://github.com/rapidsai/cugraph/blob/{revision}/python/{path}#L{lineno}" -) \ No newline at end of file +) + +breathe_projects = { + 'libcugraphops': os.environ['XML_DIR_LIBCUGRAPHOPS'], + 'libwholegraph': os.environ['XML_DIR_LIBWHOLEGRAPH'] +} +breathe_default_project = "libcugraph" From bbc8fed271deb973de01d227e81a0a2981ac2b38 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Wed, 11 Oct 2023 10:28:07 -0400 Subject: [PATCH 039/111] Update Changelog [skip ci] --- CHANGELOG.md | 94 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4832bc2fb04..33a5b2bc5e7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,97 @@ +# cuGraph 23.10.00 (11 Oct 2023) + +## 🚨 Breaking Changes + +- Rename `cugraph-nx` to `nx-cugraph` ([#3840](https://github.com/rapidsai/cugraph/pull/3840)) [@eriknw](https://github.com/eriknw) +- Remove legacy betweenness centrality ([#3829](https://github.com/rapidsai/cugraph/pull/3829)) [@jnke2016](https://github.com/jnke2016) +- Remove Deprecated Sampling Options ([#3816](https://github.com/rapidsai/cugraph/pull/3816)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- cuGraph-PyG Loader Improvements ([#3795](https://github.com/rapidsai/cugraph/pull/3795)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Expose threshold in louvain ([#3792](https://github.com/rapidsai/cugraph/pull/3792)) [@ChuckHastings](https://github.com/ChuckHastings) +- Fix ValueError Caused By Batches With No Samples ([#3789](https://github.com/rapidsai/cugraph/pull/3789)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Update to Cython 3.0.0 ([#3716](https://github.com/rapidsai/cugraph/pull/3716)) [@vyasr](https://github.com/vyasr) + +## 🐛 Bug Fixes + +- Add wget to test_notebook dependencies ([#3918](https://github.com/rapidsai/cugraph/pull/3918)) [@raydouglass](https://github.com/raydouglass) +- Increase dask-related timeouts for CI testing ([#3907](https://github.com/rapidsai/cugraph/pull/3907)) [@jnke2016](https://github.com/jnke2016) +- Remove `dask_cudf` dataframe for the `_make_plc_graph` while creating `cugraph.Graph` ([#3895](https://github.com/rapidsai/cugraph/pull/3895)) [@VibhuJawa](https://github.com/VibhuJawa) +- Adds logic to handle isolated vertices at python layer ([#3886](https://github.com/rapidsai/cugraph/pull/3886)) [@naimnv](https://github.com/naimnv) +- Update Allocator Selection in cuGraph-DGL Example ([#3877](https://github.com/rapidsai/cugraph/pull/3877)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Add file to update-version.sh ([#3870](https://github.com/rapidsai/cugraph/pull/3870)) [@raydouglass](https://github.com/raydouglass) +- Fix torch seed in `cugraph-dgl` and `-pyg` tests for conv layers ([#3869](https://github.com/rapidsai/cugraph/pull/3869)) [@tingyu66](https://github.com/tingyu66) +- MFG C++ code bug fix ([#3865](https://github.com/rapidsai/cugraph/pull/3865)) [@seunghwak](https://github.com/seunghwak) +- Fix subtle memory leak in nbr_intersection primitive ([#3858](https://github.com/rapidsai/cugraph/pull/3858)) [@ChuckHastings](https://github.com/ChuckHastings) +- Uses `conda mambabuild` rather than `mamba mambabuild` ([#3853](https://github.com/rapidsai/cugraph/pull/3853)) [@rlratzel](https://github.com/rlratzel) +- Remove the assumption made on the client data's keys ([#3835](https://github.com/rapidsai/cugraph/pull/3835)) [@jnke2016](https://github.com/jnke2016) +- Disable mg tests ([#3833](https://github.com/rapidsai/cugraph/pull/3833)) [@naimnv](https://github.com/naimnv) +- Refactor python code for similarity algos to use latest CAPI ([#3828](https://github.com/rapidsai/cugraph/pull/3828)) [@naimnv](https://github.com/naimnv) +- [BUG] Fix Batch Renumbering of Empty Batches ([#3823](https://github.com/rapidsai/cugraph/pull/3823)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Temporarily disable the deletion of the dask dataframe ([#3814](https://github.com/rapidsai/cugraph/pull/3814)) [@jnke2016](https://github.com/jnke2016) +- Fix OD shortest distance matrix computation test failures. ([#3813](https://github.com/rapidsai/cugraph/pull/3813)) [@seunghwak](https://github.com/seunghwak) +- Use rapidsai/ci:cuda11.8.0-ubuntu22.04-py3.10 for docs build ([#3811](https://github.com/rapidsai/cugraph/pull/3811)) [@naimnv](https://github.com/naimnv) +- Fix ValueError Caused By Batches With No Samples ([#3789](https://github.com/rapidsai/cugraph/pull/3789)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Update `python_run_cugraph` in `dependencies.yaml` ([#3781](https://github.com/rapidsai/cugraph/pull/3781)) [@nv-rliu](https://github.com/nv-rliu) +- Fixes `KeyError` for `get_two_hop_neighbors` when called with a small start vertices list ([#3778](https://github.com/rapidsai/cugraph/pull/3778)) [@rlratzel](https://github.com/rlratzel) + +## 📖 Documentation + +- Update the docstrings of the similarity algorithms ([#3817](https://github.com/rapidsai/cugraph/pull/3817)) [@jnke2016](https://github.com/jnke2016) + +## 🚀 New Features + +- WholeGraph Feature Store for cuGraph-PyG and cuGraph-DGL ([#3874](https://github.com/rapidsai/cugraph/pull/3874)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- similarity notebook to compare link prediction algos ([#3868](https://github.com/rapidsai/cugraph/pull/3868)) [@acostadon](https://github.com/acostadon) +- adding dining preference dataset ([#3866](https://github.com/rapidsai/cugraph/pull/3866)) [@acostadon](https://github.com/acostadon) +- Integrate C++ Renumbering and Compression ([#3841](https://github.com/rapidsai/cugraph/pull/3841)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Sampling post processing functions to accelerate MFG creation. ([#3815](https://github.com/rapidsai/cugraph/pull/3815)) [@seunghwak](https://github.com/seunghwak) +- [REVIEW] Add Pure DGL Dataloading benchmark ([#3660](https://github.com/rapidsai/cugraph/pull/3660)) [@VibhuJawa](https://github.com/VibhuJawa) + +## 🛠️ Improvements + +- nx-cugraph: handle louvain with isolated nodes ([#3897](https://github.com/rapidsai/cugraph/pull/3897)) [@eriknw](https://github.com/eriknw) +- Pin `dask` and `distributed` for `23.10` release ([#3896](https://github.com/rapidsai/cugraph/pull/3896)) [@galipremsagar](https://github.com/galipremsagar) +- Updates the source build docs to include libcugraphops as a build prerequisite ([#3893](https://github.com/rapidsai/cugraph/pull/3893)) [@rlratzel](https://github.com/rlratzel) +- fixes force atlas to allow string as vertex names ([#3891](https://github.com/rapidsai/cugraph/pull/3891)) [@acostadon](https://github.com/acostadon) +- Integrate renumbering and compression to `cugraph-dgl` to accelerate MFG creation ([#3887](https://github.com/rapidsai/cugraph/pull/3887)) [@tingyu66](https://github.com/tingyu66) +- Enable weights for MG similarity algorithms ([#3879](https://github.com/rapidsai/cugraph/pull/3879)) [@jnke2016](https://github.com/jnke2016) +- cuGraph-PyG MFG Creation and Conversion ([#3873](https://github.com/rapidsai/cugraph/pull/3873)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Update image names ([#3867](https://github.com/rapidsai/cugraph/pull/3867)) [@AyodeAwe](https://github.com/AyodeAwe) +- Update to clang 16.0.6. ([#3859](https://github.com/rapidsai/cugraph/pull/3859)) [@bdice](https://github.com/bdice) +- Updates to build and test `nx-cugraph` wheel as part of CI and nightly workflows ([#3852](https://github.com/rapidsai/cugraph/pull/3852)) [@rlratzel](https://github.com/rlratzel) +- Update `cugraph-dgl` conv layers to use improved graph class ([#3849](https://github.com/rapidsai/cugraph/pull/3849)) [@tingyu66](https://github.com/tingyu66) +- Add entry point to tell NetworkX about nx-cugraph without importing it. ([#3848](https://github.com/rapidsai/cugraph/pull/3848)) [@eriknw](https://github.com/eriknw) +- [IMP] Add ability to get batch size from the loader in cuGraph-PyG ([#3846](https://github.com/rapidsai/cugraph/pull/3846)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Refactor legacy k truss ([#3843](https://github.com/rapidsai/cugraph/pull/3843)) [@jnke2016](https://github.com/jnke2016) +- Use new `raft::compiled_static` targets ([#3842](https://github.com/rapidsai/cugraph/pull/3842)) [@divyegala](https://github.com/divyegala) +- Rename `cugraph-nx` to `nx-cugraph` ([#3840](https://github.com/rapidsai/cugraph/pull/3840)) [@eriknw](https://github.com/eriknw) +- Add cuGraph devcontainers ([#3838](https://github.com/rapidsai/cugraph/pull/3838)) [@trxcllnt](https://github.com/trxcllnt) +- Enable temporarily disabled MG tests ([#3837](https://github.com/rapidsai/cugraph/pull/3837)) [@naimnv](https://github.com/naimnv) +- Remove legacy betweenness centrality ([#3829](https://github.com/rapidsai/cugraph/pull/3829)) [@jnke2016](https://github.com/jnke2016) +- Use `copy-pr-bot` ([#3827](https://github.com/rapidsai/cugraph/pull/3827)) [@ajschmidt8](https://github.com/ajschmidt8) +- Update README.md ([#3826](https://github.com/rapidsai/cugraph/pull/3826)) [@lmeyerov](https://github.com/lmeyerov) +- Adding metadata getter methods to datasets API ([#3821](https://github.com/rapidsai/cugraph/pull/3821)) [@nv-rliu](https://github.com/nv-rliu) +- Unpin `dask` and `distributed` for `23.10` development ([#3818](https://github.com/rapidsai/cugraph/pull/3818)) [@galipremsagar](https://github.com/galipremsagar) +- Remove Deprecated Sampling Options ([#3816](https://github.com/rapidsai/cugraph/pull/3816)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- [REVIEW] Cugraph dgl block improvements ([#3810](https://github.com/rapidsai/cugraph/pull/3810)) [@VibhuJawa](https://github.com/VibhuJawa) +- Simplify wheel build scripts and allow alphas of RAPIDS dependencies ([#3809](https://github.com/rapidsai/cugraph/pull/3809)) [@vyasr](https://github.com/vyasr) +- Allow cugraph-nx to run networkx tests for nx versions 3.0, 3.1, and 3.2 ([#3808](https://github.com/rapidsai/cugraph/pull/3808)) [@eriknw](https://github.com/eriknw) +- Add `louvain_communities` to cugraph-nx ([#3803](https://github.com/rapidsai/cugraph/pull/3803)) [@eriknw](https://github.com/eriknw) +- Adds missing copyright and license text to __init__.py package files ([#3799](https://github.com/rapidsai/cugraph/pull/3799)) [@rlratzel](https://github.com/rlratzel) +- cuGraph-PyG Loader Improvements ([#3795](https://github.com/rapidsai/cugraph/pull/3795)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Adds updates to build wheel and conda packages for `cugraph-nx` ([#3793](https://github.com/rapidsai/cugraph/pull/3793)) [@rlratzel](https://github.com/rlratzel) +- Expose threshold in louvain ([#3792](https://github.com/rapidsai/cugraph/pull/3792)) [@ChuckHastings](https://github.com/ChuckHastings) +- Allow models to use a lightweight sparse structure ([#3782](https://github.com/rapidsai/cugraph/pull/3782)) [@tingyu66](https://github.com/tingyu66) +- Clean-up old testing conventions in `test_ecg.py` ([#3779](https://github.com/rapidsai/cugraph/pull/3779)) [@nv-rliu](https://github.com/nv-rliu) +- Calling `dataset.get_edgelist()` returns a copy of an edge list instead of global ([#3777](https://github.com/rapidsai/cugraph/pull/3777)) [@nv-rliu](https://github.com/nv-rliu) +- Update dgl benchmarks ([#3775](https://github.com/rapidsai/cugraph/pull/3775)) [@VibhuJawa](https://github.com/VibhuJawa) +- Forward-merge branch-23.08 to branch-23.10 ([#3774](https://github.com/rapidsai/cugraph/pull/3774)) [@nv-rliu](https://github.com/nv-rliu) +- Migrate upstream models to `cugraph-pyg` ([#3763](https://github.com/rapidsai/cugraph/pull/3763)) [@tingyu66](https://github.com/tingyu66) +- Branch 23.10 merge 23.08 ([#3743](https://github.com/rapidsai/cugraph/pull/3743)) [@vyasr](https://github.com/vyasr) +- Update to Cython 3.0.0 ([#3716](https://github.com/rapidsai/cugraph/pull/3716)) [@vyasr](https://github.com/vyasr) +- Testing util improvements and refactoring ([#3705](https://github.com/rapidsai/cugraph/pull/3705)) [@betochimas](https://github.com/betochimas) +- Add new cugraph-nx package (networkx backend using pylibcugraph) ([#3614](https://github.com/rapidsai/cugraph/pull/3614)) [@eriknw](https://github.com/eriknw) +- New mtmg API for integration ([#3521](https://github.com/rapidsai/cugraph/pull/3521)) [@ChuckHastings](https://github.com/ChuckHastings) + # cuGraph 23.08.00 (9 Aug 2023) ## 🚨 Breaking Changes From 06a840ed5a27cce954a7ef4488e1f5b430add4bf Mon Sep 17 00:00:00 2001 From: Ralph Liu <137829296+nv-rliu@users.noreply.github.com> Date: Wed, 11 Oct 2023 11:12:41 -0400 Subject: [PATCH 040/111] Update Broken Links in README.md (#3924) Closes #3915 This PR fixes two broken links in our README.md file. Authors: - Ralph Liu (https://github.com/nv-rliu) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3924 --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3daeb0570b0..560d1483242 100644 --- a/README.md +++ b/README.md @@ -43,8 +43,8 @@ - [Contributing to cuGraph](./readme_pages/CONTRIBUTING.md) - General - [Latest News](./readme_pages/news.md) - - [Current list of algorithms](./readme_pages/algorithms.md) - - [Blogs and Presentation](./docs/cugraph/source/basics/cugraph_blogs.rst) + - [Current list of algorithms](./docs/cugraph/source/graph_support/algorithms.md) + - [Blogs and Presentation](./docs/cugraph/source/tutorials/cugraph_blogs.rst) - [Performance](./readme_pages/performance/performance.md) - Packages - [cuGraph Python](./readme_pages/cugraph_python.md) From f605d6a5d842a9075dba5631d5cc12769f5777a6 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Wed, 11 Oct 2023 19:41:32 -0500 Subject: [PATCH 041/111] Use branch-23.12 workflows. (#3928) This PR switches back to using `branch-23.12` for CI workflows because the CUDA 12 ARM conda migration is complete. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/3928 --- .github/workflows/build.yaml | 20 ++++++++++---------- .github/workflows/pr.yaml | 30 +++++++++++++++--------------- .github/workflows/test.yaml | 10 +++++----- 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index b02b134b337..f8f8bbc1166 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -28,7 +28,7 @@ concurrency: jobs: cpp-build: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -38,7 +38,7 @@ jobs: python-build: needs: [cpp-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -47,7 +47,7 @@ jobs: upload-conda: needs: [cpp-build, python-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -57,7 +57,7 @@ jobs: if: github.ref_type == 'branch' needs: python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: arch: "amd64" branch: ${{ inputs.branch }} @@ -69,7 +69,7 @@ jobs: sha: ${{ inputs.sha }} wheel-build-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -82,7 +82,7 @@ jobs: wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -92,7 +92,7 @@ jobs: wheel-build-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -105,7 +105,7 @@ jobs: wheel-publish-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -115,7 +115,7 @@ jobs: wheel-build-nx-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -125,7 +125,7 @@ jobs: wheel-publish-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index e8834e58ace..5f954975818 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -27,41 +27,41 @@ jobs: - wheel-tests-nx-cugraph - devcontainer secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.12 checks: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@branch-23.12 with: enable_check_generated_files: false conda-cpp-build: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: pull-request node_type: cpu32 conda-cpp-tests: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: pull-request conda-python-build: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: pull-request conda-python-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: pull-request conda-notebook-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -71,7 +71,7 @@ jobs: docs-build: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -81,7 +81,7 @@ jobs: wheel-build-pylibcugraph: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_pylibcugraph.sh @@ -91,14 +91,14 @@ jobs: wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_pylibcugraph.sh wheel-build-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_cugraph.sh @@ -108,27 +108,27 @@ jobs: wheel-tests-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_cugraph.sh wheel-build-nx-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_nx-cugraph.sh wheel-tests-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_nx-cugraph.sh devcontainer: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.12 with: extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY build_command: | diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index dc40eae5f0d..6d2c1566a3c 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -16,7 +16,7 @@ on: jobs: conda-cpp-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -24,7 +24,7 @@ jobs: sha: ${{ inputs.sha }} conda-python-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -32,7 +32,7 @@ jobs: sha: ${{ inputs.sha }} wheel-tests-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -41,7 +41,7 @@ jobs: script: ci/test_wheel_pylibcugraph.sh wheel-tests-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -50,7 +50,7 @@ jobs: script: ci/test_wheel_cugraph.sh wheel-tests-nx-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@cuda-120-arm + uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} From 63e282e28554645a0026f2fa8e5b321fda208369 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Thu, 12 Oct 2023 10:22:11 -0400 Subject: [PATCH 042/111] new build all option (#3916) option to build all Renamed the old buildAll function to buildDefault Added a check for the "all" argument Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Joseph Nke (https://github.com/jnke2016) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3916 --- build.sh | 36 +++++++++++++++++++----------------- 1 file changed, 19 insertions(+), 17 deletions(-) diff --git a/build.sh b/build.sh index 8dca89aeedd..99082fa96fb 100755 --- a/build.sh +++ b/build.sh @@ -24,14 +24,15 @@ VALIDARGS=" uninstall libcugraph libcugraph_etl + pylibcugraph cugraph cugraph-service - pylibcugraph - cpp-mgtests cugraph-pyg cugraph-dgl nx-cugraph + cpp-mgtests docs + all -v -g -n @@ -52,13 +53,14 @@ HELP="$0 [ ...] [ ...] libcugraph - build libcugraph.so and SG test binaries libcugraph_etl - build libcugraph_etl.so and SG test binaries pylibcugraph - build the pylibcugraph Python package - cugraph-pyg - build the cugraph-pyg Python package cugraph - build the cugraph Python package - nx-cugraph - build the nx-cugraph Python package cugraph-service - build the cugraph-service_client and cugraph-service_server Python package - cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. + cugraph-pyg - build the cugraph-pyg Python package cugraph-dgl - build the cugraph-dgl extensions for DGL + nx-cugraph - build the nx-cugraph Python package + cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. docs - build the docs + all - build everything and is: -v - verbose build mode -g - build for debug @@ -71,7 +73,7 @@ HELP="$0 [ ...] [ ...] --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) -h - print this text - default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' then 'cugraph' targets + default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' and then 'cugraph' targets libcugraph build dir is: ${LIBCUGRAPH_BUILD_DIR} @@ -119,7 +121,7 @@ function hasArg { (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ") } -function buildAll { +function buildDefault { (( ${NUMARGS} == 0 )) || !(echo " ${ARGS} " | grep -q " [^-][a-zA-Z0-9\_\-]\+ ") } @@ -170,7 +172,7 @@ fi if hasArg --without_cugraphops; then BUILD_WITH_CUGRAPHOPS=OFF fi -if hasArg cpp-mgtests; then +if hasArg cpp-mgtests || hasArg all; then BUILD_CPP_MG_TESTS=ON fi if hasArg --cmake_default_generator; then @@ -240,7 +242,7 @@ fi ################################################################################ # Configure, build, and install libcugraph -if buildAll || hasArg libcugraph; then +if buildDefault || hasArg libcugraph || hasArg all; then if hasArg --clean; then if [ -d ${LIBCUGRAPH_BUILD_DIR} ]; then find ${LIBCUGRAPH_BUILD_DIR} -mindepth 1 -delete @@ -270,7 +272,7 @@ if buildAll || hasArg libcugraph; then fi # Configure, build, and install libcugraph_etl -if buildAll || hasArg libcugraph_etl; then +if buildDefault || hasArg libcugraph_etl || hasArg all; then if hasArg --clean; then if [ -d ${LIBCUGRAPH_ETL_BUILD_DIR} ]; then find ${LIBCUGRAPH_ETL_BUILD_DIR} -mindepth 1 -delete @@ -301,7 +303,7 @@ if buildAll || hasArg libcugraph_etl; then fi # Build, and install pylibcugraph -if buildAll || hasArg pylibcugraph; then +if buildDefault || hasArg pylibcugraph || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/pylibcugraph else @@ -328,7 +330,7 @@ if buildAll || hasArg pylibcugraph; then fi # Build and install the cugraph Python package -if buildAll || hasArg cugraph; then +if buildDefault || hasArg cugraph || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/cugraph else @@ -355,7 +357,7 @@ if buildAll || hasArg cugraph; then fi # Install the cugraph-service-client and cugraph-service-server Python packages -if hasArg cugraph-service; then +if hasArg cugraph-service || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/cugraph-service else @@ -365,7 +367,7 @@ if hasArg cugraph-service; then fi # Build and install the cugraph-pyg Python package -if hasArg cugraph-pyg; then +if hasArg cugraph-pyg || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/cugraph-pyg else @@ -374,7 +376,7 @@ if hasArg cugraph-pyg; then fi # Install the cugraph-dgl extensions for DGL -if hasArg cugraph-dgl; then +if hasArg cugraph-dgl || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/cugraph-dgl else @@ -383,7 +385,7 @@ if hasArg cugraph-dgl; then fi # Build and install the nx-cugraph Python package -if hasArg nx-cugraph; then +if hasArg nx-cugraph || hasArg all; then if hasArg --clean; then cleanPythonDir ${REPODIR}/python/nx-cugraph else @@ -392,7 +394,7 @@ if hasArg nx-cugraph; then fi # Build the docs -if hasArg docs; then +if hasArg docs || hasArg all; then if [ ! -d ${LIBCUGRAPH_BUILD_DIR} ]; then mkdir -p ${LIBCUGRAPH_BUILD_DIR} cd ${LIBCUGRAPH_BUILD_DIR} From 3003307f21e46689cccfd623ebc40cf60f751c2d Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Thu, 12 Oct 2023 12:57:24 -0700 Subject: [PATCH 043/111] TODO: revert this commit for re-running ci --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index 6f2d33b0eb8..f117efb3ba5 100644 --- a/print_env.sh +++ b/print_env.sh @@ -84,4 +84,4 @@ echo " " print_env | while read -r line; do echo " $line" done -echo "" +echo "" From 60c2d46614c50bff664179ac3fa269472df8adf4 Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Thu, 12 Oct 2023 12:57:46 -0700 Subject: [PATCH 044/111] DONE: reverted to re-run ci --- print_env.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/print_env.sh b/print_env.sh index f117efb3ba5..6f2d33b0eb8 100644 --- a/print_env.sh +++ b/print_env.sh @@ -84,4 +84,4 @@ echo " " print_env | while read -r line; do echo " $line" done -echo "" +echo "" From d52344db280cbd178e4950c421b620c29d2f2176 Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Thu, 12 Oct 2023 16:40:56 -0700 Subject: [PATCH 045/111] style fix for merged changes --- conda/environments/all_cuda-118_arch-x86_64.yaml | 1 + conda/environments/all_cuda-120_arch-x86_64.yaml | 1 + 2 files changed, 2 insertions(+) diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 42a218fcf66..71a8d10b935 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -75,5 +75,6 @@ dependencies: - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.35.* +- wget - wheel name: all_cuda-118_arch-x86_64 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 0a1774b769f..9179fba788e 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -74,5 +74,6 @@ dependencies: - sphinxcontrib-websupport - ucx-proc=*=gpu - ucx-py==0.35.* +- wget - wheel name: all_cuda-120_arch-x86_64 From c4570901fb1f3f2cf3a3d2099f53286c3fc83d8a Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Fri, 13 Oct 2023 08:46:07 -0700 Subject: [PATCH 046/111] bump wheel-build-plc to cpu32 --- .github/workflows/build.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index f8f8bbc1166..c92c11941f9 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -79,6 +79,7 @@ jobs: extra-repo: rapidsai/cugraph-ops extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY + node_type: cpu32 wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit From ebe792b95d70de68470fce4b98118647c95535ef Mon Sep 17 00:00:00 2001 From: Ralph Liu Date: Fri, 13 Oct 2023 10:56:07 -0700 Subject: [PATCH 047/111] update pr.yaml to bump to cpu32 --- .github/workflows/pr.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 5f954975818..55068b16cc7 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -88,6 +88,7 @@ jobs: extra-repo: rapidsai/cugraph-ops extra-repo-sha: branch-23.12 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY + node_type: cpu32 wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit From 89e884a40ba287f0bc14d4161695268beb28b792 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Sat, 14 Oct 2023 16:55:02 -0700 Subject: [PATCH 048/111] Silence spurious compiler warnings (#3913) We see compiler warnings basically saying some internal data member in `std::optional` may be used uninitialized. This lengthy warnings are very annoying and can hide important warnings/errors. Seeing https://gcc.gnu.org/bugzilla/show_bug.cgi?id=80635, it seems like this warning is not valid and due to the fact that the compiler cannot perfectly track when an `std::optional` object is valid or not in every code path. Anyways, this PR tweaks the code to silence the warnings. There is very little practical difference between the old and new code, but I don't see the annoying warnings anymore with this update. See the below for the actual warnings. ``` [1/1] Building CUDA object CMakeFiles/cugraph.dir/src/structure/graph_view_mg.cu.o In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_src_property_t::edge_src_property_t(const raft::handle_t&, const GraphViewType&) [with GraphViewType = cugraph::graph_view_t; T = int]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:425:13, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '((const std::size_t*)((char*)& + offsetof(cugraph::edge_src_property_t, int>,cugraph::edge_src_property_t, int>::property_.cugraph::detail::edge_minor_property_t::keys_.std::optional >::.std::_Optional_base, true, true>::_M_payload.std::_Optional_payload, true, true, true>::.std::_Optional_payload_base >::_M_payload)))[1]' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: note: '' declared here 522 | minor_tmp_buffer = edge_src_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_src_property_t::edge_src_property_t(const raft::handle_t&, const GraphViewType&) [with GraphViewType = cugraph::graph_view_t; T = int]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:425:13, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(long unsigned int*)((char*)& + offsetof(cugraph::edge_src_property_t, int>,cugraph::edge_src_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_size_.std::optional::.std::_Optional_base::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: note: '' declared here 522 | minor_tmp_buffer = edge_src_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_src_property_t, int>& cugraph::edge_src_property_t, int>::operator=(cugraph::edge_src_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:375:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(const int**)((char*)& + offsetof(cugraph::edge_src_property_t, int>,cugraph::edge_src_property_t, int>::property_.cugraph::detail::edge_minor_property_t::keys_.std::optional >::.std::_Optional_base, true, true>::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: note: '' declared here 522 | minor_tmp_buffer = edge_src_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_src_property_t, int>& cugraph::edge_src_property_t, int>::operator=(cugraph::edge_src_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:375:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(const int**)((char*)& + offsetof(cugraph::edge_src_property_t, int>,cugraph::edge_src_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_start_offsets_.std::optional >::.std::_Optional_base, true, true>::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: note: '' declared here 522 | minor_tmp_buffer = edge_src_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_src_property_t, int>& cugraph::edge_src_property_t, int>::operator=(cugraph::edge_src_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:375:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '((std::size_t*)((char*)& + offsetof(cugraph::edge_src_property_t, int>,cugraph::edge_src_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_start_offsets_.std::optional >::.std::_Optional_base, true, true>::_M_payload.std::_Optional_payload, true, true, true>::.std::_Optional_payload_base >::_M_payload)))[1]' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = false; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 1> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:522:20: note: '' declared here 522 | minor_tmp_buffer = edge_src_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_dst_property_t::edge_dst_property_t(const raft::handle_t&, const GraphViewType&) [with GraphViewType = cugraph::graph_view_t; T = int]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:527:13, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '((const std::size_t*)((char*)& + offsetof(cugraph::edge_dst_property_t, int>,cugraph::edge_dst_property_t, int>::property_.cugraph::detail::edge_minor_property_t::keys_.std::optional >::.std::_Optional_base, true, true>::_M_payload.std::_Optional_payload, true, true, true>::.std::_Optional_payload_base >::_M_payload)))[1]' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: note: '' declared here 524 | minor_tmp_buffer = edge_dst_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_dst_property_t::edge_dst_property_t(const raft::handle_t&, const GraphViewType&) [with GraphViewType = cugraph::graph_view_t; T = int]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:527:13, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(long unsigned int*)((char*)& + offsetof(cugraph::edge_dst_property_t, int>,cugraph::edge_dst_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_size_.std::optional::.std::_Optional_base::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: note: '' declared here 524 | minor_tmp_buffer = edge_dst_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_dst_property_t, int>& cugraph::edge_dst_property_t, int>::operator=(cugraph::edge_dst_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:467:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(const int**)((char*)& + offsetof(cugraph::edge_dst_property_t, int>,cugraph::edge_dst_property_t, int>::property_.cugraph::detail::edge_minor_property_t::keys_.std::optional >::.std::_Optional_base, true, true>::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: note: '' declared here 524 | minor_tmp_buffer = edge_dst_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_dst_property_t, int>& cugraph::edge_dst_property_t, int>::operator=(cugraph::edge_dst_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:467:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '*(const int**)((char*)& + offsetof(cugraph::edge_dst_property_t, int>,cugraph::edge_dst_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_start_offsets_.std::optional >::.std::_Optional_base, true, true>::))' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: note: '' declared here 524 | minor_tmp_buffer = edge_dst_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In member function 'cugraph::detail::edge_minor_property_t& cugraph::detail::edge_minor_property_t::operator=(cugraph::detail::edge_minor_property_t&&)', inlined from 'cugraph::edge_dst_property_t, int>& cugraph::edge_dst_property_t, int>::operator=(cugraph::edge_dst_property_t, int>&&)' at /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:467:7, inlined from 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]' at /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:18: /home/seunghwak/RAPIDS/development/cugraph/cpp/include/cugraph/edge_src_dst_property.hpp:285:7: warning: '((std::size_t*)((char*)& + offsetof(cugraph::edge_dst_property_t, int>,cugraph::edge_dst_property_t, int>::property_.cugraph::detail::edge_minor_property_t::key_chunk_start_offsets_.std::optional >::.std::_Optional_base, true, true>::_M_payload.std::_Optional_payload, true, true, true>::.std::_Optional_payload_base >::_M_payload)))[1]' may be used uninitialized [-Wmaybe-uninitialized] 285 | class edge_minor_property_t { | ^~~~~~~~~~~~~~~~~~~~~ /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh: In function 'void cugraph::detail::per_v_transform_reduce_e(const raft::handle_t&, const GraphViewType&, EdgeSrcValueInputWrapper, EdgeDstValueInputWrapper, EdgeValueInputWrapper, EdgeOp, T, ReduceOp, VertexValueOutputIterator) [with bool incoming = true; GraphViewType = cugraph::graph_view_t; EdgeSrcValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeDstValueInputWrapper = cugraph::detail::edge_endpoint_dummy_property_view_t; EdgeValueInputWrapper = cugraph::edge_dummy_property_view_t; EdgeOp = __nv_dl_wrapper_t<__nv_dl_tag (*)(const raft::handle_t&, const cugraph::graph_view_t&), cugraph::_GLOBAL__N__fbd7e12b_16_graph_view_mg_cu_854a5764_83484::compute_minor_degrees, 2> >; ReduceOp = cugraph::reduce_op::plus; T = int; VertexValueOutputIterator = int*]': /home/seunghwak/RAPIDS/development/cugraph/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh:524:20: note: '' declared here 524 | minor_tmp_buffer = edge_dst_property_t(handle, graph_view); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``` Authors: - Seunghwa Kang (https://github.com/seunghwak) - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Naim (https://github.com/naimnv) URL: https://github.com/rapidsai/cugraph/pull/3913 --- ...v_transform_reduce_incoming_outgoing_e.cuh | 28 ++++++++----------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh b/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh index 1349454f5b6..0b6c6a554bb 100644 --- a/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh +++ b/cpp/src/prims/per_v_transform_reduce_incoming_outgoing_e.cuh @@ -513,24 +513,20 @@ void per_v_transform_reduce_e(raft::handle_t const& handle, static_assert(is_arithmetic_or_thrust_tuple_of_arithmetic::value); - [[maybe_unused]] std::conditional_t, - edge_dst_property_t> - minor_tmp_buffer(handle); // relevant only when (GraphViewType::is_multi_gpu && !update_major + using minor_tmp_buffer_type = std::conditional_t, + edge_dst_property_t>; + [[maybe_unused]] std::unique_ptr minor_tmp_buffer{}; if constexpr (GraphViewType::is_multi_gpu && !update_major) { - if constexpr (GraphViewType::is_storage_transposed) { - minor_tmp_buffer = edge_src_property_t(handle, graph_view); - } else { - minor_tmp_buffer = edge_dst_property_t(handle, graph_view); - } + minor_tmp_buffer = std::make_unique(handle, graph_view); } using edge_partition_minor_output_device_view_t = - std::conditional_t>; + decltype(minor_tmp_buffer->mutable_view().value_first())>, + void /* dummy */>; if constexpr (update_major) { size_t partition_idx = 0; @@ -549,7 +545,7 @@ void per_v_transform_reduce_e(raft::handle_t const& handle, } else { if constexpr (GraphViewType::is_multi_gpu) { auto minor_init = init; - auto view = minor_tmp_buffer.view(); + auto view = minor_tmp_buffer->view(); if (view.keys()) { // defer applying the initial value to the end as minor_tmp_buffer may not // store values for the entire minor range minor_init = ReduceOp::identity_element; @@ -558,7 +554,7 @@ void per_v_transform_reduce_e(raft::handle_t const& handle, auto const major_comm_rank = major_comm.get_rank(); minor_init = (major_comm_rank == 0) ? init : ReduceOp::identity_element; } - fill_edge_minor_property(handle, graph_view, minor_init, minor_tmp_buffer.mutable_view()); + fill_edge_minor_property(handle, graph_view, minor_init, minor_tmp_buffer->mutable_view()); } else { thrust::fill(handle.get_thrust_policy(), vertex_value_output_first, @@ -699,7 +695,7 @@ void per_v_transform_reduce_e(raft::handle_t const& handle, if constexpr (update_major) { output_buffer = major_buffer_first; } else { - output_buffer = edge_partition_minor_output_device_view_t(minor_tmp_buffer.mutable_view()); + output_buffer = edge_partition_minor_output_device_view_t(minor_tmp_buffer->mutable_view()); } } else { output_buffer = vertex_value_output_first; @@ -913,7 +909,7 @@ void per_v_transform_reduce_e(raft::handle_t const& handle, auto const minor_comm_rank = minor_comm.get_rank(); auto const minor_comm_size = minor_comm.get_size(); - auto view = minor_tmp_buffer.view(); + auto view = minor_tmp_buffer->view(); if (view.keys()) { // applying the initial value is deferred to here vertex_t max_vertex_partition_size{0}; for (int i = 0; i < major_comm_size; ++i) { From 11279aa46663eaa3190ddee06c247e8f7a28d764 Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Mon, 16 Oct 2023 09:44:21 -0400 Subject: [PATCH 049/111] Updating Docs (#3923) starting with all the packages in a table Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3923 --- docs/cugraph/source/images/cugraph_logo_2.png | Bin 0 -> 4952011 bytes docs/cugraph/source/index.rst | 49 +++++++++++++----- 2 files changed, 37 insertions(+), 12 deletions(-) create mode 100644 docs/cugraph/source/images/cugraph_logo_2.png diff --git a/docs/cugraph/source/images/cugraph_logo_2.png b/docs/cugraph/source/images/cugraph_logo_2.png new file mode 100644 index 0000000000000000000000000000000000000000..62dd79c4b988ad58771e893c117b6e0a3b2c7074 GIT binary patch literal 4952011 zcmcG0XIN8PyDcK3C{hIJMFpe^g0#?kCv-v)P>S@P(2EKJ(tDBKrS}#fpmZW7ROuZ8 zp?3m-i~F8)@3;4P&bi;8`y*LvuCg+-W+ 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zch&TY20n!G>uy1~#G*d%unD1PWQ%ppl{q8sbCeaazhKa0pvs{Tr$Ki#-{Y1g?M+KM|vRyy{{>XFqhLQr}dUFUWp_B=G_ zPpz!vfG5W(18_N+NFnvRB#&nsqvsIFlNKhucOR94lVIV$4ii0=! zRHKE*dqQZ@S2kdlJ0Omhc{&k>@L+u?hLI{1i083ERZwM(B5hoN1dex0f&?wcS4x0a zYi-74<^gI4ngt|@IH_psK-rLjIXEI;k2 ziRlt#^t-^I)4%cniGa0NGm}NOp|YmN)$aFwdz+mm?)pJ&Qf9QSpTDH;YLQ?$IO|m+ zrL!g(1`WIKdA>HXZ^5%(3~?FI_H3m52?!;77K9DkmU*hfYZddwwA)zh&h+0}HdEPy zkuUkSs|Ci>$k+X+1H6Bk=67;tdZ+Q-d1(!n7r}H|fnqw;n|TQV+%qSx2S}x7b@Q2- zng&1hvO=9H8QcNXpKI%6Of07PC6m&dh+_`yg+7i#GQ~O_UDdq%c7jZkX6!_DxALe@ zJqNnv_i@128K-f5GYA8{evrTE4ulV8elhNxawQoJ@Kw&hY{m34u*sJZCCSf9pI2?@JBXskK`jEWPX)V_rW|p{@5KwUmQ{qu0#X z&`yUF@X;Bj2+2NQUw(ah2l=YlQtb*6Zcdl=9e5C@XCPDT(wL(P$VMmzJ;| Date: Tue, 17 Oct 2023 06:28:14 -0400 Subject: [PATCH 050/111] Updates to 23.12 (#3905) A couple PRs were merged after `branch-23.12` was created and contained RAPIDS versions that need to be updated in `branch-23.12`. Ref: - https://github.com/rapidsai/cugraph/pull/3838 - https://github.com/rapidsai/cugraph/pull/3852 Authors: - Ray Douglass (https://github.com/raydouglass) - Rick Ratzel (https://github.com/rlratzel) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3905 --- .devcontainer/cuda11.8-conda/devcontainer.json | 2 +- .devcontainer/cuda11.8-pip/devcontainer.json | 4 +++- .devcontainer/cuda12.0-pip/devcontainer.json | 4 +++- .github/workflows/pr.yaml | 1 + python/nx-cugraph/_nx_cugraph/__init__.py | 2 +- 5 files changed, 9 insertions(+), 4 deletions(-) diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json index 8cfe0e35990..83078a304ed 100644 --- a/.devcontainer/cuda11.8-conda/devcontainer.json +++ b/.devcontainer/cuda11.8-conda/devcontainer.json @@ -5,7 +5,7 @@ "args": { "CUDA": "11.8", "PYTHON_PACKAGE_MANAGER": "conda", - "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda11.8-mambaforge-ubuntu22.04" + "BASE": "rapidsai/devcontainers:23.12-cpp-cuda11.8-mambaforge-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json index fd3f8d22f89..d59742575b5 100644 --- a/.devcontainer/cuda11.8-pip/devcontainer.json +++ b/.devcontainer/cuda11.8-pip/devcontainer.json @@ -10,7 +10,9 @@ }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": { + "version": "1.14.1" + }, "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json index abff840ebdd..3eacf726bf0 100644 --- a/.devcontainer/cuda12.0-pip/devcontainer.json +++ b/.devcontainer/cuda12.0-pip/devcontainer.json @@ -10,7 +10,9 @@ }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": {"version": "1.14.1"}, + "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": { + "version": "1.14.1" + }, "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} }, "overrideFeatureInstallOrder": [ diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 55068b16cc7..362e826ef65 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -131,6 +131,7 @@ jobs: secrets: inherit uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.12 with: + node_type: cpu32 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY build_command: | sccache -z; diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 227caea29d5..886d7a19f74 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -24,7 +24,7 @@ "backend_name": "cugraph", "project": "nx-cugraph", "package": "nx_cugraph", - "url": "https://github.com/rapidsai/cugraph/tree/branch-23.10/python/nx-cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-23.12/python/nx-cugraph", "short_summary": "GPU-accelerated backend.", # "description": "TODO", "functions": { From f0ea63475b3932e90f1133da52b9041b4462e19f Mon Sep 17 00:00:00 2001 From: Dylan Chima-Sanchez <97180625+betochimas@users.noreply.github.com> Date: Tue, 17 Oct 2023 07:22:59 -0700 Subject: [PATCH 051/111] Add nx-cugraph notebook for showing accelerated networkX APIs (#3830) This PR adds a notebook showing how to use the `nx-cugraph` package to accelerate your NetworkX workflow. closes rapidsai/graph_dl#277 Authors: - Dylan Chima-Sanchez (https://github.com/betochimas) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/3830 --- notebooks/demo/nx_cugraph_demo.ipynb | 672 +++++++++++++++++++++++++++ 1 file changed, 672 insertions(+) create mode 100644 notebooks/demo/nx_cugraph_demo.ipynb diff --git a/notebooks/demo/nx_cugraph_demo.ipynb b/notebooks/demo/nx_cugraph_demo.ipynb new file mode 100644 index 00000000000..6e50370ed80 --- /dev/null +++ b/notebooks/demo/nx_cugraph_demo.ipynb @@ -0,0 +1,672 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `nx-cugraph`: a NetworkX backend that provides GPU acceleration with RAPIDS cuGraph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook will demonstrate the `nx-cugraph` NetworkX backend using the NetworkX betweenness_centrality algorithm.\n", + "\n", + "## Background\n", + "Networkx version 3.0 introduced a dispatching mechanism that allows users to configure NetworkX to dispatch various algorithms to third-party backends. Backends can provide different implementations of graph algorithms, allowing users to take advantage of capabilities not available in NetworkX. `nx-cugraph` is a NetworkX backend provided by the [RAPIDS](https://rapids.ai) cuGraph project that adds GPU acceleration to greatly improve performance.\n", + "\n", + "## System Requirements\n", + "Using `nx-cugraph` with this notebook requires the following: \n", + "- NVIDIA GPU, Pascal architecture or later\n", + "- CUDA 11.2, 11.4, 11.5, 11.8, or 12.0\n", + "- Python versions 3.9, 3.10, or 3.11\n", + "- NetworkX >= version 3.2\n", + " - _NetworkX 3.0 supports dispatching and is compatible with `nx-cugraph`, but this notebook will demonstrate features added in 3.2_\n", + " - At the time of this writing, NetworkX 3.2 is only available from source and can be installed by following the [development version install instructions](https://github.com/networkx/networkx/blob/main/INSTALL.rst#install-the-development-version).\n", + "- Pandas\n", + "\n", + "More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Installation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Assuming NetworkX >= 3.2 has been installed using the [development version install instructions](https://github.com/networkx/networkx/blob/main/INSTALL.rst#install-the-development-version), `nx-cugraph` can be installed using either `conda` or `pip`. \n", + "\n", + "#### conda\n", + "```\n", + "conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph\n", + "```\n", + "#### pip\n", + "```\n", + "python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com\n", + "```\n", + "#### _Notes:_\n", + " * nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above.\n", + " * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start).\n", + " * If you installed any of the packages described here since running this notebook, you may need to restart the kernel to have them visible to this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Notebook Helper Functions\n", + "\n", + "A few helper functions will be defined here that will be used in order to help keep this notebook easy to read." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "def reimport_networkx():\n", + " \"\"\"\n", + " Re-imports networkx for demonstrating different backend configuration\n", + " options applied at import-time. This is only needed for demonstration\n", + " purposes since other mechanisms are available for runtime configuration.\n", + " \"\"\"\n", + " # Using importlib.reload(networkx) has several caveats (described here:\n", + " # https://docs.python.org/3/library/imp.html?highlight=reload#imp.reload)\n", + " # which result in backend configuration not being re-applied correctly.\n", + " # Instead, manually remove all modules and re-import\n", + " nx_mods = [m for m in sys.modules.keys()\n", + " if (m.startswith(\"networkx\") or m.startswith(\"nx_cugraph\"))]\n", + " for m in nx_mods:\n", + " sys.modules.pop(m)\n", + " import networkx\n", + " return networkx\n", + "\n", + "\n", + "from pathlib import Path\n", + "import requests\n", + "import gzip\n", + "import pandas as pd\n", + "def create_cit_patents_graph(verbose=True):\n", + " \"\"\"\n", + " Downloads the cit-Patents dataset (if not previously downloaded), reads\n", + " it, and creates a nx.DiGraph from it and returns it.\n", + " cit-Patents is described here:\n", + " https://snap.stanford.edu/data/cit-Patents.html\n", + " \"\"\"\n", + " url = \"https://snap.stanford.edu/data/cit-Patents.txt.gz\"\n", + " gz_file_name = Path(url.split(\"/\")[-1])\n", + " csv_file_name = Path(gz_file_name.stem)\n", + " if csv_file_name.exists():\n", + " if verbose: print(f\"{csv_file_name} already exists, not downloading.\")\n", + " else:\n", + " if verbose: print(f\"downloading {url}...\", end=\"\", flush=True)\n", + " req = requests.get(url)\n", + " open(gz_file_name, \"wb\").write(req.content)\n", + " if verbose: print(\"done\")\n", + " if verbose: print(f\"unzipping {gz_file_name}...\", end=\"\", flush=True)\n", + " with gzip.open(gz_file_name, \"rb\") as gz_in:\n", + " with open(csv_file_name, \"wb\") as txt_out:\n", + " txt_out.write(gz_in.read())\n", + " if verbose: print(\"done\")\n", + "\n", + " if verbose: print(\"reading csv to dataframe...\", end=\"\", flush=True)\n", + " pandas_edgelist = pd.read_csv(\n", + " csv_file_name.name,\n", + " skiprows=4,\n", + " delimiter=\"\\t\",\n", + " names=[\"src\", \"dst\"],\n", + " dtype={\"src\":\"int32\", \"dst\":\"int32\"},\n", + " )\n", + " if verbose: print(\"done\")\n", + " if verbose: print(\"creating NX graph from dataframe...\", end=\"\", flush=True)\n", + " G = nx.from_pandas_edgelist(\n", + " pandas_edgelist, source=\"src\", target=\"dst\", create_using=nx.DiGraph\n", + " )\n", + " if verbose: print(\"done\")\n", + " return G" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Running `betweenness_centrality`\n", + "Let's start by running `betweenness_centrality` on the Karate Club graph using the default NetworkX implementation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Zachary's Karate Club\n", + "\n", + "Zachary's Karate Club is a small dataset consisting of 34 nodes and 78 edges which represent the friendships between members of a karate club. This dataset is small enough to make comparing results between NetworkX and `nx-cugraph` easy." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import networkx as nx\n", + "karate_club_graph = nx.karate_club_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Having NetworkX compute the `betweenness_centrality` values for each node on this graph is quick and easy." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.51 ms ± 1.02 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit global karate_nx_bc_results\n", + "karate_nx_bc_results = nx.betweenness_centrality(karate_club_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Automatic GPU acceleration\n", + "When `nx-cugraph` is installed, NetworkX will detect it on import and make it available as a backend for APIs supported by that backend. However, NetworkX does not assume the user always wants to use a particular backend, and instead looks at various configuration mechanisms in place for users to specify how NetworkX should use installed backends. Since NetworkX was not configured to use a backend for the above `betweenness_centrality` call, it used the default implementation provided by NetworkX.\n", + "\n", + "The first configuration mechanism to be demonstrated below is the `NETWORKX_AUTOMATIC_BACKENDS` environment variable. This environment variable directs NetworkX to use the backend specified everywhere it's supported and does not require the user to modify any of their existing NetworkX code.\n", + "\n", + "To use it, a user sets `NETWORKX_AUTOMATIC_BACKENDS` in their shell to the backend they'd like to use. If a user has more than one backend installed, the environment variable can also accept a comma-separated list of backends, ordered by priority in which NetworkX should use them, where the first backend that supports a particular API call will be used. For example:\n", + "```\n", + "bash> export NETWORKX_AUTOMATIC_BACKENDS=cugraph\n", + "bash> python my_nx_app.py # uses nx-cugraph wherever possible, then falls back to default implementation where it's not.\n", + "```\n", + "or in the case of multiple backends installed\n", + "```\n", + "bash> export NETWORKX_AUTOMATIC_BACKENDS=cugraph,graphblas\n", + "bash> python my_nx_app.py # uses nx-cugraph if possible, then nx-graphblas if possible, then default implementation.\n", + "```\n", + "\n", + "NetworkX looks at the environment variable and the installed backends at import time, and will not re-examine the environment after that. Because `networkx` was already imported in this notebook, the `reimport_nx()` utility will be called after the `os.environ` dictionary is updated to simulate an environment variable being set in the shell.\n", + "\n", + "**Please note, this is only needed for demonstration purposes to compare runs both with and without fully-automatic backend use enabled.**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"NETWORKX_AUTOMATIC_BACKENDS\"] = \"cugraph\"\n", + "nx = reimport_networkx()\n", + "# reimporting nx requires reinstantiating Graphs since python considers\n", + "# types from the prior nx import != types from the reimported nx\n", + "karate_club_graph = nx.karate_club_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Once the environment is updated, re-running the same `betweenness_centrality` call on the same graph requires no code changes." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "43.9 ms ± 222 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit global karate_cg_bc_results\n", + "karate_cg_bc_results = nx.betweenness_centrality(karate_club_graph)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "We may see that the same computation actually took *longer* using `nx-cugraph`. This is not too surprising given how small the graph is, since there's a small amount of overhead to copy data to and from the GPU which becomes more obvious on very small graphs. We'll see with a larger graph how this overhead becomes negligible." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Results Comparison\n", + "\n", + "Let's examine the results of each run to see how they compare. \n", + "The `betweenness_centrality` results are a dictionary mapping vertex IDs to betweenness_centrality scores. The score itself is usually not as important as the relative rank of each vertex ID (e.g. vertex A is ranked higher than vertex B in both sets of results)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NX: (0, 0.437635), CG: (0, 0.437635)\n", + "NX: (33, 0.304075), CG: (33, 0.304075)\n", + "NX: (32, 0.145247), CG: (32, 0.145247)\n", + "NX: (2, 0.143657), CG: (2, 0.143657)\n", + "NX: (31, 0.138276), CG: (31, 0.138276)\n", + "NX: (8, 0.055927), CG: (8, 0.055927)\n", + "NX: (1, 0.053937), CG: (1, 0.053937)\n", + "NX: (13, 0.045863), CG: (13, 0.045863)\n", + "NX: (19, 0.032475), CG: (19, 0.032475)\n", + "NX: (5, 0.029987), CG: (5, 0.029987)\n", + "NX: (6, 0.029987), CG: (6, 0.029987)\n", + "NX: (27, 0.022333), CG: (27, 0.022333)\n", + "NX: (23, 0.017614), CG: (23, 0.017614)\n", + "NX: (30, 0.014412), CG: (30, 0.014412)\n", + "NX: (3, 0.011909), CG: (3, 0.011909)\n", + "NX: (25, 0.003840), CG: (25, 0.003840)\n", + "NX: (29, 0.002922), CG: (29, 0.002922)\n", + "NX: (24, 0.002210), CG: (24, 0.002210)\n", + "NX: (28, 0.001795), CG: (28, 0.001795)\n", + "NX: (9, 0.000848), CG: (9, 0.000848)\n", + "NX: (4, 0.000631), CG: (4, 0.000631)\n", + "NX: (10, 0.000631), CG: (10, 0.000631)\n", + "NX: (7, 0.000000), CG: (7, 0.000000)\n", + "NX: (11, 0.000000), CG: (11, 0.000000)\n", + "NX: (12, 0.000000), CG: (12, 0.000000)\n", + "NX: (14, 0.000000), CG: (14, 0.000000)\n", + "NX: (15, 0.000000), CG: (15, 0.000000)\n", + "NX: (16, 0.000000), CG: (16, 0.000000)\n", + "NX: (17, 0.000000), CG: (17, 0.000000)\n", + "NX: (18, 0.000000), CG: (18, 0.000000)\n", + "NX: (20, 0.000000), CG: (20, 0.000000)\n", + "NX: (21, 0.000000), CG: (21, 0.000000)\n", + "NX: (22, 0.000000), CG: (22, 0.000000)\n", + "NX: (26, 0.000000), CG: (26, 0.000000)\n" + ] + } + ], + "source": [ + "# The lists contain tuples of (vertex ID, betweenness_centrality score),\n", + "# sorted based on the score.\n", + "nx_sorted = sorted(karate_nx_bc_results.items(), key=lambda t:t[1], reverse=True)\n", + "cg_sorted = sorted(karate_cg_bc_results.items(), key=lambda t:t[1], reverse=True)\n", + "\n", + "for i in range(len(nx_sorted)):\n", + " print(\"NX: (%d, %.6f), CG: (%d, %.6f)\" % (nx_sorted[i] + cg_sorted[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Here we can see that the results match exactly as expected. \n", + "\n", + "For larger graphs, results are harder to compare given that `betweenness_centrality` is an approximation algorithm influenced by the random selection of paths used to compute the betweenness_centrality score of each vertex. The argument `k` is used for limiting the number of paths used in the computation, since using every path for every vertex would be prohibitively expensive for large graphs. For small graphs, `k` need not be specified, which allows `betweenness_centrality` to use all paths for all vertices and makes for an easier comparison." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### `betweenness_centrality` on larger graphs - The U.S. Patent Citation Network1\n", + "\n", + "The U.S. Patent Citation Network dataset is much larger with over 3.7M nodes and over 16.5M edges and demonstrates how `nx-cugraph` enables NetworkX to run `betweenness_centrality` on graphs this large (and larger) in seconds instead of minutes.\n", + "\n", + "#### NetworkX default implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading https://snap.stanford.edu/data/cit-Patents.txt.gz...done\n", + "unzipping cit-Patents.txt.gz...done\n", + "reading csv to dataframe...done\n", + "creating NX graph from dataframe...done\n" + ] + } + ], + "source": [ + "import os\n", + "# Unset NETWORKX_AUTOMATIC_BACKENDS so the default NetworkX implementation is used\n", + "os.environ.pop(\"NETWORKX_AUTOMATIC_BACKENDS\", None)\n", + "nx = reimport_networkx()\n", + "# Create the cit-Patents graph - this will also download the dataset if not previously downloaded\n", + "cit_patents_graph = create_cit_patents_graph()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Since this is a large graph, a k value must be set so the computation returns in a reasonable time\n", + "k = 40" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Because this run will take time, `%%timeit` is restricted to a single pass.\n", + "\n", + "*NOTE: this run may take approximately 1 minute*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1min 4s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1\n", + "results = nx.betweenness_centrality(cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "Something to note is that `%%timeit` disables garbage collection by default, which may not be something a user is able to do. To see a more realistic real-world run time, `gc` can be enabled." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# import and run the garbage collector upfront prior to using it in the benchmark\n", + "import gc\n", + "gc.collect()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "*NOTE: this run may take approximately 7 minutes!*" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6min 50s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `nx-cugraph`\n", + "\n", + "Running on a GPU using `nx-cugraph` can result in a tremendous speedup, especially when graphs reach sizes larger than a few thousand nodes or `k` values become larger to increase accuracy.\n", + "\n", + "Rather than setting the `NETWORKX_AUTOMATIC_BACKENDS` environment variable and re-importing again, this example will demonstrate the `backend=` keyword argument to explicitly direct the NetworkX dispatcher to use the `cugraph` backend." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.1 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k, backend=\"cugraph\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "k = 150" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(cit_patents_graph, k=k, backend=\"cugraph\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "For the same graph and the same `k` value, the `\"cugraph\"` backend returns results in seconds instead of minutes. Increasing the `k` value has very little relative impact to runtime due to the high parallel processing ability of the GPU, allowing the user to get improved accuracy for virtually no additional cost." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Type-based dispatching\n", + "\n", + "NetworkX also supports automatically dispatching to backends associated with specific graph types. This requires the user to write code for a specific backend, and therefore requires the backend to be installed, but has the advantage of ensuring a particular behavior without the potential for runtime conversions.\n", + "\n", + "To use type-based dispatching with `nx-cugraph`, the user must import the backend directly in their code to access the utilities provided to create a Graph instance specifically for the `nx-cugraph` backend." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import nx_cugraph as nxcg" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The `from_networkx()` API will copy the data from the NetworkX graph instance to the GPU and return a new `nx-cugraph` graph instance. By passing an explicit `nx-cugraph` graph, the NetworkX dispatcher will automatically call the `\"cugraph\"` backend (and only the `\"cugraph\"` backend) without requiring future conversions to copy data to the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.92 s ± 2.85 ms per loop (mean ± std. dev. of 2 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 2 global nxcg_cit_patents_graph\n", + "nxcg_cit_patents_graph = nxcg.from_networkx(cit_patents_graph)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit -r 1 gc.enable()\n", + "nx.betweenness_centrality(nxcg_cit_patents_graph, k=k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated `nx-cugraph`'s support for `betweenness_centrality`. At the time of this writing, `nx-cugraph` also provides support for `edge_netweenness_centrality` and `louvain_communities`. Other algorithms are scheduled to be supported based on their availability in the cuGraph [pylibcugraph](https://github.com/rapidsai/cugraph/tree/branch-23.10/python/pylibcugraph/pylibcugraph) package and demand by the NetworkX community.\n", + "\n", + "#### Benchmark Results\n", + "The results included in this notebook were generated on a workstation with the following hardware:\n", + "\n", + "\n", + " \n", + " \n", + "
CPU:Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz, 45GB
GPU:Quatro RTX 8000, 50GB
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1 Information on the U.S. Patent Citation Network dataset used in this notebook is as follows:\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
Authors:Jure Leskovec and Andrej Krevl
Title:SNAP Datasets, Stanford Large Network Dataset Collection
URL:http://snap.stanford.edu/data
Date:June 2014
\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 9efd87d3479f510978fa61402879a051ae11d8d5 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Tue, 17 Oct 2023 17:16:21 -0500 Subject: [PATCH 052/111] Temporarily disable mg testing (#3940) This PR temporarily disables single-GPU "MG" tests in CI until the dask related transient failures are resolved. The PR to re-enable them, intended to block the 23.12 release, is [here](https://github.com/rapidsai/cugraph/pull/3941). Authors: - Joseph Nke (https://github.com/jnke2016) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3940 --- ci/test_python.sh | 3 ++- ci/test_wheel.sh | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index c7687396ff0..1690ce2f15b 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -63,6 +63,7 @@ pytest \ tests popd +# FIXME: TEMPORARILY disable single-GPU "MG" testing rapids-logger "pytest cugraph" pushd python/cugraph/cugraph DASK_WORKER_DEVICES="0" \ @@ -78,7 +79,7 @@ pytest \ --cov=cugraph \ --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cugraph-coverage.xml" \ --cov-report=term \ - -k "not test_property_graph_mg" \ + -k "not _mg" \ tests popd diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index c4adb638437..28f59f0209e 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -21,9 +21,10 @@ arch=$(uname -m) if [[ "${arch}" == "aarch64" && ${RAPIDS_BUILD_TYPE} == "pull-request" ]]; then python ./ci/wheel_smoke_test_${package_name}.py else + # FIXME: TEMPORARILY disable single-GPU "MG" testing RAPIDS_DATASET_ROOT_DIR=`pwd`/datasets \ DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT="1000s" \ - python -m pytest ./python/${package_name}/${python_package_name}/tests + python -m pytest -k "not _mg" ./python/${package_name}/${python_package_name}/tests fi From 34dce3da87f5583d421e7b0ab30421afef581366 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Tue, 17 Oct 2023 19:46:52 -0700 Subject: [PATCH 053/111] Implement the transform_e primitive (to update property values for all edges) (#3917) We have two versions of transform_e, one updating property values for all edges and another updating property values only for the edges in the given edge list. Both were declared but only the latter was implemented. This PR implements the first version (necessary to update edge masks in testing the neighbor intersection primitives with edge masking). Authors: - Seunghwa Kang (https://github.com/seunghwak) - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3917 --- .../cugraph/edge_partition_device_view.cuh | 31 ++- ...ge_partition_edge_property_device_view.cuh | 39 ++-- ...artition_endpoint_property_device_view.cuh | 33 ++-- cpp/include/cugraph/edge_property.hpp | 8 +- .../cugraph/utilities/packed_bool_utils.hpp | 7 + cpp/src/prims/transform_e.cuh | 165 +++++++++++++++- cpp/tests/prims/mg_transform_e.cu | 182 ++++++++++++++---- 7 files changed, 382 insertions(+), 83 deletions(-) diff --git a/cpp/include/cugraph/edge_partition_device_view.cuh b/cpp/include/cugraph/edge_partition_device_view.cuh index d34d639f4d9..213f9b9497a 100644 --- a/cpp/include/cugraph/edge_partition_device_view.cuh +++ b/cpp/include/cugraph/edge_partition_device_view.cuh @@ -109,6 +109,13 @@ class edge_partition_device_view_base_t { __host__ __device__ edge_t const* offsets() const { return offsets_.data(); } __host__ __device__ vertex_t const* indices() const { return indices_.data(); } + __device__ vertex_t major_idx_from_local_edge_idx_nocheck(edge_t local_edge_idx) const noexcept + { + return static_cast(thrust::distance( + offsets_.begin() + 1, + thrust::upper_bound(thrust::seq, offsets_.begin() + 1, offsets_.end(), local_edge_idx))); + } + // major_idx == major offset if CSR/CSC, major_offset != major_idx if DCSR/DCSC __device__ thrust::tuple local_edges( vertex_t major_idx) const noexcept @@ -291,8 +298,19 @@ class edge_partition_device_view_t= (*major_hypersparse_first_ - major_range_first_) + ? (*dcs_nzd_vertices_)[major_idx - (*major_hypersparse_first_ - major_range_first_)] + : major_from_major_offset_nocheck(major_idx); + } else { // major_idx == major_offset + return major_from_major_offset_nocheck(major_idx); + } + } + // major_hypersparse_idx: index within the hypersparse segment - __host__ __device__ thrust::optional major_hypersparse_idx_from_major_nocheck( + __device__ thrust::optional major_hypersparse_idx_from_major_nocheck( vertex_t major) const noexcept { if (dcs_nzd_vertices_) { @@ -303,7 +321,7 @@ class edge_partition_device_view_t major_from_major_hypersparse_idx_nocheck( + __device__ thrust::optional major_from_major_hypersparse_idx_nocheck( vertex_t major_hypersparse_idx) const noexcept { return dcs_nzd_vertices_ @@ -442,8 +460,13 @@ class edge_partition_device_view_t major_hypersparse_idx_from_major_nocheck( + __device__ thrust::optional major_hypersparse_idx_from_major_nocheck( vertex_t major) const noexcept { assert(false); @@ -451,7 +474,7 @@ class edge_partition_device_view_t major_from_major_hypersparse_idx_nocheck( + __device__ thrust::optional major_from_major_hypersparse_idx_nocheck( vertex_t major_hypersparse_idx) const noexcept { assert(false); diff --git a/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh b/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh index f71fc167d12..18091567e38 100644 --- a/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh +++ b/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh @@ -33,18 +33,21 @@ template ::value_type> class edge_partition_edge_property_device_view_t { public: + using edge_type = edge_t; + using value_type = value_t; + static constexpr bool is_packed_bool = cugraph::is_packed_bool(); + static constexpr bool has_packed_bool_element = + cugraph::has_packed_bool_element(); + static_assert( std::is_same_v::value_type, value_t> || - cugraph::has_packed_bool_element()); + has_packed_bool_element); static_assert(cugraph::is_arithmetic_or_thrust_tuple_of_arithmetic::value); - using edge_type = edge_t; - using value_type = value_t; - edge_partition_edge_property_device_view_t() = default; edge_partition_edge_property_device_view_t( - edge_property_view_t const& view, size_t partition_idx) + edge_property_view_t const& view, size_t partition_idx) : value_first_(view.value_firsts()[partition_idx]) { value_first_ = view.value_firsts()[partition_idx]; @@ -54,8 +57,8 @@ class edge_partition_edge_property_device_view_t { __device__ value_t get(edge_t offset) const { - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(offset); return static_cast(*(value_first_ + cugraph::packed_bool_offset(offset)) & mask); } else { @@ -69,8 +72,8 @@ class edge_partition_edge_property_device_view_t { void> set(edge_t offset, value_t val) const { - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(offset); if (val) { atomicOr(value_first_ + cugraph::packed_bool_offset(offset), mask); @@ -88,8 +91,8 @@ class edge_partition_edge_property_device_view_t { value_t> atomic_and(edge_t offset, value_t val) const { - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(offset); auto old = atomicAnd(value_first_ + cugraph::packed_bool_offset(offset), val ? uint32_t{0xffffffff} : ~mask); @@ -105,8 +108,8 @@ class edge_partition_edge_property_device_view_t { value_t> atomic_or(edge_t offset, value_t val) const { - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(offset); auto old = atomicOr(value_first_ + cugraph::packed_bool_offset(offset), val ? mask : uint32_t{0}); @@ -132,8 +135,8 @@ class edge_partition_edge_property_device_view_t { value_t> elementwise_atomic_cas(edge_t offset, value_t compare, value_t val) const { - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(offset); auto old = val ? atomicOr(value_first_ + cugraph::packed_bool_offset(offset), mask) : atomicAnd(value_first_ + cugraph::packed_bool_offset(offset), ~mask); @@ -170,8 +173,10 @@ class edge_partition_edge_property_device_view_t { template class edge_partition_edge_dummy_property_device_view_t { public: - using edge_type = edge_t; - using value_type = thrust::nullopt_t; + using edge_type = edge_t; + using value_type = thrust::nullopt_t; + static constexpr bool is_packed_bool = false; + static constexpr bool has_packed_bool_element = false; edge_partition_edge_dummy_property_device_view_t() = default; diff --git a/cpp/include/cugraph/edge_partition_endpoint_property_device_view.cuh b/cpp/include/cugraph/edge_partition_endpoint_property_device_view.cuh index 1ff279fbdca..7578c646175 100644 --- a/cpp/include/cugraph/edge_partition_endpoint_property_device_view.cuh +++ b/cpp/include/cugraph/edge_partition_endpoint_property_device_view.cuh @@ -39,12 +39,15 @@ template ::value_type> class edge_partition_endpoint_property_device_view_t { public: + using vertex_type = vertex_t; + using value_type = value_t; + static constexpr bool is_packed_bool = cugraph::is_packed_bool(); + static constexpr bool has_packed_bool_element = + cugraph::has_packed_bool_element(); + static_assert( std::is_same_v::value_type, value_t> || - cugraph::has_packed_bool_element()); - - using vertex_type = vertex_t; - using value_type = value_t; + has_packed_bool_element); edge_partition_endpoint_property_device_view_t() = default; @@ -77,8 +80,8 @@ class edge_partition_endpoint_property_device_view_t { __device__ value_t get(vertex_t offset) const { auto val_offset = value_offset(offset); - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(val_offset); return static_cast(*(value_first_ + cugraph::packed_bool_offset(val_offset)) & mask); } else { @@ -93,8 +96,8 @@ class edge_partition_endpoint_property_device_view_t { atomic_and(vertex_t offset, value_t val) const { auto val_offset = value_offset(offset); - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(val_offset); auto old = atomicAnd(value_first_ + cugraph::packed_bool_offset(val_offset), val ? cugraph::packed_bool_full_mask() : ~mask); @@ -111,8 +114,8 @@ class edge_partition_endpoint_property_device_view_t { atomic_or(vertex_t offset, value_t val) const { auto val_offset = value_offset(offset); - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(val_offset); auto old = atomicOr(value_first_ + cugraph::packed_bool_offset(val_offset), val ? mask : cugraph::packed_bool_empty_mask()); @@ -140,8 +143,8 @@ class edge_partition_endpoint_property_device_view_t { elementwise_atomic_cas(vertex_t offset, value_t compare, value_t val) const { auto val_offset = value_offset(offset); - if constexpr (cugraph::has_packed_bool_element()) { - static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); + if constexpr (has_packed_bool_element) { + static_assert(is_packed_bool, "unimplemented for thrust::tuple types."); auto mask = cugraph::packed_bool_mask(val_offset); auto old = val ? atomicOr(value_first_ + cugraph::packed_bool_offset(val_offset), mask) : atomicAnd(value_first_ + cugraph::packed_bool_offset(val_offset), ~mask); @@ -203,8 +206,10 @@ class edge_partition_endpoint_property_device_view_t { template class edge_partition_endpoint_dummy_property_device_view_t { public: - using vertex_type = vertex_t; - using value_type = thrust::nullopt_t; + using vertex_type = vertex_t; + using value_type = thrust::nullopt_t; + static constexpr bool is_packed_bool = false; + static constexpr bool has_packed_bool_element = false; edge_partition_endpoint_dummy_property_device_view_t() = default; diff --git a/cpp/include/cugraph/edge_property.hpp b/cpp/include/cugraph/edge_property.hpp index 8904006a2a2..d46d4e52fd4 100644 --- a/cpp/include/cugraph/edge_property.hpp +++ b/cpp/include/cugraph/edge_property.hpp @@ -72,9 +72,11 @@ class edge_property_t { public: static_assert(cugraph::is_arithmetic_or_thrust_tuple_of_arithmetic::value); - using edge_type = typename GraphViewType::edge_type; - using value_type = T; - using buffer_type = decltype(allocate_dataframe_buffer(size_t{0}, rmm::cuda_stream_view{})); + using edge_type = typename GraphViewType::edge_type; + using value_type = T; + using buffer_type = + decltype(allocate_dataframe_buffer, uint32_t, T>>( + size_t{0}, rmm::cuda_stream_view{})); edge_property_t(raft::handle_t const& handle) {} diff --git a/cpp/include/cugraph/utilities/packed_bool_utils.hpp b/cpp/include/cugraph/utilities/packed_bool_utils.hpp index 9557b11e8e0..0be5711d90c 100644 --- a/cpp/include/cugraph/utilities/packed_bool_utils.hpp +++ b/cpp/include/cugraph/utilities/packed_bool_utils.hpp @@ -47,6 +47,13 @@ has_packed_bool_element(std::index_sequence) } // namespace detail +template +constexpr bool is_packed_bool() +{ + return std::is_same_v::value_type, uint32_t> && + std::is_same_v; +} + // sizeof(uint32_t) * 8 packed Boolean values are stored using one uint32_t template constexpr bool has_packed_bool_element() diff --git a/cpp/src/prims/transform_e.cuh b/cpp/src/prims/transform_e.cuh index 7950df58a3e..edacdc8a970 100644 --- a/cpp/src/prims/transform_e.cuh +++ b/cpp/src/prims/transform_e.cuh @@ -20,6 +20,7 @@ #include #include #include +#include #include #include @@ -35,6 +36,70 @@ namespace cugraph { +namespace detail { + +int32_t constexpr transform_e_kernel_block_size = 512; + +template +__global__ void transform_e_packed_bool( + edge_partition_device_view_t edge_partition, + EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, + EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, + EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + EdgePartitionEdgeValueOutputWrapper edge_partition_e_value_output, + EdgeOp e_op) +{ + static_assert(EdgePartitionEdgeValueOutputWrapper::is_packed_bool); + static_assert(raft::warp_size() == packed_bools_per_word()); + + using edge_t = typename GraphViewType::edge_type; + + auto const tid = threadIdx.x + blockIdx.x * blockDim.x; + static_assert(transform_e_kernel_block_size % raft::warp_size() == 0); + auto const lane_id = tid % raft::warp_size(); + auto idx = static_cast(packed_bool_offset(tid)); + + auto num_edges = edge_partition.number_of_edges(); + while (idx < static_cast(packed_bool_size(num_edges))) { + auto local_edge_idx = + idx * static_cast(packed_bools_per_word()) + static_cast(lane_id); + uint32_t mask{0}; + int predicate{0}; + if (local_edge_idx < num_edges) { + auto major_idx = edge_partition.major_idx_from_local_edge_idx_nocheck(local_edge_idx); + auto major = edge_partition.major_from_major_idx_nocheck(major_idx); + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto minor = *(edge_partition.indices() + local_edge_idx); + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + + auto src = GraphViewType::is_storage_transposed ? minor : major; + auto dst = GraphViewType::is_storage_transposed ? major : minor; + auto src_offset = GraphViewType::is_storage_transposed ? minor_offset : major_offset; + auto dst_offset = GraphViewType::is_storage_transposed ? major_offset : minor_offset; + predicate = e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(local_edge_idx)) + ? int{1} + : int{0}; + } + mask = __ballot_sync(uint32_t{0xffffffff}, predicate); + if (lane_id == 0) { *(edge_partition_e_value_output.value_first() + idx) = mask; } + + idx += static_cast(gridDim.x * (blockDim.x / raft::warp_size())); + } +} + +} // namespace detail + /** * @brief Iterate over the entire set of edges and update edge property values. * @@ -84,9 +149,103 @@ void transform_e(raft::handle_t const& handle, EdgeValueOutputWrapper edge_value_output, bool do_expensive_check = false) { - // CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); + using vertex_t = typename GraphViewType::vertex_type; + using edge_t = typename GraphViewType::edge_type; - CUGRAPH_FAIL("unimplemented."); + using edge_partition_src_input_device_view_t = std::conditional_t< + std::is_same_v, + detail::edge_partition_endpoint_dummy_property_device_view_t, + detail::edge_partition_endpoint_property_device_view_t< + vertex_t, + typename EdgeSrcValueInputWrapper::value_iterator, + typename EdgeSrcValueInputWrapper::value_type>>; + using edge_partition_dst_input_device_view_t = std::conditional_t< + std::is_same_v, + detail::edge_partition_endpoint_dummy_property_device_view_t, + detail::edge_partition_endpoint_property_device_view_t< + vertex_t, + typename EdgeDstValueInputWrapper::value_iterator, + typename EdgeDstValueInputWrapper::value_type>>; + using edge_partition_e_input_device_view_t = std::conditional_t< + std::is_same_v, + detail::edge_partition_edge_dummy_property_device_view_t, + detail::edge_partition_edge_property_device_view_t< + edge_t, + typename EdgeValueInputWrapper::value_iterator, + typename EdgeValueInputWrapper::value_type>>; + using edge_partition_e_output_device_view_t = detail::edge_partition_edge_property_device_view_t< + edge_t, + typename EdgeValueOutputWrapper::value_iterator, + typename EdgeValueOutputWrapper::value_type>; + + CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); + + for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { + auto edge_partition = + edge_partition_device_view_t( + graph_view.local_edge_partition_view(i)); + + edge_partition_src_input_device_view_t edge_partition_src_value_input{}; + edge_partition_dst_input_device_view_t edge_partition_dst_value_input{}; + if constexpr (GraphViewType::is_storage_transposed) { + edge_partition_src_value_input = edge_partition_src_input_device_view_t(edge_src_value_input); + edge_partition_dst_value_input = + edge_partition_dst_input_device_view_t(edge_dst_value_input, i); + } else { + edge_partition_src_value_input = + edge_partition_src_input_device_view_t(edge_src_value_input, i); + edge_partition_dst_value_input = edge_partition_dst_input_device_view_t(edge_dst_value_input); + } + auto edge_partition_e_value_input = edge_partition_e_input_device_view_t(edge_value_input, i); + auto edge_partition_e_value_output = + edge_partition_e_output_device_view_t(edge_value_output, i); + + auto num_edges = edge_partition.number_of_edges(); + if constexpr (edge_partition_e_output_device_view_t::has_packed_bool_element) { + static_assert(edge_partition_e_output_device_view_t::is_packed_bool, + "unimplemented for thrust::tuple types."); + if (edge_partition.number_of_edges() > edge_t{0}) { + raft::grid_1d_thread_t update_grid(num_edges, + detail::transform_e_kernel_block_size, + handle.get_device_properties().maxGridSize[0]); + detail::transform_e_packed_bool + <<>>( + edge_partition, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_e_value_output, + e_op); + } + } else { + thrust::transform( + handle.get_thrust_policy(), + thrust::make_counting_iterator(edge_t{0}), + thrust::make_counting_iterator(num_edges), + edge_partition_e_value_output.value_first(), + [e_op, + edge_partition, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input] __device__(edge_t i) { + auto major_idx = edge_partition.major_idx_from_local_edge_idx_nocheck(i); + auto major = edge_partition.major_from_major_idx_nocheck(major_idx); + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto minor = *(edge_partition.indices() + i); + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + + auto src = GraphViewType::is_storage_transposed ? minor : major; + auto dst = GraphViewType::is_storage_transposed ? major : minor; + auto src_offset = GraphViewType::is_storage_transposed ? minor_offset : major_offset; + auto dst_offset = GraphViewType::is_storage_transposed ? major_offset : minor_offset; + return e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(i)); + }); + } + } } /** @@ -177,7 +336,7 @@ void transform_e(raft::handle_t const& handle, typename EdgeValueOutputWrapper::value_iterator, typename EdgeValueOutputWrapper::value_type>; - // CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); auto major_first = GraphViewType::is_storage_transposed ? edge_list.dst_begin() : edge_list.src_begin(); diff --git a/cpp/tests/prims/mg_transform_e.cu b/cpp/tests/prims/mg_transform_e.cu index 127eddd43c7..24deaad810a 100644 --- a/cpp/tests/prims/mg_transform_e.cu +++ b/cpp/tests/prims/mg_transform_e.cu @@ -51,6 +51,7 @@ #include struct Prims_Usecase { + bool use_edgelist{false}; bool check_correctness{true}; }; @@ -113,8 +114,9 @@ class Tests_MGTransformE auto mg_dst_prop = cugraph::test::generate::dst_property( *handle_, mg_graph_view, mg_vertex_prop); - cugraph::edge_bucket_t edge_list(*handle_); - { + cugraph::edge_bucket_t edge_list( + *handle_); + if (prims_usecase.use_edgelist) { rmm::device_uvector srcs(0, handle_->get_stream()); rmm::device_uvector dsts(0, handle_->get_stream()); std::tie(srcs, dsts, std::ignore, std::ignore) = cugraph::decompress_to_edgelist( @@ -154,24 +156,41 @@ class Tests_MGTransformE if (cugraph::test::g_perf) { RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement handle_->get_comms().barrier(); - hr_timer.start("MG transform_reduce_e"); + hr_timer.start("MG transform_e"); } - cugraph::transform_e( - *handle_, - mg_graph_view, - edge_list, - mg_src_prop.view(), - mg_dst_prop.view(), - cugraph::edge_dummy_property_t{}.view(), - [] __device__(auto src, auto dst, auto src_property, auto dst_property, thrust::nullopt_t) { - if (src_property < dst_property) { - return src_property; - } else { - return dst_property; - } - }, - edge_value_output.mutable_view()); + if (prims_usecase.use_edgelist) { + cugraph::transform_e( + *handle_, + mg_graph_view, + edge_list, + mg_src_prop.view(), + mg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_property, auto dst_property, thrust::nullopt_t) { + if (src_property < dst_property) { + return src_property; + } else { + return dst_property; + } + }, + edge_value_output.mutable_view()); + } else { + cugraph::transform_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_property, auto dst_property, thrust::nullopt_t) { + if (src_property < dst_property) { + return src_property; + } else { + return dst_property; + } + }, + edge_value_output.mutable_view()); + } if (cugraph::test::g_perf) { RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement @@ -183,24 +202,42 @@ class Tests_MGTransformE // 3. validate MG results if (prims_usecase.check_correctness) { - auto num_invalids = cugraph::count_if_e( - *handle_, - mg_graph_view, - mg_src_prop.view(), - mg_dst_prop.view(), - edge_value_output.view(), - [property_initial_value] __device__( - auto src, auto dst, auto src_property, auto dst_property, auto edge_property) { - if (((src + dst) % 2) == 0) { + size_t num_invalids{}; + if (prims_usecase.use_edgelist) { + num_invalids = cugraph::count_if_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + edge_value_output.view(), + [property_initial_value] __device__( + auto src, auto dst, auto src_property, auto dst_property, auto edge_property) { + if (((src + dst) % 2) == 0) { + if (src_property < dst_property) { + return edge_property != src_property; + } else { + return edge_property != dst_property; + } + } else { + return edge_property != property_initial_value; + } + }); + } else { + num_invalids = cugraph::count_if_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + edge_value_output.view(), + [property_initial_value] __device__( + auto src, auto dst, auto src_property, auto dst_property, auto edge_property) { if (src_property < dst_property) { return edge_property != src_property; } else { return edge_property != dst_property; } - } else { - return edge_property != property_initial_value; - } - }); + }); + } ASSERT_TRUE(num_invalids == 0); } @@ -278,13 +315,13 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatTupleIntFloatTransposeTrue) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatTransposeFalse) +TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatIntTransposeFalse) { auto param = GetParam(); run_current_test(std::get<0>(param), std::get<1>(param)); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatTransposeFalse) +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatIntTransposeFalse) { auto param = GetParam(); run_current_test( @@ -292,7 +329,7 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatTransposeFalse) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatTransposeFalse) +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatIntTransposeFalse) { auto param = GetParam(); run_current_test( @@ -300,7 +337,7 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatTransposeFalse) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatTransposeFalse) +TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatIntTransposeFalse) { auto param = GetParam(); run_current_test( @@ -308,13 +345,13 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatTransposeFalse) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatTransposeTrue) +TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatIntTransposeTrue) { auto param = GetParam(); run_current_test(std::get<0>(param), std::get<1>(param)); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatTransposeTrue) +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatIntTransposeTrue) { auto param = GetParam(); run_current_test( @@ -322,7 +359,7 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatTransposeTrue) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatTransposeTrue) +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatIntTransposeTrue) { auto param = GetParam(); run_current_test( @@ -330,7 +367,7 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatTransposeTrue) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } -TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatTransposeTrue) +TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatIntTransposeTrue) { auto param = GetParam(); run_current_test( @@ -338,11 +375,71 @@ TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatTransposeTrue) cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } +TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatBoolTransposeFalse) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatBoolTransposeFalse) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatBoolTransposeFalse) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatBoolTransposeFalse) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformE_File, CheckInt32Int32FloatBoolTransposeTrue) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int32FloatBoolTransposeTrue) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt32Int64FloatBoolTransposeTrue) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformE_Rmat, CheckInt64Int64FloatBoolTransposeTrue) +{ + auto param = GetParam(); + run_current_test( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGTransformE_File, ::testing::Combine( - ::testing::Values(Prims_Usecase{true}), + ::testing::Values(Prims_Usecase{false, true}, Prims_Usecase{true, true}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx"), cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), @@ -350,7 +447,8 @@ INSTANTIATE_TEST_SUITE_P( INSTANTIATE_TEST_SUITE_P(rmat_small_test, Tests_MGTransformE_Rmat, - ::testing::Combine(::testing::Values(Prims_Usecase{true}), + ::testing::Combine(::testing::Values(Prims_Usecase{false, true}, + Prims_Usecase{true, true}), ::testing::Values(cugraph::test::Rmat_Usecase( 10, 16, 0.57, 0.19, 0.19, 0, false, false)))); @@ -362,7 +460,7 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGTransformE_Rmat, ::testing::Combine( - ::testing::Values(Prims_Usecase{false}), + ::testing::Values(Prims_Usecase{false, false}, Prims_Usecase{true, false}), ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); CUGRAPH_MG_TEST_PROGRAM_MAIN() From 8877464a094370421d82a04b95d9618eff647ce5 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 17 Oct 2023 23:06:00 -0500 Subject: [PATCH 054/111] Add multigraph support to nx-cugraph (#3934) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3934 --- dependencies.yaml | 3 +- python/nx-cugraph/lint.yaml | 14 +- .../nx-cugraph/nx_cugraph/classes/__init__.py | 2 + python/nx-cugraph/nx_cugraph/classes/graph.py | 28 +- .../nx_cugraph/classes/multidigraph.py | 30 ++ .../nx_cugraph/classes/multigraph.py | 453 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/convert.py | 167 +++++-- python/nx-cugraph/nx_cugraph/interface.py | 12 +- .../nx_cugraph/tests/bench_convert.py | 16 +- .../nx_cugraph/tests/test_convert.py | 39 +- .../nx_cugraph/tests/test_multigraph.py | 72 +++ python/nx-cugraph/pyproject.toml | 3 +- 12 files changed, 752 insertions(+), 87 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/classes/multidigraph.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/multigraph.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_multigraph.py diff --git a/dependencies.yaml b/dependencies.yaml index 70fb54b4721..736aa2e019f 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -401,12 +401,13 @@ dependencies: - output_types: [conda, pyproject] packages: - networkx>=3.0 + - &numpy numpy>=1.21 python_run_cugraph_dgl: common: - output_types: [conda, pyproject] packages: - *numba - - &numpy numpy>=1.21 + - *numpy - output_types: [pyproject] packages: - &cugraph cugraph==23.12.* diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 338ca7b230e..4f604fbeb6e 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -11,7 +11,7 @@ default_language_version: python: python3 repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-added-large-files - id: check-case-conflict @@ -26,7 +26,7 @@ repos: - id: mixed-line-ending - id: trailing-whitespace - repo: https://github.com/abravalheri/validate-pyproject - rev: v0.14 + rev: v0.15 hooks: - id: validate-pyproject name: Validate pyproject.toml @@ -40,7 +40,7 @@ repos: hooks: - id: isort - repo: https://github.com/asottile/pyupgrade - rev: v3.13.0 + rev: v3.15.0 hooks: - id: pyupgrade args: [--py39-plus] @@ -50,7 +50,7 @@ repos: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.291 + rev: v0.0.292 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -70,18 +70,18 @@ repos: - id: yesqa additional_dependencies: *flake8_dependencies - repo: https://github.com/codespell-project/codespell - rev: v2.2.5 + rev: v2.2.6 hooks: - id: codespell types_or: [python, rst, markdown] additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.291 + rev: v0.0.292 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: no-commit-to-branch args: [-p, "^branch-2....$"] diff --git a/python/nx-cugraph/nx_cugraph/classes/__init__.py b/python/nx-cugraph/nx_cugraph/classes/__init__.py index e47641ae812..9916bcbe241 100644 --- a/python/nx-cugraph/nx_cugraph/classes/__init__.py +++ b/python/nx-cugraph/nx_cugraph/classes/__init__.py @@ -11,5 +11,7 @@ # See the License for the specific language governing permissions and # limitations under the License. from .graph import Graph +from .multigraph import MultiGraph from .digraph import DiGraph # isort:skip +from .multidigraph import MultiDiGraph # isort:skip diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index ded4cc3943f..f1e85c836e3 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -43,7 +43,8 @@ class Graph: # Tell networkx to dispatch calls with this object to nx-cugraph - __networkx_plugin__: ClassVar[str] = "cugraph" + __networkx_backend__: ClassVar[str] = "cugraph" # nx >=3.2 + __networkx_plugin__: ClassVar[str] = "cugraph" # nx <3.2 # networkx properties graph: dict @@ -58,7 +59,7 @@ class Graph: node_values: dict[AttrKey, cp.ndarray[NodeValue]] node_masks: dict[AttrKey, cp.ndarray[bool]] key_to_id: dict[NodeKey, IndexValue] | None - _id_to_key: dict[IndexValue, NodeKey] | None + _id_to_key: list[NodeKey] | None _N: int #################### @@ -77,7 +78,7 @@ def from_coo( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: new_graph = object.__new__(cls) @@ -88,7 +89,7 @@ def from_coo( new_graph.node_values = {} if node_values is None else dict(node_values) new_graph.node_masks = {} if node_masks is None else dict(node_masks) new_graph.key_to_id = None if key_to_id is None else dict(key_to_id) - new_graph._id_to_key = None if id_to_key is None else dict(id_to_key) + new_graph._id_to_key = None if id_to_key is None else list(id_to_key) new_graph._N = op.index(N) # Ensure N is integral new_graph.graph = new_graph.graph_attr_dict_factory() new_graph.graph.update(attr) @@ -123,7 +124,7 @@ def from_csr( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: N = indptr.size - 1 @@ -155,7 +156,7 @@ def from_csc( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: N = indptr.size - 1 @@ -189,7 +190,7 @@ def from_dcsr( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: row_indices = cp.array( @@ -222,7 +223,7 @@ def from_dcsc( node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, - id_to_key: dict[IndexValue, NodeKey] | None = None, + id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: col_indices = cp.array( @@ -295,11 +296,11 @@ def node_dtypes(self) -> dict[AttrKey, Dtype]: return {key: val.dtype for key, val in self.node_values.items()} @property - def id_to_key(self) -> dict[IndexValue, NodeKey] | None: + def id_to_key(self) -> [NodeKey] | None: if self.key_to_id is None: return None if self._id_to_key is None: - self._id_to_key = {val: key for key, val in self.key_to_id.items()} + self._id_to_key = sorted(self.key_to_id, key=self.key_to_id.__getitem__) return self._id_to_key name = nx.Graph.name @@ -370,6 +371,12 @@ def get_edge_data( v = self.key_to_id[v] except KeyError: return default + else: + try: + if u < 0 or v < 0 or u >= self._N or v >= self._N: + return default + except TypeError: + return default index = cp.nonzero((self.row_indices == u) & (self.col_indices == v))[0] if index.size == 0: return default @@ -447,6 +454,7 @@ def to_undirected(self, as_view: bool = False) -> Graph: ################### def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): + # DRY warning: see also MultiGraph._copy indptr = self.indptr row_indices = self.row_indices col_indices = self.col_indices diff --git a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py new file mode 100644 index 00000000000..5629e2c9c06 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py @@ -0,0 +1,30 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +import networkx as nx + +import nx_cugraph as nxcg + +from .digraph import DiGraph +from .multigraph import MultiGraph + +__all__ = ["MultiDiGraph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.MultiDiGraph) + + +class MultiDiGraph(MultiGraph, DiGraph): + @classmethod + def to_networkx_class(cls) -> type[nx.MultiDiGraph]: + return nx.MultiDiGraph diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py new file mode 100644 index 00000000000..499ca7ce212 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -0,0 +1,453 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from __future__ import annotations + +from copy import deepcopy +from typing import TYPE_CHECKING, ClassVar + +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg + +from .graph import Graph + +if TYPE_CHECKING: + from nx_cugraph.typing import ( + AttrKey, + EdgeKey, + EdgeValue, + IndexValue, + NodeKey, + NodeValue, + ) +__all__ = ["MultiGraph"] + +networkx_api = nxcg.utils.decorators.networkx_class(nx.MultiGraph) + + +class MultiGraph(Graph): + # networkx properties + edge_key_dict_factory: ClassVar[type] = dict + + # Not networkx properties + + # In a MultiGraph, each edge has a unique `(row, col, key)` key. + # By default, `key` is 0 if possible, else 1, else 2, etc. + # This key can be any hashable Python object in NetworkX. + # We don't use a dict for our data structure here, because + # that would require a `(row, col, key)` key. + # Instead, we keep `edge_keys` and/or `edge_indices`. + # `edge_keys` is the list of Python objects for each edge. + # `edge_indices` is for the common case of default multiedge keys, + # in which case we can store it as a cupy array. + # `edge_indices` is generally preferred. It is possible to provide + # both where edge_indices is the default and edge_keys is anything. + # It is also possible for them both to be None, which means the + # default edge indices has not yet been calculated. + edge_indices: cp.ndarray[IndexValue] | None + edge_keys: list[EdgeKey] | None + + #################### + # Creation methods # + #################### + + @classmethod + def from_coo( + cls, + N: int, + row_indices: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + new_graph = super().from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + **attr, + ) + new_graph.edge_indices = edge_indices + new_graph.edge_keys = edge_keys + # Easy and fast sanity checks + if ( + new_graph.edge_keys is not None + and len(new_graph.edge_keys) != row_indices.size + ): + raise ValueError + return new_graph + + @classmethod + def from_csr( + cls, + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + N = indptr.size - 1 + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_csc( + cls, + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + N = indptr.size - 1 + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_dcsr( + cls, + N: int, + compressed_rows: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + col_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> MultiGraph: + row_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + @classmethod + def from_dcsc( + cls, + N: int, + compressed_cols: cp.ndarray[IndexValue], + indptr: cp.ndarray[IndexValue], + row_indices: cp.ndarray[IndexValue], + edge_indices: cp.ndarray[IndexValue] | None = None, + edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, + edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + *, + key_to_id: dict[NodeKey, IndexValue] | None = None, + id_to_key: list[NodeKey] | None = None, + edge_keys: list[EdgeKey] | None = None, + **attr, + ) -> Graph: + col_indices = cp.array( + # cp.repeat is slow to use here, so use numpy instead + np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + ) + return cls.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + **attr, + ) + + def __new__( + cls, incoming_graph_data=None, multigraph_input=None, **attr + ) -> MultiGraph: + if isinstance(incoming_graph_data, dict) and multigraph_input is not False: + new_graph = nxcg.from_networkx( + nx.MultiGraph(incoming_graph_data, multigraph_input=multigraph_input), + preserve_all_attrs=True, + ) + else: + new_graph = super().__new__(cls, incoming_graph_data) + new_graph.graph.update(attr) + return new_graph + + ################# + # Class methods # + ################# + + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return False + + @classmethod + @networkx_api + def is_multigraph(cls) -> bool: + return True + + @classmethod + @networkx_api + def to_directed_class(cls) -> type[nxcg.MultiDiGraph]: + return nxcg.MultiDiGraph + + @classmethod + def to_networkx_class(cls) -> type[nx.MultiGraph]: + return nx.MultiGraph + + @classmethod + @networkx_api + def to_undirected_class(cls) -> type[MultiGraph]: + return MultiGraph + + ########################## + # NetworkX graph methods # + ########################## + + @networkx_api + def clear(self) -> None: + super().clear() + self.edge_indices = None + self.edge_keys = None + + @networkx_api + def clear_edges(self) -> None: + super().clear_edges() + self.edge_indices = None + self.edge_keys = None + + @networkx_api + def copy(self, as_view: bool = False) -> MultiGraph: + # Does shallow copy in networkx + return self._copy(as_view, self.__class__) + + @networkx_api + def get_edge_data( + self, + u: NodeKey, + v: NodeKey, + key: EdgeKey | None = None, + default: EdgeValue | None = None, + ): + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return default + else: + try: + if u < 0 or v < 0 or u >= self._N or v >= self._N: + return default + except TypeError: + return default + mask = (self.row_indices == u) & (self.col_indices == v) + if not mask.any(): + return default + if self.edge_keys is None: + if self.edge_indices is None: + self._calculate_edge_indices() + if key is not None: + try: + mask = mask & (self.edge_indices == key) + except TypeError: + return default + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return default + edge_keys = self.edge_keys + if key is not None and edge_keys is not None: + mask[[i for i in indices.tolist() if edge_keys[i] != key]] = False + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return default + if key is not None: + [index] = indices.tolist() + return { + k: v[index].tolist() + for k, v in self.edge_values.items() + if k not in self.edge_masks or self.edge_masks[k][index] + } + return { + edge_keys[index] + if edge_keys is not None + else index: { + k: v[index].tolist() + for k, v in self.edge_values.items() + if k not in self.edge_masks or self.edge_masks[k][index] + } + for index in indices.tolist() + } + + @networkx_api + def has_edge(self, u: NodeKey, v: NodeKey, key: EdgeKey | None = None) -> bool: + if self.key_to_id is not None: + try: + u = self.key_to_id[u] + v = self.key_to_id[v] + except KeyError: + return False + mask = (self.row_indices == u) & (self.col_indices == v) + if key is None or (self.edge_indices is None and self.edge_keys is None): + return bool(mask.any()) + if self.edge_keys is None: + try: + return bool((mask & (self.edge_indices == key)).any()) + except TypeError: + return False + indices = cp.nonzero(mask)[0] + if indices.size == 0: + return False + edge_keys = self.edge_keys + return any(edge_keys[i] == key for i in indices.tolist()) + + @networkx_api + def to_directed(self, as_view: bool = False) -> nxcg.MultiDiGraph: + return self._copy(as_view, self.to_directed_class()) + + @networkx_api + def to_undirected(self, as_view: bool = False) -> MultiGraph: + # Does deep copy in networkx + return self.copy(as_view) + + ################### + # Private methods # + ################### + + def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): + # DRY warning: see also Graph._copy + indptr = self.indptr + row_indices = self.row_indices + col_indices = self.col_indices + edge_indices = self.edge_indices + edge_values = self.edge_values + edge_masks = self.edge_masks + node_values = self.node_values + node_masks = self.node_masks + key_to_id = self.key_to_id + id_to_key = None if key_to_id is None else self._id_to_key + edge_keys = self.edge_keys + if not as_view: + indptr = indptr.copy() + row_indices = row_indices.copy() + col_indices = col_indices.copy() + edge_indices = edge_indices.copy() + edge_values = {key: val.copy() for key, val in edge_values.items()} + edge_masks = {key: val.copy() for key, val in edge_masks.items()} + node_values = {key: val.copy() for key, val in node_values.items()} + node_masks = {key: val.copy() for key, val in node_masks.items()} + if key_to_id is not None: + key_to_id = key_to_id.copy() + if id_to_key is not None: + id_to_key = id_to_key.copy() + if edge_keys is not None: + edge_keys = edge_keys.copy() + if reverse: + row_indices, col_indices = col_indices, row_indices + rv = cls.from_coo( + indptr, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + id_to_key=id_to_key, + edge_keys=edge_keys, + ) + if as_view: + rv.graph = self.graph + else: + rv.graph.update(deepcopy(self.graph)) + return rv diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index 1240ea71db7..e82286e5e29 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -122,8 +122,6 @@ def from_networkx( graph = G else: raise TypeError(f"Expected networkx.Graph; got {type(graph)}") - elif graph.is_multigraph(): - raise NotImplementedError("MultiGraph support is not yet implemented") if preserve_all_attrs: preserve_edge_attrs = True @@ -144,7 +142,7 @@ def from_networkx( else: node_attrs = {node_attrs: None} - if graph.__class__ in {nx.Graph, nx.DiGraph}: + if graph.__class__ in {nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph}: # This is a NetworkX private attribute, but is much faster to use adj = graph._adj else: @@ -163,7 +161,11 @@ def from_networkx( edge_attrs = None elif preserve_edge_attrs: # Using comprehensions should be just as fast starting in Python 3.11 - attr_sets = set(map(frozenset, concat(map(dict.values, adj.values())))) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + # PERF: should we add `filter(None, ...)` to remove empty data dicts? + attr_sets = set(map(frozenset, it)) attrs = frozenset.union(*attr_sets) edge_attrs = dict.fromkeys(attrs, REQUIRED) if len(attr_sets) > 1: @@ -180,11 +182,19 @@ def from_networkx( if len(required) == 1: # Fast path for the common case of a single attribute with no default [attr] = required - it = ( - attr in edgedata - for rowdata in adj.values() - for edgedata in rowdata.values() - ) + if graph.is_multigraph(): + it = ( + attr in edgedata + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() + ) + else: + it = ( + attr in edgedata + for rowdata in adj.values() + for edgedata in rowdata.values() + ) if next(it): if all(it): # All edges have data @@ -195,9 +205,10 @@ def from_networkx( del edge_attrs[attr] # Else some edges have attribute (default already None) else: - attr_sets = set( - map(required.intersection, concat(map(dict.values, adj.values()))) - ) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + attr_sets = set(map(required.intersection, it)) for attr in required - frozenset.union(*attr_sets): # No edges have these attributes del edge_attrs[attr] @@ -254,7 +265,23 @@ def from_networkx( key_to_id = None else: col_iter = map(key_to_id.__getitem__, col_iter) - col_indices = cp.fromiter(col_iter, np.int32) + if graph.is_multigraph(): + col_indices = np.fromiter(col_iter, np.int32) + num_multiedges = np.fromiter( + map(len, concat(map(dict.values, adj.values()))), np.int32 + ) + # cp.repeat is slow to use here, so use numpy instead + col_indices = cp.array(np.repeat(col_indices, num_multiedges)) + # Determine edge keys and edge ids for multigraphs + edge_keys = list(concat(concat(map(dict.values, adj.values())))) + edge_indices = cp.fromiter( + concat(map(range, map(len, concat(map(dict.values, adj.values()))))), + np.int32, + ) + if edge_keys == edge_indices.tolist(): + edge_keys = None # Prefer edge_indices + else: + col_indices = cp.fromiter(col_iter, np.int32) edge_values = {} edge_masks = {} @@ -268,16 +295,29 @@ def from_networkx( if edge_default is None: vals = [] append = vals.append - iter_mask = ( - append( - edgedata[edge_attr] - if (present := edge_attr in edgedata) - else False + if graph.is_multigraph(): + iter_mask = ( + append( + edgedata[edge_attr] + if (present := edge_attr in edgedata) + else False + ) + or present + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() + ) + else: + iter_mask = ( + append( + edgedata[edge_attr] + if (present := edge_attr in edgedata) + else False + ) + or present + for rowdata in adj.values() + for edgedata in rowdata.values() ) - or present - for rowdata in adj.values() - for edgedata in rowdata.values() - ) edge_masks[edge_attr] = cp.fromiter(iter_mask, bool) edge_values[edge_attr] = cp.array(vals, dtype) # if vals.ndim > 1: ... @@ -289,8 +329,16 @@ def from_networkx( # for rowdata in adj.values() # for edgedata in rowdata.values() # ) - iter_values = map( - op.itemgetter(edge_attr), concat(map(dict.values, adj.values())) + it = concat(map(dict.values, adj.values())) + if graph.is_multigraph(): + it = concat(map(dict.values, it)) + iter_values = map(op.itemgetter(edge_attr), it) + elif graph.is_multigraph(): + iter_values = ( + edgedata.get(edge_attr, edge_default) + for rowdata in adj.values() + for multiedges in rowdata.values() + for edgedata in multiedges.values() ) else: iter_values = ( @@ -304,13 +352,13 @@ def from_networkx( edge_values[edge_attr] = cp.fromiter(iter_values, dtype) # if vals.ndim > 1: ... - row_indices = cp.array( - # cp.repeat is slow to use here, so use numpy instead - np.repeat( - np.arange(N, dtype=np.int32), - np.fromiter(map(len, adj.values()), np.int32), - ) + # cp.repeat is slow to use here, so use numpy instead + row_indices = np.repeat( + np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) ) + if graph.is_multigraph(): + row_indices = np.repeat(row_indices, num_multiedges) + row_indices = cp.array(row_indices) node_values = {} node_masks = {} @@ -350,21 +398,38 @@ def from_networkx( else: node_values[node_attr] = cp.fromiter(iter_values, dtype) # if vals.ndim > 1: ... - - if graph.is_directed() or as_directed: - klass = nxcg.DiGraph + if graph.is_multigraph(): + if graph.is_directed() or as_directed: + klass = nxcg.MultiDiGraph + else: + klass = nxcg.MultiGraph + rv = klass.from_coo( + N, + row_indices, + col_indices, + edge_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + edge_keys=edge_keys, + ) else: - klass = nxcg.Graph - rv = klass.from_coo( - N, - row_indices, - col_indices, - edge_values, - edge_masks, - node_values, - node_masks, - key_to_id=key_to_id, - ) + if graph.is_directed() or as_directed: + klass = nxcg.DiGraph + else: + klass = nxcg.Graph + rv = klass.from_coo( + N, + row_indices, + col_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + ) if preserve_graph_attrs: rv.graph.update(graph.graph) # deepcopy? return rv @@ -427,7 +492,7 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: full_node_dicts = _iter_attr_dicts(node_values, node_masks) rv.add_nodes_from(zip(node_iter, full_node_dicts)) elif id_to_key is not None: - rv.add_nodes_from(id_to_key.values()) + rv.add_nodes_from(id_to_key) else: rv.add_nodes_from(range(len(G))) @@ -448,7 +513,17 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: if id_to_key is not None: row_iter = map(id_to_key.__getitem__, row_indices) col_iter = map(id_to_key.__getitem__, col_indices) - if edge_values: + if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): + if G.edge_keys is not None: + edge_keys = G.edge_keys + else: + edge_keys = G.edge_indices.tolist() + if edge_values: + full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) + rv.add_edges_from(zip(row_iter, col_iter, edge_keys, full_edge_dicts)) + else: + rv.add_edges_from(zip(row_iter, col_iter, edge_keys)) + elif edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) rv.add_edges_from(zip(row_iter, col_iter, full_edge_dicts)) else: diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 2ad23acd940..124b86a4af2 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -174,6 +174,10 @@ def key(testpath): ): louvain_different, key("test_louvain.py:test_none_weight_param"): louvain_different, key("test_louvain.py:test_multigraph"): louvain_different, + # See networkx#6630 + key( + "test_louvain.py:test_undirected_selfloops" + ): "self-loops not handled in Louvain", } ) @@ -189,10 +193,4 @@ def can_run(cls, name, args, kwargs): This is a proposed API to add to networkx dispatching machinery and may change. """ - return ( - hasattr(cls, name) - and getattr(cls, name).can_run(*args, **kwargs) - # We don't support MultiGraphs yet - and not any(isinstance(x, nx.MultiGraph) for x in args) - and not any(isinstance(x, nx.MultiGraph) for x in kwargs.values()) - ) + return hasattr(cls, name) and getattr(cls, name).can_run(*args, **kwargs) diff --git a/python/nx-cugraph/nx_cugraph/tests/bench_convert.py b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py index 7e6278661c2..2eb432230eb 100644 --- a/python/nx-cugraph/nx_cugraph/tests/bench_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/bench_convert.py @@ -39,6 +39,8 @@ gpubenchmark = pytest_benchmark.plugin.benchmark +CREATE_USING = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] + def _bench_helper(gpubenchmark, N, attr_kind, create_using, method): G = method(N, create_using=create_using) @@ -103,7 +105,7 @@ def _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, method, fmt): None, ], ) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_cycle_graph(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph) @@ -111,7 +113,7 @@ def bench_cycle_graph(gpubenchmark, N, attr_kind, create_using): @pytest.mark.skipif("not cugraph") @pytest.mark.parametrize("N", [1, 10**6]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("do_renumber", [True, False]) def bench_cycle_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): if N == 1 and not do_renumber: @@ -124,7 +126,7 @@ def bench_cycle_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renum @pytest.mark.skipif("not scipy") @pytest.mark.parametrize("N", [1, 10**6]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("fmt", ["coo", "csr"]) def bench_cycle_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): _bench_helper_scipy(gpubenchmark, N, attr_kind, create_using, nx.cycle_graph, fmt) @@ -143,14 +145,14 @@ def bench_cycle_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): None, ], ) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_complete_graph_edgedata(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) @pytest.mark.parametrize("N", [3000]) @pytest.mark.parametrize("attr_kind", [None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) def bench_complete_graph_noedgedata(gpubenchmark, N, attr_kind, create_using): _bench_helper(gpubenchmark, N, attr_kind, create_using, nx.complete_graph) @@ -158,7 +160,7 @@ def bench_complete_graph_noedgedata(gpubenchmark, N, attr_kind, create_using): @pytest.mark.skipif("not cugraph") @pytest.mark.parametrize("N", [1, 1500]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("do_renumber", [True, False]) def bench_complete_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_renumber): if N == 1 and not do_renumber: @@ -171,7 +173,7 @@ def bench_complete_graph_cugraph(gpubenchmark, N, attr_kind, create_using, do_re @pytest.mark.skipif("not scipy") @pytest.mark.parametrize("N", [1, 1500]) @pytest.mark.parametrize("attr_kind", ["full", None]) -@pytest.mark.parametrize("create_using", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize("create_using", CREATE_USING) @pytest.mark.parametrize("fmt", ["coo", "csr"]) def bench_complete_graph_scipy(gpubenchmark, N, attr_kind, create_using, fmt): _bench_helper_scipy( diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py index ba3cd7aaee1..83820f7621f 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py @@ -18,7 +18,9 @@ from nx_cugraph import interface -@pytest.mark.parametrize("graph_class", [nx.Graph, nx.DiGraph]) +@pytest.mark.parametrize( + "graph_class", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] +) @pytest.mark.parametrize( "kwargs", [ @@ -48,9 +50,10 @@ def test_convert_empty(graph_class, kwargs): assert G.graph == Gcg.graph == H.graph == {} -def test_convert(): +@pytest.mark.parametrize("graph_class", [nx.Graph, nx.MultiGraph]) +def test_convert(graph_class): # FIXME: can we break this into smaller tests? - G = nx.Graph() + G = graph_class() G.add_edge(0, 1, x=2) G.add_node(0, foo=10) G.add_node(1, foo=20, bar=100) @@ -88,7 +91,10 @@ def test_convert(): cp.testing.assert_array_equal(Gcg.node_values["foo"], [10, 20]) assert Gcg.edge_values == Gcg.edge_masks == {} H = nxcg.to_networkx(Gcg) - assert set(G.edges) == set(H.edges) == {(0, 1)} + if G.is_multigraph(): + assert set(G.edges) == set(H.edges) == {(0, 1, 0)} + else: + assert set(G.edges) == set(H.edges) == {(0, 1)} assert G.nodes == H.nodes # Fill completely missing attribute with default value @@ -100,6 +106,8 @@ def test_convert(): assert Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {"y": 0})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0)} # If attribute is completely missing (and no default), then just ignore it Gcg = nxcg.from_networkx(G, edge_attrs={"y": None}) @@ -108,6 +116,8 @@ def test_convert(): assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == [] H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0)} G.add_edge(0, 2) # Some edges are missing 'x' attribute; need to use a mask @@ -120,6 +130,8 @@ def test_convert(): cp.testing.assert_array_equal(Gcg.edge_values["x"][Gcg.edge_masks["x"]], [2, 2]) H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {"x": 2}), (0, 2, {})] + if Gcg.is_multigraph(): + assert set(H.edges) == {(0, 1, 0), (0, 2, 0)} with pytest.raises(KeyError, match="x"): nxcg.from_networkx(G, edge_attrs={"x": nxcg.convert.REQUIRED}) @@ -131,7 +143,7 @@ def test_convert(): nxcg.from_networkx(G, node_attrs={"bar": ...}) # Now for something more complicated... - G = nx.Graph() + G = graph_class() G.add_edge(10, 20, x=1) G.add_edge(10, 30, x=2, y=1.5) G.add_node(10, foo=100) @@ -147,7 +159,7 @@ def test_convert(): {"edge_attrs": {"x": 0, "y": None}, "edge_dtypes": {"x": int, "y": float}}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [1, 2, 1, 2]) @@ -168,7 +180,7 @@ def test_convert(): {"node_attrs": {"foo": 0, "bar": None, "missing": None}}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [100, 200, 300]) @@ -189,7 +201,7 @@ def test_convert(): {"node_attrs": {"bar": 0, "foo": None}, "node_dtypes": int}, ]: Gcg = nxcg.from_networkx(G, **kwargs) - assert Gcg.id_to_key == {0: 10, 1: 20, 2: 30} # Remap node IDs to 0, 1, ... + assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["bar"], [0, 1000, 0]) @@ -201,3 +213,14 @@ def test_convert(): interface.BackendInterface.convert_from_nx(G, edge_attrs={"x": 1}, weight="x") with pytest.raises(TypeError, match="Expected networkx.Graph"): nxcg.from_networkx({}) + + +@pytest.mark.parametrize("graph_class", [nx.MultiGraph, nx.MultiDiGraph]) +def test_multigraph(graph_class): + G = graph_class() + G.add_edge(0, 1, "key1", x=10) + G.add_edge(0, 1, "key2", y=20) + Gcg = nxcg.from_networkx(G, preserve_edge_attrs=True) + H = nxcg.to_networkx(Gcg) + assert type(G) is type(H) + assert nx.utils.graphs_equal(G, H) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py b/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py new file mode 100644 index 00000000000..a8f189a4745 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_multigraph.py @@ -0,0 +1,72 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +@pytest.mark.parametrize("test_nxcugraph", [False, True]) +def test_get_edge_data(test_nxcugraph): + G = nx.MultiGraph() + G.add_edge(0, 1, 0, x=10) + G.add_edge(0, 1, 1, y=20) + G.add_edge(0, 2, "a", x=100) + G.add_edge(0, 2, "b", y=200) + G.add_edge(0, 3) + G.add_edge(0, 3) + if test_nxcugraph: + G = nxcg.MultiGraph(G) + default = object() + assert G.get_edge_data(0, 0, default=default) is default + assert G.get_edge_data("a", "b", default=default) is default + assert G.get_edge_data(0, 1, 2, default=default) is default + assert G.get_edge_data(-1, 1, default=default) is default + assert G.get_edge_data(0, 1, 0, default=default) == {"x": 10} + assert G.get_edge_data(0, 1, 1, default=default) == {"y": 20} + assert G.get_edge_data(0, 1, default=default) == {0: {"x": 10}, 1: {"y": 20}} + assert G.get_edge_data(0, 2, "a", default=default) == {"x": 100} + assert G.get_edge_data(0, 2, "b", default=default) == {"y": 200} + assert G.get_edge_data(0, 2, default=default) == {"a": {"x": 100}, "b": {"y": 200}} + assert G.get_edge_data(0, 3, 0, default=default) == {} + assert G.get_edge_data(0, 3, 1, default=default) == {} + assert G.get_edge_data(0, 3, 2, default=default) is default + assert G.get_edge_data(0, 3, default=default) == {0: {}, 1: {}} + assert G.has_edge(0, 1) + assert G.has_edge(0, 1, 0) + assert G.has_edge(0, 1, 1) + assert not G.has_edge(0, 1, 2) + assert not G.has_edge(0, 1, "a") + assert not G.has_edge(0, -1) + assert G.has_edge(0, 2) + assert G.has_edge(0, 2, "a") + assert G.has_edge(0, 2, "b") + assert not G.has_edge(0, 2, "c") + assert not G.has_edge(0, 2, 0) + assert G.has_edge(0, 3) + assert not G.has_edge(0, 0) + assert not G.has_edge(0, 0, 0) + + G = nx.MultiGraph() + G.add_edge(0, 1) + if test_nxcugraph: + G = nxcg.MultiGraph(G) + assert G.get_edge_data(0, 1, default=default) == {0: {}} + assert G.get_edge_data(0, 1, 0, default=default) == {} + assert G.get_edge_data(0, 1, 1, default=default) is default + assert G.get_edge_data(0, 1, "b", default=default) is default + assert G.get_edge_data(-1, 2, default=default) is default + assert G.has_edge(0, 1) + assert G.has_edge(0, 1, 0) + assert not G.has_edge(0, 1, 1) + assert not G.has_edge(0, 1, "a") diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 98adbb439d8..2478a02df9b 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -32,6 +32,7 @@ classifiers = [ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", + "numpy>=1.21", "pylibcugraph==23.12.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. @@ -109,7 +110,7 @@ addopts = [ # "-ra", # Print summary of all fails/errors "--benchmark-warmup=off", "--benchmark-max-time=0", - "--benchmark-min-rounds=3", + "--benchmark-min-rounds=1", "--benchmark-columns=min,median,max", ] From 0fd58836cc54a7a1d557f0251d31eaa157043eb2 Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Wed, 18 Oct 2023 08:36:31 -0500 Subject: [PATCH 055/111] update workflow links (#3939) --- .github/workflows/build.yaml | 20 ++++++++++---------- .github/workflows/pr.yaml | 30 +++++++++++++++--------------- .github/workflows/test.yaml | 10 +++++----- ci/release/update-version.sh | 2 +- 4 files changed, 31 insertions(+), 31 deletions(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index c92c11941f9..ccfdb826812 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -28,7 +28,7 @@ concurrency: jobs: cpp-build: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -38,7 +38,7 @@ jobs: python-build: needs: [cpp-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -47,7 +47,7 @@ jobs: upload-conda: needs: [cpp-build, python-build] secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -57,7 +57,7 @@ jobs: if: github.ref_type == 'branch' needs: python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: arch: "amd64" branch: ${{ inputs.branch }} @@ -69,7 +69,7 @@ jobs: sha: ${{ inputs.sha }} wheel-build-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -83,7 +83,7 @@ jobs: wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -93,7 +93,7 @@ jobs: wheel-build-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -106,7 +106,7 @@ jobs: wheel-publish-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -116,7 +116,7 @@ jobs: wheel-build-nx-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -126,7 +126,7 @@ jobs: wheel-publish-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 362e826ef65..9d20074381e 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -27,41 +27,41 @@ jobs: - wheel-tests-nx-cugraph - devcontainer secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/pr-builder.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-23.12 checks: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/checks.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-23.12 with: enable_check_generated_files: false conda-cpp-build: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 with: build_type: pull-request node_type: cpu32 conda-cpp-tests: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: pull-request conda-python-build: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 with: build_type: pull-request conda-python-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: pull-request conda-notebook-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -71,7 +71,7 @@ jobs: docs-build: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -81,7 +81,7 @@ jobs: wheel-build-pylibcugraph: needs: checks secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_pylibcugraph.sh @@ -92,14 +92,14 @@ jobs: wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_pylibcugraph.sh wheel-build-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_cugraph.sh @@ -109,27 +109,27 @@ jobs: wheel-tests-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_cugraph.sh wheel-build-nx-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 with: build_type: pull-request script: ci/build_wheel_nx-cugraph.sh wheel-tests-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: pull-request script: ci/test_wheel_nx-cugraph.sh devcontainer: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.12 with: node_type: cpu32 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 6d2c1566a3c..a0ecb67712c 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -16,7 +16,7 @@ on: jobs: conda-cpp-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -24,7 +24,7 @@ jobs: sha: ${{ inputs.sha }} conda-python-tests: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -32,7 +32,7 @@ jobs: sha: ${{ inputs.sha }} wheel-tests-pylibcugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -41,7 +41,7 @@ jobs: script: ci/test_wheel_pylibcugraph.sh wheel-tests-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} @@ -50,7 +50,7 @@ jobs: script: ci/test_wheel_cugraph.sh wheel-tests-nx-cugraph: secrets: inherit - uses: rapidsai/shared-action-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 with: build_type: nightly branch: ${{ inputs.branch }} diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index aaeaa715434..ad6426b66ff 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -124,7 +124,7 @@ sed_runner "/^ucx_py_version:$/ {n;s/.*/ - \"${NEXT_UCX_PY_VERSION}.*\"/}" cond # CI files for FILE in .github/workflows/*.yaml; do - sed_runner "/shared-action-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}" + sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}" # Wheel builds clone cugraph-ops, update its branch sed_runner "s/extra-repo-sha: branch-.*/extra-repo-sha: branch-${NEXT_SHORT_TAG}/g" "${FILE}" # Wheel builds install dask-cuda from source, update its branch From d9a8dc7165198d5fabe6e8462df3abf36177c128 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 23 Oct 2023 17:14:44 -0500 Subject: [PATCH 056/111] nx-cugraph: xfail test_louvain.py:test_threshold in Python 3.9 (#3944) This test is flaky b/c Louvain in PLC does not have a seed or random state parameter. This test holds the seed fixed and changes the threshold, but clearly this may not work if we can't set the seed. Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Naim (https://github.com/naimnv) URL: https://github.com/rapidsai/cugraph/pull/3944 --- python/nx-cugraph/nx_cugraph/interface.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 124b86a4af2..a7b88b72ec5 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -12,6 +12,8 @@ # limitations under the License. from __future__ import annotations +import sys + import networkx as nx import nx_cugraph as nxcg @@ -180,6 +182,11 @@ def key(testpath): ): "self-loops not handled in Louvain", } ) + if sys.version_info[:2] == (3, 9): + # This test is sensitive to RNG, which depends on Python version + xfail[ + key("test_louvain.py:test_threshold") + ] = "Louvain does not support seed parameter" for item in items: kset = set(item.keywords) From 9b28458dae82e85270bad0f8abf806f318fc84c8 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Mon, 23 Oct 2023 22:47:36 -0400 Subject: [PATCH 057/111] Some MTMG code cleanup and small optimizations (#3894) Added some missing documentation. A couple of optimizations: * Modified the `append` logic to keep the mutex lock only long enough to compute what needs to be copied and where. * Modified the handle created by the resource manager for each GPU to have a stream pool to enable different threads to operate on different streams. Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/3894 --- .../mtmg/detail/per_device_edgelist.hpp | 116 ++++++++++++------ cpp/include/cugraph/mtmg/handle.hpp | 35 +++++- cpp/include/cugraph/mtmg/instance_manager.hpp | 12 +- cpp/include/cugraph/mtmg/resource_manager.hpp | 15 ++- .../cugraph/mtmg/vertex_result_view.hpp | 2 + cpp/src/mtmg/vertex_result.cu | 4 +- cpp/tests/mtmg/threaded_test.cu | 16 +-- 7 files changed, 137 insertions(+), 63 deletions(-) diff --git a/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp b/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp index 8011146ee4f..7fd5bb726e6 100644 --- a/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp +++ b/cpp/include/cugraph/mtmg/detail/per_device_edgelist.hpp @@ -16,6 +16,7 @@ #pragma once +#include #include // FIXME: Could use std::span once compiler supports C++20 @@ -58,6 +59,15 @@ class per_device_edgelist_t { per_device_edgelist_t& operator=(per_device_edgelist_t const&) = delete; per_device_edgelist_t& operator=(per_device_edgelist_t&&) = delete; + /** + * @brief Construct a new per device edgelist t object + * + * @param handle MTMG resource handle - used to identify GPU resources + * @param device_buffer_size Number of edges to store in each device buffer + * @param use_weight Whether or not the edgelist will have weights + * @param use_edge_id Whether or not the edgelist will have edge ids + * @param use_edge_type Whether or not the edgelist will have edge types + */ per_device_edgelist_t(cugraph::mtmg::handle_t const& handle, size_t device_buffer_size, bool use_weight, @@ -82,6 +92,11 @@ class per_device_edgelist_t { create_new_buffers(handle); } + /** + * @brief Move construct a new per device edgelist t object + * + * @param other Object to move into this instance + */ per_device_edgelist_t(per_device_edgelist_t&& other) : device_buffer_size_{other.device_buffer_size_}, current_pos_{other.current_pos_}, @@ -110,45 +125,72 @@ class per_device_edgelist_t { std::optional> edge_id, std::optional> edge_type) { - // FIXME: This lock guard could be on a smaller region, but it - // would require more careful coding. The raft::update_device - // calls could be done without the lock if we made a local - // of the values of *.back() and did an increment of current_pos_ - // while we hold the lock. - std::lock_guard lock(lock_); - - size_t count = src.size(); - size_t pos = 0; - - while (count > 0) { - size_t copy_count = std::min(count, (src_.back().size() - current_pos_)); - - raft::update_device( - src_.back().begin() + current_pos_, src.begin() + pos, copy_count, handle.get_stream()); - raft::update_device( - dst_.back().begin() + current_pos_, dst.begin() + pos, copy_count, handle.get_stream()); - if (wgt) - raft::update_device( - wgt_->back().begin() + current_pos_, wgt->begin() + pos, copy_count, handle.get_stream()); - if (edge_id) - raft::update_device(edge_id_->back().begin() + current_pos_, - edge_id->begin() + pos, - copy_count, - handle.get_stream()); - if (edge_type) - raft::update_device(edge_type_->back().begin() + current_pos_, - edge_type->begin() + pos, - copy_count, - handle.get_stream()); - - count -= copy_count; - pos += copy_count; - current_pos_ += copy_count; - - if (current_pos_ == src_.back().size()) { create_new_buffers(handle); } + std::vector> copy_positions; + + { + std::lock_guard lock(lock_); + + size_t count = src.size(); + size_t pos = 0; + + while (count > 0) { + size_t copy_count = std::min(count, (src_.back().size() - current_pos_)); + + copy_positions.push_back(std::make_tuple(src_.size() - 1, current_pos_, pos, copy_count)); + + count -= copy_count; + pos += copy_count; + current_pos_ += copy_count; + + if (current_pos_ == src_.back().size()) { create_new_buffers(handle); } + } } - handle.raft_handle().sync_stream(); + std::for_each(copy_positions.begin(), + copy_positions.end(), + [&handle, + &this_src = src_, + &src, + &this_dst = dst_, + &dst, + &this_wgt = wgt_, + &wgt, + &this_edge_id = edge_id_, + &edge_id, + &this_edge_type = edge_type_, + &edge_type](auto tuple) { + auto [buffer_idx, buffer_pos, input_pos, copy_count] = tuple; + + raft::update_device(this_src[buffer_idx].begin() + buffer_pos, + src.begin() + input_pos, + copy_count, + handle.get_stream()); + + raft::update_device(this_dst[buffer_idx].begin() + buffer_pos, + dst.begin() + input_pos, + copy_count, + handle.get_stream()); + + if (this_wgt) + raft::update_device((*this_wgt)[buffer_idx].begin() + buffer_pos, + wgt->begin() + input_pos, + copy_count, + handle.get_stream()); + + if (this_edge_id) + raft::update_device((*this_edge_id)[buffer_idx].begin() + buffer_pos, + edge_id->begin() + input_pos, + copy_count, + handle.get_stream()); + + if (this_edge_type) + raft::update_device((*this_edge_type)[buffer_idx].begin() + buffer_pos, + edge_type->begin() + input_pos, + copy_count, + handle.get_stream()); + }); + + handle.sync_stream(); } /** diff --git a/cpp/include/cugraph/mtmg/handle.hpp b/cpp/include/cugraph/mtmg/handle.hpp index f23bce5aeac..efdec3f0775 100644 --- a/cpp/include/cugraph/mtmg/handle.hpp +++ b/cpp/include/cugraph/mtmg/handle.hpp @@ -18,6 +18,8 @@ #include +#include + namespace cugraph { namespace mtmg { @@ -60,10 +62,41 @@ class handle_t { rmm::cuda_stream_view get_stream() const { return raft_handle_.is_stream_pool_initialized() - ? raft_handle_.get_stream_from_stream_pool(device_id_) + ? raft_handle_.get_stream_from_stream_pool(thread_rank_) : raft_handle_.get_stream(); } + /** + * @brief Sync on the cuda stream + * + * @param stream Which stream to synchronize (defaults to the stream for this handle) + */ + void sync_stream(rmm::cuda_stream_view stream) const { raft_handle_.sync_stream(stream); } + + /** + * @brief Sync on the cuda stream for this handle + */ + void sync_stream() const { sync_stream(get_stream()); } + + /** + * @brief get thrust policy for the stream + * + * @param stream Which stream to use for this thrust call + * + * @return exec policy using the current stream + */ + rmm::exec_policy get_thrust_policy(rmm::cuda_stream_view stream) const + { + return rmm::exec_policy(stream); + } + + /** + * @brief get thrust policy for the stream for this handle + * + * @return exec policy using the current stream + */ + rmm::exec_policy get_thrust_policy() const { return get_thrust_policy(get_stream()); } + /** * @brief Get thread rank * diff --git a/cpp/include/cugraph/mtmg/instance_manager.hpp b/cpp/include/cugraph/mtmg/instance_manager.hpp index 8bf62b56f4b..687c5ddbf02 100644 --- a/cpp/include/cugraph/mtmg/instance_manager.hpp +++ b/cpp/include/cugraph/mtmg/instance_manager.hpp @@ -54,18 +54,18 @@ class instance_manager_t { * the request. Threads will be assigned to GPUs in a round-robin fashion to * spread requesting threads around the GPU resources. * - * This function will be CPU thread-safe. + * This function is CPU thread-safe. * * @return a handle for this thread. */ handle_t get_handle() { - int local_id = thread_counter_++; + int local_id = thread_counter_++; + int gpu_id = local_id % raft_handle_.size(); + int thread_id = local_id / raft_handle_.size(); - RAFT_CUDA_TRY(cudaSetDevice(device_ids_[local_id % raft_handle_.size()].value())); - return handle_t(*raft_handle_[local_id % raft_handle_.size()], - local_id / raft_handle_.size(), - static_cast(local_id % raft_handle_.size())); + RAFT_CUDA_TRY(cudaSetDevice(device_ids_[gpu_id].value())); + return handle_t(*raft_handle_[gpu_id], thread_id, static_cast(gpu_id)); } /** diff --git a/cpp/include/cugraph/mtmg/resource_manager.hpp b/cpp/include/cugraph/mtmg/resource_manager.hpp index b4633626e7c..e1e1d7ffc9d 100644 --- a/cpp/include/cugraph/mtmg/resource_manager.hpp +++ b/cpp/include/cugraph/mtmg/resource_manager.hpp @@ -23,8 +23,8 @@ #include #include -#include #include +#include #include #include @@ -121,11 +121,14 @@ class resource_manager_t { * @param instance_manager_id a ncclUniqueId that is shared by all processes participating * in this instance. All processes must use the same ID in this call, it is up * to the calling code to share this ID properly before the call. + * @param n_streams The number of streams to create in a stream pool for + * each GPU. Defaults to 16. * * @return unique pointer to instance manager */ - std::unique_ptr create_instance_manager( - std::vector ranks_to_include, ncclUniqueId instance_manager_id) const + std::unique_ptr create_instance_manager(std::vector ranks_to_include, + ncclUniqueId instance_manager_id, + size_t n_streams = 16) const { std::for_each( ranks_to_include.begin(), ranks_to_include.end(), [local_ranks = local_rank_map_](int rank) { @@ -153,11 +156,11 @@ class resource_manager_t { auto pos = local_rank_map_.find(rank); RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); - raft::handle_t tmp_handle; - nccl_comms.push_back(std::make_unique()); handles.push_back( - std::make_unique(tmp_handle, per_device_rmm_resources_.find(rank)->second)); + std::make_unique(rmm::cuda_stream_per_thread, + std::make_shared(n_streams), + per_device_rmm_resources_.find(rank)->second)); device_ids.push_back(pos->second); } diff --git a/cpp/include/cugraph/mtmg/vertex_result_view.hpp b/cpp/include/cugraph/mtmg/vertex_result_view.hpp index 7a7070d6f2a..a349bb95333 100644 --- a/cpp/include/cugraph/mtmg/vertex_result_view.hpp +++ b/cpp/include/cugraph/mtmg/vertex_result_view.hpp @@ -21,6 +21,8 @@ #include #include +#include + namespace cugraph { namespace mtmg { diff --git a/cpp/src/mtmg/vertex_result.cu b/cpp/src/mtmg/vertex_result.cu index a669a127f41..97fcd291c87 100644 --- a/cpp/src/mtmg/vertex_result.cu +++ b/cpp/src/mtmg/vertex_result.cu @@ -97,7 +97,7 @@ rmm::device_uvector vertex_result_view_t::gather( return vertex_partition.local_vertex_partition_offset_from_vertex_nocheck(v); }); - thrust::gather(handle.raft_handle().get_thrust_policy(), + thrust::gather(handle.get_thrust_policy(), iter, iter + local_vertices.size(), wrapped.begin(), @@ -118,7 +118,7 @@ rmm::device_uvector vertex_result_view_t::gather( // // Finally, reorder result // - thrust::scatter(handle.raft_handle().get_thrust_policy(), + thrust::scatter(handle.get_thrust_policy(), tmp_result.begin(), tmp_result.end(), vertex_pos.begin(), diff --git a/cpp/tests/mtmg/threaded_test.cu b/cpp/tests/mtmg/threaded_test.cu index c5dc2d3c7ce..bc4d8cfef6a 100644 --- a/cpp/tests/mtmg/threaded_test.cu +++ b/cpp/tests/mtmg/threaded_test.cu @@ -94,8 +94,9 @@ class Tests_Multithreaded size_t device_buffer_size{64 * 1024 * 1024}; size_t thread_buffer_size{4 * 1024 * 1024}; + const int num_threads_per_gpu{4}; int num_gpus = gpu_list.size(); - int num_threads = num_gpus * 4; + int num_threads = num_gpus * num_threads_per_gpu; cugraph::mtmg::resource_manager_t resource_manager; @@ -106,8 +107,10 @@ class Tests_Multithreaded ncclUniqueId instance_manager_id; ncclGetUniqueId(&instance_manager_id); + // Currently the only uses for multiple streams for each CPU threads + // associated with a particular GPU, which is a constant set above auto instance_manager = resource_manager.create_instance_manager( - resource_manager.registered_ranks(), instance_manager_id); + resource_manager.registered_ranks(), instance_manager_id, num_threads_per_gpu); cugraph::mtmg::edgelist_t edgelist; cugraph::mtmg::graph_t graph; @@ -172,15 +175,6 @@ class Tests_Multithreaded per_thread_edgelist(edgelist.get(thread_handle), thread_buffer_size); for (size_t j = i; j < h_src_v.size(); j += num_threads) { -#if 0 - if (h_weights_v) { - thread_edgelist.append( - thread_handle, h_src_v[j], h_dst_v[j], (*h_weights_v)[j], std::nullopt, std::nullopt); - } else { - thread_edgelist.append( - thread_handle, h_src_v[j], h_dst_v[j], std::nullopt, std::nullopt, std::nullopt); - } -#endif per_thread_edgelist.append( thread_handle, h_src_v[j], From 629e63c164732fe8da690fa42d2bd9b990131b09 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 25 Oct 2023 16:16:00 -0500 Subject: [PATCH 058/111] nx-cugraph: add k_truss and degree centralities (#3945) New algorithms: - `degree_centrality` - `in_degree_centrality` - `k_truss` - `number_of_selfloops` - `out_degree_centrality` Also, rename `row_indices, col_indices` to `src_indices, dst_indices` Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3945 --- python/nx-cugraph/_nx_cugraph/__init__.py | 5 + python/nx-cugraph/lint.yaml | 6 +- .../nx_cugraph/algorithms/__init__.py | 1 + .../algorithms/centrality/__init__.py | 1 + .../algorithms/centrality/degree_alg.py | 48 ++++++++ .../algorithms/community/louvain.py | 2 +- .../nx-cugraph/nx_cugraph/algorithms/core.py | 96 ++++++++++++++++ .../nx_cugraph/algorithms/isolate.py | 8 +- .../nx-cugraph/nx_cugraph/classes/__init__.py | 5 +- .../nx-cugraph/nx_cugraph/classes/digraph.py | 13 ++- .../nx-cugraph/nx_cugraph/classes/function.py | 23 ++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 105 ++++++++++-------- .../nx_cugraph/classes/multigraph.py | 64 ++++++----- python/nx-cugraph/nx_cugraph/convert.py | 38 +++---- .../nx_cugraph/tests/test_convert.py | 32 +++--- .../nx_cugraph/tests/test_match_api.py | 3 + python/nx-cugraph/pyproject.toml | 5 +- 17 files changed, 330 insertions(+), 125 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/core.py create mode 100644 python/nx-cugraph/nx_cugraph/classes/function.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 886d7a19f74..965b5b232ab 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -30,11 +30,16 @@ "functions": { # BEGIN: functions "betweenness_centrality", + "degree_centrality", "edge_betweenness_centrality", + "in_degree_centrality", "is_isolate", "isolates", + "k_truss", "louvain_communities", "number_of_isolates", + "number_of_selfloops", + "out_degree_centrality", # END: functions }, "extra_docstrings": { diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 4f604fbeb6e..fef2cebc7f5 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -45,12 +45,12 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.9.1 + rev: 23.10.0 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.292 + rev: v0.1.1 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.292 + rev: v0.1.1 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index dfd9adfc61a..22600bfdc2d 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -12,4 +12,5 @@ # limitations under the License. from . import centrality, community from .centrality import * +from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py index 2ac6242e8a4..af91f227843 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py @@ -11,3 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. from .betweenness import * +from .degree_alg import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py new file mode 100644 index 00000000000..0b2fd24af79 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/degree_alg.py @@ -0,0 +1,48 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nx_cugraph.convert import _to_directed_graph, _to_graph +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"] + + +@networkx_algorithm +def degree_centrality(G): + G = _to_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) + + +@not_implemented_for("undirected") +@networkx_algorithm +def in_degree_centrality(G): + G = _to_directed_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._in_degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) + + +@not_implemented_for("undirected") +@networkx_algorithm +def out_degree_centrality(G): + G = _to_directed_graph(G) + if len(G) <= 1: + return dict.fromkeys(G, 1) + deg = G._out_degrees_array() + centrality = deg * (1 / (len(G) - 1)) + return G._nodearray_to_dict(centrality) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index dc209870c89..62261d109a2 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -42,7 +42,7 @@ def louvain_communities( # NetworkX allows both directed and undirected, but cugraph only allows undirected. seed = _seed_to_int(seed) # Unused, but ensure it's valid for future compatibility G = _to_undirected_graph(G, weight) - if G.row_indices.size == 0: + if G.src_indices.size == 0: # TODO: PLC doesn't handle empty graphs gracefully! return [{key} for key in G._nodeiter_to_iter(range(len(G)))] if max_level is None: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py new file mode 100644 index 00000000000..0a64dd71c69 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -0,0 +1,96 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +import nx_cugraph as nxcg +from nx_cugraph.utils import networkx_algorithm, not_implemented_for + +__all__ = ["k_truss"] + + +@not_implemented_for("directed") +@not_implemented_for("multigraph") +@networkx_algorithm +def k_truss(G, k): + if is_nx := isinstance(G, nx.Graph): + G = nxcg.from_networkx(G, preserve_all_attrs=True) + if nxcg.number_of_selfloops(G) > 0: + raise nx.NetworkXError( + "Input graph has self loops which is not permitted; " + "Consider using G.remove_edges_from(nx.selfloop_edges(G))." + ) + # TODO: create renumbering helper function(s) + if k < 3: + # k-truss graph is comprised of nodes incident on k-2 triangles, so k<3 is a + # boundary condition. Here, all we need to do is drop nodes with zero degree. + # Technically, it would be okay to delete this branch of code, because + # plc.k_truss_subgraph behaves the same for 0 <= k < 3. We keep this branch b/c + # it's faster and has an "early return" if there are no nodes with zero degree. + degrees = G._degrees_array() + # Renumber step 0: node indices + node_indices = degrees.nonzero()[0] + if degrees.size == node_indices.size: + # No change + return G if is_nx else G.copy() + src_indices = G.src_indices + dst_indices = G.dst_indices + # Renumber step 1: edge values (no changes needed) + edge_values = {key: val.copy() for key, val in G.edge_values.items()} + edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} + else: + # int dtype for edge_indices would be preferred + edge_indices = cp.arange(G.src_indices.size, dtype=np.float64) + src_indices, dst_indices, edge_indices, _ = plc.k_truss_subgraph( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(edge_array=edge_indices), + k=k, + do_expensive_check=False, + ) + # Renumber step 0: node indices + node_indices = cp.unique(cp.concatenate([src_indices, dst_indices])) + # Renumber step 1: edge values + edge_indices = edge_indices.astype(np.int64) + edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()} + edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()} + # Renumber step 2: edge indices + mapper = cp.zeros(len(G), src_indices.dtype) + mapper[node_indices] = cp.arange(node_indices.size, dtype=mapper.dtype) + src_indices = mapper[src_indices] + dst_indices = mapper[dst_indices] + # Renumber step 3: node values + node_values = {key: val[node_indices] for key, val in G.node_values.items()} + node_masks = {key: val[node_indices] for key, val in G.node_masks.items()} + # Renumber step 4: key_to_id + if (id_to_key := G.id_to_key) is not None: + key_to_id = { + id_to_key[old_index]: new_index + for new_index, old_index in enumerate(node_indices.tolist()) + } + else: + key_to_id = None + # Same as calling `G.from_coo`, but use __class__ to indicate it's a classmethod. + new_graph = G.__class__.from_coo( + node_indices.size, + src_indices, + dst_indices, + edge_values, + edge_masks, + node_values, + node_masks, + key_to_id=key_to_id, + ) + new_graph.graph.update(G.graph) + return new_graph diff --git a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py index 774627e84f6..d32223fb3ed 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/isolate.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/isolate.py @@ -30,18 +30,18 @@ def is_isolate(G, n): G = _to_graph(G) index = n if G.key_to_id is None else G.key_to_id[n] return not ( - (G.row_indices == index).any().tolist() + (G.src_indices == index).any().tolist() or G.is_directed() - and (G.col_indices == index).any().tolist() + and (G.dst_indices == index).any().tolist() ) def _mark_isolates(G) -> cp.ndarray[bool]: """Return a boolean mask array indicating indices of isolated nodes.""" mark_isolates = cp.ones(len(G), bool) - mark_isolates[G.row_indices] = False + mark_isolates[G.src_indices] = False if G.is_directed(): - mark_isolates[G.col_indices] = False + mark_isolates[G.dst_indices] = False return mark_isolates diff --git a/python/nx-cugraph/nx_cugraph/classes/__init__.py b/python/nx-cugraph/nx_cugraph/classes/__init__.py index 9916bcbe241..19a5357da55 100644 --- a/python/nx-cugraph/nx_cugraph/classes/__init__.py +++ b/python/nx-cugraph/nx_cugraph/classes/__init__.py @@ -11,7 +11,8 @@ # See the License for the specific language governing permissions and # limitations under the License. from .graph import Graph +from .digraph import DiGraph from .multigraph import MultiGraph +from .multidigraph import MultiDiGraph -from .digraph import DiGraph # isort:skip -from .multidigraph import MultiDiGraph # isort:skip +from .function import * diff --git a/python/nx-cugraph/nx_cugraph/classes/digraph.py b/python/nx-cugraph/nx_cugraph/classes/digraph.py index 72a1bff21a9..52ea2334c85 100644 --- a/python/nx-cugraph/nx_cugraph/classes/digraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/digraph.py @@ -14,6 +14,7 @@ from typing import TYPE_CHECKING +import cupy as cp import networkx as nx import nx_cugraph as nxcg @@ -48,7 +49,7 @@ def number_of_edges( ) -> int: if u is not None or v is not None: raise NotImplementedError - return self.row_indices.size + return self.src_indices.size ########################## # NetworkX graph methods # @@ -59,3 +60,13 @@ def reverse(self, copy: bool = True) -> DiGraph: return self._copy(not copy, self.__class__, reverse=True) # Many more methods to implement... + + ################### + # Private methods # + ################### + + def _in_degrees_array(self): + return cp.bincount(self.dst_indices, minlength=self._N) + + def _out_degrees_array(self): + return cp.bincount(self.src_indices, minlength=self._N) diff --git a/python/nx-cugraph/nx_cugraph/classes/function.py b/python/nx-cugraph/nx_cugraph/classes/function.py new file mode 100644 index 00000000000..633e4abd7f4 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/classes/function.py @@ -0,0 +1,23 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import networkx_algorithm + +__all__ = ["number_of_selfloops"] + + +@networkx_algorithm +def number_of_selfloops(G): + G = _to_graph(G) + is_selfloop = G.src_indices == G.dst_indices + return is_selfloop.sum().tolist() diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index f1e85c836e3..166b6b9dc6b 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -52,8 +52,8 @@ class Graph: # Not networkx properties # We store edge data in COO format with {row,col}_indices and edge_values. - row_indices: cp.ndarray[IndexValue] - col_indices: cp.ndarray[IndexValue] + src_indices: cp.ndarray[IndexValue] + dst_indices: cp.ndarray[IndexValue] edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] edge_masks: dict[AttrKey, cp.ndarray[bool]] node_values: dict[AttrKey, cp.ndarray[NodeValue]] @@ -70,8 +70,8 @@ class Graph: def from_coo( cls, N: int, - row_indices: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -82,8 +82,8 @@ def from_coo( **attr, ) -> Graph: new_graph = object.__new__(cls) - new_graph.row_indices = row_indices - new_graph.col_indices = col_indices + new_graph.src_indices = src_indices + new_graph.dst_indices = dst_indices new_graph.edge_values = {} if edge_values is None else dict(edge_values) new_graph.edge_masks = {} if edge_masks is None else dict(edge_masks) new_graph.node_values = {} if node_values is None else dict(node_values) @@ -93,9 +93,9 @@ def from_coo( new_graph._N = op.index(N) # Ensure N is integral new_graph.graph = new_graph.graph_attr_dict_factory() new_graph.graph.update(attr) - size = new_graph.row_indices.size + size = new_graph.src_indices.size # Easy and fast sanity checks - if size != new_graph.col_indices.size: + if size != new_graph.dst_indices.size: raise ValueError for attr in ["edge_values", "edge_masks"]: if datadict := getattr(new_graph, attr): @@ -117,7 +117,7 @@ def from_coo( def from_csr( cls, indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -128,14 +128,14 @@ def from_csr( **attr, ) -> Graph: N = indptr.size - 1 - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -149,7 +149,7 @@ def from_csr( def from_csc( cls, indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -160,14 +160,14 @@ def from_csc( **attr, ) -> Graph: N = indptr.size - 1 - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -183,7 +183,7 @@ def from_dcsr( N: int, compressed_rows: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -193,14 +193,14 @@ def from_dcsr( id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_rows.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -216,7 +216,7 @@ def from_dcsc( N: int, compressed_cols: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, @@ -226,14 +226,14 @@ def from_dcsc( id_to_key: list[NodeKey] | None = None, **attr, ) -> Graph: - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_cols.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -343,8 +343,8 @@ def clear(self) -> None: self.node_values.clear() self.node_masks.clear() self.graph.clear() - self.row_indices = cp.empty(0, self.row_indices.dtype) - self.col_indices = cp.empty(0, self.col_indices.dtype) + self.src_indices = cp.empty(0, self.src_indices.dtype) + self.dst_indices = cp.empty(0, self.dst_indices.dtype) self._N = 0 self.key_to_id = None self._id_to_key = None @@ -353,8 +353,8 @@ def clear(self) -> None: def clear_edges(self) -> None: self.edge_values.clear() self.edge_masks.clear() - self.row_indices = cp.empty(0, self.row_indices.dtype) - self.col_indices = cp.empty(0, self.col_indices.dtype) + self.src_indices = cp.empty(0, self.src_indices.dtype) + self.dst_indices = cp.empty(0, self.dst_indices.dtype) @networkx_api def copy(self, as_view: bool = False) -> Graph: @@ -377,7 +377,7 @@ def get_edge_data( return default except TypeError: return default - index = cp.nonzero((self.row_indices == u) & (self.col_indices == v))[0] + index = cp.nonzero((self.src_indices == u) & (self.dst_indices == v))[0] if index.size == 0: return default [index] = index.tolist() @@ -397,7 +397,7 @@ def has_edge(self, u: NodeKey, v: NodeKey) -> bool: v = self.key_to_id[v] except KeyError: return False - return bool(((self.row_indices == u) & (self.col_indices == v)).any()) + return bool(((self.src_indices == u) & (self.dst_indices == v)).any()) @networkx_api def has_node(self, n: NodeKey) -> bool: @@ -431,8 +431,8 @@ def order(self) -> int: def size(self, weight: AttrKey | None = None) -> int: if weight is not None: raise NotImplementedError - # If no self-edges, then `self.row_indices.size // 2` - return int((self.row_indices <= self.col_indices).sum()) + # If no self-edges, then `self.src_indices.size // 2` + return int((self.src_indices <= self.dst_indices).sum()) @networkx_api def to_directed(self, as_view: bool = False) -> nxcg.DiGraph: @@ -455,9 +455,8 @@ def to_undirected(self, as_view: bool = False) -> Graph: def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): # DRY warning: see also MultiGraph._copy - indptr = self.indptr - row_indices = self.row_indices - col_indices = self.col_indices + src_indices = self.src_indices + dst_indices = self.dst_indices edge_values = self.edge_values edge_masks = self.edge_masks node_values = self.node_values @@ -465,9 +464,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): key_to_id = self.key_to_id id_to_key = None if key_to_id is None else self._id_to_key if not as_view: - indptr = indptr.copy() - row_indices = row_indices.copy() - col_indices = col_indices.copy() + src_indices = src_indices.copy() + dst_indices = dst_indices.copy() edge_values = {key: val.copy() for key, val in edge_values.items()} edge_masks = {key: val.copy() for key, val in edge_masks.items()} node_values = {key: val.copy() for key, val in node_values.items()} @@ -477,11 +475,11 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): if id_to_key is not None: id_to_key = id_to_key.copy() if reverse: - row_indices, col_indices = col_indices, row_indices + src_indices, dst_indices = dst_indices, src_indices rv = cls.from_coo( - indptr, - row_indices, - col_indices, + self._N, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -502,8 +500,11 @@ def _get_plc_graph( edge_dtype: Dtype | None = None, *, store_transposed: bool = False, + edge_array: cp.ndarray[EdgeValue] | None = None, ): - if edge_attr is None: + if edge_array is not None: + pass + elif edge_attr is None: edge_array = None elif edge_attr not in self.edge_values: raise KeyError("Graph has no edge attribute {edge_attr!r}") @@ -532,14 +533,20 @@ def _get_plc_graph( is_multigraph=self.is_multigraph(), is_symmetric=not self.is_directed(), ), - src_or_offset_array=self.row_indices, - dst_or_index_array=self.col_indices, + src_or_offset_array=self.src_indices, + dst_or_index_array=self.dst_indices, weight_array=edge_array, store_transposed=store_transposed, renumber=False, do_expensive_check=False, ) + def _degrees_array(self): + degrees = cp.bincount(self.src_indices, minlength=self._N) + if self.is_directed(): + degrees += cp.bincount(self.dst_indices, minlength=self._N) + return degrees + def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" if (id_to_key := self.id_to_key) is not None: @@ -551,6 +558,14 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: return node_ids.tolist() return list(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_dict( + self, values: cp.ndarray[NodeValue] + ) -> dict[NodeKey, NodeValue]: + it = enumerate(values.tolist()) + if (id_to_key := self.id_to_key) is not None: + return {id_to_key[key]: val for key, val in it} + return dict(it) + def _nodearrays_to_dict( self, node_ids: cp.ndarray[IndexValue], values: cp.ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index 499ca7ce212..3d90861a328 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -67,8 +67,8 @@ class MultiGraph(Graph): def from_coo( cls, N: int, - row_indices: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -82,8 +82,8 @@ def from_coo( ) -> MultiGraph: new_graph = super().from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -97,7 +97,7 @@ def from_coo( # Easy and fast sanity checks if ( new_graph.edge_keys is not None - and len(new_graph.edge_keys) != row_indices.size + and len(new_graph.edge_keys) != src_indices.size ): raise ValueError return new_graph @@ -106,7 +106,7 @@ def from_coo( def from_csr( cls, indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -119,14 +119,14 @@ def from_csr( **attr, ) -> MultiGraph: N = indptr.size - 1 - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -142,7 +142,7 @@ def from_csr( def from_csc( cls, indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -155,14 +155,14 @@ def from_csc( **attr, ) -> MultiGraph: N = indptr.size - 1 - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -180,7 +180,7 @@ def from_dcsr( N: int, compressed_rows: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - col_indices: cp.ndarray[IndexValue], + dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -192,14 +192,14 @@ def from_dcsr( edge_keys: list[EdgeKey] | None = None, **attr, ) -> MultiGraph: - row_indices = cp.array( + src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_rows.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -217,7 +217,7 @@ def from_dcsc( N: int, compressed_cols: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], - row_indices: cp.ndarray[IndexValue], + src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, @@ -229,14 +229,14 @@ def from_dcsc( edge_keys: list[EdgeKey] | None = None, **attr, ) -> Graph: - col_indices = cp.array( + dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead np.repeat(compressed_cols.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -330,7 +330,7 @@ def get_edge_data( return default except TypeError: return default - mask = (self.row_indices == u) & (self.col_indices == v) + mask = (self.src_indices == u) & (self.dst_indices == v) if not mask.any(): return default if self.edge_keys is None: @@ -376,7 +376,7 @@ def has_edge(self, u: NodeKey, v: NodeKey, key: EdgeKey | None = None) -> bool: v = self.key_to_id[v] except KeyError: return False - mask = (self.row_indices == u) & (self.col_indices == v) + mask = (self.src_indices == u) & (self.dst_indices == v) if key is None or (self.edge_indices is None and self.edge_keys is None): return bool(mask.any()) if self.edge_keys is None: @@ -405,9 +405,8 @@ def to_undirected(self, as_view: bool = False) -> MultiGraph: def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): # DRY warning: see also Graph._copy - indptr = self.indptr - row_indices = self.row_indices - col_indices = self.col_indices + src_indices = self.src_indices + dst_indices = self.dst_indices edge_indices = self.edge_indices edge_values = self.edge_values edge_masks = self.edge_masks @@ -417,9 +416,8 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): id_to_key = None if key_to_id is None else self._id_to_key edge_keys = self.edge_keys if not as_view: - indptr = indptr.copy() - row_indices = row_indices.copy() - col_indices = col_indices.copy() + src_indices = src_indices.copy() + dst_indices = dst_indices.copy() edge_indices = edge_indices.copy() edge_values = {key: val.copy() for key, val in edge_values.items()} edge_masks = {key: val.copy() for key, val in edge_masks.items()} @@ -432,11 +430,11 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): if edge_keys is not None: edge_keys = edge_keys.copy() if reverse: - row_indices, col_indices = col_indices, row_indices + src_indices, dst_indices = dst_indices, src_indices rv = cls.from_coo( - indptr, - row_indices, - col_indices, + self._N, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index e82286e5e29..d117c8e5c03 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -266,12 +266,12 @@ def from_networkx( else: col_iter = map(key_to_id.__getitem__, col_iter) if graph.is_multigraph(): - col_indices = np.fromiter(col_iter, np.int32) + dst_indices = np.fromiter(col_iter, np.int32) num_multiedges = np.fromiter( map(len, concat(map(dict.values, adj.values()))), np.int32 ) # cp.repeat is slow to use here, so use numpy instead - col_indices = cp.array(np.repeat(col_indices, num_multiedges)) + dst_indices = cp.array(np.repeat(dst_indices, num_multiedges)) # Determine edge keys and edge ids for multigraphs edge_keys = list(concat(concat(map(dict.values, adj.values())))) edge_indices = cp.fromiter( @@ -281,7 +281,7 @@ def from_networkx( if edge_keys == edge_indices.tolist(): edge_keys = None # Prefer edge_indices else: - col_indices = cp.fromiter(col_iter, np.int32) + dst_indices = cp.fromiter(col_iter, np.int32) edge_values = {} edge_masks = {} @@ -353,12 +353,12 @@ def from_networkx( # if vals.ndim > 1: ... # cp.repeat is slow to use here, so use numpy instead - row_indices = np.repeat( + src_indices = np.repeat( np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) ) if graph.is_multigraph(): - row_indices = np.repeat(row_indices, num_multiedges) - row_indices = cp.array(row_indices) + src_indices = np.repeat(src_indices, num_multiedges) + src_indices = cp.array(src_indices) node_values = {} node_masks = {} @@ -405,8 +405,8 @@ def from_networkx( klass = nxcg.MultiGraph rv = klass.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_indices, edge_values, edge_masks, @@ -422,8 +422,8 @@ def from_networkx( klass = nxcg.Graph rv = klass.from_coo( N, - row_indices, - col_indices, + src_indices, + dst_indices, edge_values, edge_masks, node_values, @@ -496,23 +496,23 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: else: rv.add_nodes_from(range(len(G))) - row_indices = G.row_indices - col_indices = G.col_indices + src_indices = G.src_indices + dst_indices = G.dst_indices edge_values = G.edge_values edge_masks = G.edge_masks if edge_values and not G.is_directed(): # Only add upper triangle of the adjacency matrix so we don't double-add edges - mask = row_indices <= col_indices - row_indices = row_indices[mask] - col_indices = col_indices[mask] + mask = src_indices <= dst_indices + src_indices = src_indices[mask] + dst_indices = dst_indices[mask] edge_values = {k: v[mask] for k, v in edge_values.items()} if edge_masks: edge_masks = {k: v[mask] for k, v in edge_masks.items()} - row_indices = row_iter = row_indices.tolist() - col_indices = col_iter = col_indices.tolist() + src_indices = row_iter = src_indices.tolist() + dst_indices = col_iter = dst_indices.tolist() if id_to_key is not None: - row_iter = map(id_to_key.__getitem__, row_indices) - col_iter = map(id_to_key.__getitem__, col_indices) + row_iter = map(id_to_key.__getitem__, src_indices) + col_iter = map(id_to_key.__getitem__, dst_indices) if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): if G.edge_keys is not None: edge_keys = G.edge_keys diff --git a/python/nx-cugraph/nx_cugraph/tests/test_convert.py b/python/nx-cugraph/nx_cugraph/tests/test_convert.py index 83820f7621f..1a71b796861 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_convert.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_convert.py @@ -71,8 +71,8 @@ def test_convert(graph_class): ]: # All edges have "x" attribute, so all kwargs are equivalent Gcg = nxcg.from_networkx(G, **kwargs) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [2, 2]) assert len(Gcg.edge_values) == 1 assert Gcg.edge_masks == {} @@ -86,8 +86,8 @@ def test_convert(graph_class): # Structure-only graph (no edge attributes) Gcg = nxcg.from_networkx(G, preserve_node_attrs=True) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [10, 20]) assert Gcg.edge_values == Gcg.edge_masks == {} H = nxcg.to_networkx(Gcg) @@ -99,8 +99,8 @@ def test_convert(graph_class): # Fill completely missing attribute with default value Gcg = nxcg.from_networkx(G, edge_attrs={"y": 0}) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) cp.testing.assert_array_equal(Gcg.edge_values["y"], [0, 0]) assert len(Gcg.edge_values) == 1 assert Gcg.edge_masks == Gcg.node_values == Gcg.node_masks == {} @@ -111,8 +111,8 @@ def test_convert(graph_class): # If attribute is completely missing (and no default), then just ignore it Gcg = nxcg.from_networkx(G, edge_attrs={"y": None}) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 1]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 1]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 0]) assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == [] H = nxcg.to_networkx(Gcg) assert list(H.edges(data=True)) == [(0, 1, {})] @@ -123,8 +123,8 @@ def test_convert(graph_class): # Some edges are missing 'x' attribute; need to use a mask for kwargs in [{"preserve_edge_attrs": True}, {"edge_attrs": {"x": None}}]: Gcg = nxcg.from_networkx(G, **kwargs) - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) assert sorted(Gcg.edge_values) == sorted(Gcg.edge_masks) == ["x"] cp.testing.assert_array_equal(Gcg.edge_masks["x"], [True, False, True, False]) cp.testing.assert_array_equal(Gcg.edge_values["x"][Gcg.edge_masks["x"]], [2, 2]) @@ -160,8 +160,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.edge_values["x"], [1, 2, 1, 2]) assert sorted(Gcg.edge_masks) == ["y"] cp.testing.assert_array_equal(Gcg.edge_masks["y"], [False, True, False, True]) @@ -181,8 +181,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["foo"], [100, 200, 300]) assert sorted(Gcg.node_masks) == ["bar"] cp.testing.assert_array_equal(Gcg.node_masks["bar"], [False, True, False]) @@ -202,8 +202,8 @@ def test_convert(graph_class): ]: Gcg = nxcg.from_networkx(G, **kwargs) assert Gcg.id_to_key == [10, 20, 30] # Remap node IDs to 0, 1, ... - cp.testing.assert_array_equal(Gcg.row_indices, [0, 0, 1, 2]) - cp.testing.assert_array_equal(Gcg.col_indices, [1, 2, 0, 0]) + cp.testing.assert_array_equal(Gcg.src_indices, [0, 0, 1, 2]) + cp.testing.assert_array_equal(Gcg.dst_indices, [1, 2, 0, 0]) cp.testing.assert_array_equal(Gcg.node_values["bar"], [0, 1000, 0]) assert Gcg.node_masks == {} diff --git a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py index ecfda1397db..a654ff343ed 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_match_api.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_match_api.py @@ -31,6 +31,9 @@ def test_match_signature_and_names(): if is_nx_30_or_31 and name in {"louvain_communities"}: continue + if name not in nx_backends._registered_algorithms: + print(f"{name} not dispatched from networkx") + continue dispatchable_func = nx_backends._registered_algorithms[name] # nx version >=3.2 uses orig_func, version >=3.0,<3.2 uses _orig_func if is_nx_30_or_31: diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 2478a02df9b..9fec8fa0242 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -81,7 +81,10 @@ float_to_top = true default_section = "THIRDPARTY" known_first_party = "nx_cugraph" line_length = 88 -extend_skip_glob = ["nx_cugraph/__init__.py"] +extend_skip_glob = [ + "nx_cugraph/__init__.py", + "nx_cugraph/classes/__init__.py", +] [tool.pytest.ini_options] minversion = "6.0" From c90fd9480644c2ef68cf67041b97160fc174313e Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Thu, 26 Oct 2023 07:40:03 -0500 Subject: [PATCH 059/111] Unpin `dask` and `distributed` for `23.12` development (#3953) This PR relaxes `dask` and `distributed` versions pinning for `23.12` development. xref: https://github.com/rapidsai/cudf/pull/14320 Authors: - GALI PREM SAGAR (https://github.com/galipremsagar) Approvers: - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) - https://github.com/jakirkham URL: https://github.com/rapidsai/cugraph/pull/3953 --- ci/test_wheel_cugraph.sh | 2 +- conda/environments/all_cuda-118_arch-x86_64.yaml | 2 +- conda/environments/all_cuda-120_arch-x86_64.yaml | 2 +- conda/recipes/cugraph-pyg/meta.yaml | 2 +- conda/recipes/cugraph-service/meta.yaml | 2 +- conda/recipes/cugraph/meta.yaml | 6 +++--- dependencies.yaml | 2 +- 7 files changed, 9 insertions(+), 9 deletions(-) diff --git a/ci/test_wheel_cugraph.sh b/ci/test_wheel_cugraph.sh index ac18459128a..f9e2aa6d8da 100755 --- a/ci/test_wheel_cugraph.sh +++ b/ci/test_wheel_cugraph.sh @@ -9,6 +9,6 @@ RAPIDS_PY_WHEEL_NAME="pylibcugraph_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-whe python -m pip install --no-deps ./local-pylibcugraph-dep/pylibcugraph*.whl # Always install latest dask for testing -python -m pip install git+https://github.com/dask/dask.git@2023.9.2 git+https://github.com/dask/distributed.git@2023.9.2 +python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main ./ci/test_wheel.sh cugraph python/cugraph diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 71a8d10b935..2f3a9c988cf 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -20,7 +20,7 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core==2023.9.2 +- dask-core>=2023.9.2 - dask-cuda==23.12.* - dask-cudf==23.12.* - dask>=2023.7.1 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 9179fba788e..31ff503e682 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -20,7 +20,7 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core==2023.9.2 +- dask-core>=2023.9.2 - dask-cuda==23.12.* - dask-cudf==23.12.* - dask>=2023.7.1 diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index 1dc5a75c41b..f4b2e9a4ee9 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -26,7 +26,7 @@ requirements: - python - scikit-build >=0.13.1 run: - - distributed ==2023.9.2 + - distributed >=2023.9.2 - numba >=0.57 - numpy >=1.21 - python diff --git a/conda/recipes/cugraph-service/meta.yaml b/conda/recipes/cugraph-service/meta.yaml index 2daf0438351..3d001e83e1e 100644 --- a/conda/recipes/cugraph-service/meta.yaml +++ b/conda/recipes/cugraph-service/meta.yaml @@ -59,7 +59,7 @@ outputs: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - distributed ==2023.9.2 + - distributed >=2023.9.2 - numba >=0.57 - numpy >=1.21 - python diff --git a/conda/recipes/cugraph/meta.yaml b/conda/recipes/cugraph/meta.yaml index f9bf54a2ef4..3691db508d9 100644 --- a/conda/recipes/cugraph/meta.yaml +++ b/conda/recipes/cugraph/meta.yaml @@ -76,9 +76,9 @@ requirements: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - dask ==2023.9.2 - - dask-core ==2023.9.2 - - distributed ==2023.9.2 + - dask >=2023.9.2 + - dask-core >=2023.9.2 + - distributed >=2023.9.2 - fsspec>=0.6.0 - libcugraph ={{ version }} - pylibcugraph ={{ version }} diff --git a/dependencies.yaml b/dependencies.yaml index 736aa2e019f..b127d9bd29e 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -385,7 +385,7 @@ dependencies: - output_types: conda packages: - aiohttp - - &dask-core_conda dask-core==2023.9.2 + - &dask-core_conda dask-core>=2023.9.2 - fsspec>=0.6.0 - libcudf==23.12.* - requests From 4e39f209392ad604c747dc92a8e28b398a4ef1c7 Mon Sep 17 00:00:00 2001 From: Jake Awe <50372925+AyodeAwe@users.noreply.github.com> Date: Fri, 27 Oct 2023 02:25:07 -0500 Subject: [PATCH 060/111] Download `xml` docs artifact through CloudFront endpoint (#3955) * local docs build workaround * pipe output --- ci/build_docs.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/build_docs.sh b/ci/build_docs.sh index e774a6f9871..3f97f652d41 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -44,7 +44,7 @@ for PROJECT in libcugraphops libwholegraph; do rapids-logger "Download ${PROJECT} xml_tar" TMP_DIR=$(mktemp -d) export XML_DIR_${PROJECT^^}="$TMP_DIR" - aws s3 cp --only-show-errors "s3://rapidsai-docs/${PROJECT}/xml_tar/${RAPIDS_VERSION_NUMBER}/xml.tar.gz" - | tar xzf - -C "${TMP_DIR}" + curl "https://d1664dvumjb44w.cloudfront.net/${PROJECT}/xml_tar/${RAPIDS_VERSION_NUMBER}/xml.tar.gz" | tar -xzf - -C "${TMP_DIR}" done rapids-logger "Build CPP docs" From a57f779dc1c1ab8ea2575210747eb1066db5f9cf Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Fri, 27 Oct 2023 11:59:57 -0700 Subject: [PATCH 061/111] Update the neighbor intersection primitive to support edge masking. (#3550) This PR updates the detail::nbr_intersection() primitive and the per_v_pair_transform_dst_nbr_intersection primitive (which calls the detail::nbr_intersection primitive) to work with edge masking (graph_view_t object with attached edge mask). Several utility functions are updated to support edge masking as well to support primitive updates and testing. This PR is necessary to implement K-truss with the cuGraph C++ primitives. See https://github.com/rapidsai/cugraph/issues/3446#issuecomment-1499649664 for additional details. Authors: - Seunghwa Kang (https://github.com/seunghwak) - Brad Rees (https://github.com/BradReesWork) - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Naim (https://github.com/naimnv) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/3550 --- .../detail/decompress_edge_partition.cuh | 138 ++- ...ge_partition_edge_property_device_view.cuh | 12 +- cpp/include/cugraph/graph_mask.hpp | 382 -------- cpp/include/cugraph/graph_view.hpp | 24 +- .../cugraph/utilities/device_functors.cuh | 55 +- cpp/include/cugraph/utilities/mask_utils.cuh | 150 ++++ .../cugraph/utilities/packed_bool_utils.hpp | 7 + cpp/src/prims/detail/nbr_intersection.cuh | 835 +++++++++++------- .../detail/optional_dataframe_buffer.hpp | 1 + cpp/src/prims/kv_store.cuh | 6 +- ..._v_pair_transform_dst_nbr_intersection.cuh | 50 +- ...r_v_random_select_transform_outgoing_e.cuh | 2 +- ...m_reduce_dst_key_aggregated_outgoing_e.cuh | 10 +- ...t_nbr_intersection_of_e_endpoints_by_v.cuh | 35 +- cpp/src/structure/coarsen_graph_impl.cuh | 16 +- .../structure/decompress_to_edgelist_impl.cuh | 56 +- cpp/src/structure/graph_impl.cuh | 15 +- cpp/src/structure/graph_view_impl.cuh | 79 +- cpp/tests/CMakeLists.txt | 4 - ...r_v_pair_transform_dst_nbr_intersection.cu | 128 ++- ...transform_dst_nbr_weighted_intersection.cu | 77 +- cpp/tests/structure/graph_mask_test.cpp | 64 -- 22 files changed, 1085 insertions(+), 1061 deletions(-) delete mode 100644 cpp/include/cugraph/graph_mask.hpp create mode 100644 cpp/include/cugraph/utilities/mask_utils.cuh delete mode 100644 cpp/tests/structure/graph_mask_test.cpp diff --git a/cpp/include/cugraph/detail/decompress_edge_partition.cuh b/cpp/include/cugraph/detail/decompress_edge_partition.cuh index cd8739114f2..4b256a0413a 100644 --- a/cpp/include/cugraph/detail/decompress_edge_partition.cuh +++ b/cpp/include/cugraph/detail/decompress_edge_partition.cuh @@ -18,6 +18,7 @@ #include #include #include +#include #include #include @@ -46,7 +47,7 @@ __global__ void decompress_to_edgelist_mid_degree( edge_partition_device_view_t edge_partition, vertex_t major_range_first, vertex_t major_range_last, - vertex_t* majors) + raft::device_span majors) { auto const tid = threadIdx.x + blockIdx.x * blockDim.x; static_assert(decompress_edge_partition_block_size % raft::warp_size() == 0); @@ -76,7 +77,7 @@ __global__ void decompress_to_edgelist_high_degree( edge_partition_device_view_t edge_partition, vertex_t major_range_first, vertex_t major_range_last, - vertex_t* majors) + raft::device_span majors) { auto major_start_offset = static_cast(major_range_first - edge_partition.major_range_first()); @@ -103,10 +104,19 @@ template void decompress_edge_partition_to_fill_edgelist_majors( raft::handle_t const& handle, edge_partition_device_view_t edge_partition, - vertex_t* majors, + std::optional> + edge_partition_mask_view, + raft::device_span majors, std::optional> const& segment_offsets) { - auto execution_policy = handle.get_thrust_policy(); + auto tmp_buffer = edge_partition_mask_view + ? std::make_optional>( + edge_partition.number_of_edges(), handle.get_stream()) + : std::nullopt; + + auto output_buffer = + tmp_buffer ? raft::device_span((*tmp_buffer).data(), (*tmp_buffer).size()) : majors; + if (segment_offsets) { // FIXME: we may further improve performance by 1) concurrently running kernels on different // segments; 2) individually tuning block sizes for different segments; and 3) adding one more @@ -124,7 +134,7 @@ void decompress_edge_partition_to_fill_edgelist_majors( edge_partition, edge_partition.major_range_first(), edge_partition.major_range_first() + (*segment_offsets)[1], - majors); + output_buffer); } if ((*segment_offsets)[2] - (*segment_offsets)[1] > 0) { raft::grid_1d_warp_t update_grid((*segment_offsets)[2] - (*segment_offsets)[1], @@ -138,49 +148,63 @@ void decompress_edge_partition_to_fill_edgelist_majors( edge_partition, edge_partition.major_range_first() + (*segment_offsets)[1], edge_partition.major_range_first() + (*segment_offsets)[2], - majors); + output_buffer); } if ((*segment_offsets)[3] - (*segment_offsets)[2] > 0) { thrust::for_each( - execution_policy, + handle.get_thrust_policy(), thrust::make_counting_iterator(edge_partition.major_range_first()) + (*segment_offsets)[2], thrust::make_counting_iterator(edge_partition.major_range_first()) + (*segment_offsets)[3], - [edge_partition, majors] __device__(auto major) { + [edge_partition, output_buffer] __device__(auto major) { auto major_offset = edge_partition.major_offset_from_major_nocheck(major); auto local_degree = edge_partition.local_degree(major_offset); auto local_offset = edge_partition.local_offset(major_offset); - thrust::fill( - thrust::seq, majors + local_offset, majors + local_offset + local_degree, major); + thrust::fill(thrust::seq, + output_buffer.begin() + local_offset, + output_buffer.begin() + local_offset + local_degree, + major); }); } if (edge_partition.dcs_nzd_vertex_count() && (*(edge_partition.dcs_nzd_vertex_count()) > 0)) { thrust::for_each( - execution_policy, + handle.get_thrust_policy(), thrust::make_counting_iterator(vertex_t{0}), thrust::make_counting_iterator(*(edge_partition.dcs_nzd_vertex_count())), - [edge_partition, major_start_offset = (*segment_offsets)[3], majors] __device__(auto idx) { + [edge_partition, major_start_offset = (*segment_offsets)[3], output_buffer] __device__( + auto idx) { auto major = *(edge_partition.major_from_major_hypersparse_idx_nocheck(idx)); auto major_idx = major_start_offset + idx; // major_offset != major_idx in the hypersparse region auto local_degree = edge_partition.local_degree(major_idx); auto local_offset = edge_partition.local_offset(major_idx); - thrust::fill( - thrust::seq, majors + local_offset, majors + local_offset + local_degree, major); + thrust::fill(thrust::seq, + output_buffer.begin() + local_offset, + output_buffer.begin() + local_offset + local_degree, + major); }); } } else { - thrust::for_each( - execution_policy, - thrust::make_counting_iterator(edge_partition.major_range_first()), - thrust::make_counting_iterator(edge_partition.major_range_first()) + - edge_partition.major_range_size(), - [edge_partition, majors] __device__(auto major) { - auto major_offset = edge_partition.major_offset_from_major_nocheck(major); - auto local_degree = edge_partition.local_degree(major_offset); - auto local_offset = edge_partition.local_offset(major_offset); - thrust::fill( - thrust::seq, majors + local_offset, majors + local_offset + local_degree, major); - }); + thrust::for_each(handle.get_thrust_policy(), + thrust::make_counting_iterator(edge_partition.major_range_first()), + thrust::make_counting_iterator(edge_partition.major_range_first()) + + edge_partition.major_range_size(), + [edge_partition, output_buffer] __device__(auto major) { + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto local_degree = edge_partition.local_degree(major_offset); + auto local_offset = edge_partition.local_offset(major_offset); + thrust::fill(thrust::seq, + output_buffer.begin() + local_offset, + output_buffer.begin() + local_offset + local_degree, + major); + }); + } + + if (tmp_buffer) { + copy_if_mask_set(handle, + (*tmp_buffer).begin(), + (*tmp_buffer).end(), + (*edge_partition_mask_view).value_first(), + majors.begin()); } } @@ -192,33 +216,59 @@ void decompress_edge_partition_to_edgelist( edge_partition_weight_view, std::optional> edge_partition_id_view, - vertex_t* edgelist_majors /* [OUT] */, - vertex_t* edgelist_minors /* [OUT] */, - std::optional edgelist_weights /* [OUT] */, - std::optional edgelist_ids /* [OUT] */, + std::optional> + edge_partition_mask_view, + raft::device_span edgelist_majors /* [OUT] */, + raft::device_span edgelist_minors /* [OUT] */, + std::optional> edgelist_weights /* [OUT] */, + std::optional> edgelist_ids /* [OUT] */, std::optional> const& segment_offsets) { auto number_of_edges = edge_partition.number_of_edges(); decompress_edge_partition_to_fill_edgelist_majors( - handle, edge_partition, edgelist_majors, segment_offsets); - thrust::copy(handle.get_thrust_policy(), - edge_partition.indices(), - edge_partition.indices() + number_of_edges, - edgelist_minors); - if (edge_partition_id_view) { - assert(edgelist_ids.has_value()); + handle, edge_partition, edge_partition_mask_view, edgelist_majors, segment_offsets); + if (edge_partition_mask_view) { + copy_if_mask_set(handle, + edge_partition.indices(), + edge_partition.indices() + number_of_edges, + (*edge_partition_mask_view).value_first(), + edgelist_minors.begin()); + } else { thrust::copy(handle.get_thrust_policy(), - (*edge_partition_id_view).value_first(), - (*edge_partition_id_view).value_first() + number_of_edges, - (*edgelist_ids)); + edge_partition.indices(), + edge_partition.indices() + number_of_edges, + edgelist_minors.begin()); } if (edge_partition_weight_view) { assert(edgelist_weights.has_value()); - thrust::copy(handle.get_thrust_policy(), - (*edge_partition_weight_view).value_first(), - (*edge_partition_weight_view).value_first() + number_of_edges, - (*edgelist_weights)); + if (edge_partition_mask_view) { + copy_if_mask_set(handle, + (*edge_partition_weight_view).value_first(), + (*edge_partition_weight_view).value_first() + number_of_edges, + (*edge_partition_mask_view).value_first(), + (*edgelist_weights).begin()); + } else { + thrust::copy(handle.get_thrust_policy(), + (*edge_partition_weight_view).value_first(), + (*edge_partition_weight_view).value_first() + number_of_edges, + (*edgelist_weights).begin()); + } + } + if (edge_partition_id_view) { + assert(edgelist_ids.has_value()); + if (edge_partition_mask_view) { + copy_if_mask_set(handle, + (*edge_partition_id_view).value_first(), + (*edge_partition_id_view).value_first() + number_of_edges, + (*edge_partition_mask_view).value_first(), + (*edgelist_ids).begin()); + } else { + thrust::copy(handle.get_thrust_policy(), + (*edge_partition_id_view).value_first(), + (*edge_partition_id_view).value_first() + number_of_edges, + (*edgelist_ids).begin()); + } } } diff --git a/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh b/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh index 18091567e38..e5b64b1e02f 100644 --- a/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh +++ b/cpp/include/cugraph/edge_partition_edge_property_device_view.cuh @@ -33,8 +33,9 @@ template ::value_type> class edge_partition_edge_property_device_view_t { public: - using edge_type = edge_t; - using value_type = value_t; + using edge_type = edge_t; + using value_type = value_t; + static constexpr bool is_packed_bool = cugraph::is_packed_bool(); static constexpr bool has_packed_bool_element = cugraph::has_packed_bool_element(); @@ -53,7 +54,7 @@ class edge_partition_edge_property_device_view_t { value_first_ = view.value_firsts()[partition_idx]; } - __host__ __device__ ValueIterator value_first() { return value_first_; } + __host__ __device__ ValueIterator value_first() const { return value_first_; } __device__ value_t get(edge_t offset) const { @@ -173,8 +174,9 @@ class edge_partition_edge_property_device_view_t { template class edge_partition_edge_dummy_property_device_view_t { public: - using edge_type = edge_t; - using value_type = thrust::nullopt_t; + using edge_type = edge_t; + using value_type = thrust::nullopt_t; + static constexpr bool is_packed_bool = false; static constexpr bool has_packed_bool_element = false; diff --git a/cpp/include/cugraph/graph_mask.hpp b/cpp/include/cugraph/graph_mask.hpp deleted file mode 100644 index 2048d3692c7..00000000000 --- a/cpp/include/cugraph/graph_mask.hpp +++ /dev/null @@ -1,382 +0,0 @@ -/* - * Copyright (c) 2022-2023, NVIDIA CORPORATION. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#pragma once - -#include -#include -#include - -#include -#include -#include -#include - -namespace cugraph { - -/** - * Compile-time fast lookup of log2(num_bits(mask_t)) to eliminate - * the log2 computation for powers of 2. - * @tparam mask_t - */ -template -__host__ __device__ constexpr int log_bits() -{ - switch (std::numeric_limits::digits) { - case 8: return 3; - case 16: return 4; - case 32: return 5; - case 64: return 6; - default: return log2(std::numeric_limits::digits); - } -} - -/** - * Uses bit-shifting to perform a fast mod operation. This - * is used to compute the index of a specific bit - * @tparam mask_t - * @tparam T - */ -template -__host__ __device__ int bit_mod(T numerator) -{ - return numerator & (std::numeric_limits::digits - 1); -} - -namespace detail { - -/** - * Sets the bit at location h in a one-hot encoded 32-bit int array - */ -template -__device__ __host__ inline void _set_bit(mask_type* arr, mask_type h) -{ - mask_type bit = bit_mod(h); - mask_type idx = h >> log_bits(); - atomicOr(arr + idx, 1 << bit); -} - -/** - * Unsets the bit at location h in a one-hot encoded 32-bit int array - */ -template -__device__ __host__ inline void _unset_bit(mask_type* arr, mask_type h) -{ - mask_type bit = bit_mod(h); - mask_type idx = h >> log_bits(); - atomicAnd(arr + idx, ~(1 << bit)); -} - -/** - * Returns whether or not bit at location h is nonzero in a one-hot - * encoded 32-bit in array. - */ -template -__device__ __host__ inline bool _is_set(mask_type* arr, mask_type h) -{ - mask_type bit = bit_mod(h); - mask_type idx = h >> log_bits(); - return arr[idx] >> bit & 1U; -} -}; // namespace detail - -/** - * Mask view to be used in device functions for reading and updating existing mask. - * This assumes the appropriate masks (vertex/edge) have already been initialized, - * since that will need to be done from the owning object. - * @tparam vertex_t - * @tparam edge_t - * @tparam mask_t - */ -template -struct graph_mask_view_t { - public: - graph_mask_view_t() = delete; - - graph_mask_view_t(vertex_t n_vertices, - edge_t n_edges, - std::optional>& vertices, - std::optional>& edges, - bool complement = false) - : n_vertices_(n_vertices), - n_edges_(n_edges), - complement_(complement), - vertices_(vertices), - edges_(edges) - { - } - - graph_mask_view_t(graph_mask_view_t const& other) = default; - using vertex_type = vertex_t; - using edge_type = edge_t; - using mask_type = mask_t; - using size_type = std::size_t; - - ~graph_mask_view_t() = default; - graph_mask_view_t(graph_mask_view_t&&) noexcept = default; - - graph_mask_view_t& operator=(graph_mask_view_t&&) noexcept = default; - graph_mask_view_t& operator=(graph_mask_view_t const& other) = default; - - /** - * Are masks complemeneted? - * - * - !complemented means masks are inclusive (masking in) - * - complemented means masks are exclusive (masking out) - */ - __host__ __device__ bool is_complemented() const { return complement_; } - - /** - * Has the edge mask been initialized? - */ - __host__ __device__ bool has_edge_mask() const { return edges_.has_value(); } - - /** - * Has the vertex mask been initialized? - */ - __host__ __device__ bool has_vertex_mask() const { return vertices_.has_value(); } - - /** - * Get the vertex mask - */ - __host__ __device__ std::optional> get_vertex_mask() const - { - return vertices_; - } - - /** - * Get the edge mask - */ - __host__ __device__ std::optional> get_edge_mask() const - { - return edges_; - } - - __host__ __device__ edge_t get_edge_mask_size() const { return n_edges_ >> log_bits(); } - - __host__ __device__ vertex_t get_vertex_mask_size() const - { - return n_vertices_ >> log_bits(); - } - - protected: - vertex_t n_vertices_; - edge_t n_edges_; - bool complement_{false}; - std::optional> vertices_{std::nullopt}; - std::optional> edges_{std::nullopt}; -}; // struct graph_mask_view_t - -/** - * An owning container object to manage separate bitmasks for - * filtering vertices and edges. A compliment setting - * determines whether the value of 1 for corresponding - * items means they should be masked in (included) or - * masked out (excluded). - * - * Creating this object does not allocate any memory on device. - * In order to start using and querying the masks, they will - * need to first be initialized. - * - * @tparam vertex_t - * @tparam edge_t - * @tparam mask_t - */ -template -struct graph_mask_t { - public: - using vertex_type = vertex_t; - using edge_type = edge_t; - using mask_type = mask_t; - using size_type = std::size_t; - - ~graph_mask_t() = default; - graph_mask_t(graph_mask_t&&) noexcept = default; - - graph_mask_t() = delete; - - explicit graph_mask_t(raft::handle_t const& handle, - vertex_t n_vertices, - edge_t n_edges, - bool complement = false) - : handle_(handle), - n_vertices_(n_vertices), - n_edges_(n_edges), - edges_(0, handle.get_stream()), - vertices_(0, handle.get_stream()), - complement_(complement) - { - } - - explicit graph_mask_t(graph_mask_t const& other) - : handle_(other.handle_), - n_vertices_(other.n_vertices_), - n_edges_(other.n_edges_), - edges_(other.edges_, other.handle_.get_stream()), - vertices_(other.vertices_, other.handle_.get_stream()), - complement_(other.complement_) - { - } - - graph_mask_t& operator=(graph_mask_t&&) noexcept = default; - graph_mask_t& operator=(graph_mask_t const& other) = default; - - /** - * Determines whether the 1 bit in a vertex or edge position - * represents an inclusive mask or exclusive mask. Default is - * an inclusive mask (e.g. 1 bit means the corresponding vertex - * or edge should be included in computations). - * @return - */ - bool is_complemented() const { return complement_; } - - /** - * Whether or not the current mask object has been initialized - * with an edge mask. - * @return - */ - bool has_edge_mask() const { return get_edge_mask().has_value(); } - - /** - * Whether or not the current mask object has been initialized - * with a vertex mask. - * @return - */ - bool has_vertex_mask() const { return get_vertex_mask().has_value(); } - - /** - * Returns the edge mask if it has been initialized on the instance - * @return - */ - std::optional get_edge_mask() const - { - return edges_.size() > 0 ? std::make_optional(edges_.data()) : std::nullopt; - } - - /** - * Retuns the vertex mask if it has been initialized on the instance - * @return - */ - std::optional get_vertex_mask() const - { - return vertices_.size() > 0 ? std::make_optional(vertices_.data()) - : std::nullopt; - } - - vertex_t get_n_vertices() { return n_vertices_; } - - edge_t get_n_edges() { return n_edges_; } - - edge_t get_edge_mask_size() const { return n_edges_ >> log_bits(); } - - vertex_t get_vertex_mask_size() const { return n_vertices_ >> log_bits(); } - - void initialize_edge_mask(bool init = 0) - { - if (!has_edge_mask()) { - allocate_edge_mask(); - RAFT_CUDA_TRY(cudaMemsetAsync(edges_.data(), - edges_.size() * sizeof(mask_t), - std::numeric_limits::max() * init, - handle_.get_stream())); - } - } - - void initialize_vertex_mask(bool init = 0) - { - if (!has_vertex_mask()) { - allocate_vertex_mask(); - RAFT_CUDA_TRY(cudaMemsetAsync(vertices_.data(), - vertices_.size() * sizeof(mask_t), - std::numeric_limits::max() * init, - handle_.get_stream())); - } - } - - /** - * Initializes an edge mask by allocating the device memory - */ - void allocate_edge_mask() - { - if (edges_.size() == 0) { - edges_.resize(get_edge_mask_size(), handle_.get_stream()); - clear_edge_mask(); - } - } - - /** - * Initializes a vertex mask by allocating the device memory - */ - void allocate_vertex_mask() - { - if (vertices_.size() == 0) { - vertices_.resize(get_vertex_mask_size(), handle_.get_stream()); - clear_vertex_mask(); - } - } - - /** - * Clears out all the masked bits of the edge mask - */ - void clear_edge_mask() - { - if (edges_.size() > 0) { - RAFT_CUDA_TRY( - cudaMemsetAsync(edges_.data(), edges_.size() * sizeof(mask_t), 0, handle_.get_stream())); - } - } - - /** - * Clears out all the masked bits of the vertex mask - */ - void clear_vertex_mask() - { - if (vertices_.size() > 0) { - RAFT_CUDA_TRY(cudaMemsetAsync( - vertices_.data(), vertices_.size() * sizeof(mask_t), 0, handle_.get_stream())); - } - } - - /** - * Returns a view of the current mask object which can safely be used - * in device functions. - * - * Note that the view will not be able to initialize the underlying - * masks so they will need to be initialized before this method is - * invoked. - */ - auto view() - { - auto vspan = has_vertex_mask() ? std::make_optional>(vertices_.data(), - vertices_.size()) - : std::nullopt; - auto espan = has_edge_mask() - ? std::make_optional>(edges_.data(), edges_.size()) - : std::nullopt; - return graph_mask_view_t( - n_vertices_, n_edges_, vspan, espan, complement_); - } - - protected: - raft::handle_t const& handle_; - vertex_t n_vertices_; - edge_t n_edges_; - bool complement_ = false; - rmm::device_uvector vertices_; - rmm::device_uvector edges_; - -}; // struct graph_mask_t -}; // namespace cugraph \ No newline at end of file diff --git a/cpp/include/cugraph/graph_view.hpp b/cpp/include/cugraph/graph_view.hpp index 2d10b435224..f30a8b7e2af 100644 --- a/cpp/include/cugraph/graph_view.hpp +++ b/cpp/include/cugraph/graph_view.hpp @@ -444,12 +444,6 @@ class graph_view_t local_edge_partition_view( size_t partition_idx) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); - vertex_t major_range_first{}; vertex_t major_range_last{}; vertex_t minor_range_first{}; @@ -748,6 +740,11 @@ class graph_view_t> edge_mask_view() const + { + return edge_mask_view_; + } + private: std::vector edge_partition_offsets_{}; std::vector edge_partition_indices_{}; @@ -856,12 +853,6 @@ class graph_view_tnumber_of_edges(); - } - vertex_t local_edge_partition_src_range_size(size_t partition_idx = 0) const { assert(partition_idx == 0); @@ -1030,6 +1021,11 @@ class graph_view_t> edge_mask_view() const + { + return edge_mask_view_; + } + private: edge_t const* offsets_{nullptr}; vertex_t const* indices_{nullptr}; diff --git a/cpp/include/cugraph/utilities/device_functors.cuh b/cpp/include/cugraph/utilities/device_functors.cuh index 501e74cf47b..3af8ed1dd19 100644 --- a/cpp/include/cugraph/utilities/device_functors.cuh +++ b/cpp/include/cugraph/utilities/device_functors.cuh @@ -15,18 +15,11 @@ */ #pragma once -#include -#include -#include +#include -#include -#include -#include -#include +#include #include -#include -#include namespace cugraph { @@ -44,12 +37,12 @@ struct pack_bool_t { __device__ uint32_t operator()(size_t i) const { - auto first = i * (sizeof(uint32_t) * 8); - auto last = std::min((i + 1) * (sizeof(uint32_t) * 8), num_bools); + auto first = i * packed_bools_per_word(); + auto last = std::min((i + 1) * packed_bools_per_word(), num_bools); uint32_t ret{0}; for (auto j = first; j < last; ++j) { if (*(bool_first + j)) { - auto mask = uint32_t{1} << (j % (sizeof(uint32_t) * 8)); + auto mask = packed_bool_mask(j); ret |= mask; } } @@ -57,6 +50,22 @@ struct pack_bool_t { } }; +template +struct check_bit_set_t { + PackedBoolIterator bitmap_first{}; + T idx_first{}; + + static_assert( + std::is_same_v::value_type, uint32_t>); + + __device__ bool operator()(T idx) const + { + auto offset = idx - idx_first; + return static_cast(*(bitmap_first + packed_bool_offset(offset)) & + packed_bool_mask(offset)); + } +}; + template struct indirection_t { Iterator first{}; @@ -80,7 +89,14 @@ struct indirection_if_idx_valid_t { }; template -struct not_equal_t { +struct is_equal_t { + T compare{}; + + __device__ bool operator()(T val) const { return val == compare; } +}; + +template +struct is_not_equal_t { T compare{}; __device__ bool operator()(T val) const { return val != compare; } @@ -96,19 +112,6 @@ struct is_first_in_run_t { } }; -template -struct check_bit_set_t { - uint32_t const* bitmaps{nullptr}; - T idx_first{}; - - __device__ bool operator()(T idx) const - { - auto offset = idx - idx_first; - auto mask = uint32_t{1} << (offset % (sizeof(uint32_t) * 8)); - return (*(bitmaps + (offset / (sizeof(uint32_t) * 8))) & mask) > uint32_t{0}; - } -}; - template struct check_in_range_t { T min{}; // inclusive diff --git a/cpp/include/cugraph/utilities/mask_utils.cuh b/cpp/include/cugraph/utilities/mask_utils.cuh new file mode 100644 index 00000000000..ab1403d019b --- /dev/null +++ b/cpp/include/cugraph/utilities/mask_utils.cuh @@ -0,0 +1,150 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include + +#include + +#include +#include +#include +#include +#include + +namespace cugraph { + +namespace detail { + +template // should be packed bool +__device__ size_t count_set_bits(MaskIterator mask_first, size_t start_offset, size_t num_bits) +{ + static_assert( + std::is_same_v::value_type, uint32_t>); + + size_t ret{0}; + + mask_first = mask_first + packed_bool_offset(start_offset); + start_offset = start_offset % packed_bools_per_word(); + if (start_offset != 0) { + auto mask = ~packed_bool_partial_mask(start_offset); + if (start_offset + num_bits < packed_bools_per_word()) { + mask &= packed_bool_partial_mask(start_offset + num_bits); + } + ret += __popc(*mask_first & mask); + num_bits -= __popc(mask); + ++mask_first; + } + + return thrust::transform_reduce( + thrust::seq, + thrust::make_counting_iterator(size_t{0}), + thrust::make_counting_iterator(packed_bool_size(num_bits)), + [mask_first, num_bits] __device__(size_t i) { + auto word = *(mask_first + i); + if ((i + 1) * packed_bools_per_word() > num_bits) { + word &= packed_bool_partial_mask(num_bits % packed_bools_per_word()); + } + return static_cast(__popc(word)); + }, + ret, + thrust::plus{}); +} + +template ::value_type, // for packed bool support + typename output_value_type = typename thrust::iterator_traits< + OutputIterator>::value_type> // for packed bool support +__device__ size_t copy_if_mask_set(InputIterator input_first, + MaskIterator mask_first, + OutputIterator output_first, + size_t input_start_offset, + size_t output_start_offset, + size_t num_items) +{ + static_assert( + std::is_same_v::value_type, uint32_t>); + static_assert( + std::is_same_v::value_type, input_value_type> || + cugraph::has_packed_bool_element()); + static_assert(std::is_same_v::value_type, + output_value_type> || + cugraph::has_packed_bool_element()); + + static_assert(!cugraph::has_packed_bool_element() && + !cugraph::has_packed_bool_element(), + "unimplemented."); + + return static_cast(thrust::distance( + output_first + output_start_offset, + thrust::copy_if(thrust::seq, + input_first + input_start_offset, + input_first + (input_start_offset + num_items), + thrust::make_transform_iterator( + thrust::make_counting_iterator(size_t{0}), + check_bit_set_t{mask_first, size_t{0}}) + + input_start_offset, + output_first + output_start_offset, + is_equal_t{true}))); +} + +template // should be packed bool +size_t count_set_bits(raft::handle_t const& handle, MaskIterator mask_first, size_t num_bits) +{ + static_assert( + std::is_same_v::value_type, uint32_t>); + + return thrust::transform_reduce( + handle.get_thrust_policy(), + thrust::make_counting_iterator(size_t{0}), + thrust::make_counting_iterator(packed_bool_size(num_bits)), + [mask_first, num_bits] __device__(size_t i) { + auto word = *(mask_first + i); + if ((i + 1) * packed_bools_per_word() > num_bits) { + word &= packed_bool_partial_mask(num_bits % packed_bools_per_word()); + } + return static_cast(__popc(word)); + }, + size_t{0}, + thrust::plus{}); +} + +template +OutputIterator copy_if_mask_set(raft::handle_t const& handle, + InputIterator input_first, + InputIterator input_last, + MaskIterator mask_first, + OutputIterator output_first) +{ + return thrust::copy_if( + handle.get_thrust_policy(), + input_first, + input_last, + thrust::make_transform_iterator(thrust::make_counting_iterator(size_t{0}), + check_bit_set_t{mask_first, size_t{0}}), + output_first, + is_equal_t{true}); +} + +} // namespace detail + +} // namespace cugraph diff --git a/cpp/include/cugraph/utilities/packed_bool_utils.hpp b/cpp/include/cugraph/utilities/packed_bool_utils.hpp index 0be5711d90c..b418d5afc35 100644 --- a/cpp/include/cugraph/utilities/packed_bool_utils.hpp +++ b/cpp/include/cugraph/utilities/packed_bool_utils.hpp @@ -92,6 +92,13 @@ constexpr uint32_t packed_bool_mask(T bool_offset) constexpr uint32_t packed_bool_full_mask() { return uint32_t{0xffffffff}; } +template +constexpr uint32_t packed_bool_partial_mask(T num_set_bits) +{ + assert(static_cast(num_set_bits) <= sizeof(uint32_t) * 8); + return uint32_t{0xffffffff} >> (sizeof(uint32_t) * 8 - num_set_bits); +} + constexpr uint32_t packed_bool_empty_mask() { return uint32_t{0x0}; } template diff --git a/cpp/src/prims/detail/nbr_intersection.cuh b/cpp/src/prims/detail/nbr_intersection.cuh index 2f30faebb3e..32247ca3466 100644 --- a/cpp/src/prims/detail/nbr_intersection.cuh +++ b/cpp/src/prims/detail/nbr_intersection.cuh @@ -24,6 +24,7 @@ #include #include #include +#include #include #include @@ -130,6 +131,8 @@ struct update_rx_major_local_degree_t { int minor_comm_size{}; edge_partition_device_view_t edge_partition{}; + thrust::optional> + edge_partition_e_mask{}; size_t reordered_idx_first{}; size_t local_edge_partition_idx{}; @@ -151,19 +154,28 @@ struct update_rx_major_local_degree_t { auto major = rx_majors[rx_group_firsts[major_comm_rank * minor_comm_size + local_edge_partition_idx] + offset_in_local_edge_partition]; - edge_t local_degree{}; + vertex_t major_idx{0}; + edge_t local_degree{0}; if (multi_gpu && (edge_partition.major_hypersparse_first() && (major >= *(edge_partition.major_hypersparse_first())))) { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major); - local_degree = major_hypersparse_idx - ? edge_partition.local_degree((*(edge_partition.major_hypersparse_first()) - - edge_partition.major_range_first()) + - *major_hypersparse_idx) - : edge_t{0}; + if (major_hypersparse_idx) { + major_idx = + (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + + *major_hypersparse_idx; + local_degree = edge_partition.local_degree(major_idx); + } } else { - local_degree = - edge_partition.local_degree(edge_partition.major_offset_from_major_nocheck(major)); + major_idx = edge_partition.major_offset_from_major_nocheck(major); + local_degree = edge_partition.local_degree(major_idx); } + + if (edge_partition_e_mask && (local_degree > edge_t{0})) { + auto local_offset = edge_partition.local_offset(major_idx); + local_degree = static_cast( + count_set_bits((*edge_partition_e_mask).value_first(), local_offset, local_degree)); + } + local_degrees_for_rx_majors[rx_group_firsts[major_comm_rank * minor_comm_size + local_edge_partition_idx] + offset_in_local_edge_partition] = local_degree; @@ -173,7 +185,7 @@ struct update_rx_major_local_degree_t { template struct update_rx_major_local_nbrs_t { int major_comm_size{}; @@ -181,6 +193,8 @@ struct update_rx_major_local_nbrs_t { edge_partition_device_view_t edge_partition{}; edge_partition_e_input_device_view_t edge_partition_e_value_input{}; + thrust::optional> + edge_partition_e_mask{}; size_t reordered_idx_first{}; size_t local_edge_partition_idx{}; @@ -190,12 +204,13 @@ struct update_rx_major_local_nbrs_t { raft::device_span rx_majors{}; raft::device_span local_nbr_offsets_for_rx_majors{}; raft::device_span local_nbrs_for_rx_majors{}; - optional_property_buffer_view_t local_nbrs_properties_for_rx_majors{}; + optional_property_buffer_mutable_view_t local_e_property_values_for_rx_majors{}; __device__ void operator()(size_t idx) { using edge_property_value_t = typename edge_partition_e_input_device_view_t::value_type; - auto it = thrust::upper_bound( + + auto it = thrust::upper_bound( thrust::seq, rx_reordered_group_lasts.begin(), rx_reordered_group_lasts.end(), idx); auto major_comm_rank = static_cast(thrust::distance(rx_reordered_group_lasts.begin(), it)); auto offset_in_local_edge_partition = @@ -204,39 +219,76 @@ struct update_rx_major_local_nbrs_t { auto major = rx_majors[rx_group_firsts[major_comm_rank * minor_comm_size + local_edge_partition_idx] + offset_in_local_edge_partition]; - vertex_t const* indices{nullptr}; - [[maybe_unused]] edge_t edge_offset{0}; + + edge_t edge_offset{0}; edge_t local_degree{0}; if (multi_gpu && (edge_partition.major_hypersparse_first() && (major >= *(edge_partition.major_hypersparse_first())))) { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major); if (major_hypersparse_idx) { - thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges( + auto major_idx = (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + - *major_hypersparse_idx); + *major_hypersparse_idx; + edge_offset = edge_partition.local_offset(major_idx); + local_degree = edge_partition.local_degree(major_idx); } } else { - thrust::tie(indices, edge_offset, local_degree) = - edge_partition.local_edges(edge_partition.major_offset_from_major_nocheck(major)); + auto major_idx = edge_partition.major_offset_from_major_nocheck(major); + edge_offset = edge_partition.local_offset(major_idx); + local_degree = edge_partition.local_degree(major_idx); } - // FIXME: this can lead to thread-divergence with a mix of high-degree and low-degree - // vertices in a single warp (better optimize if this becomes a performance - // bottleneck) - size_t start_offset = + auto indices = edge_partition.indices(); + size_t output_start_offset = local_nbr_offsets_for_rx_majors[rx_group_firsts[major_comm_rank * minor_comm_size + local_edge_partition_idx] + offset_in_local_edge_partition]; - thrust::copy(thrust::seq, - indices, - indices + local_degree, - local_nbrs_for_rx_majors.begin() + start_offset); - if constexpr (!std::is_same_v) { - thrust::copy(thrust::seq, - edge_partition_e_value_input.value_first() + edge_offset, - edge_partition_e_value_input.value_first() + (edge_offset + local_degree), - local_nbrs_properties_for_rx_majors.begin() + start_offset); + // FIXME: this can lead to thread-divergence with a mix of high-degree and low-degree + // vertices in a single warp (better optimize if this becomes a performance + // bottleneck) + + static_assert(!edge_partition_e_input_device_view_t::has_packed_bool_element, "unimplemented."); + if (local_degree > 0) { + if (edge_partition_e_mask) { + auto mask_first = (*edge_partition_e_mask).value_first(); + if constexpr (!std::is_same_v) { + auto input_first = + thrust::make_zip_iterator(indices, edge_partition_e_value_input.value_first()) + + edge_offset; + copy_if_mask_set(input_first, + mask_first, + thrust::make_zip_iterator(local_nbrs_for_rx_majors.begin(), + local_e_property_values_for_rx_majors), + edge_offset, + output_start_offset, + local_degree); + } else { + copy_if_mask_set(indices, + mask_first, + local_nbrs_for_rx_majors.begin(), + edge_offset, + output_start_offset, + local_degree); + } + } else { + if constexpr (!std::is_same_v) { + auto input_first = + thrust::make_zip_iterator(indices, edge_partition_e_value_input.value_first()) + + edge_offset; + thrust::copy(thrust::seq, + input_first, + input_first + local_degree, + thrust::make_zip_iterator(local_nbrs_for_rx_majors.begin(), + local_e_property_values_for_rx_majors) + + output_start_offset); + } else { + thrust::copy(thrust::seq, + indices + edge_offset, + indices + (edge_offset + local_degree), + local_nbrs_for_rx_majors.begin() + output_start_offset); + } + } } } }; @@ -259,36 +311,45 @@ template struct pick_min_degree_t { FirstElementToIdxMap first_element_to_idx_map{}; - raft::device_span first_element_offsets{nullptr}; + raft::device_span first_element_offsets{nullptr}; SecondElementToIdxMap second_element_to_idx_map{}; - raft::device_span second_element_offsets{nullptr}; + raft::device_span second_element_offsets{nullptr}; edge_partition_device_view_t edge_partition{}; + thrust::optional> + edge_partition_e_mask{}; __device__ edge_t operator()(thrust::tuple pair) const { edge_t local_degree0{0}; vertex_t major0 = thrust::get<0>(pair); if constexpr (std::is_same_v) { + vertex_t major_idx{0}; if constexpr (multi_gpu) { if (edge_partition.major_hypersparse_first() && (major0 >= *(edge_partition.major_hypersparse_first()))) { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major0); - local_degree0 = - major_hypersparse_idx - ? edge_partition.local_degree((*(edge_partition.major_hypersparse_first()) - - edge_partition.major_range_first()) + - *major_hypersparse_idx) - : edge_t{0}; + if (major_hypersparse_idx) { + major_idx = + (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + + *major_hypersparse_idx; + local_degree0 = edge_partition.local_degree(major_idx); + } } else { - local_degree0 = - edge_partition.local_degree(edge_partition.major_offset_from_major_nocheck(major0)); + major_idx = edge_partition.major_offset_from_major_nocheck(major0); + local_degree0 = edge_partition.local_degree(major_idx); } } else { + major_idx = edge_partition.major_offset_from_major_nocheck(major0); + local_degree0 = edge_partition.local_degree(major_idx); + } + + if (edge_partition_e_mask && (local_degree0 > edge_t{0})) { + auto local_offset = edge_partition.local_offset(major_idx); local_degree0 = - edge_partition.local_degree(edge_partition.major_offset_from_major_nocheck(major0)); + count_set_bits((*edge_partition_e_mask).value_first(), local_offset, local_degree0); } } else { auto idx = first_element_to_idx_map.find(major0); @@ -299,24 +360,31 @@ struct pick_min_degree_t { edge_t local_degree1{0}; vertex_t major1 = thrust::get<1>(pair); if constexpr (std::is_same_v) { + vertex_t major_idx{0}; if constexpr (multi_gpu) { if (edge_partition.major_hypersparse_first() && (major1 >= *(edge_partition.major_hypersparse_first()))) { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major1); - local_degree1 = - major_hypersparse_idx - ? edge_partition.local_degree((*(edge_partition.major_hypersparse_first()) - - edge_partition.major_range_first()) + - *major_hypersparse_idx) - : edge_t{0}; + if (major_hypersparse_idx) { + major_idx = + (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + + *major_hypersparse_idx; + local_degree1 = edge_partition.local_degree(major_idx); + } } else { - local_degree1 = - edge_partition.local_degree(edge_partition.major_offset_from_major_nocheck(major1)); + major_idx = edge_partition.major_offset_from_major_nocheck(major1); + local_degree1 = edge_partition.local_degree(major_idx); } } else { + major_idx = edge_partition.major_offset_from_major_nocheck(major1); + local_degree1 = edge_partition.local_degree(major_idx); + } + + if (edge_partition_e_mask && (local_degree1 > edge_t{0})) { + auto local_offset = edge_partition.local_offset(major_idx); local_degree1 = - edge_partition.local_degree(edge_partition.major_offset_from_major_nocheck(major1)); + count_set_bits((*edge_partition_e_mask).value_first(), local_offset, local_degree1); } } else { auto idx = second_element_to_idx_map.find(major1); @@ -328,6 +396,71 @@ struct pick_min_degree_t { } }; +template +__device__ edge_t set_intersection_by_key_with_mask(InputKeyIterator0 input_key_first0, + InputKeyIterator1 input_key_first1, + InputValueIterator0 input_value_first0, + InputValueIterator1 input_value_first1, + MaskIterator mask_first, + OutputKeyIterator output_key_first, + OutputValueIterator0 output_value_first0, + OutputValueIterator1 output_value_first1, + edge_t input_start_offset0, + edge_t input_size0, + bool apply_mask0, + edge_t input_start_offset1, + edge_t input_size1, + bool apply_mask1, + size_t output_start_offset) +{ + static_assert( + std::is_same_v::value_type, uint32_t>); + static_assert(std::is_same_v == + std::is_same_v); + + check_bit_set_t check_bit_set{mask_first, edge_t{0}}; + + auto idx0 = input_start_offset0; + auto idx1 = input_start_offset1; + auto output_idx = output_start_offset; + while ((idx0 < (input_start_offset0 + input_size0)) && + (idx1 < (input_start_offset1 + input_size1))) { + bool valid0 = apply_mask0 ? check_bit_set(idx0) : true; + bool valid1 = apply_mask1 ? check_bit_set(idx1) : true; + if (!valid0) { ++idx0; } + if (!valid1) { ++idx1; } + + if (valid0 && valid1) { + auto key0 = *(input_key_first0 + idx0); + auto key1 = *(input_key_first1 + idx1); + if (key0 < key1) { + ++idx0; + } else if (key0 > key1) { + ++idx1; + } else { + *(output_key_first + output_idx) = key0; + if constexpr (!std::is_same_v) { + *(output_value_first0 + output_idx) = *(input_value_first0 + idx0); + *(output_value_first1 + output_idx) = *(input_value_first1 + idx1); + } + ++idx0; + ++idx1; + ++output_idx; + } + } + } + + return (output_idx - output_start_offset); +} + template struct copy_intersecting_nbrs_and_update_intersection_size_t { FirstElementToIdxMap first_element_to_idx_map{}; - raft::device_span first_element_offsets{}; + raft::device_span first_element_offsets{}; raft::device_span first_element_indices{nullptr}; - optional_property_buffer_view_t first_element_properties{}; + optional_property_buffer_view_t first_element_edge_property_values{}; SecondElementToIdxMap second_element_to_idx_map{}; - raft::device_span second_element_offsets{}; + raft::device_span second_element_offsets{}; raft::device_span second_element_indices{nullptr}; - optional_property_buffer_view_t second_element_properties{}; + optional_property_buffer_view_t second_element_edge_property_values{}; edge_partition_device_view_t edge_partition{}; edge_partition_e_input_device_view_t edge_partition_e_value_input{}; + thrust::optional> + edge_partition_e_mask{}; VertexPairIterator vertex_pair_first; raft::device_span nbr_intersection_offsets{nullptr}; raft::device_span nbr_intersection_indices{nullptr}; - optional_property_buffer_view_t nbr_intersection_properties0{}; - optional_property_buffer_view_t nbr_intersection_properties1{}; + optional_property_buffer_mutable_view_t nbr_intersection_e_property_values0{}; + optional_property_buffer_mutable_view_t nbr_intersection_e_property_values1{}; vertex_t invalid_id{}; + __device__ edge_t operator()(size_t i) { using edge_property_value_t = typename edge_partition_e_input_device_view_t::value_type; - using optional_const_property_buffer_view_t = - std::conditional_t, - raft::device_span, - std::byte /* dummy */>; auto pair = *(vertex_pair_first + i); + vertex_t const* indices0{nullptr}; - optional_const_property_buffer_view_t properties0{}; + std::conditional_t, + edge_property_value_t const*, + void*> + edge_property_values0{nullptr}; edge_t local_edge_offset0{0}; edge_t local_degree0{0}; if constexpr (std::is_same_v) { + indices0 = edge_partition.indices(); + if constexpr (!std::is_same_v) { + edge_property_values0 = edge_partition_e_value_input.value_first(); + } + vertex_t major = thrust::get<0>(pair); if constexpr (multi_gpu) { if (edge_partition.major_hypersparse_first() && @@ -379,42 +521,47 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major); if (major_hypersparse_idx) { - thrust::tie(indices0, local_edge_offset0, local_degree0) = edge_partition.local_edges( + auto major_idx = (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + - *major_hypersparse_idx); + *major_hypersparse_idx; + local_edge_offset0 = edge_partition.local_offset(major_idx); + local_degree0 = edge_partition.local_degree(major_idx); } } else { - thrust::tie(indices0, local_edge_offset0, local_degree0) = - edge_partition.local_edges(edge_partition.major_offset_from_major_nocheck(major)); + auto major_idx = edge_partition.major_offset_from_major_nocheck(major); + local_edge_offset0 = edge_partition.local_offset(major_idx); + local_degree0 = edge_partition.local_degree(major_idx); } } else { - thrust::tie(indices0, local_edge_offset0, local_degree0) = - edge_partition.local_edges(edge_partition.major_offset_from_major_nocheck(major)); + auto major_idx = edge_partition.major_offset_from_major_nocheck(major); + local_edge_offset0 = edge_partition.local_offset(major_idx); + local_degree0 = edge_partition.local_degree(major_idx); } - + } else { + indices0 = first_element_indices.begin(); if constexpr (!std::is_same_v) { - properties0 = raft::device_span( - edge_partition_e_value_input.value_first() + local_edge_offset0, local_degree0); + edge_property_values0 = first_element_edge_property_values; } - } else { auto idx = first_element_to_idx_map.find(thrust::get<0>(pair)); local_edge_offset0 = first_element_offsets[idx]; local_degree0 = static_cast(first_element_offsets[idx + 1] - local_edge_offset0); - indices0 = first_element_indices.begin() + local_edge_offset0; - - if constexpr (!std::is_same_v) { - properties0 = raft::device_span( - first_element_properties.begin() + local_edge_offset0, local_degree0); - } } vertex_t const* indices1{nullptr}; - optional_const_property_buffer_view_t properties1{}; + std::conditional_t, + edge_property_value_t const*, + void*> + edge_property_values1{nullptr}; - [[maybe_unused]] edge_t local_edge_offset1{0}; + edge_t local_edge_offset1{0}; edge_t local_degree1{0}; if constexpr (std::is_same_v) { + indices1 = edge_partition.indices(); + if constexpr (!std::is_same_v) { + edge_property_values1 = edge_partition_e_value_input.value_first(); + } + vertex_t major = thrust::get<1>(pair); if constexpr (multi_gpu) { if (edge_partition.major_hypersparse_first() && @@ -422,83 +569,63 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t { auto major_hypersparse_idx = edge_partition.major_hypersparse_idx_from_major_nocheck(major); if (major_hypersparse_idx) { - thrust::tie(indices1, local_edge_offset1, local_degree1) = edge_partition.local_edges( + auto major_idx = (*(edge_partition.major_hypersparse_first()) - edge_partition.major_range_first()) + - *major_hypersparse_idx); + *major_hypersparse_idx; + local_edge_offset1 = edge_partition.local_offset(major_idx); + local_degree1 = edge_partition.local_degree(major_idx); } } else { - thrust::tie(indices1, local_edge_offset1, local_degree1) = - edge_partition.local_edges(edge_partition.major_offset_from_major_nocheck(major)); + auto major_idx = edge_partition.major_offset_from_major_nocheck(major); + local_edge_offset1 = edge_partition.local_offset(major_idx); + local_degree1 = edge_partition.local_degree(major_idx); } } else { - thrust::tie(indices1, local_edge_offset1, local_degree1) = - edge_partition.local_edges(edge_partition.major_offset_from_major_nocheck(major)); + auto major_idx = edge_partition.major_offset_from_major_nocheck(major); + local_edge_offset1 = edge_partition.local_offset(major_idx); + local_degree1 = edge_partition.local_degree(major_idx); } - + } else { + indices1 = second_element_indices.begin(); if constexpr (!std::is_same_v) { - properties1 = raft::device_span( - edge_partition_e_value_input.value_first() + local_edge_offset1, local_degree1); + edge_property_values1 = second_element_edge_property_values; } - } else { auto idx = second_element_to_idx_map.find(thrust::get<1>(pair)); local_edge_offset1 = second_element_offsets[idx]; local_degree1 = static_cast(second_element_offsets[idx + 1] - local_edge_offset1); - indices1 = second_element_indices.begin() + local_edge_offset1; - - if constexpr (!std::is_same_v) { - properties1 = raft::device_span( - second_element_properties.begin() + local_edge_offset1, local_degree1); - } } // FIXME: this can lead to thread-divergence with a mix of high-degree and low-degree // vertices in a single warp (better optimize if this becomes a performance // bottleneck) - auto nbr_intersection_first = nbr_intersection_indices.begin() + nbr_intersection_offsets[i]; - - auto nbr_intersection_last = thrust::set_intersection(thrust::seq, - indices0, - indices0 + local_degree0, - indices1, - indices1 + local_degree1, - nbr_intersection_first); - thrust::fill(thrust::seq, - nbr_intersection_last, - nbr_intersection_indices.begin() + nbr_intersection_offsets[i + 1], - invalid_id); - - auto insection_size = - static_cast(thrust::distance(nbr_intersection_first, nbr_intersection_last)); - if constexpr (!std::is_same_v) { - auto ip0_start = nbr_intersection_properties0.begin() + nbr_intersection_offsets[i]; - - // copy edge properties from first vertex to common neighbors - thrust::transform(thrust::seq, - nbr_intersection_first, - nbr_intersection_last, - ip0_start, - [indices0, local_degree0, properties0] __device__(auto v) { - auto position = - thrust::lower_bound(thrust::seq, indices0, indices0 + local_degree0, v); - return properties0[thrust::distance(indices0, position)]; - }); - - auto ip1_start = nbr_intersection_properties1.begin() + nbr_intersection_offsets[i]; - - // copy edge properties from second vertex to common neighbors - thrust::transform(thrust::seq, - nbr_intersection_first, - nbr_intersection_last, - ip1_start, - [indices1, local_degree1, properties1] __device__(auto v) { - auto position = - thrust::lower_bound(thrust::seq, indices1, indices1 + local_degree1, v); - return properties1[thrust::distance(indices1, position)]; - }); - } - return insection_size; + auto mask_first = edge_partition_e_mask ? (*edge_partition_e_mask).value_first() + : static_cast(nullptr); + auto intersection_size = set_intersection_by_key_with_mask( + indices0, + indices1, + edge_property_values0, + edge_property_values1, + mask_first, + nbr_intersection_indices.begin(), + nbr_intersection_e_property_values0, + nbr_intersection_e_property_values1, + local_edge_offset0, + local_degree0, + (std::is_same_v && edge_partition_e_mask), + local_edge_offset1, + local_degree1, + (std::is_same_v && edge_partition_e_mask), + nbr_intersection_offsets[i]); + + thrust::fill( + thrust::seq, + nbr_intersection_indices.begin() + (nbr_intersection_offsets[i] + intersection_size), + nbr_intersection_indices.begin() + nbr_intersection_offsets[i + 1], + invalid_id); + + return intersection_size; } }; @@ -520,7 +647,8 @@ struct strided_accumulate_t { template + typename optional_property_buffer_view_t, + typename optional_property_buffer_mutable_view_t> struct gatherv_indices_t { size_t output_size{}; int minor_comm_size{}; @@ -530,10 +658,10 @@ struct gatherv_indices_t { raft::device_span combined_nbr_intersection_offsets{}; raft::device_span combined_nbr_intersection_indices{}; - optional_property_buffer_view_t gathered_nbr_intersection_properties0{}; - optional_property_buffer_view_t gathered_nbr_intersection_properties1{}; - optional_property_buffer_view_t combined_nbr_intersection_properties0{}; - optional_property_buffer_view_t combined_nbr_intersection_properties1{}; + optional_property_buffer_view_t gathered_nbr_intersection_e_property_values0{}; + optional_property_buffer_view_t gathered_nbr_intersection_e_property_values1{}; + optional_property_buffer_mutable_view_t combined_nbr_intersection_e_property_values0{}; + optional_property_buffer_mutable_view_t combined_nbr_intersection_e_property_values1{}; __device__ void operator()(size_t i) const { @@ -546,13 +674,13 @@ struct gatherv_indices_t { if constexpr (!std::is_same_v) { auto zipped_gathered_begin = thrust::make_zip_iterator( thrust::make_tuple(gathered_intersection_indices.begin(), - gathered_nbr_intersection_properties0.begin(), - gathered_nbr_intersection_properties1.begin())); + gathered_nbr_intersection_e_property_values0, + gathered_nbr_intersection_e_property_values1)); auto zipped_combined_begin = thrust::make_zip_iterator( thrust::make_tuple(combined_nbr_intersection_indices.begin(), - combined_nbr_intersection_properties0.begin(), - combined_nbr_intersection_properties1.begin())); + combined_nbr_intersection_e_property_values0, + combined_nbr_intersection_e_property_values1)); thrust::copy(thrust::seq, zipped_gathered_begin + gathered_intersection_offsets[output_size * j + i], @@ -694,13 +822,12 @@ nbr_intersection(raft::handle_t const& handle, using optional_property_buffer_view_t = std::conditional_t, - raft::device_span, - std::byte /* dummy */>; - - using optional_nbr_intersected_edge_partitions_t = + edge_property_value_t const*, + void*>; + using optional_property_buffer_mutable_view_t = std::conditional_t, - std::vector>, - std::byte /* dummy */>; + edge_property_value_t*, + void*>; static_assert(std::is_same_v::value_type, thrust::tuple>); @@ -729,19 +856,20 @@ nbr_intersection(raft::handle_t const& handle, "Invalid input arguments: there are invalid input vertex pairs."); } - // 2. Collect neighbor lists for unique second pair elements (for the neighbors within the minor - // range for this GPU); Note that no need to collect for first pair elements as they already - // locally reside. + // 2. Collect neighbor lists (within the minor range for this GPU in multi-GPU) for unique second + // pair elements (all-gathered over minor_comm in multi-GPU); Note that no need to collect for + // first pair elements as they already locally reside. + + auto edge_mask_view = graph_view.edge_mask_view(); std::optional>> major_to_idx_map_ptr{ std::nullopt}; - std::optional> major_nbr_offsets{std::nullopt}; + std::optional> major_nbr_offsets{std::nullopt}; std::optional> major_nbr_indices{std::nullopt}; - [[maybe_unused]] auto major_nbr_properties = + [[maybe_unused]] auto major_e_property_values = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); - optional_property_buffer_view_t optional_major_nbr_properties{}; if constexpr (GraphViewType::is_multi_gpu) { if (intersect_minor_nbr[1]) { @@ -805,7 +933,7 @@ nbr_intersection(raft::handle_t const& handle, } } - // 2.2 Send majors and group (major_comm_rank, edge_partition_idx) counts + // 2.2 Send majors and group (major_comm_rank, local edge_partition_idx) counts rmm::device_uvector rx_majors(0, handle.get_stream()); std::vector rx_major_counts{}; @@ -859,7 +987,7 @@ nbr_intersection(raft::handle_t const& handle, rmm::device_uvector local_degrees_for_rx_majors(size_t{0}, handle.get_stream()); rmm::device_uvector local_nbrs_for_rx_majors(size_t{0}, handle.get_stream()); - [[maybe_unused]] auto local_nbrs_properties_for_rx_majors = + [[maybe_unused]] auto local_e_property_values_for_rx_majors = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); @@ -901,6 +1029,13 @@ nbr_intersection(raft::handle_t const& handle, auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail:: + edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); auto reordered_idx_first = (i == size_t{0}) ? size_t{0} : h_rx_reordered_group_lasts[i * major_comm_size - 1]; @@ -913,6 +1048,7 @@ nbr_intersection(raft::handle_t const& handle, major_comm_size, minor_comm_size, edge_partition, + edge_partition_e_mask, reordered_idx_first, i, raft::device_span( @@ -936,22 +1072,28 @@ nbr_intersection(raft::handle_t const& handle, local_nbrs_for_rx_majors.resize( local_nbr_offsets_for_rx_majors.back_element(handle.get_stream()), handle.get_stream()); - optional_property_buffer_view_t optional_local_nbrs_properties{}; + optional_property_buffer_mutable_view_t optional_local_e_property_values{}; if constexpr (!std::is_same_v) { - local_nbrs_properties_for_rx_majors.resize(local_nbrs_for_rx_majors.size(), - handle.get_stream()); - optional_local_nbrs_properties = raft::device_span( - local_nbrs_properties_for_rx_majors.data(), local_nbrs_properties_for_rx_majors.size()); + local_e_property_values_for_rx_majors.resize(local_nbrs_for_rx_majors.size(), + handle.get_stream()); + optional_local_e_property_values = local_e_property_values_for_rx_majors.data(); } for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); - auto edge_partition_e_value_input = edge_partition_e_input_device_view_t(edge_value_input, i); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail:: + edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; + auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); auto reordered_idx_first = (i == size_t{0}) ? size_t{0} : h_rx_reordered_group_lasts[i * major_comm_size - 1]; @@ -964,12 +1106,13 @@ nbr_intersection(raft::handle_t const& handle, update_rx_major_local_nbrs_t{ major_comm_size, minor_comm_size, edge_partition, edge_partition_e_value_input, + edge_partition_e_mask, reordered_idx_first, i, raft::device_span( @@ -980,7 +1123,7 @@ nbr_intersection(raft::handle_t const& handle, local_nbr_offsets_for_rx_majors.size()), raft::device_span(local_nbrs_for_rx_majors.data(), local_nbrs_for_rx_majors.size()), - optional_local_nbrs_properties}); + optional_local_e_property_values}); } std::vector h_rx_offsets(rx_major_counts.size() + size_t{1}, size_t{0}); @@ -1012,7 +1155,7 @@ nbr_intersection(raft::handle_t const& handle, rmm::device_uvector local_degrees_for_unique_majors(size_t{0}, handle.get_stream()); std::tie(local_degrees_for_unique_majors, std::ignore) = shuffle_values( major_comm, local_degrees_for_rx_majors.begin(), rx_major_counts, handle.get_stream()); - major_nbr_offsets = rmm::device_uvector(local_degrees_for_unique_majors.size() + 1, + major_nbr_offsets = rmm::device_uvector(local_degrees_for_unique_majors.size() + 1, handle.get_stream()); (*major_nbr_offsets).set_element_to_zero_async(size_t{0}, handle.get_stream()); auto degree_first = thrust::make_transform_iterator(local_degrees_for_unique_majors.begin(), @@ -1027,14 +1170,11 @@ nbr_intersection(raft::handle_t const& handle, major_comm, local_nbrs_for_rx_majors.begin(), local_nbr_counts, handle.get_stream()); if constexpr (!std::is_same_v) { - std::tie(major_nbr_properties, std::ignore) = + std::tie(major_e_property_values, std::ignore) = shuffle_values(major_comm, - local_nbrs_properties_for_rx_majors.begin(), + local_e_property_values_for_rx_majors.begin(), local_nbr_counts, handle.get_stream()); - - optional_major_nbr_properties = raft::device_span( - major_nbr_properties.data(), major_nbr_properties.size()); } major_to_idx_map_ptr = std::make_unique>( @@ -1065,11 +1205,11 @@ nbr_intersection(raft::handle_t const& handle, rmm::device_uvector nbr_intersection_offsets(size_t{0}, handle.get_stream()); rmm::device_uvector nbr_intersection_indices(size_t{0}, handle.get_stream()); - [[maybe_unused]] auto nbr_intersection_properties0 = + [[maybe_unused]] auto nbr_intersection_e_property_values0 = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); - [[maybe_unused]] auto nbr_intersection_properties1 = + [[maybe_unused]] auto nbr_intersection_e_property_values1 = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); @@ -1116,15 +1256,19 @@ nbr_intersection(raft::handle_t const& handle, edge_partition_nbr_intersection_sizes.reserve(graph_view.number_of_local_edge_partitions()); edge_partition_nbr_intersection_indices.reserve(graph_view.number_of_local_edge_partitions()); - [[maybe_unused]] optional_nbr_intersected_edge_partitions_t - edge_partition_nbr_intersection_property0{}; - [[maybe_unused]] optional_nbr_intersected_edge_partitions_t - edge_partition_nbr_intersection_property1{}; + [[maybe_unused]] std::conditional_t, + std::vector>, + std::byte /* dummy */> + edge_partition_nbr_intersection_e_property_values0{}; + [[maybe_unused]] std::conditional_t, + std::vector>, + std::byte /* dummy */> + edge_partition_nbr_intersection_e_property_values1{}; if constexpr (!std::is_same_v) { - edge_partition_nbr_intersection_property0.reserve( + edge_partition_nbr_intersection_e_property_values0.reserve( graph_view.number_of_local_edge_partitions()); - edge_partition_nbr_intersection_property1.reserve( + edge_partition_nbr_intersection_e_property_values1.reserve( graph_view.number_of_local_edge_partitions()); } @@ -1144,11 +1288,11 @@ nbr_intersection(raft::handle_t const& handle, rmm::device_uvector rx_v_pair_nbr_intersection_indices(size_t{0}, handle.get_stream()); - [[maybe_unused]] auto rx_v_pair_nbr_intersection_properties0 = + [[maybe_unused]] auto rx_v_pair_nbr_intersection_e_property_values0 = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); - [[maybe_unused]] auto rx_v_pair_nbr_intersection_properties1 = + [[maybe_unused]] auto rx_v_pair_nbr_intersection_e_property_values1 = cugraph::detail::allocate_optional_dataframe_buffer( 0, handle.get_stream()); @@ -1174,9 +1318,15 @@ nbr_intersection(raft::handle_t const& handle, auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); - auto edge_partition_e_value_input = edge_partition_e_input_device_view_t(edge_value_input, i); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; + auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); rx_v_pair_nbr_intersection_sizes.resize( @@ -1193,11 +1343,12 @@ nbr_intersection(raft::handle_t const& handle, rx_v_pair_nbr_intersection_sizes.begin(), pick_min_degree_t{ nullptr, - raft::device_span(), + raft::device_span(), second_element_to_idx_map, - raft::device_span((*major_nbr_offsets).data(), + raft::device_span((*major_nbr_offsets).data(), (*major_nbr_offsets).size()), - edge_partition}); + edge_partition, + edge_partition_e_mask}); } else { CUGRAPH_FAIL("unimplemented."); } @@ -1215,25 +1366,30 @@ nbr_intersection(raft::handle_t const& handle, rx_v_pair_nbr_intersection_offsets.back_element(handle.get_stream()), handle.get_stream()); - optional_property_buffer_view_t rx_v_pair_optional_nbr_intersection_properties0{}; - optional_property_buffer_view_t rx_v_pair_optional_nbr_intersection_properties1{}; + optional_property_buffer_mutable_view_t + rx_v_pair_optional_nbr_intersection_e_property_values0{}; + optional_property_buffer_mutable_view_t + rx_v_pair_optional_nbr_intersection_e_property_values1{}; if constexpr (!std::is_same_v) { - rx_v_pair_nbr_intersection_properties0.resize(rx_v_pair_nbr_intersection_indices.size(), - handle.get_stream()); - rx_v_pair_nbr_intersection_properties1.resize(rx_v_pair_nbr_intersection_indices.size(), - handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values0.resize( + rx_v_pair_nbr_intersection_indices.size(), handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values1.resize( + rx_v_pair_nbr_intersection_indices.size(), handle.get_stream()); - rx_v_pair_optional_nbr_intersection_properties0 = - raft::device_span(rx_v_pair_nbr_intersection_properties0.data(), - rx_v_pair_nbr_intersection_properties0.size()); + rx_v_pair_optional_nbr_intersection_e_property_values0 = + rx_v_pair_nbr_intersection_e_property_values0.data(); - rx_v_pair_optional_nbr_intersection_properties1 = - raft::device_span(rx_v_pair_nbr_intersection_properties1.data(), - rx_v_pair_nbr_intersection_properties1.size()); + rx_v_pair_optional_nbr_intersection_e_property_values1 = + rx_v_pair_nbr_intersection_e_property_values1.data(); } if (intersect_minor_nbr[0] && intersect_minor_nbr[1]) { + optional_property_buffer_view_t optional_major_e_property_values{}; + if constexpr (!std::is_same_v) { + optional_major_e_property_values = major_e_property_values.data(); + } + auto second_element_to_idx_map = detail::kv_cuco_store_find_device_view_t((*major_to_idx_map_ptr)->view()); thrust::tabulate( @@ -1248,28 +1404,29 @@ nbr_intersection(raft::handle_t const& handle, edge_t, edge_partition_e_input_device_view_t, optional_property_buffer_view_t, + optional_property_buffer_mutable_view_t, true>{nullptr, - raft::device_span(), + raft::device_span(), raft::device_span(), optional_property_buffer_view_t{}, second_element_to_idx_map, - raft::device_span((*major_nbr_offsets).data(), + raft::device_span((*major_nbr_offsets).data(), (*major_nbr_offsets).size()), raft::device_span((*major_nbr_indices).data(), (*major_nbr_indices).size()), - optional_major_nbr_properties, + optional_major_e_property_values, edge_partition, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(vertex_pair_buffer), raft::device_span(rx_v_pair_nbr_intersection_offsets.data(), rx_v_pair_nbr_intersection_offsets.size()), raft::device_span(rx_v_pair_nbr_intersection_indices.data(), rx_v_pair_nbr_intersection_indices.size()), - rx_v_pair_optional_nbr_intersection_properties0, - rx_v_pair_optional_nbr_intersection_properties1, + rx_v_pair_optional_nbr_intersection_e_property_values0, + rx_v_pair_optional_nbr_intersection_e_property_values1, invalid_vertex_id::value}); - } else { CUGRAPH_FAIL("unimplemented."); } @@ -1284,31 +1441,31 @@ nbr_intersection(raft::handle_t const& handle, handle.get_stream()); rx_v_pair_nbr_intersection_indices.shrink_to_fit(handle.get_stream()); } else { - auto common_nbr_and_properties_begin = thrust::make_zip_iterator( + auto common_nbr_and_e_property_values_begin = thrust::make_zip_iterator( thrust::make_tuple(rx_v_pair_nbr_intersection_indices.begin(), - rx_v_pair_nbr_intersection_properties0.begin(), - rx_v_pair_nbr_intersection_properties1.begin())); + rx_v_pair_nbr_intersection_e_property_values0.begin(), + rx_v_pair_nbr_intersection_e_property_values1.begin())); auto last = thrust::remove_if( handle.get_thrust_policy(), - common_nbr_and_properties_begin, - common_nbr_and_properties_begin + rx_v_pair_nbr_intersection_indices.size(), + common_nbr_and_e_property_values_begin, + common_nbr_and_e_property_values_begin + rx_v_pair_nbr_intersection_indices.size(), [] __device__(auto nbr_p0_p1) { return thrust::get<0>(nbr_p0_p1) == invalid_vertex_id::value; }); rx_v_pair_nbr_intersection_indices.resize( - thrust::distance(common_nbr_and_properties_begin, last), handle.get_stream()); + thrust::distance(common_nbr_and_e_property_values_begin, last), handle.get_stream()); rx_v_pair_nbr_intersection_indices.shrink_to_fit(handle.get_stream()); - rx_v_pair_nbr_intersection_properties0.resize(rx_v_pair_nbr_intersection_indices.size(), - handle.get_stream()); - rx_v_pair_nbr_intersection_properties0.shrink_to_fit(handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values0.resize( + rx_v_pair_nbr_intersection_indices.size(), handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values0.shrink_to_fit(handle.get_stream()); - rx_v_pair_nbr_intersection_properties1.resize(rx_v_pair_nbr_intersection_indices.size(), - handle.get_stream()); - rx_v_pair_nbr_intersection_properties1.shrink_to_fit(handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values1.resize( + rx_v_pair_nbr_intersection_indices.size(), handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values1.shrink_to_fit(handle.get_stream()); } thrust::inclusive_scan(handle.get_thrust_policy(), @@ -1427,11 +1584,11 @@ nbr_intersection(raft::handle_t const& handle, rmm::device_uvector combined_nbr_intersection_indices(size_t{0}, handle.get_stream()); - [[maybe_unused]] auto combined_nbr_intersection_properties0 = + [[maybe_unused]] auto combined_nbr_intersection_e_property_values0 = cugraph::detail::allocate_optional_dataframe_buffer( size_t{0}, handle.get_stream()); - [[maybe_unused]] auto combined_nbr_intersection_properties1 = + [[maybe_unused]] auto combined_nbr_intersection_e_property_values1 = cugraph::detail::allocate_optional_dataframe_buffer( size_t{0}, handle.get_stream()); @@ -1470,47 +1627,47 @@ nbr_intersection(raft::handle_t const& handle, combined_nbr_intersection_indices.resize(gathered_nbr_intersection_indices.size(), handle.get_stream()); - [[maybe_unused]] auto gathered_nbr_intersection_properties0 = + [[maybe_unused]] auto gathered_nbr_intersection_e_property_values0 = cugraph::detail::allocate_optional_dataframe_buffer( rx_displacements.back() + gathered_nbr_intersection_index_rx_counts.back(), handle.get_stream()); - [[maybe_unused]] auto gathered_nbr_intersection_properties1 = + [[maybe_unused]] auto gathered_nbr_intersection_e_property_values1 = cugraph::detail::allocate_optional_dataframe_buffer( rx_displacements.back() + gathered_nbr_intersection_index_rx_counts.back(), handle.get_stream()); if constexpr (!std::is_same_v) { device_multicast_sendrecv(minor_comm, - rx_v_pair_nbr_intersection_properties0.begin(), + rx_v_pair_nbr_intersection_e_property_values0.begin(), rx_v_pair_nbr_intersection_index_tx_counts, tx_displacements, ranks, - gathered_nbr_intersection_properties0.begin(), + gathered_nbr_intersection_e_property_values0.begin(), gathered_nbr_intersection_index_rx_counts, rx_displacements, ranks, handle.get_stream()); - rx_v_pair_nbr_intersection_properties0.resize(size_t{0}, handle.get_stream()); - rx_v_pair_nbr_intersection_properties0.shrink_to_fit(handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values0.resize(size_t{0}, handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values0.shrink_to_fit(handle.get_stream()); - combined_nbr_intersection_properties0.resize(gathered_nbr_intersection_properties0.size(), - handle.get_stream()); + combined_nbr_intersection_e_property_values0.resize( + gathered_nbr_intersection_e_property_values0.size(), handle.get_stream()); device_multicast_sendrecv(minor_comm, - rx_v_pair_nbr_intersection_properties1.begin(), + rx_v_pair_nbr_intersection_e_property_values1.begin(), rx_v_pair_nbr_intersection_index_tx_counts, tx_displacements, ranks, - gathered_nbr_intersection_properties1.begin(), + gathered_nbr_intersection_e_property_values1.begin(), gathered_nbr_intersection_index_rx_counts, rx_displacements, ranks, handle.get_stream()); - rx_v_pair_nbr_intersection_properties1.resize(size_t{0}, handle.get_stream()); - rx_v_pair_nbr_intersection_properties1.shrink_to_fit(handle.get_stream()); - combined_nbr_intersection_properties1.resize(gathered_nbr_intersection_properties1.size(), - handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values1.resize(size_t{0}, handle.get_stream()); + rx_v_pair_nbr_intersection_e_property_values1.shrink_to_fit(handle.get_stream()); + combined_nbr_intersection_e_property_values1.resize( + gathered_nbr_intersection_e_property_values1.size(), handle.get_stream()); } if constexpr (!std::is_same_v) { @@ -1518,7 +1675,10 @@ nbr_intersection(raft::handle_t const& handle, handle.get_thrust_policy(), thrust::make_counting_iterator(size_t{0}), thrust::make_counting_iterator(rx_v_pair_counts[minor_comm_rank]), - gatherv_indices_t{ + gatherv_indices_t{ rx_v_pair_counts[minor_comm_rank], minor_comm_size, raft::device_span(gathered_nbr_intersection_offsets.data(), @@ -1529,25 +1689,19 @@ nbr_intersection(raft::handle_t const& handle, combined_nbr_intersection_offsets.size()), raft::device_span(combined_nbr_intersection_indices.data(), combined_nbr_intersection_indices.size()), - raft::device_span( - gathered_nbr_intersection_properties0.data(), - gathered_nbr_intersection_properties0.size()), - raft::device_span( - gathered_nbr_intersection_properties1.data(), - gathered_nbr_intersection_properties1.size()), - raft::device_span( - combined_nbr_intersection_properties0.data(), - combined_nbr_intersection_properties0.size()), - raft::device_span( - combined_nbr_intersection_properties1.data(), - combined_nbr_intersection_properties1.size())}); - + gathered_nbr_intersection_e_property_values0.data(), + gathered_nbr_intersection_e_property_values1.data(), + combined_nbr_intersection_e_property_values0.data(), + combined_nbr_intersection_e_property_values1.data()}); } else { thrust::for_each( handle.get_thrust_policy(), thrust::make_counting_iterator(size_t{0}), thrust::make_counting_iterator(rx_v_pair_counts[minor_comm_rank]), - gatherv_indices_t{ + gatherv_indices_t{ rx_v_pair_counts[minor_comm_rank], minor_comm_size, raft::device_span(gathered_nbr_intersection_offsets.data(), @@ -1567,10 +1721,10 @@ nbr_intersection(raft::handle_t const& handle, edge_partition_nbr_intersection_indices.push_back( std::move(combined_nbr_intersection_indices)); if constexpr (!std::is_same_v) { - edge_partition_nbr_intersection_property0.push_back( - std::move(combined_nbr_intersection_properties0)); - edge_partition_nbr_intersection_property1.push_back( - std::move(combined_nbr_intersection_properties1)); + edge_partition_nbr_intersection_e_property_values0.push_back( + std::move(combined_nbr_intersection_e_property_values0)); + edge_partition_nbr_intersection_e_property_values1.push_back( + std::move(combined_nbr_intersection_e_property_values1)); } } @@ -1581,8 +1735,10 @@ nbr_intersection(raft::handle_t const& handle, } nbr_intersection_indices.resize(num_nbr_intersection_indices, handle.get_stream()); if constexpr (!std::is_same_v) { - nbr_intersection_properties0.resize(nbr_intersection_indices.size(), handle.get_stream()); - nbr_intersection_properties1.resize(nbr_intersection_indices.size(), handle.get_stream()); + nbr_intersection_e_property_values0.resize(nbr_intersection_indices.size(), + handle.get_stream()); + nbr_intersection_e_property_values1.resize(nbr_intersection_indices.size(), + handle.get_stream()); } size_t size_offset{0}; size_t index_offset{0}; @@ -1599,14 +1755,14 @@ nbr_intersection(raft::handle_t const& handle, if constexpr (!std::is_same_v) { thrust::copy(handle.get_thrust_policy(), - edge_partition_nbr_intersection_property0[i].begin(), - edge_partition_nbr_intersection_property0[i].end(), - nbr_intersection_properties0.begin() + index_offset); + edge_partition_nbr_intersection_e_property_values0[i].begin(), + edge_partition_nbr_intersection_e_property_values0[i].end(), + nbr_intersection_e_property_values0.begin() + index_offset); thrust::copy(handle.get_thrust_policy(), - edge_partition_nbr_intersection_property1[i].begin(), - edge_partition_nbr_intersection_property1[i].end(), - nbr_intersection_properties1.begin() + index_offset); + edge_partition_nbr_intersection_e_property_values1[i].begin(), + edge_partition_nbr_intersection_e_property_values1[i].end(), + nbr_intersection_e_property_values1.begin() + index_offset); } index_offset += edge_partition_nbr_intersection_indices[i].size(); @@ -1619,13 +1775,18 @@ nbr_intersection(raft::handle_t const& handle, size_first, size_first + nbr_intersection_sizes.size(), nbr_intersection_offsets.begin() + 1); - } else { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(size_t{0})); - auto edge_partition_e_value_input = edge_partition_e_input_device_view_t(edge_value_input, 0); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, 0) + : thrust::nullopt; + rmm::device_uvector nbr_intersection_sizes( input_size, handle.get_stream()); // initially store minimum degrees (upper bound for intersection sizes) @@ -1636,10 +1797,11 @@ nbr_intersection(raft::handle_t const& handle, vertex_pair_first + input_size, nbr_intersection_sizes.begin(), pick_min_degree_t{nullptr, - raft::device_span(), + raft::device_span(), nullptr, - raft::device_span(), - edge_partition}); + raft::device_span(), + edge_partition, + edge_partition_e_mask}); } else { CUGRAPH_FAIL("unimplemented."); } @@ -1656,51 +1818,51 @@ nbr_intersection(raft::handle_t const& handle, nbr_intersection_indices.resize(nbr_intersection_offsets.back_element(handle.get_stream()), handle.get_stream()); - optional_property_buffer_view_t optional_nbr_intersection_properties0{}; - optional_property_buffer_view_t optional_nbr_intersection_properties1{}; + optional_property_buffer_mutable_view_t optional_nbr_intersection_e_property_values0{}; + optional_property_buffer_mutable_view_t optional_nbr_intersection_e_property_values1{}; if constexpr (!std::is_same_v) { - nbr_intersection_properties0.resize(nbr_intersection_indices.size(), handle.get_stream()); - nbr_intersection_properties1.resize(nbr_intersection_indices.size(), handle.get_stream()); - - optional_nbr_intersection_properties0 = raft::device_span( - nbr_intersection_properties0.data(), nbr_intersection_properties0.size()); + nbr_intersection_e_property_values0.resize(nbr_intersection_indices.size(), + handle.get_stream()); + nbr_intersection_e_property_values1.resize(nbr_intersection_indices.size(), + handle.get_stream()); - optional_nbr_intersection_properties1 = raft::device_span( - nbr_intersection_properties1.data(), nbr_intersection_properties1.size()); + optional_nbr_intersection_e_property_values0 = nbr_intersection_e_property_values0.data(); + optional_nbr_intersection_e_property_values1 = nbr_intersection_e_property_values1.data(); } if (intersect_minor_nbr[0] && intersect_minor_nbr[1]) { - thrust::tabulate( - handle.get_thrust_policy(), - nbr_intersection_sizes.begin(), - nbr_intersection_sizes.end(), - copy_intersecting_nbrs_and_update_intersection_size_t{ - nullptr, - raft::device_span(), - raft::device_span(), - optional_property_buffer_view_t{}, - nullptr, - raft::device_span(), - raft::device_span(), - optional_property_buffer_view_t{}, - edge_partition, - edge_partition_e_value_input, - vertex_pair_first, - raft::device_span(nbr_intersection_offsets.data(), - nbr_intersection_offsets.size()), - raft::device_span(nbr_intersection_indices.data(), - nbr_intersection_indices.size()), - optional_nbr_intersection_properties0, - optional_nbr_intersection_properties1, - invalid_vertex_id::value}); + thrust::tabulate(handle.get_thrust_policy(), + nbr_intersection_sizes.begin(), + nbr_intersection_sizes.end(), + copy_intersecting_nbrs_and_update_intersection_size_t< + void*, + void*, + decltype(vertex_pair_first), + vertex_t, + edge_t, + edge_partition_e_input_device_view_t, + optional_property_buffer_view_t, + optional_property_buffer_mutable_view_t, + false>{nullptr, + raft::device_span(), + raft::device_span(), + optional_property_buffer_view_t{}, + nullptr, + raft::device_span(), + raft::device_span(), + optional_property_buffer_view_t{}, + edge_partition, + edge_partition_e_value_input, + edge_partition_e_mask, + vertex_pair_first, + raft::device_span(nbr_intersection_offsets.data(), + nbr_intersection_offsets.size()), + raft::device_span(nbr_intersection_indices.data(), + nbr_intersection_indices.size()), + optional_nbr_intersection_e_property_values0, + optional_nbr_intersection_e_property_values1, + invalid_vertex_id::value}); } else { CUGRAPH_FAIL("unimplemented."); } @@ -1711,14 +1873,14 @@ nbr_intersection(raft::handle_t const& handle, thrust::count_if(handle.get_thrust_policy(), nbr_intersection_indices.begin(), nbr_intersection_indices.end(), - detail::not_equal_t{invalid_vertex_id::value}), + detail::is_not_equal_t{invalid_vertex_id::value}), handle.get_stream()); - [[maybe_unused]] auto tmp_properties0 = + [[maybe_unused]] auto tmp_property_values0 = cugraph::detail::allocate_optional_dataframe_buffer( tmp_indices.size(), handle.get_stream()); - [[maybe_unused]] auto tmp_properties1 = + [[maybe_unused]] auto tmp_property_values1 = cugraph::detail::allocate_optional_dataframe_buffer( tmp_indices.size(), handle.get_stream()); @@ -1737,39 +1899,38 @@ nbr_intersection(raft::handle_t const& handle, nbr_intersection_indices.begin() + num_scanned, nbr_intersection_indices.begin() + num_scanned + this_scan_size, tmp_indices.begin() + num_copied, - detail::not_equal_t{invalid_vertex_id::value}))); + detail::is_not_equal_t{invalid_vertex_id::value}))); } else { - auto zipped_itr_to_indices_and_properties_begin = - thrust::make_zip_iterator(thrust::make_tuple(nbr_intersection_indices.begin(), - nbr_intersection_properties0.begin(), - nbr_intersection_properties1.begin())); + auto zipped_itr_to_indices_and_e_property_values_begin = thrust::make_zip_iterator( + thrust::make_tuple(nbr_intersection_indices.begin(), + nbr_intersection_e_property_values0.begin(), + nbr_intersection_e_property_values1.begin())); auto zipped_itr_to_tmps_begin = thrust::make_zip_iterator(thrust::make_tuple( - tmp_indices.begin(), tmp_properties0.begin(), tmp_properties1.begin())); + tmp_indices.begin(), tmp_property_values0.begin(), tmp_property_values1.begin())); num_copied += static_cast(thrust::distance( zipped_itr_to_tmps_begin + num_copied, - thrust::copy_if(handle.get_thrust_policy(), - zipped_itr_to_indices_and_properties_begin + num_scanned, - zipped_itr_to_indices_and_properties_begin + num_scanned + this_scan_size, - zipped_itr_to_tmps_begin + num_copied, - [] __device__(auto nbr_p0_p1) { - auto nbr = thrust::get<0>(nbr_p0_p1); - auto p0 = thrust::get<1>(nbr_p0_p1); - auto p1 = thrust::get<2>(nbr_p0_p1); - return thrust::get<0>(nbr_p0_p1) != invalid_vertex_id::value; - }))); + thrust::copy_if( + handle.get_thrust_policy(), + zipped_itr_to_indices_and_e_property_values_begin + num_scanned, + zipped_itr_to_indices_and_e_property_values_begin + num_scanned + this_scan_size, + zipped_itr_to_tmps_begin + num_copied, + [] __device__(auto nbr_p0_p1) { + auto nbr = thrust::get<0>(nbr_p0_p1); + auto p0 = thrust::get<1>(nbr_p0_p1); + auto p1 = thrust::get<2>(nbr_p0_p1); + return thrust::get<0>(nbr_p0_p1) != invalid_vertex_id::value; + }))); } num_scanned += this_scan_size; } nbr_intersection_indices = std::move(tmp_indices); if constexpr (!std::is_same_v) { - nbr_intersection_properties0 = std::move(tmp_properties0); - nbr_intersection_properties1 = std::move(tmp_properties1); + nbr_intersection_e_property_values0 = std::move(tmp_property_values0); + nbr_intersection_e_property_values1 = std::move(tmp_property_values1); } - #else - if constexpr (std::is_same_v) { nbr_intersection_indices.resize( thrust::distance(nbr_intersection_indices.begin(), @@ -1780,10 +1941,10 @@ nbr_intersection(raft::handle_t const& handle, handle.get_stream()); } else { nbr_intersection_indices.resize( - thrust::distance(zipped_itr_to_indices_and_properties_begin, + thrust::distance(zipped_itr_to_indices_and_e_property_values_begin, thrust::remove_if(handle.get_thrust_policy(), - zipped_itr_to_indices_and_properties_begin, - zipped_itr_to_indices_and_properties_begin + + zipped_itr_to_indices_and_e_property_values_begin, + zipped_itr_to_indices_and_e_property_values_begin + nbr_intersection_indices.size(), [] __device__(auto nbr_p0_p1) { return thrust::get<0>(nbr_p0_p1) == @@ -1791,8 +1952,10 @@ nbr_intersection(raft::handle_t const& handle, })), handle.get_stream()); - nbr_intersection_properties0.resize(nbr_intersection_indices.size(), handle.get_stream()); - nbr_intersection_properties1.resize(nbr_intersection_indices.size(), handle.get_stream()); + nbr_intersection_e_property_values0.resize(nbr_intersection_indices.size(), + handle.get_stream()); + nbr_intersection_e_property_values1.resize(nbr_intersection_indices.size(), + handle.get_stream()); } #endif @@ -1811,8 +1974,8 @@ nbr_intersection(raft::handle_t const& handle, } else { return std::make_tuple(std::move(nbr_intersection_offsets), std::move(nbr_intersection_indices), - std::move(nbr_intersection_properties0), - std::move(nbr_intersection_properties1)); + std::move(nbr_intersection_e_property_values0), + std::move(nbr_intersection_e_property_values1)); } } diff --git a/cpp/src/prims/detail/optional_dataframe_buffer.hpp b/cpp/src/prims/detail/optional_dataframe_buffer.hpp index dd40e6932e4..62b2245a651 100644 --- a/cpp/src/prims/detail/optional_dataframe_buffer.hpp +++ b/cpp/src/prims/detail/optional_dataframe_buffer.hpp @@ -97,6 +97,7 @@ void shrink_to_fit_optional_dataframe_buffer( { return shrink_to_fit_dataframe_buffer(optional_dataframe_buffer, stream_view); } + } // namespace detail } // namespace cugraph diff --git a/cpp/src/prims/kv_store.cuh b/cpp/src/prims/kv_store.cuh index c46e83aa5da..f17441ad6ab 100644 --- a/cpp/src/prims/kv_store.cuh +++ b/cpp/src/prims/kv_store.cuh @@ -604,7 +604,7 @@ class kv_cuco_store_t { store_value_offsets.begin() /* map */, store_value_offsets.begin() /* stencil */, get_dataframe_buffer_begin(store_values_), - not_equal_t{std::numeric_limits::max()}); + is_not_equal_t{std::numeric_limits::max()}); } } @@ -649,7 +649,7 @@ class kv_cuco_store_t { store_value_offsets.begin() /* map */, store_value_offsets.begin() /* stencil */, get_dataframe_buffer_begin(store_values_), - not_equal_t{std::numeric_limits::max()}); + is_not_equal_t{std::numeric_limits::max()}); } } @@ -695,7 +695,7 @@ class kv_cuco_store_t { store_value_offsets.begin() /* map */, store_value_offsets.begin() /* stencil */, get_dataframe_buffer_begin(store_values_), - not_equal_t{std::numeric_limits::max()}); + is_not_equal_t{std::numeric_limits::max()}); // now perform assigns (for k,v pairs that failed to insert) diff --git a/cpp/src/prims/per_v_pair_transform_dst_nbr_intersection.cuh b/cpp/src/prims/per_v_pair_transform_dst_nbr_intersection.cuh index 640c3c04bfd..201c08325d7 100644 --- a/cpp/src/prims/per_v_pair_transform_dst_nbr_intersection.cuh +++ b/cpp/src/prims/per_v_pair_transform_dst_nbr_intersection.cuh @@ -112,8 +112,8 @@ struct call_intersection_op_t { IntersectionOp intersection_op{}; size_t const* nbr_offsets{nullptr}; typename GraphViewType::vertex_type const* nbr_indices{nullptr}; - EdgeValueInputIterator nbr_intersection_properties0{nullptr}; - EdgeValueInputIterator nbr_intersection_properties1{nullptr}; + EdgeValueInputIterator nbr_intersection_property_values0{nullptr}; + EdgeValueInputIterator nbr_intersection_property_values1{nullptr}; VertexPairIndexIterator major_minor_pair_index_first{}; VertexPairIterator major_minor_pair_first{}; VertexPairValueOutputIterator major_minor_pair_value_output_first{}; @@ -136,20 +136,20 @@ struct call_intersection_op_t { std::conditional_t, raft::device_span, std::byte /* dummy */> - properties0{}; + property_values0{}; std::conditional_t, raft::device_span, std::byte /* dummy */> - properties1{}; + property_values1{}; if constexpr (!std::is_same_v) { - properties0 = raft::device_span( - nbr_intersection_properties0 + nbr_offsets[i], - nbr_intersection_properties0 + +nbr_offsets[i + 1]); - properties1 = raft::device_span( - nbr_intersection_properties1 + nbr_offsets[i], - nbr_intersection_properties1 + +nbr_offsets[i + 1]); + property_values0 = raft::device_span( + nbr_intersection_property_values0 + nbr_offsets[i], + nbr_intersection_property_values0 + +nbr_offsets[i + 1]); + property_values1 = raft::device_span( + nbr_intersection_property_values1 + nbr_offsets[i], + nbr_intersection_property_values1 + +nbr_offsets[i + 1]); } property_t src_prop{}; @@ -174,8 +174,8 @@ struct call_intersection_op_t { dst_prop = *(vertex_property_first + dst_offset); } - *(major_minor_pair_value_output_first + index) = - intersection_op(src, dst, src_prop, dst_prop, intersection, properties0, properties1); + *(major_minor_pair_value_output_first + index) = intersection_op( + src, dst, src_prop, dst_prop, intersection, property_values0, property_values1); } }; @@ -240,8 +240,6 @@ void per_v_pair_transform_dst_nbr_intersection( using edge_property_value_t = typename EdgeValueInputIterator::value_type; using result_t = typename thrust::iterator_traits::value_type; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { auto num_invalids = detail::count_invalid_vertex_pairs(handle, graph_view, vertex_pair_first, vertex_pair_last); @@ -384,16 +382,16 @@ void per_v_pair_transform_dst_nbr_intersection( rmm::device_uvector intersection_offsets(size_t{0}, handle.get_stream()); rmm::device_uvector intersection_indices(size_t{0}, handle.get_stream()); - [[maybe_unused]] rmm::device_uvector r_nbr_intersection_properties0( - size_t{0}, handle.get_stream()); - [[maybe_unused]] rmm::device_uvector r_nbr_intersection_properties1( - size_t{0}, handle.get_stream()); + [[maybe_unused]] rmm::device_uvector + r_nbr_intersection_property_values0(size_t{0}, handle.get_stream()); + [[maybe_unused]] rmm::device_uvector + r_nbr_intersection_property_values1(size_t{0}, handle.get_stream()); if constexpr (!std::is_same_v) { std::tie(intersection_offsets, intersection_indices, - r_nbr_intersection_properties0, - r_nbr_intersection_properties1) = + r_nbr_intersection_property_values0, + r_nbr_intersection_property_values1) = detail::nbr_intersection(handle, graph_view, edge_value_input, @@ -422,7 +420,7 @@ void per_v_pair_transform_dst_nbr_intersection( detail::call_intersection_op_t< GraphViewType, decltype(vertex_value_input_for_unique_vertices_first), - typename decltype(r_nbr_intersection_properties0)::const_pointer, + typename decltype(r_nbr_intersection_property_values0)::const_pointer, IntersectionOp, decltype(chunk_vertex_pair_index_first), VertexPairIterator, @@ -433,8 +431,8 @@ void per_v_pair_transform_dst_nbr_intersection( intersection_op, intersection_offsets.data(), intersection_indices.data(), - r_nbr_intersection_properties0.data(), - r_nbr_intersection_properties1.data(), + r_nbr_intersection_property_values0.data(), + r_nbr_intersection_property_values1.data(), chunk_vertex_pair_index_first, vertex_pair_first, vertex_pair_value_output_first}); @@ -445,7 +443,7 @@ void per_v_pair_transform_dst_nbr_intersection( detail::call_intersection_op_t< GraphViewType, VertexValueInputIterator, - typename decltype(r_nbr_intersection_properties0)::const_pointer, + typename decltype(r_nbr_intersection_property_values0)::const_pointer, IntersectionOp, decltype(chunk_vertex_pair_index_first), VertexPairIterator, @@ -456,8 +454,8 @@ void per_v_pair_transform_dst_nbr_intersection( intersection_op, intersection_offsets.data(), intersection_indices.data(), - r_nbr_intersection_properties0.data(), - r_nbr_intersection_properties1.data(), + r_nbr_intersection_property_values0.data(), + r_nbr_intersection_property_values1.data(), chunk_vertex_pair_index_first, vertex_pair_first, vertex_pair_value_output_first}); diff --git a/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh b/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh index e6db21f1c7c..5fee97790f1 100644 --- a/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh +++ b/cpp/src/prims/per_v_random_select_transform_outgoing_e.cuh @@ -941,7 +941,7 @@ per_v_random_select_transform_e(raft::handle_t const& handle, minor_comm_ranks.begin(), thrust::make_zip_iterator( thrust::make_tuple(tmp_sample_local_nbr_indices.begin(), tmp_sample_key_indices.begin())), - not_equal_t{-1}); + is_not_equal_t{-1}); sample_local_nbr_indices = std::move(tmp_sample_local_nbr_indices); sample_key_indices = std::move(tmp_sample_key_indices); diff --git a/cpp/src/prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh b/cpp/src/prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh index 2e19adc34c4..d4f8606257e 100644 --- a/cpp/src/prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh +++ b/cpp/src/prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh @@ -336,8 +336,14 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( if (edge_partition.number_of_edges() > 0) { auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); - detail::decompress_edge_partition_to_fill_edgelist_majors( - handle, edge_partition, tmp_majors.data(), segment_offsets); + detail::decompress_edge_partition_to_fill_edgelist_majors( + handle, + edge_partition, + std::nullopt, + raft::device_span(tmp_majors.data(), tmp_majors.size()), + segment_offsets); auto minor_key_first = thrust::make_transform_iterator( edge_partition.indices(), diff --git a/cpp/src/prims/transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cuh b/cpp/src/prims/transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cuh index f773a102959..bcf7606c423 100644 --- a/cpp/src/prims/transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cuh +++ b/cpp/src/prims/transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cuh @@ -25,6 +25,7 @@ #include #include #include +#include #include #include @@ -269,10 +270,17 @@ void transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( vertex_value_output_first + graph_view.local_vertex_partition_range_size(), init); + auto edge_mask_view = graph_view.edge_mask_view(); for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); + auto edge_partition_e_mask = + edge_mask_view + ? std::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : std::nullopt; edge_partition_src_input_device_view_t edge_partition_src_value_input{}; edge_partition_dst_input_device_view_t edge_partition_dst_value_input{}; @@ -286,22 +294,29 @@ void transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( edge_partition_dst_value_input = edge_partition_dst_input_device_view_t(edge_dst_value_input); } - rmm::device_uvector majors(edge_partition.number_of_edges(), handle.get_stream()); + rmm::device_uvector majors( + edge_partition_e_mask + ? detail::count_set_bits( + handle, (*edge_partition_e_mask).value_first(), edge_partition.number_of_edges()) + : static_cast(edge_partition.number_of_edges()), + handle.get_stream()); rmm::device_uvector minors(majors.size(), handle.get_stream()); auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); detail::decompress_edge_partition_to_edgelist(handle, - edge_partition, - std::nullopt, - std::nullopt, - majors.data(), - minors.data(), - std::nullopt, - std::nullopt, - segment_offsets); + GraphViewType::is_multi_gpu>( + handle, + edge_partition, + std::nullopt, + std::nullopt, + edge_partition_e_mask, + raft::device_span(majors.data(), majors.size()), + raft::device_span(minors.data(), minors.size()), + std::nullopt, + std::nullopt, + segment_offsets); auto vertex_pair_first = thrust::make_zip_iterator(thrust::make_tuple(majors.begin(), minors.begin())); diff --git a/cpp/src/structure/coarsen_graph_impl.cuh b/cpp/src/structure/coarsen_graph_impl.cuh index b8dc28d563e..0704bc6f23b 100644 --- a/cpp/src/structure/coarsen_graph_impl.cuh +++ b/cpp/src/structure/coarsen_graph_impl.cuh @@ -164,16 +164,18 @@ decompress_edge_partition_to_relabeled_and_grouped_and_coarsened_edgelist( ? std::make_optional>( edgelist_majors.size(), handle.get_stream()) : std::nullopt; - detail::decompress_edge_partition_to_edgelist( + detail::decompress_edge_partition_to_edgelist( handle, edge_partition, edge_partition_weight_view, - std::optional>{ - std::nullopt}, - edgelist_majors.data(), - edgelist_minors.data(), - edgelist_weights ? std::optional{(*edgelist_weights).data()} : std::nullopt, - std::optional{std::nullopt}, + std::nullopt, + std::nullopt, + raft::device_span(edgelist_majors.data(), edgelist_majors.size()), + raft::device_span(edgelist_minors.data(), edgelist_minors.size()), + edgelist_weights ? std::make_optional>((*edgelist_weights).data(), + (*edgelist_weights).size()) + : std::nullopt, + std::nullopt, segment_offsets); auto pair_first = diff --git a/cpp/src/structure/decompress_to_edgelist_impl.cuh b/cpp/src/structure/decompress_to_edgelist_impl.cuh index d653307c620..e9e84f5bf15 100644 --- a/cpp/src/structure/decompress_to_edgelist_impl.cuh +++ b/cpp/src/structure/decompress_to_edgelist_impl.cuh @@ -23,6 +23,7 @@ #include #include #include +#include #include #include @@ -80,8 +81,11 @@ decompress_to_edgelist_impl( std::vector edgelist_edge_counts(graph_view.number_of_local_edge_partitions(), size_t{0}); for (size_t i = 0; i < edgelist_edge_counts.size(); ++i) { - edgelist_edge_counts[i] = - static_cast(graph_view.number_of_local_edge_partition_edges(i)); + edgelist_edge_counts[i] = graph_view.local_edge_partition_view(i).number_of_edges(); + if (graph_view.has_edge_mask()) { + edgelist_edge_counts[i] = detail::count_set_bits( + handle, (*(graph_view.edge_mask_view())).value_firsts()[i], edgelist_edge_counts[i]); + } } auto number_of_local_edges = std::reduce(edgelist_edge_counts.begin(), edgelist_edge_counts.end()); @@ -97,7 +101,7 @@ decompress_to_edgelist_impl( size_t cur_size{0}; for (size_t i = 0; i < edgelist_edge_counts.size(); ++i) { - detail::decompress_edge_partition_to_edgelist( + detail::decompress_edge_partition_to_edgelist( handle, edge_partition_device_view_t( graph_view.local_edge_partition_view(i)), @@ -110,11 +114,19 @@ decompress_to_edgelist_impl( detail::edge_partition_edge_property_device_view_t>( (*edge_id_view), i) : std::nullopt, - edgelist_majors.data() + cur_size, - edgelist_minors.data() + cur_size, - edgelist_weights ? std::optional{(*edgelist_weights).data() + cur_size} + graph_view.has_edge_mask() + ? std::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *(graph_view.edge_mask_view()), i) + : std::nullopt, + raft::device_span(edgelist_majors.data() + cur_size, edgelist_edge_counts[i]), + raft::device_span(edgelist_minors.data() + cur_size, edgelist_edge_counts[i]), + edgelist_weights ? std::make_optional>( + (*edgelist_weights).data() + cur_size, edgelist_edge_counts[i]) : std::nullopt, - edgelist_ids ? std::optional{(*edgelist_ids).data() + cur_size} : std::nullopt, + edgelist_ids ? std::make_optional>( + (*edgelist_ids).data() + cur_size, edgelist_edge_counts[i]) + : std::nullopt, graph_view.local_edge_partition_segment_offsets(i)); cur_size += edgelist_edge_counts[i]; } @@ -231,8 +243,13 @@ decompress_to_edgelist_impl( if (do_expensive_check) { /* currently, nothing to do */ } - rmm::device_uvector edgelist_majors(graph_view.number_of_local_edge_partition_edges(), - handle.get_stream()); + auto num_edges = graph_view.local_edge_partition_view().number_of_edges(); + if (graph_view.has_edge_mask()) { + num_edges = + detail::count_set_bits(handle, (*(graph_view.edge_mask_view())).value_firsts()[0], num_edges); + } + + rmm::device_uvector edgelist_majors(num_edges, handle.get_stream()); rmm::device_uvector edgelist_minors(edgelist_majors.size(), handle.get_stream()); auto edgelist_weights = edge_weight_view ? std::make_optional>( edgelist_majors.size(), handle.get_stream()) @@ -240,7 +257,7 @@ decompress_to_edgelist_impl( auto edgelist_ids = edge_id_view ? std::make_optional>( edgelist_majors.size(), handle.get_stream()) : std::nullopt; - detail::decompress_edge_partition_to_edgelist( + detail::decompress_edge_partition_to_edgelist( handle, edge_partition_device_view_t( graph_view.local_edge_partition_view()), @@ -253,10 +270,19 @@ decompress_to_edgelist_impl( detail::edge_partition_edge_property_device_view_t>( (*edge_id_view), 0) : std::nullopt, - edgelist_majors.data(), - edgelist_minors.data(), - edgelist_weights ? std::optional{(*edgelist_weights).data()} : std::nullopt, - edgelist_ids ? std::optional{(*edgelist_ids).data()} : std::nullopt, + graph_view.has_edge_mask() + ? std::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *(graph_view.edge_mask_view()), 0) + : std::nullopt, + raft::device_span(edgelist_majors.data(), edgelist_majors.size()), + raft::device_span(edgelist_minors.data(), edgelist_minors.size()), + edgelist_weights ? std::make_optional>((*edgelist_weights).data(), + (*edgelist_weights).size()) + : std::nullopt, + edgelist_ids ? std::make_optional>((*edgelist_ids).data(), + (*edgelist_ids).size()) + : std::nullopt, graph_view.local_edge_partition_segment_offsets()); if (renumber_map) { @@ -294,8 +320,6 @@ decompress_to_edgelist( std::optional> renumber_map, bool do_expensive_check) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - return decompress_to_edgelist_impl( handle, graph_view, edge_weight_view, edge_id_view, renumber_map, do_expensive_check); } diff --git a/cpp/src/structure/graph_impl.cuh b/cpp/src/structure/graph_impl.cuh index 97975897e08..75862266789 100644 --- a/cpp/src/structure/graph_impl.cuh +++ b/cpp/src/structure/graph_impl.cuh @@ -213,16 +213,17 @@ update_local_sorted_unique_edge_majors_minors( thrust::make_counting_iterator(minor_range_first + num_scanned), thrust::make_counting_iterator(minor_range_first + num_scanned + this_scan_size), unique_edge_minors.begin() + num_copied, - cugraph::detail::check_bit_set_t{minor_bitmaps.data(), minor_range_first}))); + cugraph::detail::check_bit_set_t{minor_bitmaps.data(), + minor_range_first}))); num_scanned += this_scan_size; } #else - thrust::copy_if( - handle.get_thrust_policy(), - thrust::make_counting_iterator(minor_range_first), - thrust::make_counting_iterator(minor_range_last), - unique_edge_minors.begin(), - cugraph::detail::check_bit_set_t{minor_bitmaps.data(), minor_range_first}); + thrust::copy_if(handle.get_thrust_policy(), + thrust::make_counting_iterator(minor_range_first), + thrust::make_counting_iterator(minor_range_last), + unique_edge_minors.begin(), + cugraph::detail::check_bit_set_t{ + minor_bitmaps.data(), minor_range_first}); #endif auto num_chunks = diff --git a/cpp/src/structure/graph_view_impl.cuh b/cpp/src/structure/graph_view_impl.cuh index 7626784c13c..64a8a3212b3 100644 --- a/cpp/src/structure/graph_view_impl.cuh +++ b/cpp/src/structure/graph_view_impl.cuh @@ -28,6 +28,7 @@ #include #include #include +#include #include #include @@ -106,6 +107,7 @@ rmm::device_uvector compute_major_degrees( std::vector const& edge_partition_offsets, std::optional> const& edge_partition_dcs_nzd_vertices, std::optional> const& edge_partition_dcs_nzd_vertex_counts, + std::optional> const& edge_partition_masks, partition_t const& partition, std::vector const& edge_partition_segment_offsets) { @@ -142,7 +144,9 @@ rmm::device_uvector compute_major_degrees( vertex_t major_range_last{}; std::tie(major_range_first, major_range_last) = partition.vertex_partition_range(major_range_vertex_partition_id); - auto p_offsets = edge_partition_offsets[i]; + auto offsets = edge_partition_offsets[i]; + auto masks = + edge_partition_masks ? thrust::make_optional((*edge_partition_masks)[i]) : thrust::nullopt; auto segment_offset_size_per_partition = edge_partition_segment_offsets.size() / static_cast(minor_comm_size); auto major_hypersparse_first = @@ -155,9 +159,16 @@ rmm::device_uvector compute_major_degrees( thrust::make_counting_iterator(vertex_t{0}), thrust::make_counting_iterator(major_hypersparse_first - major_range_first), local_degrees.begin(), - [p_offsets] __device__(auto i) { return p_offsets[i + 1] - p_offsets[i]; }); + [offsets, masks] __device__(auto i) { + auto local_degree = offsets[i + 1] - offsets[i]; + if (masks) { + local_degree = static_cast( + detail::count_set_bits(*masks, offsets[i], local_degree)); + } + return local_degree; + }); if (use_dcs) { - auto p_dcs_nzd_vertices = (*edge_partition_dcs_nzd_vertices)[i]; + auto dcs_nzd_vertices = (*edge_partition_dcs_nzd_vertices)[i]; auto dcs_nzd_vertex_count = (*edge_partition_dcs_nzd_vertex_counts)[i]; thrust::fill(execution_policy, local_degrees.begin() + (major_hypersparse_first - major_range_first), @@ -166,15 +177,20 @@ rmm::device_uvector compute_major_degrees( thrust::for_each(execution_policy, thrust::make_counting_iterator(vertex_t{0}), thrust::make_counting_iterator(dcs_nzd_vertex_count), - [p_offsets, - p_dcs_nzd_vertices, + [offsets, + dcs_nzd_vertices, + masks, major_range_first, major_hypersparse_first, local_degrees = local_degrees.data()] __device__(auto i) { - auto d = p_offsets[(major_hypersparse_first - major_range_first) + i + 1] - - p_offsets[(major_hypersparse_first - major_range_first) + i]; - auto v = p_dcs_nzd_vertices[i]; - local_degrees[v - major_range_first] = d; + auto major_idx = (major_hypersparse_first - major_range_first) + i; + auto local_degree = offsets[major_idx + 1] - offsets[major_idx]; + if (masks) { + local_degree = static_cast( + detail::count_set_bits(*masks, offsets[major_idx], local_degree)); + } + auto v = dcs_nzd_vertices[i]; + local_degrees[v - major_range_first] = local_degree; }); } minor_comm.reduce(local_degrees.data(), @@ -193,12 +209,21 @@ rmm::device_uvector compute_major_degrees( template rmm::device_uvector compute_major_degrees(raft::handle_t const& handle, edge_t const* offsets, + std::optional masks, vertex_t number_of_vertices) { rmm::device_uvector degrees(number_of_vertices, handle.get_stream()); thrust::tabulate( - handle.get_thrust_policy(), degrees.begin(), degrees.end(), [offsets] __device__(auto i) { - return offsets[i + 1] - offsets[i]; + handle.get_thrust_policy(), + degrees.begin(), + degrees.end(), + [offsets, masks = masks ? thrust::make_optional(*masks) : thrust::nullopt] __device__(auto i) { + auto local_degree = offsets[i + 1] - offsets[i]; + if (masks) { + local_degree = + static_cast(detail::count_set_bits(*masks, offsets[i], local_degree)); + } + return local_degree; }); return degrees; } @@ -512,16 +537,18 @@ rmm::device_uvector graph_view_t>:: compute_in_degrees(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); - if (store_transposed) { return compute_major_degrees(handle, this->edge_partition_offsets_, this->edge_partition_dcs_nzd_vertices_, this->edge_partition_dcs_nzd_vertex_counts_, + this->has_edge_mask() + ? std::make_optional((*(this->edge_mask_view())).value_firsts()) + : std::nullopt, this->partition_, this->edge_partition_segment_offsets_); } else { + CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); return compute_minor_degrees(handle, *this); } } @@ -531,11 +558,15 @@ rmm::device_uvector graph_view_t>:: compute_in_degrees(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); - if (store_transposed) { - return compute_major_degrees(handle, this->offsets_, this->local_vertex_partition_range_size()); + return compute_major_degrees( + handle, + this->offsets_, + this->has_edge_mask() ? std::make_optional((*(this->edge_mask_view())).value_firsts()[0]) + : std::nullopt, + this->local_vertex_partition_range_size()); } else { + CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); return compute_minor_degrees(handle, *this); } } @@ -545,15 +576,17 @@ rmm::device_uvector graph_view_t>:: compute_out_degrees(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); - if (store_transposed) { + CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); return compute_minor_degrees(handle, *this); } else { return compute_major_degrees(handle, this->edge_partition_offsets_, this->edge_partition_dcs_nzd_vertices_, this->edge_partition_dcs_nzd_vertex_counts_, + this->has_edge_mask() + ? std::make_optional((*(this->edge_mask_view())).value_firsts()) + : std::nullopt, this->partition_, this->edge_partition_segment_offsets_); } @@ -564,12 +597,16 @@ rmm::device_uvector graph_view_t>:: compute_out_degrees(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); - if (store_transposed) { + CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); return compute_minor_degrees(handle, *this); } else { - return compute_major_degrees(handle, this->offsets_, this->local_vertex_partition_range_size()); + return compute_major_degrees( + handle, + this->offsets_, + this->has_edge_mask() ? std::make_optional((*(this->edge_mask_view())).value_firsts()[0]) + : std::nullopt, + this->local_vertex_partition_range_size()); } } diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 2a4bb8ab2a5..6775ed2eb16 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -288,10 +288,6 @@ ConfigureTest(GENERATE_RMAT_TEST generators/generate_rmat_test.cpp) ConfigureTest(GENERATE_BIPARTITE_RMAT_TEST generators/generate_bipartite_rmat_test.cpp GPUS 1 PERCENT 100) -################################################################################################### -# - Graph mask tests ------------------------------------------------------------------------------ -ConfigureTest(GRAPH_MASK_TEST structure/graph_mask_test.cpp) - ################################################################################################### # - Symmetrize tests ------------------------------------------------------------------------------ ConfigureTest(SYMMETRIZE_TEST structure/symmetrize_test.cpp) diff --git a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu index a7cd8a989b0..a3edb1f6372 100644 --- a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu +++ b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu @@ -21,9 +21,13 @@ #include #include +#include +#include #include +#include #include +#include #include #include #include @@ -61,6 +65,7 @@ struct intersection_op_t { struct Prims_Usecase { size_t num_vertex_pairs{0}; + bool edge_masking{false}; bool check_correctness{true}; }; @@ -78,7 +83,7 @@ class Tests_MGPerVPairTransformDstNbrIntersection virtual void TearDown() {} // Verify the results of per_v_pair_transform_dst_nbr_intersection primitive - template + template void run_current_test(Prims_Usecase const& prims_usecase, input_usecase_t const& input_usecase) { HighResTimer hr_timer{}; @@ -109,6 +114,34 @@ class Tests_MGPerVPairTransformDstNbrIntersection auto mg_graph_view = mg_graph.view(); + std::optional> edge_mask{std::nullopt}; + if (prims_usecase.edge_masking) { + cugraph::edge_src_property_t edge_src_renumber_map( + *handle_, mg_graph_view); + cugraph::edge_dst_property_t edge_dst_renumber_map( + *handle_, mg_graph_view); + cugraph::update_edge_src_property( + *handle_, mg_graph_view, (*mg_renumber_map).begin(), edge_src_renumber_map); + cugraph::update_edge_dst_property( + *handle_, mg_graph_view, (*mg_renumber_map).begin(), edge_dst_renumber_map); + + edge_mask = cugraph::edge_property_t(*handle_, mg_graph_view); + + cugraph::transform_e( + *handle_, + mg_graph_view, + edge_src_renumber_map.view(), + edge_dst_renumber_map.view(), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_property, auto dst_property, thrust::nullopt_t) { + return ((src_property % 2 == 0) && (dst_property % 2 == 0)) + ? false + : true; // mask out the edges with even unrenumbered src & dst vertex IDs + }, + (*edge_mask).mutable_view()); + mg_graph_view.attach_edge_mask((*edge_mask).view()); + } + // 2. run MG per_v_pair_transform_dst_nbr_intersection primitive ASSERT_TRUE( @@ -224,6 +257,42 @@ class Tests_MGPerVPairTransformDstNbrIntersection if (handle_->get_comms().get_rank() == 0) { auto sg_graph_view = sg_graph.view(); + if (prims_usecase.edge_masking) { + rmm::device_uvector srcs(0, handle_->get_stream()); + rmm::device_uvector dsts(0, handle_->get_stream()); + std::tie(srcs, dsts, std::ignore, std::ignore) = + cugraph::decompress_to_edgelist( + *handle_, sg_graph_view, std::nullopt, std::nullopt, std::nullopt); + auto edge_first = thrust::make_zip_iterator(srcs.begin(), dsts.begin()); + srcs.resize(thrust::distance(edge_first, + thrust::remove_if(handle_->get_thrust_policy(), + edge_first, + edge_first + srcs.size(), + [] __device__(auto pair) { + return (thrust::get<0>(pair) % 2 == 0) && + (thrust::get<1>(pair) % 2 == 0); + })), + handle_->get_stream()); + dsts.resize(srcs.size(), handle_->get_stream()); + rmm::device_uvector vertices(sg_graph_view.number_of_vertices(), + handle_->get_stream()); + thrust::sequence( + handle_->get_thrust_policy(), vertices.begin(), vertices.end(), vertex_t{0}); + std::tie(sg_graph, std::ignore, std::ignore, std::ignore, std::ignore) = cugraph:: + create_graph_from_edgelist( + *handle_, + std::move(vertices), + std::move(srcs), + std::move(dsts), + std::nullopt, + std::nullopt, + std::nullopt, + cugraph::graph_properties_t{sg_graph_view.is_symmetric(), + sg_graph_view.is_multigraph()}, + false); + sg_graph_view = sg_graph.view(); + } + auto sg_result_buffer = cugraph::allocate_dataframe_buffer>( cugraph::size_dataframe_buffer(mg_aggregate_vertex_pair_buffer), handle_->get_stream()); auto sg_out_degrees = sg_graph_view.compute_out_degrees(*handle_); @@ -262,47 +331,16 @@ using Tests_MGPerVPairTransformDstNbrIntersection_File = using Tests_MGPerVPairTransformDstNbrIntersection_Rmat = Tests_MGPerVPairTransformDstNbrIntersection; -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_File, CheckInt32Int32FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>(std::get<0>(param), - std::get<1>(param)); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt64Int64FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_File, CheckInt32Int32Float) { auto param = GetParam(); - run_current_test(std::get<0>(param), std::get<1>(param)); + run_current_test(std::get<0>(param), std::get<1>(param)); } TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } @@ -310,7 +348,7 @@ TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32Float) TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } @@ -318,7 +356,7 @@ TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64Float) TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt64Int64Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } @@ -327,15 +365,18 @@ INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGPerVPairTransformDstNbrIntersection_File, ::testing::Combine( - ::testing::Values(Prims_Usecase{size_t{1024}, true}), + ::testing::Values(Prims_Usecase{size_t{1024}, false, true}, + Prims_Usecase{size_t{1024}, true, true}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx"), cugraph::test::File_Usecase("test/datasets/netscience.mtx")))); -INSTANTIATE_TEST_SUITE_P(rmat_small_test, - Tests_MGPerVPairTransformDstNbrIntersection_Rmat, - ::testing::Combine(::testing::Values(Prims_Usecase{size_t{1024}, true}), - ::testing::Values(cugraph::test::Rmat_Usecase( - 10, 16, 0.57, 0.19, 0.19, 0, false, false)))); +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_MGPerVPairTransformDstNbrIntersection_Rmat, + ::testing::Combine( + ::testing::Values(Prims_Usecase{size_t{1024}, false, true}, + Prims_Usecase{size_t{1024}, true, true}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); INSTANTIATE_TEST_SUITE_P( rmat_benchmark_test, /* note that scale & edge factor can be overridden in benchmarking (with @@ -345,7 +386,8 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGPerVPairTransformDstNbrIntersection_Rmat, ::testing::Combine( - ::testing::Values(Prims_Usecase{size_t{1024 * 1024}, false}), + ::testing::Values(Prims_Usecase{size_t{1024 * 1024}, false, false}, + Prims_Usecase{size_t{1024 * 1024}, true, false}), ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); CUGRAPH_MG_TEST_PROGRAM_MAIN() diff --git a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu index 3b6a6b9c4c5..4d05b0c9e65 100644 --- a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu +++ b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu @@ -50,14 +50,14 @@ template struct intersection_op_t { - __device__ thrust::tuple operator()( + __device__ thrust::tuple operator()( vertex_t a, vertex_t b, weight_t weight_a /* weighted out degree */, weight_t weight_b /* weighted out degree */, raft::device_span intersection, - raft::device_span intersected_properties_a, - raft::device_span intersected_properties_b) const + raft::device_span intersected_property_values_a, + raft::device_span intersected_property_values_b) const { weight_t min_weight_a_intersect_b = weight_t{0}; weight_t max_weight_a_intersect_b = weight_t{0}; @@ -65,10 +65,12 @@ struct intersection_op_t { weight_t sum_of_intersected_b = weight_t{0}; for (size_t k = 0; k < intersection.size(); k++) { - min_weight_a_intersect_b += min(intersected_properties_a[k], intersected_properties_b[k]); - max_weight_a_intersect_b += max(intersected_properties_a[k], intersected_properties_b[k]); - sum_of_intersected_a += intersected_properties_a[k]; - sum_of_intersected_b += intersected_properties_b[k]; + min_weight_a_intersect_b += + min(intersected_property_values_a[k], intersected_property_values_b[k]); + max_weight_a_intersect_b += + max(intersected_property_values_a[k], intersected_property_values_b[k]); + sum_of_intersected_a += intersected_property_values_a[k]; + sum_of_intersected_b += intersected_property_values_b[k]; } weight_t sum_of_uniq_a = weight_a - sum_of_intersected_a; @@ -99,7 +101,7 @@ class Tests_MGPerVPairTransformDstNbrIntersection virtual void TearDown() {} // Verify the results of per_v_pair_transform_dst_nbr_intersection primitive - template + template void run_current_test(Prims_Usecase const& prims_usecase, input_usecase_t const& input_usecase) { HighResTimer hr_timer{}; @@ -189,7 +191,7 @@ class Tests_MGPerVPairTransformDstNbrIntersection auto mg_result_buffer = cugraph::allocate_dataframe_buffer>( cugraph::size_dataframe_buffer(mg_vertex_pair_buffer), handle_->get_stream()); - auto mg_out_degrees = mg_graph_view.compute_out_degrees(*handle_); + auto mg_out_weight_sums = compute_out_weight_sums(*handle_, mg_graph_view, mg_edge_weight_view); if (cugraph::test::g_perf) { RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement @@ -197,9 +199,6 @@ class Tests_MGPerVPairTransformDstNbrIntersection hr_timer.start("MG per_v_pair_transform_dst_nbr_intersection"); } - rmm::device_uvector mg_out_weight_sums = - compute_out_weight_sums(*handle_, mg_graph_view, mg_edge_weight_view); - cugraph::per_v_pair_transform_dst_nbr_intersection( *handle_, mg_graph_view, @@ -272,7 +271,6 @@ class Tests_MGPerVPairTransformDstNbrIntersection if (handle_->get_comms().get_rank() == 0) { auto sg_graph_view = sg_graph.view(); - auto sg_result_buffer = cugraph::allocate_dataframe_buffer>( cugraph::size_dataframe_buffer(mg_aggregate_vertex_pair_buffer), handle_->get_stream()); @@ -290,11 +288,21 @@ class Tests_MGPerVPairTransformDstNbrIntersection */), sg_out_weight_sums.begin(), intersection_op_t{}, cugraph::get_dataframe_buffer_begin(sg_result_buffer)); + auto threshold_ratio = weight_t{1e-4}; + auto threshold_magnitude = std::numeric_limits::min(); + auto nearly_equal = [threshold_ratio, threshold_magnitude] __device__(auto lhs, auto rhs) { + return (fabs(thrust::get<0>(lhs) - thrust::get<0>(rhs)) < + max(max(thrust::get<0>(lhs), thrust::get<0>(rhs)) * threshold_ratio, + threshold_magnitude)) && + (fabs(thrust::get<1>(lhs) - thrust::get<1>(rhs)) < + max(max(thrust::get<1>(lhs), thrust::get<1>(rhs)) * threshold_ratio, + threshold_magnitude)); + }; bool valid = thrust::equal(handle_->get_thrust_policy(), cugraph::get_dataframe_buffer_begin(mg_aggregate_result_buffer), cugraph::get_dataframe_buffer_end(mg_aggregate_result_buffer), - cugraph::get_dataframe_buffer_begin(sg_result_buffer)); - + cugraph::get_dataframe_buffer_begin(sg_result_buffer), + nearly_equal); ASSERT_TRUE(valid); } } @@ -313,47 +321,16 @@ using Tests_MGPerVPairTransformDstNbrIntersection_File = using Tests_MGPerVPairTransformDstNbrIntersection_Rmat = Tests_MGPerVPairTransformDstNbrIntersection; -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_File, CheckInt32Int32FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>(std::get<0>(param), - std::get<1>(param)); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - -TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt64Int64FloatTupleIntFloat) -{ - auto param = GetParam(); - run_current_test>( - std::get<0>(param), - cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); -} - TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_File, CheckInt32Int32Float) { auto param = GetParam(); - run_current_test(std::get<0>(param), std::get<1>(param)); + run_current_test(std::get<0>(param), std::get<1>(param)); } TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } @@ -361,7 +338,7 @@ TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int32Float) TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } @@ -369,7 +346,7 @@ TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt32Int64Float) TEST_P(Tests_MGPerVPairTransformDstNbrIntersection_Rmat, CheckInt64Int64Float) { auto param = GetParam(); - run_current_test( + run_current_test( std::get<0>(param), cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); } diff --git a/cpp/tests/structure/graph_mask_test.cpp b/cpp/tests/structure/graph_mask_test.cpp deleted file mode 100644 index fc704d683a7..00000000000 --- a/cpp/tests/structure/graph_mask_test.cpp +++ /dev/null @@ -1,64 +0,0 @@ -/* - * Copyright (c) 2022, NVIDIA CORPORATION. - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governin_from_mtxg permissions and - * limitations under the License. - */ - -#include -#include -#include - -#include -#include -#include - -#include -#include -#include -#include - -#include - -#include -#include -#include -#include -#include - -TEST(Test_GraphMask, BasicGraphMaskTestInt64) -{ - raft::handle_t handle; - - int number_of_vertices = 500; - int number_of_edges = 1000; - - cugraph::graph_mask_t mask( - handle, number_of_vertices, number_of_edges); - - auto mask_view = mask.view(); - - ASSERT_EQ(false, mask.has_vertex_mask()); - ASSERT_EQ(false, mask.has_edge_mask()); - ASSERT_EQ(false, mask_view.has_vertex_mask()); - ASSERT_EQ(false, mask_view.has_edge_mask()); - - mask.initialize_vertex_mask(); - mask.initialize_edge_mask(); - - auto mask_view2 = mask.view(); - - ASSERT_EQ(true, mask.has_vertex_mask()); - ASSERT_EQ(true, mask.has_edge_mask()); - ASSERT_EQ(true, mask_view2.has_vertex_mask()); - ASSERT_EQ(true, mask_view2.has_edge_mask()); -} \ No newline at end of file From e0c3a9391851528407bc085eeab202e6c260c9f4 Mon Sep 17 00:00:00 2001 From: Andrei Ivanov <32910461+drivanov@users.noreply.github.com> Date: Mon, 30 Oct 2023 06:10:52 -0700 Subject: [PATCH 062/111] Fix an issue occurring in the cuGraph-DGL example for "mixed" mode. (#3927) Fixing the following bug: ``` Training in mixed mode. Loading data Traceback (most recent call last): File "/opt/rapids/cugraph/python/cugraph-dgl/examples/graphsage/node-classification.py", line 249, in g.get_node_storage(key="feat", ntype="_N") File "/usr/local/lib/python3.10/dist-packages/dgl/frame.py", line 530, in fetch return super().fetch(indices, device, pin_memory=pin_memory, **kwargs) File "/usr/local/lib/python3.10/dist-packages/dgl/storages/pytorch_tensor.py", line 40, in fetch raise ValueError( ValueError: Got indices on device cuda:0 whereas the feature tensor is on cpu. Please either (1) move the graph to GPU with to() method, or (2) pin the graph with pin_memory_() method. ``` which appears in `mixed` mode. NOTE: the option `(1) move the graph to GPU with to()` is not available because in mixed mode the graph must be on the CPU. Authors: - Andrei Ivanov (https://github.com/drivanov) - Brad Rees (https://github.com/BradReesWork) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3927 --- python/cugraph-dgl/examples/graphsage/node-classification.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/python/cugraph-dgl/examples/graphsage/node-classification.py b/python/cugraph-dgl/examples/graphsage/node-classification.py index 320890b0312..539fd86d136 100644 --- a/python/cugraph-dgl/examples/graphsage/node-classification.py +++ b/python/cugraph-dgl/examples/graphsage/node-classification.py @@ -243,7 +243,9 @@ def train(args, device, g, dataset, model): else: g = g.to("cuda" if args.mode == "gpu_dgl" else "cpu") - device = torch.device("cpu" if args.mode == "cpu" else "cuda") + device = torch.device( + "cpu" if args.mode == "cpu" or args.mode == "mixed" else "cuda" + ) # create GraphSAGE model feat_shape = ( From 975502209991c1abe7b1878585fec9e1978f5b07 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 30 Oct 2023 14:01:25 -0500 Subject: [PATCH 063/111] nx-cugraph: handle seed argument in edge_betweenness_centrality (#3943) CC @rlratzel who brought this to my attention and can test this fix Authors: - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3943 --- .../nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py index 104ac87414c..210e1f0a2b2 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py @@ -53,6 +53,7 @@ def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=No raise NotImplementedError( "Weighted implementation of betweenness centrality not currently supported" ) + seed = _seed_to_int(seed) G = _to_graph(G, weight) src_ids, dst_ids, values, _edge_ids = plc.edge_betweenness_centrality( resource_handle=plc.ResourceHandle(), From 35875a84b95e9d8ba9c691f33521c45845d3e084 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Tue, 31 Oct 2023 09:01:07 -0400 Subject: [PATCH 064/111] Errors compiling for DLFW on CUDA 12.3 (#3952) DLFW compilation found a few issues building cugraph 23.10. This PR addresses these issues. Two main issues: * We were brace initializing `raft::device_span` objects with only `nullptr`. The device span already defaults to `nullptr`, and the class hierarchy doesn't support just passing `nullptr`. * We were brace initializing the `raft::random::DeviceState`, but there's no default constructor. Changed it to not brace initialize and require a reference to make it clear that you must initialize the DeviceState. Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Robert Maynard (https://github.com/robertmaynard) URL: https://github.com/rapidsai/cugraph/pull/3952 --- cpp/src/community/detail/refine_impl.cuh | 2 +- cpp/src/prims/detail/nbr_intersection.cuh | 25 +++++++++++------------ 2 files changed, 13 insertions(+), 14 deletions(-) diff --git a/cpp/src/community/detail/refine_impl.cuh b/cpp/src/community/detail/refine_impl.cuh index e811aafc776..6b6470991bb 100644 --- a/cpp/src/community/detail/refine_impl.cuh +++ b/cpp/src/community/detail/refine_impl.cuh @@ -64,7 +64,7 @@ struct leiden_key_aggregated_edge_op_t { weight_t total_edge_weight{}; weight_t resolution{}; // resolution parameter weight_t theta{}; // scaling factor - raft::random::DeviceState device_state{}; + raft::random::DeviceState& device_state; __device__ auto operator()( vertex_t src, vertex_t neighboring_leiden_cluster, diff --git a/cpp/src/prims/detail/nbr_intersection.cuh b/cpp/src/prims/detail/nbr_intersection.cuh index 32247ca3466..cefc1836fa6 100644 --- a/cpp/src/prims/detail/nbr_intersection.cuh +++ b/cpp/src/prims/detail/nbr_intersection.cuh @@ -138,7 +138,7 @@ struct update_rx_major_local_degree_t { size_t local_edge_partition_idx{}; raft::device_span rx_reordered_group_lasts{}; - raft::device_span rx_group_firsts{nullptr}; + raft::device_span rx_group_firsts{}; raft::device_span rx_majors{}; raft::device_span local_degrees_for_rx_majors{}; @@ -200,7 +200,7 @@ struct update_rx_major_local_nbrs_t { size_t local_edge_partition_idx{}; raft::device_span rx_reordered_group_lasts{}; - raft::device_span rx_group_firsts{nullptr}; + raft::device_span rx_group_firsts{}; raft::device_span rx_majors{}; raft::device_span local_nbr_offsets_for_rx_majors{}; raft::device_span local_nbrs_for_rx_majors{}; @@ -311,10 +311,10 @@ template struct pick_min_degree_t { FirstElementToIdxMap first_element_to_idx_map{}; - raft::device_span first_element_offsets{nullptr}; + raft::device_span first_element_offsets{}; SecondElementToIdxMap second_element_to_idx_map{}; - raft::device_span second_element_offsets{nullptr}; + raft::device_span second_element_offsets{}; edge_partition_device_view_t edge_partition{}; thrust::optional> @@ -473,12 +473,12 @@ template first_element_offsets{}; - raft::device_span first_element_indices{nullptr}; + raft::device_span first_element_indices{}; optional_property_buffer_view_t first_element_edge_property_values{}; SecondElementToIdxMap second_element_to_idx_map{}; raft::device_span second_element_offsets{}; - raft::device_span second_element_indices{nullptr}; + raft::device_span second_element_indices{}; optional_property_buffer_view_t second_element_edge_property_values{}; edge_partition_device_view_t edge_partition{}; @@ -487,8 +487,8 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t { edge_partition_e_mask{}; VertexPairIterator vertex_pair_first; - raft::device_span nbr_intersection_offsets{nullptr}; - raft::device_span nbr_intersection_indices{nullptr}; + raft::device_span nbr_intersection_offsets{}; + raft::device_span nbr_intersection_indices{}; optional_property_buffer_mutable_view_t nbr_intersection_e_property_values0{}; optional_property_buffer_mutable_view_t nbr_intersection_e_property_values1{}; @@ -499,12 +499,11 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t { using edge_property_value_t = typename edge_partition_e_input_device_view_t::value_type; auto pair = *(vertex_pair_first + i); - - vertex_t const* indices0{nullptr}; + vertex_t const* indices0{}; std::conditional_t, edge_property_value_t const*, void*> - edge_property_values0{nullptr}; + edge_property_values0{}; edge_t local_edge_offset0{0}; edge_t local_degree0{0}; @@ -548,11 +547,11 @@ struct copy_intersecting_nbrs_and_update_intersection_size_t { local_degree0 = static_cast(first_element_offsets[idx + 1] - local_edge_offset0); } - vertex_t const* indices1{nullptr}; + vertex_t const* indices1{}; std::conditional_t, edge_property_value_t const*, void*> - edge_property_values1{nullptr}; + edge_property_values1{}; edge_t local_edge_offset1{0}; edge_t local_degree1{0}; From d6c7fa131ea5bb901ca4fde3094a29b4681b77bd Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 31 Oct 2023 14:10:20 -0500 Subject: [PATCH 065/111] Add many graph generators to nx-cugraph (#3954) Also, better handle dtypes for edge values passed to pylibcugraph, which only takes float32 and float64 atm. I also defined `index_dtype` (currently int32) to globally control the dtype of indices. Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3954 --- python/nx-cugraph/_nx_cugraph/__init__.py | 45 ++ python/nx-cugraph/lint.yaml | 8 +- python/nx-cugraph/nx_cugraph/__init__.py | 8 +- .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/bipartite/__init__.py | 13 + .../algorithms/bipartite/generators.py | 62 ++ .../nx-cugraph/nx_cugraph/algorithms/core.py | 11 +- python/nx-cugraph/nx_cugraph/classes/graph.py | 188 +++++- .../nx_cugraph/classes/multidigraph.py | 5 + .../nx_cugraph/classes/multigraph.py | 74 ++- python/nx-cugraph/nx_cugraph/convert.py | 78 ++- .../nx-cugraph/nx_cugraph/convert_matrix.py | 146 ++++ .../nx_cugraph/generators/__init__.py | 16 + .../nx_cugraph/generators/_utils.py | 136 ++++ .../nx_cugraph/generators/classic.py | 423 ++++++++++++ .../nx_cugraph/generators/community.py | 45 ++ .../nx-cugraph/nx_cugraph/generators/small.py | 622 ++++++++++++++++++ .../nx_cugraph/generators/social.py | 294 +++++++++ python/nx-cugraph/nx_cugraph/interface.py | 35 + .../nx_cugraph/tests/test_generators.py | 277 ++++++++ .../nx-cugraph/nx_cugraph/tests/test_utils.py | 87 +++ python/nx-cugraph/nx_cugraph/typing.py | 8 + python/nx-cugraph/nx_cugraph/utils/misc.py | 72 +- python/nx-cugraph/pyproject.toml | 3 +- 24 files changed, 2573 insertions(+), 86 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py create mode 100644 python/nx-cugraph/nx_cugraph/convert_matrix.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/_utils.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/classic.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/community.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/small.py create mode 100644 python/nx-cugraph/nx_cugraph/generators/social.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_generators.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_utils.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 965b5b232ab..af1df04644c 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -29,23 +29,68 @@ # "description": "TODO", "functions": { # BEGIN: functions + "barbell_graph", "betweenness_centrality", + "bull_graph", + "caveman_graph", + "chvatal_graph", + "circular_ladder_graph", + "complete_bipartite_graph", + "complete_graph", + "complete_multipartite_graph", + "cubical_graph", + "cycle_graph", + "davis_southern_women_graph", "degree_centrality", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", "edge_betweenness_centrality", + "empty_graph", + "florentine_families_graph", + "from_pandas_edgelist", + "from_scipy_sparse_array", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", "in_degree_centrality", "is_isolate", "isolates", "k_truss", + "karate_club_graph", + "krackhardt_kite_graph", + "ladder_graph", + "les_miserables_graph", + "lollipop_graph", "louvain_communities", + "moebius_kantor_graph", + "null_graph", "number_of_isolates", "number_of_selfloops", + "octahedral_graph", "out_degree_centrality", + "pappus_graph", + "path_graph", + "petersen_graph", + "sedgewick_maze_graph", + "star_graph", + "tadpole_graph", + "tetrahedral_graph", + "trivial_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "turan_graph", + "tutte_graph", + "wheel_graph", # END: functions }, "extra_docstrings": { # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index fef2cebc7f5..01a806e6162 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -45,12 +45,12 @@ repos: - id: pyupgrade args: [--py39-plus] - repo: https://github.com/psf/black - rev: 23.10.0 + rev: 23.10.1 hooks: - id: black # - id: black-jupyter - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.1 + rev: v0.1.3 hooks: - id: ruff args: [--fix-only, --show-fixes] @@ -58,7 +58,7 @@ repos: rev: 6.1.0 hooks: - id: flake8 - args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501'] # Why is this necessary? + args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105'] # Why is this necessary? additional_dependencies: &flake8_dependencies # These versions need updated manually - flake8==6.1.0 @@ -77,7 +77,7 @@ repos: additional_dependencies: [tomli] files: ^(nx_cugraph|docs)/ - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.1 + rev: v0.1.3 hooks: - id: ruff - repo: https://github.com/pre-commit/pre-commit-hooks diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 1eaf3ec6bbb..25d44212264 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -20,11 +20,11 @@ from . import convert from .convert import * -# from . import convert_matrix -# from .convert_matrix import * +from . import convert_matrix +from .convert_matrix import * -# from . import generators -# from .generators import * +from . import generators +from .generators import * from . import algorithms from .algorithms import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 22600bfdc2d..69feb8f6437 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,7 +10,8 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import centrality, community +from . import bipartite, centrality, community +from .bipartite import complete_bipartite_graph from .centrality import * from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py new file mode 100644 index 00000000000..062be973d55 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .generators import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py new file mode 100644 index 00000000000..1d3e762b4fd --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py @@ -0,0 +1,62 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from numbers import Integral + +import cupy as cp +import networkx as nx +import numpy as np + +from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes +from nx_cugraph.utils import index_dtype, networkx_algorithm, nodes_or_number + +__all__ = [ + "complete_bipartite_graph", +] + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def complete_bipartite_graph(n1, n2, create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + orig_n1, unused_nodes1 = n1 + orig_n2, unused_nodes2 = n2 + n1, nodes1 = _number_and_nodes(n1) + n2, nodes2 = _number_and_nodes(n2) + all_indices = cp.indices((n1, n2), dtype=index_dtype) + indices0 = all_indices[0].ravel() + indices1 = all_indices[1].ravel() + n1 + del all_indices + src_indices = cp.hstack((indices0, indices1)) + dst_indices = cp.hstack((indices1, indices0)) + bipartite = cp.zeros(n1 + n2, np.int8) + bipartite[n1:] = 1 + if isinstance(orig_n1, Integral) and isinstance(orig_n2, Integral): + nodes = None + else: + nodes = list(range(n1)) if nodes1 is None else nodes1 + nodes.extend(range(n2) if nodes2 is None else nodes2) + if len(set(nodes)) != len(nodes): + raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes") + G = graph_class.from_coo( + n1 + n2, + src_indices, + dst_indices, + node_values={"bipartite": bipartite}, + id_to_key=nodes, + name=f"complete_bipartite_graph({orig_n1}, {orig_n2})", + ) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 0a64dd71c69..33e79793553 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -12,11 +12,10 @@ # limitations under the License. import cupy as cp import networkx as nx -import numpy as np import pylibcugraph as plc import nx_cugraph as nxcg -from nx_cugraph.utils import networkx_algorithm, not_implemented_for +from nx_cugraph.utils import _get_int_dtype, networkx_algorithm, not_implemented_for __all__ = ["k_truss"] @@ -51,8 +50,8 @@ def k_truss(G, k): edge_values = {key: val.copy() for key, val in G.edge_values.items()} edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} else: - # int dtype for edge_indices would be preferred - edge_indices = cp.arange(G.src_indices.size, dtype=np.float64) + edge_dtype = _get_int_dtype(G.src_indices.size - 1) + edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype) src_indices, dst_indices, edge_indices, _ = plc.k_truss_subgraph( resource_handle=plc.ResourceHandle(), graph=G._get_plc_graph(edge_array=edge_indices), @@ -62,7 +61,9 @@ def k_truss(G, k): # Renumber step 0: node indices node_indices = cp.unique(cp.concatenate([src_indices, dst_indices])) # Renumber step 1: edge values - edge_indices = edge_indices.astype(np.int64) + if edge_indices.dtype != edge_dtype: + # The returned edge_indices may have different dtype (and float) + edge_indices = edge_indices.astype(edge_dtype) edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()} edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()} # Renumber step 2: edge indices diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 166b6b9dc6b..2048c4c3d72 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -14,7 +14,7 @@ import operator as op from copy import deepcopy -from typing import TYPE_CHECKING, ClassVar +from typing import TYPE_CHECKING import cupy as cp import networkx as nx @@ -23,8 +23,11 @@ import nx_cugraph as nxcg +from ..utils import index_dtype + if TYPE_CHECKING: # pragma: no cover from collections.abc import Iterable, Iterator + from typing import ClassVar from nx_cugraph.typing import ( AttrKey, @@ -34,6 +37,7 @@ IndexValue, NodeKey, NodeValue, + any_ndarray, ) __all__ = ["Graph"] @@ -51,17 +55,38 @@ class Graph: graph_attr_dict_factory: ClassVar[type] = dict # Not networkx properties - # We store edge data in COO format with {row,col}_indices and edge_values. + # We store edge data in COO format with {src,dst}_indices and edge_values. src_indices: cp.ndarray[IndexValue] dst_indices: cp.ndarray[IndexValue] edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] edge_masks: dict[AttrKey, cp.ndarray[bool]] - node_values: dict[AttrKey, cp.ndarray[NodeValue]] - node_masks: dict[AttrKey, cp.ndarray[bool]] + node_values: dict[AttrKey, any_ndarray[NodeValue]] + node_masks: dict[AttrKey, any_ndarray[bool]] key_to_id: dict[NodeKey, IndexValue] | None _id_to_key: list[NodeKey] | None _N: int + # Used by graph._get_plc_graph + _plc_type_map: ClassVar[dict[np.dtype, np.dtype]] = { + # signed int + np.dtype(np.int8): np.dtype(np.float32), + np.dtype(np.int16): np.dtype(np.float32), + np.dtype(np.int32): np.dtype(np.float64), + np.dtype(np.int64): np.dtype(np.float64), # raise if abs(x) > 2**53 + # unsigned int + np.dtype(np.uint8): np.dtype(np.float32), + np.dtype(np.uint16): np.dtype(np.float32), + np.dtype(np.uint32): np.dtype(np.float64), + np.dtype(np.uint64): np.dtype(np.float64), # raise if x > 2**53 + # other + np.dtype(np.bool_): np.dtype(np.float16), + np.dtype(np.float16): np.dtype(np.float32), + } + _plc_allowed_edge_types: ClassVar[set[np.dtype]] = { + np.dtype(np.float32), + np.dtype(np.float64), + } + #################### # Creation methods # #################### @@ -74,8 +99,8 @@ def from_coo( dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -111,6 +136,27 @@ def from_coo( raise ValueError if new_graph._id_to_key is not None and len(new_graph._id_to_key) != N: raise ValueError + if new_graph._id_to_key is not None and new_graph.key_to_id is None: + try: + new_graph.key_to_id = dict(zip(new_graph._id_to_key, range(N))) + except TypeError as exc: + raise ValueError("Bad type of a node value") from exc + if new_graph.src_indices.dtype != index_dtype: + src_indices = new_graph.src_indices.astype(index_dtype) + if not (new_graph.src_indices == src_indices).all(): + raise ValueError( + f"Unable to convert src_indices to {src_indices.dtype.name} " + f"(got {new_graph.src_indices.dtype.name})." + ) + new_graph.src_indices = src_indices + if new_graph.dst_indices.dtype != index_dtype: + dst_indices = new_graph.dst_indices.astype(index_dtype) + if not (new_graph.dst_indices == dst_indices).all(): + raise ValueError( + f"Unable to convert dst_indices to {dst_indices.dtype.name} " + f"(got {new_graph.dst_indices.dtype.name})." + ) + new_graph.dst_indices = dst_indices return new_graph @classmethod @@ -120,8 +166,8 @@ def from_csr( dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -130,7 +176,7 @@ def from_csr( N = indptr.size - 1 src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -152,8 +198,8 @@ def from_csc( src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -162,7 +208,7 @@ def from_csc( N = indptr.size - 1 dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -181,13 +227,13 @@ def from_csc( def from_dcsr( cls, N: int, - compressed_rows: cp.ndarray[IndexValue], + compressed_srcs: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], dst_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -195,7 +241,7 @@ def from_dcsr( ) -> Graph: src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + np.repeat(compressed_srcs.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -214,13 +260,13 @@ def from_dcsr( def from_dcsc( cls, N: int, - compressed_cols: cp.ndarray[IndexValue], + compressed_dsts: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], src_indices: cp.ndarray[IndexValue], edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -228,7 +274,7 @@ def from_dcsc( ) -> Graph: dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + np.repeat(compressed_dsts.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -245,7 +291,9 @@ def from_dcsc( def __new__(cls, incoming_graph_data=None, **attr) -> Graph: if incoming_graph_data is None: - new_graph = cls.from_coo(0, cp.empty(0, np.int32), cp.empty(0, np.int32)) + new_graph = cls.from_coo( + 0, cp.empty(0, index_dtype), cp.empty(0, index_dtype) + ) elif incoming_graph_data.__class__ is cls: new_graph = incoming_graph_data.copy() elif incoming_graph_data.__class__ is cls.to_networkx_class(): @@ -336,6 +384,17 @@ def __len__(self) -> int: # NetworkX graph methods # ########################## + @networkx_api + def add_nodes_from(self, nodes_for_adding: Iterable[NodeKey], **attr) -> None: + if self._N != 0: + raise NotImplementedError( + "add_nodes_from is not implemented for graph that already has nodes." + ) + G = self.to_networkx_class()() + G.add_nodes_from(nodes_for_adding, **attr) + G = nxcg.from_networkx(G, preserve_node_attrs=True) + self._become(G) + @networkx_api def clear(self) -> None: self.edge_values.clear() @@ -522,11 +581,38 @@ def _get_plc_graph( # Mask is all True; don't need anymore del self.edge_masks[edge_attr] edge_array = self.edge_values[edge_attr] + if edge_array is not None: + if edge_dtype is not None: + edge_dtype = np.dtype(edge_dtype) + if edge_array.dtype != edge_dtype: + edge_array = edge_array.astype(edge_dtype) + # PLC doesn't handle int edge weights right now, so cast int to float + if edge_array.dtype in self._plc_type_map: + if edge_array.dtype == np.int64: + if (val := edge_array.max().tolist()) > 2**53: + raise ValueError( + f"Integer value of value is too large (> 2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + if (val := edge_array.min().tolist()) < -(2**53): + raise ValueError( + f"Integer value of value is small large (< -2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + elif ( + edge_array.dtype == np.uint64 + and edge_array.max().tolist() > 2**53 + ): + raise ValueError( + f"Integer value of value is too large (> 2**53): {val}; " + "pylibcugraph only supports float16 and float32 dtypes." + ) + # Consider warning here if we add algorithms that may + # introduce roundoff errors when using floats as ints. + edge_array = edge_array.astype(self._plc_type_map[edge_array.dtype]) + elif edge_array.dtype not in self._plc_allowed_edge_types: + raise TypeError(edge_array.dtype) # Should we cache PLC graph? - if edge_dtype is not None: - edge_dtype = np.dtype(edge_dtype) - if edge_array.dtype != edge_dtype: - edge_array = edge_array.astype(edge_dtype) return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( @@ -541,12 +627,60 @@ def _get_plc_graph( do_expensive_check=False, ) + def _sort_edge_indices(self, primary="src"): + # DRY warning: see also MultiGraph._sort_edge_indices + if primary == "src": + stacked = cp.vstack((self.dst_indices, self.src_indices)) + elif primary == "dst": + stacked = cp.vstack((self.src_indices, self.dst_indices)) + else: + raise ValueError( + f'Bad `primary` argument; expected "src" or "dst", got {primary!r}' + ) + indices = cp.lexsort(stacked) + if (cp.diff(indices) > 0).all(): + # Already sorted + return + self.src_indices = self.src_indices[indices] + self.dst_indices = self.dst_indices[indices] + self.edge_values.update( + {key: val[indices] for key, val in self.edge_values.items()} + ) + self.edge_masks.update( + {key: val[indices] for key, val in self.edge_masks.items()} + ) + + def _become(self, other: Graph): + if self.__class__ is not other.__class__: + raise TypeError( + "Attempting to update graph inplace with graph of different type!" + ) + self.clear() + edge_values = self.edge_values + edge_masks = self.edge_masks + node_values = self.node_values + node_masks = self.node_masks + graph = self.graph + edge_values.update(other.edge_values) + edge_masks.update(other.edge_masks) + node_values.update(other.node_values) + node_masks.update(other.node_masks) + graph.update(other.graph) + self.__dict__.update(other.__dict__) + self.edge_values = edge_values + self.edge_masks = edge_masks + self.node_values = node_values + self.node_masks = node_masks + self.graph = graph + return self + def _degrees_array(self): degrees = cp.bincount(self.src_indices, minlength=self._N) if self.is_directed(): degrees += cp.bincount(self.dst_indices, minlength=self._N) return degrees + # Data conversions def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" if (id_to_key := self.id_to_key) is not None: @@ -567,7 +701,7 @@ def _nodearray_to_dict( return dict(it) def _nodearrays_to_dict( - self, node_ids: cp.ndarray[IndexValue], values: cp.ndarray[NodeValue] + self, node_ids: cp.ndarray[IndexValue], values: any_ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: it = zip(node_ids.tolist(), values.tolist()) if (id_to_key := self.id_to_key) is not None: @@ -597,7 +731,7 @@ def _dict_to_nodearrays( indices_iter = d else: indices_iter = map(self.key_to_id.__getitem__, d) - node_ids = cp.fromiter(indices_iter, np.int32) + node_ids = cp.fromiter(indices_iter, index_dtype) if dtype is None: values = cp.array(list(d.values())) else: diff --git a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py index 5629e2c9c06..2c7bfc00752 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multidigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multidigraph.py @@ -25,6 +25,11 @@ class MultiDiGraph(MultiGraph, DiGraph): + @classmethod + @networkx_api + def is_directed(cls) -> bool: + return True + @classmethod def to_networkx_class(cls) -> type[nx.MultiDiGraph]: return nx.MultiDiGraph diff --git a/python/nx-cugraph/nx_cugraph/classes/multigraph.py b/python/nx-cugraph/nx_cugraph/classes/multigraph.py index 3d90861a328..23466dc7dd4 100644 --- a/python/nx-cugraph/nx_cugraph/classes/multigraph.py +++ b/python/nx-cugraph/nx_cugraph/classes/multigraph.py @@ -21,6 +21,7 @@ import nx_cugraph as nxcg +from ..utils import index_dtype from .graph import Graph if TYPE_CHECKING: @@ -31,6 +32,7 @@ IndexValue, NodeKey, NodeValue, + any_ndarray, ) __all__ = ["MultiGraph"] @@ -43,11 +45,11 @@ class MultiGraph(Graph): # Not networkx properties - # In a MultiGraph, each edge has a unique `(row, col, key)` key. + # In a MultiGraph, each edge has a unique `(src, dst, key)` key. # By default, `key` is 0 if possible, else 1, else 2, etc. # This key can be any hashable Python object in NetworkX. # We don't use a dict for our data structure here, because - # that would require a `(row, col, key)` key. + # that would require a `(src, dst, key)` key. # Instead, we keep `edge_keys` and/or `edge_indices`. # `edge_keys` is the list of Python objects for each edge. # `edge_indices` is for the common case of default multiedge keys, @@ -72,8 +74,8 @@ def from_coo( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -110,8 +112,8 @@ def from_csr( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -121,7 +123,7 @@ def from_csr( N = indptr.size - 1 src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -146,8 +148,8 @@ def from_csc( edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -157,7 +159,7 @@ def from_csc( N = indptr.size - 1 dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(np.arange(N, dtype=np.int32), cp.diff(indptr).get()) + np.repeat(np.arange(N, dtype=index_dtype), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -178,14 +180,14 @@ def from_csc( def from_dcsr( cls, N: int, - compressed_rows: cp.ndarray[IndexValue], + compressed_srcs: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], dst_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -194,7 +196,7 @@ def from_dcsr( ) -> MultiGraph: src_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_rows.get(), cp.diff(indptr).get()) + np.repeat(compressed_srcs.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -215,14 +217,14 @@ def from_dcsr( def from_dcsc( cls, N: int, - compressed_cols: cp.ndarray[IndexValue], + compressed_dsts: cp.ndarray[IndexValue], indptr: cp.ndarray[IndexValue], src_indices: cp.ndarray[IndexValue], edge_indices: cp.ndarray[IndexValue] | None = None, edge_values: dict[AttrKey, cp.ndarray[EdgeValue]] | None = None, edge_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, - node_values: dict[AttrKey, cp.ndarray[NodeValue]] | None = None, - node_masks: dict[AttrKey, cp.ndarray[bool]] | None = None, + node_values: dict[AttrKey, any_ndarray[NodeValue]] | None = None, + node_masks: dict[AttrKey, any_ndarray[bool]] | None = None, *, key_to_id: dict[NodeKey, IndexValue] | None = None, id_to_key: list[NodeKey] | None = None, @@ -231,7 +233,7 @@ def from_dcsc( ) -> Graph: dst_indices = cp.array( # cp.repeat is slow to use here, so use numpy instead - np.repeat(compressed_cols.get(), cp.diff(indptr).get()) + np.repeat(compressed_dsts.get(), cp.diff(indptr).get()) ) return cls.from_coo( N, @@ -449,3 +451,39 @@ def _copy(self, as_view: bool, cls: type[Graph], reverse: bool = False): else: rv.graph.update(deepcopy(self.graph)) return rv + + def _sort_edge_indices(self, primary="src"): + # DRY warning: see also Graph._sort_edge_indices + if self.edge_indices is None and self.edge_keys is None: + return super()._sort_edge_indices(primary=primary) + if primary == "src": + if self.edge_indices is None: + stacked = (self.dst_indices, self.src_indices) + else: + stacked = (self.edge_indices, self.dst_indices, self.src_indices) + elif primary == "dst": + if self.edge_indices is None: + stacked = (self.src_indices, self.dst_indices) + else: + stacked = (self.edge_indices, self.dst_indices, self.src_indices) + else: + raise ValueError( + f'Bad `primary` argument; expected "src" or "dst", got {primary!r}' + ) + indices = cp.lexsort(cp.vstack(stacked)) + if (cp.diff(indices) > 0).all(): + # Already sorted + return + self.src_indices = self.src_indices[indices] + self.dst_indices = self.dst_indices[indices] + self.edge_values.update( + {key: val[indices] for key, val in self.edge_values.items()} + ) + self.edge_masks.update( + {key: val[indices] for key, val in self.edge_masks.items()} + ) + if self.edge_indices is not None: + self.edge_indices = self.edge_indices[indices] + if self.edge_keys is not None: + edge_keys = self.edge_keys + self.edge_keys = [edge_keys[i] for i in indices.tolist()] diff --git a/python/nx-cugraph/nx_cugraph/convert.py b/python/nx-cugraph/nx_cugraph/convert.py index d117c8e5c03..3c0814370d3 100644 --- a/python/nx-cugraph/nx_cugraph/convert.py +++ b/python/nx-cugraph/nx_cugraph/convert.py @@ -24,8 +24,10 @@ import nx_cugraph as nxcg +from .utils import index_dtype + if TYPE_CHECKING: # pragma: no cover - from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue + from nx_cugraph.typing import AttrKey, Dtype, EdgeValue, NodeValue, any_ndarray __all__ = [ "from_networkx", @@ -256,7 +258,7 @@ def from_networkx( node_attrs[attr] = REQUIRED key_to_id = dict(zip(adj, range(N))) - col_iter = concat(adj.values()) + dst_iter = concat(adj.values()) try: no_renumber = all(k == v for k, v in key_to_id.items()) except Exception: @@ -264,11 +266,11 @@ def from_networkx( if no_renumber: key_to_id = None else: - col_iter = map(key_to_id.__getitem__, col_iter) + dst_iter = map(key_to_id.__getitem__, dst_iter) if graph.is_multigraph(): - dst_indices = np.fromiter(col_iter, np.int32) + dst_indices = np.fromiter(dst_iter, index_dtype) num_multiedges = np.fromiter( - map(len, concat(map(dict.values, adj.values()))), np.int32 + map(len, concat(map(dict.values, adj.values()))), index_dtype ) # cp.repeat is slow to use here, so use numpy instead dst_indices = cp.array(np.repeat(dst_indices, num_multiedges)) @@ -276,12 +278,12 @@ def from_networkx( edge_keys = list(concat(concat(map(dict.values, adj.values())))) edge_indices = cp.fromiter( concat(map(range, map(len, concat(map(dict.values, adj.values()))))), - np.int32, + index_dtype, ) if edge_keys == edge_indices.tolist(): edge_keys = None # Prefer edge_indices else: - dst_indices = cp.fromiter(col_iter, np.int32) + dst_indices = cp.fromiter(dst_iter, index_dtype) edge_values = {} edge_masks = {} @@ -354,7 +356,8 @@ def from_networkx( # cp.repeat is slow to use here, so use numpy instead src_indices = np.repeat( - np.arange(N, dtype=np.int32), np.fromiter(map(len, adj.values()), np.int32) + np.arange(N, dtype=index_dtype), + np.fromiter(map(len, adj.values()), index_dtype), ) if graph.is_multigraph(): src_indices = np.repeat(src_indices, num_multiedges) @@ -383,8 +386,18 @@ def from_networkx( or present for node_id in adj ) - node_masks[node_attr] = cp.fromiter(iter_mask, bool) - node_values[node_attr] = cp.array(vals, dtype) + # Node values may be numpy or cupy arrays (useful for str, object, etc). + # Someday we'll let the user choose np or cp, and support edge values. + node_mask = np.fromiter(iter_mask, bool) + node_value = np.array(vals, dtype) + try: + node_value = cp.array(node_value) + except ValueError: + pass + else: + node_mask = cp.array(node_mask) + node_values[node_attr] = node_value + node_masks[node_attr] = node_mask # if vals.ndim > 1: ... else: if node_default is REQUIRED: @@ -393,10 +406,17 @@ def from_networkx( iter_values = ( nodes[node_id].get(node_attr, node_default) for node_id in adj ) + # Node values may be numpy or cupy arrays (useful for str, object, etc). + # Someday we'll let the user choose np or cp, and support edge values. if dtype is None: - node_values[node_attr] = cp.array(list(iter_values)) + node_value = np.array(list(iter_values)) else: - node_values[node_attr] = cp.fromiter(iter_values, dtype) + node_value = np.fromiter(iter_values, dtype) + try: + node_value = cp.array(node_value) + except ValueError: + pass + node_values[node_attr] = node_value # if vals.ndim > 1: ... if graph.is_multigraph(): if graph.is_directed() or as_directed: @@ -436,8 +456,8 @@ def from_networkx( def _iter_attr_dicts( - values: dict[AttrKey, cp.ndarray[EdgeValue | NodeValue]], - masks: dict[AttrKey, cp.ndarray[bool]], + values: dict[AttrKey, any_ndarray[EdgeValue | NodeValue]], + masks: dict[AttrKey, any_ndarray[bool]], ): full_attrs = list(values.keys() - masks.keys()) if full_attrs: @@ -463,7 +483,7 @@ def _iter_attr_dicts( return full_dicts -def to_networkx(G: nxcg.Graph) -> nx.Graph: +def to_networkx(G: nxcg.Graph, *, sort_edges: bool = False) -> nx.Graph: """Convert a nx_cugraph graph to networkx graph. All edge and node attributes and ``G.graph`` properties are converted. @@ -471,6 +491,11 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: Parameters ---------- G : nx_cugraph.Graph + sort_edges : bool, default False + Whether to sort the edge data of the input graph by (src, dst) indices + before converting. This can be useful to convert to networkx graphs + that iterate over edges consistently since edges are stored in dicts + in the order they were added. Returns ------- @@ -482,6 +507,8 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: """ rv = G.to_networkx_class()() id_to_key = G.id_to_key + if sort_edges: + G._sort_edge_indices() node_values = G.node_values node_masks = G.node_masks @@ -500,19 +527,20 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: dst_indices = G.dst_indices edge_values = G.edge_values edge_masks = G.edge_masks - if edge_values and not G.is_directed(): + if not G.is_directed(): # Only add upper triangle of the adjacency matrix so we don't double-add edges mask = src_indices <= dst_indices src_indices = src_indices[mask] dst_indices = dst_indices[mask] - edge_values = {k: v[mask] for k, v in edge_values.items()} + if edge_values: + edge_values = {k: v[mask] for k, v in edge_values.items()} if edge_masks: edge_masks = {k: v[mask] for k, v in edge_masks.items()} - src_indices = row_iter = src_indices.tolist() - dst_indices = col_iter = dst_indices.tolist() + src_indices = src_iter = src_indices.tolist() + dst_indices = dst_iter = dst_indices.tolist() if id_to_key is not None: - row_iter = map(id_to_key.__getitem__, src_indices) - col_iter = map(id_to_key.__getitem__, dst_indices) + src_iter = map(id_to_key.__getitem__, src_indices) + dst_iter = map(id_to_key.__getitem__, dst_indices) if G.is_multigraph() and (G.edge_keys is not None or G.edge_indices is not None): if G.edge_keys is not None: edge_keys = G.edge_keys @@ -520,14 +548,14 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph: edge_keys = G.edge_indices.tolist() if edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) - rv.add_edges_from(zip(row_iter, col_iter, edge_keys, full_edge_dicts)) + rv.add_edges_from(zip(src_iter, dst_iter, edge_keys, full_edge_dicts)) else: - rv.add_edges_from(zip(row_iter, col_iter, edge_keys)) + rv.add_edges_from(zip(src_iter, dst_iter, edge_keys)) elif edge_values: full_edge_dicts = _iter_attr_dicts(edge_values, edge_masks) - rv.add_edges_from(zip(row_iter, col_iter, full_edge_dicts)) + rv.add_edges_from(zip(src_iter, dst_iter, full_edge_dicts)) else: - rv.add_edges_from(zip(row_iter, col_iter)) + rv.add_edges_from(zip(src_iter, dst_iter)) rv.graph.update(G.graph) return rv diff --git a/python/nx-cugraph/nx_cugraph/convert_matrix.py b/python/nx-cugraph/nx_cugraph/convert_matrix.py new file mode 100644 index 00000000000..6c8b8fb4a1d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/convert_matrix.py @@ -0,0 +1,146 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np + +from .generators._utils import _create_using_class +from .utils import index_dtype, networkx_algorithm + +__all__ = [ + "from_pandas_edgelist", + "from_scipy_sparse_array", +] + + +@networkx_algorithm +def from_pandas_edgelist( + df, + source="source", + target="target", + edge_attr=None, + create_using=None, + edge_key=None, +): + """cudf.DataFrame inputs also supported.""" + graph_class, inplace = _create_using_class(create_using) + src_array = df[source].to_numpy() + dst_array = df[target].to_numpy() + # Renumber step 0: node keys + nodes = np.unique(np.concatenate([src_array, dst_array])) + N = nodes.size + kwargs = {} + if N > 0 and ( + nodes[0] != 0 + or nodes[N - 1] != N - 1 + or ( + nodes.dtype.kind not in {"i", "u"} + and not (nodes == np.arange(N, dtype=np.int64)).all() + ) + ): + # We need to renumber indices--np.searchsorted to the rescue! + kwargs["id_to_key"] = nodes.tolist() + src_indices = cp.array(np.searchsorted(nodes, src_array), index_dtype) + dst_indices = cp.array(np.searchsorted(nodes, dst_array), index_dtype) + else: + src_indices = cp.array(src_array) + dst_indices = cp.array(dst_array) + + if not graph_class.is_directed(): + # Symmetrize the edges + mask = src_indices != dst_indices + if mask.all(): + mask = None + src_indices, dst_indices = ( + cp.hstack( + (src_indices, dst_indices[mask] if mask is not None else dst_indices) + ), + cp.hstack( + (dst_indices, src_indices[mask] if mask is not None else src_indices) + ), + ) + + if edge_attr is not None: + # Additional columns requested for edge data + if edge_attr is True: + attr_col_headings = df.columns.difference({source, target}).to_list() + elif isinstance(edge_attr, (list, tuple)): + attr_col_headings = edge_attr + else: + attr_col_headings = [edge_attr] + if len(attr_col_headings) == 0: + raise nx.NetworkXError( + "Invalid edge_attr argument: No columns found with name: " + f"{attr_col_headings}" + ) + try: + edge_values = { + key: cp.array(val.to_numpy()) + for key, val in df[attr_col_headings].items() + } + except (KeyError, TypeError) as exc: + raise nx.NetworkXError(f"Invalid edge_attr argument: {edge_attr}") from exc + + if not graph_class.is_directed(): + # Symmetrize the edges + edge_values = { + key: cp.hstack((val, val[mask] if mask is not None else val)) + for key, val in edge_values.items() + } + kwargs["edge_values"] = edge_values + + if graph_class.is_multigraph() and edge_key is not None: + try: + edge_keys = df[edge_key].to_list() + except (KeyError, TypeError) as exc: + raise nx.NetworkXError( + f"Invalid edge_key argument: {edge_key}" + ) from exc + if not graph_class.is_directed(): + # Symmetrize the edges + edge_keys = cp.hstack( + (edge_keys, edge_keys[mask] if mask is not None else edge_keys) + ) + kwargs["edge_keys"] = edge_keys + + G = graph_class.from_coo(N, src_indices, dst_indices, **kwargs) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def from_scipy_sparse_array( + A, parallel_edges=False, create_using=None, edge_attribute="weight" +): + graph_class, inplace = _create_using_class(create_using) + m, n = A.shape + if m != n: + raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}") + if A.format != "coo": + A = A.tocoo() + if A.dtype.kind in {"i", "u"} and graph_class.is_multigraph() and parallel_edges: + src_indices = cp.array(np.repeat(A.row, A.data), index_dtype) + dst_indices = cp.array(np.repeat(A.col, A.data), index_dtype) + weight = cp.empty(src_indices.size, A.data.dtype) + weight[:] = 1 + else: + src_indices = cp.array(A.row, index_dtype) + dst_indices = cp.array(A.col, index_dtype) + weight = cp.array(A.data) + G = graph_class.from_coo( + n, src_indices, dst_indices, edge_values={"weight": weight} + ) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/__init__.py b/python/nx-cugraph/nx_cugraph/generators/__init__.py new file mode 100644 index 00000000000..c1834a4dec7 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/__init__.py @@ -0,0 +1,16 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .classic import * +from .community import * +from .small import * +from .social import * diff --git a/python/nx-cugraph/nx_cugraph/generators/_utils.py b/python/nx-cugraph/nx_cugraph/generators/_utils.py new file mode 100644 index 00000000000..e38ace5b28d --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/_utils.py @@ -0,0 +1,136 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import operator as op + +import cupy as cp +import networkx as nx + +import nx_cugraph as nxcg + +from ..utils import index_dtype + +# 3.2.1 fixed some issues in generators that occur in 3.2 and earlier +_IS_NX32_OR_LESS = (nxver := nx.__version__)[:3] <= "3.2" and ( + len(nxver) <= 3 or nxver[3] != "." and not nxver[3].isdigit() +) + + +def _ensure_int(n): + """Ensure n is integral.""" + return op.index(n) + + +def _ensure_nonnegative_int(n): + """Ensure n is a nonnegative integer.""" + n = op.index(n) + if n < 0: + raise nx.NetworkXError(f"Negative number of nodes not valid: {n}") + return n + + +def _complete_graph_indices(n): + all_indices = cp.indices((n, n), dtype=index_dtype) + src_indices = all_indices[0].ravel() + dst_indices = all_indices[1].ravel() + del all_indices + mask = src_indices != dst_indices + return (src_indices[mask], dst_indices[mask]) + + +def _common_small_graph(n, nodes, create_using, *, allow_directed=True): + """Create a "common graph" for small n. + + n == 0: empty graph + n == 1: empty graph + n == 2: complete graph + n > 2: undefined + """ + graph_class, inplace = _create_using_class(create_using) + if not allow_directed and graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n < 2: + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + else: + G = graph_class.from_coo( + n, + cp.arange(2, dtype=index_dtype), + cp.array([1, 0], index_dtype), + id_to_key=nodes, + ) + if inplace: + return create_using._become(G) + return G + + +def _create_using_class(create_using, *, default=nxcg.Graph): + """Handle ``create_using`` argument and return a Graph type from nx_cugraph.""" + inplace = False + if create_using is None: + G = default() + elif isinstance(create_using, type): + G = create_using() + elif not hasattr(create_using, "is_directed") or not hasattr( + create_using, "is_multigraph" + ): + raise TypeError("create_using is not a valid graph type or instance") + elif not isinstance(create_using, nxcg.Graph): + raise NotImplementedError( + f"create_using with object of type {type(create_using)} is not supported " + "by the cugraph backend; only nx_cugraph.Graph objects are allowed." + ) + else: + inplace = True + G = create_using + G.clear() + if not isinstance(G, nxcg.Graph): + if G.is_multigraph(): + if G.is_directed(): + graph_class = nxcg.MultiDiGraph + else: + graph_class = nxcg.MultiGraph + elif G.is_directed(): + graph_class = nxcg.DiGraph + else: + graph_class = nxcg.Graph + if G.__class__ not in {nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph}: + raise NotImplementedError( + f"create_using with type {type(G)} is not supported by the cugraph " + "backend; only standard networkx or nx_cugraph Graph objects are " + "allowed (but not customized subclasses derived from them)." + ) + else: + graph_class = G.__class__ + return graph_class, inplace + + +def _number_and_nodes(n_and_nodes): + n, nodes = n_and_nodes + try: + n = op.index(n) + except TypeError: + n = len(nodes) + if n < 0: + raise nx.NetworkXError(f"Negative number of nodes not valid: {n}") + if not isinstance(nodes, list): + nodes = list(nodes) + if not nodes: + return (n, None) + if nodes[0] == 0 and nodes[n - 1] == n - 1: + try: + if nodes == list(range(n)): + return (n, None) + except Exception: + pass + return (n, nodes) diff --git a/python/nx-cugraph/nx_cugraph/generators/classic.py b/python/nx-cugraph/nx_cugraph/generators/classic.py new file mode 100644 index 00000000000..b196c232320 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/classic.py @@ -0,0 +1,423 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools +from numbers import Integral + +import cupy as cp +import networkx as nx +import numpy as np + +import nx_cugraph as nxcg + +from ..utils import _get_int_dtype, index_dtype, networkx_algorithm, nodes_or_number +from ._utils import ( + _IS_NX32_OR_LESS, + _common_small_graph, + _complete_graph_indices, + _create_using_class, + _ensure_int, + _ensure_nonnegative_int, + _number_and_nodes, +) + +__all__ = [ + "barbell_graph", + "circular_ladder_graph", + "complete_graph", + "complete_multipartite_graph", + "cycle_graph", + "empty_graph", + "ladder_graph", + "lollipop_graph", + "null_graph", + "path_graph", + "star_graph", + "tadpole_graph", + "trivial_graph", + "turan_graph", + "wheel_graph", +] + +concat = itertools.chain.from_iterable + + +@networkx_algorithm +def barbell_graph(m1, m2, create_using=None): + # Like two complete graphs and a path_graph + m1 = _ensure_nonnegative_int(m1) + if m1 < 2: + raise nx.NetworkXError("Invalid graph description, m1 should be >=2") + m2 = _ensure_nonnegative_int(m2) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_bell1, dst_bell1 = _complete_graph_indices(m1) + src_bell2 = src_bell1 + (m1 + m2) + dst_bell2 = dst_bell1 + (m1 + m2) + if m2 == 0: + src_bar = cp.array([m1 - 1, m1], index_dtype) + dst_bar = cp.array([m1, m1 - 1], index_dtype) + else: + src_bar = cp.arange(2 * m1 - 1, 2 * m1 + 2 * m2 + 1, dtype=index_dtype) // 2 + dst_bar = ( + cp.arange(m1 - 1, m1 + m2 + 1, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + src_indices = cp.hstack((src_bell1, src_bar, src_bell2)) + dst_indices = cp.hstack((dst_bell1, dst_bar, dst_bell2)) + G = graph_class.from_coo(2 * m1 + m2, src_indices, dst_indices) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def circular_ladder_graph(n, create_using=None): + return _ladder_graph(n, create_using, is_circular=True) + + +@nodes_or_number(0) +@networkx_algorithm +def complete_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + if n < 3: + return _common_small_graph(n, nodes, create_using) + graph_class, inplace = _create_using_class(create_using) + src_indices, dst_indices = _complete_graph_indices(n) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def complete_multipartite_graph(*subset_sizes): + if not subset_sizes: + return nxcg.Graph() + try: + subset_sizes = [_ensure_int(size) for size in subset_sizes] + except TypeError: + subsets = [list(subset) for subset in subset_sizes] + subset_sizes = [len(subset) for subset in subsets] + nodes = list(concat(subsets)) + else: + subsets = nodes = None + try: + subset_sizes = [_ensure_nonnegative_int(size) for size in subset_sizes] + except nx.NetworkXError: + if _IS_NX32_OR_LESS: + raise NotImplementedError("Negative number of nodes is not supported") + raise + L1 = [] + L2 = [] + total = 0 + for size in subset_sizes: + all_indices = cp.indices((total, size), dtype=index_dtype) + L1.append(all_indices[0].ravel()) + L2.append(all_indices[1].ravel() + total) + total += size + src_indices = cp.hstack(L1 + L2) + dst_indices = cp.hstack(L2 + L1) + subsets_array = cp.array( + np.repeat( + np.arange(len(subset_sizes), dtype=_get_int_dtype(len(subset_sizes) - 1)), + subset_sizes, + ) + ) + return nxcg.Graph.from_coo( + subsets_array.size, + src_indices, + dst_indices, + node_values={"subset": subsets_array}, + id_to_key=nodes, + ) + + +@nodes_or_number(0) +@networkx_algorithm +def cycle_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if n == 1: + src_indices = cp.zeros(1, index_dtype) + dst_indices = cp.zeros(1, index_dtype) + elif n == 2 and graph_class.is_multigraph() and not graph_class.is_directed(): + # This is kind of a peculiar edge case + src_indices = cp.array([0, 0, 1, 1], index_dtype) + dst_indices = cp.array([1, 1, 0, 0], index_dtype) + elif n < 3: + return _common_small_graph(n, nodes, create_using) + elif graph_class.is_directed(): + src_indices = cp.arange(n, dtype=index_dtype) + dst_indices = cp.arange(1, n + 1, dtype=index_dtype) + dst_indices[-1] = 0 + else: + src_indices = cp.arange(2 * n, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[0] = n - 1 + dst_indices[-1] = 0 + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number(0) +@networkx_algorithm +def empty_graph(n=0, create_using=None, default=nx.Graph): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using, default=default) + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + if inplace: + return create_using._become(G) + return G + + +def _ladder_graph(n, create_using, *, is_circular=False): + # Like path path_graph with extra arange, and middle link missing + n = _ensure_nonnegative_int(n) + if n < 2: + if not is_circular: + return _common_small_graph(2 * n, None, create_using, allow_directed=False) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n == 1: + src_indices = cp.array([0, 1, 0, 1], index_dtype) + dst_indices = cp.array([0, 0, 1, 1], index_dtype) + nodes = None + elif graph_class.is_multigraph(): + src_indices = cp.array([0, 0, 1, 1], index_dtype) + dst_indices = cp.array([1, 1, 0, 0], index_dtype) + nodes = [0, -1] + else: + src_indices = cp.array([0, 1], index_dtype) + dst_indices = cp.array([1, 0], index_dtype) + nodes = [0, -1] + G = graph_class.from_coo(2, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + path_src = cp.arange(1, 2 * n - 1, dtype=index_dtype) // 2 + path_dst = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + srcs = [path_src, path_src + n, cp.arange(2 * n, dtype=index_dtype)] + dsts = [ + path_dst, + path_dst + n, + cp.arange(n, 2 * n, dtype=index_dtype), + cp.arange(0, n, dtype=index_dtype), + ] + if is_circular and (n > 2 or graph_class.is_multigraph()): + srcs.append(cp.array([0, n - 1, n, 2 * n - 1], index_dtype)) + dsts.append(cp.array([n - 1, 0, 2 * n - 1, n], index_dtype)) + src_indices = cp.hstack(srcs) + dst_indices = cp.hstack(dsts) + G = graph_class.from_coo(2 * n, src_indices, dst_indices) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def ladder_graph(n, create_using=None): + return _ladder_graph(n, create_using) + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def lollipop_graph(m, n, create_using=None): + # Like complete_graph then path_graph + orig_m, unused_nodes_m = m + orig_n, unused_nodes_n = n + m, m_nodes = _number_and_nodes(m) + if m < 2: + raise nx.NetworkXError( + "Invalid description: m should indicate at least 2 nodes" + ) + n, n_nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + msrc_indices, mdst_indices = _complete_graph_indices(m) + nsrc_indices = cp.arange(2 * m - 1, 2 * m + 2 * n - 1, dtype=index_dtype) // 2 + ndst_indices = ( + cp.arange(m - 1, m + n, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + src_indices = cp.hstack((msrc_indices, nsrc_indices)) + dst_indices = cp.hstack((mdst_indices, ndst_indices)) + if isinstance(orig_m, Integral) and isinstance(orig_n, Integral): + nodes = None + else: + nodes = list(range(m)) if m_nodes is None else m_nodes + nodes.extend(range(n) if n_nodes is None else n_nodes) + if len(set(nodes)) != len(nodes): + raise nx.NetworkXError("Nodes must be distinct in containers m and n") + G = graph_class.from_coo(m + n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def null_graph(create_using=None): + return _common_small_graph(0, None, create_using) + + +@nodes_or_number(0) +@networkx_algorithm +def path_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.arange(n - 1, dtype=index_dtype) + dst_indices = cp.arange(1, n, dtype=index_dtype) + elif n < 3: + return _common_small_graph(n, nodes, create_using) + else: + src_indices = cp.arange(1, 2 * n - 1, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(n, dtype=index_dtype)[:, None] + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number(0) +@networkx_algorithm +def star_graph(n, create_using=None): + orig_n, orig_nodes = n + n, nodes = _number_and_nodes(n) + # star_graph behaves differently whether the input was an int or iterable + if isinstance(orig_n, Integral): + if nodes is not None: + nodes.append(n) + n += 1 + if n < 3: + return _common_small_graph(n, nodes, create_using, allow_directed=False) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + flat = cp.zeros(n - 1, index_dtype) + ramp = cp.arange(1, n, dtype=index_dtype) + src_indices = cp.hstack((flat, ramp)) + dst_indices = cp.hstack((ramp, flat)) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@nodes_or_number([0, 1]) +@networkx_algorithm +def tadpole_graph(m, n, create_using=None): + orig_m, unused_nodes_m = m + orig_n, unused_nodes_n = n + m, m_nodes = _number_and_nodes(m) + if m < 2: + raise nx.NetworkXError( + "Invalid description: m should indicate at least 2 nodes" + ) + n, n_nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if isinstance(orig_m, Integral) and isinstance(orig_n, Integral): + nodes = None + else: + nodes = list(range(m)) if m_nodes is None else m_nodes + nodes.extend(range(n) if n_nodes is None else n_nodes) + if m == 2 and not graph_class.is_multigraph(): + src_indices = cp.arange(1, 2 * (m + n) - 1, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange((m + n), dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel()[1:-1] + else: + src_indices = cp.arange(2 * (m + n), dtype=index_dtype) // 2 + dst_indices = ( + cp.arange((m + n), dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[0] = m - 1 + dst_indices[-1] = 0 + G = graph_class.from_coo(m + n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def trivial_graph(create_using=None): + return _common_small_graph(1, None, create_using) + + +@networkx_algorithm +def turan_graph(n, r): + if not 1 <= r <= n: + raise nx.NetworkXError("Must satisfy 1 <= r <= n") + n_div_r, n_mod_r = divmod(n, r) + partitions = [n_div_r] * (r - n_mod_r) + [n_div_r + 1] * n_mod_r + return complete_multipartite_graph(*partitions) + + +@nodes_or_number(0) +@networkx_algorithm +def wheel_graph(n, create_using=None): + n, nodes = _number_and_nodes(n) + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + if n < 2: + G = graph_class.from_coo( + n, cp.empty(0, index_dtype), cp.empty(0, index_dtype), id_to_key=nodes + ) + else: + # Like star_graph + flat = cp.zeros(n - 1, index_dtype) + ramp = cp.arange(1, n, dtype=index_dtype) + # Like cycle_graph + if n < 3: + src_indices = cp.empty(0, index_dtype) + dst_indices = cp.empty(0, index_dtype) + elif n > 3: + src_indices = cp.arange(2, 2 * n, dtype=index_dtype) // 2 + dst_indices = ( + cp.arange(1, n, dtype=index_dtype)[:, None] + + cp.array([-1, 1], index_dtype) + ).ravel() + dst_indices[-1] = 1 + dst_indices[0] = n - 1 + elif graph_class.is_multigraph(): + src_indices = cp.array([1, 1, 2, 2], index_dtype) + dst_indices = cp.array([2, 2, 1, 1], index_dtype) + else: + src_indices = cp.array([1, 2], index_dtype) + dst_indices = cp.array([2, 1], index_dtype) + src_indices = cp.hstack((flat, ramp, src_indices)) + dst_indices = cp.hstack((ramp, flat, dst_indices)) + G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes) + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/community.py b/python/nx-cugraph/nx_cugraph/generators/community.py new file mode 100644 index 00000000000..e5cb03e8cc0 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/community.py @@ -0,0 +1,45 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp + +import nx_cugraph as nxcg + +from ..utils import networkx_algorithm +from ._utils import ( + _common_small_graph, + _complete_graph_indices, + _ensure_int, + _ensure_nonnegative_int, +) + +__all__ = [ + "caveman_graph", +] + + +@networkx_algorithm +def caveman_graph(l, k): # noqa: E741 + l = _ensure_int(l) # noqa: E741 + k = _ensure_int(k) + N = _ensure_nonnegative_int(k * l) + if l == 0 or k < 1: + return _common_small_graph(N, None, None) + k = _ensure_nonnegative_int(k) + src_clique, dst_clique = _complete_graph_indices(k) + src_cliques = [src_clique] + dst_cliques = [dst_clique] + src_cliques.extend(src_clique + i * k for i in range(1, l)) + dst_cliques.extend(dst_clique + i * k for i in range(1, l)) + src_indices = cp.hstack(src_cliques) + dst_indices = cp.hstack(dst_cliques) + return nxcg.Graph.from_coo(l * k, src_indices, dst_indices) diff --git a/python/nx-cugraph/nx_cugraph/generators/small.py b/python/nx-cugraph/nx_cugraph/generators/small.py new file mode 100644 index 00000000000..b9a189c31d5 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/small.py @@ -0,0 +1,622 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx + +import nx_cugraph as nxcg + +from ..utils import index_dtype, networkx_algorithm +from ._utils import _IS_NX32_OR_LESS, _create_using_class + +__all__ = [ + "bull_graph", + "chvatal_graph", + "cubical_graph", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", + "krackhardt_kite_graph", + "moebius_kantor_graph", + "octahedral_graph", + "pappus_graph", + "petersen_graph", + "sedgewick_maze_graph", + "tetrahedral_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "tutte_graph", +] + + +@networkx_algorithm +def bull_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 4], index_dtype) + dst_indices = cp.array([1, 2, 0, 2, 3, 0, 1, 4, 1, 2], index_dtype) + G = graph_class.from_coo(5, src_indices, dst_indices, name="Bull Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def chvatal_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, + 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, + 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 4, 6, 9, 0, 2, 5, 7, 1, 3, 6, 8, 2, 4, 7, 9, 0, 3, 5, 8, 1, 4, 10, 11, + 0, 2, 10, 11, 1, 3, 8, 11, 2, 4, 7, 10, 0, 3, 10, 11, 5, 6, 8, 9, 5, 6, + 7, 9, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(12, src_indices, dst_indices, name="Chvatal Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def cubical_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7], + index_dtype, + ) + dst_indices = cp.array( + [1, 3, 4, 0, 2, 7, 1, 3, 6, 0, 2, 5, 0, 5, 7, 3, 4, 6, 2, 5, 7, 1, 4, 6], + index_dtype, + ) + name = ("Platonic Cubical Graph",) if _IS_NX32_OR_LESS else "Platonic Cubical Graph" + G = graph_class.from_coo(8, src_indices, dst_indices, name=name) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def desargues_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 19, 0, 2, 16, 1, 3, 11, 2, 4, 14, 3, 5, 9, 0, 4, 6, 5, 7, 15, 6, 8, + 18, 7, 9, 13, 4, 8, 10, 9, 11, 19, 2, 10, 12, 11, 13, 17, 8, 12, 14, 3, + 13, 15, 6, 14, 16, 1, 15, 17, 12, 16, 18, 7, 17, 19, 0, 10, 18, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 16, 11, 14, 9, 0, 15, 18, 13, 4, 19, 2, 17, 8, 3, 6, 1, 12, 7, 10], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(20, src_indices, dst_indices, name="Desargues Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def diamond_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 1, 2, 2, 2, 3, 3], index_dtype) + dst_indices = cp.array([1, 2, 0, 2, 3, 0, 1, 3, 1, 2], index_dtype) + G = graph_class.from_coo(4, src_indices, dst_indices, name="Diamond Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def dodecahedral_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 10, 19, 0, 2, 8, 1, 3, 6, 2, 4, 19, 3, 5, 17, 4, 6, 15, 2, 5, 7, 6, 8, + 14, 1, 7, 9, 8, 10, 13, 0, 9, 11, 10, 12, 18, 11, 13, 16, 9, 12, 14, 7, + 13, 15, 5, 14, 16, 12, 15, 17, 4, 16, 18, 11, 17, 19, 0, 3, 18, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], + index_dtype, + ) + dst_indices_extra = cp.array( + [10, 8, 6, 19, 17, 15, 2, 14, 1, 13, 0, 18, 16, 9, 7, 5, 12, 4, 11, 3], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(20, src_indices, dst_indices, name="Dodecahedral Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def frucht_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array( + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 8, 8, 10], + index_dtype, + ) + dst_indices = cp.array( + [1, 7, 2, 7, 3, 8, 4, 9, 5, 9, 6, 10, 0, 10, 11, 9, 11, 11], + index_dtype, + ) + else: + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, + 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 6, 7, 0, 2, 7, 1, 3, 8, 2, 4, 9, 3, 5, 9, 4, 6, 10, 0, 5, 10, 0, + 1, 11, 2, 9, 11, 3, 4, 8, 5, 6, 11, 7, 8, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(12, src_indices, dst_indices, name="Frucht Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def heawood_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 13, 0, 2, 10, 1, 3, 7, 2, 4, 12, 3, 5, 9, 0, 4, 6, 5, 7, 11, 2, 6, + 8, 7, 9, 13, 4, 8, 10, 1, 9, 11, 6, 10, 12, 3, 11, 13, 0, 8, 12, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 10, 7, 12, 9, 0, 11, 2, 13, 4, 1, 6, 3, 8], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(14, src_indices, dst_indices, name="Heawood Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def house_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array([0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4], index_dtype) + dst_indices = cp.array([1, 2, 0, 3, 0, 3, 4, 1, 2, 4, 2, 3], index_dtype) + G = graph_class.from_coo(5, src_indices, dst_indices, name="House Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def house_x_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4], index_dtype + ) + dst_indices = cp.array( + [1, 2, 3, 0, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3], index_dtype + ) + G = graph_class.from_coo( + 5, src_indices, dst_indices, name="House-with-X-inside Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def icosahedral_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, + 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, + 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 7, 8, 11, 0, 2, 5, 6, 8, 1, 3, 6, 8, 9, 2, 4, 6, 9, 10, 3, 5, 6, + 10, 11, 0, 1, 4, 6, 11, 1, 2, 3, 4, 5, 0, 8, 9, 10, 11, 0, 1, 2, 7, 9, 2, + 3, 7, 8, 10, 3, 4, 7, 9, 11, 0, 4, 5, 7, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 12, src_indices, dst_indices, name="Platonic Icosahedral Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def krackhardt_kite_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, + 5, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 5, 0, 3, 4, 6, 0, 3, 5, 0, 1, 2, 4, 5, 6, 1, 3, 6, 0, 2, 3, 6, + 7, 1, 3, 4, 5, 7, 5, 6, 8, 7, 9, 8, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 10, src_indices, dst_indices, name="Krackhardt Kite Social Network" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def moebius_kantor_graph(create_using=None): + # This can also be defined w.r.t. LCF_graph + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, + 14, 14, 15, 15, 15, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 15, 0, 2, 12, 1, 3, 7, 2, 4, 14, 3, 5, 9, 0, 4, 6, 5, 7, 11, 2, 6, + 8, 7, 9, 13, 4, 8, 10, 9, 11, 15, 6, 10, 12, 1, 11, 13, 8, 12, 14, 3, 13, + 15, 0, 10, 14, + ], + index_dtype, + ) + # fmt: on + if graph_class.is_multigraph(): + src_indices_extra = cp.array( + [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], + index_dtype, + ) + dst_indices_extra = cp.array( + [5, 12, 7, 14, 9, 0, 11, 2, 13, 4, 15, 6, 1, 8, 3, 10], + index_dtype, + ) + src_indices = cp.hstack((src_indices, src_indices_extra)) + dst_indices = cp.hstack((dst_indices, dst_indices_extra)) + G = graph_class.from_coo(16, src_indices, dst_indices, name="Moebius-Kantor Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def octahedral_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + src_indices = cp.array( + [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5], + index_dtype, + ) + dst_indices = cp.array( + [1, 2, 3, 4, 0, 2, 3, 5, 0, 1, 4, 5, 0, 1, 4, 5, 0, 2, 3, 5, 1, 2, 3, 4], + index_dtype, + ) + G = graph_class.from_coo( + 6, src_indices, dst_indices, name="Platonic Octahedral Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def pappus_graph(): + # This can also be defined w.r.t. LCF_graph + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 5, 17, 0, 2, 8, 1, 3, 13, 2, 4, 10, 3, 5, 15, 0, 4, 6, 5, 7, 11, 6, 8, + 14, 1, 7, 9, 8, 10, 16, 3, 9, 11, 6, 10, 12, 11, 13, 17, 2, 12, 14, 7, + 13, 15, 4, 14, 16, 9, 15, 17, 0, 12, 16, + ], + index_dtype, + ) + # fmt: on + return nxcg.Graph.from_coo(18, src_indices, dst_indices, name="Pappus Graph") + + +@networkx_algorithm +def petersen_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 4, 5, 0, 2, 6, 1, 3, 7, 2, 4, 8, 0, 3, 9, 0, 7, 8, 1, 8, 9, 2, 5, 9, + 3, 5, 6, 4, 6, 7, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(10, src_indices, dst_indices, name="Petersen Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def sedgewick_maze_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array([0, 0, 0, 1, 2, 3, 3, 4, 4, 4], index_dtype) + dst_indices = cp.array([2, 5, 7, 7, 6, 4, 5, 5, 6, 7], index_dtype) + else: + src_indices = cp.array( + [0, 0, 0, 1, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 7], + index_dtype, + ) + dst_indices = cp.array( + [2, 5, 7, 7, 0, 6, 4, 5, 3, 5, 6, 7, 0, 3, 4, 2, 4, 0, 1, 4], + index_dtype, + ) + G = graph_class.from_coo(8, src_indices, dst_indices, name="Sedgewick Maze") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def tetrahedral_graph(create_using=None): + # This can also be defined w.r.t. complete_graph + graph_class, inplace = _create_using_class(create_using) + src_indices = cp.array([0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3], index_dtype) + dst_indices = cp.array([1, 2, 3, 0, 2, 3, 0, 1, 3, 0, 1, 2], index_dtype) + name = ( + "Platonic Tetrahedral graph" + if _IS_NX32_OR_LESS + else "Platonic Tetrahedral Graph" + ) + G = graph_class.from_coo(4, src_indices, dst_indices, name=name) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def truncated_cube_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, + 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 4, 0, 11, 14, 0, 3, 4, 2, 6, 8, 0, 2, 5, 4, 16, 18, 3, 7, 8, 6, 10, + 12, 3, 6, 9, 8, 17, 20, 7, 11, 12, 1, 10, 14, 7, 10, 13, 12, 21, 22, 1, + 11, 15, 14, 19, 23, 5, 17, 18, 9, 16, 20, 5, 16, 19, 15, 18, 23, 9, 17, + 21, 13, 20, 22, 13, 21, 23, 15, 19, 22, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(24, src_indices, dst_indices, name="Truncated Cube Graph") + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def truncated_tetrahedron_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + src_indices = cp.array( + [0, 0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5, 6, 7, 8, 8, 9, 10], index_dtype + ) + dst_indices = cp.array( + [1, 2, 9, 2, 6, 3, 4, 11, 5, 11, 6, 7, 7, 8, 9, 10, 10, 11], index_dtype + ) + else: + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, + 7, 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 9, 0, 2, 6, 0, 1, 3, 2, 4, 11, 3, 5, 11, 4, 6, 7, 1, 5, 7, 5, + 6, 8, 7, 9, 10, 0, 8, 10, 8, 9, 11, 3, 4, 10, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo( + 12, src_indices, dst_indices, name="Truncated Tetrahedron Graph" + ) + if inplace: + return create_using._become(G) + return G + + +@networkx_algorithm +def tutte_graph(create_using=None): + graph_class, inplace = _create_using_class(create_using) + if graph_class.is_directed(): + raise nx.NetworkXError("Directed Graph not supported") + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7, + 8, 8, 8, 9, 9, 9, 10, 10, 10, 11, 11, 11, 12, 12, 12, 13, 13, 13, 14, 14, + 14, 15, 15, 15, 16, 16, 16, 17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, + 20, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, + 26, 27, 27, 27, 28, 28, 28, 29, 29, 29, 30, 30, 30, 31, 31, 31, 32, 32, + 32, 33, 33, 33, 34, 34, 34, 35, 35, 35, 36, 36, 36, 37, 37, 37, 38, 38, + 38, 39, 39, 39, 40, 40, 40, 41, 41, 41, 42, 42, 42, 43, 43, 43, 44, 44, + 44, 45, 45, 45, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 0, 4, 26, 0, 10, 11, 0, 18, 19, 1, 5, 33, 4, 6, 29, 5, 7, 27, 6, + 8, 14, 7, 9, 38, 8, 10, 37, 2, 9, 39, 2, 12, 39, 11, 13, 35, 12, 14, 15, + 7, 13, 34, 13, 16, 22, 15, 17, 44, 16, 18, 43, 3, 17, 45, 3, 20, 45, 19, + 21, 41, 20, 22, 23, 15, 21, 40, 21, 24, 27, 23, 25, 32, 24, 26, 31, 1, + 25, 33, 6, 23, 28, 27, 29, 32, 5, 28, 30, 29, 31, 33, 25, 30, 32, 24, 28, + 31, 4, 26, 30, 14, 35, 38, 12, 34, 36, 35, 37, 39, 9, 36, 38, 8, 34, 37, + 10, 11, 36, 22, 41, 44, 20, 40, 42, 41, 43, 45, 17, 42, 44, 16, 40, 43, + 18, 19, 42, + ], + index_dtype, + ) + # fmt: on + G = graph_class.from_coo(46, src_indices, dst_indices, name="Tutte's Graph") + if inplace: + return create_using._become(G) + return G diff --git a/python/nx-cugraph/nx_cugraph/generators/social.py b/python/nx-cugraph/nx_cugraph/generators/social.py new file mode 100644 index 00000000000..3c936d07af3 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/generators/social.py @@ -0,0 +1,294 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import numpy as np + +import nx_cugraph as nxcg + +from ..utils import index_dtype, networkx_algorithm + +__all__ = [ + "davis_southern_women_graph", + "florentine_families_graph", + "karate_club_graph", + "les_miserables_graph", +] + + +@networkx_algorithm +def davis_southern_women_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, + 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, + 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, + 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, + 16, 16, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 20, 21, 21, + 21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, + 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25, + 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, + 27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 30, 30, 30, + 31, 31, 31, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 18, 19, 20, 21, 22, 23, 25, 26, 18, 19, 20, 22, 23, 24, 25, 19, 20, 21, + 22, 23, 24, 25, 26, 18, 20, 21, 22, 23, 24, 25, 20, 21, 22, 24, 20, 22, + 23, 25, 22, 23, 24, 25, 23, 25, 26, 22, 24, 25, 26, 24, 25, 26, 29, 25, + 26, 27, 29, 25, 26, 27, 29, 30, 31, 24, 25, 26, 27, 29, 30, 31, 23, 24, + 26, 27, 28, 29, 30, 31, 24, 25, 27, 28, 29, 25, 26, 26, 28, 26, 28, 0, 1, + 3, 0, 1, 2, 0, 1, 2, 3, 4, 5, 0, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 8, 0, 1, + 2, 3, 5, 6, 7, 13, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 0, 1, 2, 3, 5, 6, 7, + 8, 9, 10, 11, 12, 14, 15, 0, 2, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 10, + 11, 12, 13, 14, 13, 14, 16, 17, 9, 10, 11, 12, 13, 14, 11, 12, 13, 11, + 12, 13, + ], + index_dtype, + ) + bipartite = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, + ], + np.int8, + ) + women = [ + "Evelyn Jefferson", "Laura Mandeville", "Theresa Anderson", "Brenda Rogers", + "Charlotte McDowd", "Frances Anderson", "Eleanor Nye", "Pearl Oglethorpe", + "Ruth DeSand", "Verne Sanderson", "Myra Liddel", "Katherina Rogers", + "Sylvia Avondale", "Nora Fayette", "Helen Lloyd", "Dorothy Murchison", + "Olivia Carleton", "Flora Price", + ] + events = [ + "E1", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9", "E10", "E11", "E12", + "E13", "E14", + ] + # fmt: on + return nxcg.Graph.from_coo( + 32, + src_indices, + dst_indices, + node_values={"bipartite": bipartite}, + id_to_key=women + events, + top=women, + bottom=events, + ) + + +@networkx_algorithm +def florentine_families_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 1, 1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, + 9, 10, 10, 10, 11, 11, 11, 12, 12, 13, 13, 13, 13, 14, 14, 14, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 8, 5, 6, 8, 4, 8, 6, 10, 13, 2, 10, 13, 1, 1, 3, 7, 14, 6, 0, 1, 2, 11, + 12, 14, 12, 3, 4, 13, 8, 13, 14, 8, 9, 3, 4, 10, 11, 6, 8, 11, + ], + index_dtype, + ) + nodes = [ + "Acciaiuoli", "Albizzi", "Barbadori", "Bischeri", "Castellani", "Ginori", + "Guadagni", "Lamberteschi", "Medici", "Pazzi", "Peruzzi", "Ridolfi", + "Salviati", "Strozzi", "Tornabuoni" + ] + # fmt: on + return nxcg.Graph.from_coo(15, src_indices, dst_indices, id_to_key=nodes) + + +@networkx_algorithm +def karate_club_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 5, + 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 10, 10, 10, 11, 12, 12, 13, + 13, 13, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, 20, + 20, 21, 21, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, + 27, 27, 27, 27, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30, 31, 31, 31, + 31, 31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, + 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31, 0, 2, 3, 7, 13, + 17, 19, 21, 30, 0, 1, 3, 7, 8, 9, 13, 27, 28, 32, 0, 1, 2, 7, 12, 13, 0, + 6, 10, 0, 6, 10, 16, 0, 4, 5, 16, 0, 1, 2, 3, 0, 2, 30, 32, 33, 2, 33, + 0, 4, 5, 0, 0, 3, 0, 1, 2, 3, 33, 32, 33, 32, 33, 5, 6, 0, 1, 32, 33, 0, + 1, 33, 32, 33, 0, 1, 32, 33, 25, 27, 29, 32, 33, 25, 27, 31, 23, 24, 31, + 29, 33, 2, 23, 24, 33, 2, 31, 33, 23, 26, 32, 33, 1, 8, 32, 33, 0, 24, + 25, 28, 32, 33, 2, 8, 14, 15, 18, 20, 22, 23, 29, 30, 31, 33, 8, 9, 13, + 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32, + ], + index_dtype, + ) + weights = cp.array( + [ + 4, 5, 3, 3, 3, 3, 2, 2, 2, 3, 1, 3, 2, 2, 2, 2, 4, 6, 3, 4, 5, 1, 2, 2, + 2, 5, 6, 3, 4, 5, 1, 3, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 5, 3, 3, + 3, 2, 5, 3, 2, 4, 4, 3, 2, 5, 3, 3, 4, 1, 2, 2, 3, 3, 3, 1, 3, 3, 5, 3, + 3, 3, 3, 2, 3, 4, 3, 3, 2, 1, 1, 2, 2, 2, 1, 3, 1, 2, 2, 2, 3, 5, 4, 3, + 5, 4, 2, 3, 2, 5, 2, 7, 4, 2, 2, 4, 3, 4, 2, 2, 2, 3, 4, 4, 2, 2, 3, 3, + 3, 2, 2, 7, 2, 4, 4, 2, 3, 3, 3, 1, 3, 2, 5, 4, 3, 4, 5, 4, 2, 3, 2, 4, + 2, 1, 1, 3, 4, 2, 4, 2, 2, 3, 4, 5, + ], + np.int8, + ) + # For now, cupy doesn't handle str dtypes and we primarily handle cupy arrays. + # We try to support numpy arrays for node values, so let's use numpy here. + clubs = np.array([ + "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", + "Mr. Hi", "Mr. Hi", "Officer", "Mr. Hi", "Mr. Hi", "Mr. Hi", "Mr. Hi", + "Officer", "Officer", "Mr. Hi", "Mr. Hi", "Officer", "Mr. Hi", "Officer", + "Mr. Hi", "Officer", "Officer", "Officer", "Officer", "Officer", "Officer", + "Officer", "Officer", "Officer", "Officer", "Officer", "Officer", + ]) + # fmt: on + return nxcg.Graph.from_coo( + 34, + src_indices, + dst_indices, + edge_values={"weight": weights}, + node_values={"club": clubs}, + name="Zachary's Karate Club", + ) + + +@networkx_algorithm +def les_miserables_graph(): + # fmt: off + src_indices = cp.array( + [ + 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, + 6, 6, 6, 6, 6, 6, 6, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, + 10, 10, 10, 10, 11, 12, 12, 12, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, + 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, + 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 19, + 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 23, 23, 23, + 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, + 24, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, + 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, + 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, + 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 32, 33, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, + 35, 35, 35, 35, 35, 35, 35, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, + 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, + 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 42, 42, 42, 42, 42, + 42, 43, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 46, 46, 46, 46, 46, + 46, 46, 46, 46, 46, 46, 47, 47, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, + 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 51, 51, 51, 51, + 51, 51, 51, 52, 53, 53, 54, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 57, + 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, + 59, 59, 59, 60, 60, 61, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 64, + 65, 65, 66, 66, 66, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68, 69, 69, 69, + 69, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 71, + 71, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, + 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, + 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 75, 75, 75, 76, 76, + 76, 76, 76, 76, 76, + ], + index_dtype, + ) + dst_indices = cp.array( + [ + 25, 58, 70, 9, 15, 25, 31, 37, 39, 58, 59, 70, 73, 6, 17, 21, 24, 30, 31, + 35, 40, 46, 49, 55, 67, 8, 10, 12, 16, 27, 39, 42, 73, 34, 49, 23, 26, + 27, 29, 44, 71, 76, 2, 17, 21, 24, 30, 31, 35, 40, 46, 49, 55, 67, 73, + 70, 3, 10, 12, 16, 42, 73, 1, 15, 25, 31, 37, 59, 70, 3, 8, 12, 16, 42, + 73, 62, 3, 8, 10, 16, 42, 73, 14, 31, 13, 31, 1, 9, 24, 25, 37, 39, 58, + 59, 70, 73, 3, 8, 10, 12, 42, 73, 2, 6, 21, 24, 30, 31, 35, 40, 46, 49, + 67, 34, 39, 45, 49, 51, 58, 70, 71, 72, 73, 75, 62, 62, 2, 6, 17, 24, 25, + 30, 31, 35, 40, 46, 49, 55, 67, 62, 5, 26, 27, 29, 44, 71, 76, 2, 6, 15, + 17, 21, 30, 31, 35, 39, 40, 46, 49, 55, 67, 73, 0, 1, 9, 15, 21, 37, 46, + 49, 58, 59, 70, 5, 23, 27, 29, 44, 71, 76, 3, 5, 23, 26, 29, 39, 44, 48, + 58, 65, 69, 70, 71, 73, 76, 36, 39, 60, 73, 5, 23, 26, 27, 44, 71, 76, 2, + 6, 17, 21, 24, 31, 35, 40, 46, 49, 67, 1, 2, 6, 9, 13, 14, 17, 21, 24, + 30, 35, 37, 39, 40, 46, 49, 53, 55, 59, 67, 70, 73, 62, 73, 4, 18, 45, + 47, 49, 51, 73, 2, 6, 17, 21, 24, 30, 31, 40, 55, 67, 28, 1, 9, 15, 25, + 31, 39, 58, 59, 70, 73, 73, 1, 3, 15, 18, 24, 27, 28, 31, 37, 58, 59, 69, + 70, 72, 73, 74, 75, 2, 6, 17, 21, 24, 30, 31, 35, 46, 49, 55, 67, 53, 3, + 8, 10, 12, 16, 73, 73, 5, 23, 26, 27, 29, 71, 76, 18, 34, 49, 51, 2, 6, + 17, 21, 24, 25, 30, 31, 40, 49, 61, 34, 58, 27, 73, 2, 4, 6, 17, 18, 21, + 24, 25, 30, 31, 34, 40, 45, 46, 51, 66, 70, 71, 73, 56, 62, 73, 18, 34, + 45, 49, 52, 57, 73, 51, 31, 41, 73, 2, 6, 21, 24, 31, 35, 40, 50, 62, 73, + 51, 66, 0, 1, 15, 18, 25, 27, 37, 39, 47, 70, 73, 1, 9, 15, 25, 31, 37, + 39, 70, 73, 28, 73, 46, 11, 19, 20, 22, 32, 50, 56, 63, 64, 73, 62, 62, + 27, 69, 49, 57, 70, 2, 6, 17, 21, 24, 30, 31, 35, 40, 73, 27, 39, 65, 73, + 0, 1, 7, 9, 15, 18, 25, 27, 31, 37, 39, 49, 58, 59, 66, 73, 5, 18, 23, + 26, 27, 29, 44, 49, 76, 18, 39, 73, 1, 3, 6, 8, 10, 12, 15, 16, 18, 24, + 27, 28, 31, 33, 34, 37, 38, 39, 42, 43, 48, 49, 50, 51, 54, 56, 58, 59, + 60, 62, 68, 69, 70, 72, 74, 75, 39, 73, 18, 39, 73, 5, 23, 26, 27, 29, + 44, 71, + ], + index_dtype, + ) + weights = cp.array( + [ + 2, 1, 2, 3, 4, 1, 1, 6, 2, 1, 2, 6, 1, 4, 5, 6, 4, 3, 5, 1, 5, 2, 1, 1, + 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 3, 4, 3, 4, 4, 4, 3, 4, 9, 12, 10, 6, 5, + 3, 7, 1, 5, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 1, 3, 1, 3, 2, 2, 2, + 2, 3, 3, 1, 1, 2, 2, 2, 2, 2, 3, 2, 3, 2, 4, 1, 1, 1, 4, 1, 1, 2, 4, 1, + 1, 2, 2, 2, 2, 2, 5, 9, 13, 15, 5, 6, 1, 5, 2, 5, 2, 3, 1, 1, 21, 2, 4, + 1, 1, 2, 31, 1, 2, 1, 6, 12, 13, 17, 1, 6, 7, 2, 5, 2, 9, 1, 3, 1, 3, 3, + 4, 5, 3, 3, 4, 4, 10, 1, 15, 17, 6, 7, 3, 6, 5, 1, 7, 1, 4, 4, 2, 1, 1, + 1, 1, 1, 1, 5, 2, 1, 3, 4, 3, 3, 3, 4, 4, 3, 1, 3, 4, 3, 4, 5, 3, 2, 2, + 1, 2, 1, 3, 9, 4, 2, 1, 3, 8, 4, 5, 3, 4, 3, 3, 4, 3, 6, 5, 6, 6, 2, 1, + 5, 1, 1, 2, 1, 5, 5, 1, 2, 2, 6, 7, 7, 2, 1, 1, 1, 3, 1, 4, 2, 1, 1, 1, + 1, 1, 1, 1, 1, 3, 1, 1, 12, 9, 2, 1, 3, 1, 2, 3, 1, 1, 2, 1, 1, 2, 6, 3, + 4, 1, 1, 1, 1, 2, 5, 1, 1, 2, 1, 1, 1, 6, 5, 1, 1, 1, 1, 1, 1, 5, 1, 17, + 1, 1, 5, 7, 5, 5, 5, 5, 3, 2, 1, 2, 1, 2, 1, 2, 2, 3, 2, 2, 3, 1, 4, 3, + 4, 3, 3, 4, 3, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, + 1, 1, 5, 5, 21, 9, 7, 5, 1, 4, 12, 2, 1, 1, 6, 1, 2, 1, 19, 6, 8, 3, 2, + 9, 2, 6, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 6, 10, 3, 1, 1, 1, 1, + 1, 4, 2, 2, 1, 1, 1, 13, 7, 2, 1, 2, 1, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 1, + 1, 1, 8, 10, 1, 1, 5, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 3, 4, 2, 1, 1, 2, 1, + 2, 1, 2, 3, 2, 6, 1, 3, 4, 1, 3, 1, 1, 5, 5, 2, 13, 1, 1, 12, 4, 1, 3, 4, + 3, 3, 4, 1, 3, 2, 1, 1, 1, 2, 1, 2, 3, 2, 1, 2, 31, 4, 9, 8, 1, 1, 2, 1, + 1, 17, 3, 1, 1, 19, 3, 2, 1, 3, 7, 1, 1, 5, 1, 3, 12, 1, 2, 3, 1, 2, 1, + 1, 3, 3, 4, 3, 4, 4, 3, 3, + ], + np.int8, + ) + nodes = [ + "Anzelma", "Babet", "Bahorel", "Bamatabois", "BaronessT", "Blacheville", + "Bossuet", "Boulatruelle", "Brevet", "Brujon", "Champmathieu", + "Champtercier", "Chenildieu", "Child1", "Child2", "Claquesous", + "Cochepaille", "Combeferre", "Cosette", "Count", "CountessDeLo", + "Courfeyrac", "Cravatte", "Dahlia", "Enjolras", "Eponine", "Fameuil", + "Fantine", "Fauchelevent", "Favourite", "Feuilly", "Gavroche", "Geborand", + "Gervais", "Gillenormand", "Grantaire", "Gribier", "Gueulemer", "Isabeau", + "Javert", "Joly", "Jondrette", "Judge", "Labarre", "Listolier", + "LtGillenormand", "Mabeuf", "Magnon", "Marguerite", "Marius", + "MlleBaptistine", "MlleGillenormand", "MlleVaubois", "MmeBurgon", "MmeDeR", + "MmeHucheloup", "MmeMagloire", "MmePontmercy", "MmeThenardier", + "Montparnasse", "MotherInnocent", "MotherPlutarch", "Myriel", "Napoleon", + "OldMan", "Perpetue", "Pontmercy", "Prouvaire", "Scaufflaire", "Simplice", + "Thenardier", "Tholomyes", "Toussaint", "Valjean", "Woman1", "Woman2", + "Zephine", + ] + # fmt: on + return nxcg.Graph.from_coo( + 77, src_indices, dst_indices, edge_values={"weight": weights}, id_to_key=nodes + ) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index a7b88b72ec5..fd0b1483d73 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -65,6 +65,7 @@ def key(testpath): no_weights = "weighted implementation not currently supported" no_multigraph = "multigraphs not currently supported" louvain_different = "Louvain may be different due to RNG" + no_string_dtype = "string edge values not currently supported" xfail = {} @@ -187,6 +188,40 @@ def key(testpath): xfail[ key("test_louvain.py:test_threshold") ] = "Louvain does not support seed parameter" + if nxver.major == 3 and nxver.minor >= 2: + xfail.update( + { + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_multi_attr_incl_target" + ): no_string_dtype, + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_multidigraph_and_edge_attr" + ): no_string_dtype, + key( + "test_convert_pandas.py:TestConvertPandas." + "test_from_edgelist_int_attr_name" + ): no_string_dtype, + } + ) + if nxver.minor == 2: + different_iteration_order = "Different graph data iteration order" + xfail.update( + { + key( + "test_cycles.py:TestMinimumCycleBasis." + "test_gh6787_and_edge_attribute_names" + ): different_iteration_order, + key( + "test_euler.py:TestEulerianCircuit." + "test_eulerian_circuit_cycle" + ): different_iteration_order, + key( + "test_gml.py:TestGraph.test_special_float_label" + ): different_iteration_order, + } + ) for item in items: kset = set(item.keywords) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_generators.py b/python/nx-cugraph/nx_cugraph/tests/test_generators.py new file mode 100644 index 00000000000..511f8dcd8e2 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_generators.py @@ -0,0 +1,277 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pytest +from packaging.version import parse + +import nx_cugraph as nxcg + +nxver = parse(nx.__version__) + + +def assert_graphs_equal(Gnx, Gcg): + assert isinstance(Gnx, nx.Graph) + assert isinstance(Gcg, nxcg.Graph) + assert Gnx.number_of_nodes() == Gcg.number_of_nodes() + assert Gnx.number_of_edges() == Gcg.number_of_edges() + assert Gnx.is_directed() == Gcg.is_directed() + assert Gnx.is_multigraph() == Gcg.is_multigraph() + G = nxcg.to_networkx(Gcg) + rv = nx.utils.graphs_equal(G, Gnx) + if not rv: + print("GRAPHS ARE NOT EQUAL!") + assert sorted(G) == sorted(Gnx) + assert sorted(G._adj) == sorted(Gnx._adj) + assert sorted(G._node) == sorted(Gnx._node) + for k in sorted(G._adj): + print(k, sorted(G._adj[k]), sorted(Gnx._adj[k])) + print(nx.to_scipy_sparse_array(G).todense()) + print(nx.to_scipy_sparse_array(Gnx).todense()) + print(G.graph) + print(Gnx.graph) + assert rv + + +if nxver.major == 3 and nxver.minor < 2: + pytest.skip("Need NetworkX >=3.2 to test generators", allow_module_level=True) + + +def compare(name, create_using, *args, is_vanilla=False): + exc1 = exc2 = None + func = getattr(nx, name) + if isinstance(create_using, nxcg.Graph): + nx_create_using = nxcg.to_networkx(create_using) + elif isinstance(create_using, type) and issubclass(create_using, nxcg.Graph): + nx_create_using = create_using.to_networkx_class() + elif isinstance(create_using, nx.Graph): + nx_create_using = create_using.copy() + else: + nx_create_using = create_using + try: + if is_vanilla: + G = func(*args) + else: + G = func(*args, create_using=nx_create_using) + except Exception as exc: + exc1 = exc + try: + if is_vanilla: + Gcg = func(*args, backend="cugraph") + else: + Gcg = func(*args, create_using=create_using, backend="cugraph") + except ZeroDivisionError: + raise + except NotImplementedError as exc: + if name in {"complete_multipartite_graph"}: # nx.__version__[:3] <= "3.2" + return + exc2 = exc + except Exception as exc: + if exc1 is None: # pragma: no cover (debug) + raise + exc2 = exc + if exc1 is not None or exc2 is not None: + assert type(exc1) is type(exc2) + else: + assert_graphs_equal(G, Gcg) + + +N = list(range(-1, 5)) +CREATE_USING = [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] +COMPLETE_CREATE_USING = [ + nx.Graph, + nx.DiGraph, + nx.MultiGraph, + nx.MultiDiGraph, + nxcg.Graph, + nxcg.DiGraph, + nxcg.MultiGraph, + nxcg.MultiDiGraph, + # These raise NotImplementedError + # nx.Graph(), + # nx.DiGraph(), + # nx.MultiGraph(), + # nx.MultiDiGraph(), + nxcg.Graph(), + nxcg.DiGraph(), + nxcg.MultiGraph(), + nxcg.MultiDiGraph(), + None, + object, # Bad input + 7, # Bad input +] +GENERATORS_NOARG = [ + # classic + "null_graph", + "trivial_graph", + # small + "bull_graph", + "chvatal_graph", + "cubical_graph", + "desargues_graph", + "diamond_graph", + "dodecahedral_graph", + "frucht_graph", + "heawood_graph", + "house_graph", + "house_x_graph", + "icosahedral_graph", + "krackhardt_kite_graph", + "moebius_kantor_graph", + "octahedral_graph", + "petersen_graph", + "sedgewick_maze_graph", + "tetrahedral_graph", + "truncated_cube_graph", + "truncated_tetrahedron_graph", + "tutte_graph", +] +GENERATORS_NOARG_VANILLA = [ + # classic + "complete_multipartite_graph", + # small + "pappus_graph", + # social + "davis_southern_women_graph", + "florentine_families_graph", + "karate_club_graph", + "les_miserables_graph", +] +GENERATORS_N = [ + # classic + "circular_ladder_graph", + "complete_graph", + "cycle_graph", + "empty_graph", + "ladder_graph", + "path_graph", + "star_graph", + "wheel_graph", +] +GENERATORS_M_N = [ + # classic + "barbell_graph", + "lollipop_graph", + "tadpole_graph", + # bipartite + "complete_bipartite_graph", +] +GENERATORS_M_N_VANILLA = [ + # classic + "complete_multipartite_graph", + "turan_graph", + # community + "caveman_graph", +] + + +@pytest.mark.parametrize("name", GENERATORS_NOARG) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +def test_generator_noarg(name, create_using): + print(name, create_using, type(create_using)) + if isinstance(create_using, nxcg.Graph) and name in { + # fmt: off + "bull_graph", "chvatal_graph", "cubical_graph", "diamond_graph", + "house_graph", "house_x_graph", "icosahedral_graph", "krackhardt_kite_graph", + "octahedral_graph", "petersen_graph", "truncated_cube_graph", "tutte_graph", + # fmt: on + }: + # The _raise_on_directed decorator used in networkx doesn't like our graphs. + if create_using.is_directed(): + with pytest.raises(AssertionError): + compare(name, create_using) + else: + with pytest.raises(TypeError): + compare(name, create_using) + else: + compare(name, create_using) + + +@pytest.mark.parametrize("name", GENERATORS_NOARG_VANILLA) +def test_generator_noarg_vanilla(name): + print(name) + compare(name, None, is_vanilla=True) + + +@pytest.mark.parametrize("name", GENERATORS_N) +@pytest.mark.parametrize("n", N) +@pytest.mark.parametrize("create_using", CREATE_USING) +def test_generator_n(name, n, create_using): + print(name, n, create_using) + compare(name, create_using, n) + + +@pytest.mark.parametrize("name", GENERATORS_N) +@pytest.mark.parametrize("n", [1, 4]) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +def test_generator_n_complete(name, n, create_using): + print(name, n, create_using) + compare(name, create_using, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N) +@pytest.mark.parametrize("create_using", CREATE_USING) +@pytest.mark.parametrize("m", N) +@pytest.mark.parametrize("n", N) +def test_generator_m_n(name, create_using, m, n): + print(name, m, n, create_using) + compare(name, create_using, m, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N_VANILLA) +@pytest.mark.parametrize("m", N) +@pytest.mark.parametrize("n", N) +def test_generator_m_n_vanilla(name, m, n): + print(name, m, n) + compare(name, None, m, n, is_vanilla=True) + + +@pytest.mark.parametrize("name", GENERATORS_M_N) +@pytest.mark.parametrize("create_using", COMPLETE_CREATE_USING) +@pytest.mark.parametrize("m", [4]) +@pytest.mark.parametrize("n", [4]) +def test_generator_m_n_complete(name, create_using, m, n): + print(name, m, n, create_using) + compare(name, create_using, m, n) + + +@pytest.mark.parametrize("name", GENERATORS_M_N_VANILLA) +@pytest.mark.parametrize("m", [4]) +@pytest.mark.parametrize("n", [4]) +def test_generator_m_n_complete_vanilla(name, m, n): + print(name, m, n) + compare(name, None, m, n, is_vanilla=True) + + +def test_bad_lollipop_graph(): + compare("lollipop_graph", None, [0, 1], [1, 2]) + + +def test_can_convert_karate_club(): + # Karate club graph has string node values. + # This really tests conversions, but it's here so we can use `assert_graphs_equal`. + G = nx.karate_club_graph() + G.add_node(0, foo="bar") # string dtype with a mask + G.add_node(1, object=object()) # haha + Gcg = nxcg.from_networkx(G, preserve_all_attrs=True) + assert_graphs_equal(G, Gcg) + Gnx = nxcg.to_networkx(Gcg) + assert nx.utils.graphs_equal(G, Gnx) + assert isinstance(Gcg.node_values["club"], np.ndarray) + assert Gcg.node_values["club"].dtype.kind == "U" + assert isinstance(Gcg.node_values["foo"], np.ndarray) + assert isinstance(Gcg.node_masks["foo"], np.ndarray) + assert Gcg.node_values["foo"].dtype.kind == "U" + assert isinstance(Gcg.node_values["object"], np.ndarray) + assert Gcg.node_values["object"].dtype.kind == "O" + assert isinstance(Gcg.node_masks["object"], np.ndarray) diff --git a/python/nx-cugraph/nx_cugraph/tests/test_utils.py b/python/nx-cugraph/nx_cugraph/tests/test_utils.py new file mode 100644 index 00000000000..fdd0c91995c --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_utils.py @@ -0,0 +1,87 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import numpy as np +import pytest + +from nx_cugraph.utils import _get_int_dtype + + +def test_get_int_dtype(): + uint8 = np.dtype(np.uint8) + uint16 = np.dtype(np.uint16) + uint32 = np.dtype(np.uint32) + uint64 = np.dtype(np.uint64) + # signed + cur = np.iinfo(np.int8) + for val in [cur.min, cur.min + 1, -1, 0, 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) == np.int8 + assert _get_int_dtype(val, signed=True) == np.int8 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) == np.uint8 + assert _get_int_dtype(val + 1, unsigned=True) == np.uint8 + prev = cur + cur = np.iinfo(np.int16) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int16 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint8, uint16} + assert _get_int_dtype(val + 1, unsigned=True) in {uint8, uint16} + prev = cur + cur = np.iinfo(np.int32) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int32 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint16, uint32} + assert _get_int_dtype(val + 1, unsigned=True) in {uint16, uint32} + prev = cur + cur = np.iinfo(np.int64) + for val in [cur.min, cur.min + 1, prev.min - 1, prev.max + 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) != prev.dtype + assert _get_int_dtype(val, signed=True) == np.int64 + if val >= 0: + assert _get_int_dtype(val, unsigned=True) in {uint32, uint64} + assert _get_int_dtype(val + 1, unsigned=True) in {uint32, uint64} + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.min - 1, signed=True) + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.max + 1, signed=True) + + # unsigned + cur = np.iinfo(np.uint8) + for val in [0, 1, cur.max - 1, cur.max]: + assert _get_int_dtype(val) == (np.uint8 if val > 1 else np.int8) + assert _get_int_dtype(val, unsigned=True) == np.uint8 + assert _get_int_dtype(cur.max + 1) == np.int16 + cur = np.iinfo(np.uint16) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint16 + assert _get_int_dtype(cur.max + 1) == np.int32 + cur = np.iinfo(np.uint32) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint32 + assert _get_int_dtype(cur.max + 1) == np.int64 + cur = np.iinfo(np.uint64) + for val in [cur.max - 1, cur.max]: + assert _get_int_dtype(val, unsigned=True) == np.uint64 + with pytest.raises(ValueError, match="Value is incompatible"): + _get_int_dtype(cur.min - 1, unsigned=True) + with pytest.raises(ValueError, match="Value is too"): + _get_int_dtype(cur.max + 1, unsigned=True) + + # API + with pytest.raises(TypeError, match="incompatible"): + _get_int_dtype(7, signed=True, unsigned=True) + assert _get_int_dtype(7, signed=True, unsigned=False) == np.int8 + assert _get_int_dtype(7, signed=False, unsigned=True) == np.uint8 diff --git a/python/nx-cugraph/nx_cugraph/typing.py b/python/nx-cugraph/nx_cugraph/typing.py index d3045ab4656..b419a9085e0 100644 --- a/python/nx-cugraph/nx_cugraph/typing.py +++ b/python/nx-cugraph/nx_cugraph/typing.py @@ -15,6 +15,9 @@ from collections.abc import Hashable from typing import TypeVar +import cupy as cp +import numpy as np + AttrKey = TypeVar("AttrKey", bound=Hashable) EdgeKey = TypeVar("EdgeKey", bound=Hashable) NodeKey = TypeVar("NodeKey", bound=Hashable) @@ -23,3 +26,8 @@ NodeValue = TypeVar("NodeValue") IndexValue = TypeVar("IndexValue") Dtype = TypeVar("Dtype") + + +class any_ndarray: + def __class_getitem__(cls, item): + return cp.ndarray[item] | np.ndarray[item] diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 72e4094b8b7..9683df5e7f9 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -12,13 +12,19 @@ # limitations under the License. from __future__ import annotations +import itertools import operator as op import sys from random import Random +from typing import SupportsIndex import cupy as cp +import numpy as np -__all__ = ["_groupby", "_seed_to_int"] +__all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] + +# This may switch to np.uint32 at some point +index_dtype = np.int32 def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: @@ -58,3 +64,67 @@ def _seed_to_int(seed: int | Random | None) -> int: if isinstance(seed, Random): return seed.randint(0, sys.maxsize) return op.index(seed) # Ensure seed is integral + + +def _get_int_dtype( + val: SupportsIndex, *, signed: bool | None = None, unsigned: bool | None = None +): + """Determine the smallest integer dtype that can store the integer ``val``. + + If signed or unsigned are unspecified, then signed integers are preferred + unless the value can be represented by a smaller unsigned integer. + + Raises + ------ + ValueError : If the value cannot be represented with an int dtype. + """ + # This is similar in spirit to `np.min_scalar_type` + if signed is not None: + if unsigned is not None and (not signed) is (not unsigned): + raise TypeError( + f"signed (={signed}) and unsigned (={unsigned}) keyword arguments " + "are incompatible." + ) + signed = bool(signed) + unsigned = not signed + elif unsigned is not None: + unsigned = bool(unsigned) + signed = not unsigned + + val = op.index(val) # Ensure val is integral + if val < 0: + if unsigned: + raise ValueError(f"Value is incompatible with unsigned int: {val}.") + signed = True + unsigned = False + + if signed is not False: + # Number of bytes (and a power of two) + signed_nbytes = (val + (val < 0)).bit_length() // 8 + 1 + signed_nbytes = next( + filter( + signed_nbytes.__le__, + itertools.accumulate(itertools.repeat(2), op.mul, initial=1), + ) + ) + if unsigned is not False: + # Number of bytes (and a power of two) + unsigned_nbytes = (val.bit_length() + 7) // 8 + unsigned_nbytes = next( + filter( + unsigned_nbytes.__le__, + itertools.accumulate(itertools.repeat(2), op.mul, initial=1), + ) + ) + if signed is None and unsigned is None: + # Prefer signed int if same size + signed = signed_nbytes <= unsigned_nbytes + + if signed: + dtype_string = f"i{signed_nbytes}" + else: + dtype_string = f"u{unsigned_nbytes}" + try: + return np.dtype(dtype_string) + except TypeError as exc: + raise ValueError("Value is too large to store as integer: {val}") from exc diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 9fec8fa0242..3d3029da232 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -160,7 +160,8 @@ ignore = [ # "SIM300", # Yoda conditions are discouraged, use ... instead (Note: we're not this picky) # "SIM401", # Use dict.get ... instead of if-else-block (Note: if-else better for coverage and sometimes clearer) # "TRY004", # Prefer `TypeError` exception for invalid type (Note: good advice, but not worth the nuisance) - # "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) + "B904", # Bare `raise` inside exception clause (like TRY200; sometimes okay) + "TRY200", # Use `raise from` to specify exception cause (Note: sometimes okay to raise original exception) # Intentionally ignored "A003", # Class attribute ... is shadowing a python builtin From 24845ca319fb3922f514ef98844f1e1acb5e0123 Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Tue, 31 Oct 2023 15:38:05 -0400 Subject: [PATCH 066/111] Skip certain `cugraph-pyg` tests when `torch_sparse` is not available (#3962) The CSC (CSR) code path in cugraph-pyg requires `torch_sparse` package. However, `torch_sparse` does not seem to work out of box for rockylinux8. This PR fixes such [CI failures](https://github.com/rapidsai/cugraph/actions/runs/6691094105/job/18177667677). Closes https://github.com/rapidsai/graph_dl/issues/343 Authors: - Tingyu Wang (https://github.com/tingyu66) Approvers: - Naim (https://github.com/naimnv) - Alex Barghi (https://github.com/alexbarghi-nv) URL: https://github.com/rapidsai/cugraph/pull/3962 --- .../cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 03274948158..836b30c9df7 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -29,6 +29,7 @@ torch = import_optional("torch") torch_geometric = import_optional("torch_geometric") +torch_sparse = import_optional("torch_sparse") trim_to_layer = import_optional("torch_geometric.utils.trim_to_layer") @@ -200,6 +201,9 @@ def test_cugraph_loader_from_disk_subset(): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(torch_sparse, MissingModule), reason="torch-sparse not available" +) def test_cugraph_loader_from_disk_subset_csr(): m = [2, 9, 99, 82, 11, 13] n = torch.arange(1, 1 + len(m), dtype=torch.int32) @@ -332,6 +336,9 @@ def test_cugraph_loader_e2e_coo(): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.skipif( + isinstance(torch_sparse, MissingModule), reason="torch-sparse not available" +) @pytest.mark.parametrize("framework", ["pyg", "cugraph-ops"]) def test_cugraph_loader_e2e_csc(framework): m = [2, 9, 99, 82, 9, 3, 18, 1, 12] From eb1e515553ef133f0ea484a663b1b3ff82b1d5c6 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 31 Oct 2023 17:50:52 -0500 Subject: [PATCH 067/111] nx-cugraph: add CC for undirected graphs to fix k-truss (#3965) Fixes #3963 and add `connected_components`, `is_connected`, `node_connected_component`, and `number_connected_components`. Also updated `_groupby` to handle groups that are not consecutive integers starting with 0. Also, `plc.weakly_connected_components` does not handle isolated nodes well, and I needed to handle this at the Python layer as was done in #3897 Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3965 --- python/nx-cugraph/_nx_cugraph/__init__.py | 8 ++ .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/community/louvain.py | 2 +- .../algorithms/components/__init__.py | 13 ++ .../algorithms/components/connected.py | 130 ++++++++++++++++++ .../nx-cugraph/nx_cugraph/algorithms/core.py | 10 ++ python/nx-cugraph/nx_cugraph/classes/graph.py | 5 + python/nx-cugraph/nx_cugraph/interface.py | 9 ++ .../nx_cugraph/tests/test_ktruss.py | 30 ++++ .../nx-cugraph/nx_cugraph/utils/decorators.py | 5 +- python/nx-cugraph/nx_cugraph/utils/misc.py | 44 ++++-- python/nx-cugraph/pyproject.toml | 1 + 12 files changed, 243 insertions(+), 17 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/components/connected.py create mode 100644 python/nx-cugraph/nx_cugraph/tests/test_ktruss.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index af1df04644c..8ef976aabf1 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -38,6 +38,7 @@ "complete_bipartite_graph", "complete_graph", "complete_multipartite_graph", + "connected_components", "cubical_graph", "cycle_graph", "davis_southern_women_graph", @@ -56,6 +57,7 @@ "house_x_graph", "icosahedral_graph", "in_degree_centrality", + "is_connected", "is_isolate", "isolates", "k_truss", @@ -66,7 +68,9 @@ "lollipop_graph", "louvain_communities", "moebius_kantor_graph", + "node_connected_component", "null_graph", + "number_connected_components", "number_of_isolates", "number_of_selfloops", "octahedral_graph", @@ -91,6 +95,10 @@ "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", + "k_truss": ( + "Currently raises `NotImplementedError` for graphs with more than one connected\n" + "component when k >= 3. We expect to fix this soon." + ), "louvain_communities": "`seed` parameter is currently ignored.", # END: extra_docstrings }, diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 69feb8f6437..87b1967fa93 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,8 +10,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community +from . import bipartite, centrality, community, components from .bipartite import complete_bipartite_graph from .centrality import * +from .components import * from .core import * from .isolate import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 62261d109a2..45a3429d2ee 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -62,7 +62,7 @@ def louvain_communities( resolution=resolution, do_expensive_check=False, ) - groups = _groupby(clusters, vertices) + groups = _groupby(clusters, vertices, groups_are_canonical=True) rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix isolates = _isolates(G) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py new file mode 100644 index 00000000000..26816ef3692 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .connected import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py new file mode 100644 index 00000000000..41f3457d542 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/components/connected.py @@ -0,0 +1,130 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import itertools + +import cupy as cp +import networkx as nx +import pylibcugraph as plc + +from nx_cugraph.convert import _to_undirected_graph +from nx_cugraph.utils import _groupby, networkx_algorithm, not_implemented_for + +from ..isolate import _isolates + +__all__ = [ + "number_connected_components", + "connected_components", + "is_connected", + "node_connected_component", +] + + +@not_implemented_for("directed") +@networkx_algorithm +def number_connected_components(G): + return sum(1 for _ in connected_components(G)) + # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL + # G = _to_undirected_graph(G) + # unused_node_ids, labels = plc.weakly_connected_components( + # resource_handle=plc.ResourceHandle(), + # graph=G._get_plc_graph(), + # offsets=None, + # indices=None, + # weights=None, + # labels=None, + # do_expensive_check=False, + # ) + # return cp.unique(labels).size + + +@number_connected_components._can_run +def _(G): + # NetworkX <= 3.2.1 does not check directedness for us + try: + return not G.is_directed() + except Exception: + return False + + +@not_implemented_for("directed") +@networkx_algorithm +def connected_components(G): + G = _to_undirected_graph(G) + if G.src_indices.size == 0: + # TODO: PLC doesn't handle empty graphs (or isolated nodes) gracefully! + return [{key} for key in G._nodeiter_to_iter(range(len(G)))] + node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + groups = _groupby(labels, node_ids) + it = (G._nodearray_to_set(connected_ids) for connected_ids in groups.values()) + # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + isolates = _isolates(G) + if isolates.size > 0: + isolates = isolates[isolates > node_ids.max()] + if isolates.size > 0: + it = itertools.chain( + it, ({node} for node in G._nodearray_to_list(isolates)) + ) + return it + + +@not_implemented_for("directed") +@networkx_algorithm +def is_connected(G): + G = _to_undirected_graph(G) + if len(G) == 0: + raise nx.NetworkXPointlessConcept( + "Connectivity is undefined for the null graph." + ) + for community in connected_components(G): + return len(community) == len(G) + raise RuntimeError # pragma: no cover + # PREFERRED IMPLEMENTATION, BUT PLC DOES NOT HANDLE ISOLATED VERTICES WELL + # unused_node_ids, labels = plc.weakly_connected_components( + # resource_handle=plc.ResourceHandle(), + # graph=G._get_plc_graph(), + # offsets=None, + # indices=None, + # weights=None, + # labels=None, + # do_expensive_check=False, + # ) + # return labels.size == len(G) and cp.unique(labels).size == 1 + + +@not_implemented_for("directed") +@networkx_algorithm +def node_connected_component(G, n): + # We could also do plain BFS from n + G = _to_undirected_graph(G) + node_id = n if G.key_to_id is None else G.key_to_id[n] + node_ids, labels = plc.weakly_connected_components( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(), + offsets=None, + indices=None, + weights=None, + labels=None, + do_expensive_check=False, + ) + indices = cp.nonzero(node_ids == node_id)[0] + if indices.size == 0: + return {n} + return G._nodearray_to_set(node_ids[labels == labels[indices[0]]]) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/core.py b/python/nx-cugraph/nx_cugraph/algorithms/core.py index 33e79793553..2219388bc58 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/core.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/core.py @@ -24,6 +24,10 @@ @not_implemented_for("multigraph") @networkx_algorithm def k_truss(G, k): + """ + Currently raises `NotImplementedError` for graphs with more than one connected + component when k >= 3. We expect to fix this soon. + """ if is_nx := isinstance(G, nx.Graph): G = nxcg.from_networkx(G, preserve_all_attrs=True) if nxcg.number_of_selfloops(G) > 0: @@ -31,6 +35,7 @@ def k_truss(G, k): "Input graph has self loops which is not permitted; " "Consider using G.remove_edges_from(nx.selfloop_edges(G))." ) + # TODO: create renumbering helper function(s) if k < 3: # k-truss graph is comprised of nodes incident on k-2 triangles, so k<3 is a @@ -49,6 +54,11 @@ def k_truss(G, k): # Renumber step 1: edge values (no changes needed) edge_values = {key: val.copy() for key, val in G.edge_values.items()} edge_masks = {key: val.copy() for key, val in G.edge_masks.items()} + elif (ncc := nxcg.number_connected_components(G)) > 1: + raise NotImplementedError( + "nx_cugraph.k_truss does not yet work on graphs with more than one " + f"connected component (this graph has {ncc}). We expect to fix this soon." + ) else: edge_dtype = _get_int_dtype(G.src_indices.size - 1) edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 2048c4c3d72..23004651fc5 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -692,6 +692,11 @@ def _nodearray_to_list(self, node_ids: cp.ndarray[IndexValue]) -> list[NodeKey]: return node_ids.tolist() return list(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_set(self, node_ids: cp.ndarray[IndexValue]) -> set[NodeKey]: + if self.key_to_id is None: + return set(node_ids.tolist()) + return set(self._nodeiter_to_iter(node_ids.tolist())) + def _nodearray_to_dict( self, values: cp.ndarray[NodeValue] ) -> dict[NodeKey, NodeValue]: diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index fd0b1483d73..875f8621021 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -223,11 +223,20 @@ def key(testpath): } ) + too_slow = "Too slow to run" + skip = { + key("test_tree_isomorphism.py:test_positive"): too_slow, + key("test_tree_isomorphism.py:test_negative"): too_slow, + } + for item in items: kset = set(item.keywords) for (test_name, keywords), reason in xfail.items(): if item.name == test_name and keywords.issubset(kset): item.add_marker(pytest.mark.xfail(reason=reason)) + for (test_name, keywords), reason in skip.items(): + if item.name == test_name and keywords.issubset(kset): + item.add_marker(pytest.mark.skip(reason=reason)) @classmethod def can_run(cls, name, args, kwargs): diff --git a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py new file mode 100644 index 00000000000..a3e4cee3124 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py @@ -0,0 +1,30 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import pytest + +import nx_cugraph as nxcg + + +@pytest.mark.parametrize( + "get_graph", [nx.florentine_families_graph, nx.les_miserables_graph] +) +def test_k_truss(get_graph): + Gnx = get_graph() + Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True) + for k in range(10): + Hnx = nx.k_truss(Gnx, k) + Hcg = nxcg.k_truss(Gcg, k) + assert nx.utils.graphs_equal(Hnx, nxcg.to_networkx(Hcg)) + if Hnx.number_of_edges() == 0: + break diff --git a/python/nx-cugraph/nx_cugraph/utils/decorators.py b/python/nx-cugraph/nx_cugraph/utils/decorators.py index 0f15d236ecd..0048aee51bb 100644 --- a/python/nx-cugraph/nx_cugraph/utils/decorators.py +++ b/python/nx-cugraph/nx_cugraph/utils/decorators.py @@ -13,6 +13,7 @@ from __future__ import annotations from functools import partial, update_wrapper +from textwrap import dedent from networkx.utils.decorators import nodes_or_number, not_implemented_for @@ -65,7 +66,9 @@ def __new__( ) instance.extra_params = extra_params # The docstring on our function is added to the NetworkX docstring. - instance.extra_doc = func.__doc__ + instance.extra_doc = ( + dedent(func.__doc__.lstrip("\n").rstrip()) if func.__doc__ else None + ) # Copy __doc__ from NetworkX if instance.name in _registered_algorithms: instance.__doc__ = _registered_algorithms[instance.name].__doc__ diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 9683df5e7f9..26f023bdcec 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -21,40 +21,56 @@ import cupy as cp import numpy as np +try: + from itertools import pairwise # Python >=3.10 +except ImportError: + + def pairwise(it): + it = iter(it) + for prev in it: + for cur in it: + yield (prev, cur) + prev = cur + + __all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] # This may switch to np.uint32 at some point index_dtype = np.int32 -def _groupby(groups: cp.ndarray, values: cp.ndarray) -> dict[int, cp.ndarray]: +def _groupby( + groups: cp.ndarray, values: cp.ndarray, groups_are_canonical: bool = False +) -> dict[int, cp.ndarray]: """Perform a groupby operation given an array of group IDs and array of values. Parameters ---------- groups : cp.ndarray Array that holds the group IDs. - Group IDs are assumed to be consecutive integers from 0. values : cp.ndarray Array of values to be grouped according to groups. Must be the same size as groups array. + groups_are_canonical : bool, default False + Whether the group IDs are consecutive integers beginning with 0. Returns ------- dict with group IDs as keys and cp.ndarray as values. """ - # It would actually be easy to support groups that aren't consecutive integers, - # but let's wait until we need it to implement it. - sorted_groups = cp.argsort(groups) - sorted_values = values[sorted_groups] - rv = {} - start = 0 - for i, end in enumerate( - [*(cp.nonzero(cp.diff(groups[sorted_groups]))[0] + 1).tolist(), groups.size] - ): - rv[i] = sorted_values[start:end] - start = end - return rv + if groups.size == 0: + return {} + sort_indices = cp.argsort(groups) + sorted_groups = groups[sort_indices] + sorted_values = values[sort_indices] + prepend = 1 if groups_are_canonical else sorted_groups[0] + 1 + left_bounds = cp.nonzero(cp.diff(sorted_groups, prepend=prepend))[0] + boundaries = pairwise(itertools.chain(left_bounds.tolist(), [groups.size])) + if groups_are_canonical: + it = enumerate(boundaries) + else: + it = zip(sorted_groups[left_bounds].tolist(), boundaries) + return {group: sorted_values[start:end] for group, (start, end) in it} def _seed_to_int(seed: int | Random | None) -> int: diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 3d3029da232..7e51efd4fe4 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -218,6 +218,7 @@ ignore = [ # Allow assert, print, RNG, and no docstring "nx_cugraph/**/tests/*py" = ["S101", "S311", "T201", "D103", "D100"] "_nx_cugraph/__init__.py" = ["E501"] +"nx_cugraph/algorithms/**/*py" = ["D205", "D401"] # Allow flexible docstrings for algorithms [tool.ruff.flake8-annotations] mypy-init-return = true From 0a905630c990235783f77c461691a983f97afc9f Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Wed, 1 Nov 2023 11:21:45 -0700 Subject: [PATCH 068/111] Cut peak memory footprint in graph creation (#3966) This limits memory footprint (especially in single-GPU or multi-GPU with a small number of GPUs) to the size of edge list * 1.5 + alpha (alpha to store O(V) data, V: # vertices). Authors: - Seunghwa Kang (https://github.com/seunghwak) Approvers: - Naim (https://github.com/naimnv) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3966 --- cpp/src/c_api/capi_helper.cu | 2 +- .../create_graph_from_edgelist_impl.cuh | 425 ++++++++---------- cpp/src/structure/detail/structure_utils.cuh | 281 ++++++------ cpp/src/structure/induced_subgraph_impl.cuh | 2 + cpp/src/structure/renumber_edgelist_impl.cuh | 100 +++-- cpp/tests/community/mg_egonet_test.cu | 1 + .../structure/mg_induced_subgraph_test.cu | 1 + cpp/tests/utilities/test_utilities_impl.cuh | 61 ++- 8 files changed, 415 insertions(+), 458 deletions(-) diff --git a/cpp/src/c_api/capi_helper.cu b/cpp/src/c_api/capi_helper.cu index af0163b0512..0ee49f87265 100644 --- a/cpp/src/c_api/capi_helper.cu +++ b/cpp/src/c_api/capi_helper.cu @@ -44,7 +44,7 @@ shuffle_vertex_ids_and_offsets(raft::handle_t const& handle, thrust::make_zip_iterator(ids.end(), vertices.end())); auto return_offsets = cugraph::detail::compute_sparse_offsets( - ids.begin(), ids.end(), size_t{0}, size_t{offsets.size() - 1}, handle.get_stream()); + ids.begin(), ids.end(), size_t{0}, size_t{offsets.size() - 1}, true, handle.get_stream()); return std::make_tuple(std::move(vertices), std::move(return_offsets)); } diff --git a/cpp/src/structure/create_graph_from_edgelist_impl.cuh b/cpp/src/structure/create_graph_from_edgelist_impl.cuh index 0d4b12a3e38..8dd587e1661 100644 --- a/cpp/src/structure/create_graph_from_edgelist_impl.cuh +++ b/cpp/src/structure/create_graph_from_edgelist_impl.cuh @@ -510,7 +510,18 @@ create_graph_from_edgelist_impl( auto use_dcs = num_segments_per_vertex_partition > (detail::num_sparse_segments_per_vertex_partition + 2); - // 4. compress edge list (COO) to CSR (or CSC) or CSR + DCSR (CSC + DCSC) hybrid + // 4. sort and compress edge list (COO) to CSR (or CSC) or CSR + DCSR (CSC + DCSC) hybrid + + auto total_global_mem = handle.get_device_properties().totalGlobalMem; + size_t element_size = sizeof(vertex_t) * 2; + if (edgelist_weights) { element_size += sizeof(weight_t); } + if (edgelist_edge_ids) { element_size += sizeof(edge_id_t); } + if (edgelist_edge_types) { element_size += sizeof(edge_type_t); } + auto constexpr mem_frugal_ratio = + 0.25; // if the expected temporary buffer size exceeds the mem_frugal_ratio of the + // total_global_mem, switch to the memory frugal approach + auto mem_frugal_threshold = + static_cast(static_cast(total_global_mem / element_size) * mem_frugal_ratio); std::vector> edge_partition_offsets; std::vector> edge_partition_indices; @@ -559,154 +570,139 @@ create_graph_from_edgelist_impl( if (edgelist_weights) { if (edgelist_edge_ids) { if (edgelist_edge_types) { - auto edge_value_first = - thrust::make_zip_iterator((*edge_partition_edgelist_weights)[i].begin(), - (*edge_partition_edgelist_edge_ids)[i].begin(), - (*edge_partition_edgelist_edge_types)[i].begin()); std::forward_as_tuple( offsets, indices, std::tie(weights, edge_ids, edge_types), dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::make_tuple(std::move((*edge_partition_edgelist_weights)[i]), + std::move((*edge_partition_edgelist_edge_ids)[i]), + std::move((*edge_partition_edgelist_edge_types)[i])), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } else { - auto edge_value_first = - thrust::make_zip_iterator((*edge_partition_edgelist_weights)[i].begin(), - (*edge_partition_edgelist_edge_ids)[i].begin()); std::forward_as_tuple(offsets, indices, std::tie(weights, edge_ids), dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::make_tuple(std::move((*edge_partition_edgelist_weights)[i]), + std::move((*edge_partition_edgelist_edge_ids)[i])), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } } else { if (edgelist_edge_types) { - auto edge_value_first = - thrust::make_zip_iterator((*edge_partition_edgelist_weights)[i].begin(), - (*edge_partition_edgelist_edge_types)[i].begin()); std::forward_as_tuple(offsets, indices, std::tie(weights, edge_types), dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::make_tuple(std::move((*edge_partition_edgelist_weights)[i]), + std::move((*edge_partition_edgelist_edge_types)[i])), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } else { - auto edge_value_first = (*edge_partition_edgelist_weights)[i].begin(); std::forward_as_tuple(offsets, indices, weights, dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::move((*edge_partition_edgelist_weights)[i]), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } } } else { if (edgelist_edge_ids) { if (edgelist_edge_types) { - auto edge_value_first = - thrust::make_zip_iterator((*edge_partition_edgelist_edge_ids)[i].begin(), - (*edge_partition_edgelist_edge_types)[i].begin()); std::forward_as_tuple( offsets, indices, std::tie(edge_ids, edge_types), dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::make_tuple(std::move((*edge_partition_edgelist_edge_ids)[i]), + std::move((*edge_partition_edgelist_edge_types)[i])), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } else { - auto edge_value_first = (*edge_partition_edgelist_edge_ids)[i].begin(); std::forward_as_tuple(offsets, indices, edge_ids, dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::move((*edge_partition_edgelist_edge_ids)[i]), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } } else { if (edgelist_edge_types) { - auto edge_value_first = (*edge_partition_edgelist_edge_types)[i].begin(); std::forward_as_tuple(offsets, indices, edge_types, dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), - edge_value_first, + detail::sort_and_compress_edgelist( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), + std::move((*edge_partition_edgelist_edge_types)[i]), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } else { std::forward_as_tuple(offsets, indices, dcs_nzd_vertices) = - detail::compress_edgelist( - edge_partition_edgelist_srcs[i].begin(), - edge_partition_edgelist_srcs[i].end(), - edge_partition_edgelist_dsts[i].begin(), + detail::sort_and_compress_edgelist( + std::move(edge_partition_edgelist_srcs[i]), + std::move(edge_partition_edgelist_dsts[i]), major_range_first, major_hypersparse_first, major_range_last, minor_range_first, minor_range_last, + mem_frugal_threshold, handle.get_stream()); } } } - edge_partition_edgelist_srcs[i].resize(0, handle.get_stream()); - edge_partition_edgelist_srcs[i].shrink_to_fit(handle.get_stream()); - edge_partition_edgelist_dsts[i].resize(0, handle.get_stream()); - edge_partition_edgelist_dsts[i].shrink_to_fit(handle.get_stream()); - if (edge_partition_edgelist_weights) { - (*edge_partition_edgelist_weights)[i].resize(0, handle.get_stream()); - (*edge_partition_edgelist_weights)[i].shrink_to_fit(handle.get_stream()); - } - if (edge_partition_edgelist_edge_ids) { - (*edge_partition_edgelist_edge_ids)[i].resize(0, handle.get_stream()); - (*edge_partition_edgelist_edge_ids)[i].shrink_to_fit(handle.get_stream()); - } - if (edge_partition_edgelist_edge_types) { - (*edge_partition_edgelist_edge_types)[i].resize(0, handle.get_stream()); - (*edge_partition_edgelist_edge_types)[i].shrink_to_fit(handle.get_stream()); - } edge_partition_offsets.push_back(std::move(offsets)); edge_partition_indices.push_back(std::move(indices)); if (edge_partition_weights) { (*edge_partition_weights).push_back(std::move(*weights)); } @@ -954,6 +950,17 @@ create_graph_from_edgelist_impl( // convert edge list (COO) to compressed sparse format (CSR or CSC) + auto total_global_mem = handle.get_device_properties().totalGlobalMem; + size_t element_size = sizeof(vertex_t) * 2; + if (edgelist_weights) { element_size += sizeof(weight_t); } + if (edgelist_edge_ids) { element_size += sizeof(edge_id_t); } + if (edgelist_edge_types) { element_size += sizeof(edge_type_t); } + auto constexpr mem_frugal_ratio = + 0.25; // if the expected temporary buffer size exceeds the mem_frugal_ratio of the + // total_global_mem, switch to the memory frugal approach + auto mem_frugal_threshold = + static_cast(static_cast(total_global_mem / element_size) * mem_frugal_ratio); + rmm::device_uvector offsets(size_t{0}, handle.get_stream()); rmm::device_uvector indices(size_t{0}, handle.get_stream()); std::optional> weights{std::nullopt}; @@ -963,202 +970,130 @@ create_graph_from_edgelist_impl( if (edgelist_weights) { if (edgelist_edge_ids) { if (edgelist_edge_types) { - auto edge_value_first = thrust::make_zip_iterator((*edgelist_weights).begin(), - (*edgelist_edge_ids).begin(), - (*edgelist_edge_types).begin()); std::forward_as_tuple(offsets, indices, std::tie(weights, ids, types), std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), + std::move(*edgelist_edge_ids), + std::move(*edgelist_edge_types)), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } else { - auto edge_value_first = - thrust::make_zip_iterator((*edgelist_weights).begin(), (*edgelist_edge_ids).begin()); std::forward_as_tuple(offsets, indices, std::tie(weights, ids), std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), std::move(*edgelist_edge_ids)), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } } else { if (edgelist_edge_types) { - auto edge_value_first = - thrust::make_zip_iterator((*edgelist_weights).begin(), (*edgelist_edge_types).begin()); std::forward_as_tuple(offsets, indices, std::tie(weights, types), std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), std::move(*edgelist_edge_types)), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } else { - auto edge_value_first = (*edgelist_weights).begin(); std::forward_as_tuple(offsets, indices, weights, std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(*edgelist_weights), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } } } else { if (edgelist_edge_ids) { if (edgelist_edge_types) { - auto edge_value_first = - thrust::make_zip_iterator((*edgelist_edge_ids).begin(), (*edgelist_edge_types).begin()); std::forward_as_tuple(offsets, indices, std::tie(ids, types), std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist, + store_transposed>( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_edge_ids), std::move(*edgelist_edge_types)), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } else { - auto edge_value_first = (*edgelist_edge_ids).begin(); std::forward_as_tuple(offsets, indices, ids, std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(*edgelist_edge_ids), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } } else { if (edgelist_edge_types) { - auto edge_value_first = (*edgelist_edge_types).begin(); std::forward_as_tuple(offsets, indices, types, std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - edge_value_first, - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); + detail::sort_and_compress_edgelist( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(*edgelist_edge_types), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } else { std::forward_as_tuple(offsets, indices, std::ignore) = - detail::compress_edgelist(edgelist_srcs.begin(), - edgelist_srcs.end(), - edgelist_dsts.begin(), - vertex_t{0}, - std::optional{std::nullopt}, - num_vertices, - vertex_t{0}, - num_vertices, - handle.get_stream()); - } - } - } - - edgelist_srcs.resize(0, handle.get_stream()); - edgelist_srcs.shrink_to_fit(handle.get_stream()); - edgelist_dsts.resize(0, handle.get_stream()); - edgelist_dsts.shrink_to_fit(handle.get_stream()); - if (edgelist_weights) { - (*edgelist_weights).resize(0, handle.get_stream()); - (*edgelist_weights).shrink_to_fit(handle.get_stream()); - } - if (edgelist_edge_ids) { - (*edgelist_edge_ids).resize(0, handle.get_stream()); - (*edgelist_edge_ids).shrink_to_fit(handle.get_stream()); - } - if (edgelist_edge_types) { - (*edgelist_edge_types).resize(0, handle.get_stream()); - (*edgelist_edge_types).shrink_to_fit(handle.get_stream()); - } - - // segmented sort neighbors - - if (weights) { - if (ids) { - if (types) { - detail::sort_adjacency_list( - handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - thrust::make_zip_iterator((*weights).begin(), (*ids).begin(), (*types).begin())); - } else { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - thrust::make_zip_iterator((*weights).begin(), (*ids).begin())); - } - } else { - if (types) { - detail::sort_adjacency_list( - handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - thrust::make_zip_iterator((*weights).begin(), (*types).begin())); - } else { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - (*weights).begin()); - } - } - } else { - if (ids) { - if (types) { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - thrust::make_zip_iterator((*ids).begin(), (*types).begin())); - } else { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - (*ids).begin()); - } - } else { - if (types) { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end(), - (*types).begin()); - } else { - detail::sort_adjacency_list(handle, - raft::device_span(offsets.data(), offsets.size()), - indices.begin(), - indices.end()); + detail::sort_and_compress_edgelist( + std::move(edgelist_srcs), + std::move(edgelist_dsts), + vertex_t{0}, + std::optional{std::nullopt}, + num_vertices, + vertex_t{0}, + num_vertices, + mem_frugal_threshold, + handle.get_stream()); } } } diff --git a/cpp/src/structure/detail/structure_utils.cuh b/cpp/src/structure/detail/structure_utils.cuh index f57b549e1ef..01fbccaa53e 100644 --- a/cpp/src/structure/detail/structure_utils.cuh +++ b/cpp/src/structure/detail/structure_utils.cuh @@ -47,57 +47,38 @@ namespace cugraph { namespace detail { -template -struct update_edge_t { - raft::device_span offsets{}; - raft::device_span indices{}; - EdgeValueIterator edge_value_first{}; - vertex_t major_range_first{}; - - __device__ void operator()(typename thrust::iterator_traits::value_type e) const - { - auto s = thrust::get<0>(e); - auto d = thrust::get<1>(e); - auto major = store_transposed ? d : s; - auto minor = store_transposed ? s : d; - auto start = offsets[major - major_range_first]; - auto degree = offsets[(major - major_range_first) + 1] - start; - auto idx = - atomicAdd(&indices[start + degree - 1], vertex_t{1}); // use the last element as a counter - // FIXME: we can actually store minor - minor_range_first instead of minor to save memory if - // minor can be larger than 32 bit but minor - minor_range_first fits within 32 bit - indices[start + idx] = minor; // overwrite the counter only if idx == degree - 1 (no race) - if constexpr (!std::is_same_v) { - auto value = thrust::get<2>(e); - *(edge_value_first + (start + idx)) = value; - } - } -}; - template rmm::device_uvector compute_sparse_offsets( VertexIterator edgelist_major_first, VertexIterator edgelist_major_last, typename thrust::iterator_traits::value_type major_range_first, typename thrust::iterator_traits::value_type major_range_last, + bool edgelist_major_sorted, rmm::cuda_stream_view stream_view) { rmm::device_uvector offsets((major_range_last - major_range_first) + 1, stream_view); - thrust::fill(rmm::exec_policy(stream_view), offsets.begin(), offsets.end(), edge_t{0}); - - auto offset_view = raft::device_span(offsets.data(), offsets.size()); - thrust::for_each(rmm::exec_policy(stream_view), - edgelist_major_first, - edgelist_major_last, - [offset_view, major_range_first] __device__(auto v) { - atomicAdd(&offset_view[v - major_range_first], edge_t{1}); - }); - thrust::exclusive_scan( - rmm::exec_policy(stream_view), offsets.begin(), offsets.end(), offsets.begin()); + if (edgelist_major_sorted) { + offsets.set_element_to_zero_async(0, stream_view); + thrust::upper_bound(rmm::exec_policy(stream_view), + edgelist_major_first, + edgelist_major_last, + thrust::make_counting_iterator(major_range_first), + thrust::make_counting_iterator(major_range_last), + offsets.begin() + 1); + } else { + thrust::fill(rmm::exec_policy(stream_view), offsets.begin(), offsets.end(), edge_t{0}); + + auto offset_view = raft::device_span(offsets.data(), offsets.size()); + thrust::for_each(rmm::exec_policy(stream_view), + edgelist_major_first, + edgelist_major_last, + [offset_view, major_range_first] __device__(auto v) { + atomicAdd(&offset_view[v - major_range_first], edge_t{1}); + }); + + thrust::exclusive_scan( + rmm::exec_policy(stream_view), offsets.begin(), offsets.end(), offsets.begin()); + } return offsets; } @@ -156,61 +137,77 @@ std::tuple, rmm::device_uvector> compress_ } // compress edge list (COO) to CSR (or CSC) or CSR + DCSR (CSC + DCSC) hybrid -template -std::tuple< - rmm::device_uvector, - rmm::device_uvector::value_type>, - decltype(allocate_dataframe_buffer::value_type>(size_t{0}, rmm::cuda_stream_view{})), - std::optional::value_type>>> -compress_edgelist( - VertexIterator edgelist_src_first, - VertexIterator edgelist_src_last, - VertexIterator edgelist_dst_first, - EdgeValueIterator edge_value_first, - typename thrust::iterator_traits::value_type major_range_first, - std::optional::value_type> - major_hypersparse_first, - typename thrust::iterator_traits::value_type major_range_last, - typename thrust::iterator_traits::value_type /* minor_range_first */, - typename thrust::iterator_traits::value_type /* minor_range_last */, +template +std::tuple, + rmm::device_uvector, + decltype(allocate_dataframe_buffer(size_t{0}, rmm::cuda_stream_view{})), + std::optional>> +sort_and_compress_edgelist( + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + decltype(allocate_dataframe_buffer(0, rmm::cuda_stream_view{}))&& edgelist_values, + vertex_t major_range_first, + std::optional major_hypersparse_first, + vertex_t major_range_last, + vertex_t /* minor_range_first */, + vertex_t /* minor_range_last */, + size_t mem_frugal_threshold, rmm::cuda_stream_view stream_view) { - using vertex_t = std::remove_cv_t::value_type>; - using edge_value_t = - std::remove_cv_t::value_type>; - - auto number_of_edges = - static_cast(thrust::distance(edgelist_src_first, edgelist_src_last)); - - auto offsets = compute_sparse_offsets( - store_transposed ? edgelist_dst_first : edgelist_src_first, - store_transposed ? edgelist_dst_first + number_of_edges : edgelist_src_last, - major_range_first, - major_range_last, - stream_view); - - rmm::device_uvector indices(number_of_edges, stream_view); - thrust::fill(rmm::exec_policy(stream_view), indices.begin(), indices.end(), vertex_t{0}); - auto values = allocate_dataframe_buffer(number_of_edges, stream_view); - - auto offset_view = raft::device_span(offsets.data(), offsets.size()); - auto index_view = raft::device_span(indices.data(), indices.size()); - auto edge_first = thrust::make_zip_iterator( - thrust::make_tuple(edgelist_src_first, edgelist_dst_first, edge_value_first)); - thrust::for_each( - rmm::exec_policy(stream_view), - edge_first, - edge_first + number_of_edges, - update_edge_t{ - offset_view, index_view, get_dataframe_buffer_begin(values), major_range_first}); + auto edgelist_majors = std::move(store_transposed ? edgelist_dsts : edgelist_srcs); + auto edgelist_minors = std::move(store_transposed ? edgelist_srcs : edgelist_dsts); + + rmm::device_uvector offsets(0, stream_view); + rmm::device_uvector indices(0, stream_view); + auto values = allocate_dataframe_buffer(0, stream_view); + auto pair_first = thrust::make_zip_iterator(edgelist_majors.begin(), edgelist_minors.begin()); + if (edgelist_minors.size() > mem_frugal_threshold) { + offsets = compute_sparse_offsets(edgelist_majors.begin(), + edgelist_majors.end(), + major_range_first, + major_range_last, + false, + stream_view); + + auto pivot = major_range_first + static_cast(thrust::distance( + offsets.begin(), + thrust::lower_bound(rmm::exec_policy(stream_view), + offsets.begin(), + offsets.end(), + edgelist_minors.size() / 2))); + auto second_first = + detail::mem_frugal_partition(pair_first, + pair_first + edgelist_minors.size(), + get_dataframe_buffer_begin(edgelist_values), + thrust_tuple_get, 0>{}, + pivot, + stream_view); + thrust::sort_by_key(rmm::exec_policy(stream_view), + pair_first, + std::get<0>(second_first), + get_dataframe_buffer_begin(edgelist_values)); + thrust::sort_by_key(rmm::exec_policy(stream_view), + std::get<0>(second_first), + pair_first + edgelist_minors.size(), + std::get<1>(second_first)); + } else { + thrust::sort_by_key(rmm::exec_policy(stream_view), + pair_first, + pair_first + edgelist_minors.size(), + get_dataframe_buffer_begin(edgelist_values)); + + offsets = compute_sparse_offsets(edgelist_majors.begin(), + edgelist_majors.end(), + major_range_first, + major_range_last, + true, + stream_view); + } + indices = std::move(edgelist_minors); + values = std::move(edgelist_values); + + edgelist_majors.resize(0, stream_view); + edgelist_majors.shrink_to_fit(stream_view); std::optional> dcs_nzd_vertices{std::nullopt}; if (major_hypersparse_first) { @@ -226,47 +223,61 @@ compress_edgelist( } // compress edge list (COO) to CSR (or CSC) or CSR + DCSR (CSC + DCSC) hybrid -template -std::tuple< - rmm::device_uvector, - rmm::device_uvector::value_type>, - std::optional::value_type>>> -compress_edgelist( - VertexIterator edgelist_src_first, - VertexIterator edgelist_src_last, - VertexIterator edgelist_dst_first, - typename thrust::iterator_traits::value_type major_range_first, - std::optional::value_type> - major_hypersparse_first, - typename thrust::iterator_traits::value_type major_range_last, - typename thrust::iterator_traits::value_type /* minor_range_first */, - typename thrust::iterator_traits::value_type /* minor_range_last */, - rmm::cuda_stream_view stream_view) +template +std::tuple, + rmm::device_uvector, + std::optional>> +sort_and_compress_edgelist(rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + vertex_t major_range_first, + std::optional major_hypersparse_first, + vertex_t major_range_last, + vertex_t /* minor_range_first */, + vertex_t /* minor_range_last */, + size_t mem_frugal_threshold, + rmm::cuda_stream_view stream_view) { - using vertex_t = std::remove_cv_t::value_type>; - - auto number_of_edges = - static_cast(thrust::distance(edgelist_src_first, edgelist_src_last)); - - auto offsets = compute_sparse_offsets( - store_transposed ? edgelist_dst_first : edgelist_src_first, - store_transposed ? edgelist_dst_first + number_of_edges : edgelist_src_last, - major_range_first, - major_range_last, - stream_view); - - rmm::device_uvector indices(number_of_edges, stream_view); - thrust::fill(rmm::exec_policy(stream_view), indices.begin(), indices.end(), vertex_t{0}); - - auto offset_view = raft::device_span(offsets.data(), offsets.size()); - auto index_view = raft::device_span(indices.data(), indices.size()); - auto edge_first = - thrust::make_zip_iterator(thrust::make_tuple(edgelist_src_first, edgelist_dst_first)); - thrust::for_each(rmm::exec_policy(stream_view), - edge_first, - edge_first + number_of_edges, - update_edge_t{ - offset_view, index_view, static_cast(nullptr), major_range_first}); + auto edgelist_majors = std::move(store_transposed ? edgelist_dsts : edgelist_srcs); + auto edgelist_minors = std::move(store_transposed ? edgelist_srcs : edgelist_dsts); + + rmm::device_uvector offsets(0, stream_view); + rmm::device_uvector indices(0, stream_view); + auto edge_first = thrust::make_zip_iterator(edgelist_majors.begin(), edgelist_minors.begin()); + if (edgelist_minors.size() > mem_frugal_threshold) { + offsets = compute_sparse_offsets(edgelist_majors.begin(), + edgelist_majors.end(), + major_range_first, + major_range_last, + false, + stream_view); + + auto pivot = major_range_first + static_cast(thrust::distance( + offsets.begin(), + thrust::lower_bound(rmm::exec_policy(stream_view), + offsets.begin(), + offsets.end(), + edgelist_minors.size() / 2))); + auto second_first = + detail::mem_frugal_partition(edge_first, + edge_first + edgelist_minors.size(), + thrust_tuple_get, 0>{}, + pivot, + stream_view); + thrust::sort(rmm::exec_policy(stream_view), edge_first, second_first); + thrust::sort(rmm::exec_policy(stream_view), second_first, edge_first + edgelist_minors.size()); + } else { + thrust::sort(rmm::exec_policy(stream_view), edge_first, edge_first + edgelist_minors.size()); + offsets = compute_sparse_offsets(edgelist_majors.begin(), + edgelist_majors.end(), + major_range_first, + major_range_last, + true, + stream_view); + } + indices = std::move(edgelist_minors); + + edgelist_majors.resize(0, stream_view); + edgelist_majors.shrink_to_fit(stream_view); std::optional> dcs_nzd_vertices{std::nullopt}; if (major_hypersparse_first) { diff --git a/cpp/src/structure/induced_subgraph_impl.cuh b/cpp/src/structure/induced_subgraph_impl.cuh index 950cca5828d..18e1af32a71 100644 --- a/cpp/src/structure/induced_subgraph_impl.cuh +++ b/cpp/src/structure/induced_subgraph_impl.cuh @@ -196,6 +196,7 @@ extract_induced_subgraphs( graph_ids_v.end(), size_t{0}, size_t{subgraph_offsets.size() - 1}, + true, handle.get_stream()); dst_subgraph_offsets = @@ -290,6 +291,7 @@ extract_induced_subgraphs( subgraph_edge_graph_ids.end(), size_t{0}, size_t{subgraph_offsets.size() - 1}, + true, handle.get_stream()); #ifdef TIMING diff --git a/cpp/src/structure/renumber_edgelist_impl.cuh b/cpp/src/structure/renumber_edgelist_impl.cuh index 6bc19ff4fe1..09a4dae6c64 100644 --- a/cpp/src/structure/renumber_edgelist_impl.cuh +++ b/cpp/src/structure/renumber_edgelist_impl.cuh @@ -367,18 +367,19 @@ std::tuple, std::vector, vertex_t> compu rmm::device_uvector sorted_local_vertex_degrees(0, handle.get_stream()); std::optional> stream_pool_indices{ std::nullopt}; // FIXME: move this inside the if statement + + auto constexpr num_chunks = size_t{ + 2}; // tuning parameter, this trade-offs # binary searches (up to num_chunks times more binary + // searches can be necessary if num_unique_majors << edgelist_edge_counts[i]) and temporary + // buffer requirement (cut by num_chunks times), currently set to 2 to avoid peak memory + // usage happening in this part (especially when minor_comm_size is small) + if constexpr (multi_gpu) { auto& comm = handle.get_comms(); auto& minor_comm = handle.get_subcomm(cugraph::partition_manager::minor_comm_name()); auto const minor_comm_rank = minor_comm.get_rank(); auto const minor_comm_size = minor_comm.get_size(); - auto constexpr num_chunks = size_t{ - 2}; // tuning parameter, this trade-offs # binary searches (up to num_chunks times more - // binary searches can be necessary if num_unique_majors << edgelist_edge_counts[i]) and - // temporary buffer requirement (cut by num_chunks times), currently set to 2 to avoid - // peak memory usage happening in this part (especially when minor_comm_size is small) - assert(edgelist_majors.size() == minor_comm_size); auto edge_partition_major_range_sizes = @@ -433,29 +434,30 @@ std::tuple, std::vector, vertex_t> compu sorted_major_degrees.end(), edge_t{0}); - rmm::device_uvector tmp_majors( + rmm::device_uvector tmp_majors(0, loop_stream); + tmp_majors.reserve( (static_cast(edgelist_edge_counts[i]) + (num_chunks - 1)) / num_chunks, - handle.get_stream()); + loop_stream); size_t offset{0}; for (size_t j = 0; j < num_chunks; ++j) { size_t this_chunk_size = - std::min(tmp_majors.size(), static_cast(edgelist_edge_counts[i]) - offset); + std::min(tmp_majors.capacity(), static_cast(edgelist_edge_counts[i]) - offset); + tmp_majors.resize(this_chunk_size, loop_stream); thrust::copy(rmm::exec_policy(loop_stream), edgelist_majors[i] + offset, - edgelist_majors[i] + offset + this_chunk_size, + edgelist_majors[i] + offset + tmp_majors.size(), tmp_majors.begin()); - thrust::sort( - rmm::exec_policy(loop_stream), tmp_majors.begin(), tmp_majors.begin() + this_chunk_size); + thrust::sort(rmm::exec_policy(loop_stream), tmp_majors.begin(), tmp_majors.end()); auto num_unique_majors = thrust::count_if(rmm::exec_policy(loop_stream), thrust::make_counting_iterator(size_t{0}), - thrust::make_counting_iterator(this_chunk_size), + thrust::make_counting_iterator(tmp_majors.size()), is_first_in_run_t{tmp_majors.data()}); rmm::device_uvector tmp_keys(num_unique_majors, loop_stream); rmm::device_uvector tmp_values(num_unique_majors, loop_stream); thrust::reduce_by_key(rmm::exec_policy(loop_stream), tmp_majors.begin(), - tmp_majors.begin() + this_chunk_size, + tmp_majors.end(), thrust::make_constant_iterator(edge_t{1}), tmp_keys.begin(), tmp_values.begin()); @@ -486,44 +488,50 @@ std::tuple, std::vector, vertex_t> compu } else { assert(edgelist_majors.size() == 1); - rmm::device_uvector tmp_majors(edgelist_edge_counts[0], handle.get_stream()); - thrust::copy(handle.get_thrust_policy(), - edgelist_majors[0], - edgelist_majors[0] + edgelist_edge_counts[0], - tmp_majors.begin()); - thrust::sort(handle.get_thrust_policy(), tmp_majors.begin(), tmp_majors.end()); - auto num_unique_majors = - thrust::count_if(handle.get_thrust_policy(), - thrust::make_counting_iterator(size_t{0}), - thrust::make_counting_iterator(tmp_majors.size()), - is_first_in_run_t{tmp_majors.data()}); - rmm::device_uvector tmp_keys(num_unique_majors, handle.get_stream()); - rmm::device_uvector tmp_values(num_unique_majors, handle.get_stream()); - thrust::reduce_by_key(handle.get_thrust_policy(), - tmp_majors.begin(), - tmp_majors.end(), - thrust::make_constant_iterator(edge_t{1}), - tmp_keys.begin(), - tmp_values.begin()); - - tmp_majors.resize(0, handle.get_stream()); - tmp_majors.shrink_to_fit(handle.get_stream()); - sorted_local_vertex_degrees.resize(sorted_local_vertices.size(), handle.get_stream()); thrust::fill(handle.get_thrust_policy(), sorted_local_vertex_degrees.begin(), sorted_local_vertex_degrees.end(), edge_t{0}); - auto kv_pair_first = - thrust::make_zip_iterator(thrust::make_tuple(tmp_keys.begin(), tmp_values.begin())); - thrust::for_each(handle.get_thrust_policy(), - kv_pair_first, - kv_pair_first + tmp_keys.size(), - search_and_increment_degree_t{ - sorted_local_vertices.data(), - static_cast(sorted_local_vertices.size()), - sorted_local_vertex_degrees.data()}); + rmm::device_uvector tmp_majors(0, handle.get_stream()); + tmp_majors.reserve(static_cast(edgelist_edge_counts[0] + (num_chunks - 1)) / num_chunks, + handle.get_stream()); + size_t offset{0}; + for (size_t i = 0; i < num_chunks; ++i) { + size_t this_chunk_size = + std::min(tmp_majors.capacity(), static_cast(edgelist_edge_counts[0]) - offset); + tmp_majors.resize(this_chunk_size, handle.get_stream()); + thrust::copy(handle.get_thrust_policy(), + edgelist_majors[0] + offset, + edgelist_majors[0] + offset + tmp_majors.size(), + tmp_majors.begin()); + thrust::sort(handle.get_thrust_policy(), tmp_majors.begin(), tmp_majors.end()); + auto num_unique_majors = + thrust::count_if(handle.get_thrust_policy(), + thrust::make_counting_iterator(size_t{0}), + thrust::make_counting_iterator(tmp_majors.size()), + is_first_in_run_t{tmp_majors.data()}); + rmm::device_uvector tmp_keys(num_unique_majors, handle.get_stream()); + rmm::device_uvector tmp_values(num_unique_majors, handle.get_stream()); + thrust::reduce_by_key(handle.get_thrust_policy(), + tmp_majors.begin(), + tmp_majors.end(), + thrust::make_constant_iterator(edge_t{1}), + tmp_keys.begin(), + tmp_values.begin()); + + auto kv_pair_first = + thrust::make_zip_iterator(thrust::make_tuple(tmp_keys.begin(), tmp_values.begin())); + thrust::for_each(handle.get_thrust_policy(), + kv_pair_first, + kv_pair_first + tmp_keys.size(), + search_and_increment_degree_t{ + sorted_local_vertices.data(), + static_cast(sorted_local_vertices.size()), + sorted_local_vertex_degrees.data()}); + offset += this_chunk_size; + } } // 4. sort local vertices by degree (descending) diff --git a/cpp/tests/community/mg_egonet_test.cu b/cpp/tests/community/mg_egonet_test.cu index 42a2bba1181..6660eac3cad 100644 --- a/cpp/tests/community/mg_egonet_test.cu +++ b/cpp/tests/community/mg_egonet_test.cu @@ -215,6 +215,7 @@ class Tests_MGEgonet graph_ids_v.end(), size_t{0}, d_mg_edgelist_offsets.size() - 1, + true, handle_->get_stream()); auto [d_reference_src, d_reference_dst, d_reference_wgt, d_reference_offsets] = diff --git a/cpp/tests/structure/mg_induced_subgraph_test.cu b/cpp/tests/structure/mg_induced_subgraph_test.cu index 3f3db7c5278..b7bd22dfa63 100644 --- a/cpp/tests/structure/mg_induced_subgraph_test.cu +++ b/cpp/tests/structure/mg_induced_subgraph_test.cu @@ -210,6 +210,7 @@ class Tests_MGInducedSubgraph graph_ids_v.end(), size_t{0}, size_t{d_subgraph_offsets.size() - 1}, + true, handle_->get_stream()); auto [sg_graph, sg_edge_weights, sg_number_map] = cugraph::test::mg_graph_to_sg_graph( diff --git a/cpp/tests/utilities/test_utilities_impl.cuh b/cpp/tests/utilities/test_utilities_impl.cuh index 3025ca7908b..856c50ad35f 100644 --- a/cpp/tests/utilities/test_utilities_impl.cuh +++ b/cpp/tests/utilities/test_utilities_impl.cuh @@ -183,43 +183,42 @@ graph_to_host_csr( } } + auto total_global_mem = handle.get_device_properties().totalGlobalMem; + size_t element_size = sizeof(vertex_t) * 2; + if (d_wgt) { element_size += sizeof(weight_t); } + auto constexpr mem_frugal_ratio = + 0.25; // if the expected temporary buffer size exceeds the mem_frugal_ratio of the + // total_global_mem, switch to the memory frugal approach + auto mem_frugal_threshold = + static_cast(static_cast(total_global_mem / element_size) * mem_frugal_ratio); + rmm::device_uvector d_offsets(0, handle.get_stream()); if (d_wgt) { std::tie(d_offsets, d_dst, *d_wgt, std::ignore) = - detail::compress_edgelist(d_src.begin(), - d_src.end(), - d_dst.begin(), - d_wgt->begin(), - vertex_t{0}, - std::optional{std::nullopt}, - graph_view.number_of_vertices(), - vertex_t{0}, - graph_view.number_of_vertices(), - handle.get_stream()); - - // segmented sort neighbors - detail::sort_adjacency_list(handle, - raft::device_span(d_offsets.data(), d_offsets.size()), - d_dst.begin(), - d_dst.end(), - d_wgt->begin()); + detail::sort_and_compress_edgelist( + std::move(d_src), + std::move(d_dst), + std::move(*d_wgt), + vertex_t{0}, + std::optional{std::nullopt}, + graph_view.number_of_vertices(), + vertex_t{0}, + graph_view.number_of_vertices(), + mem_frugal_threshold, + handle.get_stream()); } else { std::tie(d_offsets, d_dst, std::ignore) = - detail::compress_edgelist(d_src.begin(), - d_src.end(), - d_dst.begin(), - vertex_t{0}, - std::optional{std::nullopt}, - graph_view.number_of_vertices(), - vertex_t{0}, - graph_view.number_of_vertices(), - handle.get_stream()); - // segmented sort neighbors - detail::sort_adjacency_list(handle, - raft::device_span(d_offsets.data(), d_offsets.size()), - d_dst.begin(), - d_dst.end()); + detail::sort_and_compress_edgelist( + std::move(d_src), + std::move(d_dst), + vertex_t{0}, + std::optional{std::nullopt}, + graph_view.number_of_vertices(), + vertex_t{0}, + graph_view.number_of_vertices(), + mem_frugal_threshold, + handle.get_stream()); } return std::make_tuple( From 5c0bc8a19fc3f9904541de6fb9bde95495298eb4 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Wed, 1 Nov 2023 15:41:03 -0400 Subject: [PATCH 069/111] [BUG] Check if Dask has quit to avoid throwing an exception and triggering a segfault on ddp exit (#3961) Currently, when training with ddp, if dask exits before the `CuGraphStore` is cleaned up, an exception is thrown, which causes ddp to quit with an error, which then causes a segfault, making users think that the workflow has failed when it has actually succeeded. This bug gracefully displays a warning if the dask dataset can't be deleted, which resolves this issue. Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Vibhu Jawa (https://github.com/VibhuJawa) - Tingyu Wang (https://github.com/tingyu66) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3961 --- python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index fd2172e6ade..6192cd621d5 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -320,7 +320,13 @@ def __init__( def __del__(self): if self.__is_graph_owner: if isinstance(self.__graph._plc_graph, dict): - distributed.get_client().unpublish_dataset("cugraph_graph") + try: + distributed.get_client().unpublish_dataset("cugraph_graph") + except TypeError: + warnings.warn( + "Could not unpublish graph dataset, most likely because" + " dask has already shut down." + ) del self.__graph def __make_offsets(self, input_dict): From f4bcdc2667a15e2e5031987550322a9d29d8f713 Mon Sep 17 00:00:00 2001 From: Divye Gala Date: Wed, 1 Nov 2023 19:19:22 -0400 Subject: [PATCH 070/111] Setup Consistent Nightly Versions for Pip and Conda (#3933) See https://github.com/rapidsai/rmm/pull/1347 Authors: - Divye Gala (https://github.com/divyegala) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3933 --- VERSION | 1 + ci/build_cpp.sh | 4 ++- ci/build_python.sh | 15 ++++++++++ ci/build_wheel.sh | 10 ++++--- ci/release/update-version.sh | 22 +++----------- conda/recipes/cugraph-dgl/meta.yaml | 4 +-- conda/recipes/cugraph-pyg/meta.yaml | 4 +-- conda/recipes/cugraph-service/meta.yaml | 4 +-- conda/recipes/cugraph/meta.yaml | 4 +-- conda/recipes/libcugraph/meta.yaml | 4 +-- conda/recipes/nx-cugraph/meta.yaml | 4 +-- conda/recipes/pylibcugraph/meta.yaml | 4 +-- python/cugraph-dgl/cugraph_dgl/VERSION | 1 + python/cugraph-dgl/cugraph_dgl/__init__.py | 2 +- python/cugraph-dgl/cugraph_dgl/_version.py | 26 +++++++++++++++++ python/cugraph-dgl/pyproject.toml | 5 +++- python/cugraph-dgl/setup.py | 7 +++-- python/cugraph-pyg/cugraph_pyg/VERSION | 1 + python/cugraph-pyg/cugraph_pyg/__init__.py | 2 +- python/cugraph-pyg/cugraph_pyg/_version.py | 26 +++++++++++++++++ python/cugraph-pyg/pyproject.toml | 5 +++- python/cugraph-pyg/setup.py | 4 ++- .../client/cugraph_service_client/VERSION | 1 + .../client/cugraph_service_client/__init__.py | 2 +- .../client/cugraph_service_client/_version.py | 29 +++++++++++++++++++ python/cugraph-service/client/pyproject.toml | 5 +++- python/cugraph-service/client/setup.py | 7 +++-- .../server/cugraph_service_server/VERSION | 1 + .../server/cugraph_service_server/__init__.py | 2 +- .../server/cugraph_service_server/_version.py | 29 +++++++++++++++++++ python/cugraph-service/server/pyproject.toml | 6 ++-- python/cugraph-service/server/setup.py | 4 ++- python/cugraph/cugraph/VERSION | 1 + python/cugraph/cugraph/__init__.py | 2 +- python/cugraph/cugraph/_version.py | 26 +++++++++++++++++ python/cugraph/pyproject.toml | 5 +++- python/cugraph/setup.py | 2 +- python/nx-cugraph/nx_cugraph/VERSION | 1 + python/nx-cugraph/nx_cugraph/__init__.py | 2 +- python/nx-cugraph/nx_cugraph/_version.py | 26 +++++++++++++++++ python/nx-cugraph/pyproject.toml | 5 +++- python/nx-cugraph/setup.py | 7 +++-- python/pylibcugraph/pylibcugraph/VERSION | 1 + python/pylibcugraph/pylibcugraph/__init__.py | 2 +- python/pylibcugraph/pylibcugraph/_version.py | 26 +++++++++++++++++ python/pylibcugraph/pyproject.toml | 5 +++- python/pylibcugraph/setup.py | 2 +- 47 files changed, 296 insertions(+), 62 deletions(-) create mode 100644 VERSION create mode 120000 python/cugraph-dgl/cugraph_dgl/VERSION create mode 100644 python/cugraph-dgl/cugraph_dgl/_version.py create mode 120000 python/cugraph-pyg/cugraph_pyg/VERSION create mode 100644 python/cugraph-pyg/cugraph_pyg/_version.py create mode 120000 python/cugraph-service/client/cugraph_service_client/VERSION create mode 100644 python/cugraph-service/client/cugraph_service_client/_version.py create mode 120000 python/cugraph-service/server/cugraph_service_server/VERSION create mode 100644 python/cugraph-service/server/cugraph_service_server/_version.py create mode 120000 python/cugraph/cugraph/VERSION create mode 100644 python/cugraph/cugraph/_version.py create mode 120000 python/nx-cugraph/nx_cugraph/VERSION create mode 100644 python/nx-cugraph/nx_cugraph/_version.py create mode 120000 python/pylibcugraph/pylibcugraph/VERSION create mode 100644 python/pylibcugraph/pylibcugraph/_version.py diff --git a/VERSION b/VERSION new file mode 100644 index 00000000000..a193fff41e8 --- /dev/null +++ b/VERSION @@ -0,0 +1 @@ +23.12.00 diff --git a/ci/build_cpp.sh b/ci/build_cpp.sh index 3fb72cac08b..d0d13f99448 100755 --- a/ci/build_cpp.sh +++ b/ci/build_cpp.sh @@ -9,8 +9,10 @@ export CMAKE_GENERATOR=Ninja rapids-print-env +version=$(rapids-generate-version) + rapids-logger "Begin cpp build" -rapids-conda-retry mambabuild conda/recipes/libcugraph +RAPIDS_PACKAGE_VERSION=${version} rapids-conda-retry mambabuild conda/recipes/libcugraph rapids-upload-conda-to-s3 cpp diff --git a/ci/build_python.sh b/ci/build_python.sh index 62eb6c2ccec..90a40c539ff 100755 --- a/ci/build_python.sh +++ b/ci/build_python.sh @@ -11,8 +11,19 @@ rapids-print-env CPP_CHANNEL=$(rapids-download-conda-from-s3 cpp) +version=$(rapids-generate-version) +git_commit=$(git rev-parse HEAD) +export RAPIDS_PACKAGE_VERSION=${version} +echo "${version}" > VERSION + rapids-logger "Begin py build" +package_dir="python" +for package_name in pylibcugraph cugraph nx-cugraph cugraph-pyg cugraph-dgl; do + underscore_package_name=$(echo "${package_name}" | tr "-" "_") + sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" "${package_dir}/${package_name}/${underscore_package_name}/_version.py" +done + # TODO: Remove `--no-test` flags once importing on a CPU # node works correctly rapids-conda-retry mambabuild \ @@ -40,6 +51,10 @@ rapids-conda-retry mambabuild \ # built on each CUDA platform to ensure they are included in each set of # artifacts, since test scripts only install from one set of artifacts based on # the CUDA version used for the test run. +version_file_cugraph_service_client="python/cugraph-service/client/cugraph_service_client/_version.py" +sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" ${version_file_cugraph_service_client} +version_file_cugraph_service_server="python/cugraph-service/server/cugraph_service_server/_version.py" +sed -i "/^__git_commit__/ s/= .*/= \"${git_commit}\"/g" ${version_file_cugraph_service_server} rapids-conda-retry mambabuild \ --no-test \ --channel "${CPP_CHANNEL}" \ diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh index 821aa25c1b9..c888c908056 100755 --- a/ci/build_wheel.sh +++ b/ci/build_wheel.sh @@ -5,13 +5,13 @@ set -euo pipefail package_name=$1 package_dir=$2 +underscore_package_name=$(echo "${package_name}" | tr "-" "_") source rapids-configure-sccache source rapids-date-string -# Use gha-tools rapids-pip-wheel-version to generate wheel version then -# update the necessary files -version_override="$(rapids-pip-wheel-version ${RAPIDS_DATE_STRING})" +version=$(rapids-generate-version) +git_commit=$(git rev-parse HEAD) RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})" @@ -21,9 +21,11 @@ PACKAGE_CUDA_SUFFIX="-${RAPIDS_PY_CUDA_SUFFIX}" # Patch project metadata files to include the CUDA version suffix and version override. pyproject_file="${package_dir}/pyproject.toml" +version_file="${package_dir}/${underscore_package_name}/_version.py" -sed -i "s/^version = .*/version = \"${version_override}\"/g" ${pyproject_file} sed -i "s/name = \"${package_name}\"/name = \"${package_name}${PACKAGE_CUDA_SUFFIX}\"/g" ${pyproject_file} +echo "${version}" > VERSION +sed -i "/^__git_commit__ / s/= .*/= \"${git_commit}\"/g" ${version_file} # For nightlies we want to ensure that we're pulling in alphas as well. The # easiest way to do so is to augment the spec with a constraint containing a diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index ad6426b66ff..d3dbed6ae46 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -54,24 +54,10 @@ sed_runner "s/set(cugraph_version .*)/set(cugraph_version ${NEXT_FULL_TAG})/g" p sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/cugraph/source/conf.py sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/cugraph/source/conf.py -# Python __init__.py updates -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph/cugraph/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph-dgl/cugraph_dgl/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph-pyg/cugraph_pyg/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph-service/client/cugraph_service_client/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/cugraph-service/server/cugraph_service_server/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/pylibcugraph/pylibcugraph/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/nx_cugraph/__init__.py -sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/_nx_cugraph/__init__.py - -# Python pyproject.toml updates -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph-dgl/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph-pyg/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph-service/client/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/cugraph-service/server/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/pylibcugraph/pyproject.toml -sed_runner "s/^version = .*/version = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/pyproject.toml +# Centralized version file update +# NOTE: Any script that runs in CI will need to use gha-tool `rapids-generate-version` +# and echo it to `VERSION` file to get an alpha spec of the current version +echo "${NEXT_FULL_TAG}" > VERSION # Wheel testing script sed_runner "s/branch-.*/branch-${NEXT_SHORT_TAG}/g" ci/test_wheel_cugraph.sh diff --git a/conda/recipes/cugraph-dgl/meta.yaml b/conda/recipes/cugraph-dgl/meta.yaml index 9e9fcd2faf1..bb85734098a 100644 --- a/conda/recipes/cugraph-dgl/meta.yaml +++ b/conda/recipes/cugraph-dgl/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set date_string = environ['RAPIDS_DATE_STRING'] %} @@ -10,7 +10,7 @@ package: version: {{ version }} source: - git_url: ../../.. + path: ../../.. build: number: {{ GIT_DESCRIBE_NUMBER }} diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index f4b2e9a4ee9..2714dcfa55a 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2022-2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set date_string = environ['RAPIDS_DATE_STRING'] %} @@ -10,7 +10,7 @@ package: version: {{ version }} source: - git_url: ../../.. + path: ../../.. build: number: {{ GIT_DESCRIBE_NUMBER }} diff --git a/conda/recipes/cugraph-service/meta.yaml b/conda/recipes/cugraph-service/meta.yaml index 3d001e83e1e..ae8074ba7d3 100644 --- a/conda/recipes/cugraph-service/meta.yaml +++ b/conda/recipes/cugraph-service/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2018-2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set date_string = environ['RAPIDS_DATE_STRING'] %} @@ -9,7 +9,7 @@ package: name: cugraph-service-split source: - git_url: ../../.. + path: ../../.. outputs: - name: cugraph-service-client diff --git a/conda/recipes/cugraph/meta.yaml b/conda/recipes/cugraph/meta.yaml index 3691db508d9..65403bc8d73 100644 --- a/conda/recipes/cugraph/meta.yaml +++ b/conda/recipes/cugraph/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2018-2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %} @@ -12,7 +12,7 @@ package: version: {{ version }} source: - git_url: ../../.. + path: ../../.. build: number: {{ GIT_DESCRIBE_NUMBER }} diff --git a/conda/recipes/libcugraph/meta.yaml b/conda/recipes/libcugraph/meta.yaml index 83c82adf703..66f72e6b6b5 100644 --- a/conda/recipes/libcugraph/meta.yaml +++ b/conda/recipes/libcugraph/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2018-2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %} {% set cuda_major = cuda_version.split('.')[0] %} @@ -10,7 +10,7 @@ package: name: libcugraph-split source: - git_url: ../../.. + path: ../../.. build: script_env: diff --git a/conda/recipes/nx-cugraph/meta.yaml b/conda/recipes/nx-cugraph/meta.yaml index 556d72e8548..cdb7bc13c23 100644 --- a/conda/recipes/nx-cugraph/meta.yaml +++ b/conda/recipes/nx-cugraph/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set date_string = environ['RAPIDS_DATE_STRING'] %} @@ -10,7 +10,7 @@ package: version: {{ version }} source: - git_url: ../../.. + path: ../../.. build: number: {{ GIT_DESCRIBE_NUMBER }} diff --git a/conda/recipes/pylibcugraph/meta.yaml b/conda/recipes/pylibcugraph/meta.yaml index 083998be053..ad59c4de66f 100644 --- a/conda/recipes/pylibcugraph/meta.yaml +++ b/conda/recipes/pylibcugraph/meta.yaml @@ -1,6 +1,6 @@ # Copyright (c) 2023, NVIDIA CORPORATION. -{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') %} +{% set version = environ['RAPIDS_PACKAGE_VERSION'].lstrip('v') + environ.get('VERSION_SUFFIX', '') %} {% set minor_version = version.split('.')[0] + '.' + version.split('.')[1] %} {% set py_version = environ['CONDA_PY'] %} {% set cuda_version = '.'.join(environ['RAPIDS_CUDA_VERSION'].split('.')[:2]) %} @@ -12,7 +12,7 @@ package: version: {{ version }} source: - git_url: ../../.. + path: ../../.. build: number: {{ GIT_DESCRIBE_NUMBER }} diff --git a/python/cugraph-dgl/cugraph_dgl/VERSION b/python/cugraph-dgl/cugraph_dgl/VERSION new file mode 120000 index 00000000000..d62dc733efd --- /dev/null +++ b/python/cugraph-dgl/cugraph_dgl/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/cugraph-dgl/cugraph_dgl/__init__.py b/python/cugraph-dgl/cugraph_dgl/__init__.py index 74be4fdea3f..03ff50896a4 100644 --- a/python/cugraph-dgl/cugraph_dgl/__init__.py +++ b/python/cugraph-dgl/cugraph_dgl/__init__.py @@ -20,4 +20,4 @@ import cugraph_dgl.dataloading import cugraph_dgl.nn -__version__ = "23.12.00" +from cugraph_dgl._version import __git_commit__, __version__ diff --git a/python/cugraph-dgl/cugraph_dgl/_version.py b/python/cugraph-dgl/cugraph_dgl/_version.py new file mode 100644 index 00000000000..f95a4705467 --- /dev/null +++ b/python/cugraph-dgl/cugraph_dgl/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("cugraph_dgl").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/cugraph-dgl/pyproject.toml b/python/cugraph-dgl/pyproject.toml index fa9e1c5abe5..eff7a20f0aa 100644 --- a/python/cugraph-dgl/pyproject.toml +++ b/python/cugraph-dgl/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-dgl" -version = "23.12.00" +dynamic = ["version"] description = "cugraph extensions for DGL" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -35,6 +35,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "cugraph_dgl/VERSION"} + [tool.setuptools.packages.find] include = [ "cugraph_dgl*", diff --git a/python/cugraph-dgl/setup.py b/python/cugraph-dgl/setup.py index 6991b23b0fb..afb8002af42 100644 --- a/python/cugraph-dgl/setup.py +++ b/python/cugraph-dgl/setup.py @@ -11,6 +11,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup +from setuptools import find_packages, setup -setup() +packages = find_packages(include=["cugraph_dgl*"]) +setup( + package_data={key: ["VERSION"] for key in packages}, +) diff --git a/python/cugraph-pyg/cugraph_pyg/VERSION b/python/cugraph-pyg/cugraph_pyg/VERSION new file mode 120000 index 00000000000..d62dc733efd --- /dev/null +++ b/python/cugraph-pyg/cugraph_pyg/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/cugraph-pyg/cugraph_pyg/__init__.py b/python/cugraph-pyg/cugraph_pyg/__init__.py index ecd2f271a00..719751c966a 100644 --- a/python/cugraph-pyg/cugraph_pyg/__init__.py +++ b/python/cugraph-pyg/cugraph_pyg/__init__.py @@ -11,4 +11,4 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "23.12.00" +from cugraph_pyg._version import __git_commit__, __version__ diff --git a/python/cugraph-pyg/cugraph_pyg/_version.py b/python/cugraph-pyg/cugraph_pyg/_version.py new file mode 100644 index 00000000000..963052da909 --- /dev/null +++ b/python/cugraph-pyg/cugraph_pyg/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("cugraph_pyg").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/cugraph-pyg/pyproject.toml b/python/cugraph-pyg/pyproject.toml index 84d30221d55..95b1fa27402 100644 --- a/python/cugraph-pyg/pyproject.toml +++ b/python/cugraph-pyg/pyproject.toml @@ -12,7 +12,7 @@ testpaths = ["cugraph_pyg/tests"] [project] name = "cugraph_pyg" -version = "23.12.00" +dynamic = ["version"] description = "cugraph_pyg - PyG support for cuGraph massive-scale, ultra-fast GPU graph analytics." authors = [ { name = "NVIDIA Corporation" }, @@ -38,6 +38,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "cugraph_pyg/VERSION"} + [tool.setuptools.packages.find] include = [ "cugraph_pyg*", diff --git a/python/cugraph-pyg/setup.py b/python/cugraph-pyg/setup.py index 1f7db1d3772..50f023050bf 100644 --- a/python/cugraph-pyg/setup.py +++ b/python/cugraph-pyg/setup.py @@ -14,7 +14,7 @@ import os import shutil -from setuptools import Command, setup +from setuptools import Command, find_packages, setup from setuputils import get_environment_option @@ -59,6 +59,8 @@ def run(self): os.system("rm -rf *.egg-info") +packages = find_packages(include=["cugraph_pyg*"]) setup( cmdclass={"clean": CleanCommand}, + package_data={key: ["VERSION"] for key in packages}, ) diff --git a/python/cugraph-service/client/cugraph_service_client/VERSION b/python/cugraph-service/client/cugraph_service_client/VERSION new file mode 120000 index 00000000000..a4e948506b8 --- /dev/null +++ b/python/cugraph-service/client/cugraph_service_client/VERSION @@ -0,0 +1 @@ +../../../../VERSION \ No newline at end of file diff --git a/python/cugraph-service/client/cugraph_service_client/__init__.py b/python/cugraph-service/client/cugraph_service_client/__init__.py index a0361abedd3..a9a96ae6c16 100644 --- a/python/cugraph-service/client/cugraph_service_client/__init__.py +++ b/python/cugraph-service/client/cugraph_service_client/__init__.py @@ -35,4 +35,4 @@ from cugraph_service_client.client import CugraphServiceClient from cugraph_service_client.remote_graph import RemoteGraph -__version__ = "23.12.00" +from cugraph_service_client._version import __git_commit__, __version__ diff --git a/python/cugraph-service/client/cugraph_service_client/_version.py b/python/cugraph-service/client/cugraph_service_client/_version.py new file mode 100644 index 00000000000..344361973bb --- /dev/null +++ b/python/cugraph-service/client/cugraph_service_client/_version.py @@ -0,0 +1,29 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("cugraph_service_client") + .joinpath("VERSION") + .read_text() + .strip() +) +__git_commit__ = "" diff --git a/python/cugraph-service/client/pyproject.toml b/python/cugraph-service/client/pyproject.toml index 7f702252f02..59539693877 100644 --- a/python/cugraph-service/client/pyproject.toml +++ b/python/cugraph-service/client/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-service-client" -version = "23.12.00" +dynamic = ["version"] description = "cuGraph Service client" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -35,6 +35,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "cugraph_service_client/VERSION"} + [tool.setuptools.packages.find] include = [ "cugraph_service_client", diff --git a/python/cugraph-service/client/setup.py b/python/cugraph-service/client/setup.py index 811a12c50b7..61c758cef4a 100644 --- a/python/cugraph-service/client/setup.py +++ b/python/cugraph-service/client/setup.py @@ -11,6 +11,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup +from setuptools import find_packages, setup -setup() +packages = find_packages(include=["cugraph_service_client*"]) +setup( + package_data={key: ["VERSION"] for key in packages}, +) diff --git a/python/cugraph-service/server/cugraph_service_server/VERSION b/python/cugraph-service/server/cugraph_service_server/VERSION new file mode 120000 index 00000000000..a4e948506b8 --- /dev/null +++ b/python/cugraph-service/server/cugraph_service_server/VERSION @@ -0,0 +1 @@ +../../../../VERSION \ No newline at end of file diff --git a/python/cugraph-service/server/cugraph_service_server/__init__.py b/python/cugraph-service/server/cugraph_service_server/__init__.py index 87d35005195..02473f0ea47 100644 --- a/python/cugraph-service/server/cugraph_service_server/__init__.py +++ b/python/cugraph-service/server/cugraph_service_server/__init__.py @@ -61,4 +61,4 @@ def start_server_blocking( server.serve() # blocks until Ctrl-C (kill -2) -__version__ = "23.12.00" +from cugraph_service_server._version import __git_commit__, __version__ diff --git a/python/cugraph-service/server/cugraph_service_server/_version.py b/python/cugraph-service/server/cugraph_service_server/_version.py new file mode 100644 index 00000000000..7da31f78767 --- /dev/null +++ b/python/cugraph-service/server/cugraph_service_server/_version.py @@ -0,0 +1,29 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("cugraph_service_server") + .joinpath("VERSION") + .read_text() + .strip() +) +__git_commit__ = "" diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index 3c77cf01c2c..f50b33b3f15 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "cugraph-service-server" -version = "23.12.00" +dynamic = ["version", "entry-points"] description = "cuGraph Service server" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -39,7 +39,6 @@ classifiers = [ "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", ] -dynamic = ["entry-points"] [project.optional-dependencies] test = [ @@ -62,6 +61,9 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "cugraph_service_server/VERSION"} + [tool.setuptools.packages.find] include = [ "cugraph_service_server", diff --git a/python/cugraph-service/server/setup.py b/python/cugraph-service/server/setup.py index 5203b76c659..91864168e2c 100644 --- a/python/cugraph-service/server/setup.py +++ b/python/cugraph-service/server/setup.py @@ -11,12 +11,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup +from setuptools import find_packages, setup +packages = find_packages(include=["cugraph_service_server*"]) setup( entry_points={ "console_scripts": [ "cugraph-service-server=cugraph_service_server.__main__:main" ], }, + package_data={key: ["VERSION"] for key in packages}, ) diff --git a/python/cugraph/cugraph/VERSION b/python/cugraph/cugraph/VERSION new file mode 120000 index 00000000000..d62dc733efd --- /dev/null +++ b/python/cugraph/cugraph/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/cugraph/cugraph/__init__.py b/python/cugraph/cugraph/__init__.py index f3a335183f3..f635d215696 100644 --- a/python/cugraph/cugraph/__init__.py +++ b/python/cugraph/cugraph/__init__.py @@ -120,4 +120,4 @@ from cugraph import exceptions -__version__ = "23.12.00" +from cugraph._version import __git_commit__, __version__ diff --git a/python/cugraph/cugraph/_version.py b/python/cugraph/cugraph/_version.py new file mode 100644 index 00000000000..710afb87e29 --- /dev/null +++ b/python/cugraph/cugraph/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("cugraph").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index 5e17be40e7b..aaa301fa05f 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -20,7 +20,7 @@ testpaths = ["cugraph/tests"] [project] name = "cugraph" -version = "23.12.00" +dynamic = ["version"] description = "cuGraph - RAPIDS GPU Graph Analytics" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -69,3 +69,6 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] + +[tool.setuptools.dynamic] +version = {file = "cugraph/VERSION"} diff --git a/python/cugraph/setup.py b/python/cugraph/setup.py index aa3a5fb56a7..81916444cfd 100644 --- a/python/cugraph/setup.py +++ b/python/cugraph/setup.py @@ -46,7 +46,7 @@ def run(self): packages = find_packages(include=["cugraph*"]) setup( packages=packages, - package_data={key: ["*.pxd", "*.yaml"] for key in packages}, + package_data={key: ["VERSION", "*.pxd", "*.yaml"] for key in packages}, cmdclass={"clean": CleanCommand}, zip_safe=False, ) diff --git a/python/nx-cugraph/nx_cugraph/VERSION b/python/nx-cugraph/nx_cugraph/VERSION new file mode 120000 index 00000000000..d62dc733efd --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/nx-cugraph/nx_cugraph/__init__.py b/python/nx-cugraph/nx_cugraph/__init__.py index 25d44212264..3a8f0996e9c 100644 --- a/python/nx-cugraph/nx_cugraph/__init__.py +++ b/python/nx-cugraph/nx_cugraph/__init__.py @@ -29,4 +29,4 @@ from . import algorithms from .algorithms import * -__version__ = "23.12.00" +from nx_cugraph._version import __git_commit__, __version__ diff --git a/python/nx-cugraph/nx_cugraph/_version.py b/python/nx-cugraph/nx_cugraph/_version.py new file mode 100644 index 00000000000..868a2e19475 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("nx_cugraph").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index 7e51efd4fe4..f309f4797a7 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta" [project] name = "nx-cugraph" -version = "23.12.00" +dynamic = ["version"] description = "cugraph backend for NetworkX" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -61,6 +61,9 @@ cugraph = "_nx_cugraph:get_info" [tool.setuptools] license-files = ["LICENSE"] +[tool.setuptools.dynamic] +version = {file = "nx_cugraph/VERSION"} + [tool.setuptools.packages.find] include = [ "nx_cugraph*", diff --git a/python/nx-cugraph/setup.py b/python/nx-cugraph/setup.py index 87c0e10646d..c4ab535923b 100644 --- a/python/nx-cugraph/setup.py +++ b/python/nx-cugraph/setup.py @@ -10,6 +10,9 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from setuptools import setup +from setuptools import find_packages, setup -setup() +packages = find_packages(include=["nx_cugraph*"]) +setup( + package_data={key: ["VERSION"] for key in packages}, +) diff --git a/python/pylibcugraph/pylibcugraph/VERSION b/python/pylibcugraph/pylibcugraph/VERSION new file mode 120000 index 00000000000..d62dc733efd --- /dev/null +++ b/python/pylibcugraph/pylibcugraph/VERSION @@ -0,0 +1 @@ +../../../VERSION \ No newline at end of file diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index 2aec0b98a25..30f1c2d0fb1 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -98,4 +98,4 @@ from pylibcugraph import exceptions -__version__ = "23.12.00" +from pylibcugraph._version import __git_commit__, __version__ diff --git a/python/pylibcugraph/pylibcugraph/_version.py b/python/pylibcugraph/pylibcugraph/_version.py new file mode 100644 index 00000000000..5dca7e48b3f --- /dev/null +++ b/python/pylibcugraph/pylibcugraph/_version.py @@ -0,0 +1,26 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import importlib.resources + +# Read VERSION file from the module that is symlinked to VERSION file +# in the root of the repo at build time or copied to the moudle at +# installation. VERSION is a separate file that allows CI build-time scripts +# to update version info (including commit hashes) without modifying +# source files. +__version__ = ( + importlib.resources.files("pylibcugraph").joinpath("VERSION").read_text().strip() +) +__git_commit__ = "" diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index f1b439debd2..96f5ec84efb 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -19,7 +19,7 @@ testpaths = ["pylibcugraph/tests"] [project] name = "pylibcugraph" -version = "23.12.00" +dynamic = ["version"] description = "pylibcugraph - Python bindings for the libcugraph cuGraph C/C++/CUDA library" readme = { file = "README.md", content-type = "text/markdown" } authors = [ @@ -56,3 +56,6 @@ Documentation = "https://docs.rapids.ai/api/cugraph/stable/" [tool.setuptools] license-files = ["LICENSE"] + +[tool.setuptools.dynamic] +version = {file = "pylibcugraph/VERSION"} diff --git a/python/pylibcugraph/setup.py b/python/pylibcugraph/setup.py index f1a419f31bb..a6c1bda3b5b 100644 --- a/python/pylibcugraph/setup.py +++ b/python/pylibcugraph/setup.py @@ -54,7 +54,7 @@ def exclude_libcxx_symlink(cmake_manifest): packages = find_packages(include=["pylibcugraph*"]) setup( packages=packages, - package_data={key: ["*.pxd"] for key in packages}, + package_data={key: ["VERSION", "*.pxd"] for key in packages}, cmake_process_manifest_hook=exclude_libcxx_symlink, cmdclass={"clean": CleanCommand}, zip_safe=False, From 2bdb7358878a8f1e2405b1f5c3e7938d54a67759 Mon Sep 17 00:00:00 2001 From: Tingyu Wang Date: Fri, 3 Nov 2023 15:20:32 -0400 Subject: [PATCH 071/111] Skip certain `cugraph-pyg` tests when torch-sparse is not available (#3970) A follow-up PR to fix the test failure again on rockylinux (see [log](https://github.com/rapidsai/cugraph/actions/runs/6718664898/job/18258783035#step:7:9297)). `import_optional` only catches `ModuleNotFoundError`, however, it threw an `OSError` on rockylinux. Let's just catch all exceptions during imports in this case. Closes https://github.com/rapidsai/graph_dl/issues/373 Authors: - Tingyu Wang (https://github.com/tingyu66) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3970 --- .../cugraph_pyg/tests/test_cugraph_loader.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 836b30c9df7..853836dc2a6 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -29,9 +29,15 @@ torch = import_optional("torch") torch_geometric = import_optional("torch_geometric") -torch_sparse = import_optional("torch_sparse") trim_to_layer = import_optional("torch_geometric.utils.trim_to_layer") +try: + import torch_sparse # noqa: F401 + + HAS_TORCH_SPARSE = True +except: # noqa: E722 + HAS_TORCH_SPARSE = False + @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") def test_cugraph_loader_basic(karate_gnn): @@ -201,9 +207,7 @@ def test_cugraph_loader_from_disk_subset(): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") -@pytest.mark.skipif( - isinstance(torch_sparse, MissingModule), reason="torch-sparse not available" -) +@pytest.mark.skipif(not HAS_TORCH_SPARSE, reason="torch-sparse not available") def test_cugraph_loader_from_disk_subset_csr(): m = [2, 9, 99, 82, 11, 13] n = torch.arange(1, 1 + len(m), dtype=torch.int32) @@ -336,9 +340,7 @@ def test_cugraph_loader_e2e_coo(): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") -@pytest.mark.skipif( - isinstance(torch_sparse, MissingModule), reason="torch-sparse not available" -) +@pytest.mark.skipif(not HAS_TORCH_SPARSE, reason="torch-sparse not available") @pytest.mark.parametrize("framework", ["pyg", "cugraph-ops"]) def test_cugraph_loader_e2e_csc(framework): m = [2, 9, 99, 82, 9, 3, 18, 1, 12] From 586451da8cbdf54e04a420725982c0d60de42356 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Mon, 6 Nov 2023 10:28:05 -0500 Subject: [PATCH 072/111] MTMG multi node (#3932) This PR extends the MTMG framework to support a multi-node configuration. Multi-node configuration of MTMG assumes some externally available mechanism for communicating configuration parameters and the NCCL unique id between the processes that will participate. For testing purposes we assume a shared file system where we can create a directory and read/write configuration information rather than constructing a more complex communication mechanism (NFS is sufficient for testing). Closes https://github.com/rapidsai/graph_dl/issues/329 Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Joseph Nke (https://github.com/jnke2016) - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/3932 --- cpp/include/cugraph/mtmg/handle.hpp | 8 - cpp/include/cugraph/mtmg/instance_manager.hpp | 27 +- cpp/include/cugraph/mtmg/resource_manager.hpp | 124 +++-- cpp/tests/CMakeLists.txt | 10 + cpp/tests/mtmg/multi_node_threaded_test.cu | 516 ++++++++++++++++++ 5 files changed, 626 insertions(+), 59 deletions(-) create mode 100644 cpp/tests/mtmg/multi_node_threaded_test.cu diff --git a/cpp/include/cugraph/mtmg/handle.hpp b/cpp/include/cugraph/mtmg/handle.hpp index efdec3f0775..6223de1781d 100644 --- a/cpp/include/cugraph/mtmg/handle.hpp +++ b/cpp/include/cugraph/mtmg/handle.hpp @@ -111,14 +111,6 @@ class handle_t { */ int get_size() const { return raft_handle_.get_comms().get_size(); } - /** - * @brief Get number of local gpus - * - * @return number of local gpus - */ - // FIXME: wrong for multi-node - int get_local_size() const { return raft_handle_.get_comms().get_size(); } - /** * @brief Get gpu rank * diff --git a/cpp/include/cugraph/mtmg/instance_manager.hpp b/cpp/include/cugraph/mtmg/instance_manager.hpp index 687c5ddbf02..f819a5a0abe 100644 --- a/cpp/include/cugraph/mtmg/instance_manager.hpp +++ b/cpp/include/cugraph/mtmg/instance_manager.hpp @@ -18,7 +18,7 @@ #include -#include +#include #include @@ -37,16 +37,27 @@ class instance_manager_t { */ instance_manager_t(std::vector>&& handles, std::vector>&& nccl_comms, - std::vector&& device_ids, - int local_gpu_count) + std::vector&& device_ids) : thread_counter_{0}, raft_handle_{std::move(handles)}, nccl_comms_{std::move(nccl_comms)}, - device_ids_{std::move(device_ids)}, - local_gpu_count_{local_gpu_count} + device_ids_{std::move(device_ids)} { } + ~instance_manager_t() + { + int current_device{}; + RAFT_CUDA_TRY(cudaGetDevice(¤t_device)); + + for (size_t i = 0; i < nccl_comms_.size(); ++i) { + RAFT_CUDA_TRY(cudaSetDevice(device_ids_[i].value())); + RAFT_NCCL_TRY(ncclCommDestroy(*nccl_comms_[i])); + } + + RAFT_CUDA_TRY(cudaSetDevice(current_device)); + } + /** * @brief Get handle * @@ -79,7 +90,7 @@ class instance_manager_t { /** * @brief Number of local GPUs in the instance */ - int get_local_gpu_count() { return local_gpu_count_; } + int get_local_gpu_count() { return static_cast(raft_handle_.size()); } private: // FIXME: Should this be an std::map<> where the key is the rank? @@ -87,9 +98,11 @@ class instance_manager_t { // (or no) GPUs, so mapping rank to a handle might be a challenge // std::vector> raft_handle_{}; + + // FIXME: Explore what RAFT changes might be desired to allow the ncclComm_t + // to be managed by RAFT instead of cugraph::mtmg std::vector> nccl_comms_{}; std::vector device_ids_{}; - int local_gpu_count_{}; std::atomic thread_counter_{0}; }; diff --git a/cpp/include/cugraph/mtmg/resource_manager.hpp b/cpp/include/cugraph/mtmg/resource_manager.hpp index e1e1d7ffc9d..127944cf7ba 100644 --- a/cpp/include/cugraph/mtmg/resource_manager.hpp +++ b/cpp/include/cugraph/mtmg/resource_manager.hpp @@ -41,6 +41,11 @@ namespace mtmg { * register_local_gpu (or register_remote_gpu once we support a multi-node * configuration) to allocate resources that can be used in the mtmg space. * + * Each GPU in the cluster should be given a unique global rank, an integer + * that will be used to reference the GPU within the resource manager. It + * is recommended that the GPUs be numbered sequentially from 0, although this + * is not required. + * * When we want to execute some graph computations, we need to create an instance for execution. * Based on how big a subset of the desired compute resources is desired, we can allocate some * number of GPUs to the problem (up to the total set of managed resources). @@ -48,7 +53,7 @@ namespace mtmg { * The returned instance can be used to create a graph, execute one or more algorithms, etc. Once * we are done the caller can delete the instance. * - * At the moment, the caller is assumed to be responsible for scheduling use of the resources. + * The caller is assumed to be responsible for scheduling use of the resources. * * For our first release, we will only consider a single node multi-GPU configuration, so the remote * GPU methods are currently disabled via ifdef. @@ -63,25 +68,28 @@ class resource_manager_t { /** * @brief add a local GPU to the resource manager. * - * @param rank The rank to assign to the local GPU - * @param device_id The device_id corresponding to this rank + * @param global_rank The global rank to assign to the local GPU + * @param local_device_id The local device_id corresponding to this rank */ - void register_local_gpu(int rank, rmm::cuda_device_id device_id) + void register_local_gpu(int global_rank, rmm::cuda_device_id local_device_id) { std::lock_guard lock(lock_); - CUGRAPH_EXPECTS(local_rank_map_.find(rank) == local_rank_map_.end(), - "cannot register same rank multiple times"); + CUGRAPH_EXPECTS(remote_rank_set_.find(global_rank) == remote_rank_set_.end(), + "cannot register same global_rank as local and remote"); + CUGRAPH_EXPECTS(local_rank_map_.find(global_rank) == local_rank_map_.end(), + "cannot register same global_rank multiple times"); int num_gpus_this_node; RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_this_node)); - CUGRAPH_EXPECTS((device_id.value() >= 0) && (device_id.value() < num_gpus_this_node), - "device id out of range"); + CUGRAPH_EXPECTS( + (local_device_id.value() >= 0) && (local_device_id.value() < num_gpus_this_node), + "local device id out of range"); - local_rank_map_.insert(std::pair(rank, device_id)); + local_rank_map_.insert(std::pair(global_rank, local_device_id)); - RAFT_CUDA_TRY(cudaSetDevice(device_id.value())); + RAFT_CUDA_TRY(cudaSetDevice(local_device_id.value())); // FIXME: There is a bug in the cuda_memory_resource that results in a Hang. // using the pool resource as a work-around. @@ -89,23 +97,43 @@ class resource_manager_t { // There is a deprecated environment variable: NCCL_LAUNCH_MODE=GROUP // which should temporarily work around this problem. // + // Further NOTE: multi-node requires the NCCL_LAUNCH_MODE=GROUP feature + // to be enabled even with the pool memory resource. + // // Ultimately there should be some RMM parameters passed into this function // (or the constructor of the object) to configure this behavior #if 0 auto per_device_it = per_device_rmm_resources_.insert( - std::pair{rank, std::make_shared()}); + std::pair{global_rank, std::make_shared()}); #else auto const [free, total] = rmm::detail::available_device_memory(); auto const min_alloc = rmm::detail::align_down(std::min(free, total / 6), rmm::detail::CUDA_ALLOCATION_ALIGNMENT); auto per_device_it = per_device_rmm_resources_.insert( - std::pair{rank, + std::pair{global_rank, rmm::mr::make_owning_wrapper( std::make_shared(), min_alloc)}); #endif - rmm::mr::set_per_device_resource(device_id, per_device_it.first->second.get()); + rmm::mr::set_per_device_resource(local_device_id, per_device_it.first->second.get()); + } + + /** + * @brief add a remote GPU to the resource manager. + * + * @param global_rank The global rank to assign to the remote GPU + */ + void register_remote_gpu(int global_rank) + { + std::lock_guard lock(lock_); + + CUGRAPH_EXPECTS(local_rank_map_.find(global_rank) == local_rank_map_.end(), + "cannot register same global_rank as local and remote"); + CUGRAPH_EXPECTS(remote_rank_set_.find(global_rank) == remote_rank_set_.end(), + "cannot register same global_rank multiple times"); + + remote_rank_set_.insert(global_rank); } /** @@ -130,29 +158,36 @@ class resource_manager_t { ncclUniqueId instance_manager_id, size_t n_streams = 16) const { - std::for_each( - ranks_to_include.begin(), ranks_to_include.end(), [local_ranks = local_rank_map_](int rank) { - CUGRAPH_EXPECTS(local_ranks.find(rank) != local_ranks.end(), - "requesting inclusion of an invalid rank"); - }); + std::vector local_ranks_to_include; + + std::copy_if(ranks_to_include.begin(), + ranks_to_include.end(), + std::back_inserter(local_ranks_to_include), + [&local_ranks = local_rank_map_](int rank) { + return (local_ranks.find(rank) != local_ranks.end()); + }); + // FIXME: Explore what RAFT changes might be desired to allow the ncclComm_t + // to be managed by RAFT instead of cugraph::mtmg std::vector> nccl_comms{}; std::vector> handles{}; std::vector device_ids{}; - nccl_comms.reserve(ranks_to_include.size()); - handles.reserve(ranks_to_include.size()); - device_ids.reserve(ranks_to_include.size()); + nccl_comms.reserve(local_ranks_to_include.size()); + handles.reserve(local_ranks_to_include.size()); + device_ids.reserve(local_ranks_to_include.size()); - // FIXME: not quite right for multi-node auto gpu_row_comm_size = static_cast(sqrt(static_cast(ranks_to_include.size()))); while (ranks_to_include.size() % gpu_row_comm_size != 0) { --gpu_row_comm_size; } - // FIXME: not quite right for multi-node - for (size_t i = 0; i < ranks_to_include.size(); ++i) { - int rank = ranks_to_include[i]; + int current_device{}; + RAFT_CUDA_TRY(cudaGetDevice(¤t_device)); + RAFT_NCCL_TRY(ncclGroupStart()); + + for (size_t i = 0; i < local_ranks_to_include.size(); ++i) { + int rank = local_ranks_to_include[i]; auto pos = local_rank_map_.find(rank); RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); @@ -162,26 +197,28 @@ class resource_manager_t { std::make_shared(n_streams), per_device_rmm_resources_.find(rank)->second)); device_ids.push_back(pos->second); + + RAFT_NCCL_TRY( + ncclCommInitRank(nccl_comms[i].get(), ranks_to_include.size(), instance_manager_id, rank)); + raft::comms::build_comms_nccl_only( + handles[i].get(), *nccl_comms[i], ranks_to_include.size(), rank); } + RAFT_NCCL_TRY(ncclGroupEnd()); + RAFT_CUDA_TRY(cudaSetDevice(current_device)); std::vector running_threads; - for (size_t i = 0; i < ranks_to_include.size(); ++i) { + for (size_t i = 0; i < local_ranks_to_include.size(); ++i) { running_threads.emplace_back([instance_manager_id, idx = i, gpu_row_comm_size, comm_size = ranks_to_include.size(), - &ranks_to_include, - &local_rank_map = local_rank_map_, + &local_ranks_to_include, + &device_ids, &nccl_comms, &handles]() { - int rank = ranks_to_include[idx]; - auto pos = local_rank_map.find(rank); - RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); - - NCCL_TRY(ncclCommInitRank(nccl_comms[idx].get(), comm_size, instance_manager_id, rank)); - - raft::comms::build_comms_nccl_only(handles[idx].get(), *nccl_comms[idx], comm_size, rank); + int rank = local_ranks_to_include[idx]; + RAFT_CUDA_TRY(cudaSetDevice(device_ids[idx].value())); cugraph::partition_manager::init_subcomm(*handles[idx], gpu_row_comm_size); }); @@ -189,9 +226,8 @@ class resource_manager_t { std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); - // FIXME: Update for multi-node return std::make_unique( - std::move(handles), std::move(nccl_comms), std::move(device_ids), ranks_to_include.size()); + std::move(handles), std::move(nccl_comms), std::move(device_ids)); } /** @@ -203,24 +239,24 @@ class resource_manager_t { { std::lock_guard lock(lock_); - // - // C++20 mechanism: - // return std::vector{ std::views::keys(local_rank_map_).begin(), - // std::views::keys(local_rank_map_).end() }; - // Would need a bit more complicated to handle remote_rank_map_ also - // - std::vector registered_ranks(local_rank_map_.size()); + std::vector registered_ranks(local_rank_map_.size() + remote_rank_set_.size()); std::transform( local_rank_map_.begin(), local_rank_map_.end(), registered_ranks.begin(), [](auto pair) { return pair.first; }); + std::copy(remote_rank_set_.begin(), + remote_rank_set_.end(), + registered_ranks.begin() + local_rank_map_.size()); + + std::sort(registered_ranks.begin(), registered_ranks.end()); return registered_ranks; } private: mutable std::mutex lock_{}; std::map local_rank_map_{}; + std::set remote_rank_set_{}; std::map> per_device_rmm_resources_{}; }; diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 6775ed2eb16..2f69cf9cb0d 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -680,6 +680,16 @@ if(BUILD_CUGRAPH_MG_TESTS) ConfigureCTestMG(MG_CAPI_TWO_HOP_NEIGHBORS_TEST c_api/mg_two_hop_neighbors_test.c) rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing_mg DESTINATION bin/gtests/libcugraph_mg) + + ############################################################################################### + # - Multi-node MTMG tests --------------------------------------------------------------------- + ConfigureTest(MTMG_MULTINODE_TEST mtmg/multi_node_threaded_test.cu utilities/mg_utilities.cpp) + target_link_libraries(MTMG_MULTINODE_TEST + PRIVATE + cugraphmgtestutil + UCP::UCP + ) + endif() ################################################################################################### diff --git a/cpp/tests/mtmg/multi_node_threaded_test.cu b/cpp/tests/mtmg/multi_node_threaded_test.cu new file mode 100644 index 00000000000..e5a7de07781 --- /dev/null +++ b/cpp/tests/mtmg/multi_node_threaded_test.cu @@ -0,0 +1,516 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#include + +#include + +#include +#include +#include + +#include +#include + +struct Multithreaded_Usecase { + bool test_weighted{false}; + bool check_correctness{true}; +}; + +// Global variable defining resource manager +static cugraph::mtmg::resource_manager_t g_resource_manager{}; +static int g_node_rank{-1}; +static int g_num_nodes{-1}; + +template +class Tests_Multithreaded + : public ::testing::TestWithParam> { + public: + Tests_Multithreaded() {} + + virtual void SetUp() {} + virtual void TearDown() {} + + std::vector get_gpu_list() + { + int num_gpus_per_node{1}; + RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_per_node)); + + std::vector gpu_list(num_gpus_per_node); + std::iota(gpu_list.begin(), gpu_list.end(), 0); + + return gpu_list; + } + + template + void run_current_test( + std::tuple const& param, + std::vector gpu_list) + { + using edge_type_t = int32_t; + + constexpr bool renumber = true; + constexpr bool do_expensive_check = false; + + auto [multithreaded_usecase, input_usecase] = param; + + raft::handle_t handle{}; + + result_t constexpr alpha{0.85}; + result_t constexpr epsilon{1e-6}; + + size_t device_buffer_size{64 * 1024 * 1024}; + size_t thread_buffer_size{4 * 1024 * 1024}; + + int num_local_gpus = gpu_list.size(); + int num_threads = num_local_gpus * 4; + + ncclUniqueId instance_manager_id{}; + + if (g_node_rank == 0) RAFT_NCCL_TRY(ncclGetUniqueId(&instance_manager_id)); + + RAFT_MPI_TRY( + MPI_Bcast(&instance_manager_id, sizeof(instance_manager_id), MPI_CHAR, 0, MPI_COMM_WORLD)); + + auto instance_manager = g_resource_manager.create_instance_manager( + g_resource_manager.registered_ranks(), instance_manager_id); + + cugraph::mtmg::edgelist_t edgelist; + cugraph::mtmg::graph_t graph; + cugraph::mtmg::graph_view_t graph_view; + cugraph::mtmg::vertex_result_t pageranks; + std::optional> renumber_map = + std::make_optional>(); + + auto edge_weights = multithreaded_usecase.test_weighted + ? std::make_optional, + weight_t>>() + : std::nullopt; + + // + // Simulate graph creation by spawning threads to walk through the + // local COO and add edges + // + std::vector running_threads; + + // Initialize shared edgelist object, one per GPU + for (int i = 0; i < num_local_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &edgelist, + device_buffer_size, + use_weight = true, + use_edge_id = false, + use_edge_type = false]() { + auto thread_handle = instance_manager->get_handle(); + + edgelist.set(thread_handle, device_buffer_size, use_weight, use_edge_id, use_edge_type); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + // Load SG edge list + auto [d_src_v, d_dst_v, d_weights_v, d_vertices_v, is_symmetric] = + input_usecase.template construct_edgelist( + handle, multithreaded_usecase.test_weighted, false, false); + + auto h_src_v = cugraph::test::to_host(handle, d_src_v); + auto h_dst_v = cugraph::test::to_host(handle, d_dst_v); + auto h_weights_v = cugraph::test::to_host(handle, d_weights_v); + auto unique_vertices = cugraph::test::to_host(handle, d_vertices_v); + + // Load edgelist from different threads. We'll use more threads than GPUs here + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back([&instance_manager, + thread_buffer_size, + &edgelist, + &h_src_v, + &h_dst_v, + &h_weights_v, + starting_edge_offset = g_node_rank * num_threads + i, + stride = g_num_nodes * num_threads]() { + auto thread_handle = instance_manager->get_handle(); + cugraph::mtmg::per_thread_edgelist_t + per_thread_edgelist(edgelist.get(thread_handle), thread_buffer_size); + + for (size_t j = starting_edge_offset; j < h_src_v.size(); j += stride) { + per_thread_edgelist.append( + thread_handle, + h_src_v[j], + h_dst_v[j], + h_weights_v ? std::make_optional((*h_weights_v)[j]) : std::nullopt, + std::nullopt, + std::nullopt); + } + + per_thread_edgelist.flush(thread_handle); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + for (int i = 0; i < num_local_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph, + &edge_weights, + &edgelist, + &renumber_map, + &pageranks, + &h_src_v, // debugging + is_symmetric = is_symmetric, + renumber, + do_expensive_check]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + std::optional, + edge_t>> + edge_ids{std::nullopt}; + std::optional, + int32_t>> + edge_types{std::nullopt}; + + edgelist.finalize_buffer(thread_handle); + edgelist.consolidate_and_shuffle(thread_handle, true); + + cugraph::mtmg:: + create_graph_from_edgelist( + thread_handle, + edgelist, + cugraph::graph_properties_t{is_symmetric, true}, + renumber, + graph, + edge_weights, + edge_ids, + edge_types, + renumber_map, + do_expensive_check); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + graph_view = graph.view(); + + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back( + [&instance_manager, &graph_view, &edge_weights, &pageranks, alpha, epsilon]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + auto [local_pageranks, metadata] = + cugraph::pagerank( + thread_handle.raft_handle(), + graph_view.get(thread_handle), + edge_weights ? std::make_optional(edge_weights->get(thread_handle).view()) + : std::nullopt, + std::nullopt, + std::nullopt, + std::nullopt, + alpha, + epsilon, + 500, + true); + + pageranks.set(thread_handle, std::move(local_pageranks)); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + std::vector, std::vector>> computed_pageranks_v; + std::mutex computed_pageranks_lock{}; + + auto pageranks_view = pageranks.view(); + auto renumber_map_view = renumber_map ? std::make_optional(renumber_map->view()) : std::nullopt; + + // Load computed_pageranks from different threads. + for (int i = 0; i < num_local_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph_view, + &renumber_map_view, + &pageranks_view, + &computed_pageranks_lock, + &computed_pageranks_v, + &h_src_v, + &h_dst_v, + &h_weights_v, + &unique_vertices, + i, + num_threads]() { + auto thread_handle = instance_manager->get_handle(); + + auto number_of_vertices = unique_vertices->size(); + + std::vector my_vertex_list; + my_vertex_list.reserve((number_of_vertices + num_threads - 1) / num_threads); + + for (size_t j = i; j < number_of_vertices; j += num_threads) { + my_vertex_list.push_back((*unique_vertices)[j]); + } + + rmm::device_uvector d_my_vertex_list(my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + raft::update_device(d_my_vertex_list.data(), + my_vertex_list.data(), + my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + + auto d_my_pageranks = pageranks_view.gather( + thread_handle, + raft::device_span{d_my_vertex_list.data(), d_my_vertex_list.size()}, + graph_view, + renumber_map_view); + + std::vector my_pageranks(d_my_pageranks.size()); + raft::update_host(my_pageranks.data(), + d_my_pageranks.data(), + d_my_pageranks.size(), + thread_handle.raft_handle().get_stream()); + + { + std::lock_guard lock(computed_pageranks_lock); + computed_pageranks_v.push_back( + std::make_tuple(std::move(my_vertex_list), std::move(my_pageranks))); + } + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + if (multithreaded_usecase.check_correctness) { + // Want to compare the results in computed_pageranks_v with SG results + cugraph::graph_t sg_graph(handle); + std::optional< + cugraph::edge_property_t, weight_t>> + sg_edge_weights{std::nullopt}; + std::optional> sg_renumber_map{std::nullopt}; + + std::tie(sg_graph, sg_edge_weights, std::ignore, std::ignore, sg_renumber_map) = cugraph:: + create_graph_from_edgelist( + handle, + std::nullopt, + std::move(d_src_v), + std::move(d_dst_v), + std::move(d_weights_v), + std::nullopt, + std::nullopt, + cugraph::graph_properties_t{is_symmetric, true}, + true); + + auto [sg_pageranks, meta] = cugraph::pagerank( + handle, + sg_graph.view(), + sg_edge_weights ? std::make_optional(sg_edge_weights->view()) : std::nullopt, + std::nullopt, + std::nullopt, + std::nullopt, + alpha, + epsilon); + + auto h_sg_pageranks = cugraph::test::to_host(handle, sg_pageranks); + auto h_sg_renumber_map = cugraph::test::to_host(handle, sg_renumber_map); + auto compare_functor = cugraph::test::nearly_equal{ + weight_t{1e-3}, + weight_t{(weight_t{1} / static_cast(h_sg_pageranks.size())) * weight_t{1e-3}}}; + + std::for_each(computed_pageranks_v.begin(), + computed_pageranks_v.end(), + [h_sg_pageranks, compare_functor, h_sg_renumber_map](auto t1) { + std::for_each( + thrust::make_zip_iterator(std::get<0>(t1).begin(), std::get<1>(t1).begin()), + thrust::make_zip_iterator(std::get<0>(t1).end(), std::get<1>(t1).end()), + [h_sg_pageranks, compare_functor, h_sg_renumber_map](auto t2) { + vertex_t v = thrust::get<0>(t2); + weight_t pr = thrust::get<1>(t2); + + auto pos = + std::find(h_sg_renumber_map->begin(), h_sg_renumber_map->end(), v); + auto offset = std::distance(h_sg_renumber_map->begin(), pos); + + if (pos == h_sg_renumber_map->end()) { + ASSERT_TRUE(compare_functor(pr, weight_t{0})) + << "vertex " << v << ", SG result = " << h_sg_pageranks[offset] + << ", mtmg result = " << pr << ", not in renumber map"; + } else { + ASSERT_TRUE(compare_functor(pr, h_sg_pageranks[offset])) + << "vertex " << v << ", SG result = " << h_sg_pageranks[offset] + << ", mtmg result = " << pr + << ", renumber map = " << (*h_sg_renumber_map)[offset]; + } + }); + }); + } + } +}; + +using Tests_Multithreaded_File = Tests_Multithreaded; +using Tests_Multithreaded_Rmat = Tests_Multithreaded; + +// FIXME: add tests for type combinations +TEST_P(Tests_Multithreaded_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam()), get_gpu_list()); +} + +TEST_P(Tests_Multithreaded_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam()), get_gpu_list()); +} + +INSTANTIATE_TEST_SUITE_P(file_test, + Tests_Multithreaded_File, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{false, true}, + Multithreaded_Usecase{true, true}), + ::testing::Values(cugraph::test::File_Usecase("karate.csv"), + cugraph::test::File_Usecase("dolphins.csv")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_Multithreaded_Rmat, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{false, true}, Multithreaded_Usecase{true, true}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + +INSTANTIATE_TEST_SUITE_P( + file_benchmark_test, /* note that the test filename can be overridden in benchmarking (with + --gtest_filter to select only the file_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one File_Usecase that differ only in filename + (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_File, + ::testing::Combine( + // disable correctness checks + ::testing::Values(Multithreaded_Usecase{false, false}, Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_benchmark_test, /* note that scale & edge factor can be overridden in benchmarking (with + --gtest_filter to select only the rmat_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one Rmat_Usecase that differ only in scale or edge + factor (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_Rmat, + ::testing::Combine( + // disable correctness checks for large graphs + ::testing::Values(Multithreaded_Usecase{false, false}, Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + +// +// Need to customize the test configuration to support multi-node comms not using MPI +// +int main(int argc, char** argv) +{ + cugraph::test::initialize_mpi(argc, argv); + auto comm_rank = cugraph::test::query_mpi_comm_world_rank(); + auto comm_size = cugraph::test::query_mpi_comm_world_size(); + + ::testing::InitGoogleTest(&argc, argv); + auto const cmd_opts = parse_test_options(argc, argv); + auto const rmm_mode = cmd_opts["rmm_mode"].as(); + auto resource = cugraph::test::create_memory_resource(rmm_mode); + rmm::mr::set_current_device_resource(resource.get()); + cugraph::test::g_perf = cmd_opts["perf"].as(); + cugraph::test::g_rmat_scale = (cmd_opts.count("rmat_scale") > 0) + ? std::make_optional(cmd_opts["rmat_scale"].as()) + : std::nullopt; + cugraph::test::g_rmat_edge_factor = + (cmd_opts.count("rmat_edge_factor") > 0) + ? std::make_optional(cmd_opts["rmat_edge_factor"].as()) + : std::nullopt; + cugraph::test::g_test_file_name = + (cmd_opts.count("test_file_name") > 0) + ? std::make_optional(cmd_opts["test_file_name"].as()) + : std::nullopt; + + // + // Set global values for the test. Need to know the rank of this process, + // the comm size, number of GPUs per node, and the NCCL Id for rank 0. + // + int num_gpus_this_node{-1}; + std::vector num_gpus_per_node{}; + + g_node_rank = comm_rank; + g_num_nodes = comm_size; + + num_gpus_per_node.resize(comm_size); + + RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_this_node)); + RAFT_MPI_TRY(MPI_Allgather( + &num_gpus_this_node, 1, MPI_INT, num_gpus_per_node.data(), 1, MPI_INT, MPI_COMM_WORLD)); + + int node_rank{0}; + + for (int i = 0; i < comm_size; ++i) { + for (int j = 0; j < num_gpus_per_node[i]; ++j) { + if (i != comm_rank) + g_resource_manager.register_remote_gpu(node_rank++); + else + g_resource_manager.register_local_gpu(node_rank++, rmm::cuda_device_id{j}); + } + } + + auto result = RUN_ALL_TESTS(); + cugraph::test::finalize_mpi(); + return result; +} From a3cda5de57e7c5849c85befd5b27ccea21b10255 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 6 Nov 2023 21:28:06 -0500 Subject: [PATCH 073/111] [BUG] Restore the original default order of CSR, which does not reverse edges in cuGraph-PyG (#3980) OGB and many other datasets express the edge index in CSR order, but in release 23.10, the default order was changed to CSC, which broke downstream workflows. This PR restores the original default, which should be acceptable for the most common datasets, and familiar to most PyG users. Closes #3969 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Tingyu Wang (https://github.com/tingyu66) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3980 --- python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index 6192cd621d5..d1b24543956 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -214,7 +214,7 @@ def __init__( num_nodes_dict: Dict[str, int], *, multi_gpu: bool = False, - order: str = "CSC", + order: str = "CSR", ): """ Constructs a new CuGraphStore from the provided @@ -260,11 +260,11 @@ def __init__( Whether the store should be backed by a multi-GPU graph. Requires dask to have been set up. - order: str (Optional ["CSR", "CSC"], default = CSC) - The order to use for sampling. Should nearly always be CSC - unless there is a specific expectation of "reverse" sampling. - It is also not uncommon to use CSR order for correctness - testing, which some cuGraph-PyG tests do. + order: str (Optional ["CSR", "CSC"], default = CSR) + The order to use for sampling. CSR corresponds to the + standard OGB dataset order that is usually used in PyG. + CSC order constructs the same graph as CSR, but with + edges in the opposite direction. """ if None in G: From ac5b981dabc0ca2645713164cc74367b4366cd8b Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Tue, 7 Nov 2023 15:17:16 -0500 Subject: [PATCH 074/111] Fix eigenvector testing and HITS testing discrepancies (#3979) Address a couple of bugs discovered in nx-cugraph testing. HITS should raise an exception if it fails to converge. Eigenvector computation was not quite right, nx-cugraph testing discovered an edge condition where it was obvious. Closes #3971 Closes #3972 Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Naim (https://github.com/naimnv) - Seunghwa Kang (https://github.com/seunghwak) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3979 --- .../eigenvector_centrality_impl.cuh | 10 +++++++++- cpp/src/link_analysis/hits_impl.cuh | 17 ++++++++++------- cpp/tests/c_api/eigenvector_centrality_test.c | 19 +++++++++++++++++++ .../eigenvector_centrality_test.cpp | 1 - cpp/tests/link_analysis/hits_test.cpp | 4 ++-- .../cugraph/tests/link_analysis/test_hits.py | 6 +++++- 6 files changed, 45 insertions(+), 12 deletions(-) diff --git a/cpp/src/centrality/eigenvector_centrality_impl.cuh b/cpp/src/centrality/eigenvector_centrality_impl.cuh index 291abf18455..8d1bea4004d 100644 --- a/cpp/src/centrality/eigenvector_centrality_impl.cuh +++ b/cpp/src/centrality/eigenvector_centrality_impl.cuh @@ -96,7 +96,8 @@ rmm::device_uvector eigenvector_centrality( centralities.end(), old_centralities.data()); - update_edge_src_property(handle, pull_graph_view, centralities.begin(), edge_src_centralities); + update_edge_src_property( + handle, pull_graph_view, old_centralities.begin(), edge_src_centralities); if (edge_weight_view) { per_v_transform_reduce_incoming_e( @@ -122,6 +123,13 @@ rmm::device_uvector eigenvector_centrality( centralities.begin()); } + thrust::transform(handle.get_thrust_policy(), + centralities.begin(), + centralities.end(), + old_centralities.begin(), + centralities.begin(), + thrust::plus()); + // Normalize the centralities auto hypotenuse = sqrt(transform_reduce_v( handle, diff --git a/cpp/src/link_analysis/hits_impl.cuh b/cpp/src/link_analysis/hits_impl.cuh index 9badb041218..674046745b1 100644 --- a/cpp/src/link_analysis/hits_impl.cuh +++ b/cpp/src/link_analysis/hits_impl.cuh @@ -112,7 +112,8 @@ std::tuple hits(raft::handle_t const& handle, prev_hubs + graph_view.local_vertex_partition_range_size(), result_t{1.0} / num_vertices); } - for (size_t iter = 0; iter < max_iterations; ++iter) { + size_t iter{0}; + while (true) { // Update current destination authorities property per_v_transform_reduce_incoming_e( handle, @@ -162,17 +163,19 @@ std::tuple hits(raft::handle_t const& handle, thrust::make_zip_iterator(thrust::make_tuple(curr_hubs, prev_hubs)), [] __device__(auto, auto val) { return std::abs(thrust::get<0>(val) - thrust::get<1>(val)); }, result_t{0}); - if (diff_sum < epsilon) { - final_iteration_count = iter; - std::swap(prev_hubs, curr_hubs); - break; - } update_edge_src_property(handle, graph_view, curr_hubs, prev_src_hubs); // Swap pointers for the next iteration // After this swap call, prev_hubs has the latest value of hubs std::swap(prev_hubs, curr_hubs); + iter++; + + if (diff_sum < epsilon) { + break; + } else if (iter >= max_iterations) { + CUGRAPH_FAIL("HITS failed to converge."); + } } if (normalize) { @@ -188,7 +191,7 @@ std::tuple hits(raft::handle_t const& handle, hubs); } - return std::make_tuple(diff_sum, final_iteration_count); + return std::make_tuple(diff_sum, iter); } } // namespace detail diff --git a/cpp/tests/c_api/eigenvector_centrality_test.c b/cpp/tests/c_api/eigenvector_centrality_test.c index 9fd2d2bee6f..8bc5971a70c 100644 --- a/cpp/tests/c_api/eigenvector_centrality_test.c +++ b/cpp/tests/c_api/eigenvector_centrality_test.c @@ -109,11 +109,30 @@ int test_eigenvector_centrality() h_src, h_dst, h_wgt, h_result, num_vertices, num_edges, TRUE, epsilon, max_iterations); } +int test_eigenvector_centrality_3971() +{ + size_t num_edges = 4; + size_t num_vertices = 3; + + vertex_t h_src[] = {0, 1, 1, 2}; + vertex_t h_dst[] = {1, 0, 2, 1}; + weight_t h_wgt[] = {1.0f, 1.0f, 1.0f, 1.0f}; + weight_t h_result[] = {0.5, 0.707107, 0.5}; + + double epsilon = 1e-6; + size_t max_iterations = 1000; + + // Eigenvector centrality wants store_transposed = TRUE + return generic_eigenvector_centrality_test( + h_src, h_dst, h_wgt, h_result, num_vertices, num_edges, TRUE, epsilon, max_iterations); +} + /******************************************************************************/ int main(int argc, char** argv) { int result = 0; result |= RUN_TEST(test_eigenvector_centrality); + result |= RUN_TEST(test_eigenvector_centrality_3971); return result; } diff --git a/cpp/tests/centrality/eigenvector_centrality_test.cpp b/cpp/tests/centrality/eigenvector_centrality_test.cpp index 7cafcfbde85..6c3bd510abd 100644 --- a/cpp/tests/centrality/eigenvector_centrality_test.cpp +++ b/cpp/tests/centrality/eigenvector_centrality_test.cpp @@ -60,7 +60,6 @@ void eigenvector_centrality_reference(vertex_t const* src, size_t iter{0}; while (true) { std::copy(tmp_centralities.begin(), tmp_centralities.end(), old_centralities.begin()); - std::fill(tmp_centralities.begin(), tmp_centralities.end(), double{0}); for (size_t e = 0; e < num_edges; ++e) { auto w = weights ? (*weights)[e] : weight_t{1.0}; diff --git a/cpp/tests/link_analysis/hits_test.cpp b/cpp/tests/link_analysis/hits_test.cpp index 44fa619b503..d0e77769034 100644 --- a/cpp/tests/link_analysis/hits_test.cpp +++ b/cpp/tests/link_analysis/hits_test.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2022, NVIDIA CORPORATION. + * Copyright (c) 2020-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -176,7 +176,7 @@ class Tests_Hits : public ::testing::TestWithParam d_hubs(graph_view.local_vertex_partition_range_size(), handle.get_stream()); diff --git a/python/cugraph/cugraph/tests/link_analysis/test_hits.py b/python/cugraph/cugraph/tests/link_analysis/test_hits.py index 1c5a135e944..fcfd8cc5318 100644 --- a/python/cugraph/cugraph/tests/link_analysis/test_hits.py +++ b/python/cugraph/cugraph/tests/link_analysis/test_hits.py @@ -38,7 +38,11 @@ def setup_function(): fixture_params = gen_fixture_params_product( (datasets, "graph_file"), ([50], "max_iter"), - ([1.0e-6], "tol"), + # FIXME: Changed this from 1.0e-6 to 1.0e-5. NX defaults to + # FLOAT64 computation, cuGraph C++ defaults to whatever the edge weight + # is, cugraph python defaults that to FLOAT32. Does not converge at + # 1e-6 for larger graphs and FLOAT32. + ([1.0e-5], "tol"), ) From 663d95fab0a9e6312f93d1190d6dfd590b081cd5 Mon Sep 17 00:00:00 2001 From: Vibhu Jawa Date: Tue, 7 Nov 2023 16:44:18 -0800 Subject: [PATCH 075/111] [REVIEW]Optimize cugraph-DGL csc codepath (#3977) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This PR optimizes `cugraph-DGL` csc codepath and adds an end to end benchmark using cugraph-dgl. ```python3 sampled_dir = "/raid/vjawa/nov_1_bulksampling_benchmarks/ogbn_papers100M[2]_b512_f[10, 10, 10]" dataset = HomogenousBulkSamplerDataset(meta_json_d["total_num_nodes"], edge_dir=edge_dir, sparse_format="csc", return_type="cugraph_dgl.nn.SparseGraph") dataset.set_input_files(input_directory=sampled_dir+"/samples") dataloader = torch.utils.data.DataLoader(dataset, collate_fn=lambda x:x, shuffle=False, num_workers=0, batch_size=None) def run(dataloader): for input_nodes, output_nodes, blocks in dataloader: pass ``` ```python3 %%timeit run(dataloader) ``` With PR : ```python3 2.48 s ± 14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` MAIN: ```python3 %%timeit 9.52 s ± 151 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ``` ### E2E Benchmarks: ``` python3 cugraph_dgl_benchmark.py ``` PR: ```python3 ... Epoch time = 70.41 seconds Time to create MFG = 3.37 seconds Time analysis for fanout = [10, 10, 10], batch_size = 512 mfg_creation_time_per_epoch = 3.37 seconds feature_time_per_epoch = 44.09 seconds m_fwd_time_per_epoch = 7.31 seconds m_bkwd_time_per_epoch = 15.60 seconds ``` MAIN: ```python3 .... Epoch time = 84.72 seconds Time to create MFG = 10.79 seconds Time analysis for fanout = [10, 10, 10], batch_size = 512 mfg_creation_time_per_epoch = 10.79 seconds feature_time_per_epoch = 47.09 seconds m_fwd_time_per_epoch = 8.24 seconds m_bkwd_time_per_epoch = 18.58 seconds ``` Authors: - Vibhu Jawa (https://github.com/VibhuJawa) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Tingyu Wang (https://github.com/tingyu66) URL: https://github.com/rapidsai/cugraph/pull/3977 --- .../scale-benchmarks/cugraph_dgl_benchmark.py | 152 ++++++++++++++++++ .../cugraph-dgl/scale-benchmarks/model.py | 17 +- .../bulk_sampling/cugraph_bulk_sampling.py | 55 +++++-- .../dataloading/utils/sampling_helpers.py | 71 ++++---- 4 files changed, 244 insertions(+), 51 deletions(-) create mode 100644 benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py b/benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py new file mode 100644 index 00000000000..85f43b97b90 --- /dev/null +++ b/benchmarks/cugraph-dgl/scale-benchmarks/cugraph_dgl_benchmark.py @@ -0,0 +1,152 @@ +# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import os + +os.environ["LIBCUDF_CUFILE_POLICY"] = "KVIKIO" +os.environ["KVIKIO_NTHREADS"] = "64" +os.environ["RAPIDS_NO_INITIALIZE"] = "1" +import json +import pandas as pd +import os +import time +from rmm.allocators.torch import rmm_torch_allocator +import rmm +import torch +from cugraph_dgl.dataloading import HomogenousBulkSamplerDataset +from model import run_1_epoch +from argparse import ArgumentParser +from load_graph_feats import load_node_labels, load_node_features + + +def create_dataloader(sampled_dir, total_num_nodes, sparse_format, return_type): + print("Creating dataloader", flush=True) + st = time.time() + dataset = HomogenousBulkSamplerDataset( + total_num_nodes, + edge_dir="in", + sparse_format=sparse_format, + return_type=return_type, + ) + + dataset.set_input_files(sampled_dir) + dataloader = torch.utils.data.DataLoader( + dataset, collate_fn=lambda x: x, shuffle=False, num_workers=0, batch_size=None + ) + et = time.time() + print(f"Time to create dataloader = {et - st:.2f} seconds", flush=True) + return dataloader + + +def setup_common_pool(): + rmm.reinitialize(initial_pool_size=5e9, pool_allocator=True) + torch.cuda.memory.change_current_allocator(rmm_torch_allocator) + + +def main(args): + print( + f"Running cugraph-dgl dataloading benchmark with the following parameters:\n" + f"Dataset path = {args.dataset_path}\n" + f"Sampling path = {args.sampling_path}\n" + ) + with open(os.path.join(args.dataset_path, "meta.json"), "r") as f: + input_meta = json.load(f) + + sampled_dirs = [ + os.path.join(args.sampling_path, f) for f in os.listdir(args.sampling_path) + ] + + time_ls = [] + for sampled_dir in sampled_dirs: + with open(os.path.join(sampled_dir, "output_meta.json"), "r") as f: + sampled_meta_d = json.load(f) + + replication_factor = sampled_meta_d["replication_factor"] + feat_load_st = time.time() + label_data = load_node_labels( + args.dataset_path, replication_factor, input_meta + )["paper"]["y"] + feat_data = feat_data = load_node_features( + args.dataset_path, replication_factor, node_type="paper" + ) + print( + f"Feature and label data loading took = {time.time()-feat_load_st}", + flush=True, + ) + + r_time_ls = e2e_benchmark(sampled_dir, feat_data, label_data, sampled_meta_d) + [x.update({"replication_factor": replication_factor}) for x in r_time_ls] + [x.update({"num_edges": sampled_meta_d["total_num_edges"]}) for x in r_time_ls] + time_ls.extend(r_time_ls) + + print( + f"Benchmark completed for replication factor = {replication_factor}\n{'=' * 30}", + flush=True, + ) + + df = pd.DataFrame(time_ls) + df.to_csv("cugraph_dgl_e2e_benchmark.csv", index=False) + print(f"Benchmark completed for all replication factors\n{'=' * 30}", flush=True) + + +def e2e_benchmark( + sampled_dir: str, feat: torch.Tensor, y: torch.Tensor, sampled_meta_d: dict +): + """ + Run the e2e_benchmark + Args: + sampled_dir: directory containing the sampled graph + feat: node features + y: node labels + sampled_meta_d: dictionary containing the sampled graph metadata + """ + time_ls = [] + + # TODO: Make this a parameter in bulk sampling script + sampled_meta_d["sparse_format"] = "csc" + sampled_dir = os.path.join(sampled_dir, "samples") + dataloader = create_dataloader( + sampled_dir, + sampled_meta_d["total_num_nodes"], + sampled_meta_d["sparse_format"], + return_type="cugraph_dgl.nn.SparseGraph", + ) + time_d = run_1_epoch( + dataloader, + feat, + y, + fanout=sampled_meta_d["fanout"], + batch_size=sampled_meta_d["batch_size"], + model_backend="cugraph_dgl", + ) + time_ls.append(time_d) + print("=" * 30) + return time_ls + + +def parse_arguments(): + parser = ArgumentParser() + parser.add_argument( + "--dataset_path", type=str, default="/raid/vjawa/ogbn_papers100M/" + ) + parser.add_argument( + "--sampling_path", + type=str, + default="/raid/vjawa/nov_1_bulksampling_benchmarks/", + ) + return parser.parse_args() + + +if __name__ == "__main__": + setup_common_pool() + arguments = parse_arguments() + main(arguments) diff --git a/benchmarks/cugraph-dgl/scale-benchmarks/model.py b/benchmarks/cugraph-dgl/scale-benchmarks/model.py index 08ae0e8b1ee..9a9dfe58f96 100644 --- a/benchmarks/cugraph-dgl/scale-benchmarks/model.py +++ b/benchmarks/cugraph-dgl/scale-benchmarks/model.py @@ -57,11 +57,11 @@ def create_model(feat_size, num_classes, num_layers, model_backend="dgl"): def train_model(model, dataloader, opt, feat, y): - times = {key: 0 for key in ["mfg_creation", "feature", "m_fwd", "m_bkwd"]} + times_d = {key: 0 for key in ["mfg_creation", "feature", "m_fwd", "m_bkwd"]} epoch_st = time.time() mfg_st = time.time() for input_nodes, output_nodes, blocks in dataloader: - times["mfg_creation"] += time.time() - mfg_st + times_d["mfg_creation"] += time.time() - mfg_st if feat is not None: fst = time.time() input_nodes = input_nodes.to("cpu") @@ -71,23 +71,24 @@ def train_model(model, dataloader, opt, feat, y): output_nodes = output_nodes["paper"] output_nodes = output_nodes.to(y.device) y_batch = y[output_nodes].to("cuda") - times["feature"] += time.time() - fst + times_d["feature"] += time.time() - fst m_fwd_st = time.time() y_hat = model(blocks, input_feat) - times["m_fwd"] += time.time() - m_fwd_st + times_d["m_fwd"] += time.time() - m_fwd_st m_bkwd_st = time.time() loss = F.cross_entropy(y_hat, y_batch) opt.zero_grad() loss.backward() opt.step() - times["m_bkwd"] += time.time() - m_bkwd_st + times_d["m_bkwd"] += time.time() - m_bkwd_st mfg_st = time.time() print(f"Epoch time = {time.time() - epoch_st:.2f} seconds") + print(f"Time to create MFG = {times_d['mfg_creation']:.2f} seconds") - return times + return times_d def analyze_time(dataloader, times, epoch_time, fanout, batch_size): @@ -119,6 +120,10 @@ def run_1_epoch(dataloader, feat, y, fanout, batch_size, model_backend): else: model = None opt = None + + # Warmup RUN + times = train_model(model, dataloader, opt, feat, y) + epoch_st = time.time() times = train_model(model, dataloader, opt, feat, y) epoch_time = time.time() - epoch_st diff --git a/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py b/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py index a8c0658767d..1ca5d6db637 100644 --- a/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py +++ b/benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py @@ -22,7 +22,6 @@ get_allocation_counts_dask_lazy, sizeof_fmt, get_peak_output_ratio_across_workers, - restart_client, start_dask_client, stop_dask_client, enable_spilling, @@ -187,10 +186,10 @@ def sample_graph( output_path, seed=42, batch_size=500, - seeds_per_call=200000, + seeds_per_call=400000, batches_per_partition=100, fanout=[5, 5, 5], - persist=False, + sampling_kwargs={}, ): cupy.random.seed(seed) @@ -204,6 +203,7 @@ def sample_graph( seeds_per_call=seeds_per_call, batches_per_partition=batches_per_partition, log_level=logging.INFO, + **sampling_kwargs, ) n_workers = len(default_client().scheduler_info()["workers"]) @@ -469,6 +469,7 @@ def benchmark_cugraph_bulk_sampling( batch_size, seeds_per_call, fanout, + sampling_target_framework, reverse_edges=True, dataset_dir=".", replication_factor=1, @@ -564,17 +565,39 @@ def benchmark_cugraph_bulk_sampling( output_sample_path = os.path.join(output_subdir, "samples") os.makedirs(output_sample_path) - batches_per_partition = 200_000 // batch_size + if sampling_target_framework == "cugraph_dgl_csr": + sampling_kwargs = { + "deduplicate_sources": True, + "prior_sources_behavior": "carryover", + "renumber": True, + "compression": "CSR", + "compress_per_hop": True, + "use_legacy_names": False, + "include_hop_column": False, + } + else: + # FIXME: Update these arguments when CSC mode is fixed in cuGraph-PyG (release 24.02) + sampling_kwargs = { + "deduplicate_sources": True, + "prior_sources_behavior": "exclude", + "renumber": True, + "compression": "COO", + "compress_per_hop": False, + "use_legacy_names": False, + "include_hop_column": True, + } + + batches_per_partition = 400_000 // batch_size execution_time, allocation_counts = sample_graph( - G, - dask_label_df, - output_sample_path, + G=G, + label_df=dask_label_df, + output_path=output_sample_path, seed=seed, batch_size=batch_size, seeds_per_call=seeds_per_call, batches_per_partition=batches_per_partition, fanout=fanout, - persist=persist, + sampling_kwargs=sampling_kwargs, ) output_meta = { @@ -701,7 +724,13 @@ def get_args(): required=False, default=False, ) - + parser.add_argument( + "--sampling_target_framework", + type=str, + help="The target framework for sampling (i.e. cugraph_dgl_csr, cugraph_pyg_csc, ...)", + required=False, + default=None, + ) parser.add_argument( "--dask_worker_devices", type=str, @@ -738,6 +767,12 @@ def get_args(): logging.basicConfig() args = get_args() + if args.sampling_target_framework not in ["cugraph_dgl_csr", None]: + raise ValueError( + "sampling_target_framework must be one of cugraph_dgl_csr or None", + "Other frameworks are not supported at this time.", + ) + fanouts = [ [int(f) for f in fanout.split("_")] for fanout in args.fanouts.split(",") ] @@ -785,6 +820,7 @@ def get_args(): batch_size=batch_size, seeds_per_call=seeds_per_call, fanout=fanout, + sampling_target_framework=args.sampling_target_framework, dataset_dir=args.dataset_root, reverse_edges=args.reverse_edges, replication_factor=replication_factor, @@ -809,7 +845,6 @@ def get_args(): warnings.warn("An Exception Occurred!") print(e) traceback.print_exc() - restart_client(client) sleep(10) stats_df = pd.DataFrame( diff --git a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py index a4f64668348..f674bece8be 100644 --- a/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py +++ b/python/cugraph-dgl/cugraph_dgl/dataloading/utils/sampling_helpers.py @@ -14,7 +14,6 @@ from typing import List, Tuple, Dict, Optional from collections import defaultdict import cudf -import cupy from cugraph.utilities.utils import import_optional from cugraph_dgl.nn import SparseGraph @@ -444,53 +443,58 @@ def _process_sampled_df_csc( destinations, respectively. """ # dropna - major_offsets = df.major_offsets.dropna().values - label_hop_offsets = df.label_hop_offsets.dropna().values - renumber_map_offsets = df.renumber_map_offsets.dropna().values - renumber_map = df.map.dropna().values - minors = df.minors.dropna().values + major_offsets = cast_to_tensor(df.major_offsets.dropna()) + label_hop_offsets = cast_to_tensor(df.label_hop_offsets.dropna()) + renumber_map_offsets = cast_to_tensor(df.renumber_map_offsets.dropna()) + renumber_map = cast_to_tensor(df.map.dropna()) + minors = cast_to_tensor(df.minors.dropna()) - n_batches = renumber_map_offsets.size - 1 - n_hops = int((label_hop_offsets.size - 1) / n_batches) + n_batches = len(renumber_map_offsets) - 1 + n_hops = int((len(label_hop_offsets) - 1) / n_batches) # make global offsets local - major_offsets -= major_offsets[0] - label_hop_offsets -= label_hop_offsets[0] - renumber_map_offsets -= renumber_map_offsets[0] + # Have to make a clone as pytorch does not allow + # in-place operations on tensors + major_offsets -= major_offsets[0].clone() + label_hop_offsets -= label_hop_offsets[0].clone() + renumber_map_offsets -= renumber_map_offsets[0].clone() # get the sizes of each adjacency matrix (for MFGs) mfg_sizes = (label_hop_offsets[1:] - label_hop_offsets[:-1]).reshape( (n_batches, n_hops) ) n_nodes = renumber_map_offsets[1:] - renumber_map_offsets[:-1] - mfg_sizes = cupy.hstack((mfg_sizes, n_nodes.reshape(n_batches, -1))) + mfg_sizes = torch.hstack((mfg_sizes, n_nodes.reshape(n_batches, -1))) if reverse_hop_id: - mfg_sizes = mfg_sizes[:, ::-1] + mfg_sizes = mfg_sizes.flip(1) tensors_dict = {} renumber_map_list = [] + # Note: minors and major_offsets from BulkSampler are of type int32 + # and int64 respectively. Since pylibcugraphops binding code doesn't + # support distinct node and edge index type, we simply casting both + # to int32 for now. + minors = minors.int() + major_offsets = major_offsets.int() + # Note: We transfer tensors to CPU here to avoid the overhead of + # transferring them in each iteration of the for loop below. + major_offsets_cpu = major_offsets.to("cpu").numpy() + label_hop_offsets_cpu = label_hop_offsets.to("cpu").numpy() + for batch_id in range(n_batches): batch_dict = {} - for hop_id in range(n_hops): hop_dict = {} idx = batch_id * n_hops + hop_id # idx in label_hop_offsets - major_offsets_start = label_hop_offsets[idx].item() - major_offsets_end = label_hop_offsets[idx + 1].item() - minors_start = major_offsets[major_offsets_start].item() - minors_end = major_offsets[major_offsets_end].item() - # Note: minors and major_offsets from BulkSampler are of type int32 - # and int64 respectively. Since pylibcugraphops binding code doesn't - # support distinct node and edge index type, we simply casting both - # to int32 for now. - hop_dict["minors"] = torch.as_tensor( - minors[minors_start:minors_end], device="cuda" - ).int() - hop_dict["major_offsets"] = torch.as_tensor( + major_offsets_start = label_hop_offsets_cpu[idx] + major_offsets_end = label_hop_offsets_cpu[idx + 1] + minors_start = major_offsets_cpu[major_offsets_start] + minors_end = major_offsets_cpu[major_offsets_end] + hop_dict["minors"] = minors[minors_start:minors_end] + hop_dict["major_offsets"] = ( major_offsets[major_offsets_start : major_offsets_end + 1] - - major_offsets[major_offsets_start], - device="cuda", - ).int() + - major_offsets[major_offsets_start] + ) if reverse_hop_id: batch_dict[n_hops - 1 - hop_id] = hop_dict else: @@ -499,12 +503,9 @@ def _process_sampled_df_csc( tensors_dict[batch_id] = batch_dict renumber_map_list.append( - torch.as_tensor( - renumber_map[ - renumber_map_offsets[batch_id] : renumber_map_offsets[batch_id + 1] - ], - device="cuda", - ) + renumber_map[ + renumber_map_offsets[batch_id] : renumber_map_offsets[batch_id + 1] + ], ) return tensors_dict, renumber_map_list, mfg_sizes.tolist() From 9b9ce55070c42811a26d4ec1bed675d26962c36f Mon Sep 17 00:00:00 2001 From: brandon-b-miller <53796099+brandon-b-miller@users.noreply.github.com> Date: Wed, 8 Nov 2023 10:16:16 -0600 Subject: [PATCH 076/111] CuGraph compatibility fixes (#3973) This PR contains two changes. - A change that defers to `numpy.asarray` to process array objects that `cugraph` consumes - An update from using `unique` to use `drop_duplicates` in `simpleGraph.py`, which always always returns a Series to preserve pandas compatibility. Authors: - https://github.com/brandon-b-miller Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3973 --- python/cugraph/cugraph/structure/graph_classes.py | 3 +-- .../structure/graph_implementation/simpleGraph.py | 10 +++++++--- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/python/cugraph/cugraph/structure/graph_classes.py b/python/cugraph/cugraph/structure/graph_classes.py index 6f6c7e5a26c..03efcba0307 100644 --- a/python/cugraph/cugraph/structure/graph_classes.py +++ b/python/cugraph/cugraph/structure/graph_classes.py @@ -469,8 +469,7 @@ def from_numpy_array(self, np_array, nodes=None): nodes: array-like or None, optional (default=None) A list of column names, acting as labels for nodes """ - if not isinstance(np_array, np.ndarray): - raise TypeError("np_array input is not a Numpy array") + np_array = np.asarray(np_array) if len(np_array.shape) != 2: raise ValueError("np_array is not a 2D matrix") diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 2b23d3a26b7..22d82eb1796 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -1286,9 +1286,13 @@ def nodes(self): else: return df[df.columns[0]] else: - return cudf.concat( - [df[simpleGraphImpl.srcCol], df[simpleGraphImpl.dstCol]] - ).unique() + return ( + cudf.concat( + [df[simpleGraphImpl.srcCol], df[simpleGraphImpl.dstCol]] + ) + .drop_duplicates() + .reset_index(drop=True) + ) if self.adjlist is not None: return cudf.Series(np.arange(0, self.number_of_nodes())) From 77bb2917729926b4d923e0f68a0fb76fabc90e8a Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 8 Nov 2023 10:51:33 -0600 Subject: [PATCH 077/111] Updates README file to include nx-cugraph user documentation, adds nx-cugraph to main README (#3984) * Updates the nx-cugraph README file to include nx-cugraph user documentation * Adds nx-cugraph and new News section to main README * Updates software strack diagram to remove DGL and PyG since they are not currently using PropertyGraph or cugraph-service Authors: - Rick Ratzel (https://github.com/rlratzel) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Brad Rees (https://github.com/BradReesWork) --- README.md | 14 +++- img/Stack2.png | Bin 457436 -> 285038 bytes python/nx-cugraph/README.md | 135 +++++++++++++++++++++++++++++++----- 3 files changed, 132 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 560d1483242..8026e4feb64 100644 --- a/README.md +++ b/README.md @@ -35,8 +35,18 @@ ----- +## News -## Table of content +___NEW!___ _[nx-cugraph](./python/nx-cugraph/README.md)_, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change. +``` +> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com +> export NETWORKX_AUTOMATIC_BACKENDS=cugraph +``` +That's it. NetworkX now leverages cuGraph for accelerated graph algorithms. + +----- + +## Table of contents - Installation - [Getting cuGraph Packages](./docs/cugraph/source/installation/getting_cugraph.md) - [Building from Source](./docs/cugraph/source/installation/source_build.md) @@ -52,6 +62,7 @@ - [External Data Types](./readme_pages/data_types.md) - [pylibcugraph](./readme_pages/pylibcugraph.md) - [libcugraph (C/C++/CUDA)](./readme_pages/libcugraph.md) + - [nx-cugraph](./python/nx-cugraph/README.md) - [cugraph-service](./readme_pages/cugraph_service.md) - [cugraph-dgl](./readme_pages/cugraph_dgl.md) - [cugraph-ops](./readme_pages/cugraph_ops.md) @@ -116,6 +127,7 @@ df_page.sort_values('pagerank', ascending=False).head(10) * ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/ * CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" - https://cupy.dev/ * Memgraph - In-memory Graph database - https://memgraph.com/ 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zFJ0fz8u@iY_o^LU;lKFlW*?T@u_x`$D7o_YU-g@({OWnUZ0Qu=JG8c;Yp7TM1^lV1 MXkN%TfBVt@0fYxuh5!Hn diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index ab267e5a756..148fb2f0993 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -2,31 +2,134 @@ ## Description [RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) -with minimal dependencies (`networkx`, `cupy`, and `pylibcugraph`) to run graph algorithms on the GPU. +to run supported algorithms with GPU acceleration. -### Contribute +## System Requirements -Follow instructions for [contributing to cugraph](https://github.com/rapidsai/cugraph/blob/branch-23.12 -and [building from source](https://docs.rapids.ai/api/cugraph/stable/installation/source_build/), then build nx-cugraph in develop (i.e., editable) mode: -``` -$ ./build.sh nx-cugraph --pydevelop -``` +nx-cugraph requires the following: + + * NVIDIA GPU, Pascal architecture or later + * CUDA 11.2, 11.4, 11.5, 11.8, or 12.0 + * Python versions 3.9, 3.10, or 3.11 + * NetworkX >= version 3.2 -### Run tests +More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).. -Run nx-cugraph tests from `cugraph/python/nx-cugraph` directory: +## Installation + +nx-cugraph can be installed using either conda or pip. + +### conda ``` -$ pytest +conda install -c rapidsai-nightly -c conda-forge -c nvidia nx-cugraph ``` -Run nx-cugraph benchmarks: +### pip ``` -$ pytest --bench +python -m pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com ``` -Run networkx tests (requires networkx version 3.2): +Notes: + + * Nightly wheel builds will not be available until the 23.12 release, therefore the index URL for the stable release version is being used in the pip install command above. + * Additional information relevant to installing any RAPIDS package can be found [here](https://rapids.ai/#quick-start). + +## Enabling nx-cugraph + +NetworkX will use nx-cugraph as the graph analytics backend if any of the +following are are used: + +### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. +The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. +Set `NETWORKX_AUTOMATIC_BACKENDS=cugraph` to use nx-cugraph to GPU accelerate supported APIs with no code changes. +Example: ``` -$ ./run_nx_tests.sh +bash> NETWORKX_AUTOMATIC_BACKENDS=cugraph python my_networkx_script.py ``` -Additional arguments may be passed to pytest such as: + +### `backend=` keyword argument +To explicitly specify a particular backend for an API, use the `backend=` +keyword argument. This argument takes precedence over the +`NETWORKX_AUTOMATIC_BACKENDS` environment variable. This requires anyone +running code that uses the `backend=` keyword argument to have the specified +backend installed. + +Example: ``` -$ ./run_nx_tests.sh -x --sw -k betweenness +nx.betweenness_centrality(cit_patents_graph, k=k, backend="cugraph") ``` + +### Type-based dispatching + +NetworkX also supports automatically dispatching to backends associated with +specific graph types. Like the `backend=` keyword argument example above, this +requires the user to write code for a specific backend, and therefore requires +the backend to be installed, but has the advantage of ensuring a particular +behavior without the potential for runtime conversions. + +To use type-based dispatching with nx-cugraph, the user must import the backend +directly in their code to access the utilities provided to create a Graph +instance specifically for the nx-cugraph backend. + +Example: +``` +import networkx as nx +import nx_cugraph as nxcg + +G = nx.Graph() +... +nxcg_G = nxcg.from_networkx(G) # conversion happens once here +nx.betweenness_centrality(nxcg_G, k=1000) # nxcg Graph type causes cugraph backend + # to be used, no conversion necessary +``` + +## Supported Algorithms + +The nx-cugraph backend to NetworkX connects +[pylibcugraph](../../readme_pages/pylibcugraph.md) (cuGraph's low-level python +interface to its CUDA-based graph analytics library) and +[CuPy](https://cupy.dev/) (a GPU-accelerated array library) to NetworkX's +familiar and easy-to-use API. + +Below is the list of algorithms (many listed using pylibcugraph names), +available today in pylibcugraph or implemented using CuPy, that are or will be +supported in nx-cugraph. + +| feature/algo | release/target version | +| ----- | ----- | +| analyze_clustering_edge_cut | ? | +| analyze_clustering_modularity | ? | +| analyze_clustering_ratio_cut | ? | +| balanced_cut_clustering | ? | +| betweenness_centrality | 23.10 | +| bfs | ? | +| core_number | ? | +| degree_centrality | 23.12 | +| ecg | ? | +| edge_betweenness_centrality | 23.10 | +| ego_graph | ? | +| eigenvector_centrality | 23.12 | +| get_two_hop_neighbors | ? | +| hits | 23.12 | +| in_degree_centrality | 23.12 | +| induced_subgraph | ? | +| jaccard_coefficients | ? | +| katz_centrality | 23.12 | +| k_core | ? | +| k_truss_subgraph | 23.12 | +| leiden | ? | +| louvain | 23.10 | +| node2vec | ? | +| out_degree_centrality | 23.12 | +| overlap_coefficients | ? | +| pagerank | 23.12 | +| personalized_pagerank | ? | +| sorensen_coefficients | ? | +| spectral_modularity_maximization | ? | +| sssp | 23.12 | +| strongly_connected_components | ? | +| triangle_count | ? | +| uniform_neighbor_sample | ? | +| uniform_random_walks | ? | +| weakly_connected_components | ? | + +To request nx-cugraph backend support for a NetworkX API that is not listed +above, visit the [cuGraph GitHub repo](https://github.com/rapidsai/cugraph). From c3b3ceec056723fc0ac9dfadeba7373e1e88387b Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 8 Nov 2023 11:07:29 -0600 Subject: [PATCH 078/111] Fixes typos, updates link to NX backend docs. (#3989) * Fixes typos (repeated word, extra periods) * Updates link to the current docs on NetworkX backends Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Ray Douglass (https://github.com/raydouglass) --- python/nx-cugraph/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 148fb2f0993..273a6112d77 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -1,7 +1,7 @@ # nx-cugraph ## Description -[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends) +[RAPIDS](https://rapids.ai) nx-cugraph is a [backend to NetworkX](https://networkx.org/documentation/stable/reference/utils.html#backends) to run supported algorithms with GPU acceleration. ## System Requirements @@ -13,7 +13,7 @@ nx-cugraph requires the following: * Python versions 3.9, 3.10, or 3.11 * NetworkX >= version 3.2 -More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req).. +More details about system requirements can be found in the [RAPIDS System Requirements documentation](https://docs.rapids.ai/install#system-req). ## Installation @@ -35,7 +35,7 @@ Notes: ## Enabling nx-cugraph NetworkX will use nx-cugraph as the graph analytics backend if any of the -following are are used: +following are used: ### `NETWORKX_AUTOMATIC_BACKENDS` environment variable. The `NETWORKX_AUTOMATIC_BACKENDS` environment variable can be used to have NetworkX automatically dispatch to specified backends an API is called that the backend supports. From ff943332512e6b5bd3bdbad4893ce46987fd9b1b Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Tue, 14 Nov 2023 09:29:03 -0600 Subject: [PATCH 079/111] Adds update-version.sh support for proper versioning to cu version suffix pacakges and nx-cugraph meta-data files. (#3994) Adds `update-version.sh` support for: * nx-cugraph meta-data files * `cugraph-pyg`, `cugraph-dgl` env yaml files * `*-cu11`, `*-cu12` wheel dependencies in dependencies.yaml Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Alex Barghi (https://github.com/alexbarghi-nv) - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) --- ci/release/update-version.sh | 8 +++++++- python/nx-cugraph/_nx_cugraph/__init__.py | 1 + 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index d3dbed6ae46..0f2d4a3b914 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -90,8 +90,9 @@ DEPENDENCIES=( ucx-py ) for DEP in "${DEPENDENCIES[@]}"; do - for FILE in dependencies.yaml conda/environments/*.yaml; do + for FILE in dependencies.yaml conda/environments/*.yaml python/cugraph-{pyg,dgl}/conda/*.yaml; do sed_runner "/-.* ${DEP}==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*/g" ${FILE} + sed_runner "/-.* ${DEP}-cu[0-9][0-9]==/ s/==.*/==${NEXT_SHORT_TAG_PEP440}.*/g" ${FILE} sed_runner "/-.* ucx-py==/ s/==.*/==${NEXT_UCX_PY_VERSION}.*/g" ${FILE} done for FILE in python/**/pyproject.toml python/**/**/pyproject.toml; do @@ -108,6 +109,11 @@ sed_runner "/^ucx_py_version:$/ {n;s/.*/ - \"${NEXT_UCX_PY_VERSION}.*\"/}" cond sed_runner "/^ucx_py_version:$/ {n;s/.*/ - \"${NEXT_UCX_PY_VERSION}.*\"/}" conda/recipes/cugraph-service/conda_build_config.yaml sed_runner "/^ucx_py_version:$/ {n;s/.*/ - \"${NEXT_UCX_PY_VERSION}.*\"/}" conda/recipes/pylibcugraph/conda_build_config.yaml +# nx-cugraph NetworkX entry-point meta-data +sed_runner "s@branch-[0-9][0-9].[0-9][0-9]@branch-${NEXT_SHORT_TAG}@g" python/nx-cugraph/_nx_cugraph/__init__.py +# FIXME: can this use the standard VERSION file and update mechanism? +sed_runner "s/__version__ = .*/__version__ = \"${NEXT_FULL_TAG}\"/g" python/nx-cugraph/_nx_cugraph/__init__.py + # CI files for FILE in .github/workflows/*.yaml; do sed_runner "/shared-workflows/ s/@.*/@branch-${NEXT_SHORT_TAG}/g" "${FILE}" diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 8ef976aabf1..26638d1e735 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -136,6 +136,7 @@ def get_info(): return d +# FIXME: can this use the standard VERSION file and update mechanism? __version__ = "23.12.00" if __name__ == "__main__": From 2f44a9cefa3116ac69d1f8c5cfdca87b50a083c5 Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Tue, 14 Nov 2023 11:31:50 -0500 Subject: [PATCH 080/111] v24.02 Updates [skip ci] --- .../cuda11.8-conda/devcontainer.json | 4 +- .devcontainer/cuda11.8-pip/devcontainer.json | 6 +- .../cuda12.0-conda/devcontainer.json | 4 +- .devcontainer/cuda12.0-pip/devcontainer.json | 6 +- .github/workflows/build.yaml | 24 +++---- .github/workflows/pr.yaml | 34 +++++----- .github/workflows/test.yaml | 10 +-- VERSION | 2 +- ci/build_docs.sh | 2 +- .../all_cuda-118_arch-x86_64.yaml | 28 ++++---- .../all_cuda-120_arch-x86_64.yaml | 28 ++++---- .../cugraph-service/conda_build_config.yaml | 2 +- conda/recipes/cugraph/conda_build_config.yaml | 2 +- .../pylibcugraph/conda_build_config.yaml | 2 +- cpp/CMakeLists.txt | 2 +- cpp/doxygen/Doxyfile | 2 +- cpp/libcugraph_etl/CMakeLists.txt | 2 +- dependencies.yaml | 66 +++++++++---------- docs/cugraph/source/conf.py | 4 +- fetch_rapids.cmake | 2 +- .../conda/cugraph_dgl_dev_cuda-118.yaml | 4 +- python/cugraph-dgl/pyproject.toml | 2 +- .../conda/cugraph_pyg_dev_cuda-118.yaml | 4 +- python/cugraph-pyg/pyproject.toml | 2 +- python/cugraph-service/server/pyproject.toml | 14 ++-- python/cugraph/CMakeLists.txt | 2 +- python/cugraph/pyproject.toml | 20 +++--- python/nx-cugraph/_nx_cugraph/__init__.py | 4 +- python/nx-cugraph/pyproject.toml | 2 +- python/pylibcugraph/CMakeLists.txt | 2 +- python/pylibcugraph/pyproject.toml | 10 +-- 31 files changed, 149 insertions(+), 149 deletions(-) diff --git a/.devcontainer/cuda11.8-conda/devcontainer.json b/.devcontainer/cuda11.8-conda/devcontainer.json index 83078a304ed..e48301e4d14 100644 --- a/.devcontainer/cuda11.8-conda/devcontainer.json +++ b/.devcontainer/cuda11.8-conda/devcontainer.json @@ -5,12 +5,12 @@ "args": { "CUDA": "11.8", "PYTHON_PACKAGE_MANAGER": "conda", - "BASE": "rapidsai/devcontainers:23.12-cpp-cuda11.8-mambaforge-ubuntu22.04" + "BASE": "rapidsai/devcontainers:24.02-cpp-cuda11.8-mambaforge-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda11.8-pip/devcontainer.json b/.devcontainer/cuda11.8-pip/devcontainer.json index d59742575b5..a57ea0d163b 100644 --- a/.devcontainer/cuda11.8-pip/devcontainer.json +++ b/.devcontainer/cuda11.8-pip/devcontainer.json @@ -5,15 +5,15 @@ "args": { "CUDA": "11.8", "PYTHON_PACKAGE_MANAGER": "pip", - "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda11.8-ubuntu22.04" + "BASE": "rapidsai/devcontainers:24.02-cpp-llvm16-cuda11.8-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": { + "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": { "version": "1.14.1" }, - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda12.0-conda/devcontainer.json b/.devcontainer/cuda12.0-conda/devcontainer.json index bc4a2cb6fb4..10ba2f8fd3d 100644 --- a/.devcontainer/cuda12.0-conda/devcontainer.json +++ b/.devcontainer/cuda12.0-conda/devcontainer.json @@ -5,12 +5,12 @@ "args": { "CUDA": "12.0", "PYTHON_PACKAGE_MANAGER": "conda", - "BASE": "rapidsai/devcontainers:23.12-cpp-mambaforge-ubuntu22.04" + "BASE": "rapidsai/devcontainers:24.02-cpp-mambaforge-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.devcontainer/cuda12.0-pip/devcontainer.json b/.devcontainer/cuda12.0-pip/devcontainer.json index 3eacf726bf0..a112483a6db 100644 --- a/.devcontainer/cuda12.0-pip/devcontainer.json +++ b/.devcontainer/cuda12.0-pip/devcontainer.json @@ -5,15 +5,15 @@ "args": { "CUDA": "12.0", "PYTHON_PACKAGE_MANAGER": "pip", - "BASE": "rapidsai/devcontainers:23.12-cpp-llvm16-cuda12.0-ubuntu22.04" + "BASE": "rapidsai/devcontainers:24.02-cpp-llvm16-cuda12.0-ubuntu22.04" } }, "hostRequirements": {"gpu": "optional"}, "features": { - "ghcr.io/rapidsai/devcontainers/features/ucx:23.12": { + "ghcr.io/rapidsai/devcontainers/features/ucx:24.2": { "version": "1.14.1" }, - "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:23.12": {} + "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils:24.2": {} }, "overrideFeatureInstallOrder": [ "ghcr.io/rapidsai/devcontainers/features/rapids-build-utils" diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index ccfdb826812..6f12a76dc18 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -28,7 +28,7 @@ concurrency: jobs: cpp-build: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -38,7 +38,7 @@ jobs: python-build: needs: [cpp-build] secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -47,7 +47,7 @@ jobs: upload-conda: needs: [cpp-build, python-build] secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-upload-packages.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -57,7 +57,7 @@ jobs: if: github.ref_type == 'branch' needs: python-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02 with: arch: "amd64" branch: ${{ inputs.branch }} @@ -69,7 +69,7 @@ jobs: sha: ${{ inputs.sha }} wheel-build-pylibcugraph: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -77,13 +77,13 @@ jobs: date: ${{ inputs.date }} script: ci/build_wheel_pylibcugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.12 + extra-repo-sha: branch-24.02 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY node_type: cpu32 wheel-publish-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -93,7 +93,7 @@ jobs: wheel-build-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -101,12 +101,12 @@ jobs: date: ${{ inputs.date }} script: ci/build_wheel_cugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.12 + extra-repo-sha: branch-24.02 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-publish-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -116,7 +116,7 @@ jobs: wheel-build-nx-cugraph: needs: wheel-publish-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} @@ -126,7 +126,7 @@ jobs: wheel-publish-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-publish.yaml@branch-24.02 with: build_type: ${{ inputs.build_type || 'branch' }} branch: ${{ inputs.branch }} diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 9d20074381e..82c71efffdb 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -27,41 +27,41 @@ jobs: - wheel-tests-nx-cugraph - devcontainer secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/pr-builder.yaml@branch-24.02 checks: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/checks.yaml@branch-24.02 with: enable_check_generated_files: false conda-cpp-build: needs: checks secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-build.yaml@branch-24.02 with: build_type: pull-request node_type: cpu32 conda-cpp-tests: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.02 with: build_type: pull-request conda-python-build: needs: conda-cpp-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-build.yaml@branch-24.02 with: build_type: pull-request conda-python-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.02 with: build_type: pull-request conda-notebook-tests: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -71,7 +71,7 @@ jobs: docs-build: needs: conda-python-build secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/custom-job.yaml@branch-24.02 with: build_type: pull-request node_type: "gpu-v100-latest-1" @@ -81,55 +81,55 @@ jobs: wheel-build-pylibcugraph: needs: checks secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: pull-request script: ci/build_wheel_pylibcugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.12 + extra-repo-sha: branch-24.02 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY node_type: cpu32 wheel-tests-pylibcugraph: needs: wheel-build-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: pull-request script: ci/test_wheel_pylibcugraph.sh wheel-build-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: pull-request script: ci/build_wheel_cugraph.sh extra-repo: rapidsai/cugraph-ops - extra-repo-sha: branch-23.12 + extra-repo-sha: branch-24.02 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY wheel-tests-cugraph: needs: wheel-build-cugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: pull-request script: ci/test_wheel_cugraph.sh wheel-build-nx-cugraph: needs: wheel-tests-pylibcugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-build.yaml@branch-24.02 with: build_type: pull-request script: ci/build_wheel_nx-cugraph.sh wheel-tests-nx-cugraph: needs: wheel-build-nx-cugraph secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: pull-request script: ci/test_wheel_nx-cugraph.sh devcontainer: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/build-in-devcontainer.yaml@branch-24.02 with: node_type: cpu32 extra-repo-deploy-key: CUGRAPH_OPS_SSH_PRIVATE_DEPLOY_KEY diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index a0ecb67712c..0d9f4d291c3 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -16,7 +16,7 @@ on: jobs: conda-cpp-tests: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-cpp-tests.yaml@branch-24.02 with: build_type: nightly branch: ${{ inputs.branch }} @@ -24,7 +24,7 @@ jobs: sha: ${{ inputs.sha }} conda-python-tests: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/conda-python-tests.yaml@branch-24.02 with: build_type: nightly branch: ${{ inputs.branch }} @@ -32,7 +32,7 @@ jobs: sha: ${{ inputs.sha }} wheel-tests-pylibcugraph: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: nightly branch: ${{ inputs.branch }} @@ -41,7 +41,7 @@ jobs: script: ci/test_wheel_pylibcugraph.sh wheel-tests-cugraph: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: nightly branch: ${{ inputs.branch }} @@ -50,7 +50,7 @@ jobs: script: ci/test_wheel_cugraph.sh wheel-tests-nx-cugraph: secrets: inherit - uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-23.12 + uses: rapidsai/shared-workflows/.github/workflows/wheels-test.yaml@branch-24.02 with: build_type: nightly branch: ${{ inputs.branch }} diff --git a/VERSION b/VERSION index a193fff41e8..3c6c5e2b706 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -23.12.00 +24.02.00 diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 3f97f652d41..e3b136b9662 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -37,7 +37,7 @@ rapids-mamba-retry install \ rapids-logger "Install cugraph-dgl" rapids-mamba-retry install "${PYTHON_CHANNEL}/linux-64/cugraph-dgl-*.tar.bz2" -export RAPIDS_VERSION_NUMBER="23.12" +export RAPIDS_VERSION_NUMBER="24.02" export RAPIDS_DOCS_DIR="$(mktemp -d)" for PROJECT in libcugraphops libwholegraph; do diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 2f3a9c988cf..fc3154d36ee 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -16,13 +16,13 @@ dependencies: - cmake>=3.26.4 - cuda-version=11.8 - cudatoolkit -- cudf==23.12.* +- cudf==24.2.* - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 - dask-core>=2023.9.2 -- dask-cuda==23.12.* -- dask-cudf==23.12.* +- dask-cuda==24.2.* +- dask-cudf==24.2.* - dask>=2023.7.1 - distributed>=2023.7.1 - doxygen @@ -32,11 +32,11 @@ dependencies: - graphviz - gtest>=1.13.0 - ipython -- libcudf==23.12.* -- libcugraphops==23.12.* -- libraft-headers==23.12.* -- libraft==23.12.* -- librmm==23.12.* +- libcudf==24.2.* +- libcugraphops==24.2.* +- libraft-headers==24.2.* +- libraft==24.2.* +- librmm==24.2.* - nbsphinx - nccl>=2.9.9 - networkx>=2.5.1 @@ -52,19 +52,19 @@ dependencies: - pandas - pre-commit - pydata-sphinx-theme -- pylibcugraphops==23.12.* -- pylibraft==23.12.* -- pylibwholegraph==23.12.* +- pylibcugraphops==24.2.* +- pylibraft==24.2.* +- pylibwholegraph==24.2.* - pytest - pytest-benchmark - pytest-cov - pytest-mpl - pytest-xdist - python-louvain -- raft-dask==23.12.* +- raft-dask==24.2.* - recommonmark - requests -- rmm==23.12.* +- rmm==24.2.* - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy @@ -74,7 +74,7 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.35.* +- ucx-py==0.36.* - wget - wheel name: all_cuda-118_arch-x86_64 diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 31ff503e682..1fa88754835 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -16,13 +16,13 @@ dependencies: - cmake>=3.26.4 - cuda-nvcc - cuda-version=12.0 -- cudf==23.12.* +- cudf==24.2.* - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 - dask-core>=2023.9.2 -- dask-cuda==23.12.* -- dask-cudf==23.12.* +- dask-cuda==24.2.* +- dask-cudf==24.2.* - dask>=2023.7.1 - distributed>=2023.7.1 - doxygen @@ -32,11 +32,11 @@ dependencies: - graphviz - gtest>=1.13.0 - ipython -- libcudf==23.12.* -- libcugraphops==23.12.* -- libraft-headers==23.12.* -- libraft==23.12.* -- librmm==23.12.* +- libcudf==24.2.* +- libcugraphops==24.2.* +- libraft-headers==24.2.* +- libraft==24.2.* +- librmm==24.2.* - nbsphinx - nccl>=2.9.9 - networkx>=2.5.1 @@ -51,19 +51,19 @@ dependencies: - pandas - pre-commit - pydata-sphinx-theme -- pylibcugraphops==23.12.* -- pylibraft==23.12.* -- pylibwholegraph==23.12.* +- pylibcugraphops==24.2.* +- pylibraft==24.2.* +- pylibwholegraph==24.2.* - pytest - pytest-benchmark - pytest-cov - pytest-mpl - pytest-xdist - python-louvain -- raft-dask==23.12.* +- raft-dask==24.2.* - recommonmark - requests -- rmm==23.12.* +- rmm==24.2.* - scikit-build>=0.13.1 - scikit-learn>=0.23.1 - scipy @@ -73,7 +73,7 @@ dependencies: - sphinx<6 - sphinxcontrib-websupport - ucx-proc=*=gpu -- ucx-py==0.35.* +- ucx-py==0.36.* - wget - wheel name: all_cuda-120_arch-x86_64 diff --git a/conda/recipes/cugraph-service/conda_build_config.yaml b/conda/recipes/cugraph-service/conda_build_config.yaml index b971a73fd39..6a0124983fd 100644 --- a/conda/recipes/cugraph-service/conda_build_config.yaml +++ b/conda/recipes/cugraph-service/conda_build_config.yaml @@ -1,2 +1,2 @@ ucx_py_version: - - "0.35.*" + - "0.36.*" diff --git a/conda/recipes/cugraph/conda_build_config.yaml b/conda/recipes/cugraph/conda_build_config.yaml index c03d515b9f6..387f3451d8d 100644 --- a/conda/recipes/cugraph/conda_build_config.yaml +++ b/conda/recipes/cugraph/conda_build_config.yaml @@ -17,4 +17,4 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.35.*" + - "0.36.*" diff --git a/conda/recipes/pylibcugraph/conda_build_config.yaml b/conda/recipes/pylibcugraph/conda_build_config.yaml index c03d515b9f6..387f3451d8d 100644 --- a/conda/recipes/pylibcugraph/conda_build_config.yaml +++ b/conda/recipes/pylibcugraph/conda_build_config.yaml @@ -17,4 +17,4 @@ sysroot_version: - "2.17" ucx_py_version: - - "0.35.*" + - "0.36.*" diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 41870cbc92b..c19241f3e67 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -25,7 +25,7 @@ include(rapids-find) rapids_cuda_init_architectures(CUGRAPH) -project(CUGRAPH VERSION 23.12.00 LANGUAGES C CXX CUDA) +project(CUGRAPH VERSION 24.02.00 LANGUAGES C CXX CUDA) if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0) diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile index 482ff988098..a5ac2baa694 100644 --- a/cpp/doxygen/Doxyfile +++ b/cpp/doxygen/Doxyfile @@ -38,7 +38,7 @@ PROJECT_NAME = "libcugraph" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER=23.12 +PROJECT_NUMBER=24.02 # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a diff --git a/cpp/libcugraph_etl/CMakeLists.txt b/cpp/libcugraph_etl/CMakeLists.txt index ac0cb6959e8..8874c75896c 100644 --- a/cpp/libcugraph_etl/CMakeLists.txt +++ b/cpp/libcugraph_etl/CMakeLists.txt @@ -25,7 +25,7 @@ include(rapids-find) rapids_cuda_init_architectures(CUGRAPH_ETL) -project(CUGRAPH_ETL VERSION 23.12.00 LANGUAGES C CXX CUDA) +project(CUGRAPH_ETL VERSION 24.02.00 LANGUAGES C CXX CUDA) if(CMAKE_CUDA_COMPILER_ID STREQUAL "NVIDIA" AND CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0) diff --git a/dependencies.yaml b/dependencies.yaml index b127d9bd29e..a78f6ea5858 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -297,10 +297,10 @@ dependencies: - cxx-compiler - gmock>=1.13.0 - gtest>=1.13.0 - - libcugraphops==23.12.* - - libraft-headers==23.12.* - - libraft==23.12.* - - librmm==23.12.* + - libcugraphops==24.2.* + - libraft-headers==24.2.* + - libraft==24.2.* + - librmm==24.2.* - openmpi # Required for building cpp-mgtests (multi-GPU tests) specific: - output_types: [conda] @@ -379,15 +379,15 @@ dependencies: packages: - &dask dask>=2023.7.1 - &distributed distributed>=2023.7.1 - - &dask_cuda dask-cuda==23.12.* + - &dask_cuda dask-cuda==24.2.* - &numba numba>=0.57 - - &ucx_py ucx-py==0.35.* + - &ucx_py ucx-py==0.36.* - output_types: conda packages: - aiohttp - &dask-core_conda dask-core>=2023.9.2 - fsspec>=0.6.0 - - libcudf==23.12.* + - libcudf==24.2.* - requests - nccl>=2.9.9 - ucx-proc=*=gpu @@ -410,7 +410,7 @@ dependencies: - *numpy - output_types: [pyproject] packages: - - &cugraph cugraph==23.12.* + - &cugraph cugraph==24.2.* python_run_cugraph_pyg: common: - output_types: [conda, pyproject] @@ -442,7 +442,7 @@ dependencies: - output_types: pyproject packages: - *cugraph - - cugraph-service-client==23.12.* + - cugraph-service-client==24.2.* test_cpp: common: - output_types: conda @@ -477,7 +477,7 @@ dependencies: - scikit-learn>=0.23.1 - output_types: [conda] packages: - - pylibwholegraph==23.12.* + - pylibwholegraph==24.2.* test_python_pylibcugraph: common: - output_types: [conda, pyproject] @@ -494,7 +494,7 @@ dependencies: common: - output_types: [conda] packages: - - cugraph==23.12.* + - cugraph==24.2.* - pytorch>=2.0 - pytorch-cuda==11.8 - dgl>=1.1.0.cu* @@ -502,7 +502,7 @@ dependencies: common: - output_types: [conda] packages: - - cugraph==23.12.* + - cugraph==24.2.* - pytorch==2.0 - pytorch-cuda==11.8 - pyg=2.3.1=*torch_2.0.0*cu118* @@ -511,7 +511,7 @@ dependencies: common: - output_types: conda packages: - - &rmm_conda rmm==23.12.* + - &rmm_conda rmm==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -521,12 +521,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &rmm_packages_pip_cu12 - - rmm-cu12==23.12.* + - rmm-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *rmm_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *rmm_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &rmm_packages_pip_cu11 - - rmm-cu11==23.12.* + - rmm-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *rmm_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *rmm_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *rmm_packages_pip_cu11} @@ -536,7 +536,7 @@ dependencies: common: - output_types: conda packages: - - &cudf_conda cudf==23.12.* + - &cudf_conda cudf==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -546,12 +546,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &cudf_packages_pip_cu12 - - cudf-cu12==23.12.* + - cudf-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *cudf_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *cudf_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &cudf_packages_pip_cu11 - - cudf-cu11==23.12.* + - cudf-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *cudf_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *cudf_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *cudf_packages_pip_cu11} @@ -561,7 +561,7 @@ dependencies: common: - output_types: conda packages: - - &dask_cudf_conda dask-cudf==23.12.* + - &dask_cudf_conda dask-cudf==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -571,12 +571,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &dask_cudf_packages_pip_cu12 - - dask-cudf-cu12==23.12.* + - dask-cudf-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *dask_cudf_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *dask_cudf_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &dask_cudf_packages_pip_cu11 - - dask-cudf-cu11==23.12.* + - dask-cudf-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *dask_cudf_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *dask_cudf_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *dask_cudf_packages_pip_cu11} @@ -586,7 +586,7 @@ dependencies: common: - output_types: conda packages: - - &pylibraft_conda pylibraft==23.12.* + - &pylibraft_conda pylibraft==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -596,12 +596,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &pylibraft_packages_pip_cu12 - - pylibraft-cu12==23.12.* + - pylibraft-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *pylibraft_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *pylibraft_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &pylibraft_packages_pip_cu11 - - pylibraft-cu11==23.12.* + - pylibraft-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *pylibraft_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *pylibraft_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *pylibraft_packages_pip_cu11} @@ -611,7 +611,7 @@ dependencies: common: - output_types: conda packages: - - &raft_dask_conda raft-dask==23.12.* + - &raft_dask_conda raft-dask==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -621,12 +621,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &raft_dask_packages_pip_cu12 - - raft-dask-cu12==23.12.* + - raft-dask-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *raft_dask_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *raft_dask_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &raft_dask_packages_pip_cu11 - - raft-dask-cu11==23.12.* + - raft-dask-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *raft_dask_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *raft_dask_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *raft_dask_packages_pip_cu11} @@ -636,7 +636,7 @@ dependencies: common: - output_types: conda packages: - - &pylibcugraph_conda pylibcugraph==23.12.* + - &pylibcugraph_conda pylibcugraph==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -646,12 +646,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &pylibcugraph_packages_pip_cu12 - - pylibcugraph-cu12==23.12.* + - pylibcugraph-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *pylibcugraph_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *pylibcugraph_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &pylibcugraph_packages_pip_cu11 - - pylibcugraph-cu11==23.12.* + - pylibcugraph-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *pylibcugraph_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *pylibcugraph_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *pylibcugraph_packages_pip_cu11} @@ -661,7 +661,7 @@ dependencies: common: - output_types: conda packages: - - &pylibcugraphops_conda pylibcugraphops==23.12.* + - &pylibcugraphops_conda pylibcugraphops==24.2.* - output_types: requirements packages: # pip recognizes the index as a global option for the requirements.txt file @@ -671,12 +671,12 @@ dependencies: matrices: - matrix: {cuda: "12.2"} packages: &pylibcugraphops_packages_pip_cu12 - - pylibcugraphops-cu12==23.12.* + - pylibcugraphops-cu12==24.2.* - {matrix: {cuda: "12.1"}, packages: *pylibcugraphops_packages_pip_cu12} - {matrix: {cuda: "12.0"}, packages: *pylibcugraphops_packages_pip_cu12} - matrix: {cuda: "11.8"} packages: &pylibcugraphops_packages_pip_cu11 - - pylibcugraphops-cu11==23.12.* + - pylibcugraphops-cu11==24.2.* - {matrix: {cuda: "11.5"}, packages: *pylibcugraphops_packages_pip_cu11} - {matrix: {cuda: "11.4"}, packages: *pylibcugraphops_packages_pip_cu11} - {matrix: {cuda: "11.2"}, packages: *pylibcugraphops_packages_pip_cu11} diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 470086b4faa..940d2144273 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -77,9 +77,9 @@ # built documents. # # The short X.Y version. -version = '23.12' +version = '24.02' # The full version, including alpha/beta/rc tags. -release = '23.12.00' +release = '24.02.00' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/fetch_rapids.cmake b/fetch_rapids.cmake index 2c1dd855cb5..1f099e8f85f 100644 --- a/fetch_rapids.cmake +++ b/fetch_rapids.cmake @@ -12,7 +12,7 @@ # the License. # ============================================================================= if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUGRAPH_RAPIDS.cmake) - file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-23.12/RAPIDS.cmake + file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-24.02/RAPIDS.cmake ${CMAKE_CURRENT_BINARY_DIR}/CUGRAPH_RAPIDS.cmake ) endif() diff --git a/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml b/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml index b73ccb0cf9a..c6df338ab9a 100644 --- a/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml +++ b/python/cugraph-dgl/conda/cugraph_dgl_dev_cuda-118.yaml @@ -10,11 +10,11 @@ channels: - conda-forge - nvidia dependencies: -- cugraph==23.12.* +- cugraph==24.2.* - dgl>=1.1.0.cu* - pandas - pre-commit -- pylibcugraphops==23.12.* +- pylibcugraphops==24.2.* - pytest - pytest-benchmark - pytest-cov diff --git a/python/cugraph-dgl/pyproject.toml b/python/cugraph-dgl/pyproject.toml index eff7a20f0aa..62fa8ab6368 100644 --- a/python/cugraph-dgl/pyproject.toml +++ b/python/cugraph-dgl/pyproject.toml @@ -19,7 +19,7 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cugraph==23.12.*", + "cugraph==24.2.*", "numba>=0.57", "numpy>=1.21", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. diff --git a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml index f98eab430ba..ec75846e81e 100644 --- a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml +++ b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml @@ -10,11 +10,11 @@ channels: - conda-forge - nvidia dependencies: -- cugraph==23.12.* +- cugraph==24.2.* - pandas - pre-commit - pyg=2.3.1=*torch_2.0.0*cu118* -- pylibcugraphops==23.12.* +- pylibcugraphops==24.2.* - pytest - pytest-benchmark - pytest-cov diff --git a/python/cugraph-pyg/pyproject.toml b/python/cugraph-pyg/pyproject.toml index 95b1fa27402..b0671644982 100644 --- a/python/cugraph-pyg/pyproject.toml +++ b/python/cugraph-pyg/pyproject.toml @@ -26,7 +26,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", ] dependencies = [ - "cugraph==23.12.*", + "cugraph==24.2.*", "numba>=0.57", "numpy>=1.21", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index f50b33b3f15..c0e1e6d0bb9 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -19,19 +19,19 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.12.*", - "cugraph-service-client==23.12.*", - "cugraph==23.12.*", + "cudf==24.2.*", + "cugraph-service-client==24.2.*", + "cugraph==24.2.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.12.*", - "dask-cudf==23.12.*", + "dask-cuda==24.2.*", + "dask-cudf==24.2.*", "dask>=2023.7.1", "distributed>=2023.7.1", "numba>=0.57", "numpy>=1.21", - "rmm==23.12.*", + "rmm==24.2.*", "thriftpy2", - "ucx-py==0.35.*", + "ucx-py==0.36.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/cugraph/CMakeLists.txt b/python/cugraph/CMakeLists.txt index 8693c0e9e1f..a1ec12c6e07 100644 --- a/python/cugraph/CMakeLists.txt +++ b/python/cugraph/CMakeLists.txt @@ -14,7 +14,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR) -set(cugraph_version 23.12.00) +set(cugraph_version 24.02.00) include(../../fetch_rapids.cmake) diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index aaa301fa05f..da898583d1b 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -6,9 +6,9 @@ requires = [ "cmake>=3.26.4", "cython>=3.0.0", "ninja", - "pylibcugraph==23.12.*", - "pylibraft==23.12.*", - "rmm==23.12.*", + "pylibcugraph==24.2.*", + "pylibraft==24.2.*", + "rmm==24.2.*", "scikit-build>=0.13.1", "setuptools>=61.0.0", "wheel", @@ -29,18 +29,18 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "cudf==23.12.*", + "cudf==24.2.*", "cupy-cuda11x>=12.0.0", - "dask-cuda==23.12.*", - "dask-cudf==23.12.*", + "dask-cuda==24.2.*", + "dask-cudf==24.2.*", "dask>=2023.7.1", "distributed>=2023.7.1", "fsspec[http]>=0.6.0", "numba>=0.57", - "pylibcugraph==23.12.*", - "raft-dask==23.12.*", - "rmm==23.12.*", - "ucx-py==0.35.*", + "pylibcugraph==24.2.*", + "raft-dask==24.2.*", + "rmm==24.2.*", + "ucx-py==0.36.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 26638d1e735..2e5a16aa445 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -24,7 +24,7 @@ "backend_name": "cugraph", "project": "nx-cugraph", "package": "nx_cugraph", - "url": "https://github.com/rapidsai/cugraph/tree/branch-23.12/python/nx-cugraph", + "url": "https://github.com/rapidsai/cugraph/tree/branch-24.02/python/nx-cugraph", "short_summary": "GPU-accelerated backend.", # "description": "TODO", "functions": { @@ -137,7 +137,7 @@ def get_info(): # FIXME: can this use the standard VERSION file and update mechanism? -__version__ = "23.12.00" +__version__ = "24.02.00" if __name__ == "__main__": from pathlib import Path diff --git a/python/nx-cugraph/pyproject.toml b/python/nx-cugraph/pyproject.toml index f309f4797a7..b29578b036f 100644 --- a/python/nx-cugraph/pyproject.toml +++ b/python/nx-cugraph/pyproject.toml @@ -33,7 +33,7 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "networkx>=3.0", "numpy>=1.21", - "pylibcugraph==23.12.*", + "pylibcugraph==24.2.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. [project.optional-dependencies] diff --git a/python/pylibcugraph/CMakeLists.txt b/python/pylibcugraph/CMakeLists.txt index 057f30ef3ad..7d5dc790ad0 100644 --- a/python/pylibcugraph/CMakeLists.txt +++ b/python/pylibcugraph/CMakeLists.txt @@ -14,7 +14,7 @@ cmake_minimum_required(VERSION 3.26.4 FATAL_ERROR) -set(pylibcugraph_version 23.12.00) +set(pylibcugraph_version 24.02.00) include(../../fetch_rapids.cmake) diff --git a/python/pylibcugraph/pyproject.toml b/python/pylibcugraph/pyproject.toml index 96f5ec84efb..0f2d742e7c5 100644 --- a/python/pylibcugraph/pyproject.toml +++ b/python/pylibcugraph/pyproject.toml @@ -6,8 +6,8 @@ requires = [ "cmake>=3.26.4", "cython>=3.0.0", "ninja", - "pylibraft==23.12.*", - "rmm==23.12.*", + "pylibraft==24.2.*", + "rmm==24.2.*", "scikit-build>=0.13.1", "setuptools>=61.0.0", "wheel", @@ -28,8 +28,8 @@ authors = [ license = { text = "Apache 2.0" } requires-python = ">=3.9" dependencies = [ - "pylibraft==23.12.*", - "rmm==23.12.*", + "pylibraft==24.2.*", + "rmm==24.2.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. classifiers = [ "Intended Audience :: Developers", @@ -40,7 +40,7 @@ classifiers = [ [project.optional-dependencies] test = [ - "cudf==23.12.*", + "cudf==24.2.*", "numpy>=1.21", "pandas", "pytest", From 96a7a6eaf12e4550256c5835a0f0486c78db539e Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Tue, 14 Nov 2023 18:25:39 +0100 Subject: [PATCH 081/111] Move MTMG_TEST to MG tests block (#3993) * Move MTMG_TEST to MG tests block * Remove extra newline * remove reference to get_ucp.cmake * make building MTMG a separate build flag * Updates PropertyGraph for latest cudf changes. * style fixes. --------- Co-authored-by: Naim Co-authored-by: Charles Hastings Co-authored-by: Rick Ratzel --- build.sh | 8 ++++ cpp/CMakeLists.txt | 5 ++- cpp/tests/CMakeLists.txt | 37 +++++++++++-------- .../cugraph/structure/property_graph.py | 24 +++--------- 4 files changed, 39 insertions(+), 35 deletions(-) diff --git a/build.sh b/build.sh index 99082fa96fb..1723e750978 100755 --- a/build.sh +++ b/build.sh @@ -31,6 +31,7 @@ VALIDARGS=" cugraph-dgl nx-cugraph cpp-mgtests + cpp-mtmgtests docs all -v @@ -59,6 +60,7 @@ HELP="$0 [ ...] [ ...] cugraph-dgl - build the cugraph-dgl extensions for DGL nx-cugraph - build the nx-cugraph Python package cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. + cpp-mtmgtests - build libcugraph MTMG tests. Adds UCX as a dependency (temporary). docs - build the docs all - build everything and is: @@ -105,6 +107,7 @@ BUILD_TYPE=Release INSTALL_TARGET="--target install" BUILD_CPP_TESTS=ON BUILD_CPP_MG_TESTS=OFF +BUILD_CPP_MTMG_TESTS=OFF BUILD_ALL_GPU_ARCH=0 BUILD_WITH_CUGRAPHOPS=ON CMAKE_GENERATOR_OPTION="-G Ninja" @@ -172,6 +175,9 @@ fi if hasArg --without_cugraphops; then BUILD_WITH_CUGRAPHOPS=OFF fi +if hasArg cpp-mtmgtests; then + BUILD_CPP_MTMG_TESTS=ON +fi if hasArg cpp-mgtests || hasArg all; then BUILD_CPP_MG_TESTS=ON fi @@ -264,6 +270,7 @@ if buildDefault || hasArg libcugraph || hasArg all; then -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ -DBUILD_TESTS=${BUILD_CPP_TESTS} \ -DBUILD_CUGRAPH_MG_TESTS=${BUILD_CPP_MG_TESTS} \ + -DBUILD_CUGRAPH_MTMG_TESTS=${BUILD_CPP_MTMG_TESTS} \ -DUSE_CUGRAPH_OPS=${BUILD_WITH_CUGRAPHOPS} \ ${CMAKE_GENERATOR_OPTION} \ ${CMAKE_VERBOSE_OPTION} @@ -294,6 +301,7 @@ if buildDefault || hasArg libcugraph_etl || hasArg all; then -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ -DBUILD_TESTS=${BUILD_CPP_TESTS} \ -DBUILD_CUGRAPH_MG_TESTS=${BUILD_CPP_MG_TESTS} \ + -DBUILD_CUGRAPH_MTMG_TESTS=${BUILD_CPP_MTMG_TESTS} \ -DCMAKE_PREFIX_PATH=${LIBCUGRAPH_BUILD_DIR} \ ${CMAKE_GENERATOR_OPTION} \ ${CMAKE_VERBOSE_OPTION} \ diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 41870cbc92b..360165e688d 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -166,7 +166,10 @@ endif() include(cmake/thirdparty/get_nccl.cmake) include(cmake/thirdparty/get_cuhornet.cmake) -include(cmake/thirdparty/get_ucp.cmake) + +if (BUILD_CUGRAPH_MTMG_TESTS) + include(cmake/thirdparty/get_ucp.cmake) +endif() if(BUILD_TESTS) include(cmake/thirdparty/get_gtest.cmake) diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 2f69cf9cb0d..6530a25d178 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -415,13 +415,6 @@ ConfigureTest(K_HOP_NBRS_TEST traversal/k_hop_nbrs_test.cpp) # - install tests --------------------------------------------------------------------------------- rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing DESTINATION bin/gtests/libcugraph) -################################################################################################### -# - MTMG tests ------------------------------------------------------------------------- -ConfigureTest(MTMG_TEST mtmg/threaded_test.cu) -target_link_libraries(MTMG_TEST - PRIVATE - UCP::UCP - ) ################################################################################################### # - MG tests -------------------------------------------------------------------------------------- @@ -681,15 +674,6 @@ if(BUILD_CUGRAPH_MG_TESTS) rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing_mg DESTINATION bin/gtests/libcugraph_mg) - ############################################################################################### - # - Multi-node MTMG tests --------------------------------------------------------------------- - ConfigureTest(MTMG_MULTINODE_TEST mtmg/multi_node_threaded_test.cu utilities/mg_utilities.cpp) - target_link_libraries(MTMG_MULTINODE_TEST - PRIVATE - cugraphmgtestutil - UCP::UCP - ) - endif() ################################################################################################### @@ -749,4 +733,25 @@ ConfigureCTest(CAPI_EGONET_TEST c_api/egonet_test.c) ConfigureCTest(CAPI_TWO_HOP_NEIGHBORS_TEST c_api/two_hop_neighbors_test.c) ConfigureCTest(CAPI_LEGACY_K_TRUSS_TEST c_api/legacy_k_truss_test.c) +if (BUILD_CUGRAPH_MTMG_TESTS) + ################################################################################################### + # - MTMG tests ------------------------------------------------------------------------- + ConfigureTest(MTMG_TEST mtmg/threaded_test.cu) + target_link_libraries(MTMG_TEST + PRIVATE + UCP::UCP + ) + + if(BUILD_CUGRAPH_MG_TESTS) + ############################################################################################### + # - Multi-node MTMG tests --------------------------------------------------------------------- + ConfigureTest(MTMG_MULTINODE_TEST mtmg/multi_node_threaded_test.cu utilities/mg_utilities.cpp) + target_link_libraries(MTMG_MULTINODE_TEST + PRIVATE + cugraphmgtestutil + UCP::UCP + ) + endif(BUILD_CUGRAPH_MG_TESTS) +endif(BUILD_CUGRAPH_MTMG_TESTS) + rapids_test_install_relocatable(INSTALL_COMPONENT_SET testing_c DESTINATION bin/gtests/libcugraph_c) diff --git a/python/cugraph/cugraph/structure/property_graph.py b/python/cugraph/cugraph/structure/property_graph.py index 36ce5baa212..513798f35f9 100644 --- a/python/cugraph/cugraph/structure/property_graph.py +++ b/python/cugraph/cugraph/structure/property_graph.py @@ -800,15 +800,9 @@ def add_vertex_data( tmp_df.index = tmp_df.index.rename(self.vertex_col_name) # FIXME: handle case of a type_name column already being in tmp_df - if self.__series_type is cudf.Series: - # cudf does not yet support initialization with a scalar - tmp_df[TCN] = cudf.Series( - cudf.Series([type_name], dtype=cat_dtype).repeat(len(tmp_df)), - index=tmp_df.index, - ) - else: - # pandas is oddly slow if dtype is passed to the constructor here - tmp_df[TCN] = pd.Series(type_name, index=tmp_df.index).astype(cat_dtype) + tmp_df[TCN] = self.__series_type(type_name, index=tmp_df.index).astype( + cat_dtype + ) if property_columns: # all columns @@ -1207,15 +1201,9 @@ def add_edge_data( tmp_df[self.src_col_name] = tmp_df[vertex_col_names[0]] tmp_df[self.dst_col_name] = tmp_df[vertex_col_names[1]] - if self.__series_type is cudf.Series: - # cudf does not yet support initialization with a scalar - tmp_df[TCN] = cudf.Series( - cudf.Series([type_name], dtype=cat_dtype).repeat(len(tmp_df)), - index=tmp_df.index, - ) - else: - # pandas is oddly slow if dtype is passed to the constructor here - tmp_df[TCN] = pd.Series(type_name, index=tmp_df.index).astype(cat_dtype) + tmp_df[TCN] = self.__series_type(type_name, index=tmp_df.index).astype( + cat_dtype + ) # Add unique edge IDs to the new rows. This is just a count for each # row starting from the last edge ID value, with initial edge ID 0. From b90e1a53e4c2ae6fdde73258c2d366dde596a154 Mon Sep 17 00:00:00 2001 From: GALI PREM SAGAR Date: Tue, 14 Nov 2023 14:35:20 -0600 Subject: [PATCH 082/111] Use new `rapids-dask-dependency` metapackage for managing `dask` versions (#3991) Currently dask versions are pinned as part of every release cycle and then unpinned for the next development cycle across all of RAPIDS. This introduces a great deal of churn. To centralize the dependency, we have created a metapackage to manage the required dask version and this PR introduces that metapackage as a dependency of cugraph. xref: https://github.com/rapidsai/cudf/pull/14364 Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) - Vyas Ramasubramani (https://github.com/vyasr) - GALI PREM SAGAR (https://github.com/galipremsagar) - Naim (naim@uib.no) Approvers: - Jake Awe (https://github.com/AyodeAwe) --- ci/build_wheel.sh | 7 +++++-- ci/release/update-version.sh | 1 + ci/test_wheel_cugraph.sh | 3 --- conda/environments/all_cuda-118_arch-x86_64.yaml | 4 +--- conda/environments/all_cuda-120_arch-x86_64.yaml | 4 +--- conda/recipes/cugraph-pyg/meta.yaml | 2 +- conda/recipes/cugraph-service/meta.yaml | 2 +- conda/recipes/cugraph/meta.yaml | 4 +--- dependencies.yaml | 8 +------- python/cugraph-service/server/pyproject.toml | 3 +-- python/cugraph/pyproject.toml | 3 +-- 11 files changed, 14 insertions(+), 27 deletions(-) diff --git a/ci/build_wheel.sh b/ci/build_wheel.sh index c888c908056..163520ea1da 100755 --- a/ci/build_wheel.sh +++ b/ci/build_wheel.sh @@ -40,8 +40,11 @@ for dep in rmm cudf raft-dask pylibcugraph pylibraft ucx-py; do sed -r -i "s/${dep}==(.*)\"/${dep}${PACKAGE_CUDA_SUFFIX}==\1${alpha_spec}\"/g" ${pyproject_file} done -# dask-cuda doesn't get a suffix, but it does get an alpha spec. -sed -r -i "s/dask-cuda==(.*)\"/dask-cuda==\1${alpha_spec}\"/g" ${pyproject_file} +# dask-cuda & rapids-dask-dependency doesn't get a suffix, but it does get an alpha spec. +for dep in dask-cuda rapids-dask-dependency; do + sed -r -i "s/${dep}==(.*)\"/${dep}==\1${alpha_spec}\"/g" ${pyproject_file} +done + if [[ $PACKAGE_CUDA_SUFFIX == "-cu12" ]]; then sed -i "s/cupy-cuda11x/cupy-cuda12x/g" ${pyproject_file} diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index 0f2d4a3b914..69eb085e7ed 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -88,6 +88,7 @@ DEPENDENCIES=( raft-dask rmm ucx-py + rapids-dask-dependency ) for DEP in "${DEPENDENCIES[@]}"; do for FILE in dependencies.yaml conda/environments/*.yaml python/cugraph-{pyg,dgl}/conda/*.yaml; do diff --git a/ci/test_wheel_cugraph.sh b/ci/test_wheel_cugraph.sh index f9e2aa6d8da..d351ea21624 100755 --- a/ci/test_wheel_cugraph.sh +++ b/ci/test_wheel_cugraph.sh @@ -8,7 +8,4 @@ RAPIDS_PY_CUDA_SUFFIX="$(rapids-wheel-ctk-name-gen ${RAPIDS_CUDA_VERSION})" RAPIDS_PY_WHEEL_NAME="pylibcugraph_${RAPIDS_PY_CUDA_SUFFIX}" rapids-download-wheels-from-s3 ./local-pylibcugraph-dep python -m pip install --no-deps ./local-pylibcugraph-dep/pylibcugraph*.whl -# Always install latest dask for testing -python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main - ./ci/test_wheel.sh cugraph python/cugraph diff --git a/conda/environments/all_cuda-118_arch-x86_64.yaml b/conda/environments/all_cuda-118_arch-x86_64.yaml index 2f3a9c988cf..aa38defcd7c 100644 --- a/conda/environments/all_cuda-118_arch-x86_64.yaml +++ b/conda/environments/all_cuda-118_arch-x86_64.yaml @@ -20,11 +20,8 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core>=2023.9.2 - dask-cuda==23.12.* - dask-cudf==23.12.* -- dask>=2023.7.1 -- distributed>=2023.7.1 - doxygen - fsspec>=0.6.0 - gcc_linux-64=11.* @@ -62,6 +59,7 @@ dependencies: - pytest-xdist - python-louvain - raft-dask==23.12.* +- rapids-dask-dependency==23.12.* - recommonmark - requests - rmm==23.12.* diff --git a/conda/environments/all_cuda-120_arch-x86_64.yaml b/conda/environments/all_cuda-120_arch-x86_64.yaml index 31ff503e682..a9f793b15f5 100644 --- a/conda/environments/all_cuda-120_arch-x86_64.yaml +++ b/conda/environments/all_cuda-120_arch-x86_64.yaml @@ -20,11 +20,8 @@ dependencies: - cupy>=12.0.0 - cxx-compiler - cython>=3.0.0 -- dask-core>=2023.9.2 - dask-cuda==23.12.* - dask-cudf==23.12.* -- dask>=2023.7.1 -- distributed>=2023.7.1 - doxygen - fsspec>=0.6.0 - gcc_linux-64=11.* @@ -61,6 +58,7 @@ dependencies: - pytest-xdist - python-louvain - raft-dask==23.12.* +- rapids-dask-dependency==23.12.* - recommonmark - requests - rmm==23.12.* diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index 2714dcfa55a..a6744a9f340 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -26,7 +26,7 @@ requirements: - python - scikit-build >=0.13.1 run: - - distributed >=2023.9.2 + - rapids-dask-dependency ={{ version }} - numba >=0.57 - numpy >=1.21 - python diff --git a/conda/recipes/cugraph-service/meta.yaml b/conda/recipes/cugraph-service/meta.yaml index ae8074ba7d3..d52a004db05 100644 --- a/conda/recipes/cugraph-service/meta.yaml +++ b/conda/recipes/cugraph-service/meta.yaml @@ -59,10 +59,10 @@ outputs: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - distributed >=2023.9.2 - numba >=0.57 - numpy >=1.21 - python + - rapids-dask-dependency ={{ minor_version }} - thriftpy2 >=0.4.15 - ucx-py {{ ucx_py_version }} diff --git a/conda/recipes/cugraph/meta.yaml b/conda/recipes/cugraph/meta.yaml index 65403bc8d73..58b9ea220d4 100644 --- a/conda/recipes/cugraph/meta.yaml +++ b/conda/recipes/cugraph/meta.yaml @@ -76,15 +76,13 @@ requirements: - cupy >=12.0.0 - dask-cuda ={{ minor_version }} - dask-cudf ={{ minor_version }} - - dask >=2023.9.2 - - dask-core >=2023.9.2 - - distributed >=2023.9.2 - fsspec>=0.6.0 - libcugraph ={{ version }} - pylibcugraph ={{ version }} - pylibraft ={{ minor_version }} - python - raft-dask ={{ minor_version }} + - rapids-dask-dependency ={{ minor_version }} - requests - ucx-proc=*=gpu - ucx-py {{ ucx_py_version }} diff --git a/dependencies.yaml b/dependencies.yaml index b127d9bd29e..13f100610cf 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -377,15 +377,13 @@ dependencies: common: - output_types: [conda, pyproject] packages: - - &dask dask>=2023.7.1 - - &distributed distributed>=2023.7.1 + - &dask rapids-dask-dependency==23.12.* - &dask_cuda dask-cuda==23.12.* - &numba numba>=0.57 - &ucx_py ucx-py==0.35.* - output_types: conda packages: - aiohttp - - &dask-core_conda dask-core>=2023.9.2 - fsspec>=0.6.0 - libcudf==23.12.* - requests @@ -431,14 +429,10 @@ dependencies: packages: - *dask - *dask_cuda - - *distributed - *numba - *numpy - *thrift - *ucx_py - - output_types: conda - packages: - - *dask-core_conda - output_types: pyproject packages: - *cugraph diff --git a/python/cugraph-service/server/pyproject.toml b/python/cugraph-service/server/pyproject.toml index f50b33b3f15..d68f8055ded 100644 --- a/python/cugraph-service/server/pyproject.toml +++ b/python/cugraph-service/server/pyproject.toml @@ -25,10 +25,9 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "dask-cuda==23.12.*", "dask-cudf==23.12.*", - "dask>=2023.7.1", - "distributed>=2023.7.1", "numba>=0.57", "numpy>=1.21", + "rapids-dask-dependency==23.12.*", "rmm==23.12.*", "thriftpy2", "ucx-py==0.35.*", diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index aaa301fa05f..319900b3de3 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -33,12 +33,11 @@ dependencies = [ "cupy-cuda11x>=12.0.0", "dask-cuda==23.12.*", "dask-cudf==23.12.*", - "dask>=2023.7.1", - "distributed>=2023.7.1", "fsspec[http]>=0.6.0", "numba>=0.57", "pylibcugraph==23.12.*", "raft-dask==23.12.*", + "rapids-dask-dependency==23.12.*", "rmm==23.12.*", "ucx-py==0.35.*", ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`. From 8c104a52bbece783f91001c64cc6bf0732f10594 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Wed, 15 Nov 2023 17:01:59 -0600 Subject: [PATCH 083/111] Pin to minor versions of packages outside the cuGraph repository. (#4004) This PR fixes some pinnings in cuGraph conda recipes. The problem is similar to that handled in https://github.com/rapidsai/cudf/pull/14420. The `{{ version }}` variable can only be used to constrain conda packages built by CI workflows in the _same repository_ because `{{ version }}` includes information about the git commit. We must use `{{ minor_version }}` to constrain other RAPIDS packages. In cuGraph, that means that `pylibcugraphops` (which is built by the cugraph-ops repository) and `rapids-dask-dependency` must pin with `={{ minor_version }}` instead of `={{ version }}`. Authors: - Bradley Dice (https://github.com/bdice) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) - Ray Douglass (https://github.com/raydouglass) --- conda/recipes/cugraph-dgl/meta.yaml | 2 +- conda/recipes/cugraph-pyg/meta.yaml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/conda/recipes/cugraph-dgl/meta.yaml b/conda/recipes/cugraph-dgl/meta.yaml index bb85734098a..aaa1cd8a936 100644 --- a/conda/recipes/cugraph-dgl/meta.yaml +++ b/conda/recipes/cugraph-dgl/meta.yaml @@ -26,7 +26,7 @@ requirements: - dgl >=1.1.0.cu* - numba >=0.57 - numpy >=1.21 - - pylibcugraphops ={{ version }} + - pylibcugraphops ={{ minor_version }} - python - pytorch diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index a6744a9f340..07caf07daab 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -26,14 +26,14 @@ requirements: - python - scikit-build >=0.13.1 run: - - rapids-dask-dependency ={{ version }} + - rapids-dask-dependency ={{ minor_version }} - numba >=0.57 - numpy >=1.21 - python - pytorch >=2.0 - cupy >=12.0.0 - cugraph ={{ version }} - - pylibcugraphops ={{ version }} + - pylibcugraphops ={{ minor_version }} - pyg >=2.3,<2.4 tests: From 119816cfe04be421bf96baa8075d342f72cbbfc4 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Fri, 17 Nov 2023 09:16:31 -0500 Subject: [PATCH 084/111] [BUG] Fix Incorrect Edge Index, Directory Selection in cuGraph-PyG Loader (#3978) Fixes three major bugs: 1. Edge index is set to [dst, dst] instead of [dst, src] in some cases 2. The sample directory is always set to a new temporary directory rather than the path given 3. The version of `pylibcugraphops` in `meta.yaml` is wrong and causes the wrong packages to be resolved This PR also simplifies `ci/test_python.sh` by doing only a single conda install when creating the `test_cugraph_pyg` environment. Closes #3959 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Naim (https://github.com/naimnv) Approvers: - Brad Rees (https://github.com/BradReesWork) - Ray Douglass (https://github.com/raydouglass) URL: https://github.com/rapidsai/cugraph/pull/3978 --- ci/test_python.sh | 31 ++++++----- conda/recipes/cugraph-pyg/meta.yaml | 2 +- dependencies.yaml | 4 +- .../conda/cugraph_pyg_dev_cuda-118.yaml | 4 +- .../cugraph_pyg/data/cugraph_store.py | 12 +++-- .../cugraph_pyg/loader/cugraph_node_loader.py | 48 ++++++++++------- .../tests/mg/test_mg_cugraph_store.py | 2 +- .../cugraph_pyg/tests/test_cugraph_loader.py | 54 +++++++++++++++++-- .../cugraph_pyg/tests/test_cugraph_store.py | 2 +- 9 files changed, 112 insertions(+), 47 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index 1690ce2f15b..273d3c93482 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -197,27 +197,26 @@ if [[ "${RAPIDS_CUDA_VERSION}" == "11.8.0" ]]; then conda activate test_cugraph_pyg set -u - # Install pytorch + # Will automatically install built dependencies of cuGraph-PyG rapids-mamba-retry install \ - --force-reinstall \ - --channel pyg \ + --channel "${CPP_CHANNEL}" \ + --channel "${PYTHON_CHANNEL}" \ --channel pytorch \ --channel nvidia \ - 'pyg=2.3' \ - 'pytorch=2.0.0' \ - 'pytorch-cuda=11.8' + --channel pyg \ + --channel rapidsai-nightly \ + "cugraph-pyg" \ + "pytorch>=2.0,<2.1" \ + "pytorch-cuda=11.8" # Install pyg dependencies (which requires pip) - pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.0+cu118.html - - rapids-mamba-retry install \ - --channel "${CPP_CHANNEL}" \ - --channel "${PYTHON_CHANNEL}" \ - libcugraph \ - pylibcugraph \ - pylibcugraphops \ - cugraph \ - cugraph-pyg + pip install \ + pyg_lib \ + torch_scatter \ + torch_sparse \ + torch_cluster \ + torch_spline_conv \ + -f https://data.pyg.org/whl/torch-2.0.0+cu118.html rapids-print-env diff --git a/conda/recipes/cugraph-pyg/meta.yaml b/conda/recipes/cugraph-pyg/meta.yaml index 07caf07daab..a2a02a1d9f6 100644 --- a/conda/recipes/cugraph-pyg/meta.yaml +++ b/conda/recipes/cugraph-pyg/meta.yaml @@ -34,7 +34,7 @@ requirements: - cupy >=12.0.0 - cugraph ={{ version }} - pylibcugraphops ={{ minor_version }} - - pyg >=2.3,<2.4 + - pyg >=2.3,<2.5 tests: imports: diff --git a/dependencies.yaml b/dependencies.yaml index 13f100610cf..a89acd9288b 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -497,9 +497,9 @@ dependencies: - output_types: [conda] packages: - cugraph==23.12.* - - pytorch==2.0 + - pytorch>=2.0 - pytorch-cuda==11.8 - - pyg=2.3.1=*torch_2.0.0*cu118* + - pyg>=2.4.0 depends_on_rmm: common: diff --git a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml index f98eab430ba..71d1c7e389c 100644 --- a/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml +++ b/python/cugraph-pyg/conda/cugraph_pyg_dev_cuda-118.yaml @@ -13,13 +13,13 @@ dependencies: - cugraph==23.12.* - pandas - pre-commit -- pyg=2.3.1=*torch_2.0.0*cu118* +- pyg>=2.4.0 - pylibcugraphops==23.12.* - pytest - pytest-benchmark - pytest-cov - pytest-xdist - pytorch-cuda==11.8 -- pytorch==2.0 +- pytorch>=2.0 - scipy name: cugraph_pyg_dev_cuda-118 diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index d1b24543956..edeeface4c4 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -210,7 +210,10 @@ class EXPERIMENTAL__CuGraphStore: def __init__( self, F: cugraph.gnn.FeatureStore, - G: Union[Dict[str, Tuple[TensorType]], Dict[str, int]], + G: Union[ + Dict[Tuple[str, str, str], Tuple[TensorType]], + Dict[Tuple[str, str, str], int], + ], num_nodes_dict: Dict[str, int], *, multi_gpu: bool = False, @@ -744,7 +747,7 @@ def _subgraph(self, edge_types: List[tuple] = None) -> cugraph.MultiGraph: def _get_vertex_groups_from_sample( self, nodes_of_interest: TensorType, is_sorted: bool = False - ) -> dict: + ) -> Dict[str, torch.Tensor]: """ Given a tensor of nodes of interest, this method a single dictionary, noi_index. @@ -808,7 +811,10 @@ def _get_sample_from_vertex_groups( def _get_renumbered_edge_groups_from_sample( self, sampling_results: cudf.DataFrame, noi_index: dict - ) -> Tuple[dict, dict]: + ) -> Tuple[ + Dict[Tuple[str, str, str], torch.Tensor], + Tuple[Dict[Tuple[str, str, str], torch.Tensor]], + ]: """ Given a cudf (NOT dask_cudf) DataFrame of sampling results and a dictionary of non-renumbered vertex ids grouped by vertex type, this method diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index 8552e7412e0..ad8d22e255e 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -15,6 +15,7 @@ import os import re +import warnings import cupy import cudf @@ -159,23 +160,34 @@ def __init__( if batch_size is None or batch_size < 1: raise ValueError("Batch size must be >= 1") - self.__directory = tempfile.TemporaryDirectory(dir=directory) + self.__directory = ( + tempfile.TemporaryDirectory() if directory is None else directory + ) if isinstance(num_neighbors, dict): raise ValueError("num_neighbors dict is currently unsupported!") - renumber = ( - True - if ( - (len(self.__graph_store.node_types) == 1) - and (len(self.__graph_store.edge_types) == 1) + if "renumber" in kwargs: + warnings.warn( + "Setting renumbering manually could result in invalid output," + " please ensure you intended to do this." + ) + renumber = kwargs.pop("renumber") + else: + renumber = ( + True + if ( + (len(self.__graph_store.node_types) == 1) + and (len(self.__graph_store.edge_types) == 1) + ) + else False ) - else False - ) bulk_sampler = BulkSampler( batch_size, - self.__directory.name, + self.__directory + if isinstance(self.__directory, str) + else self.__directory.name, self.__graph_store._subgraph(edge_types), fanout_vals=num_neighbors, with_replacement=replace, @@ -219,7 +231,13 @@ def __init__( ) bulk_sampler.flush() - self.__input_files = iter(os.listdir(self.__directory.name)) + self.__input_files = iter( + os.listdir( + self.__directory + if isinstance(self.__directory, str) + else self.__directory.name + ) + ) def __next__(self): from time import perf_counter @@ -423,9 +441,6 @@ def __next__(self): sampler_output.edge, ) else: - if self.__graph_store.order == "CSR": - raise ValueError("CSR format incompatible with CSC output") - out = filter_cugraph_store_csc( self.__feature_store, self.__graph_store, @@ -437,11 +452,8 @@ def __next__(self): # Account for CSR format in cuGraph vs. CSC format in PyG if self.__coo and self.__graph_store.order == "CSC": - for node_type in out.edge_index_dict: - out[node_type].edge_index[0], out[node_type].edge_index[1] = ( - out[node_type].edge_index[1], - out[node_type].edge_index[0], - ) + for edge_type in out.edge_index_dict: + out[edge_type].edge_index = out[edge_type].edge_index.flip(dims=[0]) out.set_value_dict("num_sampled_nodes", sampler_output.num_sampled_nodes) out.set_value_dict("num_sampled_edges", sampler_output.num_sampled_edges) diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py index ed7f70034e2..13c9c90c7c2 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py @@ -120,7 +120,7 @@ def test_get_edge_index(graph, edge_index_type, dask_client): G[et][0] = dask_cudf.from_cudf(cudf.Series(G[et][0]), npartitions=1) G[et][1] = dask_cudf.from_cudf(cudf.Series(G[et][1]), npartitions=1) - cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) + cugraph_store = CuGraphStore(F, G, N, order="CSC", multi_gpu=True) for pyg_can_edge_type in G: src, dst = cugraph_store.get_edge_index( diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 853836dc2a6..27b73bf7d35 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -18,6 +18,7 @@ import cudf import cupy +import numpy as np from cugraph_pyg.loader import CuGraphNeighborLoader from cugraph_pyg.loader import BulkSampleLoader @@ -27,6 +28,8 @@ from cugraph.gnn import FeatureStore from cugraph.utilities.utils import import_optional, MissingModule +from typing import Dict, Tuple + torch = import_optional("torch") torch_geometric = import_optional("torch_geometric") trim_to_layer = import_optional("torch_geometric.utils.trim_to_layer") @@ -40,7 +43,11 @@ @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") -def test_cugraph_loader_basic(karate_gnn): +def test_cugraph_loader_basic( + karate_gnn: Tuple[ + FeatureStore, Dict[Tuple[str, str, str], np.ndarray], Dict[str, int] + ] +): F, G, N = karate_gnn cugraph_store = CuGraphStore(F, G, N, order="CSR") loader = CuGraphNeighborLoader( @@ -66,7 +73,11 @@ def test_cugraph_loader_basic(karate_gnn): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") -def test_cugraph_loader_hetero(karate_gnn): +def test_cugraph_loader_hetero( + karate_gnn: Tuple[ + FeatureStore, Dict[Tuple[str, str, str], np.ndarray], Dict[str, int] + ] +): F, G, N = karate_gnn cugraph_store = CuGraphStore(F, G, N, order="CSR") loader = CuGraphNeighborLoader( @@ -342,7 +353,7 @@ def test_cugraph_loader_e2e_coo(): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") @pytest.mark.skipif(not HAS_TORCH_SPARSE, reason="torch-sparse not available") @pytest.mark.parametrize("framework", ["pyg", "cugraph-ops"]) -def test_cugraph_loader_e2e_csc(framework): +def test_cugraph_loader_e2e_csc(framework: str): m = [2, 9, 99, 82, 9, 3, 18, 1, 12] x = torch.randint(3000, (256, 256)).to(torch.float32) F = FeatureStore() @@ -442,3 +453,40 @@ def test_cugraph_loader_e2e_csc(framework): x = x.narrow(dim=0, start=0, length=s - num_sampled_nodes[1]) assert list(x.shape) == [1, 1] + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.parametrize("directory", ["local", "temp"]) +def test_load_directory( + karate_gnn: Tuple[ + FeatureStore, Dict[Tuple[str, str, str], np.ndarray], Dict[str, int] + ], + directory: str, +): + if directory == "local": + local_dir = tempfile.TemporaryDirectory(dir=".") + + cugraph_store = CuGraphStore(*karate_gnn) + cugraph_loader = CuGraphNeighborLoader( + (cugraph_store, cugraph_store), + torch.arange(8, dtype=torch.int64), + 2, + num_neighbors=[8, 4, 2], + random_state=62, + replace=False, + directory=None if directory == "temp" else local_dir.name, + batches_per_partition=1, + ) + + it = iter(cugraph_loader) + next_batch = next(it) + assert next_batch is not None + + if directory == "local": + assert len(os.listdir(local_dir.name)) == 4 + + count = 1 + while next(it, None) is not None: + count += 1 + + assert count == 4 diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py index da3043760d4..b39ebad8254 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_store.py @@ -113,7 +113,7 @@ def test_get_edge_index(graph, edge_index_type): G[et][0] = cudf.Series(G[et][0]) G[et][1] = cudf.Series(G[et][1]) - cugraph_store = CuGraphStore(F, G, N) + cugraph_store = CuGraphStore(F, G, N, order="CSC") for pyg_can_edge_type in G: src, dst = cugraph_store.get_edge_index( From 06f082b43aab84408d63a907327b9524f1cd0229 Mon Sep 17 00:00:00 2001 From: Bradley Dice Date: Fri, 17 Nov 2023 10:36:35 -0600 Subject: [PATCH 085/111] Enable build concurrency for nightly and merge triggers. (#4009) --- .github/workflows/build.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yaml b/.github/workflows/build.yaml index ccfdb826812..0f490283795 100644 --- a/.github/workflows/build.yaml +++ b/.github/workflows/build.yaml @@ -22,7 +22,7 @@ on: default: nightly concurrency: - group: ${{ github.workflow }}-${{ github.ref }} + group: ${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }} cancel-in-progress: true jobs: From 5d43f1463c1829322d2a92684f2bb11269730af3 Mon Sep 17 00:00:00 2001 From: Vyas Ramasubramani Date: Fri, 17 Nov 2023 18:17:47 -0800 Subject: [PATCH 086/111] Find rmm before cuco (#4011) RAPIDS currently relies on copies of CCCL headers bundled into rmm. This dependency is centralized by virtue of rmm installing these into the package and everything else finding those installed packages. To do this, however, rmm must be loaded first so that the libcudacxx install location is patched into CMake's search paths. cugraph also uses cuco, which requires libcudacxx but does not bundle its own, so rmm must be found first so that cuco can find libcudacxx where rmm installed it. Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - Bradley Dice (https://github.com/bdice) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4011 --- cpp/CMakeLists.txt | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 360165e688d..3e867643041 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -153,6 +153,11 @@ rapids_cpm_init() # lags behind. ### +# Need to make sure rmm is found before cuco so that rmm patches the libcudacxx +# directory to be found by cuco. +include(${rapids-cmake-dir}/cpm/rmm.cmake) +rapids_cpm_rmm(BUILD_EXPORT_SET cugraph-exports + INSTALL_EXPORT_SET cugraph-exports) # Putting this before raft to override RAFT from pulling them in. include(cmake/thirdparty/get_libcudacxx.cmake) include(${rapids-cmake-dir}/cpm/cuco.cmake) From 0684f9de2015c4b7d45f28605f2bb2f9bf523359 Mon Sep 17 00:00:00 2001 From: Ralph Liu <137829296+nv-rliu@users.noreply.github.com> Date: Sun, 19 Nov 2023 07:09:32 -0500 Subject: [PATCH 087/111] `Resultset` and `Dataset` Refactors (#3957) This PR replaces and is a continuation of #3857 (by @betochimas) > This PR primarily adds testing for the `Resultset` class, introduced earlier in 23.10. The tests take a similar approach to test_dataset, creating a temporary directory to test downloading all result files. To align `Resultset` and `Dataset`, the setter and getter for each download directory is moved into `DefaultDownloadDir`, so that each class shares an instance of `DefaultDownloadDir` and can be configured independently, although their default locations are still both dependent on the RAPIDS_DATASET_ROOT_DIR_PATH environment variable. The old patterns are present but commented-out, so this change would be breaking. This PR also removes the deprecated `experimental.datasets` package due to it being promoted to stable for >=1 release. Authors: - Ralph Liu (https://github.com/nv-rliu) - Dylan Chima-Sanchez (https://github.com/betochimas) - Rick Ratzel (https://github.com/rlratzel) - Brad Rees (https://github.com/BradReesWork) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3957 --- datasets/README.md | 4 + .../cugraph-pyg/cugraph_pyg/tests/conftest.py | 2 +- .../testing/benchmark_server_extension.py | 2 +- .../cugraph/cugraph/dask/community/leiden.py | 2 +- .../cugraph/cugraph/dask/community/louvain.py | 2 +- python/cugraph/cugraph/datasets/__init__.py | 10 + python/cugraph/cugraph/datasets/dataset.py | 86 +++-- .../datasets/metadata/cit-patents.yaml | 22 ++ .../cugraph/datasets/metadata/europe_osm.yaml | 21 ++ .../cugraph/datasets/metadata/hollywood.yaml | 26 ++ .../datasets/metadata/soc-livejournal1.yaml | 22 ++ .../datasets/metadata/soc-twitter-2010.yaml | 22 ++ .../cugraph/experimental/datasets/__init__.py | 79 ----- .../cugraph/experimental/datasets/dataset.py | 312 ------------------ .../datasets/datasets_config.yaml | 5 - .../datasets/metadata/__init__.py | 13 - .../experimental/datasets/metadata/cyber.yaml | 22 -- .../datasets/metadata/dolphins.yaml | 25 -- .../datasets/metadata/email-Eu-core.yaml | 22 -- .../datasets/metadata/karate-disjoint.yaml | 22 -- .../datasets/metadata/karate.yaml | 24 -- .../datasets/metadata/karate_asymmetric.yaml | 24 -- .../datasets/metadata/karate_data.yaml | 22 -- .../datasets/metadata/karate_undirected.yaml | 22 -- .../datasets/metadata/ktruss_polbooks.yaml | 23 -- .../datasets/metadata/netscience.yaml | 22 -- .../datasets/metadata/polbooks.yaml | 22 -- .../datasets/metadata/small_line.yaml | 22 -- .../datasets/metadata/small_tree.yaml | 22 -- .../datasets/metadata/toy_graph.yaml | 22 -- .../metadata/toy_graph_undirected.yaml | 22 -- python/cugraph/cugraph/testing/__init__.py | 8 +- .../cugraph/testing/generate_resultsets.py | 9 +- python/cugraph/cugraph/testing/resultset.py | 90 ++--- .../test_edge_betweenness_centrality_mg.py | 4 +- .../cugraph/tests/nx/test_compat_pr.py | 2 +- .../cugraph/tests/utils/test_dataset.py | 109 +++--- .../cugraph/tests/utils/test_resultset.py | 71 ++++ 38 files changed, 379 insertions(+), 882 deletions(-) create mode 100644 python/cugraph/cugraph/datasets/metadata/cit-patents.yaml create mode 100644 python/cugraph/cugraph/datasets/metadata/europe_osm.yaml create mode 100644 python/cugraph/cugraph/datasets/metadata/hollywood.yaml create mode 100644 python/cugraph/cugraph/datasets/metadata/soc-livejournal1.yaml create mode 100644 python/cugraph/cugraph/datasets/metadata/soc-twitter-2010.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/__init__.py delete mode 100644 python/cugraph/cugraph/experimental/datasets/dataset.py delete mode 100644 python/cugraph/cugraph/experimental/datasets/datasets_config.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/__init__.py delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/cyber.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/dolphins.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/email-Eu-core.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/karate-disjoint.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/karate.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/karate_asymmetric.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/karate_data.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/karate_undirected.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/ktruss_polbooks.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/netscience.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/polbooks.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/small_line.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/small_tree.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/toy_graph.yaml delete mode 100644 python/cugraph/cugraph/experimental/datasets/metadata/toy_graph_undirected.yaml create mode 100644 python/cugraph/cugraph/tests/utils/test_resultset.py diff --git a/datasets/README.md b/datasets/README.md index e42413fc996..a23dc644081 100644 --- a/datasets/README.md +++ b/datasets/README.md @@ -120,9 +120,13 @@ The benchmark datasets are described below: | soc-twitter-2010 | 21,297,772 | 265,025,809 | No | No | **cit-Patents** : A citation graph that includes all citations made by patents granted between 1975 and 1999, totaling 16,522,438 citations. + **soc-LiveJournal** : A graph of the LiveJournal social network. + **europe_osm** : A graph of OpenStreetMap data for Europe. + **hollywood** : A graph of movie actors where vertices are actors, and two actors are joined by an edge whenever they appeared in a movie together. + **soc-twitter-2010** : A network of follower relationships from a snapshot of Twitter in 2010, where an edge from i to j indicates that j is a follower of i. _NOTE: the benchmark datasets were converted to a CSV format from their original format described in the reference URL below, and in doing so had edge weights and isolated vertices discarded._ diff --git a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py index 083c4a2b37b..1512901822a 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/conftest.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/conftest.py @@ -24,7 +24,7 @@ import torch import numpy as np from cugraph.gnn import FeatureStore -from cugraph.experimental.datasets import karate +from cugraph.datasets import karate import tempfile diff --git a/python/cugraph-service/server/cugraph_service_server/testing/benchmark_server_extension.py b/python/cugraph-service/server/cugraph_service_server/testing/benchmark_server_extension.py index 5f9eac6b2a3..361226c8071 100644 --- a/python/cugraph-service/server/cugraph_service_server/testing/benchmark_server_extension.py +++ b/python/cugraph-service/server/cugraph_service_server/testing/benchmark_server_extension.py @@ -17,7 +17,7 @@ import cugraph from cugraph.experimental import PropertyGraph, MGPropertyGraph -from cugraph.experimental import datasets +from cugraph import datasets from cugraph.generators import rmat diff --git a/python/cugraph/cugraph/dask/community/leiden.py b/python/cugraph/cugraph/dask/community/leiden.py index 75582fa48f7..67bd0876ce6 100644 --- a/python/cugraph/cugraph/dask/community/leiden.py +++ b/python/cugraph/cugraph/dask/community/leiden.py @@ -125,7 +125,7 @@ def leiden( Examples -------- - >>> from cugraph.experimental.datasets import karate + >>> from cugraph.datasets import karate >>> G = karate.get_graph(fetch=True) >>> parts, modularity_score = cugraph.leiden(G) diff --git a/python/cugraph/cugraph/dask/community/louvain.py b/python/cugraph/cugraph/dask/community/louvain.py index 8efbbafaf7b..1b091817a1a 100644 --- a/python/cugraph/cugraph/dask/community/louvain.py +++ b/python/cugraph/cugraph/dask/community/louvain.py @@ -129,7 +129,7 @@ def louvain( Examples -------- - >>> from cugraph.experimental.datasets import karate + >>> from cugraph.datasets import karate >>> G = karate.get_graph(fetch=True) >>> parts = cugraph.louvain(G) diff --git a/python/cugraph/cugraph/datasets/__init__.py b/python/cugraph/cugraph/datasets/__init__.py index 65a820f108b..ac18274d354 100644 --- a/python/cugraph/cugraph/datasets/__init__.py +++ b/python/cugraph/cugraph/datasets/__init__.py @@ -39,3 +39,13 @@ small_tree = Dataset(meta_path / "small_tree.yaml") toy_graph = Dataset(meta_path / "toy_graph.yaml") toy_graph_undirected = Dataset(meta_path / "toy_graph_undirected.yaml") + +# Benchmarking datasets: be mindful of memory usage +# 250 MB +soc_livejournal = Dataset(meta_path / "soc-livejournal1.yaml") +# 965 MB +cit_patents = Dataset(meta_path / "cit-patents.yaml") +# 1.8 GB +europe_osm = Dataset(meta_path / "europe_osm.yaml") +# 1.5 GB +hollywood = Dataset(meta_path / "hollywood.yaml") diff --git a/python/cugraph/cugraph/datasets/dataset.py b/python/cugraph/cugraph/datasets/dataset.py index 877eade7708..dd7aa0df00a 100644 --- a/python/cugraph/cugraph/datasets/dataset.py +++ b/python/cugraph/cugraph/datasets/dataset.py @@ -14,44 +14,45 @@ import cudf import yaml import os +import pandas as pd from pathlib import Path from cugraph.structure.graph_classes import Graph class DefaultDownloadDir: """ - Maintains the path to the download directory used by Dataset instances. + Maintains a path to be used as a default download directory. + + All DefaultDownloadDir instances are based on RAPIDS_DATASET_ROOT_DIR if + set, or _default_base_dir if not set. + Instances of this class are typically shared by several Dataset instances in order to allow for the download directory to be defined and updated by a single object. """ - def __init__(self): - self._path = Path( - os.environ.get("RAPIDS_DATASET_ROOT_DIR", Path.home() / ".cugraph/datasets") - ) + _default_base_dir = Path.home() / ".cugraph/datasets" - @property - def path(self): + def __init__(self, *, subdir=""): """ - If `path` is not set, set it to the environment variable - RAPIDS_DATASET_ROOT_DIR. If the variable is not set, default to the - user's home directory. + subdir can be specified to provide a specialized dir under the base dir. """ - if self._path is None: - self._path = Path( - os.environ.get( - "RAPIDS_DATASET_ROOT_DIR", Path.home() / ".cugraph/datasets" - ) - ) - return self._path + self._subdir = Path(subdir) + self.reset() + + @property + def path(self): + return self._path.absolute() @path.setter def path(self, new): self._path = Path(new) - def clear(self): - self._path = None + def reset(self): + self._basedir = Path( + os.environ.get("RAPIDS_DATASET_ROOT_DIR", self._default_base_dir) + ) + self._path = self._basedir / self._subdir default_download_dir = DefaultDownloadDir() @@ -159,7 +160,7 @@ def unload(self): """ self._edgelist = None - def get_edgelist(self, download=False): + def get_edgelist(self, download=False, reader="cudf"): """ Return an Edgelist @@ -168,6 +169,9 @@ def get_edgelist(self, download=False): download : Boolean (default=False) Automatically download the dataset from the 'url' location within the YAML file. + + reader : 'cudf' or 'pandas' (default='cudf') + The library used to read a CSV and return an edgelist DataFrame. """ if self._edgelist is None: full_path = self.get_path() @@ -180,14 +184,29 @@ def get_edgelist(self, download=False): " exist. Try setting download=True" " to download the datafile" ) + header = None if isinstance(self.metadata["header"], int): header = self.metadata["header"] - self._edgelist = cudf.read_csv( - full_path, + + if reader == "cudf": + self.__reader = cudf.read_csv + elif reader == "pandas": + self.__reader = pd.read_csv + else: + raise ValueError( + "reader must be a module with a read_csv function compatible with \ + cudf.read_csv" + ) + + self._edgelist = self.__reader( + filepath_or_buffer=full_path, delimiter=self.metadata["delim"], names=self.metadata["col_names"], - dtype=self.metadata["col_types"], + dtype={ + self.metadata["col_names"][i]: self.metadata["col_types"][i] + for i in range(len(self.metadata["col_types"])) + }, header=header, ) @@ -219,6 +238,10 @@ def get_graph( dataset -if present- will be applied to the Graph. If the dataset does not contain weights, the Graph returned will be unweighted regardless of ignore_weights. + + store_transposed: Boolean (default=False) + If True, stores the transpose of the adjacency matrix. Required + for certain algorithms, such as pagerank. """ if self._edgelist is None: self.get_edgelist(download) @@ -237,20 +260,19 @@ def get_graph( "(or subclass) type or instance, got: " f"{type(create_using)}" ) - if len(self.metadata["col_names"]) > 2 and not (ignore_weights): G.from_cudf_edgelist( self._edgelist, - source="src", - destination="dst", - edge_attr="wgt", + source=self.metadata["col_names"][0], + destination=self.metadata["col_names"][1], + edge_attr=self.metadata["col_names"][2], store_transposed=store_transposed, ) else: G.from_cudf_edgelist( self._edgelist, - source="src", - destination="dst", + source=self.metadata["col_names"][0], + destination=self.metadata["col_names"][1], store_transposed=store_transposed, ) return G @@ -331,7 +353,7 @@ def download_all(force=False): def set_download_dir(path): """ - Set the download location fors datasets + Set the download location for datasets Parameters ---------- @@ -339,10 +361,10 @@ def set_download_dir(path): Location used to store datafiles """ if path is None: - default_download_dir.clear() + default_download_dir.reset() else: default_download_dir.path = path def get_download_dir(): - return default_download_dir.path.absolute() + return default_download_dir.path diff --git a/python/cugraph/cugraph/datasets/metadata/cit-patents.yaml b/python/cugraph/cugraph/datasets/metadata/cit-patents.yaml new file mode 100644 index 00000000000..d5c4cf195bd --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/cit-patents.yaml @@ -0,0 +1,22 @@ +name: cit-Patents +file_type: .csv +description: A citation graph that includes all citations made by patents granted between 1975 and 1999, totaling 16,522,438 citations. +author: NBER +refs: + J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time Densification Laws, Shrinking Diameters and Possible Explanations. + ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005. +delim: " " +header: None +col_names: + - src + - dst +col_types: + - int32 + - int32 +has_loop: true +is_directed: true +is_multigraph: false +is_symmetric: false +number_of_edges: 16518948 +number_of_nodes: 3774768 +url: https://data.rapids.ai/cugraph/datasets/cit-Patents.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/datasets/metadata/europe_osm.yaml b/python/cugraph/cugraph/datasets/metadata/europe_osm.yaml new file mode 100644 index 00000000000..fe0e42a4b86 --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/europe_osm.yaml @@ -0,0 +1,21 @@ +name: europe_osm +file_type: .csv +description: A graph of OpenStreetMap data for Europe. +author: M. Kobitzsh / Geofabrik GmbH +refs: + Rossi, Ryan. Ahmed, Nesreen. The Network Data Respoistory with Interactive Graph Analytics and Visualization. +delim: " " +header: None +col_names: + - src + - dst +col_types: + - int32 + - int32 +has_loop: false +is_directed: false +is_multigraph: false +is_symmetric: true +number_of_edges: 54054660 +number_of_nodes: 50912018 +url: https://data.rapids.ai/cugraph/datasets/europe_osm.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/datasets/metadata/hollywood.yaml b/python/cugraph/cugraph/datasets/metadata/hollywood.yaml new file mode 100644 index 00000000000..2f09cf7679b --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/hollywood.yaml @@ -0,0 +1,26 @@ +name: hollywood +file_type: .csv +description: + A graph of movie actors where vertices are actors, and two actors are + joined by an edge whenever they appeared in a movie together. +author: Laboratory for Web Algorithmics (LAW) +refs: + The WebGraph Framework I Compression Techniques, Paolo Boldi + and Sebastiano Vigna, Proc. of the Thirteenth International + World Wide Web Conference (WWW 2004), 2004, Manhattan, USA, + pp. 595--601, ACM Press. +delim: " " +header: None +col_names: + - src + - dst +col_types: + - int32 + - int32 +has_loop: false +is_directed: false +is_multigraph: false +is_symmetric: true +number_of_edges: 57515616 +number_of_nodes: 1139905 +url: https://data.rapids.ai/cugraph/datasets/hollywood.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/datasets/metadata/soc-livejournal1.yaml b/python/cugraph/cugraph/datasets/metadata/soc-livejournal1.yaml new file mode 100644 index 00000000000..fafc68acb9b --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/soc-livejournal1.yaml @@ -0,0 +1,22 @@ +name: soc-LiveJournal1 +file_type: .csv +description: A graph of the LiveJournal social network. +author: L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan +refs: + L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan. Group Formation in + Large Social Networks Membership, Growth, and Evolution. KDD, 2006. +delim: " " +header: None +col_names: + - src + - dst +col_types: + - int32 + - int32 +has_loop: true +is_directed: true +is_multigraph: false +is_symmetric: false +number_of_edges: 68993773 +number_of_nodes: 4847571 +url: https://data.rapids.ai/cugraph/datasets/soc-LiveJournal1.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/datasets/metadata/soc-twitter-2010.yaml b/python/cugraph/cugraph/datasets/metadata/soc-twitter-2010.yaml new file mode 100644 index 00000000000..df5df5735af --- /dev/null +++ b/python/cugraph/cugraph/datasets/metadata/soc-twitter-2010.yaml @@ -0,0 +1,22 @@ +name: soc-twitter-2010 +file_type: .csv +description: A network of follower relationships from a snapshot of Twitter in 2010, where an edge from i to j indicates that j is a follower of i. +author: H. Kwak, C. Lee, H. Park, S. Moon +refs: + J. Yang, J. Leskovec. Temporal Variation in Online Media. ACM Intl. + Conf. on Web Search and Data Mining (WSDM '11), 2011. +delim: " " +header: None +col_names: + - src + - dst +col_types: + - int32 + - int32 +has_loop: false +is_directed: false +is_multigraph: false +is_symmetric: false +number_of_edges: 530051354 +number_of_nodes: 21297772 +url: https://data.rapids.ai/cugraph/datasets/soc-twitter-2010.csv \ No newline at end of file diff --git a/python/cugraph/cugraph/experimental/datasets/__init__.py b/python/cugraph/cugraph/experimental/datasets/__init__.py deleted file mode 100644 index 18220243df1..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/__init__.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) 2022-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from cugraph.experimental.datasets.dataset import ( - Dataset, - load_all, - set_download_dir, - get_download_dir, - default_download_dir, -) -from cugraph.experimental.datasets import metadata -from pathlib import Path - -from cugraph.utilities.api_tools import promoted_experimental_warning_wrapper - - -Dataset = promoted_experimental_warning_wrapper(Dataset) -load_all = promoted_experimental_warning_wrapper(load_all) -set_download_dir = promoted_experimental_warning_wrapper(set_download_dir) -get_download_dir = promoted_experimental_warning_wrapper(get_download_dir) - -meta_path = Path(__file__).parent / "metadata" - - -# individual dataset objects -karate = Dataset(meta_path / "karate.yaml") -karate_data = Dataset(meta_path / "karate_data.yaml") -karate_undirected = Dataset(meta_path / "karate_undirected.yaml") -karate_asymmetric = Dataset(meta_path / "karate_asymmetric.yaml") -karate_disjoint = Dataset(meta_path / "karate-disjoint.yaml") -dolphins = Dataset(meta_path / "dolphins.yaml") -polbooks = Dataset(meta_path / "polbooks.yaml") -netscience = Dataset(meta_path / "netscience.yaml") -cyber = Dataset(meta_path / "cyber.yaml") -small_line = Dataset(meta_path / "small_line.yaml") -small_tree = Dataset(meta_path / "small_tree.yaml") -toy_graph = Dataset(meta_path / "toy_graph.yaml") -toy_graph_undirected = Dataset(meta_path / "toy_graph_undirected.yaml") -email_Eu_core = Dataset(meta_path / "email-Eu-core.yaml") -ktruss_polbooks = Dataset(meta_path / "ktruss_polbooks.yaml") - - -# batches of datasets -DATASETS_UNDIRECTED = [karate, dolphins] - -DATASETS_UNDIRECTED_WEIGHTS = [netscience] - -DATASETS_UNRENUMBERED = [karate_disjoint] - -DATASETS = [dolphins, netscience, karate_disjoint] - -DATASETS_SMALL = [karate, dolphins, polbooks] - -STRONGDATASETS = [dolphins, netscience, email_Eu_core] - -DATASETS_KTRUSS = [(polbooks, ktruss_polbooks)] - -MEDIUM_DATASETS = [polbooks] - -SMALL_DATASETS = [karate, dolphins, netscience] - -RLY_SMALL_DATASETS = [small_line, small_tree] - -ALL_DATASETS = [karate, dolphins, netscience, polbooks, small_line, small_tree] - -ALL_DATASETS_WGT = [karate, dolphins, netscience, polbooks, small_line, small_tree] - -TEST_GROUP = [dolphins, netscience] diff --git a/python/cugraph/cugraph/experimental/datasets/dataset.py b/python/cugraph/cugraph/experimental/datasets/dataset.py deleted file mode 100644 index 6b395d50fef..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/dataset.py +++ /dev/null @@ -1,312 +0,0 @@ -# Copyright (c) 2022-2023, NVIDIA CORPORATION. -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import cudf -import yaml -import os -from pathlib import Path -from cugraph.structure.graph_classes import Graph - - -class DefaultDownloadDir: - """ - Maintains the path to the download directory used by Dataset instances. - Instances of this class are typically shared by several Dataset instances - in order to allow for the download directory to be defined and updated by - a single object. - """ - - def __init__(self): - self._path = Path( - os.environ.get("RAPIDS_DATASET_ROOT_DIR", Path.home() / ".cugraph/datasets") - ) - - @property - def path(self): - """ - If `path` is not set, set it to the environment variable - RAPIDS_DATASET_ROOT_DIR. If the variable is not set, default to the - user's home directory. - """ - if self._path is None: - self._path = Path( - os.environ.get( - "RAPIDS_DATASET_ROOT_DIR", Path.home() / ".cugraph/datasets" - ) - ) - return self._path - - @path.setter - def path(self, new): - self._path = Path(new) - - def clear(self): - self._path = None - - -default_download_dir = DefaultDownloadDir() - - -class Dataset: - """ - A Dataset Object, used to easily import edgelist data and cuGraph.Graph - instances. - - Parameters - ---------- - meta_data_file_name : yaml file - The metadata file for the specific graph dataset, which includes - information on the name, type, url link, data loading format, graph - properties - """ - - def __init__( - self, - metadata_yaml_file=None, - csv_file=None, - csv_header=None, - csv_delim=" ", - csv_col_names=None, - csv_col_dtypes=None, - ): - self._metadata_file = None - self._dl_path = default_download_dir - self._edgelist = None - self._path = None - - if metadata_yaml_file is not None and csv_file is not None: - raise ValueError("cannot specify both metadata_yaml_file and csv_file") - - elif metadata_yaml_file is not None: - with open(metadata_yaml_file, "r") as file: - self.metadata = yaml.safe_load(file) - self._metadata_file = Path(metadata_yaml_file) - - elif csv_file is not None: - if csv_col_names is None or csv_col_dtypes is None: - raise ValueError( - "csv_col_names and csv_col_dtypes must both be " - "not None when csv_file is specified." - ) - self._path = Path(csv_file) - if self._path.exists() is False: - raise FileNotFoundError(csv_file) - self.metadata = { - "name": self._path.with_suffix("").name, - "file_type": ".csv", - "url": None, - "header": csv_header, - "delim": csv_delim, - "col_names": csv_col_names, - "col_types": csv_col_dtypes, - } - - else: - raise ValueError("must specify either metadata_yaml_file or csv_file") - - def __str__(self): - """ - Use the basename of the meta_data_file the instance was constructed with, - without any extension, as the string repr. - """ - # The metadata file is likely to have a more descriptive file name, so - # use that one first if present. - # FIXME: this may need to provide a more unique or descriptive string repr - if self._metadata_file is not None: - return self._metadata_file.with_suffix("").name - else: - return self.get_path().with_suffix("").name - - def __download_csv(self, url): - """ - Downloads the .csv file from url to the current download path - (self._dl_path), updates self._path with the full path to the - downloaded file, and returns the latest value of self._path. - """ - self._dl_path.path.mkdir(parents=True, exist_ok=True) - - filename = self.metadata["name"] + self.metadata["file_type"] - if self._dl_path.path.is_dir(): - df = cudf.read_csv(url) - self._path = self._dl_path.path / filename - df.to_csv(self._path, index=False) - - else: - raise RuntimeError( - f"The directory {self._dl_path.path.absolute()}" "does not exist" - ) - return self._path - - def unload(self): - - """ - Remove all saved internal objects, forcing them to be re-created when - accessed. - - NOTE: This will cause calls to get_*() to re-read the dataset file from - disk. The caller should ensure the file on disk has not moved/been - deleted/changed. - """ - self._edgelist = None - - def get_edgelist(self, fetch=False): - """ - Return an Edgelist - - Parameters - ---------- - fetch : Boolean (default=False) - Automatically fetch for the dataset from the 'url' location within - the YAML file. - """ - if self._edgelist is None: - full_path = self.get_path() - if not full_path.is_file(): - if fetch: - full_path = self.__download_csv(self.metadata["url"]) - else: - raise RuntimeError( - f"The datafile {full_path} does not" - " exist. Try get_edgelist(fetch=True)" - " to download the datafile" - ) - header = None - if isinstance(self.metadata["header"], int): - header = self.metadata["header"] - self._edgelist = cudf.read_csv( - full_path, - delimiter=self.metadata["delim"], - names=self.metadata["col_names"], - dtype=self.metadata["col_types"], - header=header, - ) - - return self._edgelist - - def get_graph( - self, - fetch=False, - create_using=Graph, - ignore_weights=False, - store_transposed=False, - ): - """ - Return a Graph object. - - Parameters - ---------- - fetch : Boolean (default=False) - Downloads the dataset from the web. - - create_using: cugraph.Graph (instance or class), optional - (default=Graph) - Specify the type of Graph to create. Can pass in an instance to - create a Graph instance with specified 'directed' attribute. - - ignore_weights : Boolean (default=False) - Ignores weights in the dataset if True, resulting in an - unweighted Graph. If False (the default), weights from the - dataset -if present- will be applied to the Graph. If the - dataset does not contain weights, the Graph returned will - be unweighted regardless of ignore_weights. - """ - if self._edgelist is None: - self.get_edgelist(fetch) - - if create_using is None: - G = Graph() - elif isinstance(create_using, Graph): - # what about BFS if trnaposed is True - attrs = {"directed": create_using.is_directed()} - G = type(create_using)(**attrs) - elif type(create_using) is type: - G = create_using() - else: - raise TypeError( - "create_using must be a cugraph.Graph " - "(or subclass) type or instance, got: " - f"{type(create_using)}" - ) - - if len(self.metadata["col_names"]) > 2 and not (ignore_weights): - G.from_cudf_edgelist( - self._edgelist, - source="src", - destination="dst", - edge_attr="wgt", - store_transposed=store_transposed, - ) - else: - G.from_cudf_edgelist( - self._edgelist, - source="src", - destination="dst", - store_transposed=store_transposed, - ) - return G - - def get_path(self): - """ - Returns the location of the stored dataset file - """ - if self._path is None: - self._path = self._dl_path.path / ( - self.metadata["name"] + self.metadata["file_type"] - ) - - return self._path.absolute() - - -def load_all(force=False): - """ - Looks in `metadata` directory and fetches all datafiles from the the URLs - provided in each YAML file. - - Parameters - force : Boolean (default=False) - Overwrite any existing copies of datafiles. - """ - default_download_dir.path.mkdir(parents=True, exist_ok=True) - - meta_path = Path(__file__).parent.absolute() / "metadata" - for file in meta_path.iterdir(): - meta = None - if file.suffix == ".yaml": - with open(meta_path / file, "r") as metafile: - meta = yaml.safe_load(metafile) - - if "url" in meta: - filename = meta["name"] + meta["file_type"] - save_to = default_download_dir.path / filename - if not save_to.is_file() or force: - df = cudf.read_csv(meta["url"]) - df.to_csv(save_to, index=False) - - -def set_download_dir(path): - """ - Set the download directory for fetching datasets - - Parameters - ---------- - path : String - Location used to store datafiles - """ - if path is None: - default_download_dir.clear() - else: - default_download_dir.path = path - - -def get_download_dir(): - return default_download_dir.path.absolute() diff --git a/python/cugraph/cugraph/experimental/datasets/datasets_config.yaml b/python/cugraph/cugraph/experimental/datasets/datasets_config.yaml deleted file mode 100644 index 69a79db9cd9..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/datasets_config.yaml +++ /dev/null @@ -1,5 +0,0 @@ ---- -fetch: "False" -force: "False" -# path where datasets will be downloaded to and stored -download_dir: "datasets" diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/__init__.py b/python/cugraph/cugraph/experimental/datasets/metadata/__init__.py deleted file mode 100644 index 081b2ae8260..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022-2023, NVIDIA CORPORATION. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/cyber.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/cyber.yaml deleted file mode 100644 index 93ab5345442..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/cyber.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: cyber -file_type: .csv -author: N/A -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/cyber.csv -refs: N/A -col_names: - - idx - - srcip - - dstip -col_types: - - int32 - - str - - str -delim: "," -header: 0 -has_loop: true -is_directed: true -is_multigraph: false -is_symmetric: false -number_of_edges: 2546575 -number_of_nodes: 706529 -number_of_lines: 2546576 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/dolphins.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/dolphins.yaml deleted file mode 100644 index e4951375321..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/dolphins.yaml +++ /dev/null @@ -1,25 +0,0 @@ -name: dolphins -file_type: .csv -author: D. Lusseau -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/dolphins.csv -refs: - D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, - The bottlenose dolphin community of Doubtful Sound features a large proportion of - long-lasting associations, Behavioral Ecology and Sociobiology 54, 396-405 (2003). -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -delim: " " -header: None -has_loop: false -is_directed: true -is_multigraph: false -is_symmetric: false -number_of_edges: 318 -number_of_nodes: 62 -number_of_lines: 318 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/email-Eu-core.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/email-Eu-core.yaml deleted file mode 100644 index 97d0dc82ee3..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/email-Eu-core.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: email-Eu-core -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/email-Eu-core.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: false -is_multigraph: false -is_symmetric: true -number_of_edges: 25571 -number_of_nodes: 1005 -number_of_lines: 25571 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/karate-disjoint.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/karate-disjoint.yaml deleted file mode 100644 index 0c0eaf78b63..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/karate-disjoint.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: karate-disjoint -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/karate-disjoint.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: True -is_multigraph: false -is_symmetric: true -number_of_edges: 312 -number_of_nodes: 68 -number_of_lines: 312 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/karate.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/karate.yaml deleted file mode 100644 index 273381ed368..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/karate.yaml +++ /dev/null @@ -1,24 +0,0 @@ -name: karate -file_type: .csv -author: Zachary W. -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/karate.csv -refs: - W. W. Zachary, An information flow model for conflict and fission in small groups, - Journal of Anthropological Research 33, 452-473 (1977). -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: true -is_directed: true -is_multigraph: false -is_symmetric: true -number_of_edges: 156 -number_of_nodes: 34 -number_of_lines: 156 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/karate_asymmetric.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/karate_asymmetric.yaml deleted file mode 100644 index 3616b8fb3a5..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/karate_asymmetric.yaml +++ /dev/null @@ -1,24 +0,0 @@ -name: karate-asymmetric -file_type: .csv -author: Zachary W. -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/karate-asymmetric.csv -delim: " " -header: None -refs: - W. W. Zachary, An information flow model for conflict and fission in small groups, - Journal of Anthropological Research 33, 452-473 (1977). -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: true -is_directed: false -is_multigraph: false -is_symmetric: false -number_of_edges: 78 -number_of_nodes: 34 -number_of_lines: 78 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/karate_data.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/karate_data.yaml deleted file mode 100644 index 9a8b27f21ae..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/karate_data.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: karate-data -file_type: .csv -author: Zachary W. -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/karate-data.csv -refs: - W. W. Zachary, An information flow model for conflict and fission in small groups, - Journal of Anthropological Research 33, 452-473 (1977). -delim: "\t" -header: None -col_names: - - src - - dst -col_types: - - int32 - - int32 -has_loop: true -is_directed: true -is_multigraph: false -is_symmetric: true -number_of_edges: 156 -number_of_nodes: 34 -number_of_lines: 156 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/karate_undirected.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/karate_undirected.yaml deleted file mode 100644 index 1b45f86caee..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/karate_undirected.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: karate_undirected -file_type: .csv -author: Zachary W. -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/karate_undirected.csv -refs: - W. W. Zachary, An information flow model for conflict and fission in small groups, - Journal of Anthropological Research 33, 452-473 (1977). -delim: "\t" -header: None -col_names: - - src - - dst -col_types: - - int32 - - int32 -has_loop: true -is_directed: false -is_multigraph: false -is_symmetric: true -number_of_edges: 78 -number_of_nodes: 34 -number_of_lines: 78 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/ktruss_polbooks.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/ktruss_polbooks.yaml deleted file mode 100644 index 1ef29b3917e..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/ktruss_polbooks.yaml +++ /dev/null @@ -1,23 +0,0 @@ -name: ktruss_polbooks -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/ref/ktruss/polbooks.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: true -is_multigraph: false -is_symmetric: false -number_of_edges: 233 -number_of_nodes: 58 -number_of_lines: 233 - diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/netscience.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/netscience.yaml deleted file mode 100644 index 2dca702df3d..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/netscience.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: netscience -file_type: .csv -author: Newman, Mark EJ -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/netscience.csv -refs: Finding community structure in networks using the eigenvectors of matrices. -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: true -is_multigraph: false -is_symmetric: true -number_of_edges: 2742 -number_of_nodes: 1461 -number_of_lines: 5484 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/polbooks.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/polbooks.yaml deleted file mode 100644 index 5816e5672fd..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/polbooks.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: polbooks -file_type: .csv -author: V. Krebs -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/polbooks.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -is_directed: true -has_loop: null -is_multigraph: null -is_symmetric: true -number_of_edges: 882 -number_of_nodes: 105 -number_of_lines: 882 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/small_line.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/small_line.yaml deleted file mode 100644 index 5b724ac99fd..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/small_line.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: small_line -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/small_line.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: false -is_multigraph: false -is_symmetric: true -number_of_edges: 9 -number_of_nodes: 10 -number_of_lines: 8 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/small_tree.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/small_tree.yaml deleted file mode 100644 index 8eeac346d2a..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/small_tree.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: small_tree -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/small_tree.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: true -is_multigraph: false -is_symmetric: true -number_of_edges: 11 -number_of_nodes: 9 -number_of_lines: 11 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph.yaml deleted file mode 100644 index 819aad06f6a..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: toy_graph -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/toy_graph.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: false -is_multigraph: false -is_symmetric: true -number_of_edges: 16 -number_of_nodes: 6 -number_of_lines: 16 diff --git a/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph_undirected.yaml b/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph_undirected.yaml deleted file mode 100644 index c6e86bdf334..00000000000 --- a/python/cugraph/cugraph/experimental/datasets/metadata/toy_graph_undirected.yaml +++ /dev/null @@ -1,22 +0,0 @@ -name: toy_graph_undirected -file_type: .csv -author: null -url: https://raw.githubusercontent.com/rapidsai/cugraph/branch-22.08/datasets/toy_graph_undirected.csv -refs: null -delim: " " -header: None -col_names: - - src - - dst - - wgt -col_types: - - int32 - - int32 - - float32 -has_loop: false -is_directed: false -is_multigraph: false -is_symmetric: true -number_of_edges: 8 -number_of_nodes: 6 -number_of_lines: 8 diff --git a/python/cugraph/cugraph/testing/__init__.py b/python/cugraph/cugraph/testing/__init__.py index f5f0bcb06eb..2b4a4fd3ebf 100644 --- a/python/cugraph/cugraph/testing/__init__.py +++ b/python/cugraph/cugraph/testing/__init__.py @@ -19,7 +19,7 @@ Resultset, load_resultset, get_resultset, - results_dir_path, + default_resultset_download_dir, ) from cugraph.datasets import ( cyber, @@ -34,6 +34,11 @@ email_Eu_core, toy_graph, toy_graph_undirected, + soc_livejournal, + cit_patents, + europe_osm, + hollywood, + # twitter, ) # @@ -66,3 +71,4 @@ toy_graph_undirected, ] DEFAULT_DATASETS = [dolphins, netscience, karate_disjoint] +BENCHMARKING_DATASETS = [soc_livejournal, cit_patents, europe_osm, hollywood] diff --git a/python/cugraph/cugraph/testing/generate_resultsets.py b/python/cugraph/cugraph/testing/generate_resultsets.py index 9724aca32dc..2ae0f52d88b 100644 --- a/python/cugraph/cugraph/testing/generate_resultsets.py +++ b/python/cugraph/cugraph/testing/generate_resultsets.py @@ -20,8 +20,14 @@ import cudf import cugraph from cugraph.datasets import dolphins, netscience, karate_disjoint, karate -from cugraph.testing import utils, Resultset, SMALL_DATASETS, results_dir_path +# from cugraph.testing import utils, Resultset, SMALL_DATASETS, results_dir_path +from cugraph.testing import ( + utils, + Resultset, + SMALL_DATASETS, + default_resultset_download_dir, +) _resultsets = {} @@ -224,6 +230,7 @@ def add_resultset(result_data_dictionary, **kwargs): ] ) # Generating ALL results files + results_dir_path = default_resultset_download_dir.path if not results_dir_path.exists(): results_dir_path.mkdir(parents=True, exist_ok=True) diff --git a/python/cugraph/cugraph/testing/resultset.py b/python/cugraph/cugraph/testing/resultset.py index 490e3a7c4ff..9570d7f3e04 100644 --- a/python/cugraph/cugraph/testing/resultset.py +++ b/python/cugraph/cugraph/testing/resultset.py @@ -16,10 +16,12 @@ import urllib.request import cudf -from cugraph.testing import utils +from cugraph.datasets.dataset import ( + DefaultDownloadDir, + default_download_dir, +) - -results_dir_path = utils.RAPIDS_DATASET_ROOT_DIR_PATH / "tests" / "resultsets" +# results_dir_path = utils.RAPIDS_DATASET_ROOT_DIR_PATH / "tests" / "resultsets" class Resultset: @@ -48,6 +50,42 @@ def get_cudf_dataframe(self): _resultsets = {} +def get_resultset(resultset_name, **kwargs): + """ + Returns the golden results for a specific test. + + Parameters + ---------- + resultset_name : String + Name of the test's module (currently just 'traversal' is supported) + + kwargs : + All distinct test details regarding the choice of algorithm, dataset, + and graph + """ + arg_dict = dict(kwargs) + arg_dict["resultset_name"] = resultset_name + # Example: + # {'a': 1, 'z': 9, 'c': 5, 'b': 2} becomes 'a-1-b-2-c-5-z-9' + resultset_key = "-".join( + [ + str(val) + for arg_dict_pair in sorted(arg_dict.items()) + for val in arg_dict_pair + ] + ) + uuid = _resultsets.get(resultset_key) + if uuid is None: + raise KeyError(f"results for {arg_dict} not found") + + results_dir_path = default_resultset_download_dir.path + results_filename = results_dir_path / (uuid + ".csv") + return cudf.read_csv(results_filename) + + +default_resultset_download_dir = DefaultDownloadDir(subdir="tests/resultsets") + + def load_resultset(resultset_name, resultset_download_url): """ Read a mapping file (.csv) in the _results_dir and save the @@ -56,17 +94,21 @@ def load_resultset(resultset_name, resultset_download_url): _results_dir, use resultset_download_url to download a file to install/unpack/etc. to _results_dir first. """ - mapping_file_path = results_dir_path / (resultset_name + "_mappings.csv") + # curr_resultset_download_dir = get_resultset_download_dir() + curr_resultset_download_dir = default_resultset_download_dir.path + # curr_download_dir = path + curr_download_dir = default_download_dir.path + mapping_file_path = curr_resultset_download_dir / (resultset_name + "_mappings.csv") if not mapping_file_path.exists(): # Downloads a tar gz from s3 bucket, then unpacks the results files - compressed_file_dir = utils.RAPIDS_DATASET_ROOT_DIR_PATH / "tests" + compressed_file_dir = curr_download_dir / "tests" compressed_file_path = compressed_file_dir / "resultsets.tar.gz" - if not results_dir_path.exists(): - results_dir_path.mkdir(parents=True, exist_ok=True) + if not curr_resultset_download_dir.exists(): + curr_resultset_download_dir.mkdir(parents=True, exist_ok=True) if not compressed_file_path.exists(): urllib.request.urlretrieve(resultset_download_url, compressed_file_path) tar = tarfile.open(str(compressed_file_path), "r:gz") - tar.extractall(str(results_dir_path)) + tar.extractall(str(curr_resultset_download_dir)) tar.close() # FIXME: This assumes separator is " ", but should this be configurable? @@ -102,35 +144,3 @@ def load_resultset(resultset_name, resultset_download_url): ) _resultsets[resultset_key] = uuid - - -def get_resultset(resultset_name, **kwargs): - """ - Returns the golden results for a specific test. - - Parameters - ---------- - resultset_name : String - Name of the test's module (currently just 'traversal' is supported) - - kwargs : - All distinct test details regarding the choice of algorithm, dataset, - and graph - """ - arg_dict = dict(kwargs) - arg_dict["resultset_name"] = resultset_name - # Example: - # {'a': 1, 'z': 9, 'c': 5, 'b': 2} becomes 'a-1-b-2-c-5-z-9' - resultset_key = "-".join( - [ - str(val) - for arg_dict_pair in sorted(arg_dict.items()) - for val in arg_dict_pair - ] - ) - uuid = _resultsets.get(resultset_key) - if uuid is None: - raise KeyError(f"results for {arg_dict} not found") - - results_filename = results_dir_path / (uuid + ".csv") - return cudf.read_csv(results_filename) diff --git a/python/cugraph/cugraph/tests/centrality/test_edge_betweenness_centrality_mg.py b/python/cugraph/cugraph/tests/centrality/test_edge_betweenness_centrality_mg.py index 4277f94a396..478b7e655d5 100644 --- a/python/cugraph/cugraph/tests/centrality/test_edge_betweenness_centrality_mg.py +++ b/python/cugraph/cugraph/tests/centrality/test_edge_betweenness_centrality_mg.py @@ -16,7 +16,7 @@ import dask_cudf from pylibcugraph.testing.utils import gen_fixture_params_product -from cugraph.experimental.datasets import DATASETS_UNDIRECTED +from cugraph.datasets import karate, dolphins import cugraph import cugraph.dask as dcg @@ -41,7 +41,7 @@ def setup_function(): # email_Eu_core is too expensive to test -datasets = DATASETS_UNDIRECTED +datasets = [karate, dolphins] # ============================================================================= diff --git a/python/cugraph/cugraph/tests/nx/test_compat_pr.py b/python/cugraph/cugraph/tests/nx/test_compat_pr.py index 9be3912a33f..45cab7a5674 100644 --- a/python/cugraph/cugraph/tests/nx/test_compat_pr.py +++ b/python/cugraph/cugraph/tests/nx/test_compat_pr.py @@ -24,7 +24,7 @@ import numpy as np from cugraph.testing import utils -from cugraph.experimental.datasets import karate +from cugraph.datasets import karate from pylibcugraph.testing.utils import gen_fixture_params_product diff --git a/python/cugraph/cugraph/tests/utils/test_dataset.py b/python/cugraph/cugraph/tests/utils/test_dataset.py index c2a4f7c6072..60bc6dbb45a 100644 --- a/python/cugraph/cugraph/tests/utils/test_dataset.py +++ b/python/cugraph/cugraph/tests/utils/test_dataset.py @@ -13,11 +13,10 @@ import os import gc -import sys -import warnings from pathlib import Path from tempfile import TemporaryDirectory +import pandas import pytest import cudf @@ -27,6 +26,7 @@ ALL_DATASETS, WEIGHTED_DATASETS, SMALL_DATASETS, + BENCHMARKING_DATASETS, ) from cugraph import datasets @@ -74,27 +74,14 @@ def setup(tmpdir): gc.collect() -@pytest.fixture() -def setup_deprecation_warning_tests(): - """ - Fixture used to set warning filters to 'default' and reload - experimental.datasets module if it has been previously - imported. Tests that import this fixture are expected to - import cugraph.experimental.datasets - """ - warnings.filterwarnings("default") - - if "cugraph.experimental.datasets" in sys.modules: - del sys.modules["cugraph.experimental.datasets"] - - yield - - ############################################################################### # Helpers # check if there is a row where src == dst -def has_loop(df): +def has_selfloop(dataset): + if not dataset.metadata["is_directed"]: + return False + df = dataset.get_edgelist(download=True) df.rename(columns={df.columns[0]: "src", df.columns[1]: "dst"}, inplace=True) res = df.where(df["src"] == df["dst"]) @@ -109,7 +96,13 @@ def is_symmetric(dataset): else: df = dataset.get_edgelist(download=True) df_a = df.sort_values("src") - df_b = df_a[["dst", "src", "wgt"]] + + # create df with swapped src/dst columns + df_b = None + if "wgt" in df_a.columns: + df_b = df_a[["dst", "src", "wgt"]] + else: + df_b = df_a[["dst", "src"]] df_b.rename(columns={"dst": "src", "src": "dst"}, inplace=True) # created a df by appending the two res = cudf.concat([df_a, df_b]) @@ -157,6 +150,27 @@ def test_download(dataset): assert dataset.get_path().is_file() +@pytest.mark.parametrize("dataset", SMALL_DATASETS) +def test_reader(dataset): + # defaults to using cudf.read_csv + E = dataset.get_edgelist(download=True) + + assert E is not None + assert isinstance(E, cudf.core.dataframe.DataFrame) + dataset.unload() + + # using pandas + E_pd = dataset.get_edgelist(download=True, reader="pandas") + + assert E_pd is not None + assert isinstance(E_pd, pandas.core.frame.DataFrame) + dataset.unload() + + with pytest.raises(ValueError): + dataset.get_edgelist(reader="fail") + dataset.get_edgelist(reader=None) + + @pytest.mark.parametrize("dataset", ALL_DATASETS) def test_get_edgelist(dataset): E = dataset.get_edgelist(download=True) @@ -172,7 +186,6 @@ def test_get_graph(dataset): @pytest.mark.parametrize("dataset", ALL_DATASETS) def test_metadata(dataset): M = dataset.metadata - assert M is not None @@ -310,10 +323,8 @@ def test_is_directed(dataset): @pytest.mark.parametrize("dataset", ALL_DATASETS) -def test_has_loop(dataset): - df = dataset.get_edgelist(download=True) - - assert has_loop(df) == dataset.metadata["has_loop"] +def test_has_selfloop(dataset): + assert has_selfloop(dataset) == dataset.metadata["has_loop"] @pytest.mark.parametrize("dataset", ALL_DATASETS) @@ -328,6 +339,25 @@ def test_is_multigraph(dataset): assert G.is_multigraph() == dataset.metadata["is_multigraph"] +# The datasets used for benchmarks are in their own test, since downloading them +# repeatedly would increase testing overhead significantly +@pytest.mark.parametrize("dataset", BENCHMARKING_DATASETS) +def test_benchmarking_datasets(dataset): + dataset_is_directed = dataset.metadata["is_directed"] + G = dataset.get_graph( + download=True, create_using=Graph(directed=dataset_is_directed) + ) + + assert G.is_directed() == dataset.metadata["is_directed"] + assert G.number_of_nodes() == dataset.metadata["number_of_nodes"] + assert G.number_of_edges() == dataset.metadata["number_of_edges"] + assert has_selfloop(dataset) == dataset.metadata["has_loop"] + assert is_symmetric(dataset) == dataset.metadata["is_symmetric"] + assert G.is_multigraph() == dataset.metadata["is_multigraph"] + + dataset.unload() + + @pytest.mark.parametrize("dataset", ALL_DATASETS) def test_object_getters(dataset): assert dataset.is_directed() == dataset.metadata["is_directed"] @@ -336,32 +366,3 @@ def test_object_getters(dataset): assert dataset.number_of_nodes() == dataset.metadata["number_of_nodes"] assert dataset.number_of_vertices() == dataset.metadata["number_of_nodes"] assert dataset.number_of_edges() == dataset.metadata["number_of_edges"] - - -# -# Test experimental for DeprecationWarnings -# -def test_experimental_dataset_import(setup_deprecation_warning_tests): - with pytest.deprecated_call(): - from cugraph.experimental.datasets import karate - - # unload() is called to pass flake8 - karate.unload() - - -def test_experimental_method_warnings(setup_deprecation_warning_tests): - from cugraph.experimental.datasets import ( - load_all, - set_download_dir, - get_download_dir, - ) - - warnings.filterwarnings("default") - tmpd = TemporaryDirectory() - - with pytest.deprecated_call(): - set_download_dir(tmpd.name) - get_download_dir() - load_all() - - tmpd.cleanup() diff --git a/python/cugraph/cugraph/tests/utils/test_resultset.py b/python/cugraph/cugraph/tests/utils/test_resultset.py new file mode 100644 index 00000000000..5c2298bedb7 --- /dev/null +++ b/python/cugraph/cugraph/tests/utils/test_resultset.py @@ -0,0 +1,71 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from pathlib import Path +from tempfile import TemporaryDirectory + +import cudf +from cugraph.datasets.dataset import ( + set_download_dir, + get_download_dir, +) +from cugraph.testing.resultset import load_resultset, default_resultset_download_dir + +############################################################################### + + +def test_load_resultset(): + with TemporaryDirectory() as tmpd: + + set_download_dir(Path(tmpd)) + default_resultset_download_dir.path = Path(tmpd) / "tests" / "resultsets" + default_resultset_download_dir.path.mkdir(parents=True, exist_ok=True) + + datasets_download_dir = get_download_dir() + resultsets_download_dir = default_resultset_download_dir.path + assert "tests" in os.listdir(datasets_download_dir) + assert "resultsets.tar.gz" not in os.listdir(datasets_download_dir / "tests") + assert "traversal_mappings.csv" not in os.listdir(resultsets_download_dir) + + load_resultset( + "traversal", "https://data.rapids.ai/cugraph/results/resultsets.tar.gz" + ) + + assert "resultsets.tar.gz" in os.listdir(datasets_download_dir / "tests") + assert "traversal_mappings.csv" in os.listdir(resultsets_download_dir) + + +def test_verify_resultset_load(): + # This test is more detailed than test_load_resultset, where for each module, + # we check that every single resultset file is included along with the + # corresponding mapping file. + with TemporaryDirectory() as tmpd: + set_download_dir(Path(tmpd)) + default_resultset_download_dir.path = Path(tmpd) / "tests" / "resultsets" + default_resultset_download_dir.path.mkdir(parents=True, exist_ok=True) + + resultsets_download_dir = default_resultset_download_dir.path + + load_resultset( + "traversal", "https://data.rapids.ai/cugraph/results/resultsets.tar.gz" + ) + + resultsets = os.listdir(resultsets_download_dir) + downloaded_results = cudf.read_csv( + resultsets_download_dir / "traversal_mappings.csv", sep=" " + ) + downloaded_uuids = downloaded_results["#UUID"].values + for resultset_uuid in downloaded_uuids: + assert str(resultset_uuid) + ".csv" in resultsets From 0f28b2ee45130486ca891b757574780ac58dd720 Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 20 Nov 2023 13:04:06 -0500 Subject: [PATCH 088/111] [BUG] Fix Graph Construction From Pandas in cuGraph-PyG (#3985) The current graph construction creates a single pandas dataframe, which for larger datasets (i.e. ogbn-papers100M) cannot be serialized. This PR resolves this by breaking up the dataframe into scattered numpy arrays that are then reassembled. Merge after #3978 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Naim (https://github.com/naimnv) Approvers: - Vibhu Jawa (https://github.com/VibhuJawa) - Brad Rees (https://github.com/BradReesWork) - Tingyu Wang (https://github.com/tingyu66) URL: https://github.com/rapidsai/cugraph/pull/3985 --- .../cugraph_pyg/data/cugraph_store.py | 75 +++++++++++++------ .../tests/mg/test_mg_cugraph_loader.py | 1 - .../tests/mg/test_mg_cugraph_store.py | 26 +++++++ 3 files changed, 80 insertions(+), 22 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py index edeeface4c4..14dc5d84f90 100644 --- a/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/data/cugraph_store.py @@ -27,11 +27,12 @@ import cugraph import warnings -from cugraph.utilities.utils import import_optional, MissingModule +import dask.array as dar +import dask.dataframe as dd +import dask.distributed as distributed +import dask_cudf -dd = import_optional("dask.dataframe") -distributed = import_optional("dask.distributed") -dask_cudf = import_optional("dask_cudf") +from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") torch_geometric = import_optional("torch_geometric") @@ -367,6 +368,13 @@ def __infer_offsets( } ) + def __dask_array_from_numpy(self, array: np.ndarray, npartitions: int): + return dar.from_array( + array, + meta=np.array([], dtype=array.dtype), + chunks=max(1, len(array) // npartitions), + ) + def __construct_graph( self, edge_info: Dict[Tuple[str, str, str], List[TensorType]], @@ -464,22 +472,32 @@ def __construct_graph( ] ) - df = pandas.DataFrame( - { - "src": pandas.Series(na_dst) - if order == "CSC" - else pandas.Series(na_src), - "dst": pandas.Series(na_src) - if order == "CSC" - else pandas.Series(na_dst), - "etp": pandas.Series(na_etp), - } - ) - vertex_dtype = df.src.dtype + vertex_dtype = na_src.dtype if multi_gpu: - nworkers = len(distributed.get_client().scheduler_info()["workers"]) - df = dd.from_pandas(df, npartitions=nworkers if len(df) > 32 else 1) + client = distributed.get_client() + nworkers = len(client.scheduler_info()["workers"]) + npartitions = nworkers * 4 + + src_dar = self.__dask_array_from_numpy(na_src, npartitions) + del na_src + + dst_dar = self.__dask_array_from_numpy(na_dst, npartitions) + del na_dst + + etp_dar = self.__dask_array_from_numpy(na_etp, npartitions) + del na_etp + + df = dd.from_dask_array(etp_dar, columns=["etp"]) + df["src"] = dst_dar if order == "CSC" else src_dar + df["dst"] = src_dar if order == "CSC" else dst_dar + + del src_dar + del dst_dar + del etp_dar + + if df.etp.dtype != "int32": + raise ValueError("Edge type must be int32!") # Ensure the dataframe is constructed on each partition # instead of adding additional synchronization head from potential @@ -487,9 +505,9 @@ def __construct_graph( def get_empty_df(): return cudf.DataFrame( { + "etp": cudf.Series([], dtype="int32"), "src": cudf.Series([], dtype=vertex_dtype), "dst": cudf.Series([], dtype=vertex_dtype), - "etp": cudf.Series([], dtype="int32"), } ) @@ -500,9 +518,23 @@ def get_empty_df(): if len(f) > 0 else get_empty_df(), meta=get_empty_df(), - ).reset_index(drop=True) + ).reset_index( + drop=True + ) # should be ok for dask else: - df = cudf.from_pandas(df).reset_index(drop=True) + df = pandas.DataFrame( + { + "src": pandas.Series(na_dst) + if order == "CSC" + else pandas.Series(na_src), + "dst": pandas.Series(na_src) + if order == "CSC" + else pandas.Series(na_dst), + "etp": pandas.Series(na_etp), + } + ) + df = cudf.from_pandas(df) + df.reset_index(drop=True, inplace=True) graph = cugraph.MultiGraph(directed=True) if multi_gpu: @@ -521,6 +553,7 @@ def get_empty_df(): edge_type="etp", ) + del df return graph @property diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py index 55aebf305da..f5035a38621 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_loader.py @@ -15,7 +15,6 @@ from cugraph_pyg.loader import CuGraphNeighborLoader from cugraph_pyg.data import CuGraphStore - from cugraph.utilities.utils import import_optional, MissingModule torch = import_optional("torch") diff --git a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py index 13c9c90c7c2..be8f8245807 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/mg/test_mg_cugraph_store.py @@ -386,3 +386,29 @@ def test_mg_frame_handle(graph, dask_client): F, G, N = graph cugraph_store = CuGraphStore(F, G, N, multi_gpu=True) assert isinstance(cugraph_store._EXPERIMENTAL__CuGraphStore__graph._plc_graph, dict) + + +@pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +def test_cugraph_loader_large_index(dask_client): + large_index = ( + np.random.randint(0, 1_000_000, (100_000_000,)), + np.random.randint(0, 1_000_000, (100_000_000,)), + ) + + large_features = np.random.randint(0, 50, (1_000_000,)) + F = cugraph.gnn.FeatureStore(backend="torch") + F.add_data(large_features, "N", "f") + + store = CuGraphStore( + F, + {("N", "e", "N"): large_index}, + {"N": 1_000_000}, + multi_gpu=True, + ) + + graph = store._subgraph() + assert isinstance(graph, cugraph.Graph) + + el = graph.view_edge_list().compute() + assert (el["src"].values_host - large_index[0]).sum() == 0 + assert (el["dst"].values_host - large_index[1]).sum() == 0 From d34e3d6522f1f3d8e9fbea6581b7ce37de7e1005 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Mon, 20 Nov 2023 12:33:35 -0800 Subject: [PATCH 089/111] Address FIXMEs (#3988) This PR works on addressing FIXMEs (and reduce the number of outstanding FIXMEs). Authors: - Seunghwa Kang (https://github.com/seunghwak) - Naim (https://github.com/naimnv) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Naim (https://github.com/naimnv) - Joseph Nke (https://github.com/jnke2016) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/3988 --- cpp/include/cugraph/algorithms.hpp | 45 --------- cpp/include/cugraph/utilities/device_comm.hpp | 8 +- .../cugraph/utilities/host_scalar_comm.hpp | 98 ++++++++++++++----- .../cugraph/utilities/shuffle_comm.cuh | 5 - cpp/src/centrality/katz_centrality_impl.cuh | 2 - .../weakly_connected_components_impl.cuh | 40 ++------ 6 files changed, 83 insertions(+), 115 deletions(-) diff --git a/cpp/include/cugraph/algorithms.hpp b/cpp/include/cugraph/algorithms.hpp index 78846bc5766..8501eedce5c 100644 --- a/cpp/include/cugraph/algorithms.hpp +++ b/cpp/include/cugraph/algorithms.hpp @@ -464,51 +464,6 @@ k_truss_subgraph(raft::handle_t const& handle, size_t number_of_vertices, int k); -// FIXME: Internally distances is of int (signed 32-bit) data type, but current -// template uses data from VT, ET, WT from the legacy::GraphCSR View even if weights -// are not considered -/** - * @Synopsis Performs a breadth first search traversal of a graph starting from a vertex. - * - * @throws cugraph::logic_error with a custom message when an error occurs. - * - * @tparam VT Type of vertex identifiers. Supported value : int (signed, - * 32-bit) - * @tparam ET Type of edge identifiers. Supported value : int (signed, - * 32-bit) - * @tparam WT Type of edge weights. Supported values : int (signed, 32-bit) - * - * @param[in] handle Library handle (RAFT). If a communicator is set in the handle, - the multi GPU version will be selected. - * @param[in] graph cuGraph graph descriptor, should contain the connectivity - * information as a CSR - * - * @param[out] distances If set to a valid pointer, this is populated by distance of - * every vertex in the graph from the starting vertex - * - * @param[out] predecessors If set to a valid pointer, this is populated by bfs traversal - * predecessor of every vertex - * - * @param[out] sp_counters If set to a valid pointer, this is populated by bfs traversal - * shortest_path counter of every vertex - * - * @param[in] start_vertex The starting vertex for breadth first search traversal - * - * @param[in] directed Treat the input graph as directed - * - * @param[in] mg_batch If set to true use SG BFS path when comms are initialized. - * - */ -template -void bfs(raft::handle_t const& handle, - legacy::GraphCSRView const& graph, - VT* distances, - VT* predecessors, - double* sp_counters, - const VT start_vertex, - bool directed = true, - bool mg_batch = false); - /** * @brief Compute Hungarian algorithm on a weighted bipartite graph * diff --git a/cpp/include/cugraph/utilities/device_comm.hpp b/cpp/include/cugraph/utilities/device_comm.hpp index 7087724921a..990074e781b 100644 --- a/cpp/include/cugraph/utilities/device_comm.hpp +++ b/cpp/include/cugraph/utilities/device_comm.hpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2020-2022, NVIDIA CORPORATION. + * Copyright (c) 2020-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -806,9 +806,6 @@ device_sendrecv(raft::comms::comms_t const& comm, size_t constexpr tuple_size = thrust::tuple_size::value_type>::value; - // FIXME: NCCL 2.7 supports only one ncclSend and one ncclRecv for a source rank and destination - // rank inside ncclGroupStart/ncclGroupEnd, so we cannot place this inside - // ncclGroupStart/ncclGroupEnd, this restriction will be lifted in NCCL 2.8 detail::device_sendrecv_tuple_iterator_element_impl::value_type>::value; - // FIXME: NCCL 2.7 supports only one ncclSend and one ncclRecv for a source rank and destination - // rank inside ncclGroupStart/ncclGroupEnd, so we cannot place this inside - // ncclGroupStart/ncclGroupEnd, this restriction will be lifted in NCCL 2.8 detail::device_multicast_sendrecv_tuple_iterator_element_impl std::enable_if_t::value, std::vector> host_scalar_allgather( raft::comms::comms_t const& comm, T input, cudaStream_t stream) { - std::vector rx_counts(comm.get_size(), size_t{1}); - std::vector displacements(rx_counts.size(), size_t{0}); - std::iota(displacements.begin(), displacements.end(), size_t{0}); - rmm::device_uvector d_outputs(rx_counts.size(), stream); + rmm::device_uvector d_outputs(comm.get_size(), stream); raft::update_device(d_outputs.data() + comm.get_rank(), &input, 1, stream); - // FIXME: better use allgather - comm.allgatherv(d_outputs.data() + comm.get_rank(), - d_outputs.data(), - rx_counts.data(), - displacements.data(), - stream); - std::vector h_outputs(rx_counts.size()); - raft::update_host(h_outputs.data(), d_outputs.data(), rx_counts.size(), stream); + comm.allgather(d_outputs.data() + comm.get_rank(), d_outputs.data(), size_t{1}, stream); + std::vector h_outputs(d_outputs.size()); + raft::update_host(h_outputs.data(), d_outputs.data(), d_outputs.size(), stream); auto status = comm.sync_stream(stream); CUGRAPH_EXPECTS(status == raft::comms::status_t::SUCCESS, "sync_stream() failure."); return h_outputs; @@ -277,11 +269,6 @@ std::enable_if_t::value, std::vector::value; - std::vector rx_counts(comm.get_size(), tuple_size); - std::vector displacements(rx_counts.size(), size_t{0}); - for (size_t i = 0; i < displacements.size(); ++i) { - displacements[i] = i * tuple_size; - } std::vector h_tuple_scalar_elements(tuple_size); rmm::device_uvector d_allgathered_tuple_scalar_elements(comm.get_size() * tuple_size, stream); @@ -292,12 +279,10 @@ host_scalar_allgather(raft::comms::comms_t const& comm, T input, cudaStream_t st h_tuple_scalar_elements.data(), tuple_size, stream); - // FIXME: better use allgather - comm.allgatherv(d_allgathered_tuple_scalar_elements.data() + comm.get_rank() * tuple_size, - d_allgathered_tuple_scalar_elements.data(), - rx_counts.data(), - displacements.data(), - stream); + comm.allgather(d_allgathered_tuple_scalar_elements.data() + comm.get_rank() * tuple_size, + d_allgathered_tuple_scalar_elements.data(), + tuple_size, + stream); std::vector h_allgathered_tuple_scalar_elements(comm.get_size() * tuple_size); raft::update_host(h_allgathered_tuple_scalar_elements.data(), d_allgathered_tuple_scalar_elements.data(), @@ -318,6 +303,71 @@ host_scalar_allgather(raft::comms::comms_t const& comm, T input, cudaStream_t st return ret; } +template +std::enable_if_t::value, T> host_scalar_scatter( + raft::comms::comms_t const& comm, + std::vector const& inputs, // relevant only in root + int root, + cudaStream_t stream) +{ + CUGRAPH_EXPECTS( + ((comm.get_rank() == root) && (inputs.size() == static_cast(comm.get_size()))) || + ((comm.get_rank() != root) && (inputs.size() == 0)), + "inputs.size() should match with comm.get_size() in root and should be 0 otherwise."); + rmm::device_uvector d_outputs(comm.get_size(), stream); + if (comm.get_rank() == root) { + raft::update_device(d_outputs.data(), inputs.data(), inputs.size(), stream); + } + comm.bcast(d_outputs.data(), d_outputs.size(), root, stream); + T h_output{}; + raft::update_host(&h_output, d_outputs.data() + comm.get_rank(), 1, stream); + auto status = comm.sync_stream(stream); + CUGRAPH_EXPECTS(status == raft::comms::status_t::SUCCESS, "sync_stream() failure."); + return h_output; +} + +template +std::enable_if_t::value, T> host_scalar_scatter( + raft::comms::comms_t const& comm, + std::vector const& inputs, // relevant only in root + int root, + cudaStream_t stream) +{ + CUGRAPH_EXPECTS( + ((comm.get_rank() == root) && (inputs.size() == static_cast(comm.get_size()))) || + ((comm.get_rank() != root) && (inputs.size() == 0)), + "inputs.size() should match with comm.get_size() in root and should be 0 otherwise."); + size_t constexpr tuple_size = thrust::tuple_size::value; + rmm::device_uvector d_scatter_tuple_scalar_elements(comm.get_size() * tuple_size, + stream); + if (comm.get_rank() == root) { + for (int i = 0; i < comm.get_size(); ++i) { + std::vector h_tuple_scalar_elements(tuple_size); + detail::update_vector_of_tuple_scalar_elements_from_tuple_impl() + .update(h_tuple_scalar_elements, inputs[i]); + raft::update_device(d_scatter_tuple_scalar_elements.data() + i * tuple_size, + h_tuple_scalar_elements.data(), + tuple_size, + stream); + } + } + comm.bcast( + d_scatter_tuple_scalar_elements.data(), d_scatter_tuple_scalar_elements.size(), root, stream); + std::vector h_tuple_scalar_elements(tuple_size); + raft::update_host(h_tuple_scalar_elements.data(), + d_scatter_tuple_scalar_elements.data() + comm.get_rank() * tuple_size, + tuple_size, + stream); + auto status = comm.sync_stream(stream); + CUGRAPH_EXPECTS(status == raft::comms::status_t::SUCCESS, "sync_stream() failure."); + + T ret{}; + detail::update_tuple_from_vector_of_tuple_scalar_elements_impl().update( + ret, h_tuple_scalar_elements); + + return ret; +} + // Return value is valid only in root (return value may better be std::optional in C++17 or later) template std::enable_if_t::value, std::vector> host_scalar_gather( diff --git a/cpp/include/cugraph/utilities/shuffle_comm.cuh b/cpp/include/cugraph/utilities/shuffle_comm.cuh index 6a260144324..ab6a54cc1c0 100644 --- a/cpp/include/cugraph/utilities/shuffle_comm.cuh +++ b/cpp/include/cugraph/utilities/shuffle_comm.cuh @@ -80,7 +80,6 @@ compute_tx_rx_counts_offsets_ranks(raft::comms::comms_t const& comm, rmm::device_uvector d_rx_value_counts(comm_size, stream_view); - // FIXME: this needs to be replaced with AlltoAll once NCCL 2.8 is released. std::vector tx_counts(comm_size, size_t{1}); std::vector tx_offsets(comm_size); std::iota(tx_offsets.begin(), tx_offsets.end(), size_t{0}); @@ -835,7 +834,6 @@ auto shuffle_values(raft::comms::comms_t const& comm, allocate_dataframe_buffer::value_type>( rx_offsets.size() > 0 ? rx_offsets.back() + rx_counts.back() : size_t{0}, stream_view); - // FIXME: this needs to be replaced with AlltoAll once NCCL 2.8 is released // (if num_tx_dst_ranks == num_rx_src_ranks == comm_size). device_multicast_sendrecv(comm, tx_value_first, @@ -889,7 +887,6 @@ auto groupby_gpu_id_and_shuffle_values(raft::comms::comms_t const& comm, allocate_dataframe_buffer::value_type>( rx_offsets.size() > 0 ? rx_offsets.back() + rx_counts.back() : size_t{0}, stream_view); - // FIXME: this needs to be replaced with AlltoAll once NCCL 2.8 is released // (if num_tx_dst_ranks == num_rx_src_ranks == comm_size). device_multicast_sendrecv(comm, tx_value_first, @@ -946,7 +943,6 @@ auto groupby_gpu_id_and_shuffle_kv_pairs(raft::comms::comms_t const& comm, allocate_dataframe_buffer::value_type>( rx_keys.size(), stream_view); - // FIXME: this needs to be replaced with AlltoAll once NCCL 2.8 is released // (if num_tx_dst_ranks == num_rx_src_ranks == comm_size). device_multicast_sendrecv(comm, tx_key_first, @@ -959,7 +955,6 @@ auto groupby_gpu_id_and_shuffle_kv_pairs(raft::comms::comms_t const& comm, rx_src_ranks, stream_view); - // FIXME: this needs to be replaced with AlltoAll once NCCL 2.8 is released // (if num_tx_dst_ranks == num_rx_src_ranks == comm_size). device_multicast_sendrecv(comm, tx_value_first, diff --git a/cpp/src/centrality/katz_centrality_impl.cuh b/cpp/src/centrality/katz_centrality_impl.cuh index 202d00a5771..ac31043d862 100644 --- a/cpp/src/centrality/katz_centrality_impl.cuh +++ b/cpp/src/centrality/katz_centrality_impl.cuh @@ -74,8 +74,6 @@ void katz_centrality( CUGRAPH_EXPECTS(epsilon >= 0.0, "Invalid input argument: epsilon should be non-negative."); if (do_expensive_check) { - // FIXME: should I check for betas? - if (has_initial_guess) { auto num_negative_values = count_if_v(handle, pull_graph_view, katz_centralities, [] __device__(auto, auto val) { diff --git a/cpp/src/components/weakly_connected_components_impl.cuh b/cpp/src/components/weakly_connected_components_impl.cuh index 615a50ded54..b7b6e139cfa 100644 --- a/cpp/src/components/weakly_connected_components_impl.cuh +++ b/cpp/src/components/weakly_connected_components_impl.cuh @@ -236,18 +236,16 @@ struct v_op_t { auto tag = thrust::get<1>(tagged_v); auto v_offset = vertex_partition.local_vertex_partition_offset_from_vertex_nocheck(thrust::get<0>(tagged_v)); - // FIXME: better switch to atomic_ref after - // https://github.com/nvidia/libcudacxx/milestone/2 - auto old = - atomicCAS(level_components + v_offset, invalid_component_id::value, tag); - if (old != invalid_component_id::value && old != tag) { // conflict + cuda::atomic_ref v_component(*(level_components + v_offset)); + auto old = invalid_component_id::value; + bool success = v_component.compare_exchange_strong(old, tag, cuda::std::memory_order_relaxed); + if (!success && (old != tag)) { // conflict return thrust::make_tuple(thrust::optional{bucket_idx_conflict}, thrust::optional{std::byte{0}} /* dummy */); } else { - auto update = (old == invalid_component_id::value); return thrust::make_tuple( - update ? thrust::optional{bucket_idx_next} : thrust::nullopt, - update ? thrust::optional{std::byte{0}} /* dummy */ : thrust::nullopt); + success ? thrust::optional{bucket_idx_next} : thrust::nullopt, + success ? thrust::optional{std::byte{0}} /* dummy */ : thrust::nullopt); } } @@ -457,33 +455,11 @@ void weakly_connected_components_impl(raft::handle_t const& handle, std::numeric_limits::max()); } - // FIXME: we need to add host_scalar_scatter -#if 1 - rmm::device_uvector d_counts(comm_size, handle.get_stream()); - raft::update_device(d_counts.data(), - init_max_new_root_counts.data(), - init_max_new_root_counts.size(), - handle.get_stream()); - device_bcast( - comm, d_counts.data(), d_counts.data(), d_counts.size(), int{0}, handle.get_stream()); - raft::update_host( - &init_max_new_roots, d_counts.data() + comm_rank, size_t{1}, handle.get_stream()); -#else init_max_new_roots = - host_scalar_scatter(comm, init_max_new_root_counts.data(), int{0}, handle.get_stream()); -#endif + host_scalar_scatter(comm, init_max_new_root_counts, int{0}, handle.get_stream()); } else { - // FIXME: we need to add host_scalar_scatter -#if 1 - rmm::device_uvector d_counts(comm_size, handle.get_stream()); - device_bcast( - comm, d_counts.data(), d_counts.data(), d_counts.size(), int{0}, handle.get_stream()); - raft::update_host( - &init_max_new_roots, d_counts.data() + comm_rank, size_t{1}, handle.get_stream()); -#else init_max_new_roots = - host_scalar_scatter(comm, init_max_new_root_counts.data(), int{0}, handle.get_stream()); -#endif + host_scalar_scatter(comm, std::vector{}, int{0}, handle.get_stream()); } handle.sync_stream(); From 8549b546ef1a97b4c25a0f25b73700802d563d17 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Mon, 20 Nov 2023 21:39:53 +0100 Subject: [PATCH 090/111] Fix Leiden refinement phase (#3990) - Normalization factor was missing in the equation to decide if a node and a refined community is strongly connected inside their Louvain community. This PR adds that factor. - Disable random moves in the refinement phase. We plan to expose a flag to enable/disable random moves in a future PR. - Adds new function to flatten Leiden dendrogram as dendrogram flattening process needs additional info to unroll hierarchical leiden clustering Closes #3850 Closes #3749 Authors: - Naim (https://github.com/naimnv) - Alex Barghi (https://github.com/alexbarghi-nv) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Seunghwa Kang (https://github.com/seunghwak) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/3990 --- cpp/src/community/detail/common_methods.cuh | 46 +++- cpp/src/community/detail/refine_impl.cuh | 22 +- cpp/src/community/flatten_dendrogram.hpp | 29 ++- cpp/src/community/leiden_impl.cuh | 200 ++++++++++-------- cpp/tests/c_api/leiden_test.c | 4 +- cpp/tests/c_api/louvain_test.c | 39 +++- cpp/tests/community/louvain_test.cpp | 81 +------ .../cugraph/tests/community/test_leiden.py | 28 +-- 8 files changed, 242 insertions(+), 207 deletions(-) diff --git a/cpp/src/community/detail/common_methods.cuh b/cpp/src/community/detail/common_methods.cuh index b388ba53e81..f67d4d939ad 100644 --- a/cpp/src/community/detail/common_methods.cuh +++ b/cpp/src/community/detail/common_methods.cuh @@ -52,7 +52,7 @@ struct is_bitwise_comparable> : std::true_type {}; namespace cugraph { namespace detail { -// a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct key_aggregated_edge_op_t { weight_t total_edge_weight{}; @@ -80,7 +80,7 @@ struct key_aggregated_edge_op_t { } }; -// a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct reduce_op_t { using type = thrust::tuple; @@ -100,7 +100,28 @@ struct reduce_op_t { } }; -// a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +template +struct count_updown_moves_op_t { + bool up_down{}; + __device__ auto operator()(thrust::tuple> p) const + { + vertex_t old_cluster = thrust::get<0>(p); + auto new_cluster_gain_pair = thrust::get<1>(p); + vertex_t new_cluster = thrust::get<0>(new_cluster_gain_pair); + weight_t delta_modularity = thrust::get<1>(new_cluster_gain_pair); + + auto result_assignment = + (delta_modularity > weight_t{0}) + ? (((new_cluster > old_cluster) != up_down) ? old_cluster : new_cluster) + : old_cluster; + + return (delta_modularity > weight_t{0}) + ? (((new_cluster > old_cluster) != up_down) ? false : true) + : false; + } +}; +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct cluster_update_op_t { bool up_down{}; @@ -115,7 +136,7 @@ struct cluster_update_op_t { } }; -// a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct return_edge_weight_t { __device__ auto operator()( @@ -125,7 +146,7 @@ struct return_edge_weight_t { } }; -// a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used +// FIXME: a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template struct return_one_t { __device__ auto operator()( @@ -394,6 +415,21 @@ rmm::device_uvector update_clustering_by_delta_modularity( detail::reduce_op_t{}, cugraph::get_dataframe_buffer_begin(output_buffer)); + int nr_moves = thrust::count_if( + handle.get_thrust_policy(), + thrust::make_zip_iterator(thrust::make_tuple( + next_clusters_v.begin(), cugraph::get_dataframe_buffer_begin(output_buffer))), + thrust::make_zip_iterator( + thrust::make_tuple(next_clusters_v.end(), cugraph::get_dataframe_buffer_end(output_buffer))), + detail::count_updown_moves_op_t{up_down}); + + if (multi_gpu) { + nr_moves = host_scalar_allreduce( + handle.get_comms(), nr_moves, raft::comms::op_t::SUM, handle.get_stream()); + } + + if (nr_moves == 0) { up_down = !up_down; } + thrust::transform(handle.get_thrust_policy(), next_clusters_v.begin(), next_clusters_v.end(), diff --git a/cpp/src/community/detail/refine_impl.cuh b/cpp/src/community/detail/refine_impl.cuh index 6b6470991bb..ebaae498d04 100644 --- a/cpp/src/community/detail/refine_impl.cuh +++ b/cpp/src/community/detail/refine_impl.cuh @@ -89,8 +89,9 @@ struct leiden_key_aggregated_edge_op_t { // E(Cr, S-Cr) > ||Cr||*(||S|| -||Cr||) bool is_dst_leiden_cluster_well_connected = - dst_leiden_cut_to_louvain > - resolution * dst_leiden_volume * (louvain_cluster_volume - dst_leiden_volume); + dst_leiden_cut_to_louvain > resolution * dst_leiden_volume * + (louvain_cluster_volume - dst_leiden_volume) / + total_edge_weight; // E(v, Cr-v) - ||v||* ||Cr-v||/||V(G)|| // aggregated_weight_to_neighboring_leiden_cluster == E(v, Cr-v)? @@ -98,11 +99,11 @@ struct leiden_key_aggregated_edge_op_t { weight_t mod_gain = -1.0; if (is_src_active > 0) { if ((louvain_of_dst_leiden_cluster == src_louvain_cluster) && - is_dst_leiden_cluster_well_connected) { + (dst_leiden_cluster_id != src_leiden_cluster) && is_dst_leiden_cluster_well_connected) { mod_gain = aggregated_weight_to_neighboring_leiden_cluster - - resolution * src_weighted_deg * (dst_leiden_volume - src_weighted_deg) / - total_edge_weight; - + resolution * src_weighted_deg * dst_leiden_volume / total_edge_weight; +// FIXME: Disable random moves in refinement phase for now. +#if 0 weight_t random_number{0.0}; if (mod_gain > 0.0) { auto flat_id = uint64_t{threadIdx.x + blockIdx.x * blockDim.x}; @@ -117,6 +118,8 @@ struct leiden_key_aggregated_edge_op_t { ? __expf(static_cast((2.0 * mod_gain) / (theta * total_edge_weight))) * random_number : -1.0; +#endif + mod_gain = mod_gain > 0.0 ? mod_gain : -1.0; } } @@ -240,11 +243,12 @@ refine_clustering( wcut_deg_and_cluster_vol_triple_begin, wcut_deg_and_cluster_vol_triple_end, singleton_and_connected_flags.begin(), - [resolution] __device__(auto wcut_wdeg_and_louvain_volume) { + [resolution, total_edge_weight] __device__(auto wcut_wdeg_and_louvain_volume) { auto wcut = thrust::get<0>(wcut_wdeg_and_louvain_volume); auto wdeg = thrust::get<1>(wcut_wdeg_and_louvain_volume); auto louvain_volume = thrust::get<2>(wcut_wdeg_and_louvain_volume); - return wcut > (resolution * wdeg * (louvain_volume - wdeg)); + return wcut > + (resolution * wdeg * (louvain_volume - wdeg) / total_edge_weight); }); edge_src_property_t src_louvain_cluster_weight_cache(handle); @@ -478,7 +482,7 @@ refine_clustering( auto values_for_leiden_cluster_keys = thrust::make_zip_iterator( thrust::make_tuple(refined_community_volumes.begin(), refined_community_cuts.begin(), - leiden_keys_used_in_edge_reduction.begin(), // redundant + leiden_keys_used_in_edge_reduction.begin(), louvain_of_leiden_keys_used_in_edge_reduction.begin())); using value_t = thrust::tuple; diff --git a/cpp/src/community/flatten_dendrogram.hpp b/cpp/src/community/flatten_dendrogram.hpp index 9a0c103c01f..eac20389765 100644 --- a/cpp/src/community/flatten_dendrogram.hpp +++ b/cpp/src/community/flatten_dendrogram.hpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2022, NVIDIA CORPORATION. + * Copyright (c) 2021-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -59,4 +59,31 @@ void partition_at_level(raft::handle_t const& handle, }); } +template +void leiden_partition_at_level(raft::handle_t const& handle, + Dendrogram const& dendrogram, + vertex_t* d_partition, + size_t level) +{ + vertex_t local_num_verts = dendrogram.get_level_size_nocheck(0); + raft::copy( + d_partition, dendrogram.get_level_ptr_nocheck(0), local_num_verts, handle.get_stream()); + + rmm::device_uvector local_vertex_ids_v(local_num_verts, handle.get_stream()); + + std::for_each( + thrust::make_counting_iterator(0), + thrust::make_counting_iterator((level - 1) / 2), + [&handle, &dendrogram, &local_vertex_ids_v, &d_partition, local_num_verts](size_t l) { + cugraph::relabel( + handle, + std::tuple(dendrogram.get_level_ptr_nocheck(2 * l + 1), + dendrogram.get_level_ptr_nocheck(2 * l + 2)), + dendrogram.get_level_size_nocheck(2 * l + 1), + d_partition, + local_num_verts, + false); + }); +} + } // namespace cugraph diff --git a/cpp/src/community/leiden_impl.cuh b/cpp/src/community/leiden_impl.cuh index a9faf2f2d82..b6e20272de9 100644 --- a/cpp/src/community/leiden_impl.cuh +++ b/cpp/src/community/leiden_impl.cuh @@ -43,6 +43,34 @@ void check_clustering(graph_view_t const& gr if (graph_view.local_vertex_partition_range_size() > 0) CUGRAPH_EXPECTS(clustering != nullptr, "Invalid input argument: clustering is null"); } +template +vertex_t remove_duplicates(raft::handle_t const& handle, rmm::device_uvector& input_array) +{ + thrust::sort(handle.get_thrust_policy(), input_array.begin(), input_array.end()); + + auto nr_unique_elements = static_cast(thrust::distance( + input_array.begin(), + thrust::unique(handle.get_thrust_policy(), input_array.begin(), input_array.end()))); + + input_array.resize(nr_unique_elements, handle.get_stream()); + + if constexpr (multi_gpu) { + input_array = cugraph::detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning( + handle, std::move(input_array)); + + thrust::sort(handle.get_thrust_policy(), input_array.begin(), input_array.end()); + + nr_unique_elements = static_cast(thrust::distance( + input_array.begin(), + thrust::unique(handle.get_thrust_policy(), input_array.begin(), input_array.end()))); + + input_array.resize(nr_unique_elements, handle.get_stream()); + + nr_unique_elements = host_scalar_allreduce( + handle.get_comms(), nr_unique_elements, raft::comms::op_t::SUM, handle.get_stream()); + } + return nr_unique_elements; +} template >, weight_t> leiden( rmm::device_uvector louvain_of_refined_graph(0, handle.get_stream()); // #V - while (dendrogram->num_levels() < max_level) { + while (dendrogram->num_levels() < 2 * max_level + 1) { // // Initialize every cluster to reference each vertex to itself // @@ -353,40 +381,8 @@ std::pair>, weight_t> leiden( dendrogram->current_level_begin(), dendrogram->current_level_begin() + dendrogram->current_level_size(), copied_louvain_partition.begin()); - - thrust::sort( - handle.get_thrust_policy(), copied_louvain_partition.begin(), copied_louvain_partition.end()); - auto nr_unique_louvain_clusters = - static_cast(thrust::distance(copied_louvain_partition.begin(), - thrust::unique(handle.get_thrust_policy(), - copied_louvain_partition.begin(), - copied_louvain_partition.end()))); - - copied_louvain_partition.resize(nr_unique_louvain_clusters, handle.get_stream()); - - if constexpr (graph_view_t::is_multi_gpu) { - copied_louvain_partition = - cugraph::detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning( - handle, std::move(copied_louvain_partition)); - - thrust::sort(handle.get_thrust_policy(), - copied_louvain_partition.begin(), - copied_louvain_partition.end()); - - nr_unique_louvain_clusters = - static_cast(thrust::distance(copied_louvain_partition.begin(), - thrust::unique(handle.get_thrust_policy(), - copied_louvain_partition.begin(), - copied_louvain_partition.end()))); - - copied_louvain_partition.resize(nr_unique_louvain_clusters, handle.get_stream()); - - nr_unique_louvain_clusters = host_scalar_allreduce(handle.get_comms(), - nr_unique_louvain_clusters, - raft::comms::op_t::SUM, - handle.get_stream()); - } + remove_duplicates(handle, copied_louvain_partition); terminate = terminate || (nr_unique_louvain_clusters == current_graph_view.number_of_vertices()); @@ -481,6 +477,15 @@ std::pair>, weight_t> leiden( (*cluster_assignment).data(), (*cluster_assignment).size(), false); + // louvain assignment of aggregated graph which is necessary to flatten dendrogram + dendrogram->add_level(current_graph_view.local_vertex_partition_range_first(), + current_graph_view.local_vertex_partition_range_size(), + handle.get_stream()); + + raft::copy(dendrogram->current_level_begin(), + (*cluster_assignment).begin(), + (*cluster_assignment).size(), + handle.get_stream()); louvain_of_refined_graph.resize(current_graph_view.local_vertex_partition_range_size(), handle.get_stream()); @@ -492,47 +497,6 @@ std::pair>, weight_t> leiden( } } - // Relabel dendrogram - vertex_t local_cluster_id_first{0}; - if constexpr (multi_gpu) { - auto unique_cluster_range_lasts = cugraph::partition_manager::compute_partition_range_lasts( - handle, static_cast(copied_louvain_partition.size())); - - auto& comm = handle.get_comms(); - auto const comm_size = comm.get_size(); - auto const comm_rank = comm.get_rank(); - auto& major_comm = handle.get_subcomm(cugraph::partition_manager::major_comm_name()); - auto const major_comm_size = major_comm.get_size(); - auto const major_comm_rank = major_comm.get_rank(); - auto& minor_comm = handle.get_subcomm(cugraph::partition_manager::minor_comm_name()); - auto const minor_comm_size = minor_comm.get_size(); - auto const minor_comm_rank = minor_comm.get_rank(); - - auto vertex_partition_id = - partition_manager::compute_vertex_partition_id_from_graph_subcomm_ranks( - major_comm_size, minor_comm_size, major_comm_rank, minor_comm_rank); - - local_cluster_id_first = vertex_partition_id == 0 - ? vertex_t{0} - : unique_cluster_range_lasts[vertex_partition_id - 1]; - } - - rmm::device_uvector numbering_indices(copied_louvain_partition.size(), - handle.get_stream()); - detail::sequence_fill(handle.get_stream(), - numbering_indices.data(), - numbering_indices.size(), - local_cluster_id_first); - - relabel( - handle, - std::make_tuple(static_cast(copied_louvain_partition.begin()), - static_cast(numbering_indices.begin())), - copied_louvain_partition.size(), - dendrogram->current_level_begin(), - dendrogram->current_level_size(), - false); - copied_louvain_partition.resize(0, handle.get_stream()); copied_louvain_partition.shrink_to_fit(handle.get_stream()); @@ -550,23 +514,71 @@ std::pair>, weight_t> leiden( return std::make_pair(std::move(dendrogram), best_modularity); } -// FIXME: Can we have a common flatten_dendrogram to be used by both -// Louvain and Leiden, and possibly other clustering methods? +template +void relabel_cluster_ids(raft::handle_t const& handle, + rmm::device_uvector& unique_cluster_ids, + vertex_t* clustering, + size_t num_nodes) +{ + vertex_t local_cluster_id_first{0}; + if constexpr (multi_gpu) { + auto unique_cluster_range_lasts = cugraph::partition_manager::compute_partition_range_lasts( + handle, static_cast(unique_cluster_ids.size())); + + auto& comm = handle.get_comms(); + auto const comm_size = comm.get_size(); + auto const comm_rank = comm.get_rank(); + auto& major_comm = handle.get_subcomm(cugraph::partition_manager::major_comm_name()); + auto const major_comm_size = major_comm.get_size(); + auto const major_comm_rank = major_comm.get_rank(); + auto& minor_comm = handle.get_subcomm(cugraph::partition_manager::minor_comm_name()); + auto const minor_comm_size = minor_comm.get_size(); + auto const minor_comm_rank = minor_comm.get_rank(); + + auto vertex_partition_id = + partition_manager::compute_vertex_partition_id_from_graph_subcomm_ranks( + major_comm_size, minor_comm_size, major_comm_rank, minor_comm_rank); + + local_cluster_id_first = + vertex_partition_id == 0 ? vertex_t{0} : unique_cluster_range_lasts[vertex_partition_id - 1]; + } + + rmm::device_uvector numbering_indices(unique_cluster_ids.size(), handle.get_stream()); + detail::sequence_fill(handle.get_stream(), + numbering_indices.data(), + numbering_indices.size(), + local_cluster_id_first); + + relabel( + handle, + std::make_tuple(static_cast(unique_cluster_ids.begin()), + static_cast(numbering_indices.begin())), + unique_cluster_ids.size(), + clustering, + num_nodes, + false); +} + template -void flatten_dendrogram(raft::handle_t const& handle, - graph_view_t const& graph_view, - Dendrogram const& dendrogram, - vertex_t* clustering) +void flatten_leiden_dendrogram(raft::handle_t const& handle, + graph_view_t const& graph_view, + Dendrogram const& dendrogram, + vertex_t* clustering) { - rmm::device_uvector vertex_ids_v(graph_view.number_of_vertices(), handle.get_stream()); + leiden_partition_at_level( + handle, dendrogram, clustering, dendrogram.num_levels()); + + rmm::device_uvector unique_cluster_ids(graph_view.number_of_vertices(), + handle.get_stream()); + thrust::copy(handle.get_thrust_policy(), + clustering, + clustering + graph_view.number_of_vertices(), + unique_cluster_ids.begin()); - thrust::sequence(handle.get_thrust_policy(), - vertex_ids_v.begin(), - vertex_ids_v.end(), - graph_view.local_vertex_partition_range_first()); + remove_duplicates(handle, unique_cluster_ids); - partition_at_level( - handle, dendrogram, vertex_ids_v.data(), clustering, dendrogram.num_levels()); + relabel_cluster_ids( + handle, unique_cluster_ids, clustering, graph_view.number_of_vertices()); } } // namespace detail @@ -588,14 +600,14 @@ std::pair>, weight_t> leiden( } template -void flatten_dendrogram(raft::handle_t const& handle, - graph_view_t const& graph_view, - Dendrogram const& dendrogram, - vertex_t* clustering) +void flatten_leiden_dendrogram(raft::handle_t const& handle, + graph_view_t const& graph_view, + Dendrogram const& dendrogram, + vertex_t* clustering) { CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - detail::flatten_dendrogram(handle, graph_view, dendrogram, clustering); + detail::flatten_leiden_dendrogram(handle, graph_view, dendrogram, clustering); } template @@ -620,7 +632,7 @@ std::pair leiden( std::tie(dendrogram, modularity) = detail::leiden(handle, rng_state, graph_view, edge_weight_view, max_level, resolution, theta); - detail::flatten_dendrogram(handle, graph_view, *dendrogram, clustering); + detail::flatten_leiden_dendrogram(handle, graph_view, *dendrogram, clustering); return std::make_pair(dendrogram->num_levels(), modularity); } diff --git a/cpp/tests/c_api/leiden_test.c b/cpp/tests/c_api/leiden_test.c index 9e91adf9f89..df206ebd1ed 100644 --- a/cpp/tests/c_api/leiden_test.c +++ b/cpp/tests/c_api/leiden_test.c @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -161,7 +161,7 @@ int test_leiden_no_weights() vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; vertex_t h_result[] = {1, 1, 1, 2, 0, 0}; - weight_t expected_modularity = 0.0859375; + weight_t expected_modularity = 0.125; // Louvain wants store_transposed = FALSE return generic_leiden_test(h_src, diff --git a/cpp/tests/c_api/louvain_test.c b/cpp/tests/c_api/louvain_test.c index e9ac5c9ff06..41d777545b2 100644 --- a/cpp/tests/c_api/louvain_test.c +++ b/cpp/tests/c_api/louvain_test.c @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -46,22 +46,39 @@ int generic_louvain_test(vertex_t* h_src, cugraph_graph_t* p_graph = NULL; cugraph_hierarchical_clustering_result_t* p_result = NULL; - data_type_id_t vertex_tid = INT32; - data_type_id_t edge_tid = INT32; - data_type_id_t weight_tid = FLOAT32; + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; data_type_id_t edge_id_tid = INT32; data_type_id_t edge_type_tid = INT32; p_handle = cugraph_create_resource_handle(NULL); TEST_ASSERT(test_ret_value, p_handle != NULL, "resource handle creation failed."); - ret_code = create_sg_test_graph(p_handle, vertex_tid, edge_tid, h_src, h_dst, weight_tid, h_wgt, edge_type_tid, NULL, edge_id_tid, NULL, num_edges, store_transposed, FALSE, FALSE, FALSE, &p_graph, &ret_error); + ret_code = create_sg_test_graph(p_handle, + vertex_tid, + edge_tid, + h_src, + h_dst, + weight_tid, + h_wgt, + edge_type_tid, + NULL, + edge_id_tid, + NULL, + num_edges, + store_transposed, + FALSE, + FALSE, + FALSE, + &p_graph, + &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "create_test_graph failed."); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); - ret_code = - cugraph_louvain(p_handle, p_graph, max_level, threshold, resolution, FALSE, &p_result, &ret_error); + ret_code = cugraph_louvain( + p_handle, p_graph, max_level, threshold, resolution, FALSE, &p_result, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, "cugraph_louvain failed."); @@ -141,10 +158,10 @@ int test_louvain_no_weight() weight_t threshold = 1e-7; weight_t resolution = 1.0; - vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; - vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; - vertex_t h_result[] = {1, 1, 1, 2, 0, 0}; - weight_t expected_modularity = 0.0859375; + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_result[] = {1, 1, 1, 1, 0, 0}; + weight_t expected_modularity = 0.125; // Louvain wants store_transposed = FALSE return generic_louvain_test(h_src, diff --git a/cpp/tests/community/louvain_test.cpp b/cpp/tests/community/louvain_test.cpp index 1e1fb6d4c33..284dcc94b8c 100644 --- a/cpp/tests/community/louvain_test.cpp +++ b/cpp/tests/community/louvain_test.cpp @@ -317,72 +317,6 @@ TEST(louvain_legacy, success) } } -TEST(louvain_legacy_renumbered, success) -{ - raft::handle_t handle; - - auto stream = handle.get_stream(); - - std::vector off_h = {0, 16, 25, 30, 34, 38, 42, 44, 46, 48, 50, 52, - 54, 56, 73, 85, 95, 101, 107, 112, 117, 121, 125, 129, - 132, 135, 138, 141, 144, 147, 149, 151, 153, 155, 156}; - std::vector ind_h = { - 1, 3, 7, 11, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 30, 33, 0, 5, 11, 15, 16, 19, 21, - 25, 30, 4, 13, 14, 22, 27, 0, 9, 20, 24, 2, 13, 15, 26, 1, 13, 14, 18, 13, 15, 0, 16, - 13, 14, 3, 20, 13, 14, 0, 1, 13, 22, 2, 4, 5, 6, 8, 10, 12, 14, 17, 18, 19, 22, 25, - 28, 29, 31, 32, 2, 5, 8, 10, 13, 15, 17, 18, 22, 29, 31, 32, 0, 1, 4, 6, 14, 16, 18, - 19, 21, 28, 0, 1, 7, 15, 19, 21, 0, 13, 14, 26, 27, 28, 0, 5, 13, 14, 15, 0, 1, 13, - 16, 16, 0, 3, 9, 23, 0, 1, 15, 16, 2, 12, 13, 14, 0, 20, 24, 0, 3, 23, 0, 1, 13, - 4, 17, 27, 2, 17, 26, 13, 15, 17, 13, 14, 0, 1, 13, 14, 13, 14, 0}; - - std::vector w_h = { - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, - 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; - - int num_verts = off_h.size() - 1; - int num_edges = ind_h.size(); - - rmm::device_uvector offsets_v(num_verts + 1, stream); - rmm::device_uvector indices_v(num_edges, stream); - rmm::device_uvector weights_v(num_edges, stream); - rmm::device_uvector result_v(num_verts, stream); - - raft::update_device(offsets_v.data(), off_h.data(), off_h.size(), stream); - raft::update_device(indices_v.data(), ind_h.data(), ind_h.size(), stream); - raft::update_device(weights_v.data(), w_h.data(), w_h.size(), stream); - - cugraph::legacy::GraphCSRView G( - offsets_v.data(), indices_v.data(), weights_v.data(), num_verts, num_edges); - - float modularity{0.0}; - size_t num_level = 40; - - // "FIXME": remove this check once we drop support for Pascal - // - // Calling louvain on Pascal will throw an exception, we'll check that - // this is the behavior while we still support Pascal (device_prop.major < 7) - // - if (handle.get_device_properties().major < 7) { - EXPECT_THROW(cugraph::louvain(handle, G, result_v.data()), cugraph::logic_error); - } else { - std::tie(num_level, modularity) = cugraph::louvain(handle, G, result_v.data()); - - auto cluster_id = cugraph::test::to_host(handle, result_v); - - int min = *min_element(cluster_id.begin(), cluster_id.end()); - - ASSERT_GE(min, 0); - ASSERT_FLOAT_EQ(modularity, 0.41880345); - } -} - using Tests_Louvain_File = Tests_Louvain; using Tests_Louvain_File32 = Tests_Louvain; using Tests_Louvain_File64 = Tests_Louvain; @@ -390,11 +324,15 @@ using Tests_Louvain_Rmat = Tests_Louvain; using Tests_Louvain_Rmat32 = Tests_Louvain; using Tests_Louvain_Rmat64 = Tests_Louvain; +#if 0 +// FIXME: Reenable legacy tests once threshold parameter is exposed +// by louvain legacy API. TEST_P(Tests_Louvain_File, CheckInt32Int32FloatFloatLegacy) { run_legacy_test( override_File_Usecase_with_cmd_line_arguments(GetParam())); } +#endif TEST_P(Tests_Louvain_File, CheckInt32Int32FloatFloat) { @@ -458,11 +396,12 @@ TEST_P(Tests_Louvain_Rmat64, CheckInt64Int64FloatFloat) INSTANTIATE_TEST_SUITE_P( simple_test, Tests_Louvain_File, - ::testing::Combine( - ::testing::Values(Louvain_Usecase{std::nullopt, std::nullopt, std::nullopt, true, 3, 0.408695}, - Louvain_Usecase{20, double{1e-4}, std::nullopt, true, 3, 0.408695}, - Louvain_Usecase{100, double{1e-4}, double{0.8}, true, 3, 0.48336622}), - ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + ::testing::Combine(::testing::Values( + Louvain_Usecase{ + std::nullopt, std::nullopt, std::nullopt, true, 3, 0.39907956}, + Louvain_Usecase{20, double{1e-3}, std::nullopt, true, 3, 0.39907956}, + Louvain_Usecase{100, double{1e-3}, double{0.8}, true, 3, 0.47547662}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); INSTANTIATE_TEST_SUITE_P( file_benchmark_test, /* note that the test filename can be overridden in benchmarking (with diff --git a/python/cugraph/cugraph/tests/community/test_leiden.py b/python/cugraph/cugraph/tests/community/test_leiden.py index a06b0dd22c5..71117c4210f 100644 --- a/python/cugraph/cugraph/tests/community/test_leiden.py +++ b/python/cugraph/cugraph/tests/community/test_leiden.py @@ -22,8 +22,6 @@ from cugraph.testing import utils, UNDIRECTED_DATASETS from cugraph.datasets import karate_asymmetric -from cudf.testing.testing import assert_series_equal - # ============================================================================= # Test data @@ -43,8 +41,8 @@ "resolution": 1.0, "input_type": "COO", "expected_output": { - "partition": [1, 0, 1, 2, 2, 2], - "modularity_score": 0.1757322, + "partition": [0, 0, 0, 1, 1, 1], + "modularity_score": 0.215969, }, }, "data_2": { @@ -85,10 +83,10 @@ "input_type": "CSR", "expected_output": { # fmt: off - "partition": [6, 6, 3, 3, 1, 5, 5, 3, 0, 3, 1, 6, 3, 3, 4, 4, 5, 6, 4, 6, 4, - 6, 4, 4, 2, 2, 4, 4, 2, 4, 0, 2, 4, 4], + "partition": [3, 3, 3, 3, 2, 2, 2, 3, 1, 3, 2, 3, 3, 3, 1, 1, 2, 3, 1, 3, + 1, 3, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1], # fmt: on - "modularity_score": 0.3468113, + "modularity_score": 0.41880345, }, }, } @@ -138,7 +136,7 @@ def input_and_expected_output(request): # Create graph from csr offsets = src_or_offset_array indices = dst_or_index_array - G.from_cudf_adjlist(offsets, indices, weight) + G.from_cudf_adjlist(offsets, indices, weight, renumber=False) parts, mod = cugraph.leiden(G, max_level, resolution) @@ -223,9 +221,7 @@ def test_leiden_directed_graph(): @pytest.mark.sg def test_leiden_golden_results(input_and_expected_output): - expected_partition = cudf.Series( - input_and_expected_output["expected_output"]["partition"] - ) + expected_partition = input_and_expected_output["expected_output"]["partition"] expected_mod = input_and_expected_output["expected_output"]["modularity_score"] result_partition = input_and_expected_output["result_output"]["partition"] @@ -233,6 +229,10 @@ def test_leiden_golden_results(input_and_expected_output): assert abs(expected_mod - result_mod) < 0.0001 - assert_series_equal( - expected_partition, result_partition, check_dtype=False, check_names=False - ) + expected_to_result_map = {} + for e, r in zip(expected_partition, list(result_partition.to_pandas())): + if e in expected_to_result_map.keys(): + assert r == expected_to_result_map[e] + + else: + expected_to_result_map[e] = r From 6e765bb36e7a8d2d7120a3afd034bb005b48eda9 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Mon, 20 Nov 2023 17:36:50 -0600 Subject: [PATCH 091/111] nx-cugraph: add SSSP (unweighted) (#3976) There are many more traversal algorithms to implement, but these get us started! Authors: - Erik Welch (https://github.com/eriknw) - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3976 --- python/nx-cugraph/_nx_cugraph/__init__.py | 2 + .../nx_cugraph/algorithms/__init__.py | 3 +- .../algorithms/shortest_paths/__init__.py | 13 +++++ .../algorithms/shortest_paths/unweighted.py | 53 +++++++++++++++++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 9 +++- python/nx-cugraph/nx_cugraph/interface.py | 2 + 6 files changed, 79 insertions(+), 3 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 26638d1e735..910db1bc379 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -79,6 +79,8 @@ "path_graph", "petersen_graph", "sedgewick_maze_graph", + "single_source_shortest_path_length", + "single_target_shortest_path_length", "star_graph", "tadpole_graph", "tetrahedral_graph", diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 87b1967fa93..32cd6f31a47 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,9 +10,10 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community, components +from . import bipartite, centrality, community, components, shortest_paths from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * from .isolate import * +from .shortest_paths import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py new file mode 100644 index 00000000000..b7d6b742176 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py @@ -0,0 +1,13 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .unweighted import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py new file mode 100644 index 00000000000..3413a637b32 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py @@ -0,0 +1,53 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import index_dtype, networkx_algorithm + +__all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"] + + +@networkx_algorithm +def single_source_shortest_path_length(G, source, cutoff=None): + return _single_shortest_path_length(G, source, cutoff, "Source") + + +@networkx_algorithm +def single_target_shortest_path_length(G, target, cutoff=None): + return _single_shortest_path_length(G, target, cutoff, "Target") + + +def _single_shortest_path_length(G, source, cutoff, kind): + G = _to_graph(G) + if source not in G: + raise nx.NodeNotFound(f"{kind} {source} is not in G") + if G.src_indices.size == 0: + return {source: 0} + if cutoff is None: + cutoff = -1 + src_index = source if G.key_to_id is None else G.key_to_id[source] + distances, predecessors, node_ids = plc.bfs( + handle=plc.ResourceHandle(), + graph=G._get_plc_graph(switch_indices=kind == "Target"), + sources=cp.array([src_index], index_dtype), + direction_optimizing=False, # True for undirected only; what's recommended? + depth_limit=cutoff, + compute_predecessors=False, + do_expensive_check=False, + ) + mask = distances != np.iinfo(distances.dtype).max + return G._nodearrays_to_dict(node_ids[mask], distances[mask]) diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index 23004651fc5..fea318e036e 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -559,6 +559,7 @@ def _get_plc_graph( edge_dtype: Dtype | None = None, *, store_transposed: bool = False, + switch_indices: bool = False, edge_array: cp.ndarray[EdgeValue] | None = None, ): if edge_array is not None: @@ -613,14 +614,18 @@ def _get_plc_graph( elif edge_array.dtype not in self._plc_allowed_edge_types: raise TypeError(edge_array.dtype) # Should we cache PLC graph? + src_indices = self.src_indices + dst_indices = self.dst_indices + if switch_indices: + src_indices, dst_indices = dst_indices, src_indices return plc.SGGraph( resource_handle=plc.ResourceHandle(), graph_properties=plc.GraphProperties( is_multigraph=self.is_multigraph(), is_symmetric=not self.is_directed(), ), - src_or_offset_array=self.src_indices, - dst_or_index_array=self.dst_indices, + src_or_offset_array=src_indices, + dst_or_index_array=dst_indices, weight_array=edge_array, store_transposed=store_transposed, renumber=False, diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 875f8621021..8903fdc541e 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -224,9 +224,11 @@ def key(testpath): ) too_slow = "Too slow to run" + maybe_oom = "out of memory in CI" skip = { key("test_tree_isomorphism.py:test_positive"): too_slow, key("test_tree_isomorphism.py:test_negative"): too_slow, + key("test_efficiency.py:TestEfficiency.test_using_ego_graph"): maybe_oom, } for item in items: From f3eecdad957a362b5b1aa14b4fd27afc18d322ca Mon Sep 17 00:00:00 2001 From: Alex Barghi <105237337+alexbarghi-nv@users.noreply.github.com> Date: Mon, 20 Nov 2023 18:59:30 -0500 Subject: [PATCH 092/111] Support `drop_last` Argument in cuGraph-PyG Loader (#3995) Supports the `drop_last` argument in cuGraph-PyG for better compatibility with native PyG workflows. Closes #3949 Merge after #3985 Authors: - Alex Barghi (https://github.com/alexbarghi-nv) - Naim (https://github.com/naimnv) Approvers: - Tingyu Wang (https://github.com/tingyu66) - Brad Rees (https://github.com/BradReesWork) - Vibhu Jawa (https://github.com/VibhuJawa) URL: https://github.com/rapidsai/cugraph/pull/3995 --- .../cugraph_pyg/loader/cugraph_node_loader.py | 37 ++++++++++-------- .../cugraph_pyg/tests/test_cugraph_loader.py | 38 +++++++++++++++++++ 2 files changed, 60 insertions(+), 15 deletions(-) diff --git a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py index ad8d22e255e..200a82b460b 100644 --- a/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/loader/cugraph_node_loader.py @@ -52,7 +52,9 @@ def __init__( graph_store: CuGraphStore, input_nodes: InputNodes = None, batch_size: int = 0, + *, shuffle: bool = False, + drop_last: bool = True, edge_types: Sequence[Tuple[str]] = None, directory: Union[str, tempfile.TemporaryDirectory] = None, input_files: List[str] = None, @@ -209,26 +211,31 @@ def __init__( # Truncate if we can't evenly divide the input array stop = (len(input_nodes) // batch_size) * batch_size - input_nodes = input_nodes[:stop] + input_nodes, remainder = cupy.array_split(input_nodes, [stop]) # Split into batches - input_nodes = cupy.split(input_nodes, len(input_nodes) // batch_size) + input_nodes = cupy.split(input_nodes, max(len(input_nodes) // batch_size, 1)) + + if not drop_last: + input_nodes.append(remainder) self.__num_batches = 0 for batch_num, batch_i in enumerate(input_nodes): - self.__num_batches += 1 - bulk_sampler.add_batches( - cudf.DataFrame( - { - "start": batch_i, - "batch": cupy.full( - batch_size, batch_num + starting_batch_id, dtype="int32" - ), - } - ), - start_col_name="start", - batch_col_name="batch", - ) + batch_len = len(batch_i) + if batch_len > 0: + self.__num_batches += 1 + bulk_sampler.add_batches( + cudf.DataFrame( + { + "start": batch_i, + "batch": cupy.full( + batch_len, batch_num + starting_batch_id, dtype="int32" + ), + } + ), + start_col_name="start", + batch_col_name="batch", + ) bulk_sampler.flush() self.__input_files = iter( diff --git a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py index 27b73bf7d35..9813fa933ee 100644 --- a/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py +++ b/python/cugraph-pyg/cugraph_pyg/tests/test_cugraph_loader.py @@ -456,6 +456,44 @@ def test_cugraph_loader_e2e_csc(framework: str): @pytest.mark.skipif(isinstance(torch, MissingModule), reason="torch not available") +@pytest.mark.parametrize("drop_last", [True, False]) +def test_drop_last(drop_last): + N = {"N": 10} + G = { + ("N", "e", "N"): torch.stack( + [torch.tensor([0, 1, 2, 3, 4]), torch.tensor([5, 6, 7, 8, 9])] + ) + } + F = FeatureStore(backend="torch") + F.add_data(torch.arange(10), "N", "z") + + store = CuGraphStore(F, G, N) + with tempfile.TemporaryDirectory() as dir: + loader = CuGraphNeighborLoader( + (store, store), + input_nodes=torch.tensor([0, 1, 2, 3, 4]), + num_neighbors=[1], + batch_size=2, + shuffle=False, + drop_last=drop_last, + batches_per_partition=1, + directory=dir, + ) + + t = torch.tensor([]) + for batch in loader: + t = torch.concat([t, batch["N"].z]) + + t = t.tolist() + + files = os.listdir(dir) + assert len(files) == 2 if drop_last else 3 + assert "batch=0-0.parquet" in files + assert "batch=1-1.parquet" in files + if not drop_last: + assert "batch=2-2.parquet" in files + + @pytest.mark.parametrize("directory", ["local", "temp"]) def test_load_directory( karate_gnn: Tuple[ From 6b3d3e3f06044c86f26d9773b6daf7ab68857321 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Mon, 20 Nov 2023 19:44:48 -0600 Subject: [PATCH 093/111] Enable parallel mode (#3875) This PR enables parallel mode Closes https://github.com/rapidsai/graph_dl/issues/328 Authors: - Joseph Nke (https://github.com/jnke2016) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Naim (https://github.com/naimnv) - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - Jake Awe (https://github.com/AyodeAwe) URL: https://github.com/rapidsai/cugraph/pull/3875 --- cpp/CMakeLists.txt | 2 + .../cugraph/detail/collect_comm_wrapper.hpp | 42 +++ cpp/include/cugraph_c/graph_functions.h | 47 ++- cpp/src/c_api/allgather.cpp | 282 ++++++++++++++ cpp/src/c_api/extract_ego.cpp | 2 + cpp/src/c_api/induced_subgraph.cpp | 3 + cpp/src/c_api/induced_subgraph_result.cpp | 24 ++ cpp/src/c_api/induced_subgraph_result.hpp | 4 +- cpp/src/c_api/legacy_k_truss.cpp | 3 + cpp/src/detail/collect_comm_wrapper.cu | 57 +++ dependencies.yaml | 3 +- python/cugraph/cugraph/structure/__init__.py | 5 + .../cugraph/structure/replicate_edgelist.py | 351 ++++++++++++++++++ .../community/test_induced_subgraph_mg.py | 2 +- .../internals/test_replicate_edgelist_mg.py | 128 +++++++ python/cugraph/pyproject.toml | 1 + .../pylibcugraph/pylibcugraph/CMakeLists.txt | 1 + python/pylibcugraph/pylibcugraph/__init__.py | 2 + .../_cugraph_c/graph_functions.pxd | 26 +- .../pylibcugraph/replicate_edgelist.pyx | 202 ++++++++++ 20 files changed, 1182 insertions(+), 5 deletions(-) create mode 100644 cpp/include/cugraph/detail/collect_comm_wrapper.hpp create mode 100644 cpp/src/c_api/allgather.cpp create mode 100644 cpp/src/detail/collect_comm_wrapper.cu create mode 100644 python/cugraph/cugraph/structure/replicate_edgelist.py create mode 100644 python/cugraph/cugraph/tests/internals/test_replicate_edgelist_mg.py create mode 100644 python/pylibcugraph/pylibcugraph/replicate_edgelist.pyx diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 3e867643041..626d62cffa5 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -192,6 +192,7 @@ set(CUGRAPH_SOURCES src/detail/shuffle_vertex_pairs.cu src/detail/collect_local_vertex_values.cu src/detail/groupby_and_count.cu + src/detail/collect_comm_wrapper.cu src/sampling/random_walks_mg.cu src/community/detail/common_methods_mg.cu src/community/detail/common_methods_sg.cu @@ -443,6 +444,7 @@ add_library(cugraph_c src/c_api/labeling_result.cpp src/c_api/weakly_connected_components.cpp src/c_api/strongly_connected_components.cpp + src/c_api/allgather.cpp src/c_api/legacy_k_truss.cpp ) add_library(cugraph::cugraph_c ALIAS cugraph_c) diff --git a/cpp/include/cugraph/detail/collect_comm_wrapper.hpp b/cpp/include/cugraph/detail/collect_comm_wrapper.hpp new file mode 100644 index 00000000000..b791c593f41 --- /dev/null +++ b/cpp/include/cugraph/detail/collect_comm_wrapper.hpp @@ -0,0 +1,42 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include +#include + +#include + +namespace cugraph { +namespace detail { + +/** + * @brief Gather the span of data from all ranks and broadcast the combined data to all ranks. + * + * @param[in] handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, + * and handles to various CUDA libraries) to run graph algorithms. + * @param[in] comm Raft comms that manages underlying NCCL comms handles across the ranks. + * @param[in] d_input The span of data to perform the 'allgatherv'. + * + * @return A vector containing the combined data of all ranks. + */ +template +rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input); + +} // namespace detail +} // namespace cugraph diff --git a/cpp/include/cugraph_c/graph_functions.h b/cpp/include/cugraph_c/graph_functions.h index 655324df284..19b69922fa5 100644 --- a/cpp/include/cugraph_c/graph_functions.h +++ b/cpp/include/cugraph_c/graph_functions.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -136,6 +136,24 @@ cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_destinatio cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_edge_weights( cugraph_induced_subgraph_result_t* induced_subgraph); +/** + * @brief Get the edge ids + * + * @param [in] induced_subgraph Opaque pointer to induced subgraph + * @return type erased array view of edge ids + */ +cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_edge_ids( + cugraph_induced_subgraph_result_t* induced_subgraph); + +/** + * @brief Get the edge types + * + * @param [in] induced_subgraph Opaque pointer to induced subgraph + * @return type erased array view of edge types + */ +cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_edge_type_ids( + cugraph_induced_subgraph_result_t* induced_subgraph); + /** * @brief Get the subgraph offsets * @@ -184,6 +202,33 @@ cugraph_error_code_t cugraph_extract_induced_subgraph( cugraph_induced_subgraph_result_t** result, cugraph_error_t** error); +// FIXME: Rename the return type +/** + * @brief Gather edgelist + * + * This function collects the edgelist from all ranks and stores the combine edgelist + * in each rank + * + * @param [in] handle Handle for accessing resources. + * @param [in] src Device array containing the source vertex ids. + * @param [in] dst Device array containing the destination vertex ids + * @param [in] weights Optional device array containing the edge weights + * @param [in] edge_ids Optional device array containing the edge ids for each edge. + * @param [in] edge_type_ids Optional device array containing the edge types for each edge + * @param [out] result Opaque pointer to gathered edgelist result + * @param [out] error Pointer to an error object storing details of any error. Will + * be populated if error code is not CUGRAPH_SUCCESS + * @return error code + */ +cugraph_error_code_t cugraph_allgather(const cugraph_resource_handle_t* handle, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + cugraph_induced_subgraph_result_t** result, + cugraph_error_t** error); + #ifdef __cplusplus } #endif diff --git a/cpp/src/c_api/allgather.cpp b/cpp/src/c_api/allgather.cpp new file mode 100644 index 00000000000..7ef401aa6b7 --- /dev/null +++ b/cpp/src/c_api/allgather.cpp @@ -0,0 +1,282 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include + +namespace { + +struct create_allgather_functor : public cugraph::c_api::abstract_functor { + raft::handle_t const& handle_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* src_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids_; + cugraph::c_api::cugraph_induced_subgraph_result_t* result_{}; + + create_allgather_functor( + raft::handle_t const& handle, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* src, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids) + : abstract_functor(), + handle_(handle), + src_(src), + dst_(dst), + weights_(weights), + edge_ids_(edge_ids), + edge_type_ids_(edge_type_ids) + { + } + + template + void operator()() + { + std::optional> edgelist_srcs{std::nullopt}; + if (src_) { + edgelist_srcs = rmm::device_uvector(src_->size_, handle_.get_stream()); + raft::copy( + edgelist_srcs->data(), src_->as_type(), src_->size_, handle_.get_stream()); + } + + std::optional> edgelist_dsts{std::nullopt}; + if (dst_) { + edgelist_dsts = rmm::device_uvector(dst_->size_, handle_.get_stream()); + raft::copy( + edgelist_dsts->data(), dst_->as_type(), dst_->size_, handle_.get_stream()); + } + + std::optional> edgelist_weights{std::nullopt}; + if (weights_) { + edgelist_weights = rmm::device_uvector(weights_->size_, handle_.get_stream()); + raft::copy(edgelist_weights->data(), + weights_->as_type(), + weights_->size_, + handle_.get_stream()); + } + + std::optional> edgelist_ids{std::nullopt}; + if (edge_ids_) { + edgelist_ids = rmm::device_uvector(edge_ids_->size_, handle_.get_stream()); + raft::copy( + edgelist_ids->data(), edge_ids_->as_type(), edge_ids_->size_, handle_.get_stream()); + } + + std::optional> edgelist_type_ids{std::nullopt}; + if (edge_type_ids_) { + edgelist_type_ids = + rmm::device_uvector(edge_type_ids_->size_, handle_.get_stream()); + raft::copy(edgelist_type_ids->data(), + edge_type_ids_->as_type(), + edge_type_ids_->size_, + handle_.get_stream()); + } + + auto& comm = handle_.get_comms(); + + if (edgelist_srcs) { + edgelist_srcs = cugraph::detail::device_allgatherv( + handle_, + comm, + raft::device_span(edgelist_srcs->data(), edgelist_srcs->size())); + } + + if (edgelist_dsts) { + edgelist_dsts = cugraph::detail::device_allgatherv( + handle_, + comm, + raft::device_span(edgelist_dsts->data(), edgelist_dsts->size())); + } + + rmm::device_uvector edge_offsets(2, handle_.get_stream()); + + std::vector h_edge_offsets{ + {0, edgelist_srcs ? edgelist_srcs->size() : edgelist_weights->size()}}; + raft::update_device( + edge_offsets.data(), h_edge_offsets.data(), h_edge_offsets.size(), handle_.get_stream()); + + cugraph::c_api::cugraph_induced_subgraph_result_t* result = NULL; + + if (edgelist_weights) { + edgelist_weights = cugraph::detail::device_allgatherv( + handle_, + comm, + raft::device_span(edgelist_weights->data(), edgelist_weights->size())); + } + + if (edgelist_ids) { + edgelist_ids = cugraph::detail::device_allgatherv( + handle_, comm, raft::device_span(edgelist_ids->data(), edgelist_ids->size())); + } + + if (edgelist_type_ids) { + edgelist_type_ids = + cugraph::detail::device_allgatherv(handle_, + comm, + raft::device_span( + edgelist_type_ids->data(), edgelist_type_ids->size())); + } + + result = new cugraph::c_api::cugraph_induced_subgraph_result_t{ + edgelist_srcs + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*edgelist_srcs, src_->type_) + : NULL, + edgelist_dsts + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*edgelist_dsts, dst_->type_) + : NULL, + edgelist_weights + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*edgelist_weights, weights_->type_) + : NULL, + edgelist_ids + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*edgelist_ids, edge_ids_->type_) + : NULL, + edgelist_type_ids ? new cugraph::c_api::cugraph_type_erased_device_array_t( + *edgelist_type_ids, edge_type_ids_->type_) + : NULL, + new cugraph::c_api::cugraph_type_erased_device_array_t(edge_offsets, + cugraph_data_type_id_t::SIZE_T)}; + + result_ = reinterpret_cast(result); + } +}; + +} // namespace + +extern "C" cugraph_error_code_t cugraph_allgather( + const cugraph_resource_handle_t* handle, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + cugraph_induced_subgraph_result_t** edgelist, + cugraph_error_t** error) +{ + *edgelist = nullptr; + *error = nullptr; + + auto p_handle = reinterpret_cast(handle); + auto p_src = + reinterpret_cast(src); + auto p_dst = + reinterpret_cast(dst); + auto p_weights = + reinterpret_cast(weights); + + auto p_edge_ids = + reinterpret_cast(edge_ids); + + auto p_edge_type_ids = + reinterpret_cast(edge_type_ids); + + CAPI_EXPECTS((dst == nullptr) || (src == nullptr) || p_src->size_ == p_dst->size_, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != dst size.", + *error); + CAPI_EXPECTS((dst == nullptr) || (src == nullptr) || p_src->type_ == p_dst->type_, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src type != dst type.", + *error); + + CAPI_EXPECTS((weights == nullptr) || (src == nullptr) || (p_weights->size_ == p_src->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != weights size.", + *error); + + cugraph_data_type_id_t vertex_type; + cugraph_data_type_id_t edge_type; + cugraph_data_type_id_t weight_type; + cugraph_data_type_id_t edge_type_id_type; + + if (src != nullptr) { + vertex_type = p_src->type_; + } else { + vertex_type = cugraph_data_type_id_t::INT32; + } + + if (weights != nullptr) { + weight_type = p_weights->type_; + } else { + weight_type = cugraph_data_type_id_t::FLOAT32; + } + + if (edge_ids != nullptr) { + edge_type = p_edge_ids->type_; + } else { + edge_type = cugraph_data_type_id_t::INT32; + } + + if (edge_type_ids != nullptr) { + edge_type_id_type = p_edge_type_ids->type_; + } else { + edge_type_id_type = cugraph_data_type_id_t::INT32; + } + + if (src != nullptr) { + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge id prop size", + *error); + + CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge type prop size", + *error); + } + + constexpr bool multi_gpu = false; + constexpr bool store_transposed = false; + + ::create_allgather_functor functor( + *p_handle->handle_, p_src, p_dst, p_weights, p_edge_ids, p_edge_type_ids); + + try { + cugraph::c_api::vertex_dispatcher( + vertex_type, edge_type, weight_type, edge_type_id_type, store_transposed, multi_gpu, functor); + + if (functor.error_code_ != CUGRAPH_SUCCESS) { + *error = reinterpret_cast(functor.error_.release()); + return functor.error_code_; + } + + *edgelist = reinterpret_cast(functor.result_); + } catch (std::exception const& ex) { + *error = reinterpret_cast(new cugraph::c_api::cugraph_error_t{ex.what()}); + return CUGRAPH_UNKNOWN_ERROR; + } + + return CUGRAPH_SUCCESS; +} diff --git a/cpp/src/c_api/extract_ego.cpp b/cpp/src/c_api/extract_ego.cpp index 8f510b79023..931d58b5185 100644 --- a/cpp/src/c_api/extract_ego.cpp +++ b/cpp/src/c_api/extract_ego.cpp @@ -135,6 +135,8 @@ struct extract_ego_functor : public cugraph::c_api::abstract_functor { new cugraph::c_api::cugraph_type_erased_device_array_t(dst, graph_->vertex_type_), wgt ? new cugraph::c_api::cugraph_type_erased_device_array_t(*wgt, graph_->weight_type_) : NULL, + NULL, + NULL, new cugraph::c_api::cugraph_type_erased_device_array_t(edge_offsets, cugraph_data_type_id_t::SIZE_T)}; } diff --git a/cpp/src/c_api/induced_subgraph.cpp b/cpp/src/c_api/induced_subgraph.cpp index a1bbcb60825..ac56301e231 100644 --- a/cpp/src/c_api/induced_subgraph.cpp +++ b/cpp/src/c_api/induced_subgraph.cpp @@ -147,11 +147,14 @@ struct induced_subgraph_functor : public cugraph::c_api::abstract_functor { graph_view.vertex_partition_range_lasts(), do_expensive_check_); + // FIXME: Add support for edge_id and edge_type_id. result_ = new cugraph::c_api::cugraph_induced_subgraph_result_t{ new cugraph::c_api::cugraph_type_erased_device_array_t(src, graph_->vertex_type_), new cugraph::c_api::cugraph_type_erased_device_array_t(dst, graph_->vertex_type_), wgt ? new cugraph::c_api::cugraph_type_erased_device_array_t(*wgt, graph_->weight_type_) : NULL, + NULL, + NULL, new cugraph::c_api::cugraph_type_erased_device_array_t(graph_offsets, SIZE_T)}; } } diff --git a/cpp/src/c_api/induced_subgraph_result.cpp b/cpp/src/c_api/induced_subgraph_result.cpp index b9ad0e0d66f..5226872d404 100644 --- a/cpp/src/c_api/induced_subgraph_result.cpp +++ b/cpp/src/c_api/induced_subgraph_result.cpp @@ -45,6 +45,28 @@ extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get internal_pointer->wgt_->view()); } +extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_edge_ids( + cugraph_induced_subgraph_result_t* induced_subgraph) +{ + auto internal_pointer = + reinterpret_cast(induced_subgraph); + return (internal_pointer->edge_ids_ == nullptr) + ? NULL + : reinterpret_cast( + internal_pointer->edge_ids_->view()); +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_edge_type_ids( + cugraph_induced_subgraph_result_t* induced_subgraph) +{ + auto internal_pointer = + reinterpret_cast(induced_subgraph); + return (internal_pointer->edge_type_ids_ == nullptr) + ? NULL + : reinterpret_cast( + internal_pointer->edge_type_ids_->view()); +} + extern "C" cugraph_type_erased_device_array_view_t* cugraph_induced_subgraph_get_subgraph_offsets( cugraph_induced_subgraph_result_t* induced_subgraph) { @@ -62,6 +84,8 @@ extern "C" void cugraph_induced_subgraph_result_free( delete internal_pointer->src_; delete internal_pointer->dst_; delete internal_pointer->wgt_; + delete internal_pointer->edge_ids_; + delete internal_pointer->edge_type_ids_; delete internal_pointer->subgraph_offsets_; delete internal_pointer; } diff --git a/cpp/src/c_api/induced_subgraph_result.hpp b/cpp/src/c_api/induced_subgraph_result.hpp index acc99b617f4..6f02a699605 100644 --- a/cpp/src/c_api/induced_subgraph_result.hpp +++ b/cpp/src/c_api/induced_subgraph_result.hpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -25,6 +25,8 @@ struct cugraph_induced_subgraph_result_t { cugraph_type_erased_device_array_t* src_{}; cugraph_type_erased_device_array_t* dst_{}; cugraph_type_erased_device_array_t* wgt_{}; + cugraph_type_erased_device_array_t* edge_ids_{}; + cugraph_type_erased_device_array_t* edge_type_ids_{}; cugraph_type_erased_device_array_t* subgraph_offsets_{}; }; diff --git a/cpp/src/c_api/legacy_k_truss.cpp b/cpp/src/c_api/legacy_k_truss.cpp index 90e0894783a..90db9fc133c 100644 --- a/cpp/src/c_api/legacy_k_truss.cpp +++ b/cpp/src/c_api/legacy_k_truss.cpp @@ -123,12 +123,15 @@ struct k_truss_functor : public cugraph::c_api::abstract_functor { raft::update_device( edge_offsets.data(), h_edge_offsets.data(), h_edge_offsets.size(), handle_.get_stream()); + // FIXME: Add support for edge_id and edge_type_id. result_ = new cugraph::c_api::cugraph_induced_subgraph_result_t{ new cugraph::c_api::cugraph_type_erased_device_array_t(result_src, graph_->vertex_type_), new cugraph::c_api::cugraph_type_erased_device_array_t(result_dst, graph_->vertex_type_), wgt ? new cugraph::c_api::cugraph_type_erased_device_array_t(*result_wgt, graph_->weight_type_) : NULL, + NULL, + NULL, new cugraph::c_api::cugraph_type_erased_device_array_t(edge_offsets, cugraph_data_type_id_t::SIZE_T)}; } diff --git a/cpp/src/detail/collect_comm_wrapper.cu b/cpp/src/detail/collect_comm_wrapper.cu new file mode 100644 index 00000000000..7ce2241c677 --- /dev/null +++ b/cpp/src/detail/collect_comm_wrapper.cu @@ -0,0 +1,57 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include +#include + +#include +#include +#include + +#include +#include + +namespace cugraph { +namespace detail { + +template +rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input) +{ + auto gathered_v = cugraph::device_allgatherv(handle, comm, d_input); + + return gathered_v; +} + +template rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input); + +template rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input); + +template rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input); + +template rmm::device_uvector device_allgatherv(raft::handle_t const& handle, + raft::comms::comms_t const& comm, + raft::device_span d_input); + +} // namespace detail +} // namespace cugraph diff --git a/dependencies.yaml b/dependencies.yaml index a89acd9288b..2c0918ad117 100644 --- a/dependencies.yaml +++ b/dependencies.yaml @@ -380,6 +380,7 @@ dependencies: - &dask rapids-dask-dependency==23.12.* - &dask_cuda dask-cuda==23.12.* - &numba numba>=0.57 + - &numpy numpy>=1.21 - &ucx_py ucx-py==0.35.* - output_types: conda packages: @@ -399,7 +400,7 @@ dependencies: - output_types: [conda, pyproject] packages: - networkx>=3.0 - - &numpy numpy>=1.21 + - *numpy python_run_cugraph_dgl: common: - output_types: [conda, pyproject] diff --git a/python/cugraph/cugraph/structure/__init__.py b/python/cugraph/cugraph/structure/__init__.py index d7e0ff62358..94f34fd23f3 100644 --- a/python/cugraph/cugraph/structure/__init__.py +++ b/python/cugraph/cugraph/structure/__init__.py @@ -25,6 +25,11 @@ ) from cugraph.structure.number_map import NumberMap from cugraph.structure.symmetrize import symmetrize, symmetrize_df, symmetrize_ddf +from cugraph.structure.replicate_edgelist import ( + replicate_edgelist, + replicate_cudf_dataframe, + replicate_cudf_series, +) from cugraph.structure.convert_matrix import ( from_edgelist, from_cudf_edgelist, diff --git a/python/cugraph/cugraph/structure/replicate_edgelist.py b/python/cugraph/cugraph/structure/replicate_edgelist.py new file mode 100644 index 00000000000..d413e50e485 --- /dev/null +++ b/python/cugraph/cugraph/structure/replicate_edgelist.py @@ -0,0 +1,351 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import dask_cudf +import cudf +from dask.distributed import wait, default_client +import numpy as np +from pylibcugraph import ( + ResourceHandle, + replicate_edgelist as pylibcugraph_replicate_edgelist, +) + +from cugraph.dask.common.part_utils import ( + get_persisted_df_worker_map, + persist_dask_df_equal_parts_per_worker, +) + +import dask +import cupy as cp +import cugraph.dask.comms.comms as Comms +from typing import Union, Tuple + + +# FIXME: Convert it to a general-purpose util function +def _convert_to_cudf(cp_arrays: Tuple[cp.ndarray], col_names: list) -> cudf.DataFrame: + """ + Creates a cudf Dataframe from cupy arrays + """ + src, dst, wgt, edge_id, edge_type_id, _ = cp_arrays + gathered_edgelist_df = cudf.DataFrame() + gathered_edgelist_df[col_names[0]] = src + gathered_edgelist_df[col_names[1]] = dst + if wgt is not None: + gathered_edgelist_df[col_names[2]] = wgt + if edge_id is not None: + gathered_edgelist_df[col_names[3]] = edge_id + if edge_type_id is not None: + gathered_edgelist_df[col_names[4]] = edge_type_id + + return gathered_edgelist_df + + +def _call_plc_replicate_edgelist( + sID: bytes, edgelist_df: cudf.DataFrame, col_names: list +) -> cudf.DataFrame: + edgelist_df = edgelist_df[0] + cp_arrays = pylibcugraph_replicate_edgelist( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + src_array=edgelist_df[col_names[0]], + dst_array=edgelist_df[col_names[1]], + weight_array=edgelist_df[col_names[2]] if len(col_names) > 2 else None, + edge_id_array=edgelist_df[col_names[3]] if len(col_names) > 3 else None, + edge_type_id_array=edgelist_df[col_names[4]] if len(col_names) > 4 else None, + ) + return _convert_to_cudf(cp_arrays, col_names) + + +def _call_plc_replicate_dataframe(sID: bytes, df: cudf.DataFrame) -> cudf.DataFrame: + df = df[0] + df_replicated = cudf.DataFrame() + for col_name in df.columns: + cp_array = pylibcugraph_replicate_edgelist( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + src_array=df[col_name] + if df[col_name].dtype in [np.int32, np.int64] + else None, + dst_array=None, + weight_array=df[col_name] + if df[col_name].dtype in [np.float32, np.float64] + else None, + edge_id_array=None, + edge_type_id_array=None, + ) + src, _, wgt, _, _, _ = cp_array + if src is not None: + df_replicated[col_name] = src + elif wgt is not None: + df_replicated[col_name] = wgt + + return df_replicated + + +def _call_plc_replicate_series(sID: bytes, series: cudf.Series) -> cudf.Series: + series = series[0] + series_replicated = cudf.Series() + cp_array = pylibcugraph_replicate_edgelist( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + src_array=series if series.dtype in [np.int32, np.int64] else None, + dst_array=None, + weight_array=series if series.dtype in [np.float32, np.float64] else None, + edge_id_array=None, + edge_type_id_array=None, + ) + src, _, wgt, _, _, _ = cp_array + if src is not None: + series_replicated = cudf.Series(src) + elif wgt is not None: + series_replicated = cudf.Series(wgt) + + return series_replicated + + +def _mg_call_plc_replicate( + client: dask.distributed.client.Client, + sID: bytes, + dask_object: dict, + input_type: str, + col_names: list, +) -> Union[dask_cudf.DataFrame, dask_cudf.Series]: + + if input_type == "dataframe": + result = [ + client.submit( + _call_plc_replicate_dataframe, + sID, + edata, + workers=[w], + allow_other_workers=False, + pure=False, + ) + for w, edata in dask_object.items() + ] + elif input_type == "dataframe": + result = [ + client.submit( + _call_plc_replicate_series, + sID, + edata, + workers=[w], + allow_other_workers=False, + pure=False, + ) + for w, edata in dask_object.items() + ] + elif input_type == "edgelist": + result = [ + client.submit( + _call_plc_replicate_edgelist, + sID, + edata, + col_names, + workers=[w], + allow_other_workers=False, + pure=False, + ) + for w, edata in dask_object.items() + ] + + ddf = dask_cudf.from_delayed(result, verify_meta=False).persist() + wait(ddf) + wait([r.release() for r in result]) + return ddf + + +def replicate_edgelist( + edgelist_ddf: Union[dask_cudf.DataFrame, cudf.DataFrame] = None, + source="src", + destination="dst", + weight=None, + edge_id=None, + edge_type=None, +) -> dask_cudf.DataFrame: + """ + Replicate edges across all GPUs + + Parameters + ---------- + + edgelist_ddf: cudf.DataFrame or dask_cudf.DataFrame + A DataFrame that contains edge information. + + source : str or array-like + source column name or array of column names + + destination : str or array-like + destination column name or array of column names + + weight : str, optional (default=None) + Name of the weight column in the input dataframe. + + edge_id : str, optional (default=None) + Name of the edge id column in the input dataframe. + + edge_type : str, optional (default=None) + Name of the edge type column in the input dataframe. + + Returns + ------- + df : dask_cudf.DataFrame + A distributed dataframe where each partition contains the + combined edgelist from all GPUs. If a cudf.DataFrame was passed + as input, the edgelist will be replicated across all the other + GPUs in the cluster. If as dask_cudf.DataFrame was passed as input, + each partition will be filled with the edges of all partitions + in the dask_cudf.DataFrame. + + """ + + _client = default_client() + + if isinstance(edgelist_ddf, cudf.DataFrame): + edgelist_ddf = dask_cudf.from_cudf( + edgelist_ddf, npartitions=len(Comms.get_workers()) + ) + col_names = [source, destination] + + if weight is not None: + col_names.append(weight) + if edge_id is not None: + col_names.append(edge_id) + if edge_type is not None: + col_names.append(edge_type) + + if not (set(col_names).issubset(set(edgelist_ddf.columns))): + raise ValueError( + "Invalid column names were provided: valid columns names are " + f"{edgelist_ddf.columns}" + ) + + edgelist_ddf = persist_dask_df_equal_parts_per_worker(edgelist_ddf, _client) + edgelist_ddf = get_persisted_df_worker_map(edgelist_ddf, _client) + + ddf = _mg_call_plc_replicate( + _client, + Comms.get_session_id(), + edgelist_ddf, + "edgelist", + col_names, + ) + + return ddf + + +def replicate_cudf_dataframe(cudf_dataframe): + """ + Replicate dataframe across all GPUs + + Parameters + ---------- + + cudf_dataframe: cudf.DataFrame or dask_cudf.DataFrame + + Returns + ------- + df : dask_cudf.DataFrame + A distributed dataframe where each partition contains the + combined dataframe from all GPUs. If a cudf.DataFrame was passed + as input, the dataframe will be replicated across all the other + GPUs in the cluster. If as dask_cudf.DataFrame was passed as input, + each partition will be filled with the datafame of all partitions + in the dask_cudf.DataFrame. + + """ + + supported_types = [np.int32, np.int64, np.float32, np.float64] + if not all(dtype in supported_types for dtype in cudf_dataframe.dtypes): + raise TypeError( + "The supported types are 'int32', 'int64', 'float32', 'float64'" + ) + + _client = default_client() + + if not isinstance(cudf_dataframe, dask_cudf.DataFrame): + if isinstance(cudf_dataframe, cudf.DataFrame): + df = dask_cudf.from_cudf( + cudf_dataframe, npartitions=len(Comms.get_workers()) + ) + elif not isinstance(cudf_dataframe, dask_cudf.DataFrame): + raise TypeError( + "The variable 'cudf_dataframe' must be of type " + f"'cudf/dask_cudf.dataframe', got type {type(cudf_dataframe)}" + ) + else: + df = cudf_dataframe + + df = persist_dask_df_equal_parts_per_worker(df, _client) + df = get_persisted_df_worker_map(df, _client) + + ddf = _mg_call_plc_replicate( + _client, + Comms.get_session_id(), + df, + "dataframe", + ) + + return ddf + + +def replicate_cudf_series(cudf_series): + """ + Replicate series across all GPUs + + Parameters + ---------- + + cudf_series: cudf.Series or dask_cudf.Series + + Returns + ------- + series : dask_cudf.Series + A distributed series where each partition contains the + combined series from all GPUs. If a cudf.Series was passed + as input, the Series will be replicated across all the other + GPUs in the cluster. If as dask_cudf.Series was passed as input, + each partition will be filled with the series of all partitions + in the dask_cudf.Series. + + """ + + supported_types = [np.int32, np.int64, np.float32, np.float64] + if cudf_series.dtype not in supported_types: + raise TypeError( + "The supported types are 'int32', 'int64', 'float32', 'float64'" + ) + + _client = default_client() + + if not isinstance(cudf_series, dask_cudf.Series): + if isinstance(cudf_series, cudf.Series): + series = dask_cudf.from_cudf( + cudf_series, npartitions=len(Comms.get_workers()) + ) + elif not isinstance(cudf_series, dask_cudf.Series): + raise TypeError( + "The variable 'cudf_series' must be of type " + f"'cudf/dask_cudf.series', got type {type(cudf_series)}" + ) + else: + series = cudf_series + + series = persist_dask_df_equal_parts_per_worker(series, _client) + series = get_persisted_df_worker_map(series, _client) + + series = _mg_call_plc_replicate( + _client, + Comms.get_session_id(), + series, + "series", + ) + + return series diff --git a/python/cugraph/cugraph/tests/community/test_induced_subgraph_mg.py b/python/cugraph/cugraph/tests/community/test_induced_subgraph_mg.py index d93fa3b547d..8d80611a54c 100644 --- a/python/cugraph/cugraph/tests/community/test_induced_subgraph_mg.py +++ b/python/cugraph/cugraph/tests/community/test_induced_subgraph_mg.py @@ -93,7 +93,7 @@ def input_expected_output(input_combo): srcs = G.view_edge_list()["0"] dsts = G.view_edge_list()["1"] vertices = cudf.concat([srcs, dsts]).drop_duplicates() - vertices = vertices.sample(num_seeds).astype("int32") + vertices = vertices.sample(num_seeds, replace=True).astype("int32") # print randomly sample n seeds from the graph print("\nvertices: \n", vertices) diff --git a/python/cugraph/cugraph/tests/internals/test_replicate_edgelist_mg.py b/python/cugraph/cugraph/tests/internals/test_replicate_edgelist_mg.py new file mode 100644 index 00000000000..3bdb5c079ef --- /dev/null +++ b/python/cugraph/cugraph/tests/internals/test_replicate_edgelist_mg.py @@ -0,0 +1,128 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc + +import pytest + +import dask_cudf +import numpy as np +from cugraph.testing import UNDIRECTED_DATASETS, karate_disjoint + +from cugraph.structure.replicate_edgelist import replicate_edgelist +from cudf.testing.testing import assert_frame_equal +from pylibcugraph.testing.utils import gen_fixture_params_product + + +# ============================================================================= +# Pytest Setup / Teardown - called for each test function +# ============================================================================= +def setup_function(): + gc.collect() + + +edgeWeightCol = "weights" +edgeIdCol = "edge_id" +edgeTypeCol = "edge_type" +srcCol = "src" +dstCol = "dst" + + +input_data = UNDIRECTED_DATASETS + [karate_disjoint] +datasets = [pytest.param(d) for d in input_data] + +fixture_params = gen_fixture_params_product( + (datasets, "graph_file"), + ([True, False], "distributed"), + ([True, False], "use_weights"), + ([True, False], "use_edge_ids"), + ([True, False], "use_edge_type_ids"), +) + + +@pytest.fixture(scope="module", params=fixture_params) +def input_combo(request): + """ + Simply return the current combination of params as a dictionary for use in + tests or other parameterized fixtures. + """ + return dict( + zip( + ( + "graph_file", + "use_weights", + "use_edge_ids", + "use_edge_type_ids", + "distributed", + ), + request.param, + ) + ) + + +# ============================================================================= +# Tests +# ============================================================================= +# @pytest.mark.skipif( +# is_single_gpu(), reason="skipping MG testing on Single GPU system" +# ) +@pytest.mark.mg +def test_mg_replicate_edgelist(dask_client, input_combo): + df = input_combo["graph_file"].get_edgelist() + distributed = input_combo["distributed"] + + use_weights = input_combo["use_weights"] + use_edge_ids = input_combo["use_edge_ids"] + use_edge_type_ids = input_combo["use_edge_type_ids"] + + columns = [srcCol, dstCol] + weight = None + edge_id = None + edge_type = None + + if use_weights: + df = df.rename(columns={"wgt": edgeWeightCol}) + columns.append(edgeWeightCol) + weight = edgeWeightCol + if use_edge_ids: + df = df.reset_index().rename(columns={"index": edgeIdCol}) + df[edgeIdCol] = df[edgeIdCol].astype(df[srcCol].dtype) + columns.append(edgeIdCol) + edge_id = edgeIdCol + if use_edge_type_ids: + df[edgeTypeCol] = np.random.randint(0, 10, size=len(df)) + df[edgeTypeCol] = df[edgeTypeCol].astype(df[srcCol].dtype) + columns.append(edgeTypeCol) + edge_type = edgeTypeCol + + if distributed: + # Distribute the edges across all ranks + num_workers = len(dask_client.scheduler_info()["workers"]) + df = dask_cudf.from_cudf(df, npartitions=num_workers) + ddf = replicate_edgelist( + df[columns], weight=weight, edge_id=edge_id, edge_type=edge_type + ) + + if distributed: + df = df.compute() + + for i in range(ddf.npartitions): + result_df = ( + ddf.get_partition(i) + .compute() + .sort_values([srcCol, dstCol]) + .reset_index(drop=True) + ) + expected_df = df[columns].sort_values([srcCol, dstCol]).reset_index(drop=True) + + assert_frame_equal(expected_df, result_df, check_dtype=False, check_like=True) diff --git a/python/cugraph/pyproject.toml b/python/cugraph/pyproject.toml index 319900b3de3..bd426291c8d 100644 --- a/python/cugraph/pyproject.toml +++ b/python/cugraph/pyproject.toml @@ -35,6 +35,7 @@ dependencies = [ "dask-cudf==23.12.*", "fsspec[http]>=0.6.0", "numba>=0.57", + "numpy>=1.21", "pylibcugraph==23.12.*", "raft-dask==23.12.*", "rapids-dask-dependency==23.12.*", diff --git a/python/pylibcugraph/pylibcugraph/CMakeLists.txt b/python/pylibcugraph/pylibcugraph/CMakeLists.txt index 6618c50122c..c2e22fc1ff7 100644 --- a/python/pylibcugraph/pylibcugraph/CMakeLists.txt +++ b/python/pylibcugraph/pylibcugraph/CMakeLists.txt @@ -56,6 +56,7 @@ set(cython_sources uniform_random_walks.pyx utils.pyx weakly_connected_components.pyx + replicate_edgelist.pyx ) set(linked_libraries cugraph::cugraph;cugraph::cugraph_c) diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index 30f1c2d0fb1..1d02498ea30 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -87,6 +87,8 @@ from pylibcugraph.generate_rmat_edgelists import generate_rmat_edgelists +from pylibcugraph.replicate_edgelist import replicate_edgelist + from pylibcugraph.k_truss_subgraph import k_truss_subgraph from pylibcugraph.jaccard_coefficients import jaccard_coefficients diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd index f18e9848182..8b3a629956c 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -138,6 +138,16 @@ cdef extern from "cugraph_c/graph_functions.h": cugraph_induced_subgraph_result_t* induced_subgraph ) + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_induced_subgraph_get_edge_ids( + cugraph_induced_subgraph_result_t* induced_subgraph + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_induced_subgraph_get_edge_type_ids( + cugraph_induced_subgraph_result_t* induced_subgraph + ) + cdef cugraph_type_erased_device_array_view_t* \ cugraph_induced_subgraph_get_subgraph_offsets( cugraph_induced_subgraph_result_t* induced_subgraph @@ -158,3 +168,17 @@ cdef extern from "cugraph_c/graph_functions.h": cugraph_induced_subgraph_result_t** result, cugraph_error_t** error ) + + ########################################################################### + # allgather + cdef cugraph_error_code_t \ + cugraph_allgather( + const cugraph_resource_handle_t* handle, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + cugraph_induced_subgraph_result_t** result, + cugraph_error_t** error + ) diff --git a/python/pylibcugraph/pylibcugraph/replicate_edgelist.pyx b/python/pylibcugraph/pylibcugraph/replicate_edgelist.pyx new file mode 100644 index 00000000000..3763d4bc69d --- /dev/null +++ b/python/pylibcugraph/pylibcugraph/replicate_edgelist.pyx @@ -0,0 +1,202 @@ +# Copyright (c) 2022-2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Have cython use python 3 syntax +# cython: language_level = 3 + + +from pylibcugraph._cugraph_c.resource_handle cimport ( + cugraph_resource_handle_t, +) +from pylibcugraph._cugraph_c.error cimport ( + cugraph_error_code_t, + cugraph_error_t, +) +from pylibcugraph._cugraph_c.array cimport ( + cugraph_type_erased_device_array_view_t, + cugraph_type_erased_device_array_view_free, +) +from pylibcugraph._cugraph_c.graph_functions cimport ( + cugraph_allgather, + cugraph_induced_subgraph_result_t, + cugraph_induced_subgraph_get_sources, + cugraph_induced_subgraph_get_destinations, + cugraph_induced_subgraph_get_edge_weights, + cugraph_induced_subgraph_get_edge_ids, + cugraph_induced_subgraph_get_edge_type_ids, + cugraph_induced_subgraph_get_subgraph_offsets, + cugraph_induced_subgraph_result_free, +) +from pylibcugraph.resource_handle cimport ( + ResourceHandle, +) +from pylibcugraph.utils cimport ( + assert_success, + assert_CAI_type, + copy_to_cupy_array, + create_cugraph_type_erased_device_array_view_from_py_obj +) + + +def replicate_edgelist(ResourceHandle resource_handle, + src_array, + dst_array, + weight_array, + edge_id_array, + edge_type_id_array): + """ + Replicate edges across all GPUs + + Parameters + ---------- + resource_handle : ResourceHandle + Handle to the underlying device resources needed for referencing data + and running algorithms. + + src_array : device array type, optional + Device array containing the vertex identifiers of the source of each + directed edge. The order of the array corresponds to the ordering of the + dst_array, where the ith item in src_array and the ith item in dst_array + define the ith edge of the graph. + + dst_array : device array type, optional + Device array containing the vertex identifiers of the destination of + each directed edge. The order of the array corresponds to the ordering + of the src_array, where the ith item in src_array and the ith item in + dst_array define the ith edge of the graph. + + weight_array : device array type, optional + Device array containing the weight values of each directed edge. The + order of the array corresponds to the ordering of the src_array and + dst_array arrays, where the ith item in weight_array is the weight value + of the ith edge of the graph. + + edge_id_array : device array type, optional + Device array containing the edge id values of each directed edge. The + order of the array corresponds to the ordering of the src_array and + dst_array arrays, where the ith item in edge_id_array is the id value + of the ith edge of the graph. + + edge_type_id_array : device array type, optional + Device array containing the edge type id values of each directed edge. The + order of the array corresponds to the ordering of the src_array and + dst_array arrays, where the ith item in edge_type_id_array is the type id + value of the ith edge of the graph. + + Returns + ------- + return cupy arrays of 'src' and/or 'dst' and/or 'weight'and/or 'edge_id' + and/or 'edge_type_id'. + """ + assert_CAI_type(src_array, "src_array", True) + assert_CAI_type(dst_array, "dst_array", True) + assert_CAI_type(weight_array, "weight_array", True) + assert_CAI_type(edge_id_array, "edge_id_array", True) + assert_CAI_type(edge_type_id_array, "edge_type_id_array", True) + cdef cugraph_resource_handle_t* c_resource_handle_ptr = \ + resource_handle.c_resource_handle_ptr + + cdef cugraph_induced_subgraph_result_t* result_ptr + cdef cugraph_error_code_t error_code + cdef cugraph_error_t* error_ptr + + cdef cugraph_type_erased_device_array_view_t* srcs_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj(src_array) + + cdef cugraph_type_erased_device_array_view_t* dsts_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj(dst_array) + + + cdef cugraph_type_erased_device_array_view_t* weights_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj(weight_array) + + cdef cugraph_type_erased_device_array_view_t* edge_ids_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj(edge_id_array) + + cdef cugraph_type_erased_device_array_view_t* edge_type_ids_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj(edge_type_id_array) + + error_code = cugraph_allgather(c_resource_handle_ptr, + srcs_view_ptr, + dsts_view_ptr, + weights_view_ptr, + edge_ids_view_ptr, + edge_type_ids_view_ptr, + &result_ptr, + &error_ptr) + assert_success(error_code, error_ptr, "replicate_edgelist") + # Extract individual device array pointers from result and copy to cupy + # arrays for returning. + cdef cugraph_type_erased_device_array_view_t* sources_ptr + if src_array is not None: + sources_ptr = cugraph_induced_subgraph_get_sources(result_ptr) + cdef cugraph_type_erased_device_array_view_t* destinations_ptr + if dst_array is not None: + destinations_ptr = cugraph_induced_subgraph_get_destinations(result_ptr) + cdef cugraph_type_erased_device_array_view_t* edge_weights_ptr = \ + cugraph_induced_subgraph_get_edge_weights(result_ptr) + cdef cugraph_type_erased_device_array_view_t* edge_ids_ptr = \ + cugraph_induced_subgraph_get_edge_ids(result_ptr) + cdef cugraph_type_erased_device_array_view_t* edge_type_ids_ptr = \ + cugraph_induced_subgraph_get_edge_type_ids(result_ptr) + cdef cugraph_type_erased_device_array_view_t* subgraph_offsets_ptr = \ + cugraph_induced_subgraph_get_subgraph_offsets(result_ptr) + + # FIXME: Get ownership of the result data instead of performing a copy + # for perfomance improvement + + cupy_sources = None + cupy_destinations = None + cupy_edge_weights = None + cupy_edge_ids = None + cupy_edge_type_ids = None + + if src_array is not None: + cupy_sources = copy_to_cupy_array( + c_resource_handle_ptr, sources_ptr) + + if dst_array is not None: + cupy_destinations = copy_to_cupy_array( + c_resource_handle_ptr, destinations_ptr) + + if weight_array is not None: + cupy_edge_weights = copy_to_cupy_array( + c_resource_handle_ptr, edge_weights_ptr) + + if edge_id_array is not None: + cupy_edge_ids = copy_to_cupy_array( + c_resource_handle_ptr, edge_ids_ptr) + + if edge_type_id_array is not None: + cupy_edge_type_ids = copy_to_cupy_array( + c_resource_handle_ptr, edge_type_ids_ptr) + + cupy_subgraph_offsets = copy_to_cupy_array( + c_resource_handle_ptr, subgraph_offsets_ptr) + + # Free pointer + cugraph_induced_subgraph_result_free(result_ptr) + if src_array is not None: + cugraph_type_erased_device_array_view_free(srcs_view_ptr) + if dst_array is not None: + cugraph_type_erased_device_array_view_free(dsts_view_ptr) + if weight_array is not None: + cugraph_type_erased_device_array_view_free(weights_view_ptr) + if edge_id_array is not None: + cugraph_type_erased_device_array_view_free(edge_ids_view_ptr) + if edge_type_id_array is not None: + cugraph_type_erased_device_array_view_free(edge_type_ids_view_ptr) + + return (cupy_sources, cupy_destinations, + cupy_edge_weights, cupy_edge_ids, + cupy_edge_type_ids, cupy_subgraph_offsets) From 3f1c7b5ef8cd296aa5ad012cf855abdd2dfeb84b Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Mon, 20 Nov 2023 21:17:24 -0500 Subject: [PATCH 094/111] Update C API graph creation function signatures (#3982) Updating the C API graph creation functions to support the following: * Add support for isolated vertices * Add MG optimization to support multiple device arrays per rank as input and concatenate them internally * Add MG optimization to internally compute the number of edges via allreduce rather than requiring it as an input parameter (this can be expensive to compute in python) This PR implements these features. Some simple tests exist to check for isolate vertices (by running pagerank which generates a different result if the graph has isolated vertices). A simple test for multiple input arrays exists for the MG case. Closes #3947 Closes #3974 Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Naim (https://github.com/naimnv) Approvers: - Naim (https://github.com/naimnv) - Joseph Nke (https://github.com/jnke2016) - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/3982 --- cpp/CMakeLists.txt | 2 + cpp/include/cugraph/graph_functions.hpp | 67 +++ cpp/include/cugraph_c/graph.h | 193 ++++++- cpp/include/cugraph_c/resource_handle.h | 12 + cpp/src/c_api/graph_mg.cpp | 511 +++++++++++------- cpp/src/c_api/graph_sg.cpp | 170 +++++- cpp/src/c_api/resource_handle.cpp | 9 +- cpp/src/structure/detail/structure_utils.cuh | 61 ++- cpp/src/structure/remove_multi_edges.cu | 92 ++++ cpp/src/structure/remove_multi_edges_impl.cuh | 310 +++++++++++ cpp/src/structure/remove_self_loops.cu | 92 ++++ cpp/src/structure/remove_self_loops_impl.cuh | 94 ++++ cpp/tests/c_api/create_graph_test.c | 498 ++++++++++++++++- cpp/tests/c_api/mg_create_graph_test.c | 400 +++++++++++++- 14 files changed, 2265 insertions(+), 246 deletions(-) create mode 100644 cpp/src/structure/remove_multi_edges.cu create mode 100644 cpp/src/structure/remove_multi_edges_impl.cuh create mode 100644 cpp/src/structure/remove_self_loops.cu create mode 100644 cpp/src/structure/remove_self_loops_impl.cuh diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 626d62cffa5..836d5569ef7 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -202,6 +202,8 @@ set(CUGRAPH_SOURCES src/community/detail/mis_mg.cu src/detail/utility_wrappers.cu src/structure/graph_view_mg.cu + src/structure/remove_self_loops.cu + src/structure/remove_multi_edges.cu src/utilities/path_retrieval.cu src/structure/legacy/graph.cu src/linear_assignment/legacy/hungarian.cu diff --git a/cpp/include/cugraph/graph_functions.hpp b/cpp/include/cugraph/graph_functions.hpp index 5c1e9d5311f..6a75a420bf8 100644 --- a/cpp/include/cugraph/graph_functions.hpp +++ b/cpp/include/cugraph/graph_functions.hpp @@ -973,4 +973,71 @@ renumber_sampled_edgelist( label_offsets, bool do_expensive_check = false); +/** + * @brief Remove self loops from an edge list + * + * @tparam vertex_t Type of vertex identifiers. Needs to be an integral type. + * @tparam edge_t Type of edge identifiers. Needs to be an integral type. + * @tparam weight_t Type of edge weight. Currently float and double are supported. + * @tparam edge_type_t Type of edge type. Needs to be an integral type. + * + * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and + * handles to various CUDA libraries) to run graph algorithms. + * @param edgelist_srcs List of source vertex ids + * @param edgelist_dsts List of destination vertex ids + * @param edgelist_weights Optional list of edge weights + * @param edgelist_edge_ids Optional list of edge ids + * @param edgelist_edge_types Optional list of edge types + * @return Tuple of vectors storing edge sources, destinations, optional weights, + * optional edge ids, optional edge types. + */ +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +/** + * @brief Remove all but one edge when a multi-edge exists. Note that this function does not use + * stable methods. When a multi-edge exists, one of the edges will remain, there is no + * guarantee on which one will remain. + * + * In an MG context it is assumed that edges have been shuffled to the proper GPU, + * in which case any multi-edges will be on the same GPU. + * + * @tparam vertex_t Type of vertex identifiers. Needs to be an integral type. + * @tparam edge_t Type of edge identifiers. Needs to be an integral type. + * @tparam weight_t Type of edge weight. Currently float and double are supported. + * @tparam edge_type_t Type of edge type. Needs to be an integral type. + * + * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and + * handles to various CUDA libraries) to run graph algorithms. + * @param edgelist_srcs List of source vertex ids + * @param edgelist_dsts List of destination vertex ids + * @param edgelist_weights Optional list of edge weights + * @param edgelist_edge_ids Optional list of edge ids + * @param edgelist_edge_types Optional list of edge types + * @return Tuple of vectors storing edge sources, destinations, optional weights, + * optional edge ids, optional edge types. + */ +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + } // namespace cugraph diff --git a/cpp/include/cugraph_c/graph.h b/cpp/include/cugraph_c/graph.h index e910d8b1244..88176a9c1b6 100644 --- a/cpp/include/cugraph_c/graph.h +++ b/cpp/include/cugraph_c/graph.h @@ -35,10 +35,11 @@ typedef struct { bool_t is_multigraph; } cugraph_graph_properties_t; -// FIXME: Add support for specifying isolated vertices /** * @brief Construct an SG graph * + * @deprecated This API will be deleted, use cugraph_graph_create_sg instead + * * @param [in] handle Handle for accessing resources * @param [in] properties Properties of the constructed graph * @param [in] src Device array containing the source vertex ids. @@ -51,11 +52,11 @@ typedef struct { argument that can be NULL if edge types are not used. * @param [in] store_transposed If true create the graph initially in transposed format * @param [in] renumber If true, renumber vertices to make an efficient data structure. - * If false, do not renumber. Renumbering is required if the vertices are not sequential - * integer values from 0 to num_vertices. + * If false, do not renumber. Renumbering enables some significant optimizations within + * the graph primitives library, so it is strongly encouraged. Renumbering is required if + * the vertices are not sequential integer values from 0 to num_vertices. * @param [in] do_expensive_check If true, do expensive checks to validate the input data * is consistent with software assumptions. If false bypass these checks. - * @param [in] properties Properties of the graph * @param [out] graph A pointer to the graph object * @param [out] error Pointer to an error object storing details of any error. Will * be populated if error code is not CUGRAPH_SUCCESS @@ -76,9 +77,63 @@ cugraph_error_code_t cugraph_sg_graph_create( cugraph_graph_t** graph, cugraph_error_t** error); +/** + * @brief Construct an SG graph + * + * @param [in] handle Handle for accessing resources + * @param [in] properties Properties of the constructed graph + * @param [in] vertices Optional device array containing a list of vertex ids + * (specify NULL if we should create vertex ids from the + * unique contents of @p src and @p dst) + * @param [in] src Device array containing the source vertex ids. + * @param [in] dst Device array containing the destination vertex ids + * @param [in] weights Device array containing the edge weights. Note that an unweighted + * graph can be created by passing weights == NULL. + * @param [in] edge_ids Device array containing the edge ids for each edge. Optional + argument that can be NULL if edge ids are not used. + * @param [in] edge_type_ids Device array containing the edge types for each edge. Optional + argument that can be NULL if edge types are not used. + * @param [in] store_transposed If true create the graph initially in transposed format + * @param [in] renumber If true, renumber vertices to make an efficient data structure. + * If false, do not renumber. Renumbering enables some significant optimizations within + * the graph primitives library, so it is strongly encouraged. Renumbering is required if + * the vertices are not sequential integer values from 0 to num_vertices. + * @param [in] drop_self_loops If true, drop any self loops that exist in the provided edge list. + * @param [in] drop_multi_edges If true, drop any multi edges that exist in the provided edge list. + * Note that setting this flag will arbitrarily select one instance of a multi edge to be the + * edge that survives. If the edges have properties that should be honored (e.g. sum the + weights, + * or take the maximum weight), the caller should do that on not rely on this flag. + * @param [in] do_expensive_check If true, do expensive checks to validate the input data + * is consistent with software assumptions. If false bypass these checks. + * @param [out] graph A pointer to the graph object + * @param [out] error Pointer to an error object storing details of any error. Will + * be populated if error code is not CUGRAPH_SUCCESS + * + * @return error code + */ +cugraph_error_code_t cugraph_graph_create_sg( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* vertices, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + bool_t store_transposed, + bool_t renumber, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error); + /** * @brief Construct an SG graph from a CSR input * + * @deprecated This API will be deleted, use cugraph_graph_create_sg_from_csr instead + * * @param [in] handle Handle for accessing resources * @param [in] properties Properties of the constructed graph * @param [in] offsets Device array containing the CSR offsets array @@ -91,11 +146,11 @@ cugraph_error_code_t cugraph_sg_graph_create( argument that can be NULL if edge types are not used. * @param [in] store_transposed If true create the graph initially in transposed format * @param [in] renumber If true, renumber vertices to make an efficient data structure. - * If false, do not renumber. Renumbering is required if the vertices are not sequential - * integer values from 0 to num_vertices. + * If false, do not renumber. Renumbering enables some significant optimizations within + * the graph primitives library, so it is strongly encouraged. Renumbering is required if + * the vertices are not sequential integer values from 0 to num_vertices. * @param [in] do_expensive_check If true, do expensive checks to validate the input data * is consistent with software assumptions. If false bypass these checks. - * @param [in] properties Properties of the graph * @param [out] graph A pointer to the graph object * @param [out] error Pointer to an error object storing details of any error. Will * be populated if error code is not CUGRAPH_SUCCESS @@ -117,18 +172,50 @@ cugraph_error_code_t cugraph_sg_graph_create_from_csr( cugraph_error_t** error); /** - * @brief Destroy an SG graph + * @brief Construct an SG graph from a CSR input * - * @param [in] graph A pointer to the graph object to destroy + * @param [in] handle Handle for accessing resources + * @param [in] properties Properties of the constructed graph + * @param [in] offsets Device array containing the CSR offsets array + * @param [in] indices Device array containing the destination vertex ids + * @param [in] weights Device array containing the edge weights. Note that an unweighted + * graph can be created by passing weights == NULL. + * @param [in] edge_ids Device array containing the edge ids for each edge. Optional + argument that can be NULL if edge ids are not used. + * @param [in] edge_type_ids Device array containing the edge types for each edge. Optional + argument that can be NULL if edge types are not used. + * @param [in] store_transposed If true create the graph initially in transposed format + * @param [in] renumber If true, renumber vertices to make an efficient data structure. + * If false, do not renumber. Renumbering enables some significant optimizations within + * the graph primitives library, so it is strongly encouraged. Renumbering is required if + * the vertices are not sequential integer values from 0 to num_vertices. + * @param [in] do_expensive_check If true, do expensive checks to validate the input data + * is consistent with software assumptions. If false bypass these checks. + * @param [out] graph A pointer to the graph object + * @param [out] error Pointer to an error object storing details of any error. Will + * be populated if error code is not CUGRAPH_SUCCESS + * + * @return error code */ -// FIXME: This should probably just be cugraph_graph_free -// but didn't want to confuse with original cugraph_free_graph -void cugraph_sg_graph_free(cugraph_graph_t* graph); +cugraph_error_code_t cugraph_graph_create_sg_from_csr( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* offsets, + const cugraph_type_erased_device_array_view_t* indices, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + bool_t store_transposed, + bool_t renumber, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error); -// FIXME: Add support for specifying isolated vertices /** * @brief Construct an MG graph * + * @deprecated This API will be deleted, use cugraph_graph_create_mg instead + * * @param [in] handle Handle for accessing resources * @param [in] properties Properties of the constructed graph * @param [in] src Device array containing the source vertex ids @@ -165,13 +252,89 @@ cugraph_error_code_t cugraph_mg_graph_create( cugraph_graph_t** graph, cugraph_error_t** error); +/** + * @brief Construct an MG graph + * + * @param [in] handle Handle for accessing resources + * @param [in] properties Properties of the constructed graph + * @param [in] vertices List of device arrays containing the unique vertex ids. + * If NULL we will construct this internally using the unique + * entries specified in src and dst + * All entries in this list will be concatenated on this GPU + * into a single array. + * @param [in] src List of device array containing the source vertex ids + * All entries in this list will be concatenated on this GPU + * into a single array. + * @param [in] dst List of device array containing the destination vertex ids + * All entries in this list will be concatenated on this GPU + * into a single array. + * @param [in] weights List of device array containing the edge weights. Note that an + * unweighted graph can be created by passing weights == NULL. If a weighted graph is to be + * created, the weights device array should be created on each rank, but the pointer can be NULL and + * the size 0 if there are no inputs provided by this rank All entries in this list will be + * concatenated on this GPU into a single array. + * @param [in] edge_ids List of device array containing the edge ids for each edge. Optional + * argument that can be NULL if edge ids are not used. + * All entries in this list will be concatenated on this GPU + * into a single array. + * @param [in] edge_type_ids List of device array containing the edge types for each edge. + * Optional argument that can be NULL if edge types are not used. All entries in this list will be + * concatenated on this GPU into a single array. + * @param [in] store_transposed If true create the graph initially in transposed format + * @param [in] num_arrays The number of arrays specified in @p vertices, @p src, @p dst, @p + * weights, @p edge_ids and @p edge_type_ids + * @param [in] drop_self_loops If true, drop any self loops that exist in the provided edge list. + * @param [in] drop_multi_edges If true, drop any multi edges that exist in the provided edge list. + * Note that setting this flag will arbitrarily select one instance of a multi edge to be the + * edge that survives. If the edges have properties that should be honored (e.g. sum the + * weights, or take the maximum weight), the caller should do that on not rely on this flag. + * @param [in] do_expensive_check If true, do expensive checks to validate the input data + * is consistent with software assumptions. If false bypass these checks. + * @param [out] graph A pointer to the graph object + * @param [out] error Pointer to an error object storing details of any error. Will + * be populated if error code is not CUGRAPH_SUCCESS + * @return error code + */ +cugraph_error_code_t cugraph_graph_create_mg( + cugraph_resource_handle_t const* handle, + cugraph_graph_properties_t const* properties, + cugraph_type_erased_device_array_view_t const* const* vertices, + cugraph_type_erased_device_array_view_t const* const* src, + cugraph_type_erased_device_array_view_t const* const* dst, + cugraph_type_erased_device_array_view_t const* const* weights, + cugraph_type_erased_device_array_view_t const* const* edge_ids, + cugraph_type_erased_device_array_view_t const* const* edge_type_ids, + bool_t store_transposed, + size_t num_arrays, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error); + +/** + * @brief Destroy an graph + * + * @param [in] graph A pointer to the graph object to destroy + */ +void cugraph_graph_free(cugraph_graph_t* graph); + +/** + * @brief Destroy an SG graph + * + * @deprecated This API will be deleted, use cugraph_graph_free instead + * + * @param [in] graph A pointer to the graph object to destroy + */ +void cugraph_sg_graph_free(cugraph_graph_t* graph); + /** * @brief Destroy an MG graph * + * @deprecated This API will be deleted, use cugraph_graph_free instead + * * @param [in] graph A pointer to the graph object to destroy */ -// FIXME: This should probably just be cugraph_graph_free -// but didn't want to confuse with original cugraph_free_graph void cugraph_mg_graph_free(cugraph_graph_t* graph); /** diff --git a/cpp/include/cugraph_c/resource_handle.h b/cpp/include/cugraph_c/resource_handle.h index a239c24afe9..0e45102aae2 100644 --- a/cpp/include/cugraph_c/resource_handle.h +++ b/cpp/include/cugraph_c/resource_handle.h @@ -57,6 +57,18 @@ typedef struct cugraph_resource_handle_ { */ cugraph_resource_handle_t* cugraph_create_resource_handle(void* raft_handle); +/** + * @brief get comm_size from resource handle + * + * If the resource handle has been configured for multi-gpu, this will return + * the comm_size for this cluster. If the resource handle has not been configured for + * multi-gpu this will always return 1. + * + * @param [in] handle Handle for accessing resources + * @return comm_size + */ +int cugraph_resource_handle_get_comm_size(const cugraph_resource_handle_t* handle); + /** * @brief get rank from resource handle * diff --git a/cpp/src/c_api/graph_mg.cpp b/cpp/src/c_api/graph_mg.cpp index f50c7c08fb6..5413949e3a3 100644 --- a/cpp/src/c_api/graph_mg.cpp +++ b/cpp/src/c_api/graph_mg.cpp @@ -31,40 +31,85 @@ namespace { +template +rmm::device_uvector concatenate( + raft::handle_t const& handle, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* values, + size_t num_arrays) +{ + size_t num_values = std::transform_reduce( + values, values + num_arrays, size_t{0}, std::plus{}, [](auto p) { return p->size_; }); + + rmm::device_uvector results(num_values, handle.get_stream()); + size_t concat_pos{0}; + + for (size_t i = 0; i < num_arrays; ++i) { + raft::copy(results.data() + concat_pos, + values[i]->as_type(), + values[i]->size_, + handle.get_stream()); + concat_pos += values[i]->size_; + } + + return results; +} + struct create_graph_functor : public cugraph::c_api::abstract_functor { raft::handle_t const& handle_; cugraph_graph_properties_t const* properties_; - cugraph::c_api::cugraph_type_erased_device_array_view_t const* src_; - cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst_; - cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights_; - cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids_; - cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids_; - bool_t renumber_; - bool_t check_; + cugraph_data_type_id_t vertex_type_; cugraph_data_type_id_t edge_type_; + cugraph_data_type_id_t weight_type_; + cugraph_data_type_id_t edge_type_id_type_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* vertices_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* src_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* dst_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* weights_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* edge_ids_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* edge_type_ids_; + size_t num_arrays_; + bool_t renumber_; + bool_t drop_self_loops_; + bool_t drop_multi_edges_; + bool_t do_expensive_check_; cugraph::c_api::cugraph_graph_t* result_{}; - create_graph_functor(raft::handle_t const& handle, - cugraph_graph_properties_t const* properties, - cugraph::c_api::cugraph_type_erased_device_array_view_t const* src, - cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst, - cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights, - cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids, - cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids, - bool_t renumber, - bool_t check, - cugraph_data_type_id_t edge_type) + create_graph_functor( + raft::handle_t const& handle, + cugraph_graph_properties_t const* properties, + cugraph_data_type_id_t vertex_type, + cugraph_data_type_id_t edge_type, + cugraph_data_type_id_t weight_type, + cugraph_data_type_id_t edge_type_id_type, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* vertices, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* src, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* dst, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* weights, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* edge_ids, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* const* edge_type_ids, + size_t num_arrays, + bool_t renumber, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t do_expensive_check) : abstract_functor(), properties_(properties), + vertex_type_(vertex_type), + edge_type_(edge_type), + weight_type_(weight_type), + edge_type_id_type_(edge_type_id_type), handle_(handle), + vertices_(vertices), src_(src), dst_(dst), weights_(weights), edge_ids_(edge_ids), edge_type_ids_(edge_type_ids), + num_arrays_(num_arrays), renumber_(renumber), - check_(check), - edge_type_(edge_type) + drop_self_loops_(drop_self_loops), + drop_multi_edges_(drop_multi_edges), + do_expensive_check_(do_expensive_check) { } @@ -96,49 +141,27 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { edge_type_id_t>> new_edge_types{std::nullopt}; - rmm::device_uvector edgelist_srcs(src_->size_, handle_.get_stream()); - rmm::device_uvector edgelist_dsts(dst_->size_, handle_.get_stream()); + std::optional> vertex_list = + vertices_ ? std::make_optional(concatenate(handle_, vertices_, num_arrays_)) + : std::nullopt; - raft::copy( - edgelist_srcs.data(), src_->as_type(), src_->size_, handle_.get_stream()); - raft::copy( - edgelist_dsts.data(), dst_->as_type(), dst_->size_, handle_.get_stream()); + rmm::device_uvector edgelist_srcs = + concatenate(handle_, src_, num_arrays_); + rmm::device_uvector edgelist_dsts = + concatenate(handle_, dst_, num_arrays_); std::optional> edgelist_weights = - weights_ - ? std::make_optional(rmm::device_uvector(weights_->size_, handle_.get_stream())) - : std::nullopt; - - if (edgelist_weights) { - raft::copy(edgelist_weights->data(), - weights_->as_type(), - weights_->size_, - handle_.get_stream()); - } + weights_ ? std::make_optional(concatenate(handle_, weights_, num_arrays_)) + : std::nullopt; std::optional> edgelist_edge_ids = - edge_ids_ - ? std::make_optional(rmm::device_uvector(edge_ids_->size_, handle_.get_stream())) - : std::nullopt; - - if (edgelist_edge_ids) { - raft::copy(edgelist_edge_ids->data(), - edge_ids_->as_type(), - edge_ids_->size_, - handle_.get_stream()); - } + edge_ids_ ? std::make_optional(concatenate(handle_, edge_ids_, num_arrays_)) + : std::nullopt; std::optional> edgelist_edge_types = - edge_type_ids_ ? std::make_optional(rmm::device_uvector( - edge_type_ids_->size_, handle_.get_stream())) - : std::nullopt; - - if (edgelist_edge_types) { - raft::copy(edgelist_edge_types->data(), - edge_type_ids_->as_type(), - edge_type_ids_->size_, - handle_.get_stream()); - } + edge_type_ids_ + ? std::make_optional(concatenate(handle_, edge_type_ids_, num_arrays_)) + : std::nullopt; std::tie(store_transposed ? edgelist_dsts : edgelist_srcs, store_transposed ? edgelist_srcs : edgelist_dsts, @@ -153,6 +176,11 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { std::move(edgelist_edge_ids), std::move(edgelist_edge_types)); + if (vertex_list) { + vertex_list = cugraph::detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning( + handle_, std::move(*vertex_list)); + } + auto graph = new cugraph::graph_t(handle_); rmm::device_uvector* number_map = @@ -170,6 +198,28 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { cugraph::graph_view_t, edge_type_id_t>(handle_); + if (drop_self_loops_) { + std::tie( + edgelist_srcs, edgelist_dsts, edgelist_weights, edgelist_edge_ids, edgelist_edge_types) = + cugraph::remove_self_loops(handle_, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); + } + + if (drop_multi_edges_) { + std::tie( + edgelist_srcs, edgelist_dsts, edgelist_weights, edgelist_edge_ids, edgelist_edge_types) = + cugraph::remove_multi_edges(handle_, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); + } + std::tie(*graph, new_edge_weights, new_edge_ids, new_edge_types, new_number_map) = cugraph::create_graph_from_edgelist( handle_, - std::nullopt, + std::move(vertex_list), std::move(edgelist_srcs), std::move(edgelist_dsts), std::move(edgelist_weights), @@ -187,7 +237,7 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { std::move(edgelist_edge_types), cugraph::graph_properties_t{properties_->is_symmetric, properties_->is_multigraph}, renumber_, - check_); + do_expensive_check_); if (renumber_) { *number_map = std::move(new_number_map.value()); @@ -204,90 +254,39 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { if (new_edge_types) { *edge_types = std::move(new_edge_types.value()); } // Set up return - auto result = new cugraph::c_api::cugraph_graph_t{ - src_->type_, - edge_type_, - weights_ ? weights_->type_ : cugraph_data_type_id_t::FLOAT32, - edge_type_ids_ ? edge_type_ids_->type_ : cugraph_data_type_id_t::INT32, - store_transposed, - multi_gpu, - graph, - number_map, - new_edge_weights ? edge_weights : nullptr, - new_edge_ids ? edge_ids : nullptr, - new_edge_types ? edge_types : nullptr}; + auto result = new cugraph::c_api::cugraph_graph_t{vertex_type_, + edge_type_, + weight_type_, + edge_type_id_type_, + store_transposed, + multi_gpu, + graph, + number_map, + new_edge_weights ? edge_weights : nullptr, + new_edge_ids ? edge_ids : nullptr, + new_edge_types ? edge_types : nullptr}; result_ = reinterpret_cast(result); } } }; -struct destroy_graph_functor : public cugraph::c_api::abstract_functor { - void* graph_; - void* number_map_; - void* edge_weights_; - void* edge_ids_; - void* edge_types_; - - destroy_graph_functor( - void* graph, void* number_map, void* edge_weights, void* edge_ids, void* edge_types) - : abstract_functor(), - graph_(graph), - number_map_(number_map), - edge_weights_(edge_weights), - edge_ids_(edge_ids), - edge_types_(edge_types) - { - } - - template - void operator()() - { - auto internal_graph_pointer = - reinterpret_cast*>(graph_); - - delete internal_graph_pointer; - - auto internal_number_map_pointer = - reinterpret_cast*>(number_map_); - - delete internal_number_map_pointer; - - auto internal_edge_weight_pointer = reinterpret_cast< - cugraph::edge_property_t, - weight_t>*>(edge_weights_); - if (internal_edge_weight_pointer) { delete internal_edge_weight_pointer; } - - auto internal_edge_id_pointer = reinterpret_cast< - cugraph::edge_property_t, - edge_t>*>(edge_ids_); - if (internal_edge_id_pointer) { delete internal_edge_id_pointer; } - - auto internal_edge_type_pointer = reinterpret_cast< - cugraph::edge_property_t, - edge_type_id_t>*>(edge_types_); - if (internal_edge_type_pointer) { delete internal_edge_type_pointer; } - } -}; - } // namespace -extern "C" cugraph_error_code_t cugraph_mg_graph_create( - const cugraph_resource_handle_t* handle, - const cugraph_graph_properties_t* properties, - const cugraph_type_erased_device_array_view_t* src, - const cugraph_type_erased_device_array_view_t* dst, - const cugraph_type_erased_device_array_view_t* weights, - const cugraph_type_erased_device_array_view_t* edge_ids, - const cugraph_type_erased_device_array_view_t* edge_type_ids, +extern "C" cugraph_error_code_t cugraph_graph_create_mg( + cugraph_resource_handle_t const* handle, + cugraph_graph_properties_t const* properties, + cugraph_type_erased_device_array_view_t const* const* vertices, + cugraph_type_erased_device_array_view_t const* const* src, + cugraph_type_erased_device_array_view_t const* const* dst, + cugraph_type_erased_device_array_view_t const* const* weights, + cugraph_type_erased_device_array_view_t const* const* edge_ids, + cugraph_type_erased_device_array_view_t const* const* edge_type_ids, bool_t store_transposed, - size_t num_edges, - bool_t check, + size_t num_arrays, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t do_expensive_check, cugraph_graph_t** graph, cugraph_error_t** error) { @@ -298,87 +297,198 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( *error = nullptr; auto p_handle = reinterpret_cast(handle); + auto p_vertices = + reinterpret_cast( + vertices); auto p_src = - reinterpret_cast(src); + reinterpret_cast(src); auto p_dst = - reinterpret_cast(dst); + reinterpret_cast(dst); auto p_weights = - reinterpret_cast(weights); + reinterpret_cast( + weights); auto p_edge_ids = - reinterpret_cast(edge_ids); + reinterpret_cast( + edge_ids); auto p_edge_type_ids = - reinterpret_cast(edge_type_ids); + reinterpret_cast( + edge_type_ids); + + size_t local_num_edges{0}; + + // + // Determine the type of vertex, weight, edge_type_id across + // multiple input arrays and acros multiple GPUs. Also compute + // the number of edges so we can determine what type to use for + // edge_t + // + cugraph_data_type_id_t vertex_type{cugraph_data_type_id_t::NTYPES}; + cugraph_data_type_id_t weight_type{cugraph_data_type_id_t::NTYPES}; + + for (size_t i = 0; i < num_arrays; ++i) { + CAPI_EXPECTS(p_src[i]->size_ == p_dst[i]->size_, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != dst size.", + *error); + + CAPI_EXPECTS(p_src[i]->type_ == p_dst[i]->type_, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src type != dst type.", + *error); + + CAPI_EXPECTS((p_vertices == nullptr) || (p_src[i]->type_ == p_vertices[i]->type_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src type != vertices type.", + *error); + + CAPI_EXPECTS((weights == nullptr) || (p_weights[i]->size_ == p_src[i]->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != weights size.", + *error); + + local_num_edges += p_src[i]->size_; + + if (vertex_type == cugraph_data_type_id_t::NTYPES) vertex_type = p_src[i]->type_; + + if (weights != nullptr) { + if (weight_type == cugraph_data_type_id_t::NTYPES) weight_type = p_weights[i]->type_; + } - CAPI_EXPECTS(p_src->size_ == p_dst->size_, - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != dst size.", - *error); - CAPI_EXPECTS(p_src->type_ == p_dst->type_, + CAPI_EXPECTS(p_src[i]->type_ == vertex_type, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: all vertex types must match", + *error); + + CAPI_EXPECTS((weights == nullptr) || (p_weights[i]->type_ == weight_type), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: all weight types must match", + *error); + } + + size_t num_edges = cugraph::host_scalar_allreduce(p_handle->handle_->get_comms(), + local_num_edges, + raft::comms::op_t::SUM, + p_handle->handle_->get_stream()); + + auto vertex_types = cugraph::host_scalar_allgather( + p_handle->handle_->get_comms(), static_cast(vertex_type), p_handle->handle_->get_stream()); + + auto weight_types = cugraph::host_scalar_allgather( + p_handle->handle_->get_comms(), static_cast(weight_type), p_handle->handle_->get_stream()); + + if (vertex_type == cugraph_data_type_id_t::NTYPES) { + // Only true if this GPU had no vertex arrays + vertex_type = static_cast( + *std::min_element(vertex_types.begin(), vertex_types.end())); + } + + if (weight_type == cugraph_data_type_id_t::NTYPES) { + // Only true if this GPU had no weight arrays + weight_type = static_cast( + *std::min_element(weight_types.begin(), weight_types.end())); + } + + CAPI_EXPECTS(std::all_of(vertex_types.begin(), + vertex_types.end(), + [vertex_type](auto t) { return vertex_type == static_cast(t); }), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src type != dst type.", + "different vertex type used on different GPUs", *error); - CAPI_EXPECTS((weights == nullptr) || (p_weights->size_ == p_src->size_), + CAPI_EXPECTS(std::all_of(weight_types.begin(), + weight_types.end(), + [weight_type](auto t) { return weight_type == static_cast(t); }), CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != weights size.", + "different weight type used on different GPUs", *error); cugraph_data_type_id_t edge_type; - cugraph_data_type_id_t weight_type; if (num_edges < int32_threshold) { - edge_type = p_src->type_; + edge_type = static_cast(vertex_types[0]); } else { edge_type = cugraph_data_type_id_t::INT64; } - if (weights != nullptr) { - weight_type = p_weights->type_; - } else { + if (weight_type == cugraph_data_type_id_t::NTYPES) { weight_type = cugraph_data_type_id_t::FLOAT32; } - CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->type_ == edge_type), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: Edge id type must match edge type", - *error); + cugraph_data_type_id_t edge_type_id_type{cugraph_data_type_id_t::NTYPES}; - CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids->size_ == p_src->size_), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != edge id prop size", - *error); + for (size_t i = 0; i < num_arrays; ++i) { + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids[i]->type_ == edge_type), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: Edge id type must match edge type", + *error); - CAPI_EXPECTS((edge_type_ids == nullptr) || (p_edge_type_ids->size_ == p_src->size_), - CUGRAPH_INVALID_INPUT, - "Invalid input arguments: src size != edge type prop size", - *error); + CAPI_EXPECTS((edge_ids == nullptr) || (p_edge_ids[i]->size_ == p_src[i]->size_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge id prop size", + *error); + + if (edge_type_ids != nullptr) { + CAPI_EXPECTS(p_edge_type_ids[i]->size_ == p_src[i]->size_, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge type prop size", + *error); + + if (edge_type_id_type == cugraph_data_type_id_t::NTYPES) + edge_type_id_type = p_edge_type_ids[i]->type_; + + CAPI_EXPECTS(p_edge_type_ids[i]->type_ == edge_type_id_type, + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src size != edge type prop size", + *error); + } + } + + auto edge_type_id_types = cugraph::host_scalar_allgather(p_handle->handle_->get_comms(), + static_cast(edge_type_id_type), + p_handle->handle_->get_stream()); + + if (edge_type_id_type == cugraph_data_type_id_t::NTYPES) { + // Only true if this GPU had no edge_type_id arrays + edge_type_id_type = static_cast( + *std::min_element(edge_type_id_types.begin(), edge_type_id_types.end())); + } + + CAPI_EXPECTS( + std::all_of(edge_type_id_types.begin(), + edge_type_id_types.end(), + [edge_type_id_type](auto t) { return edge_type_id_type == static_cast(t); }), + CUGRAPH_INVALID_INPUT, + "different edge_type_id type used on different GPUs", + *error); - cugraph_data_type_id_t edge_type_id_type; - if (edge_type_ids == nullptr) { + if (edge_type_id_type == cugraph_data_type_id_t::NTYPES) { edge_type_id_type = cugraph_data_type_id_t::INT32; - } else { - edge_type_id_type = p_edge_type_ids->type_; } + // + // Now we know enough to create the graph + // create_graph_functor functor(*p_handle->handle_, properties, + vertex_type, + edge_type, + weight_type, + edge_type_id_type, + p_vertices, p_src, p_dst, p_weights, p_edge_ids, p_edge_type_ids, + num_arrays, bool_t::TRUE, - check, - edge_type); + drop_self_loops, + drop_multi_edges, + do_expensive_check); try { - cugraph::c_api::vertex_dispatcher(p_src->type_, - edge_type, - weight_type, - edge_type_id_type, - store_transposed, - multi_gpu, - functor); + cugraph::c_api::vertex_dispatcher( + vertex_type, edge_type, weight_type, edge_type_id_type, store_transposed, multi_gpu, functor); if (functor.error_code_ != CUGRAPH_SUCCESS) { *error = reinterpret_cast(functor.error_.release()); @@ -394,25 +504,38 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( return CUGRAPH_SUCCESS; } +extern "C" cugraph_error_code_t cugraph_mg_graph_create( + cugraph_resource_handle_t const* handle, + cugraph_graph_properties_t const* properties, + cugraph_type_erased_device_array_view_t const* src, + cugraph_type_erased_device_array_view_t const* dst, + cugraph_type_erased_device_array_view_t const* weights, + cugraph_type_erased_device_array_view_t const* edge_ids, + cugraph_type_erased_device_array_view_t const* edge_type_ids, + bool_t store_transposed, + size_t num_edges, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error) +{ + return cugraph_graph_create_mg(handle, + properties, + NULL, + &src, + &dst, + &weights, + &edge_ids, + &edge_type_ids, + store_transposed, + 1, + FALSE, + FALSE, + do_expensive_check, + graph, + error); +} + extern "C" void cugraph_mg_graph_free(cugraph_graph_t* ptr_graph) { - if (ptr_graph != NULL) { - auto internal_pointer = reinterpret_cast(ptr_graph); - - destroy_graph_functor functor(internal_pointer->graph_, - internal_pointer->number_map_, - internal_pointer->edge_weights_, - internal_pointer->edge_ids_, - internal_pointer->edge_types_); - - cugraph::c_api::vertex_dispatcher(internal_pointer->vertex_type_, - internal_pointer->edge_type_, - internal_pointer->weight_type_, - internal_pointer->edge_type_id_type_, - internal_pointer->store_transposed_, - internal_pointer->multi_gpu_, - functor); - - delete internal_pointer; - } + if (ptr_graph != NULL) { cugraph_graph_free(ptr_graph); } } diff --git a/cpp/src/c_api/graph_sg.cpp b/cpp/src/c_api/graph_sg.cpp index 9536869f123..7793458b53a 100644 --- a/cpp/src/c_api/graph_sg.cpp +++ b/cpp/src/c_api/graph_sg.cpp @@ -33,35 +33,44 @@ namespace { struct create_graph_functor : public cugraph::c_api::abstract_functor { raft::handle_t const& handle_; cugraph_graph_properties_t const* properties_; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* vertices_; cugraph::c_api::cugraph_type_erased_device_array_view_t const* src_; cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst_; cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights_; cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids_; cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids_; bool_t renumber_; + bool_t drop_self_loops_; + bool_t drop_multi_edges_; bool_t do_expensive_check_; cugraph_data_type_id_t edge_type_; cugraph::c_api::cugraph_graph_t* result_{}; create_graph_functor(raft::handle_t const& handle, cugraph_graph_properties_t const* properties, + cugraph::c_api::cugraph_type_erased_device_array_view_t const* vertices, cugraph::c_api::cugraph_type_erased_device_array_view_t const* src, cugraph::c_api::cugraph_type_erased_device_array_view_t const* dst, cugraph::c_api::cugraph_type_erased_device_array_view_t const* weights, cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_ids, cugraph::c_api::cugraph_type_erased_device_array_view_t const* edge_type_ids, bool_t renumber, + bool_t drop_self_loops, + bool_t drop_multi_edges, bool_t do_expensive_check, cugraph_data_type_id_t edge_type) : abstract_functor(), properties_(properties), handle_(handle), + vertices_(vertices), src_(src), dst_(dst), weights_(weights), edge_ids_(edge_ids), edge_type_ids_(edge_type_ids), renumber_(renumber), + drop_self_loops_(drop_self_loops), + drop_multi_edges_(drop_multi_edges), do_expensive_check_(do_expensive_check), edge_type_(edge_type) { @@ -99,6 +108,18 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { edge_type_id_t>> new_edge_types{std::nullopt}; + std::optional> vertex_list = + vertices_ ? std::make_optional( + rmm::device_uvector(vertices_->size_, handle_.get_stream())) + : std::nullopt; + + if (vertex_list) { + raft::copy(vertex_list->data(), + vertices_->as_type(), + vertices_->size_, + handle_.get_stream()); + } + rmm::device_uvector edgelist_srcs(src_->size_, handle_.get_stream()); rmm::device_uvector edgelist_dsts(dst_->size_, handle_.get_stream()); @@ -160,6 +181,28 @@ struct create_graph_functor : public cugraph::c_api::abstract_functor { cugraph::graph_view_t, edge_type_id_t>(handle_); + if (drop_self_loops_) { + std::tie( + edgelist_srcs, edgelist_dsts, edgelist_weights, edgelist_edge_ids, edgelist_edge_types) = + cugraph::remove_self_loops(handle_, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); + } + + if (drop_multi_edges_) { + std::tie( + edgelist_srcs, edgelist_dsts, edgelist_weights, edgelist_edge_ids, edgelist_edge_types) = + cugraph::remove_multi_edges(handle_, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); + } + std::tie(*graph, new_edge_weights, new_edge_ids, new_edge_types, new_number_map) = cugraph::create_graph_from_edgelist( handle_, - std::nullopt, + std::move(vertex_list), std::move(edgelist_srcs), std::move(edgelist_dsts), std::move(edgelist_weights), @@ -279,6 +322,12 @@ struct create_graph_csr_functor : public cugraph::c_api::abstract_functor { edge_type_id_t>> new_edge_types{std::nullopt}; + std::optional> vertex_list = std::make_optional( + rmm::device_uvector(offsets_->size_ - 1, handle_.get_stream())); + + cugraph::detail::sequence_fill( + handle_.get_stream(), vertex_list->data(), vertex_list->size(), vertex_t{0}); + rmm::device_uvector edgelist_srcs(0, handle_.get_stream()); rmm::device_uvector edgelist_dsts(indices_->size_, handle_.get_stream()); @@ -354,7 +403,7 @@ struct create_graph_csr_functor : public cugraph::c_api::abstract_functor { store_transposed, multi_gpu>( handle_, - std::nullopt, + std::move(vertex_list), std::move(edgelist_srcs), std::move(edgelist_dsts), std::move(edgelist_weights), @@ -452,9 +501,10 @@ struct destroy_graph_functor : public cugraph::c_api::abstract_functor { } // namespace -extern "C" cugraph_error_code_t cugraph_sg_graph_create( +extern "C" cugraph_error_code_t cugraph_graph_create_sg( const cugraph_resource_handle_t* handle, const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* vertices, const cugraph_type_erased_device_array_view_t* src, const cugraph_type_erased_device_array_view_t* dst, const cugraph_type_erased_device_array_view_t* weights, @@ -462,6 +512,8 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( const cugraph_type_erased_device_array_view_t* edge_type_ids, bool_t store_transposed, bool_t renumber, + bool_t drop_self_loops, + bool_t drop_multi_edges, bool_t do_expensive_check, cugraph_graph_t** graph, cugraph_error_t** error) @@ -473,6 +525,8 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( *error = nullptr; auto p_handle = reinterpret_cast(handle); + auto p_vertices = + reinterpret_cast(vertices); auto p_src = reinterpret_cast(src); auto p_dst = @@ -488,6 +542,12 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( CUGRAPH_INVALID_INPUT, "Invalid input arguments: src size != dst size.", *error); + + CAPI_EXPECTS((p_vertices == nullptr) || (p_src->type_ == p_vertices->type_), + CUGRAPH_INVALID_INPUT, + "Invalid input arguments: src type != vertices type.", + *error); + CAPI_EXPECTS(p_src->type_ == p_dst->type_, CUGRAPH_INVALID_INPUT, "Invalid input arguments: src type != dst type.", @@ -533,12 +593,15 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( ::create_graph_functor functor(*p_handle->handle_, properties, + p_vertices, p_src, p_dst, p_weights, p_edge_ids, p_edge_type_ids, renumber, + drop_self_loops, + drop_multi_edges, do_expensive_check, edge_type); @@ -565,7 +628,38 @@ extern "C" cugraph_error_code_t cugraph_sg_graph_create( return CUGRAPH_SUCCESS; } -cugraph_error_code_t cugraph_sg_graph_create_from_csr( +extern "C" cugraph_error_code_t cugraph_sg_graph_create( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + bool_t store_transposed, + bool_t renumber, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error) +{ + return cugraph_graph_create_sg(handle, + properties, + NULL, + src, + dst, + weights, + edge_ids, + edge_type_ids, + store_transposed, + renumber, + FALSE, + FALSE, + do_expensive_check, + graph, + error); +} + +cugraph_error_code_t cugraph_graph_create_sg_from_csr( const cugraph_resource_handle_t* handle, const cugraph_graph_properties_t* properties, const cugraph_type_erased_device_array_view_t* offsets, @@ -662,23 +756,55 @@ cugraph_error_code_t cugraph_sg_graph_create_from_csr( return CUGRAPH_SUCCESS; } -extern "C" void cugraph_sg_graph_free(cugraph_graph_t* ptr_graph) +cugraph_error_code_t cugraph_sg_graph_create_from_csr( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* offsets, + const cugraph_type_erased_device_array_view_t* indices, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + bool_t store_transposed, + bool_t renumber, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error) { - auto internal_pointer = reinterpret_cast(ptr_graph); - - destroy_graph_functor functor(internal_pointer->graph_, - internal_pointer->number_map_, - internal_pointer->edge_weights_, - internal_pointer->edge_ids_, - internal_pointer->edge_types_); - - cugraph::c_api::vertex_dispatcher(internal_pointer->vertex_type_, - internal_pointer->edge_type_, - internal_pointer->weight_type_, - internal_pointer->edge_type_id_type_, - internal_pointer->store_transposed_, - internal_pointer->multi_gpu_, - functor); - - delete internal_pointer; + return cugraph_graph_create_sg_from_csr(handle, + properties, + offsets, + indices, + weights, + edge_ids, + edge_type_ids, + store_transposed, + renumber, + do_expensive_check, + graph, + error); } + +extern "C" void cugraph_graph_free(cugraph_graph_t* ptr_graph) +{ + if (ptr_graph != NULL) { + auto internal_pointer = reinterpret_cast(ptr_graph); + + destroy_graph_functor functor(internal_pointer->graph_, + internal_pointer->number_map_, + internal_pointer->edge_weights_, + internal_pointer->edge_ids_, + internal_pointer->edge_types_); + + cugraph::c_api::vertex_dispatcher(internal_pointer->vertex_type_, + internal_pointer->edge_type_, + internal_pointer->weight_type_, + internal_pointer->edge_type_id_type_, + internal_pointer->store_transposed_, + internal_pointer->multi_gpu_, + functor); + + delete internal_pointer; + } +} + +extern "C" void cugraph_sg_graph_free(cugraph_graph_t* ptr_graph) { cugraph_graph_free(ptr_graph); } diff --git a/cpp/src/c_api/resource_handle.cpp b/cpp/src/c_api/resource_handle.cpp index 767a6f0add6..75b9537ef49 100644 --- a/cpp/src/c_api/resource_handle.cpp +++ b/cpp/src/c_api/resource_handle.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -41,3 +41,10 @@ extern "C" int cugraph_resource_handle_get_rank(const cugraph_resource_handle_t* auto& comm = internal->handle_->get_comms(); return static_cast(comm.get_rank()); } + +extern "C" int cugraph_resource_handle_get_comm_size(const cugraph_resource_handle_t* handle) +{ + auto internal = reinterpret_cast(handle); + auto& comm = internal->handle_->get_comms(); + return static_cast(comm.get_size()); +} diff --git a/cpp/src/structure/detail/structure_utils.cuh b/cpp/src/structure/detail/structure_utils.cuh index 01fbccaa53e..c49b62e4543 100644 --- a/cpp/src/structure/detail/structure_utils.cuh +++ b/cpp/src/structure/detail/structure_utils.cuh @@ -21,6 +21,7 @@ #include #include #include +#include #include #include @@ -33,6 +34,7 @@ #include #include #include +#include #include #include #include @@ -496,6 +498,63 @@ void sort_adjacency_list(raft::handle_t const& handle, } } -} // namespace detail +template +std::tuple> mark_entries(raft::handle_t const& handle, + size_t num_entries, + comparison_t comparison) +{ + rmm::device_uvector marked_entries(cugraph::packed_bool_size(num_entries), + handle.get_stream()); + + thrust::tabulate(handle.get_thrust_policy(), + marked_entries.begin(), + marked_entries.end(), + [comparison, num_entries] __device__(size_t idx) { + auto word = cugraph::packed_bool_empty_mask(); + size_t start_index = idx * cugraph::packed_bools_per_word(); + size_t bits_in_this_word = + (start_index + cugraph::packed_bools_per_word() < num_entries) + ? cugraph::packed_bools_per_word() + : (num_entries - start_index); + + for (size_t bit = 0; bit < bits_in_this_word; ++bit) { + if (comparison(start_index + bit)) word |= cugraph::packed_bool_mask(bit); + } + + return word; + }); + + size_t bit_count = thrust::transform_reduce( + handle.get_thrust_policy(), + marked_entries.begin(), + marked_entries.end(), + [] __device__(auto word) { return __popc(word); }, + size_t{0}, + thrust::plus()); + + return std::make_tuple(bit_count, std::move(marked_entries)); +} +template +rmm::device_uvector remove_flagged_elements(raft::handle_t const& handle, + rmm::device_uvector&& vector, + raft::device_span remove_flags, + size_t remove_count) +{ + rmm::device_uvector result(vector.size() - remove_count, handle.get_stream()); + + thrust::copy_if( + handle.get_thrust_policy(), + thrust::make_counting_iterator(size_t{0}), + thrust::make_counting_iterator(vector.size()), + thrust::make_transform_output_iterator(result.begin(), + indirection_t{vector.data()}), + [remove_flags] __device__(size_t i) { + return !(remove_flags[cugraph::packed_bool_offset(i)] & cugraph::packed_bool_mask(i)); + }); + + return result; +} + +} // namespace detail } // namespace cugraph diff --git a/cpp/src/structure/remove_multi_edges.cu b/cpp/src/structure/remove_multi_edges.cu new file mode 100644 index 00000000000..ba07d068c0e --- /dev/null +++ b/cpp/src/structure/remove_multi_edges.cu @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include + +namespace cugraph { + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +} // namespace cugraph diff --git a/cpp/src/structure/remove_multi_edges_impl.cuh b/cpp/src/structure/remove_multi_edges_impl.cuh new file mode 100644 index 00000000000..ab6b1fba8eb --- /dev/null +++ b/cpp/src/structure/remove_multi_edges_impl.cuh @@ -0,0 +1,310 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include + +#include +// FIXME: mem_frugal_partition should probably not be in shuffle_comm.hpp +// It's used here without any notion of shuffling +#include + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include + +#include +#include + +namespace cugraph { + +namespace detail { + +template +struct hash_src_dst_pair { + int32_t num_groups; + + int32_t __device__ operator()(thrust::tuple t) const + { + vertex_t pair[2]; + pair[0] = thrust::get<0>(t); + pair[1] = thrust::get<1>(t); + cuco::detail::MurmurHash3_32 hash_func{}; + return hash_func.compute_hash(reinterpret_cast(pair), 2 * sizeof(vertex_t)) % + num_groups; + } +}; + +template +std::tuple, rmm::device_uvector> group_multi_edges( + raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + size_t mem_frugal_threshold) +{ + auto pair_first = thrust::make_zip_iterator(edgelist_srcs.begin(), edgelist_dsts.begin()); + + if (edgelist_srcs.size() > mem_frugal_threshold) { + // FIXME: Tuning parameter to address high frequency multi-edges + // Defaulting to 2 which makes the code easier. If + // num_groups > 2 we can evaluate whether to find a good + // midpoint to do 2 sorts, or if we should do more than 2 sorts. + const size_t num_groups{2}; + + auto group_counts = groupby_and_count(pair_first, + pair_first + edgelist_srcs.size(), + hash_src_dst_pair{}, + num_groups, + mem_frugal_threshold, + handle.get_stream()); + + std::vector h_group_counts(group_counts.size()); + raft::update_host( + h_group_counts.data(), group_counts.data(), group_counts.size(), handle.get_stream()); + + thrust::sort(handle.get_thrust_policy(), pair_first, pair_first + h_group_counts[0]); + thrust::sort(handle.get_thrust_policy(), + pair_first + h_group_counts[0], + pair_first + edgelist_srcs.size()); + } else { + thrust::sort(handle.get_thrust_policy(), pair_first, pair_first + edgelist_srcs.size()); + } + + return std::make_tuple(std::move(edgelist_srcs), std::move(edgelist_dsts)); +} + +template +std::tuple, + rmm::device_uvector, + decltype(allocate_dataframe_buffer(size_t{0}, rmm::cuda_stream_view{}))> +group_multi_edges( + raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + decltype(allocate_dataframe_buffer(0, rmm::cuda_stream_view{}))&& edgelist_values, + size_t mem_frugal_threshold) +{ + auto pair_first = thrust::make_zip_iterator(edgelist_srcs.begin(), edgelist_dsts.begin()); + auto value_first = get_dataframe_buffer_begin(edgelist_values); + + if (edgelist_srcs.size() > mem_frugal_threshold) { + // FIXME: Tuning parameter to address high frequency multi-edges + // Defaulting to 2 which makes the code easier. If + // num_groups > 2 we can evaluate whether to find a good + // midpoint to do 2 sorts, or if we should do more than 2 sorts. + const size_t num_groups{2}; + + auto group_counts = groupby_and_count(pair_first, + pair_first + edgelist_srcs.size(), + value_first, + hash_src_dst_pair{}, + num_groups, + mem_frugal_threshold, + handle.get_stream()); + + std::vector h_group_counts(group_counts.size()); + raft::update_host( + h_group_counts.data(), group_counts.data(), group_counts.size(), handle.get_stream()); + + thrust::sort_by_key(handle.get_thrust_policy(), + pair_first, + pair_first + h_group_counts[0], + get_dataframe_buffer_begin(edgelist_values)); + thrust::sort_by_key(handle.get_thrust_policy(), + pair_first + h_group_counts[0], + pair_first + edgelist_srcs.size(), + get_dataframe_buffer_begin(edgelist_values) + h_group_counts[0]); + } else { + thrust::sort_by_key(handle.get_thrust_policy(), + pair_first, + pair_first + edgelist_srcs.size(), + get_dataframe_buffer_begin(edgelist_values)); + } + + return std::make_tuple( + std::move(edgelist_srcs), std::move(edgelist_dsts), std::move(edgelist_values)); +} + +} // namespace detail + +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_multi_edges(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types) +{ + auto total_global_mem = handle.get_device_properties().totalGlobalMem; + size_t element_size = sizeof(vertex_t) * 2; + if (edgelist_weights) { element_size += sizeof(weight_t); } + if (edgelist_edge_ids) { element_size += sizeof(edge_t); } + if (edgelist_edge_types) { element_size += sizeof(edge_type_t); } + + auto constexpr mem_frugal_ratio = + 0.25; // if the expected temporary buffer size exceeds the mem_frugal_ratio of the + // total_global_mem, switch to the memory frugal approach + auto mem_frugal_threshold = + static_cast(static_cast(total_global_mem / element_size) * mem_frugal_ratio); + + if (edgelist_weights) { + if (edgelist_edge_ids) { + if (edgelist_edge_types) { + std::forward_as_tuple(edgelist_srcs, + edgelist_dsts, + std::tie(edgelist_weights, edgelist_edge_ids, edgelist_edge_types)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), + std::move(*edgelist_edge_ids), + std::move(*edgelist_edge_types)), + mem_frugal_threshold); + } else { + std::forward_as_tuple( + edgelist_srcs, edgelist_dsts, std::tie(edgelist_weights, edgelist_edge_ids)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), std::move(*edgelist_edge_ids)), + mem_frugal_threshold); + } + } else { + if (edgelist_edge_types) { + std::forward_as_tuple( + edgelist_srcs, edgelist_dsts, std::tie(edgelist_weights, edgelist_edge_types)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights), std::move(*edgelist_edge_types)), + mem_frugal_threshold); + } else { + std::forward_as_tuple(edgelist_srcs, edgelist_dsts, std::tie(edgelist_weights)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_weights)), + mem_frugal_threshold); + } + } + } else { + if (edgelist_edge_ids) { + if (edgelist_edge_types) { + std::forward_as_tuple( + edgelist_srcs, edgelist_dsts, std::tie(edgelist_edge_ids, edgelist_edge_types)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_edge_ids), std::move(*edgelist_edge_types)), + mem_frugal_threshold); + } else { + std::forward_as_tuple(edgelist_srcs, edgelist_dsts, std::tie(edgelist_edge_ids)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_edge_ids)), + mem_frugal_threshold); + } + } else { + if (edgelist_edge_types) { + std::forward_as_tuple(edgelist_srcs, edgelist_dsts, std::tie(edgelist_edge_types)) = + detail::group_multi_edges>( + handle, + std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::make_tuple(std::move(*edgelist_edge_types)), + mem_frugal_threshold); + } else { + std::tie(edgelist_srcs, edgelist_dsts) = detail::group_multi_edges( + handle, std::move(edgelist_srcs), std::move(edgelist_dsts), mem_frugal_threshold); + } + } + } + + auto [multi_edge_count, multi_edges_to_delete] = + detail::mark_entries(handle, + edgelist_srcs.size(), + [d_edgelist_srcs = edgelist_srcs.data(), + d_edgelist_dsts = edgelist_dsts.data()] __device__(auto idx) { + return (idx > 0) && (d_edgelist_srcs[idx - 1] == d_edgelist_srcs[idx]) && + (d_edgelist_dsts[idx - 1] == d_edgelist_dsts[idx]); + }); + + if (multi_edge_count > 0) { + edgelist_srcs = detail::remove_flagged_elements( + handle, + std::move(edgelist_srcs), + raft::device_span{multi_edges_to_delete.data(), multi_edges_to_delete.size()}, + multi_edge_count); + edgelist_dsts = detail::remove_flagged_elements( + handle, + std::move(edgelist_dsts), + raft::device_span{multi_edges_to_delete.data(), multi_edges_to_delete.size()}, + multi_edge_count); + + if (edgelist_weights) + edgelist_weights = detail::remove_flagged_elements( + handle, + std::move(*edgelist_weights), + raft::device_span{multi_edges_to_delete.data(), + multi_edges_to_delete.size()}, + multi_edge_count); + + if (edgelist_edge_ids) + edgelist_edge_ids = detail::remove_flagged_elements( + handle, + std::move(*edgelist_edge_ids), + raft::device_span{multi_edges_to_delete.data(), + multi_edges_to_delete.size()}, + multi_edge_count); + + if (edgelist_edge_types) + edgelist_edge_types = detail::remove_flagged_elements( + handle, + std::move(*edgelist_edge_types), + raft::device_span{multi_edges_to_delete.data(), + multi_edges_to_delete.size()}, + multi_edge_count); + } + + return std::make_tuple(std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); +} + +} // namespace cugraph diff --git a/cpp/src/structure/remove_self_loops.cu b/cpp/src/structure/remove_self_loops.cu new file mode 100644 index 00000000000..8a66c1e05e3 --- /dev/null +++ b/cpp/src/structure/remove_self_loops.cu @@ -0,0 +1,92 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include + +namespace cugraph { + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types); + +} // namespace cugraph diff --git a/cpp/src/structure/remove_self_loops_impl.cuh b/cpp/src/structure/remove_self_loops_impl.cuh new file mode 100644 index 00000000000..161ffeae28e --- /dev/null +++ b/cpp/src/structure/remove_self_loops_impl.cuh @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#pragma once + +#include + +#include +#include + +#include +#include +#include +#include +#include + +#include +#include + +namespace cugraph { + +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +remove_self_loops(raft::handle_t const& handle, + rmm::device_uvector&& edgelist_srcs, + rmm::device_uvector&& edgelist_dsts, + std::optional>&& edgelist_weights, + std::optional>&& edgelist_edge_ids, + std::optional>&& edgelist_edge_types) +{ + auto [self_loop_count, self_loops_to_delete] = + detail::mark_entries(handle, + edgelist_srcs.size(), + [d_srcs = edgelist_srcs.data(), d_dsts = edgelist_dsts.data()] __device__( + size_t i) { return d_srcs[i] == d_dsts[i]; }); + + if (self_loop_count > 0) { + edgelist_srcs = detail::remove_flagged_elements( + handle, + std::move(edgelist_srcs), + raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, + self_loop_count); + edgelist_dsts = detail::remove_flagged_elements( + handle, + std::move(edgelist_dsts), + raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, + self_loop_count); + + if (edgelist_weights) + edgelist_weights = detail::remove_flagged_elements( + handle, + std::move(*edgelist_weights), + raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, + self_loop_count); + + if (edgelist_edge_ids) + edgelist_edge_ids = detail::remove_flagged_elements( + handle, + std::move(*edgelist_edge_ids), + raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, + self_loop_count); + + if (edgelist_edge_types) + edgelist_edge_types = detail::remove_flagged_elements( + handle, + std::move(*edgelist_edge_types), + raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, + self_loop_count); + } + + return std::make_tuple(std::move(edgelist_srcs), + std::move(edgelist_dsts), + std::move(edgelist_weights), + std::move(edgelist_edge_ids), + std::move(edgelist_edge_types)); +} + +} // namespace cugraph diff --git a/cpp/tests/c_api/create_graph_test.c b/cpp/tests/c_api/create_graph_test.c index 736db761ebd..11da2eb8589 100644 --- a/cpp/tests/c_api/create_graph_test.c +++ b/cpp/tests/c_api/create_graph_test.c @@ -91,8 +91,9 @@ int test_create_sg_graph_simple() handle, wgt_view, (byte_t*)h_wgt, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); - ret_code = cugraph_sg_graph_create(handle, + ret_code = cugraph_graph_create_sg(handle, &properties, + NULL, src_view, dst_view, wgt_view, @@ -101,11 +102,13 @@ int test_create_sg_graph_simple() FALSE, FALSE, FALSE, + FALSE, + FALSE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); - cugraph_sg_graph_free(graph); + cugraph_graph_free(graph); cugraph_type_erased_device_array_view_free(wgt_view); cugraph_type_erased_device_array_view_free(dst_view); @@ -300,7 +303,7 @@ int test_create_sg_graph_csr() } cugraph_sample_result_free(result); - cugraph_sg_graph_free(graph); + cugraph_graph_free(graph); cugraph_type_erased_device_array_view_free(wgt_view); cugraph_type_erased_device_array_view_free(indices_view); cugraph_type_erased_device_array_view_free(offsets_view); @@ -382,8 +385,9 @@ int test_create_sg_graph_symmetric_error() handle, wgt_view, (byte_t*)h_wgt, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); - ret_code = cugraph_sg_graph_create(handle, + ret_code = cugraph_graph_create_sg(handle, &properties, + NULL, src_view, dst_view, wgt_view, @@ -391,19 +395,500 @@ int test_create_sg_graph_symmetric_error() NULL, FALSE, FALSE, + FALSE, + FALSE, TRUE, &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code != CUGRAPH_SUCCESS, "graph creation succeeded but should have failed."); - if (ret_code == CUGRAPH_SUCCESS) cugraph_sg_graph_free(graph); + if (ret_code == CUGRAPH_SUCCESS) cugraph_graph_free(graph); + + cugraph_type_erased_device_array_view_free(wgt_view); + cugraph_type_erased_device_array_view_free(dst_view); + cugraph_type_erased_device_array_view_free(src_view); + cugraph_type_erased_device_array_free(wgt); + cugraph_type_erased_device_array_free(dst); + cugraph_type_erased_device_array_free(src); + + cugraph_free_resource_handle(handle); + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_create_sg_graph_with_isolated_vertices() +{ + int test_ret_value = 0; + + typedef int32_t vertex_t; + typedef int32_t edge_t; + typedef float weight_t; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + size_t num_edges = 8; + size_t num_vertices = 7; + double alpha = 0.95; + double epsilon = 0.0001; + size_t max_iterations = 20; + + vertex_t h_vertices[] = { 0, 1, 2, 3, 4, 5, 6 }; + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5}; + weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; + weight_t h_result[] = { 0.0859168, 0.158029, 0.0616337, 0.179675, 0.113239, 0.339873, 0.0616337 }; + + cugraph_resource_handle_t* handle = NULL; + cugraph_graph_t* graph = NULL; + cugraph_graph_properties_t properties; + + properties.is_symmetric = FALSE; + properties.is_multigraph = FALSE; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + + handle = cugraph_create_resource_handle(NULL); + TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); + + cugraph_type_erased_device_array_t* vertices; + cugraph_type_erased_device_array_t* src; + cugraph_type_erased_device_array_t* dst; + cugraph_type_erased_device_array_t* wgt; + cugraph_type_erased_device_array_view_t* vertices_view; + cugraph_type_erased_device_array_view_t* src_view; + cugraph_type_erased_device_array_view_t* dst_view; + cugraph_type_erased_device_array_view_t* wgt_view; + + ret_code = + cugraph_type_erased_device_array_create(handle, num_vertices, vertex_tid, &vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + vertices_view = cugraph_type_erased_device_array_view(vertices); + src_view = cugraph_type_erased_device_array_view(src); + dst_view = cugraph_type_erased_device_array_view(dst); + wgt_view = cugraph_type_erased_device_array_view(wgt); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, vertices_view, (byte_t*)h_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, src_view, (byte_t*)h_src, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, dst_view, (byte_t*)h_dst, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view, (byte_t*)h_wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + + ret_code = cugraph_graph_create_sg(handle, + &properties, + vertices_view, + src_view, + dst_view, + wgt_view, + NULL, + NULL, + FALSE, + FALSE, + FALSE, + FALSE, + FALSE, + &graph, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + + cugraph_centrality_result_t* result = NULL; + + // To verify we will call pagerank + ret_code = cugraph_pagerank(handle, + graph, + NULL, + NULL, + NULL, + NULL, + alpha, + epsilon, + max_iterations, + FALSE, + &result, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_pagerank failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* pageranks; + + result_vertices = cugraph_centrality_result_get_vertices(result); + pageranks = cugraph_centrality_result_get_values(result); + + vertex_t h_result_vertices[num_vertices]; + weight_t h_pageranks[num_vertices]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_vertices, result_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_pageranks, pageranks, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + for (int i = 0; (i < num_vertices) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + nearlyEqual(h_result[h_result_vertices[i]], h_pageranks[i], 0.001), + "pagerank results don't match"); + } + + cugraph_centrality_result_free(result); + cugraph_graph_free(graph); + + cugraph_type_erased_device_array_view_free(wgt_view); + cugraph_type_erased_device_array_view_free(dst_view); + cugraph_type_erased_device_array_view_free(src_view); + cugraph_type_erased_device_array_view_free(vertices_view); + cugraph_type_erased_device_array_free(wgt); + cugraph_type_erased_device_array_free(dst); + cugraph_type_erased_device_array_free(src); + cugraph_type_erased_device_array_free(vertices); + + cugraph_free_resource_handle(handle); + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_create_sg_graph_csr_with_isolated() +{ + int test_ret_value = 0; + + typedef int32_t vertex_t; + typedef int32_t edge_t; + typedef float weight_t; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + size_t num_edges = 8; + size_t num_vertices = 7; + double alpha = 0.95; + double epsilon = 0.0001; + size_t max_iterations = 20; + + /* + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5}; + */ + edge_t h_offsets[] = {0, 1, 3, 6, 7, 8, 8, 8}; + vertex_t h_indices[] = {1, 3, 4, 0, 1, 3, 5, 5}; + vertex_t h_start[] = {0, 1, 2, 3, 4, 5}; + weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; + weight_t h_result[] = { 0.0859168, 0.158029, 0.0616337, 0.179675, 0.113239, 0.339873, 0.0616337 }; + + cugraph_resource_handle_t* handle = NULL; + cugraph_graph_t* graph = NULL; + cugraph_graph_properties_t properties; + + properties.is_symmetric = FALSE; + properties.is_multigraph = FALSE; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + + handle = cugraph_create_resource_handle(NULL); + TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); + + cugraph_type_erased_device_array_t* offsets; + cugraph_type_erased_device_array_t* indices; + cugraph_type_erased_device_array_t* wgt; + cugraph_type_erased_device_array_view_t* offsets_view; + cugraph_type_erased_device_array_view_t* indices_view; + cugraph_type_erased_device_array_view_t* wgt_view; + + ret_code = cugraph_type_erased_device_array_create( + handle, num_vertices + 1, vertex_tid, &offsets, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "offsets create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &indices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "indices create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + offsets_view = cugraph_type_erased_device_array_view(offsets); + indices_view = cugraph_type_erased_device_array_view(indices); + wgt_view = cugraph_type_erased_device_array_view(wgt); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, offsets_view, (byte_t*)h_offsets, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "offsets copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, indices_view, (byte_t*)h_indices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "indices copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view, (byte_t*)h_wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + + ret_code = cugraph_sg_graph_create_from_csr(handle, + &properties, + offsets_view, + indices_view, + wgt_view, + NULL, + NULL, + FALSE, + FALSE, + FALSE, + &graph, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + + cugraph_centrality_result_t* result = NULL; + + // To verify we will call pagerank + ret_code = cugraph_pagerank(handle, + graph, + NULL, + NULL, + NULL, + NULL, + alpha, + epsilon, + max_iterations, + FALSE, + &result, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_pagerank failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* pageranks; + + result_vertices = cugraph_centrality_result_get_vertices(result); + pageranks = cugraph_centrality_result_get_values(result); + + vertex_t h_result_vertices[num_vertices]; + weight_t h_pageranks[num_vertices]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_vertices, result_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_pageranks, pageranks, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + for (int i = 0; (i < num_vertices) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + nearlyEqual(h_result[h_result_vertices[i]], h_pageranks[i], 0.001), + "pagerank results don't match"); + } + + cugraph_centrality_result_free(result); + cugraph_graph_free(graph); + cugraph_type_erased_device_array_view_free(wgt_view); + cugraph_type_erased_device_array_view_free(indices_view); + cugraph_type_erased_device_array_view_free(offsets_view); + cugraph_type_erased_device_array_free(wgt); + cugraph_type_erased_device_array_free(indices); + cugraph_type_erased_device_array_free(offsets); + + cugraph_free_resource_handle(handle); + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_create_sg_graph_with_isolated_vertices_multi_input() +{ + int test_ret_value = 0; + + typedef int32_t vertex_t; + typedef int32_t edge_t; + typedef float weight_t; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + size_t num_edges = 66; + size_t num_vertices = 7; + double alpha = 0.95; + double epsilon = 0.0001; + size_t max_iterations = 20; + + vertex_t h_vertices[] = { 0, 1, 2, 3, 4, 5, 6 }; + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5, + 0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5, + 0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5, + 0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5, + 0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5, + 0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5, + 1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5, + 1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5, + 1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5, + 1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5, + 1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5}; + weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f, + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f, + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f, + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f, + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f, + 0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.7f}; + weight_t h_result[] = { 0.0859168, 0.158029, 0.0616337, 0.179675, 0.113239, 0.339873, 0.0616337 }; + + cugraph_resource_handle_t* handle = NULL; + cugraph_graph_t* graph = NULL; + cugraph_graph_properties_t properties; + + properties.is_symmetric = FALSE; + properties.is_multigraph = FALSE; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + + handle = cugraph_create_resource_handle(NULL); + TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); + + cugraph_type_erased_device_array_t* vertices; + cugraph_type_erased_device_array_t* src; + cugraph_type_erased_device_array_t* dst; + cugraph_type_erased_device_array_t* wgt; + cugraph_type_erased_device_array_view_t* vertices_view; + cugraph_type_erased_device_array_view_t* src_view; + cugraph_type_erased_device_array_view_t* dst_view; + cugraph_type_erased_device_array_view_t* wgt_view; + + ret_code = + cugraph_type_erased_device_array_create(handle, num_vertices, vertex_tid, &vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + vertices_view = cugraph_type_erased_device_array_view(vertices); + src_view = cugraph_type_erased_device_array_view(src); + dst_view = cugraph_type_erased_device_array_view(dst); + wgt_view = cugraph_type_erased_device_array_view(wgt); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, vertices_view, (byte_t*)h_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, src_view, (byte_t*)h_src, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, dst_view, (byte_t*)h_dst, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view, (byte_t*)h_wgt, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + + ret_code = cugraph_graph_create_sg(handle, + &properties, + vertices_view, + src_view, + dst_view, + wgt_view, + NULL, + NULL, + FALSE, + FALSE, + TRUE, + TRUE, + FALSE, + &graph, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + + cugraph_centrality_result_t* result = NULL; + + // To verify we will call pagerank + ret_code = cugraph_pagerank(handle, + graph, + NULL, + NULL, + NULL, + NULL, + alpha, + epsilon, + max_iterations, + FALSE, + &result, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_pagerank failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* pageranks; + + result_vertices = cugraph_centrality_result_get_vertices(result); + pageranks = cugraph_centrality_result_get_values(result); + + vertex_t h_result_vertices[num_vertices]; + weight_t h_pageranks[num_vertices]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_vertices, result_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_pageranks, pageranks, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + for (int i = 0; (i < num_vertices) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + nearlyEqual(h_result[h_result_vertices[i]], h_pageranks[i], 0.001), + "pagerank results don't match"); + } + + cugraph_centrality_result_free(result); + cugraph_graph_free(graph); cugraph_type_erased_device_array_view_free(wgt_view); cugraph_type_erased_device_array_view_free(dst_view); cugraph_type_erased_device_array_view_free(src_view); + cugraph_type_erased_device_array_view_free(vertices_view); cugraph_type_erased_device_array_free(wgt); cugraph_type_erased_device_array_free(dst); cugraph_type_erased_device_array_free(src); + cugraph_type_erased_device_array_free(vertices); cugraph_free_resource_handle(handle); cugraph_error_free(ret_error); @@ -419,5 +904,8 @@ int main(int argc, char** argv) result |= RUN_TEST(test_create_sg_graph_simple); result |= RUN_TEST(test_create_sg_graph_csr); result |= RUN_TEST(test_create_sg_graph_symmetric_error); + result |= RUN_TEST(test_create_sg_graph_with_isolated_vertices); + result |= RUN_TEST(test_create_sg_graph_csr_with_isolated); + result |= RUN_TEST(test_create_sg_graph_with_isolated_vertices_multi_input); return result; } diff --git a/cpp/tests/c_api/mg_create_graph_test.c b/cpp/tests/c_api/mg_create_graph_test.c index 4c8f2f22982..fec319d1881 100644 --- a/cpp/tests/c_api/mg_create_graph_test.c +++ b/cpp/tests/c_api/mg_create_graph_test.c @@ -17,6 +17,8 @@ #include "c_test_utils.h" /* RUN_TEST */ #include "mg_test_utils.h" /* RUN_TEST */ +#include + #include #include #include @@ -41,7 +43,7 @@ int test_create_mg_graph_simple(const cugraph_resource_handle_t* handle) vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5}; weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; - cugraph_graph_t* p_graph = NULL; + cugraph_graph_t* graph = NULL; cugraph_graph_properties_t properties; properties.is_symmetric = FALSE; @@ -94,21 +96,25 @@ int test_create_mg_graph_simple(const cugraph_resource_handle_t* handle) handle, wgt_view, (byte_t*)h_wgt, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); - ret_code = cugraph_mg_graph_create(handle, + ret_code = cugraph_graph_create_mg(handle, &properties, - src_view, - dst_view, - wgt_view, + NULL, + (cugraph_type_erased_device_array_view_t const* const*) &src_view, + (cugraph_type_erased_device_array_view_t const* const*) &dst_view, + (cugraph_type_erased_device_array_view_t const* const*) &wgt_view, NULL, NULL, FALSE, - num_edges, + 1, + FALSE, + FALSE, TRUE, - &p_graph, + &graph, &ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); - cugraph_mg_graph_free(p_graph); + cugraph_graph_free(graph); cugraph_type_erased_device_array_view_free(wgt_view); cugraph_type_erased_device_array_view_free(dst_view); @@ -122,6 +128,382 @@ int test_create_mg_graph_simple(const cugraph_resource_handle_t* handle) return test_ret_value; } +int test_create_mg_graph_multiple_edge_lists(const cugraph_resource_handle_t* handle) +{ + int test_ret_value = 0; + + typedef int32_t vertex_t; + typedef int32_t edge_t; + typedef float weight_t; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + size_t num_edges = 8; + size_t num_vertices = 7; + + double alpha = 0.95; + double epsilon = 0.0001; + size_t max_iterations = 20; + + vertex_t h_vertices[] = { 0, 1, 2, 3, 4, 5, 6 }; + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5}; + weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f}; + weight_t h_result[] = { 0.0859168, 0.158029, 0.0616337, 0.179675, 0.113239, 0.339873, 0.0616337 }; + + cugraph_graph_t* graph = NULL; + cugraph_graph_properties_t properties; + + properties.is_symmetric = FALSE; + properties.is_multigraph = FALSE; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + + const size_t num_local_arrays = 2; + + cugraph_type_erased_device_array_t* vertices[num_local_arrays]; + cugraph_type_erased_device_array_t* src[num_local_arrays]; + cugraph_type_erased_device_array_t* dst[num_local_arrays]; + cugraph_type_erased_device_array_t* wgt[num_local_arrays]; + cugraph_type_erased_device_array_view_t* vertices_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* src_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* dst_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* wgt_view[num_local_arrays]; + + int my_rank = cugraph_resource_handle_get_rank(handle); + int comm_size = cugraph_resource_handle_get_comm_size(handle); + + size_t local_num_vertices = (num_vertices + comm_size - 1) / comm_size; + size_t local_start_vertex = my_rank * local_num_vertices; + size_t local_num_edges = (num_edges + comm_size - 1) / comm_size; + size_t local_start_edge = my_rank * local_num_edges; + + local_num_edges = (local_num_edges < (num_edges - local_start_edge)) ? local_num_edges : (num_edges - local_start_edge); + local_num_vertices = (local_num_vertices < (num_vertices - local_start_vertex)) ? local_num_vertices : (num_vertices - local_start_vertex); + + for (size_t i = 0 ; i < num_local_arrays ; ++i) { + size_t vertex_count = (local_num_vertices + num_local_arrays - 1) / num_local_arrays; + size_t vertex_start = i * vertex_count; + vertex_count = (vertex_count < (local_num_vertices - vertex_start)) ? vertex_count : (local_num_vertices - vertex_start); + + ret_code = + cugraph_type_erased_device_array_create(handle, vertex_count, vertex_tid, vertices + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + size_t edge_count = (local_num_edges + num_local_arrays - 1) / num_local_arrays; + size_t edge_start = i * edge_count; + edge_count = (edge_count < (local_num_edges - edge_start)) ? edge_count : (local_num_edges - edge_start); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, vertex_tid, src + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, vertex_tid, dst + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, weight_tid, wgt + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + vertices_view[i] = cugraph_type_erased_device_array_view(vertices[i]); + src_view[i] = cugraph_type_erased_device_array_view(src[i]); + dst_view[i] = cugraph_type_erased_device_array_view(dst[i]); + wgt_view[i] = cugraph_type_erased_device_array_view(wgt[i]); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, vertices_view[i], (byte_t*)(h_vertices + local_start_vertex + vertex_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, src_view[i], (byte_t*)(h_src + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, dst_view[i], (byte_t*)(h_dst + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view[i], (byte_t*)(h_wgt + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + } + + ret_code = cugraph_graph_create_mg(handle, + &properties, + (cugraph_type_erased_device_array_view_t const* const*) vertices_view, + (cugraph_type_erased_device_array_view_t const* const*) src_view, + (cugraph_type_erased_device_array_view_t const* const*) dst_view, + (cugraph_type_erased_device_array_view_t const* const*) wgt_view, + NULL, + NULL, + FALSE, + num_local_arrays, + FALSE, + FALSE, + TRUE, + &graph, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + // + // Now call pagerank and check results... + // + cugraph_centrality_result_t* result = NULL; + + ret_code = cugraph_pagerank(handle, + graph, + NULL, + NULL, + NULL, + NULL, + alpha, + epsilon, + max_iterations, + FALSE, + &result, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_pagerank failed."); + + // NOTE: Because we get back vertex ids and pageranks, we can simply compare + // the returned values with the expected results for the entire + // graph. Each GPU will have a subset of the total vertices, so + // they will do a subset of the comparisons. + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* pageranks; + + result_vertices = cugraph_centrality_result_get_vertices(result); + pageranks = cugraph_centrality_result_get_values(result); + + size_t num_local_vertices = cugraph_type_erased_device_array_view_size(result_vertices); + + vertex_t h_result_vertices[num_local_vertices]; + weight_t h_pageranks[num_local_vertices]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_vertices, result_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_pageranks, pageranks, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + for (int i = 0; (i < num_local_vertices) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + nearlyEqual(h_result[h_result_vertices[i]], h_pageranks[i], 0.001), + "pagerank results don't match"); + } + + cugraph_centrality_result_free(result); + cugraph_graph_free(graph); + + for (size_t i = 0 ; i < num_local_arrays ; ++i) { + cugraph_type_erased_device_array_view_free(wgt_view[i]); + cugraph_type_erased_device_array_view_free(dst_view[i]); + cugraph_type_erased_device_array_view_free(src_view[i]); + cugraph_type_erased_device_array_view_free(vertices_view[i]); + cugraph_type_erased_device_array_free(wgt[i]); + cugraph_type_erased_device_array_free(dst[i]); + cugraph_type_erased_device_array_free(src[i]); + cugraph_type_erased_device_array_free(vertices[i]); + } + + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_create_mg_graph_multiple_edge_lists_multi_edge(const cugraph_resource_handle_t* handle) +{ + int test_ret_value = 0; + + typedef int32_t vertex_t; + typedef int32_t edge_t; + typedef float weight_t; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + size_t num_edges = 11; + size_t num_vertices = 7; + + double alpha = 0.95; + double epsilon = 0.0001; + size_t max_iterations = 20; + + vertex_t h_vertices[] = { 0, 1, 2, 3, 4, 5, 6 }; + vertex_t h_src[] = {0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 5}; + vertex_t h_dst[] = {1, 3, 4, 0, 1, 3, 5, 5, 5, 5, 5}; + weight_t h_wgt[] = {0.1f, 2.1f, 1.1f, 5.1f, 3.1f, 4.1f, 7.2f, 3.2f, 3.2f, 3.2f, 1.1f}; + weight_t h_result[] = { 0.0859168, 0.158029, 0.0616337, 0.179675, 0.113239, 0.339873, 0.0616337 }; + + cugraph_graph_t* graph = NULL; + cugraph_graph_properties_t properties; + + properties.is_symmetric = FALSE; + properties.is_multigraph = FALSE; + + data_type_id_t vertex_tid = INT32; + data_type_id_t edge_tid = INT32; + data_type_id_t weight_tid = FLOAT32; + + const size_t num_local_arrays = 2; + + cugraph_type_erased_device_array_t* vertices[num_local_arrays]; + cugraph_type_erased_device_array_t* src[num_local_arrays]; + cugraph_type_erased_device_array_t* dst[num_local_arrays]; + cugraph_type_erased_device_array_t* wgt[num_local_arrays]; + cugraph_type_erased_device_array_view_t* vertices_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* src_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* dst_view[num_local_arrays]; + cugraph_type_erased_device_array_view_t* wgt_view[num_local_arrays]; + + int my_rank = cugraph_resource_handle_get_rank(handle); + int comm_size = cugraph_resource_handle_get_comm_size(handle); + + size_t local_num_vertices = (num_vertices + comm_size - 1) / comm_size; + size_t local_start_vertex = my_rank * local_num_vertices; + size_t local_num_edges = (num_edges + comm_size - 1) / comm_size; + size_t local_start_edge = my_rank * local_num_edges; + + local_num_edges = (local_num_edges < (num_edges - local_start_edge)) ? local_num_edges : (num_edges - local_start_edge); + local_num_vertices = (local_num_vertices < (num_vertices - local_start_vertex)) ? local_num_vertices : (num_vertices - local_start_vertex); + + for (size_t i = 0 ; i < num_local_arrays ; ++i) { + size_t vertex_count = (local_num_vertices + num_local_arrays - 1) / num_local_arrays; + size_t vertex_start = i * vertex_count; + vertex_count = (vertex_count < (local_num_vertices - vertex_start)) ? vertex_count : (local_num_vertices - vertex_start); + + ret_code = + cugraph_type_erased_device_array_create(handle, vertex_count, vertex_tid, vertices + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "vertices create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + size_t edge_count = (local_num_edges + num_local_arrays - 1) / num_local_arrays; + size_t edge_start = i * edge_count; + edge_count = (edge_count < (local_num_edges - edge_start)) ? edge_count : (local_num_edges - edge_start); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, vertex_tid, src + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, vertex_tid, dst + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + + ret_code = + cugraph_type_erased_device_array_create(handle, edge_count, weight_tid, wgt + i, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); + + vertices_view[i] = cugraph_type_erased_device_array_view(vertices[i]); + src_view[i] = cugraph_type_erased_device_array_view(src[i]); + dst_view[i] = cugraph_type_erased_device_array_view(dst[i]); + wgt_view[i] = cugraph_type_erased_device_array_view(wgt[i]); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, vertices_view[i], (byte_t*)(h_vertices + local_start_vertex + vertex_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, src_view[i], (byte_t*)(h_src + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, dst_view[i], (byte_t*)(h_dst + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst copy_from_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_from_host( + handle, wgt_view[i], (byte_t*)(h_wgt + local_start_edge + edge_start), &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt copy_from_host failed."); + } + + ret_code = cugraph_graph_create_mg(handle, + &properties, + (cugraph_type_erased_device_array_view_t const* const*) vertices_view, + (cugraph_type_erased_device_array_view_t const* const*) src_view, + (cugraph_type_erased_device_array_view_t const* const*) dst_view, + (cugraph_type_erased_device_array_view_t const* const*) wgt_view, + NULL, + NULL, + FALSE, + num_local_arrays, + TRUE, + TRUE, + TRUE, + &graph, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "graph creation failed."); + TEST_ALWAYS_ASSERT(ret_code == CUGRAPH_SUCCESS, cugraph_error_message(ret_error)); + + // + // Now call pagerank and check results... + // + cugraph_centrality_result_t* result = NULL; + + ret_code = cugraph_pagerank(handle, + graph, + NULL, + NULL, + NULL, + NULL, + alpha, + epsilon, + max_iterations, + FALSE, + &result, + &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_pagerank failed."); + + // NOTE: Because we get back vertex ids and pageranks, we can simply compare + // the returned values with the expected results for the entire + // graph. Each GPU will have a subset of the total vertices, so + // they will do a subset of the comparisons. + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* pageranks; + + result_vertices = cugraph_centrality_result_get_vertices(result); + pageranks = cugraph_centrality_result_get_values(result); + + size_t num_local_vertices = cugraph_type_erased_device_array_view_size(result_vertices); + + vertex_t h_result_vertices[num_local_vertices]; + weight_t h_pageranks[num_local_vertices]; + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_vertices, result_vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_pageranks, pageranks, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + + for (int i = 0; (i < num_local_vertices) && (test_ret_value == 0); ++i) { + TEST_ASSERT(test_ret_value, + nearlyEqual(h_result[h_result_vertices[i]], h_pageranks[i], 0.001), + "pagerank results don't match"); + } + + cugraph_centrality_result_free(result); + cugraph_graph_free(graph); + + for (size_t i = 0 ; i < num_local_arrays ; ++i) { + cugraph_type_erased_device_array_view_free(wgt_view[i]); + cugraph_type_erased_device_array_view_free(dst_view[i]); + cugraph_type_erased_device_array_view_free(src_view[i]); + cugraph_type_erased_device_array_view_free(vertices_view[i]); + cugraph_type_erased_device_array_free(wgt[i]); + cugraph_type_erased_device_array_free(dst[i]); + cugraph_type_erased_device_array_free(src[i]); + cugraph_type_erased_device_array_free(vertices[i]); + } + + cugraph_error_free(ret_error); + + return test_ret_value; +} + /******************************************************************************/ int main(int argc, char** argv) @@ -131,6 +513,8 @@ int main(int argc, char** argv) int result = 0; result |= RUN_MG_TEST(test_create_mg_graph_simple, handle); + result |= RUN_MG_TEST(test_create_mg_graph_multiple_edge_lists, handle); + result |= RUN_MG_TEST(test_create_mg_graph_multiple_edge_lists_multi_edge, handle); cugraph_free_resource_handle(handle); free_mg_raft_handle(raft_handle); From fceb6b7a4b34bc821700ba5729423a0cf76fbbdf Mon Sep 17 00:00:00 2001 From: Brad Rees <34135411+BradReesWork@users.noreply.github.com> Date: Tue, 21 Nov 2023 09:48:41 -0500 Subject: [PATCH 095/111] adding C/C++ API docs (#3938) adding C and C++ API Adding WholeGraph APIs Adding cugraph-ops APIs closes #3406 Authors: - Brad Rees (https://github.com/BradReesWork) - GALI PREM SAGAR (https://github.com/galipremsagar) - Ralph Liu (https://github.com/nv-rliu) - Don Acosta (https://github.com/acostadon) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Don Acosta (https://github.com/acostadon) - Rick Ratzel (https://github.com/rlratzel) - AJ Schmidt (https://github.com/ajschmidt8) URL: https://github.com/rapidsai/cugraph/pull/3938 --- .gitignore | 4 +- build.sh | 22 + ci/build_docs.sh | 7 +- ci/release/update-version.sh | 4 + cpp/doxygen/Doxyfile | 765 ++++++++++++------ cpp/include/cugraph_c/centrality_algorithms.h | 26 +- cpp/include/cugraph_c/community_algorithms.h | 1 - cpp/include/cugraph_c/core_algorithms.h | 14 +- cpp/include/cugraph_c/labeling_algorithms.h | 11 +- cpp/include/cugraph_c/sampling_algorithms.h | 3 +- cpp/include/cugraph_c/similarity_algorithms.h | 7 +- docs/cugraph/Makefile | 2 +- .../cugraph-ops/bipartite_operators.rst | 16 - .../api_docs/cugraph-ops/c_cpp/index.rst | 3 + .../api_docs/cugraph-ops/fg_operators.rst | 83 -- .../api_docs/cugraph-ops/graph_types.rst | 33 - .../source/api_docs/cugraph-ops/index.rst | 11 +- .../api_docs/cugraph-ops/mfg_operators.rst | 31 - .../cugraph-ops/{ => python}/dimenet.rst | 4 +- .../cugraph-ops/python/graph_types.rst | 34 + .../api_docs/cugraph-ops/python/index.rst | 13 + .../api_docs/cugraph-ops/python/operators.rst | 93 +++ .../cugraph-ops/{ => python}/pytorch.rst | 22 +- .../api_docs/cugraph-ops/static_operators.rst | 16 - .../api_docs/cugraph-pyg/cugraph_pyg.rst | 4 +- .../source/api_docs/cugraph_c/c_and_cpp.rst | 4 - .../source/api_docs/cugraph_c/centrality.rst | 51 ++ .../source/api_docs/cugraph_c/community.rst | 63 ++ .../source/api_docs/cugraph_c/core.rst | 21 + .../source/api_docs/cugraph_c/index.rst | 16 + .../source/api_docs/cugraph_c/labeling.rst | 20 + .../source/api_docs/cugraph_c/sampling.rst | 37 + .../source/api_docs/cugraph_c/similarity.rst | 25 + .../source/api_docs/cugraph_c/traversal.rst | 30 + docs/cugraph/source/api_docs/index.rst | 33 +- .../service/cugraph_service_client.rst | 2 +- .../service/cugraph_service_server.rst | 2 +- .../source/api_docs/wholegraph/index.rst | 11 + .../wholegraph/libwholegraph/index.rst | 228 ++++++ .../wholegraph/pylibwholegraph/index.rst | 38 + docs/cugraph/source/basics/cugraph_intro.md | 9 +- docs/cugraph/source/conf.py | 5 +- .../source/graph_support/algorithms.md | 12 +- .../graph_support/algorithms/Centrality.md | 12 +- .../graph_support/algorithms/Similarity.md | 6 +- docs/cugraph/source/index.rst | 10 +- .../source/wholegraph/basics/index.rst | 11 + .../wholegraph/basics/wholegraph_intro.md | 135 ++++ .../wholememory_implementation_details.md | 58 ++ .../wholegraph/basics/wholememory_intro.md | 123 +++ .../imgs/device_chunked_wholememory_step1.png | Bin 0 -> 23136 bytes .../imgs/device_chunked_wholememory_step2.png | Bin 0 -> 28201 bytes .../device_continuous_wholememory_step1.png | Bin 0 -> 21107 bytes .../device_continuous_wholememory_step2.png | Bin 0 -> 26434 bytes .../imgs/distributed_wholememory.png | Bin 0 -> 23138 bytes .../wholegraph/imgs/general_wholememory.png | Bin 0 -> 14073 bytes .../imgs/host_mapped_wholememory_step1.png | Bin 0 -> 21733 bytes .../imgs/host_mapped_wholememory_step2.png | Bin 0 -> 32110 bytes .../wholegraph/imgs/wholememory_tensor.png | Bin 0 -> 40367 bytes docs/cugraph/source/wholegraph/index.rst | 14 + .../wholegraph/installation/container.md | 29 + .../installation/getting_wholegraph.md | 48 ++ .../source/wholegraph/installation/index.rst | 9 + .../wholegraph/installation/source_build.md | 187 +++++ 64 files changed, 1951 insertions(+), 527 deletions(-) delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst rename docs/cugraph/source/api_docs/cugraph-ops/{ => python}/dimenet.rst (89%) create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/index.rst create mode 100644 docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst rename docs/cugraph/source/api_docs/cugraph-ops/{ => python}/pytorch.rst (59%) delete mode 100644 docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst delete mode 100644 docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/centrality.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/community.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/core.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/index.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/labeling.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/sampling.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/similarity.rst create mode 100644 docs/cugraph/source/api_docs/cugraph_c/traversal.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/index.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst create mode 100644 docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst create mode 100644 docs/cugraph/source/wholegraph/basics/index.rst create mode 100644 docs/cugraph/source/wholegraph/basics/wholegraph_intro.md create mode 100644 docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md create mode 100644 docs/cugraph/source/wholegraph/basics/wholememory_intro.md create mode 100644 docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_continuous_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/device_continuous_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/distributed_wholememory.png create mode 100644 docs/cugraph/source/wholegraph/imgs/general_wholememory.png create mode 100644 docs/cugraph/source/wholegraph/imgs/host_mapped_wholememory_step1.png create mode 100644 docs/cugraph/source/wholegraph/imgs/host_mapped_wholememory_step2.png create mode 100644 docs/cugraph/source/wholegraph/imgs/wholememory_tensor.png create mode 100644 docs/cugraph/source/wholegraph/index.rst create mode 100644 docs/cugraph/source/wholegraph/installation/container.md create mode 100644 docs/cugraph/source/wholegraph/installation/getting_wholegraph.md create mode 100644 docs/cugraph/source/wholegraph/installation/index.rst create mode 100644 docs/cugraph/source/wholegraph/installation/source_build.md diff --git a/.gitignore b/.gitignore index c6bcf6965d7..358650cfc5a 100644 --- a/.gitignore +++ b/.gitignore @@ -84,8 +84,10 @@ datasets/* # Jupyter Notebooks .ipynb_checkpoints -## Doxygen +## Doxygen and Docs cpp/doxygen/html +docs/cugraph/lib* +docs/cugraph/api/* # created by Dask tests python/dask-worker-space diff --git a/build.sh b/build.sh index 1723e750978..eef19046d85 100755 --- a/build.sh +++ b/build.sh @@ -18,6 +18,8 @@ ARGS=$* # script, and that this script resides in the repo dir! REPODIR=$(cd $(dirname $0); pwd) +RAPIDS_VERSION=23.12 + # Valid args to this script (all possible targets and options) - only one per line VALIDARGS=" clean @@ -412,8 +414,28 @@ if hasArg docs || hasArg all; then ${CMAKE_GENERATOR_OPTION} \ ${CMAKE_VERBOSE_OPTION} fi + + for PROJECT in libcugraphops libwholegraph; do + XML_DIR="${REPODIR}/docs/cugraph/${PROJECT}" + rm -rf "${XML_DIR}" + mkdir -p "${XML_DIR}" + export XML_DIR_${PROJECT^^}="$XML_DIR" + + echo "downloading xml for ${PROJECT} into ${XML_DIR}. Environment variable XML_DIR_${PROJECT^^} is set to ${XML_DIR}" + curl -O "https://d1664dvumjb44w.cloudfront.net/${PROJECT}/xml_tar/${RAPIDS_VERSION}/xml.tar.gz" + tar -xzf xml.tar.gz -C "${XML_DIR}" + rm "./xml.tar.gz" + done + cd ${LIBCUGRAPH_BUILD_DIR} cmake --build "${LIBCUGRAPH_BUILD_DIR}" -j${PARALLEL_LEVEL} --target docs_cugraph ${VERBOSE_FLAG} + + echo "making libcugraph doc dir" + rm -rf ${REPODIR}/docs/cugraph/libcugraph + mkdir -p ${REPODIR}/docs/cugraph/libcugraph + + export XML_DIR_LIBCUGRAPH="${REPODIR}/cpp/doxygen/xml" + cd ${REPODIR}/docs/cugraph make html fi diff --git a/ci/build_docs.sh b/ci/build_docs.sh index 3f97f652d41..3f765704bdb 100755 --- a/ci/build_docs.sh +++ b/ci/build_docs.sh @@ -29,7 +29,9 @@ rapids-mamba-retry install \ cugraph-pyg \ cugraph-service-server \ cugraph-service-client \ - libcugraph_etl + libcugraph_etl \ + pylibcugraphops \ + pylibwholegraph # This command installs `cugraph-dgl` without its dependencies # since this package can currently only run in `11.6` CTK environments @@ -50,8 +52,7 @@ done rapids-logger "Build CPP docs" pushd cpp/doxygen doxygen Doxyfile -mkdir -p "${RAPIDS_DOCS_DIR}/libcugraph/html" -mv html/* "${RAPIDS_DOCS_DIR}/libcugraph/html" +export XML_DIR_LIBCUGRAPH="$(pwd)/xml" popd rapids-logger "Build Python docs" diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh index 69eb085e7ed..c091bd1ed33 100755 --- a/ci/release/update-version.sh +++ b/ci/release/update-version.sh @@ -54,6 +54,10 @@ sed_runner "s/set(cugraph_version .*)/set(cugraph_version ${NEXT_FULL_TAG})/g" p sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/cugraph/source/conf.py sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/cugraph/source/conf.py + +# build.sh script +sed_runner 's/RAPIDS_VERSION=.*/RAPIDS_VERSION='${NEXT_SHORT_TAG}'/g' build.sh + # Centralized version file update # NOTE: Any script that runs in CI will need to use gha-tool `rapids-generate-version` # and echo it to `VERSION` file to get an alpha spec of the current version diff --git a/cpp/doxygen/Doxyfile b/cpp/doxygen/Doxyfile index 482ff988098..6946bd38bfe 100644 --- a/cpp/doxygen/Doxyfile +++ b/cpp/doxygen/Doxyfile @@ -1,4 +1,4 @@ -# Doxyfile 1.8.20 +# Doxyfile 1.9.8 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project. @@ -12,6 +12,16 @@ # For lists, items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (\" \"). +# +# Note: +# +# Use doxygen to compare the used configuration file with the template +# configuration file: +# doxygen -x [configFile] +# Use doxygen to compare the used configuration file with the template +# configuration file without replacing the environment variables or CMake type +# replacement variables: +# doxygen -x_noenv [configFile] #--------------------------------------------------------------------------- # Project related configuration options @@ -32,19 +42,19 @@ DOXYFILE_ENCODING = UTF-8 # title of most generated pages and in a few other places. # The default value is: My Project. -PROJECT_NAME = "libcugraph" +PROJECT_NAME = libcugraph # The PROJECT_NUMBER tag can be used to enter a project or revision number. This # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER=23.12 +PROJECT_NUMBER = 23.12 # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a # quick idea about the purpose of the project. Keep the description short. -PROJECT_BRIEF = GPU accelerated graph analytics +PROJECT_BRIEF = "GPU accelerated graph analytics" # With the PROJECT_LOGO tag one can specify a logo or an icon that is included # in the documentation. The maximum height of the logo should not exceed 55 @@ -60,16 +70,28 @@ PROJECT_LOGO = OUTPUT_DIRECTORY = -# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub- -# directories (in 2 levels) under the output directory of each output format and -# will distribute the generated files over these directories. Enabling this +# If the CREATE_SUBDIRS tag is set to YES then doxygen will create up to 4096 +# sub-directories (in 2 levels) under the output directory of each output format +# and will distribute the generated files over these directories. Enabling this # option can be useful when feeding doxygen a huge amount of source files, where # putting all generated files in the same directory would otherwise causes -# performance problems for the file system. +# performance problems for the file system. Adapt CREATE_SUBDIRS_LEVEL to +# control the number of sub-directories. # The default value is: NO. CREATE_SUBDIRS = NO +# Controls the number of sub-directories that will be created when +# CREATE_SUBDIRS tag is set to YES. Level 0 represents 16 directories, and every +# level increment doubles the number of directories, resulting in 4096 +# directories at level 8 which is the default and also the maximum value. The +# sub-directories are organized in 2 levels, the first level always has a fixed +# number of 16 directories. +# Minimum value: 0, maximum value: 8, default value: 8. +# This tag requires that the tag CREATE_SUBDIRS is set to YES. + +CREATE_SUBDIRS_LEVEL = 8 + # If the ALLOW_UNICODE_NAMES tag is set to YES, doxygen will allow non-ASCII # characters to appear in the names of generated files. If set to NO, non-ASCII # characters will be escaped, for example _xE3_x81_x84 will be used for Unicode @@ -81,26 +103,18 @@ ALLOW_UNICODE_NAMES = NO # The OUTPUT_LANGUAGE tag is used to specify the language in which all # documentation generated by doxygen is written. Doxygen will use this # information to generate all constant output in the proper language. -# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Catalan, Chinese, -# Chinese-Traditional, Croatian, Czech, Danish, Dutch, English (United States), -# Esperanto, Farsi (Persian), Finnish, French, German, Greek, Hungarian, -# Indonesian, Italian, Japanese, Japanese-en (Japanese with English messages), -# Korean, Korean-en (Korean with English messages), Latvian, Lithuanian, -# Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, Romanian, Russian, -# Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, Swedish, Turkish, -# Ukrainian and Vietnamese. +# Possible values are: Afrikaans, Arabic, Armenian, Brazilian, Bulgarian, +# Catalan, Chinese, Chinese-Traditional, Croatian, Czech, Danish, Dutch, English +# (United States), Esperanto, Farsi (Persian), Finnish, French, German, Greek, +# Hindi, Hungarian, Indonesian, Italian, Japanese, Japanese-en (Japanese with +# English messages), Korean, Korean-en (Korean with English messages), Latvian, +# Lithuanian, Macedonian, Norwegian, Persian (Farsi), Polish, Portuguese, +# Romanian, Russian, Serbian, Serbian-Cyrillic, Slovak, Slovene, Spanish, +# Swedish, Turkish, Ukrainian and Vietnamese. # The default value is: English. OUTPUT_LANGUAGE = English -# The OUTPUT_TEXT_DIRECTION tag is used to specify the direction in which all -# documentation generated by doxygen is written. Doxygen will use this -# information to generate all generated output in the proper direction. -# Possible values are: None, LTR, RTL and Context. -# The default value is: None. - -OUTPUT_TEXT_DIRECTION = None - # If the BRIEF_MEMBER_DESC tag is set to YES, doxygen will include brief member # descriptions after the members that are listed in the file and class # documentation (similar to Javadoc). Set to NO to disable this. @@ -248,16 +262,16 @@ TAB_SIZE = 4 # the documentation. An alias has the form: # name=value # For example adding -# "sideeffect=@par Side Effects:\n" +# "sideeffect=@par Side Effects:^^" # will allow you to put the command \sideeffect (or @sideeffect) in the # documentation, which will result in a user-defined paragraph with heading -# "Side Effects:". You can put \n's in the value part of an alias to insert -# newlines (in the resulting output). You can put ^^ in the value part of an -# alias to insert a newline as if a physical newline was in the original file. -# When you need a literal { or } or , in the value part of an alias you have to -# escape them by means of a backslash (\), this can lead to conflicts with the -# commands \{ and \} for these it is advised to use the version @{ and @} or use -# a double escape (\\{ and \\}) +# "Side Effects:". Note that you cannot put \n's in the value part of an alias +# to insert newlines (in the resulting output). You can put ^^ in the value part +# of an alias to insert a newline as if a physical newline was in the original +# file. When you need a literal { or } or , in the value part of an alias you +# have to escape them by means of a backslash (\), this can lead to conflicts +# with the commands \{ and \} for these it is advised to use the version @{ and +# @} or use a double escape (\\{ and \\}) ALIASES = @@ -302,8 +316,8 @@ OPTIMIZE_OUTPUT_SLICE = NO # extension. Doxygen has a built-in mapping, but you can override or extend it # using this tag. The format is ext=language, where ext is a file extension, and # language is one of the parsers supported by doxygen: IDL, Java, JavaScript, -# Csharp (C#), C, C++, D, PHP, md (Markdown), Objective-C, Python, Slice, VHDL, -# Fortran (fixed format Fortran: FortranFixed, free formatted Fortran: +# Csharp (C#), C, C++, Lex, D, PHP, md (Markdown), Objective-C, Python, Slice, +# VHDL, Fortran (fixed format Fortran: FortranFixed, free formatted Fortran: # FortranFree, unknown formatted Fortran: Fortran. In the later case the parser # tries to guess whether the code is fixed or free formatted code, this is the # default for Fortran type files). For instance to make doxygen treat .inc files @@ -313,7 +327,10 @@ OPTIMIZE_OUTPUT_SLICE = NO # Note: For files without extension you can use no_extension as a placeholder. # # Note that for custom extensions you also need to set FILE_PATTERNS otherwise -# the files are not read by doxygen. +# the files are not read by doxygen. When specifying no_extension you should add +# * to the FILE_PATTERNS. +# +# Note see also the list of default file extension mappings. EXTENSION_MAPPING = cu=C++ \ cuh=C++ @@ -337,6 +354,17 @@ MARKDOWN_SUPPORT = YES TOC_INCLUDE_HEADINGS = 5 +# The MARKDOWN_ID_STYLE tag can be used to specify the algorithm used to +# generate identifiers for the Markdown headings. Note: Every identifier is +# unique. +# Possible values are: DOXYGEN use a fixed 'autotoc_md' string followed by a +# sequence number starting at 0 and GITHUB use the lower case version of title +# with any whitespace replaced by '-' and punctuation characters removed. +# The default value is: DOXYGEN. +# This tag requires that the tag MARKDOWN_SUPPORT is set to YES. + +MARKDOWN_ID_STYLE = DOXYGEN + # When enabled doxygen tries to link words that correspond to documented # classes, or namespaces to their corresponding documentation. Such a link can # be prevented in individual cases by putting a % sign in front of the word or @@ -448,19 +476,27 @@ TYPEDEF_HIDES_STRUCT = NO LOOKUP_CACHE_SIZE = 0 -# The NUM_PROC_THREADS specifies the number threads doxygen is allowed to use +# The NUM_PROC_THREADS specifies the number of threads doxygen is allowed to use # during processing. When set to 0 doxygen will based this on the number of # cores available in the system. You can set it explicitly to a value larger # than 0 to get more control over the balance between CPU load and processing # speed. At this moment only the input processing can be done using multiple # threads. Since this is still an experimental feature the default is set to 1, -# which efficively disables parallel processing. Please report any issues you +# which effectively disables parallel processing. Please report any issues you # encounter. Generating dot graphs in parallel is controlled by the # DOT_NUM_THREADS setting. # Minimum value: 0, maximum value: 32, default value: 1. NUM_PROC_THREADS = 1 +# If the TIMESTAMP tag is set different from NO then each generated page will +# contain the date or date and time when the page was generated. Setting this to +# NO can help when comparing the output of multiple runs. +# Possible values are: YES, NO, DATETIME and DATE. +# The default value is: NO. + +TIMESTAMP = NO + #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- @@ -524,6 +560,13 @@ EXTRACT_LOCAL_METHODS = NO EXTRACT_ANON_NSPACES = NO +# If this flag is set to YES, the name of an unnamed parameter in a declaration +# will be determined by the corresponding definition. By default unnamed +# parameters remain unnamed in the output. +# The default value is: YES. + +RESOLVE_UNNAMED_PARAMS = YES + # If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all # undocumented members inside documented classes or files. If set to NO these # members will be included in the various overviews, but no documentation @@ -535,7 +578,8 @@ HIDE_UNDOC_MEMBERS = NO # If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. If set # to NO, these classes will be included in the various overviews. This option -# has no effect if EXTRACT_ALL is enabled. +# will also hide undocumented C++ concepts if enabled. This option has no effect +# if EXTRACT_ALL is enabled. # The default value is: NO. HIDE_UNDOC_CLASSES = NO @@ -561,12 +605,20 @@ HIDE_IN_BODY_DOCS = NO INTERNAL_DOCS = NO -# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file -# names in lower-case letters. If set to YES, upper-case letters are also -# allowed. This is useful if you have classes or files whose names only differ -# in case and if your file system supports case sensitive file names. Windows -# (including Cygwin) and Mac users are advised to set this option to NO. -# The default value is: system dependent. +# With the correct setting of option CASE_SENSE_NAMES doxygen will better be +# able to match the capabilities of the underlying filesystem. In case the +# filesystem is case sensitive (i.e. it supports files in the same directory +# whose names only differ in casing), the option must be set to YES to properly +# deal with such files in case they appear in the input. For filesystems that +# are not case sensitive the option should be set to NO to properly deal with +# output files written for symbols that only differ in casing, such as for two +# classes, one named CLASS and the other named Class, and to also support +# references to files without having to specify the exact matching casing. On +# Windows (including Cygwin) and MacOS, users should typically set this option +# to NO, whereas on Linux or other Unix flavors it should typically be set to +# YES. +# Possible values are: SYSTEM, NO and YES. +# The default value is: SYSTEM. CASE_SENSE_NAMES = YES @@ -584,6 +636,12 @@ HIDE_SCOPE_NAMES = NO HIDE_COMPOUND_REFERENCE= NO +# If the SHOW_HEADERFILE tag is set to YES then the documentation for a class +# will show which file needs to be included to use the class. +# The default value is: YES. + +SHOW_HEADERFILE = YES + # If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of # the files that are included by a file in the documentation of that file. # The default value is: YES. @@ -741,7 +799,8 @@ FILE_VERSION_FILTER = # output files in an output format independent way. To create the layout file # that represents doxygen's defaults, run doxygen with the -l option. You can # optionally specify a file name after the option, if omitted DoxygenLayout.xml -# will be used as the name of the layout file. +# will be used as the name of the layout file. See also section "Changing the +# layout of pages" for information. # # Note that if you run doxygen from a directory containing a file called # DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE @@ -787,24 +846,50 @@ WARNINGS = YES WARN_IF_UNDOCUMENTED = YES # If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for -# potential errors in the documentation, such as not documenting some parameters -# in a documented function, or documenting parameters that don't exist or using -# markup commands wrongly. +# potential errors in the documentation, such as documenting some parameters in +# a documented function twice, or documenting parameters that don't exist or +# using markup commands wrongly. # The default value is: YES. WARN_IF_DOC_ERROR = YES +# If WARN_IF_INCOMPLETE_DOC is set to YES, doxygen will warn about incomplete +# function parameter documentation. If set to NO, doxygen will accept that some +# parameters have no documentation without warning. +# The default value is: YES. + +WARN_IF_INCOMPLETE_DOC = YES + # This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that # are documented, but have no documentation for their parameters or return -# value. If set to NO, doxygen will only warn about wrong or incomplete -# parameter documentation, but not about the absence of documentation. If -# EXTRACT_ALL is set to YES then this flag will automatically be disabled. +# value. If set to NO, doxygen will only warn about wrong parameter +# documentation, but not about the absence of documentation. If EXTRACT_ALL is +# set to YES then this flag will automatically be disabled. See also +# WARN_IF_INCOMPLETE_DOC # The default value is: NO. WARN_NO_PARAMDOC = YES +# If WARN_IF_UNDOC_ENUM_VAL option is set to YES, doxygen will warn about +# undocumented enumeration values. If set to NO, doxygen will accept +# undocumented enumeration values. If EXTRACT_ALL is set to YES then this flag +# will automatically be disabled. +# The default value is: NO. + +WARN_IF_UNDOC_ENUM_VAL = NO + # If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when -# a warning is encountered. +# a warning is encountered. If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS +# then doxygen will continue running as if WARN_AS_ERROR tag is set to NO, but +# at the end of the doxygen process doxygen will return with a non-zero status. +# If the WARN_AS_ERROR tag is set to FAIL_ON_WARNINGS_PRINT then doxygen behaves +# like FAIL_ON_WARNINGS but in case no WARN_LOGFILE is defined doxygen will not +# write the warning messages in between other messages but write them at the end +# of a run, in case a WARN_LOGFILE is defined the warning messages will be +# besides being in the defined file also be shown at the end of a run, unless +# the WARN_LOGFILE is defined as - i.e. standard output (stdout) in that case +# the behavior will remain as with the setting FAIL_ON_WARNINGS. +# Possible values are: NO, YES, FAIL_ON_WARNINGS and FAIL_ON_WARNINGS_PRINT. # The default value is: NO. WARN_AS_ERROR = NO @@ -815,13 +900,27 @@ WARN_AS_ERROR = NO # and the warning text. Optionally the format may contain $version, which will # be replaced by the version of the file (if it could be obtained via # FILE_VERSION_FILTER) +# See also: WARN_LINE_FORMAT # The default value is: $file:$line: $text. WARN_FORMAT = "$file:$line: $text" +# In the $text part of the WARN_FORMAT command it is possible that a reference +# to a more specific place is given. To make it easier to jump to this place +# (outside of doxygen) the user can define a custom "cut" / "paste" string. +# Example: +# WARN_LINE_FORMAT = "'vi $file +$line'" +# See also: WARN_FORMAT +# The default value is: at line $line of file $file. + +WARN_LINE_FORMAT = "at line $line of file $file" + # The WARN_LOGFILE tag can be used to specify a file to which warning and error # messages should be written. If left blank the output is written to standard -# error (stderr). +# error (stderr). In case the file specified cannot be opened for writing the +# warning and error messages are written to standard error. When as file - is +# specified the warning and error messages are written to standard output +# (stdout). WARN_LOGFILE = @@ -842,12 +941,23 @@ INPUT = main_page.md \ # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses # libiconv (or the iconv built into libc) for the transcoding. See the libiconv -# documentation (see: https://www.gnu.org/software/libiconv/) for the list of -# possible encodings. +# documentation (see: +# https://www.gnu.org/software/libiconv/) for the list of possible encodings. +# See also: INPUT_FILE_ENCODING # The default value is: UTF-8. INPUT_ENCODING = UTF-8 +# This tag can be used to specify the character encoding of the source files +# that doxygen parses The INPUT_FILE_ENCODING tag can be used to specify +# character encoding on a per file pattern basis. Doxygen will compare the file +# name with each pattern and apply the encoding instead of the default +# INPUT_ENCODING) if there is a match. The character encodings are a list of the +# form: pattern=encoding (like *.php=ISO-8859-1). See cfg_input_encoding +# "INPUT_ENCODING" for further information on supported encodings. + +INPUT_FILE_ENCODING = + # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and # *.h) to filter out the source-files in the directories. @@ -856,13 +966,15 @@ INPUT_ENCODING = UTF-8 # need to set EXTENSION_MAPPING for the extension otherwise the files are not # read by doxygen. # -# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, -# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, -# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, -# *.m, *.markdown, *.md, *.mm, *.dox (to be provided as doxygen C comment), -# *.doc (to be provided as doxygen C comment), *.txt (to be provided as doxygen -# C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, *.f18, *.f, *.for, *.vhd, -# *.vhdl, *.ucf, *.qsf and *.ice. +# Note the list of default checked file patterns might differ from the list of +# default file extension mappings. +# +# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cxxm, +# *.cpp, *.cppm, *.c++, *.c++m, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, +# *.ddl, *.odl, *.h, *.hh, *.hxx, *.hpp, *.h++, *.ixx, *.l, *.cs, *.d, *.php, +# *.php4, *.php5, *.phtml, *.inc, *.m, *.markdown, *.md, *.mm, *.dox (to be +# provided as doxygen C comment), *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, +# *.f18, *.f, *.for, *.vhd, *.vhdl, *.ucf, *.qsf and *.ice. FILE_PATTERNS = *.cpp \ *.hpp \ @@ -907,10 +1019,7 @@ EXCLUDE_PATTERNS = */nvtx/* \ # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, -# AClass::ANamespace, ANamespace::*Test -# -# Note that the wildcards are matched against the file with absolute path, so to -# exclude all test directories use the pattern */test/* +# ANamespace::AClass, ANamespace::*Test EXCLUDE_SYMBOLS = org::apache @@ -955,6 +1064,11 @@ IMAGE_PATH = # code is scanned, but not when the output code is generated. If lines are added # or removed, the anchors will not be placed correctly. # +# Note that doxygen will use the data processed and written to standard output +# for further processing, therefore nothing else, like debug statements or used +# commands (so in case of a Windows batch file always use @echo OFF), should be +# written to standard output. +# # Note that for custom extensions or not directly supported extensions you also # need to set EXTENSION_MAPPING for the extension otherwise the files are not # properly processed by doxygen. @@ -996,6 +1110,15 @@ FILTER_SOURCE_PATTERNS = USE_MDFILE_AS_MAINPAGE = main_page.md +# The Fortran standard specifies that for fixed formatted Fortran code all +# characters from position 72 are to be considered as comment. A common +# extension is to allow longer lines before the automatic comment starts. The +# setting FORTRAN_COMMENT_AFTER will also make it possible that longer lines can +# be processed before the automatic comment starts. +# Minimum value: 7, maximum value: 10000, default value: 72. + +FORTRAN_COMMENT_AFTER = 72 + #--------------------------------------------------------------------------- # Configuration options related to source browsing #--------------------------------------------------------------------------- @@ -1093,17 +1216,11 @@ VERBATIM_HEADERS = YES ALPHABETICAL_INDEX = YES -# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in -# which the alphabetical index list will be split. -# Minimum value: 1, maximum value: 20, default value: 5. -# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. - -COLS_IN_ALPHA_INDEX = 5 - -# In case all classes in a project start with a common prefix, all classes will -# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag -# can be used to specify a prefix (or a list of prefixes) that should be ignored -# while generating the index headers. +# The IGNORE_PREFIX tag can be used to specify a prefix (or a list of prefixes) +# that should be ignored while generating the index headers. The IGNORE_PREFIX +# tag works for classes, function and member names. The entity will be placed in +# the alphabetical list under the first letter of the entity name that remains +# after removing the prefix. # This tag requires that the tag ALPHABETICAL_INDEX is set to YES. IGNORE_PREFIX = @@ -1115,7 +1232,7 @@ IGNORE_PREFIX = # If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output # The default value is: YES. -GENERATE_HTML = YES +GENERATE_HTML = NO # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of @@ -1182,7 +1299,12 @@ HTML_STYLESHEET = # Doxygen will copy the style sheet files to the output directory. # Note: The order of the extra style sheet files is of importance (e.g. the last # style sheet in the list overrules the setting of the previous ones in the -# list). For an example see the documentation. +# list). +# Note: Since the styling of scrollbars can currently not be overruled in +# Webkit/Chromium, the styling will be left out of the default doxygen.css if +# one or more extra stylesheets have been specified. So if scrollbar +# customization is desired it has to be added explicitly. For an example see the +# documentation. # This tag requires that the tag GENERATE_HTML is set to YES. HTML_EXTRA_STYLESHEET = @@ -1197,9 +1319,22 @@ HTML_EXTRA_STYLESHEET = HTML_EXTRA_FILES = +# The HTML_COLORSTYLE tag can be used to specify if the generated HTML output +# should be rendered with a dark or light theme. +# Possible values are: LIGHT always generate light mode output, DARK always +# generate dark mode output, AUTO_LIGHT automatically set the mode according to +# the user preference, use light mode if no preference is set (the default), +# AUTO_DARK automatically set the mode according to the user preference, use +# dark mode if no preference is set and TOGGLE allow to user to switch between +# light and dark mode via a button. +# The default value is: AUTO_LIGHT. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE = AUTO_LIGHT + # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen # will adjust the colors in the style sheet and background images according to -# this color. Hue is specified as an angle on a colorwheel, see +# this color. Hue is specified as an angle on a color-wheel, see # https://en.wikipedia.org/wiki/Hue for more information. For instance the value # 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 # purple, and 360 is red again. @@ -1209,7 +1344,7 @@ HTML_EXTRA_FILES = HTML_COLORSTYLE_HUE = 270 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors -# in the HTML output. For a value of 0 the output will use grayscales only. A +# in the HTML output. For a value of 0 the output will use gray-scales only. A # value of 255 will produce the most vivid colors. # Minimum value: 0, maximum value: 255, default value: 100. # This tag requires that the tag GENERATE_HTML is set to YES. @@ -1227,15 +1362,6 @@ HTML_COLORSTYLE_SAT = 255 HTML_COLORSTYLE_GAMMA = 80 -# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML -# page will contain the date and time when the page was generated. Setting this -# to YES can help to show when doxygen was last run and thus if the -# documentation is up to date. -# The default value is: NO. -# This tag requires that the tag GENERATE_HTML is set to YES. - -HTML_TIMESTAMP = NO - # If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML # documentation will contain a main index with vertical navigation menus that # are dynamically created via JavaScript. If disabled, the navigation index will @@ -1255,6 +1381,13 @@ HTML_DYNAMIC_MENUS = YES HTML_DYNAMIC_SECTIONS = NO +# If the HTML_CODE_FOLDING tag is set to YES then classes and functions can be +# dynamically folded and expanded in the generated HTML source code. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_CODE_FOLDING = YES + # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries # shown in the various tree structured indices initially; the user can expand # and collapse entries dynamically later on. Doxygen will expand the tree to @@ -1270,10 +1403,11 @@ HTML_INDEX_NUM_ENTRIES = 100 # If the GENERATE_DOCSET tag is set to YES, additional index files will be # generated that can be used as input for Apple's Xcode 3 integrated development -# environment (see: https://developer.apple.com/xcode/), introduced with OSX -# 10.5 (Leopard). To create a documentation set, doxygen will generate a -# Makefile in the HTML output directory. Running make will produce the docset in -# that directory and running make install will install the docset in +# environment (see: +# https://developer.apple.com/xcode/), introduced with OSX 10.5 (Leopard). To +# create a documentation set, doxygen will generate a Makefile in the HTML +# output directory. Running make will produce the docset in that directory and +# running make install will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at # startup. See https://developer.apple.com/library/archive/featuredarticles/Doxy # genXcode/_index.html for more information. @@ -1290,6 +1424,13 @@ GENERATE_DOCSET = NO DOCSET_FEEDNAME = "Doxygen generated docs" +# This tag determines the URL of the docset feed. A documentation feed provides +# an umbrella under which multiple documentation sets from a single provider +# (such as a company or product suite) can be grouped. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_FEEDURL = + # This tag specifies a string that should uniquely identify the documentation # set bundle. This should be a reverse domain-name style string, e.g. # com.mycompany.MyDocSet. Doxygen will append .docset to the name. @@ -1315,8 +1456,12 @@ DOCSET_PUBLISHER_NAME = Publisher # If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three # additional HTML index files: index.hhp, index.hhc, and index.hhk. The # index.hhp is a project file that can be read by Microsoft's HTML Help Workshop -# (see: https://www.microsoft.com/en-us/download/details.aspx?id=21138) on -# Windows. +# on Windows. In the beginning of 2021 Microsoft took the original page, with +# a.o. the download links, offline the HTML help workshop was already many years +# in maintenance mode). You can download the HTML help workshop from the web +# archives at Installation executable (see: +# http://web.archive.org/web/20160201063255/http://download.microsoft.com/downlo +# ad/0/A/9/0A939EF6-E31C-430F-A3DF-DFAE7960D564/htmlhelp.exe). # # The HTML Help Workshop contains a compiler that can convert all HTML output # generated by doxygen into a single compiled HTML file (.chm). Compiled HTML @@ -1373,6 +1518,16 @@ BINARY_TOC = NO TOC_EXPAND = NO +# The SITEMAP_URL tag is used to specify the full URL of the place where the +# generated documentation will be placed on the server by the user during the +# deployment of the documentation. The generated sitemap is called sitemap.xml +# and placed on the directory specified by HTML_OUTPUT. In case no SITEMAP_URL +# is specified no sitemap is generated. For information about the sitemap +# protocol see https://www.sitemaps.org +# This tag requires that the tag GENERATE_HTML is set to YES. + +SITEMAP_URL = + # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that # can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help @@ -1391,7 +1546,8 @@ QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help # Project output. For more information please see Qt Help Project / Namespace -# (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace). +# (see: +# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#namespace). # The default value is: org.doxygen.Project. # This tag requires that the tag GENERATE_QHP is set to YES. @@ -1399,8 +1555,8 @@ QHP_NAMESPACE = org.doxygen.Project # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt # Help Project output. For more information please see Qt Help Project / Virtual -# Folders (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual- -# folders). +# Folders (see: +# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#virtual-folders). # The default value is: doc. # This tag requires that the tag GENERATE_QHP is set to YES. @@ -1408,16 +1564,16 @@ QHP_VIRTUAL_FOLDER = doc # If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom # filter to add. For more information please see Qt Help Project / Custom -# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom- -# filters). +# Filters (see: +# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters). # This tag requires that the tag GENERATE_QHP is set to YES. QHP_CUST_FILTER_NAME = # The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the # custom filter to add. For more information please see Qt Help Project / Custom -# Filters (see: https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom- -# filters). +# Filters (see: +# https://doc.qt.io/archives/qt-4.8/qthelpproject.html#custom-filters). # This tag requires that the tag GENERATE_QHP is set to YES. QHP_CUST_FILTER_ATTRS = @@ -1429,9 +1585,9 @@ QHP_CUST_FILTER_ATTRS = QHP_SECT_FILTER_ATTRS = -# The QHG_LOCATION tag can be used to specify the location of Qt's -# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the -# generated .qhp file. +# The QHG_LOCATION tag can be used to specify the location (absolute path +# including file name) of Qt's qhelpgenerator. If non-empty doxygen will try to +# run qhelpgenerator on the generated .qhp file. # This tag requires that the tag GENERATE_QHP is set to YES. QHG_LOCATION = @@ -1474,16 +1630,28 @@ DISABLE_INDEX = NO # to work a browser that supports JavaScript, DHTML, CSS and frames is required # (i.e. any modern browser). Windows users are probably better off using the # HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can -# further fine-tune the look of the index. As an example, the default style -# sheet generated by doxygen has an example that shows how to put an image at -# the root of the tree instead of the PROJECT_NAME. Since the tree basically has -# the same information as the tab index, you could consider setting -# DISABLE_INDEX to YES when enabling this option. +# further fine tune the look of the index (see "Fine-tuning the output"). As an +# example, the default style sheet generated by doxygen has an example that +# shows how to put an image at the root of the tree instead of the PROJECT_NAME. +# Since the tree basically has the same information as the tab index, you could +# consider setting DISABLE_INDEX to YES when enabling this option. # The default value is: NO. # This tag requires that the tag GENERATE_HTML is set to YES. GENERATE_TREEVIEW = NO +# When both GENERATE_TREEVIEW and DISABLE_INDEX are set to YES, then the +# FULL_SIDEBAR option determines if the side bar is limited to only the treeview +# area (value NO) or if it should extend to the full height of the window (value +# YES). Setting this to YES gives a layout similar to +# https://docs.readthedocs.io with more room for contents, but less room for the +# project logo, title, and description. If either GENERATE_TREEVIEW or +# DISABLE_INDEX is set to NO, this option has no effect. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +FULL_SIDEBAR = NO + # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that # doxygen will group on one line in the generated HTML documentation. # @@ -1508,6 +1676,13 @@ TREEVIEW_WIDTH = 250 EXT_LINKS_IN_WINDOW = NO +# If the OBFUSCATE_EMAILS tag is set to YES, doxygen will obfuscate email +# addresses. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +OBFUSCATE_EMAILS = YES + # If the HTML_FORMULA_FORMAT option is set to svg, doxygen will use the pdf2svg # tool (see https://github.com/dawbarton/pdf2svg) or inkscape (see # https://inkscape.org) to generate formulas as SVG images instead of PNGs for @@ -1528,17 +1703,6 @@ HTML_FORMULA_FORMAT = png FORMULA_FONTSIZE = 10 -# Use the FORMULA_TRANSPARENT tag to determine whether or not the images -# generated for formulas are transparent PNGs. Transparent PNGs are not -# supported properly for IE 6.0, but are supported on all modern browsers. -# -# Note that when changing this option you need to delete any form_*.png files in -# the HTML output directory before the changes have effect. -# The default value is: YES. -# This tag requires that the tag GENERATE_HTML is set to YES. - -FORMULA_TRANSPARENT = YES - # The FORMULA_MACROFILE can contain LaTeX \newcommand and \renewcommand commands # to create new LaTeX commands to be used in formulas as building blocks. See # the section "Including formulas" for details. @@ -1556,11 +1720,29 @@ FORMULA_MACROFILE = USE_MATHJAX = NO +# With MATHJAX_VERSION it is possible to specify the MathJax version to be used. +# Note that the different versions of MathJax have different requirements with +# regards to the different settings, so it is possible that also other MathJax +# settings have to be changed when switching between the different MathJax +# versions. +# Possible values are: MathJax_2 and MathJax_3. +# The default value is: MathJax_2. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_VERSION = MathJax_2 + # When MathJax is enabled you can set the default output format to be used for -# the MathJax output. See the MathJax site (see: -# http://docs.mathjax.org/en/latest/output.html) for more details. +# the MathJax output. For more details about the output format see MathJax +# version 2 (see: +# http://docs.mathjax.org/en/v2.7-latest/output.html) and MathJax version 3 +# (see: +# http://docs.mathjax.org/en/latest/web/components/output.html). # Possible values are: HTML-CSS (which is slower, but has the best -# compatibility), NativeMML (i.e. MathML) and SVG. +# compatibility. This is the name for Mathjax version 2, for MathJax version 3 +# this will be translated into chtml), NativeMML (i.e. MathML. Only supported +# for NathJax 2. For MathJax version 3 chtml will be used instead.), chtml (This +# is the name for Mathjax version 3, for MathJax version 2 this will be +# translated into HTML-CSS) and SVG. # The default value is: HTML-CSS. # This tag requires that the tag USE_MATHJAX is set to YES. @@ -1573,22 +1755,29 @@ MATHJAX_FORMAT = HTML-CSS # MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax # Content Delivery Network so you can quickly see the result without installing # MathJax. However, it is strongly recommended to install a local copy of -# MathJax from https://www.mathjax.org before deployment. -# The default value is: https://cdn.jsdelivr.net/npm/mathjax@2. +# MathJax from https://www.mathjax.org before deployment. The default value is: +# - in case of MathJax version 2: https://cdn.jsdelivr.net/npm/mathjax@2 +# - in case of MathJax version 3: https://cdn.jsdelivr.net/npm/mathjax@3 # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_RELPATH = http://cdn.mathjax.org/mathjax/latest # The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax # extension names that should be enabled during MathJax rendering. For example +# for MathJax version 2 (see +# https://docs.mathjax.org/en/v2.7-latest/tex.html#tex-and-latex-extensions): # MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols +# For example for MathJax version 3 (see +# http://docs.mathjax.org/en/latest/input/tex/extensions/index.html): +# MATHJAX_EXTENSIONS = ams # This tag requires that the tag USE_MATHJAX is set to YES. MATHJAX_EXTENSIONS = # The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces # of code that will be used on startup of the MathJax code. See the MathJax site -# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an +# (see: +# http://docs.mathjax.org/en/v2.7-latest/output.html) for more details. For an # example see the documentation. # This tag requires that the tag USE_MATHJAX is set to YES. @@ -1635,7 +1824,8 @@ SERVER_BASED_SEARCH = NO # # Doxygen ships with an example indexer (doxyindexer) and search engine # (doxysearch.cgi) which are based on the open source search engine library -# Xapian (see: https://xapian.org/). +# Xapian (see: +# https://xapian.org/). # # See the section "External Indexing and Searching" for details. # The default value is: NO. @@ -1648,8 +1838,9 @@ EXTERNAL_SEARCH = NO # # Doxygen ships with an example indexer (doxyindexer) and search engine # (doxysearch.cgi) which are based on the open source search engine library -# Xapian (see: https://xapian.org/). See the section "External Indexing and -# Searching" for details. +# Xapian (see: +# https://xapian.org/). See the section "External Indexing and Searching" for +# details. # This tag requires that the tag SEARCHENGINE is set to YES. SEARCHENGINE_URL = @@ -1758,29 +1949,31 @@ PAPER_TYPE = a4 EXTRA_PACKAGES = -# The LATEX_HEADER tag can be used to specify a personal LaTeX header for the -# generated LaTeX document. The header should contain everything until the first -# chapter. If it is left blank doxygen will generate a standard header. See -# section "Doxygen usage" for information on how to let doxygen write the -# default header to a separate file. +# The LATEX_HEADER tag can be used to specify a user-defined LaTeX header for +# the generated LaTeX document. The header should contain everything until the +# first chapter. If it is left blank doxygen will generate a standard header. It +# is highly recommended to start with a default header using +# doxygen -w latex new_header.tex new_footer.tex new_stylesheet.sty +# and then modify the file new_header.tex. See also section "Doxygen usage" for +# information on how to generate the default header that doxygen normally uses. # -# Note: Only use a user-defined header if you know what you are doing! The -# following commands have a special meaning inside the header: $title, -# $datetime, $date, $doxygenversion, $projectname, $projectnumber, -# $projectbrief, $projectlogo. Doxygen will replace $title with the empty -# string, for the replacement values of the other commands the user is referred -# to HTML_HEADER. +# Note: Only use a user-defined header if you know what you are doing! +# Note: The header is subject to change so you typically have to regenerate the +# default header when upgrading to a newer version of doxygen. The following +# commands have a special meaning inside the header (and footer): For a +# description of the possible markers and block names see the documentation. # This tag requires that the tag GENERATE_LATEX is set to YES. LATEX_HEADER = -# The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for the -# generated LaTeX document. The footer should contain everything after the last -# chapter. If it is left blank doxygen will generate a standard footer. See +# The LATEX_FOOTER tag can be used to specify a user-defined LaTeX footer for +# the generated LaTeX document. The footer should contain everything after the +# last chapter. If it is left blank doxygen will generate a standard footer. See # LATEX_HEADER for more information on how to generate a default footer and what -# special commands can be used inside the footer. -# -# Note: Only use a user-defined footer if you know what you are doing! +# special commands can be used inside the footer. See also section "Doxygen +# usage" for information on how to generate the default footer that doxygen +# normally uses. Note: Only use a user-defined footer if you know what you are +# doing! # This tag requires that the tag GENERATE_LATEX is set to YES. LATEX_FOOTER = @@ -1823,10 +2016,16 @@ PDF_HYPERLINKS = YES USE_PDFLATEX = YES -# If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \batchmode -# command to the generated LaTeX files. This will instruct LaTeX to keep running -# if errors occur, instead of asking the user for help. This option is also used -# when generating formulas in HTML. +# The LATEX_BATCHMODE tag signals the behavior of LaTeX in case of an error. +# Possible values are: NO same as ERROR_STOP, YES same as BATCH, BATCH In batch +# mode nothing is printed on the terminal, errors are scrolled as if is +# hit at every error; missing files that TeX tries to input or request from +# keyboard input (\read on a not open input stream) cause the job to abort, +# NON_STOP In nonstop mode the diagnostic message will appear on the terminal, +# but there is no possibility of user interaction just like in batch mode, +# SCROLL In scroll mode, TeX will stop only for missing files to input or if +# keyboard input is necessary and ERROR_STOP In errorstop mode, TeX will stop at +# each error, asking for user intervention. # The default value is: NO. # This tag requires that the tag GENERATE_LATEX is set to YES. @@ -1839,16 +2038,6 @@ LATEX_BATCHMODE = NO LATEX_HIDE_INDICES = NO -# If the LATEX_SOURCE_CODE tag is set to YES then doxygen will include source -# code with syntax highlighting in the LaTeX output. -# -# Note that which sources are shown also depends on other settings such as -# SOURCE_BROWSER. -# The default value is: NO. -# This tag requires that the tag GENERATE_LATEX is set to YES. - -LATEX_SOURCE_CODE = NO - # The LATEX_BIB_STYLE tag can be used to specify the style to use for the # bibliography, e.g. plainnat, or ieeetr. See # https://en.wikipedia.org/wiki/BibTeX and \cite for more info. @@ -1857,14 +2046,6 @@ LATEX_SOURCE_CODE = NO LATEX_BIB_STYLE = plain -# If the LATEX_TIMESTAMP tag is set to YES then the footer of each generated -# page will contain the date and time when the page was generated. Setting this -# to NO can help when comparing the output of multiple runs. -# The default value is: NO. -# This tag requires that the tag GENERATE_LATEX is set to YES. - -LATEX_TIMESTAMP = NO - # The LATEX_EMOJI_DIRECTORY tag is used to specify the (relative or absolute) # path from which the emoji images will be read. If a relative path is entered, # it will be relative to the LATEX_OUTPUT directory. If left blank the @@ -1929,16 +2110,6 @@ RTF_STYLESHEET_FILE = RTF_EXTENSIONS_FILE = -# If the RTF_SOURCE_CODE tag is set to YES then doxygen will include source code -# with syntax highlighting in the RTF output. -# -# Note that which sources are shown also depends on other settings such as -# SOURCE_BROWSER. -# The default value is: NO. -# This tag requires that the tag GENERATE_RTF is set to YES. - -RTF_SOURCE_CODE = NO - #--------------------------------------------------------------------------- # Configuration options related to the man page output #--------------------------------------------------------------------------- @@ -1991,7 +2162,7 @@ MAN_LINKS = NO # captures the structure of the code including all documentation. # The default value is: NO. -GENERATE_XML = NO +GENERATE_XML = YES # The XML_OUTPUT tag is used to specify where the XML pages will be put. If a # relative path is entered the value of OUTPUT_DIRECTORY will be put in front of @@ -2035,27 +2206,44 @@ GENERATE_DOCBOOK = NO DOCBOOK_OUTPUT = docbook -# If the DOCBOOK_PROGRAMLISTING tag is set to YES, doxygen will include the -# program listings (including syntax highlighting and cross-referencing -# information) to the DOCBOOK output. Note that enabling this will significantly -# increase the size of the DOCBOOK output. -# The default value is: NO. -# This tag requires that the tag GENERATE_DOCBOOK is set to YES. - -DOCBOOK_PROGRAMLISTING = NO - #--------------------------------------------------------------------------- # Configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES, doxygen will generate an -# AutoGen Definitions (see http://autogen.sourceforge.net/) file that captures +# AutoGen Definitions (see https://autogen.sourceforge.net/) file that captures # the structure of the code including all documentation. Note that this feature # is still experimental and incomplete at the moment. # The default value is: NO. GENERATE_AUTOGEN_DEF = NO +#--------------------------------------------------------------------------- +# Configuration options related to Sqlite3 output +#--------------------------------------------------------------------------- + +# If the GENERATE_SQLITE3 tag is set to YES doxygen will generate a Sqlite3 +# database with symbols found by doxygen stored in tables. +# The default value is: NO. + +GENERATE_SQLITE3 = NO + +# The SQLITE3_OUTPUT tag is used to specify where the Sqlite3 database will be +# put. If a relative path is entered the value of OUTPUT_DIRECTORY will be put +# in front of it. +# The default directory is: sqlite3. +# This tag requires that the tag GENERATE_SQLITE3 is set to YES. + +SQLITE3_OUTPUT = sqlite3 + +# The SQLITE3_OVERWRITE_DB tag is set to YES, the existing doxygen_sqlite3.db +# database file will be recreated with each doxygen run. If set to NO, doxygen +# will warn if an a database file is already found and not modify it. +# The default value is: YES. +# This tag requires that the tag GENERATE_SQLITE3 is set to YES. + +SQLITE3_RECREATE_DB = YES + #--------------------------------------------------------------------------- # Configuration options related to the Perl module output #--------------------------------------------------------------------------- @@ -2130,7 +2318,8 @@ SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by the -# preprocessor. +# preprocessor. Note that the INCLUDE_PATH is not recursive, so the setting of +# RECURSIVE has no effect here. # This tag requires that the tag SEARCH_INCLUDES is set to YES. INCLUDE_PATH = @@ -2197,15 +2386,15 @@ TAGFILES = rmm.tag=https://docs.rapids.ai/api/librmm/22.08 GENERATE_TAGFILE = -# If the ALLEXTERNALS tag is set to YES, all external class will be listed in -# the class index. If set to NO, only the inherited external classes will be -# listed. +# If the ALLEXTERNALS tag is set to YES, all external classes and namespaces +# will be listed in the class and namespace index. If set to NO, only the +# inherited external classes will be listed. # The default value is: NO. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES, all external groups will be listed -# in the modules index. If set to NO, only the current project's groups will be +# in the topic index. If set to NO, only the current project's groups will be # listed. # The default value is: YES. @@ -2219,25 +2408,9 @@ EXTERNAL_GROUPS = YES EXTERNAL_PAGES = YES #--------------------------------------------------------------------------- -# Configuration options related to the dot tool +# Configuration options related to diagram generator tools #--------------------------------------------------------------------------- -# If the CLASS_DIAGRAMS tag is set to YES, doxygen will generate a class diagram -# (in HTML and LaTeX) for classes with base or super classes. Setting the tag to -# NO turns the diagrams off. Note that this option also works with HAVE_DOT -# disabled, but it is recommended to install and use dot, since it yields more -# powerful graphs. -# The default value is: YES. - -CLASS_DIAGRAMS = YES - -# You can include diagrams made with dia in doxygen documentation. Doxygen will -# then run dia to produce the diagram and insert it in the documentation. The -# DIA_PATH tag allows you to specify the directory where the dia binary resides. -# If left empty dia is assumed to be found in the default search path. - -DIA_PATH = - # If set to YES the inheritance and collaboration graphs will hide inheritance # and usage relations if the target is undocumented or is not a class. # The default value is: YES. @@ -2246,7 +2419,7 @@ HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz (see: -# http://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent +# https://www.graphviz.org/), a graph visualization toolkit from AT&T and Lucent # Bell Labs. The other options in this section have no effect if this option is # set to NO # The default value is: NO. @@ -2263,49 +2436,73 @@ HAVE_DOT = YES DOT_NUM_THREADS = 0 -# When you want a differently looking font in the dot files that doxygen -# generates you can specify the font name using DOT_FONTNAME. You need to make -# sure dot is able to find the font, which can be done by putting it in a -# standard location or by setting the DOTFONTPATH environment variable or by -# setting DOT_FONTPATH to the directory containing the font. -# The default value is: Helvetica. +# DOT_COMMON_ATTR is common attributes for nodes, edges and labels of +# subgraphs. When you want a differently looking font in the dot files that +# doxygen generates you can specify fontname, fontcolor and fontsize attributes. +# For details please see Node, +# Edge and Graph Attributes specification You need to make sure dot is able +# to find the font, which can be done by putting it in a standard location or by +# setting the DOTFONTPATH environment variable or by setting DOT_FONTPATH to the +# directory containing the font. Default graphviz fontsize is 14. +# The default value is: fontname=Helvetica,fontsize=10. # This tag requires that the tag HAVE_DOT is set to YES. -DOT_FONTNAME = Helvetica +DOT_COMMON_ATTR = "fontname=Helvetica,fontsize=10" -# The DOT_FONTSIZE tag can be used to set the size (in points) of the font of -# dot graphs. -# Minimum value: 4, maximum value: 24, default value: 10. +# DOT_EDGE_ATTR is concatenated with DOT_COMMON_ATTR. For elegant style you can +# add 'arrowhead=open, arrowtail=open, arrowsize=0.5'. Complete documentation about +# arrows shapes. +# The default value is: labelfontname=Helvetica,labelfontsize=10. # This tag requires that the tag HAVE_DOT is set to YES. -DOT_FONTSIZE = 10 +DOT_EDGE_ATTR = "labelfontname=Helvetica,labelfontsize=10" -# By default doxygen will tell dot to use the default font as specified with -# DOT_FONTNAME. If you specify a different font using DOT_FONTNAME you can set -# the path where dot can find it using this tag. +# DOT_NODE_ATTR is concatenated with DOT_COMMON_ATTR. For view without boxes +# around nodes set 'shape=plain' or 'shape=plaintext' Shapes specification +# The default value is: shape=box,height=0.2,width=0.4. +# This tag requires that the tag HAVE_DOT is set to YES. + +DOT_NODE_ATTR = "shape=box,height=0.2,width=0.4" + +# You can set the path where dot can find font specified with fontname in +# DOT_COMMON_ATTR and others dot attributes. # This tag requires that the tag HAVE_DOT is set to YES. DOT_FONTPATH = -# If the CLASS_GRAPH tag is set to YES then doxygen will generate a graph for -# each documented class showing the direct and indirect inheritance relations. -# Setting this tag to YES will force the CLASS_DIAGRAMS tag to NO. +# If the CLASS_GRAPH tag is set to YES or GRAPH or BUILTIN then doxygen will +# generate a graph for each documented class showing the direct and indirect +# inheritance relations. In case the CLASS_GRAPH tag is set to YES or GRAPH and +# HAVE_DOT is enabled as well, then dot will be used to draw the graph. In case +# the CLASS_GRAPH tag is set to YES and HAVE_DOT is disabled or if the +# CLASS_GRAPH tag is set to BUILTIN, then the built-in generator will be used. +# If the CLASS_GRAPH tag is set to TEXT the direct and indirect inheritance +# relations will be shown as texts / links. +# Possible values are: NO, YES, TEXT, GRAPH and BUILTIN. # The default value is: YES. -# This tag requires that the tag HAVE_DOT is set to YES. CLASS_GRAPH = YES # If the COLLABORATION_GRAPH tag is set to YES then doxygen will generate a # graph for each documented class showing the direct and indirect implementation # dependencies (inheritance, containment, and class references variables) of the -# class with other documented classes. +# class with other documented classes. Explicit enabling a collaboration graph, +# when COLLABORATION_GRAPH is set to NO, can be accomplished by means of the +# command \collaborationgraph. Disabling a collaboration graph can be +# accomplished by means of the command \hidecollaborationgraph. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. COLLABORATION_GRAPH = YES # If the GROUP_GRAPHS tag is set to YES then doxygen will generate a graph for -# groups, showing the direct groups dependencies. +# groups, showing the direct groups dependencies. Explicit enabling a group +# dependency graph, when GROUP_GRAPHS is set to NO, can be accomplished by means +# of the command \groupgraph. Disabling a directory graph can be accomplished by +# means of the command \hidegroupgraph. See also the chapter Grouping in the +# manual. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. @@ -2328,10 +2525,32 @@ UML_LOOK = NO # but if the number exceeds 15, the total amount of fields shown is limited to # 10. # Minimum value: 0, maximum value: 100, default value: 10. -# This tag requires that the tag HAVE_DOT is set to YES. +# This tag requires that the tag UML_LOOK is set to YES. UML_LIMIT_NUM_FIELDS = 10 +# If the DOT_UML_DETAILS tag is set to NO, doxygen will show attributes and +# methods without types and arguments in the UML graphs. If the DOT_UML_DETAILS +# tag is set to YES, doxygen will add type and arguments for attributes and +# methods in the UML graphs. If the DOT_UML_DETAILS tag is set to NONE, doxygen +# will not generate fields with class member information in the UML graphs. The +# class diagrams will look similar to the default class diagrams but using UML +# notation for the relationships. +# Possible values are: NO, YES and NONE. +# The default value is: NO. +# This tag requires that the tag UML_LOOK is set to YES. + +DOT_UML_DETAILS = NO + +# The DOT_WRAP_THRESHOLD tag can be used to set the maximum number of characters +# to display on a single line. If the actual line length exceeds this threshold +# significantly it will wrapped across multiple lines. Some heuristics are apply +# to avoid ugly line breaks. +# Minimum value: 0, maximum value: 1000, default value: 17. +# This tag requires that the tag HAVE_DOT is set to YES. + +DOT_WRAP_THRESHOLD = 17 + # If the TEMPLATE_RELATIONS tag is set to YES then the inheritance and # collaboration graphs will show the relations between templates and their # instances. @@ -2343,7 +2562,9 @@ TEMPLATE_RELATIONS = NO # If the INCLUDE_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are set to # YES then doxygen will generate a graph for each documented file showing the # direct and indirect include dependencies of the file with other documented -# files. +# files. Explicit enabling an include graph, when INCLUDE_GRAPH is is set to NO, +# can be accomplished by means of the command \includegraph. Disabling an +# include graph can be accomplished by means of the command \hideincludegraph. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. @@ -2352,7 +2573,10 @@ INCLUDE_GRAPH = YES # If the INCLUDED_BY_GRAPH, ENABLE_PREPROCESSING and SEARCH_INCLUDES tags are # set to YES then doxygen will generate a graph for each documented file showing # the direct and indirect include dependencies of the file with other documented -# files. +# files. Explicit enabling an included by graph, when INCLUDED_BY_GRAPH is set +# to NO, can be accomplished by means of the command \includedbygraph. Disabling +# an included by graph can be accomplished by means of the command +# \hideincludedbygraph. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. @@ -2392,16 +2616,26 @@ GRAPHICAL_HIERARCHY = YES # If the DIRECTORY_GRAPH tag is set to YES then doxygen will show the # dependencies a directory has on other directories in a graphical way. The # dependency relations are determined by the #include relations between the -# files in the directories. +# files in the directories. Explicit enabling a directory graph, when +# DIRECTORY_GRAPH is set to NO, can be accomplished by means of the command +# \directorygraph. Disabling a directory graph can be accomplished by means of +# the command \hidedirectorygraph. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. DIRECTORY_GRAPH = YES +# The DIR_GRAPH_MAX_DEPTH tag can be used to limit the maximum number of levels +# of child directories generated in directory dependency graphs by dot. +# Minimum value: 1, maximum value: 25, default value: 1. +# This tag requires that the tag DIRECTORY_GRAPH is set to YES. + +DIR_GRAPH_MAX_DEPTH = 1 + # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. For an explanation of the image formats see the section # output formats in the documentation of the dot tool (Graphviz (see: -# http://www.graphviz.org/)). +# https://www.graphviz.org/)). # Note: If you choose svg you need to set HTML_FILE_EXTENSION to xhtml in order # to make the SVG files visible in IE 9+ (other browsers do not have this # requirement). @@ -2438,11 +2672,12 @@ DOT_PATH = DOTFILE_DIRS = -# The MSCFILE_DIRS tag can be used to specify one or more directories that -# contain msc files that are included in the documentation (see the \mscfile -# command). +# You can include diagrams made with dia in doxygen documentation. Doxygen will +# then run dia to produce the diagram and insert it in the documentation. The +# DIA_PATH tag allows you to specify the directory where the dia binary resides. +# If left empty dia is assumed to be found in the default search path. -MSCFILE_DIRS = +DIA_PATH = # The DIAFILE_DIRS tag can be used to specify one or more directories that # contain dia files that are included in the documentation (see the \diafile @@ -2451,10 +2686,10 @@ MSCFILE_DIRS = DIAFILE_DIRS = # When using plantuml, the PLANTUML_JAR_PATH tag should be used to specify the -# path where java can find the plantuml.jar file. If left blank, it is assumed -# PlantUML is not used or called during a preprocessing step. Doxygen will -# generate a warning when it encounters a \startuml command in this case and -# will not generate output for the diagram. +# path where java can find the plantuml.jar file or to the filename of jar file +# to be used. If left blank, it is assumed PlantUML is not used or called during +# a preprocessing step. Doxygen will generate a warning when it encounters a +# \startuml command in this case and will not generate output for the diagram. PLANTUML_JAR_PATH = @@ -2492,18 +2727,6 @@ DOT_GRAPH_MAX_NODES = 50 MAX_DOT_GRAPH_DEPTH = 0 -# Set the DOT_TRANSPARENT tag to YES to generate images with a transparent -# background. This is disabled by default, because dot on Windows does not seem -# to support this out of the box. -# -# Warning: Depending on the platform used, enabling this option may lead to -# badly anti-aliased labels on the edges of a graph (i.e. they become hard to -# read). -# The default value is: NO. -# This tag requires that the tag HAVE_DOT is set to YES. - -DOT_TRANSPARENT = NO - # Set the DOT_MULTI_TARGETS tag to YES to allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) support @@ -2516,14 +2739,34 @@ DOT_MULTI_TARGETS = NO # If the GENERATE_LEGEND tag is set to YES doxygen will generate a legend page # explaining the meaning of the various boxes and arrows in the dot generated # graphs. +# Note: This tag requires that UML_LOOK isn't set, i.e. the doxygen internal +# graphical representation for inheritance and collaboration diagrams is used. # The default value is: YES. # This tag requires that the tag HAVE_DOT is set to YES. GENERATE_LEGEND = YES -# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate dot +# If the DOT_CLEANUP tag is set to YES, doxygen will remove the intermediate # files that are used to generate the various graphs. +# +# Note: This setting is not only used for dot files but also for msc temporary +# files. # The default value is: YES. -# This tag requires that the tag HAVE_DOT is set to YES. DOT_CLEANUP = YES + +# You can define message sequence charts within doxygen comments using the \msc +# command. If the MSCGEN_TOOL tag is left empty (the default), then doxygen will +# use a built-in version of mscgen tool to produce the charts. Alternatively, +# the MSCGEN_TOOL tag can also specify the name an external tool. For instance, +# specifying prog as the value, doxygen will call the tool as prog -T +# -o . The external tool should support +# output file formats "png", "eps", "svg", and "ismap". + +MSCGEN_TOOL = + +# The MSCFILE_DIRS tag can be used to specify one or more directories that +# contain msc files that are included in the documentation (see the \mscfile +# command). + +MSCFILE_DIRS = diff --git a/cpp/include/cugraph_c/centrality_algorithms.h b/cpp/include/cugraph_c/centrality_algorithms.h index 0ac0e58540f..fb5d4b63b9c 100644 --- a/cpp/include/cugraph_c/centrality_algorithms.h +++ b/cpp/include/cugraph_c/centrality_algorithms.h @@ -23,8 +23,6 @@ #include /** @defgroup centrality Centrality algorithms - * @ingroup c_api - * @{ */ #ifdef __cplusplus @@ -39,7 +37,8 @@ typedef struct { } cugraph_centrality_result_t; /** - * @brief Get the vertex ids from the centrality result + * @ingroup centrality + * @brief Get the vertex ids from the centrality result * * @param [in] result The result from a centrality algorithm * @return type erased array of vertex ids @@ -48,7 +47,8 @@ cugraph_type_erased_device_array_view_t* cugraph_centrality_result_get_vertices( cugraph_centrality_result_t* result); /** - * @brief Get the centrality values from a centrality algorithm result + * @ingroup centrality + * @brief Get the centrality values from a centrality algorithm result * * @param [in] result The result from a centrality algorithm * @return type erased array view of centrality values @@ -57,6 +57,7 @@ cugraph_type_erased_device_array_view_t* cugraph_centrality_result_get_values( cugraph_centrality_result_t* result); /** + * @ingroup centrality * @brief Get the number of iterations executed from the algorithm metadata * * @param [in] result The result from a centrality algorithm @@ -65,6 +66,7 @@ cugraph_type_erased_device_array_view_t* cugraph_centrality_result_get_values( size_t cugraph_centrality_result_get_num_iterations(cugraph_centrality_result_t* result); /** + * @ingroup centrality * @brief Returns true if the centrality algorithm converged * * @param [in] result The result from a centrality algorithm @@ -73,6 +75,7 @@ size_t cugraph_centrality_result_get_num_iterations(cugraph_centrality_result_t* bool_t cugraph_centrality_result_converged(cugraph_centrality_result_t* result); /** + * @ingroup centrality * @brief Free centrality result * * @param [in] result The result from a centrality algorithm @@ -409,6 +412,7 @@ typedef struct { } cugraph_edge_centrality_result_t; /** + * @ingroup centrality * @brief Get the src vertex ids from an edge centrality result * * @param [in] result The result from an edge centrality algorithm @@ -418,6 +422,7 @@ cugraph_type_erased_device_array_view_t* cugraph_edge_centrality_result_get_src_ cugraph_edge_centrality_result_t* result); /** + * @ingroup centrality * @brief Get the dst vertex ids from an edge centrality result * * @param [in] result The result from an edge centrality algorithm @@ -427,6 +432,7 @@ cugraph_type_erased_device_array_view_t* cugraph_edge_centrality_result_get_dst_ cugraph_edge_centrality_result_t* result); /** + * @ingroup centrality * @brief Get the edge ids from an edge centrality result * * @param [in] result The result from an edge centrality algorithm @@ -436,6 +442,7 @@ cugraph_type_erased_device_array_view_t* cugraph_edge_centrality_result_get_edge cugraph_edge_centrality_result_t* result); /** + * @ingroup centrality * @brief Get the centrality values from an edge centrality algorithm result * * @param [in] result The result from an edge centrality algorithm @@ -445,6 +452,7 @@ cugraph_type_erased_device_array_view_t* cugraph_edge_centrality_result_get_valu cugraph_edge_centrality_result_t* result); /** + * @ingroup centrality * @brief Free centrality result * * @param [in] result The result from a centrality algorithm @@ -491,6 +499,7 @@ typedef struct { } cugraph_hits_result_t; /** + * @ingroup centrality * @brief Get the vertex ids from the hits result * * @param [in] result The result from hits @@ -500,6 +509,7 @@ cugraph_type_erased_device_array_view_t* cugraph_hits_result_get_vertices( cugraph_hits_result_t* result); /** + * @ingroup centrality * @brief Get the hubs values from the hits result * * @param [in] result The result from hits @@ -509,6 +519,7 @@ cugraph_type_erased_device_array_view_t* cugraph_hits_result_get_hubs( cugraph_hits_result_t* result); /** + * @ingroup centrality * @brief Get the authorities values from the hits result * * @param [in] result The result from hits @@ -518,6 +529,7 @@ cugraph_type_erased_device_array_view_t* cugraph_hits_result_get_authorities( cugraph_hits_result_t* result); /** + * @ingroup centrality * @brief Get the score differences between the last two iterations * * @param [in] result The result from hits @@ -526,6 +538,7 @@ cugraph_type_erased_device_array_view_t* cugraph_hits_result_get_authorities( double cugraph_hits_result_get_hub_score_differences(cugraph_hits_result_t* result); /** + * @ingroup centrality * @brief Get the actual number of iterations * * @param [in] result The result from hits @@ -534,6 +547,7 @@ double cugraph_hits_result_get_hub_score_differences(cugraph_hits_result_t* resu size_t cugraph_hits_result_get_number_of_iterations(cugraph_hits_result_t* result); /** + * @ingroup centrality * @brief Free hits result * * @param [in] result The result from hits @@ -585,7 +599,3 @@ cugraph_error_code_t cugraph_hits( #ifdef __cplusplus } #endif - -/** - * @} - */ diff --git a/cpp/include/cugraph_c/community_algorithms.h b/cpp/include/cugraph_c/community_algorithms.h index 8f1015f8632..feab15c7eeb 100644 --- a/cpp/include/cugraph_c/community_algorithms.h +++ b/cpp/include/cugraph_c/community_algorithms.h @@ -23,7 +23,6 @@ #include /** @defgroup community Community algorithms - * @ingroup c_api * @{ */ diff --git a/cpp/include/cugraph_c/core_algorithms.h b/cpp/include/cugraph_c/core_algorithms.h index c0e348c3cf4..6db3269f61e 100644 --- a/cpp/include/cugraph_c/core_algorithms.h +++ b/cpp/include/cugraph_c/core_algorithms.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -21,6 +21,9 @@ #include #include +/** @defgroup core Core algorithms + */ + #ifdef __cplusplus extern "C" { #endif @@ -40,6 +43,7 @@ typedef struct { } cugraph_k_core_result_t; /** + * @ingroup core * @brief Create a core_number result (in case it was previously extracted) * * @param [in] handle Handle for accessing resources @@ -58,6 +62,7 @@ cugraph_error_code_t cugraph_core_result_create( cugraph_error_t** error); /** + * @ingroup core * @brief Get the vertex ids from the core result * * @param [in] result The result from core number @@ -67,6 +72,7 @@ cugraph_type_erased_device_array_view_t* cugraph_core_result_get_vertices( cugraph_core_result_t* result); /** + * @ingroup core * @brief Get the core numbers from the core result * * @param [in] result The result from core number @@ -76,6 +82,7 @@ cugraph_type_erased_device_array_view_t* cugraph_core_result_get_core_numbers( cugraph_core_result_t* result); /** + * @ingroup core * @brief Free core result * * @param [in] result The result from core number @@ -83,6 +90,7 @@ cugraph_type_erased_device_array_view_t* cugraph_core_result_get_core_numbers( void cugraph_core_result_free(cugraph_core_result_t* result); /** + * @ingroup core * @brief Get the src vertex ids from the k-core result * * @param [in] result The result from k-core @@ -92,6 +100,7 @@ cugraph_type_erased_device_array_view_t* cugraph_k_core_result_get_src_vertices( cugraph_k_core_result_t* result); /** + * @ingroup core * @brief Get the dst vertex ids from the k-core result * * @param [in] result The result from k-core @@ -101,6 +110,7 @@ cugraph_type_erased_device_array_view_t* cugraph_k_core_result_get_dst_vertices( cugraph_k_core_result_t* result); /** + * @ingroup core * @brief Get the weights from the k-core result * * Returns NULL if the graph is unweighted @@ -112,6 +122,7 @@ cugraph_type_erased_device_array_view_t* cugraph_k_core_result_get_weights( cugraph_k_core_result_t* result); /** + * @ingroup core * @brief Free k-core result * * @param [in] result The result from k-core @@ -119,6 +130,7 @@ cugraph_type_erased_device_array_view_t* cugraph_k_core_result_get_weights( void cugraph_k_core_result_free(cugraph_k_core_result_t* result); /** + * @ingroup core * @brief Enumeration for computing core number */ typedef enum { diff --git a/cpp/include/cugraph_c/labeling_algorithms.h b/cpp/include/cugraph_c/labeling_algorithms.h index f3e634dafe6..53dcc0d9419 100644 --- a/cpp/include/cugraph_c/labeling_algorithms.h +++ b/cpp/include/cugraph_c/labeling_algorithms.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -25,8 +25,6 @@ extern "C" { #endif /** @defgroup labeling Labeling algorithms - * @ingroup c_api - * @{ */ /** @@ -37,6 +35,7 @@ typedef struct { } cugraph_labeling_result_t; /** + * @ingroup labeling * @brief Get the vertex ids from the labeling result * * @param [in] result The result from a labeling algorithm @@ -46,6 +45,7 @@ cugraph_type_erased_device_array_view_t* cugraph_labeling_result_get_vertices( cugraph_labeling_result_t* result); /** + * @ingroup labeling * @brief Get the label values from the labeling result * * @param [in] result The result from a labeling algorithm @@ -55,6 +55,7 @@ cugraph_type_erased_device_array_view_t* cugraph_labeling_result_get_labels( cugraph_labeling_result_t* result); /** + * @ingroup labeling * @brief Free labeling result * * @param [in] result The result from a labeling algorithm @@ -104,7 +105,3 @@ cugraph_error_code_t cugraph_strongly_connected_components(const cugraph_resourc #ifdef __cplusplus } #endif - -/** - * @} - */ diff --git a/cpp/include/cugraph_c/sampling_algorithms.h b/cpp/include/cugraph_c/sampling_algorithms.h index 92fe50ef622..782bb5a3790 100644 --- a/cpp/include/cugraph_c/sampling_algorithms.h +++ b/cpp/include/cugraph_c/sampling_algorithms.h @@ -21,8 +21,7 @@ #include #include -/** @defgroup sampling Sampling algorithms - * @ingroup c_api +/** @defgroup samplingC Sampling algorithms * @{ */ diff --git a/cpp/include/cugraph_c/similarity_algorithms.h b/cpp/include/cugraph_c/similarity_algorithms.h index 1417d8ac566..b8f61b46545 100644 --- a/cpp/include/cugraph_c/similarity_algorithms.h +++ b/cpp/include/cugraph_c/similarity_algorithms.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -22,6 +22,9 @@ #include #include +/** @defgroup similarity Similarity algorithms + */ + #ifdef __cplusplus extern "C" { #endif @@ -34,6 +37,7 @@ typedef struct { } cugraph_similarity_result_t; /** + * @ingroup similarity * @brief Get the similarity coefficient array * * @param [in] result The result from a similarity algorithm @@ -43,6 +47,7 @@ cugraph_type_erased_device_array_view_t* cugraph_similarity_result_get_similarit cugraph_similarity_result_t* result); /** + * @ingroup similarity * @brief Free similarity result * * @param [in] result The result from a similarity algorithm diff --git a/docs/cugraph/Makefile b/docs/cugraph/Makefile index 32237aa2cc0..f92d0be6910 100644 --- a/docs/cugraph/Makefile +++ b/docs/cugraph/Makefile @@ -2,7 +2,7 @@ # # You can set these variables from the command line. -SPHINXOPTS = +SPHINXOPTS = "-v" SPHINXBUILD = sphinx-build SPHINXPROJ = cugraph SOURCEDIR = source diff --git a/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst deleted file mode 100644 index e172309fae2..00000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/bipartite_operators.rst +++ /dev/null @@ -1,16 +0,0 @@ -============================= -Operators on Bipartite Graphs -============================= - -.. currentmodule:: pylibcugraphops - -Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.update_efeat_bipartite_e2e_concat_fwd - operators.update_efeat_bipartite_e2e_concat_bwd - - operators.update_efeat_bipartite_e2e_sum_fwd - operators.update_efeat_bipartite_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst new file mode 100644 index 00000000000..5545bebe975 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/c_cpp/index.rst @@ -0,0 +1,3 @@ +cugraph-ops C++ API Reference +============================= + diff --git a/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst deleted file mode 100644 index 387844f684a..00000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/fg_operators.rst +++ /dev/null @@ -1,83 +0,0 @@ -======================== -Operators on Full Graphs -======================== - -.. currentmodule:: pylibcugraphops - -Simple Neighborhood Aggregator (SAGEConv) ------------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_simple_fg_n2n_fwd - operators.agg_simple_fg_n2n_bwd - operators.agg_simple_fg_e2n_fwd - operators.agg_simple_fg_e2n_bwd - operators.agg_simple_fg_n2n_e2n_fwd - operators.agg_simple_fg_n2n_e2n_bwd - - operators.agg_concat_fg_n2n_fwd - operators.agg_concat_fg_n2n_bwd - operators.agg_concat_fg_e2n_fwd - operators.agg_concat_fg_e2n_bwd - operators.agg_concat_fg_n2n_e2n_fwd - operators.agg_concat_fg_n2n_e2n_bwd - -Weighted Neighborhood Aggregation ---------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_weighted_fg_n2n_fwd - operators.agg_weighted_fg_n2n_bwd - operators.agg_concat_weighted_fg_n2n_fwd - operators.agg_concat_weighted_fg_n2n_bwd - -Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_hg_basis_fg_n2n_post_fwd - operators.agg_hg_basis_fg_n2n_post_bwd - -Graph Attention (GATConv/GATv2Conv) ------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_fg_n2n_fwd - operators.mha_gat_fg_n2n_bwd - operators.mha_gat_fg_n2n_efeat_fwd - operators.mha_gat_fg_n2n_efeat_bwd - - operators.mha_gat_v2_fg_n2n_fwd - operators.mha_gat_v2_fg_n2n_bwd - operators.mha_gat_v2_fg_n2n_efeat_fwd - operators.mha_gat_v2_fg_n2n_efeat_bwd - -Transformer-like Graph Attention (TransformerConv) --------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_v2_fg_n2n_fwd - operators.mha_gat_v2_fg_n2n_bwd - operators.mha_gat_v2_fg_n2n_efeat_fwd - operators.mha_gat_v2_fg_n2n_efeat_bwd - -Directional Message-Passing (DMPNN) ------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_dmpnn_fg_e2e_fwd - operators.agg_dmpnn_fg_e2e_bwd - -Graph Pooling -------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.pool_fg_n2s_fwd - operators.pool_fg_n2s_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst deleted file mode 100644 index 9289ce53e39..00000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/graph_types.rst +++ /dev/null @@ -1,33 +0,0 @@ -=========== -Graph types -=========== - -.. currentmodule:: pylibcugraphops - -Message-Flow Graph (MFG) -------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_mfg_csr - -Heterogenous MFG ----------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_mfg_csr_hg - -"Full" Graph (FG) ------------------ -.. autosummary:: - :toctree: ../api/ops/ - - make_fg_csr - -Heterogenous FG ---------------- -.. autosummary:: - :toctree: ../api/ops/ - - make_fg_csr_hg diff --git a/docs/cugraph/source/api_docs/cugraph-ops/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/index.rst index e2338dc1833..fdfd5baab96 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/index.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/index.rst @@ -1,4 +1,3 @@ -========================= cugraph-ops API reference ========================= @@ -8,11 +7,5 @@ This page provides a list of all publicly accessible modules, methods and classe :maxdepth: 2 :caption: API Documentation - graph_types - pytorch - mfg_operators - bipartite_operators - static_operators - fg_operators - dimenet - pytorch + python/index + c_cpp/index \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst deleted file mode 100644 index f3dd1faa245..00000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/mfg_operators.rst +++ /dev/null @@ -1,31 +0,0 @@ -================================ -Operators on Message-Flow Graphs -================================ - -.. currentmodule:: pylibcugraphops - -Simple Neighborhood Aggregator (SAGEConv) ------------------------------------------ -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_simple_mfg_n2n_fwd - operators.agg_simple_mfg_n2n_bwd - operators.agg_concat_mfg_n2n_fwd - operators.agg_concat_mfg_n2n_bwd - -Graph Attention (GATConv) -------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.mha_gat_mfg_n2n_fwd - operators.mha_gat_mfg_n2n_bwd - -Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.agg_hg_basis_mfg_n2n_post_fwd - operators.agg_hg_basis_mfg_n2n_post_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst similarity index 89% rename from docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst rename to docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst index b709464c7e6..6fadcc57b22 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/dimenet.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/dimenet.rst @@ -7,7 +7,7 @@ Dimenet operators Radial Basis Functions ---------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops dimenet.radial_basis_fwd dimenet.radial_basis_bwd @@ -16,7 +16,7 @@ Radial Basis Functions Edge-to-Edge Aggregation ------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops dimenet.agg_edge_to_edge_fwd dimenet.agg_edge_to_edge_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst new file mode 100644 index 00000000000..141d40393a5 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/graph_types.rst @@ -0,0 +1,34 @@ +=========== +Graph types +=========== + +.. currentmodule:: pylibcugraphops + + +CSC Graph +----------------- +.. autosummary:: + :toctree: ../../api/ops + + make_csc + +Heterogenous CSC Graph +---------------------- +.. autosummary:: + :toctree: ../../api/ops + + make_csc_hg + +Bipartite Graph +----------------- +.. autosummary:: + :toctree: ../../api/ops + + make_bipartite_csc + +Heterogenous Bipartite Graph +---------------------------- +.. autosummary:: + :toctree: ../../api/ops + + make_bipartite_csc_hg diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst new file mode 100644 index 00000000000..082c7741f23 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/index.rst @@ -0,0 +1,13 @@ +cugraph-ops Python API reference +================================ + +This page provides a list of all publicly accessible modules, methods and classes through `pylibcugraphops.*` namespace. + +.. toctree:: + :maxdepth: 2 + :caption: API Documentation + + graph_types + operators + dimenet + pytorch diff --git a/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst new file mode 100644 index 00000000000..3e6664b2db5 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/operators.rst @@ -0,0 +1,93 @@ +============================= +Operators for Message-Passing +============================= + +.. currentmodule:: pylibcugraphops + +Simple Neighborhood Aggregator (SAGEConv) +----------------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_simple_n2n_fwd + operators.agg_simple_n2n_bwd + operators.agg_simple_e2n_fwd + operators.agg_simple_e2n_bwd + operators.agg_simple_n2n_e2n_fwd + operators.agg_simple_n2n_e2n_bwd + + operators.agg_concat_n2n_fwd + operators.agg_concat_n2n_bwd + operators.agg_concat_e2n_fwd + operators.agg_concat_e2n_bwd + operators.agg_concat_n2n_e2n_fwd + operators.agg_concat_n2n_e2n_bwd + + +Weighted Neighborhood Aggregation +--------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_weighted_n2n_fwd + operators.agg_weighted_n2n_bwd + operators.agg_concat_weighted_n2n_fwd + operators.agg_concat_weighted_n2n_bwd + +Heterogenous Aggregator using Basis Decomposition (RGCNConv) +------------------------------------------------------------ +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_hg_basis_n2n_post_fwd + operators.agg_hg_basis_n2n_post_bwd + +Graph Attention (GATConv/GATv2Conv) +----------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.mha_gat_n2n_fwd + operators.mha_gat_n2n_bwd + operators.mha_gat_n2n_efeat_fwd + operators.mha_gat_n2n_efeat_bwd + + operators.mha_gat_v2_n2n_fwd + operators.mha_gat_v2_n2n_bwd + operators.mha_gat_v2_n2n_efeat_fwd + operators.mha_gat_v2_n2n_efeat_bwd + +Transformer-like Graph Attention (TransformerConv) +-------------------------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.mha_gat_v2_n2n_fwd + operators.mha_gat_v2_n2n_bwd + operators.mha_gat_v2_n2n_efeat_fwd + operators.mha_gat_v2_n2n_efeat_bwd + +Directional Message-Passing (DMPNN) +----------------------------------- +.. autosummary:: + :toctree: ../../api/ops + + operators.agg_dmpnn_e2e_fwd + operators.agg_dmpnn_e2e_bwd + +Update Edges: Concatenation or Sum of Edge and Node Features +------------------------------------------------------------ +.. autosummary:: + :toctree: ../../api/ops + + operators.update_efeat_e2e_concat_fwd + operators.update_efeat_e2e_concat_bwd + + operators.update_efeat_e2e_sum_fwd + operators.update_efeat_e2e_sum_bwd + + operators.update_efeat_e2e_concat_fwd + operators.update_efeat_e2e_concat_bwd + + operators.update_efeat_e2e_sum_fwd + operators.update_efeat_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst b/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst similarity index 59% rename from docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst rename to docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst index 83800fbc546..d2074df15b0 100644 --- a/docs/cugraph/source/api_docs/cugraph-ops/pytorch.rst +++ b/docs/cugraph/source/api_docs/cugraph-ops/python/pytorch.rst @@ -2,35 +2,35 @@ PyTorch Autograd Wrappers ========================== -.. currentmodule:: pylibcugraphops +.. currentmodule:: pylibcugraphops.pytorch Simple Neighborhood Aggregator (SAGEConv) ----------------------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.agg_concat_n2n + operators.agg_concat_n2n Graph Attention (GATConv/GATv2Conv) ----------------------------------- .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.mha_gat_n2n - pytorch.operators.mha_gat_v2_n2n + operators.mha_gat_n2n + operators.mha_gat_v2_n2n Heterogenous Aggregator using Basis Decomposition (RGCNConv) ------------------------------------------------------------ .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.agg_hg_basis_n2n_post + operators.agg_hg_basis_n2n_post Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------ .. autosummary:: - :toctree: ../api/ops/ + :toctree: ../../api/ops - pytorch.operators.update_efeat_bipartite_e2e - pytorch.operators.update_efeat_static_e2e + operators.update_efeat_e2e + operators.update_efeat_e2e diff --git a/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst b/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst deleted file mode 100644 index f3ecc068f22..00000000000 --- a/docs/cugraph/source/api_docs/cugraph-ops/static_operators.rst +++ /dev/null @@ -1,16 +0,0 @@ -========================== -Operators on Static Graphs -========================== - -.. currentmodule:: pylibcugraphops - -Update Edges: Concatenation or Sum of Edge and Node Features ------------------------------------------------------------- -.. autosummary:: - :toctree: ../api/ops/ - - operators.update_efeat_static_e2e_concat_fwd - operators.update_efeat_static_e2e_concat_bwd - - operators.update_efeat_static_e2e_sum_fwd - operators.update_efeat_static_e2e_sum_bwd diff --git a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst index 2cd8969aa66..f7d7f5f2262 100644 --- a/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst +++ b/docs/cugraph/source/api_docs/cugraph-pyg/cugraph_pyg.rst @@ -9,6 +9,6 @@ cugraph-pyg .. autosummary:: :toctree: ../api/cugraph-pyg/ - cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore - cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler +.. cugraph_pyg.data.cugraph_store.EXPERIMENTAL__CuGraphStore +.. cugraph_pyg.sampler.cugraph_sampler.EXPERIMENTAL__CuGraphSampler diff --git a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst b/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst deleted file mode 100644 index 34b812785d3..00000000000 --- a/docs/cugraph/source/api_docs/cugraph_c/c_and_cpp.rst +++ /dev/null @@ -1,4 +0,0 @@ -CuGraph C and C++ API Links -=========================== - -coming soon - see https://docs.rapids.ai/api/libcugraph/nightly/ \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/centrality.rst b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst new file mode 100644 index 00000000000..f34e26ad76e --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/centrality.rst @@ -0,0 +1,51 @@ +Centrality +========== + +PageRank +-------- +.. doxygenfunction:: cugraph_pagerank + :project: libcugraph + +.. doxygenfunction:: cugraph_pagerank_allow_nonconvergence + :project: libcugraph + +Personalized PageRank +--------------------- +.. doxygenfunction:: cugraph_personalized_pagerank + :project: libcugraph + +.. doxygenfunction:: cugraph_personalized_pagerank_allow_nonconvergence + :project: libcugraph + +Eigenvector Centrality +---------------------- +.. doxygenfunction:: cugraph_eigenvector_centrality + :project: libcugraph + +Katz Centrality +--------------- +.. doxygenfunction:: cugraph_katz_centrality + :project: libcugraph + +Betweenness Centrality +---------------------- +.. doxygenfunction:: cugraph_betweenness_centrality + :project: libcugraph + +Edge Betweenness Centrality +--------------------------- +.. doxygenfunction:: cugraph_edge_betweenness_centrality + :project: libcugraph + +HITS Centrality +--------------- +.. doxygenfunction:: cugraph_hits + :project: libcugraph + +Centrality Support Functions +---------------------------- + .. doxygengroup:: centrality + :project: libcugraph + :members: + :content-only: + diff --git a/docs/cugraph/source/api_docs/cugraph_c/community.rst b/docs/cugraph/source/api_docs/cugraph_c/community.rst new file mode 100644 index 00000000000..0bbfe365c4d --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/community.rst @@ -0,0 +1,63 @@ +Community +========= + +.. role:: py(code) + :language: c + :class: highlight + +``#include `` + +Triangle Counting +----------------- +.. doxygenfunction:: cugraph_triangle_count + :project: libcugraph + +Louvain +------- +.. doxygenfunction:: cugraph_louvain + :project: libcugraph + +Leiden +------ +.. doxygenfunction:: cugraph_leiden + :project: libcugraph + +ECG +--- +.. doxygenfunction:: cugraph_ecg + :project: libcugraph + +Extract Egonet +-------------- +.. doxygenfunction:: cugraph_extract_ego + :project: libcugraph + +Balanced Cut +------------ +.. doxygenfunction:: cugraph_balanced_cut_clustering + :project: libcugraph + +Spectral Clustering - Modularity Maximization +--------------------------------------------- +.. doxygenfunction:: cugraph_spectral_modularity_maximization + :project: libcugraph + +.. doxygenfunction:: cugraph_analyze_clustering_modularity + :project: libcugraph + +Spectral Clusteriong - Edge Cut +------------------------------- +.. doxygenfunction:: cugraph_analyze_clustering_edge_cut + :project: libcugraph + +.. doxygenfunction:: cugraph_analyze_clustering_ratio_cut + :project: libcugraph + + +Community Support Functions +--------------------------- + .. doxygengroup:: community + :project: libcugraph + :members: + :content-only: + diff --git a/docs/cugraph/source/api_docs/cugraph_c/core.rst b/docs/cugraph/source/api_docs/cugraph_c/core.rst new file mode 100644 index 00000000000..34456c65e43 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/core.rst @@ -0,0 +1,21 @@ +Core +==== + + +Core Number +----------- +.. doxygenfunction:: cugraph_core_number + :project: libcugraph + +K-Core +------ +.. doxygenfunction:: cugraph_k_core + :project: libcugraph + + +Core Support Functions +---------------------- + .. doxygengroup:: core + :project: libcugraph + :members: + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/index.rst b/docs/cugraph/source/api_docs/cugraph_c/index.rst new file mode 100644 index 00000000000..3dd37dbc374 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/index.rst @@ -0,0 +1,16 @@ +=========================== +cuGraph C API documentation +=========================== + + +.. toctree:: + :maxdepth: 3 + :caption: API Documentation + + centrality.rst + community.rst + core.rst + labeling.rst + sampling.rst + similarity.rst + traversal.rst diff --git a/docs/cugraph/source/api_docs/cugraph_c/labeling.rst b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst new file mode 100644 index 00000000000..af105ee8fc9 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/labeling.rst @@ -0,0 +1,20 @@ +Components +========== + + +Weakly Connected Components +--------------------------- +.. doxygenfunction:: cugraph_weakly_connected_components + :project: libcugraph + +Strongly Connected Components +----------------------------- +.. doxygenfunction:: cugraph_strongly_connected_components + :project: libcugraph + +Support +------- + .. doxygengroup:: labeling + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/sampling.rst b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst new file mode 100644 index 00000000000..21b837daf93 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/sampling.rst @@ -0,0 +1,37 @@ +Sampling +======== + +Uniform Random Walks +-------------------- +.. doxygenfunction:: cugraph_uniform_random_walks + :project: libcugraph + +Biased Random Walks +-------------------- +.. doxygenfunction:: cugraph_biased_random_walks + :project: libcugraph + +Random Walks via Node2Vec +------------------------- +.. doxygenfunction:: cugraph_node2vec_random_walks + :project: libcugraph + +Node2Vec +-------- +.. doxygenfunction:: cugraph_node2vec + :project: libcugraph + +Uniform Neighborhood Sampling +----------------------------- +.. doxygenfunction:: cugraph_uniform_neighbor_sample_with_edge_properties + :project: libcugraph + +.. doxygenfunction:: cugraph_uniform_neighbor_sample + :project: libcugraph + +Support +------- +.. doxygengroup:: samplingC + :project: libcugraph + :members: + :content-only: diff --git a/docs/cugraph/source/api_docs/cugraph_c/similarity.rst b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst new file mode 100644 index 00000000000..fba07ad206c --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/similarity.rst @@ -0,0 +1,25 @@ +Similarity +========== + + +Jaccard +------- +.. doxygenfunction:: cugraph_jaccard_coefficients + :project: libcugraph + +Sorensen +-------- +.. doxygenfunction:: cugraph_sorensen_coefficients + :project: libcugraph + +Overlap +------- +.. doxygenfunction:: cugraph_overlap_coefficients + :project: libcugraph + +Support +------- +.. doxygengroup:: similarity + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/cugraph_c/traversal.rst b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst new file mode 100644 index 00000000000..c90760e9e79 --- /dev/null +++ b/docs/cugraph/source/api_docs/cugraph_c/traversal.rst @@ -0,0 +1,30 @@ +Traversal +========== + + +Breadth First Search (BFS) +-------------------------- +.. doxygenfunction:: cugraph_bfs + :project: libcugraph + +Single-Source Shortest-Path (SSSP) +---------------------------------- +.. doxygenfunction:: cugraph_sssp + :project: libcugraph + +Path Extraction +--------------- +.. doxygenfunction:: cugraph_extract_paths + :project: libcugraph + +Extract Max Path Length +----------------------- +.. doxygenfunction:: cugraph_extract_paths_result_get_max_path_length + :project: libcugraph + +Support +------- +.. doxygengroup:: traversal + :project: libcugraph + :members: + :content-only: \ No newline at end of file diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 45f7210f5a2..74ca98bb98d 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -1,16 +1,39 @@ -Python API reference -==================== +API Reference +============= This page provides a list of all publicly accessible Python modules with in the Graph collection +Core Graph API Documentation +---------------------------- + .. toctree:: - :maxdepth: 2 - :caption: Python API Documentation + :maxdepth: 3 + :caption: Core Graph API Documentation cugraph/index.rst plc/pylibcugraph.rst + cugraph_c/index.rst + cugraph_cpp/index.rst + +Graph Nerual Networks API Documentation +--------------------------------------- + +.. toctree:: + :maxdepth: 3 + :caption: Graph Nerual Networks API Documentation + cugraph-dgl/cugraph_dgl.rst cugraph-pyg/cugraph_pyg.rst - service/index.rst cugraph-ops/index.rst + wholegraph/index.rst + +Additional Graph Packages API Documentation +---------------------------------- + +.. toctree:: + :maxdepth: 3 + :caption: Additional Graph Packages API Documentation + + service/index.rst + diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst index 383b31d269a..7e344d326f7 100644 --- a/docs/cugraph/source/api_docs/service/cugraph_service_client.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_client.rst @@ -9,7 +9,7 @@ cugraph-service .. autosummary:: :toctree: ../api/service/ - cugraph_service_client.client.RunAsyncioThread +.. cugraph_service_client.client.RunAsyncioThread cugraph_service_client.client.run_async cugraph_service_client.client.DeviceArrayAllocator cugraph_service_client.client.CugraphServiceClient diff --git a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst index a7e8b547573..09ca8360b6c 100644 --- a/docs/cugraph/source/api_docs/service/cugraph_service_server.rst +++ b/docs/cugraph/source/api_docs/service/cugraph_service_server.rst @@ -9,6 +9,6 @@ cugraph-service .. autosummary:: :toctree: ../api/service/ - cugraph_service_server.cugraph_handler.call_algo +.. cugraph_service_server.cugraph_handler.call_algo cugraph_service_server.cugraph_handler.ExtensionServerFacade cugraph_service_server.cugraph_handler.CugraphHandler diff --git a/docs/cugraph/source/api_docs/wholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/index.rst new file mode 100644 index 00000000000..80e231d4610 --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/index.rst @@ -0,0 +1,11 @@ +WholeGraph API reference +======================== + +This page provides WholeGraph API reference + +.. toctree:: + :maxdepth: 2 + :caption: WholeGraph API Documentation + + libwholegraph/index.rst + pylibwholegraph/index.rst diff --git a/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst new file mode 100644 index 00000000000..4ef68abef2d --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/libwholegraph/index.rst @@ -0,0 +1,228 @@ +===================== +libwholegraph API doc +===================== + +Doxygen WholeGraph C API documentation +-------------------------------------- +For doxygen documentation, please refer to `Doxygen Documentation <../../doxygen_docs/libwholegraph/html/index.html>`_ + +WholeGraph C API documentation +------------------------------ + +Library Level APIs +++++++++++++++++++ + +.. doxygenenum:: wholememory_error_code_t + :project: libwholegraph +.. doxygenfunction:: wholememory_init + :project: libwholegraph +.. doxygenfunction:: wholememory_finalize + :project: libwholegraph +.. doxygenfunction:: fork_get_device_count + :project: libwholegraph + +WholeMemory Communicator APIs ++++++++++++++++++++++++++++++ + +.. doxygentypedef:: wholememory_comm_t + :project: libwholegraph +.. doxygenstruct:: wholememory_unique_id_t + :project: libwholegraph +.. doxygenfunction:: wholememory_create_unique_id + :project: libwholegraph +.. doxygenfunction:: wholememory_create_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_get_rank + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_get_size + :project: libwholegraph +.. doxygenfunction:: wholememory_communicator_barrier + :project: libwholegraph + +WholeMemoryHandle APIs +++++++++++++++++++++++ + +.. doxygenenum:: wholememory_memory_type_t + :project: libwholegraph +.. doxygenenum:: wholememory_memory_location_t + :project: libwholegraph +.. doxygentypedef:: wholememory_handle_t + :project: libwholegraph +.. doxygenstruct:: wholememory_gref_t + :project: libwholegraph +.. doxygenfunction:: wholememory_malloc + :project: libwholegraph +.. doxygenfunction:: wholememory_free + :project: libwholegraph +.. doxygenfunction:: wholememory_get_communicator + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_type + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_location + :project: libwholegraph +.. doxygenfunction:: wholememory_get_total_size + :project: libwholegraph +.. doxygenfunction:: wholememory_get_data_granularity + :project: libwholegraph +.. doxygenfunction:: wholememory_get_local_memory + :project: libwholegraph +.. doxygenfunction:: wholememory_get_rank_memory + :project: libwholegraph +.. doxygenfunction:: wholememory_get_global_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_get_global_reference + :project: libwholegraph +.. doxygenfunction:: wholememory_determine_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_determine_entry_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_get_partition_plan + :project: libwholegraph +.. doxygenfunction:: wholememory_load_from_file + :project: libwholegraph +.. doxygenfunction:: wholememory_store_to_file + :project: libwholegraph + +WholeMemoryTensor APIs +++++++++++++++++++++++ + +.. doxygenenum:: wholememory_dtype_t + :project: libwholegraph +.. doxygenstruct:: wholememory_array_description_t + :project: libwholegraph +.. doxygenstruct:: wholememory_matrix_description_t + :project: libwholegraph +.. doxygenstruct:: wholememory_tensor_description_t + :project: libwholegraph +.. doxygentypedef:: wholememory_tensor_t + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_get_element_size + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_is_floating_number + :project: libwholegraph +.. doxygenfunction:: wholememory_dtype_is_integer_number + :project: libwholegraph +.. doxygenfunction:: wholememory_create_array_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_create_matrix_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_initialize_tensor_desc + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_array_desc_to_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_array_desc_to_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_copy_matrix_desc_to_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_convert_tensor_desc_to_array + :project: libwholegraph +.. doxygenfunction:: wholememory_convert_tensor_desc_to_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_array + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_array + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_matrix + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_element_count_from_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_get_memory_size_from_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_unsqueeze_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_create_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_make_tensor_from_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_make_tensor_from_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_has_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_memory_handle + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_tensor_description + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_global_reference + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_map_local_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_data_pointer + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_entry_per_partition + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_subtensor + :project: libwholegraph +.. doxygenfunction:: wholememory_tensor_get_root + :project: libwholegraph + +Ops on WholeMemory Tensors +++++++++++++++++++++++++++ + +.. doxygenfunction:: wholememory_gather + :project: libwholegraph +.. doxygenfunction:: wholememory_scatter + :project: libwholegraph + +WholeTensorEmbedding APIs ++++++++++++++++++++++++++ + +.. doxygentypedef:: wholememory_embedding_cache_policy_t + :project: libwholegraph +.. doxygentypedef:: wholememory_embedding_optimizer_t + :project: libwholegraph +.. doxygentypedef:: wholememory_embedding_t + :project: libwholegraph +.. doxygenenum:: wholememory_access_type_t + :project: libwholegraph +.. doxygenenum:: wholememory_optimizer_type_t + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding_optimizer + :project: libwholegraph +.. doxygenfunction:: wholememory_optimizer_set_parameter + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding_optimizer + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding_cache_policy + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding_cache_policy + :project: libwholegraph +.. doxygenfunction:: wholememory_create_embedding + :project: libwholegraph +.. doxygenfunction:: wholememory_destroy_embedding + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_embedding_tensor + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_gather + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_gather_gradient_apply + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_optimizer_state_names + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_get_optimizer_state + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_writeback_cache + :project: libwholegraph +.. doxygenfunction:: wholememory_embedding_drop_all_cache + :project: libwholegraph + +Ops on graphs stored in WholeMemory ++++++++++++++++++++++++++++++++++++ + +.. doxygenfunction:: wholegraph_csr_unweighted_sample_without_replacement + :project: libwholegraph +.. doxygenfunction:: wholegraph_csr_weighted_sample_without_replacement + :project: libwholegraph + +Miscellaneous Ops for graph ++++++++++++++++++++++++++++ + +.. doxygenfunction:: graph_append_unique + :project: libwholegraph +.. doxygenfunction:: csr_add_self_loop + :project: libwholegraph diff --git a/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst b/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst new file mode 100644 index 00000000000..67aab00acef --- /dev/null +++ b/docs/cugraph/source/api_docs/wholegraph/pylibwholegraph/index.rst @@ -0,0 +1,38 @@ +======================= +pylibwholegraph API doc +======================= + +.. currentmodule:: pylibwholegraph + +APIs +---- +.. autosummary:: + :toctree: ../../api/wg + + torch.initialize.init_torch_env + torch.initialize.init_torch_env_and_create_wm_comm + torch.initialize.finalize + torch.comm.WholeMemoryCommunicator + torch.comm.set_world_info + torch.comm.create_group_communicator + torch.comm.destroy_communicator + torch.comm.get_global_communicator + torch.comm.get_local_node_communicator + torch.comm.get_local_device_communicator + torch.tensor.WholeMemoryTensor + torch.tensor.create_wholememory_tensor + torch.tensor.create_wholememory_tensor_from_filelist + torch.tensor.destroy_wholememory_tensor + torch.embedding.WholeMemoryOptimizer + torch.embedding.create_wholememory_optimizer + torch.embedding.destroy_wholememory_optimizer + torch.embedding.WholeMemoryCachePolicy + torch.embedding.create_wholememory_cache_policy + torch.embedding.create_builtin_cache_policy + torch.embedding.destroy_wholememory_cache_policy + torch.embedding.WholeMemoryEmbedding + torch.embedding.create_embedding + torch.embedding.create_embedding_from_filelist + torch.embedding.destroy_embedding + torch.embedding.WholeMemoryEmbeddingModule + torch.graph_structure.GraphStructure diff --git a/docs/cugraph/source/basics/cugraph_intro.md b/docs/cugraph/source/basics/cugraph_intro.md index 0684129503f..10d14f8a0d7 100644 --- a/docs/cugraph/source/basics/cugraph_intro.md +++ b/docs/cugraph/source/basics/cugraph_intro.md @@ -21,7 +21,7 @@ call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or CuPy or SciPy sparse Matrix. -# Vision +## Vision The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the point that users just think in terms of analysis and not technologies or frameworks___. This is a goal that many of us on the cuGraph team have been @@ -49,7 +49,7 @@ RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support multi-billion edge graphs. -# Terminology +## Terminology cuGraph is a collection of GPU accelerated graph algorithms and graph utility functions. The application of graph analysis covers a lot of areas. @@ -67,8 +67,7 @@ documentation we will mostly use the terms __Node__ and __Edge__ to better match NetworkX preferred term use, as well as other Python-based tools. At the CUDA/C layer, we favor the mathematical terms of __Vertex__ and __Edge__. -# Roadmap -GitHub does not provide a robust project management interface, and so a roadmap turns into simply a projection of when work will be completed and not a complete picture of everything that needs to be done. To capture the work that requires multiple steps, issues are labels as “EPIC” and include multiple subtasks that could span multiple releases. The EPIC will be in the release where work in expected to be completed. A better roadmap is being worked an image of the roadmap will be posted when ready. - * GitHub Project Board: https://github.com/rapidsai/cugraph/projects/28 + + \ No newline at end of file diff --git a/docs/cugraph/source/conf.py b/docs/cugraph/source/conf.py index 470086b4faa..3f7ef7deb03 100644 --- a/docs/cugraph/source/conf.py +++ b/docs/cugraph/source/conf.py @@ -181,11 +181,10 @@ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'cugraph', 'cugraph Documentation', - author, 'cugraph', 'One line description of project.', + author, 'cugraph', 'GPU-accelerated graph analysis.', 'Miscellaneous'), ] - # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'https://docs.python.org/': None} @@ -209,7 +208,9 @@ def setup(app): ) breathe_projects = { + 'libcugraph': os.environ['XML_DIR_LIBCUGRAPH'], 'libcugraphops': os.environ['XML_DIR_LIBCUGRAPHOPS'], 'libwholegraph': os.environ['XML_DIR_LIBWHOLEGRAPH'] } + breathe_default_project = "libcugraph" diff --git a/docs/cugraph/source/graph_support/algorithms.md b/docs/cugraph/source/graph_support/algorithms.md index f6cb7c0d8b1..a1b80e92751 100644 --- a/docs/cugraph/source/graph_support/algorithms.md +++ b/docs/cugraph/source/graph_support/algorithms.md @@ -22,7 +22,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | Category | Notebooks | Scale | Notes | | ----------------- | ---------------------------------- | ------------------- | --------------------------------------------------------------- | -| [Centrality](./algorithms/Centrality.md) | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | +| [Centrality](./algorithms/Centrality.html ) | [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb) | | | | | [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) | __Multi-GPU__ | | | | [Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.06 | | | [Edge Betweenness Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) | __Multi-GPU__ | MG as of 23.08 | @@ -31,12 +31,12 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | Community | | | | | | [Leiden](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | MG as of 23.06 | | | [Louvain](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Louvain.ipynb) | __Multi-GPU__ | | -| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | MG planned for 23.10 | +| | [Ensemble Clustering for Graphs](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ECG.ipynb) | Single-GPU | MG planned for 24.02 | | | [Spectral-Clustering - Balanced Cut](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | | | [Spectral-Clustering - Modularity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Spectral-Clustering.ipynb) | Single-GPU | | | | [Subgraph Extraction](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Subgraph-Extraction.ipyn) | Single-GPU | | | | [Triangle Counting](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/Triangle-Counting.ipynb) | __Multi-GPU__ | | -| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | MG planned for 23.10 | +| | [K-Truss](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/community/ktruss.ipynb) | Single-GPU | MG planned for 2024 | | Components | | | | | | [Weakly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | __Multi-GPU__ | | | | [Strongly Connected Components](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/components/ConnectedComponents.ipynb) | Single-GPU | | @@ -55,7 +55,7 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | | [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Pagerank) | | | [Personal Pagerank]() | __Multi-GPU__ | [C++ README](cpp/src/centrality/README.md#Personalized-Pagerank) | | | [HITS](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/HITS.ipynb) | __Multi-GPU__ | | -| [Link Prediction](./algorithms/Similarity.md) | | | | +| [Link Prediction](algorithms/Similarity.html) | | | | | | [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | __Multi-GPU__ | Directed graph only | | | [Weighted Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) | Single-GPU | | | | [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) | **Multi-GPU** | | @@ -65,8 +65,8 @@ Note: Multi-GPU, or MG, includes support for Multi-Node Multi-GPU (also called M | | [Uniform Random Walks RW](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/sampling/RandomWalk.ipynb) | __Multi-GPU__ | | | | *Biased Random Walks (RW)* | --- | | | | Egonet | __Multi-GPU__ | | -| | Node2Vec | Single-GPU | | -| | Uniform Neighborhood sampling | __Multi-GPU__ | | +| | Node2Vec | __Multi-GPU__ | | +| | Neighborhood sampling | __Multi-GPU__ | | | Traversal | | | | | | Breadth First Search (BFS) | __Multi-GPU__ | with cutoff support [C++ README](cpp/src/traversal/README.md#BFS) | | | Single Source Shortest Path (SSSP) | __Multi-GPU__ | [C++ README](cpp/src/traversal/README.md#SSSP) | diff --git a/docs/cugraph/source/graph_support/algorithms/Centrality.md b/docs/cugraph/source/graph_support/algorithms/Centrality.md index e42bbe238c6..f82a1fe123b 100644 --- a/docs/cugraph/source/graph_support/algorithms/Centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/Centrality.md @@ -1,7 +1,7 @@ # cuGraph Centrality Notebooks - + The RAPIDS cuGraph Centrality folder contains a collection of Jupyter Notebooks that demonstrate algorithms to identify and quantify the importance of vertices to the structure of the graph. In the diagram above, the highlighted vertices are highly important and are likely answers to questions like: @@ -15,13 +15,13 @@ But which vertices are most important? The answer depends on which measure/algor |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Degree Centrality](./degree_centrality.md)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| -|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| -|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| -|[Katz Centrality](./katz_centrality.md)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | +|[Degree Centrality](./degree_centrality.html)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| +|[Betweenness Centrality](./betweenness_centrality.html)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| +|[Eigenvector Centrality](./eigenvector_centrality.html)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| +|[Katz Centrality](./katz_centrality.html)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | |Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | -[System Requirements](../../README.md#requirements) +[System Requirements](../../README.html#requirements) | Author Credit | Date | Update | cuGraph Version | Test Hardware | | --------------|------------|------------------|-----------------|----------------| diff --git a/docs/cugraph/source/graph_support/algorithms/Similarity.md b/docs/cugraph/source/graph_support/algorithms/Similarity.md index 450beb373a2..18c0a94d519 100644 --- a/docs/cugraph/source/graph_support/algorithms/Similarity.md +++ b/docs/cugraph/source/graph_support/algorithms/Similarity.md @@ -15,9 +15,9 @@ Manipulation of the data before or after the graph analytic is not covered here. |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Jaccard Smiliarity](./jaccard_similarity.md)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) || -|[Overlap Similarity](./overlap_similarity.md)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) || -|[Sorensen](./sorensen_coefficient.md)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)|| +|[Jaccard Smiliarity](./jaccard_similarity.html)| [Jaccard Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb) || +|[Overlap Similarity](./overlap_similarity.html)| [Overlap Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Overlap-Similarity.ipynb) || +|[Sorensen](./sorensen_coefficient.html)|[Sorensen Similarity](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_prediction/Sorensen_coefficient.ipynb)|| |Personal Pagerank|[Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) || diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index c5303c21674..955eb6d54db 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -25,12 +25,12 @@ RAPIDS Graph documentation * - :abbr:`libcugraph_etl (C++ renumbering function for strings)` - :abbr:`wholegraph (Shared memory-based GPU-accelerated GNN training)` - - .. - -| | +~~~~~~~~~~~~ +Introduction +~~~~~~~~~~~~ cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or @@ -39,6 +39,7 @@ even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update the docs and links +| .. toctree:: :maxdepth: 2 @@ -48,9 +49,8 @@ the docs and links installation/index tutorials/index graph_support/index + wholegraph/index references/index - dev_resources/index - releases/index api_docs/index Indices and tables diff --git a/docs/cugraph/source/wholegraph/basics/index.rst b/docs/cugraph/source/wholegraph/basics/index.rst new file mode 100644 index 00000000000..429fe35d601 --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/index.rst @@ -0,0 +1,11 @@ +====== +Basics +====== + + +.. toctree:: + :maxdepth: 2 + + wholegraph_intro + wholememory_intro + wholememory_implementation_details diff --git a/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md b/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md new file mode 100644 index 00000000000..360f8e0e36b --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholegraph_intro.md @@ -0,0 +1,135 @@ +# WholeGraph Introduction +WholeGraph helps train large-scale Graph Neural Networks(GNN). +WholeGraph provides underlying storage structure called WholeMemory. +WholeMemory is a Tensor like storage and provides multi-GPU support. +It is optimized for NVLink systems like DGX A100 servers. +By working together with cuGraph, cuGraph-Ops, cuGraph-DGL, cuGraph-PyG, and upstream DGL and PyG, +it will be easy to build GNN applications. + +## WholeMemory +WholeMemory can be regarded as a whole view of GPU memory. +WholeMemory exposes a handle of the memory instance no matter how the underlying data is stored across multiple GPUs. +WholeMemory assumes that separate process is used to control each GPU. + +### WholeMemory Basics +To define WholeMemory, we need to specify the following: + +#### 1. Specify the set of GPU to handle the Memory + +Since WholeMemory is owned by a set of GPUs, you must specify the set of GPUs. +This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specifying the WholeMemory Communicator when creating WholeMemory. + +#### 2. Specify the location of the memory + +Although WholeMemory is owned by a set of GPUs, the memory itself can be located in host memory or in device memory. +The location of the memory need to be specified, two types of locations can be specified. + +- **Host memory**: will use pinned host memory as underlying storage. +- **Device memory**: will use GPU device memory as underlying storage. + +#### 3. Specify the address mapping mode of the memory + +As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping. +There are three types of address mapping modes (also known as WholeMemory types), they are: + +- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU. + In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal + device memory. Software will see no difference. Hardware peer to peer access will handle the underlying communication. + +- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU. + In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory. + However, an abstract layer may help to hide this. + +- **Distributed**: Memory from other GPUs are not mapped into current GPU, so no direct access is supported. + To access memory of other GPU, explicit communication is needed. + +To learn more details about WholeMemory locations and WholeMemory types, please refer to +[WholeMemory Implementation Details](wholememory_implementation_details.md) + +### WholeMemory Communicator +WholeMemory Communicator has two main purpose: + +- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that + wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same. +- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs + during the WholeMemory creation and some OPs on some types of WholeMemory. + +To Create WholeMemory Communicator, a WholeMemory Unique ID needs to be created first, it is usually created by the first GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator +will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as +well as all GPU count. + +### WholeMemory Granularity +As underlying storage may be partitioned into multiple GPUs physically, this is usually not wanted inside one single +user data block. To help on this, when creating WholeMemory, the granularity of data can be specified. Then the +WholeMemory is considered as multiple block of the same granularity and will not get split inside the granularity. + +### WholeMemory Mapping +As WholeMemory provides a whole view of memory to GPU, to access WholeMemory, mapping is usually needed. +Different types of WholeMemory have different mapping methods supported as their names. +Some mappings supported include +- All the WholeMemory types support mapping the memory range that local GPU is responsible for. + That is, each rank can directly access "Local" memory in all types of WholeMemory. + Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU, + but it is guaranteed to be directly accessed by current GPU. +- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into + current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of + different chunks are not guaranteed to be continuous. +- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a + single range of virtual memory, accessing to different position of this memory will physically access to different + GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable). + +### Operations on WholeMemory +There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory. +#### Local Operation +As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be +either read or write. Just use it as GPU memory of current device is OK. +#### Load and Store +To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory uses raw binary +file format for disk operation. For Load, the input file can be a single file or a list of files, if it is a list, they +will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing +a list of files. +#### Gather and Scatter +WholeMemory also supports Gather / Scatter operation, usually they operate on a +[WholeMemory Tensor](#wholememory-tensor). + +### WholeMemory Tensor +Compared to PyTorch, WholeMemory is like PyTorch Storage while a WholeMemory Tensor is like a PyTorch Tensor. +For now, WholeMemory supports only 1D and 2D tensors, or arrays and matrices. Only first dimension is partitioned. + +### WholeMemory Embedding +WholeMemory Embedding is just like a 2D WholeMemory Tensor, with two features added. They support cache and sparse +optimizers. +#### Cache Support +To create WholeMemory Embedding with a cache, WholeMemory CachePolicy needs to be be created first. WholeMemoryCachePolicy can be created with following fields: +- **WholeMemory Communicator**: WholeMemory CachePolicy also needs WholeMemory Communicator. + WholeMemory Communicator defines the set of GPUs that cache all the Embedding. + It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding. +- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of cache. +- **WholeMemory location**: WholeMemory CachePolicy use WholeMemory location to specify the location of the cache. +- **Access type**: Access type can be readonly or readwrite. +- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the + whole embedding. + +The two most commonly used caches are: +- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory + Communicator used to create WholeMemory Embedding, it means that the cache has same GPU set as WholeMemory Embedding. + So each GPU just caches its own part of raw Embedding. + Most commonly, when raw WholeMemory Embedding is located on host memory, and the cache is on device + memory, each GPU just caches its own part of host memory. +- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the + WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings. + Normally, when raw WholeMemory Embedding is partitioned on different machine nodes, and we + want to cache some embeddings in local machine or local GPU, then the subset of GPU can be all the GPUs in the local + machine. For local cached global memory, only readonly is supported. + +#### WholeMemory Embedding Sparse Optimizer +Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training. +To efficiently train large embedding tables, a sparse optimizer is needed. +WholeMemory Embedding Sparse Optimizer can run on a cached or noncached WholeMemory Embedding. +Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad. + +## Graph Structure +Graph structure in WholeGraph is also based on WholeMemory. +In WholeGraph, graph is stored in [CSR format](https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)). +Both ROW_INDEX (noted as `csr_row_ptr`) and COL_INDEX (notated as `csr_col_ind`) are stored in a +WholeMemory Tensor. So loading Graph Structure can use [WholeMemory Tensor Loading mechanism](#load-and-store). diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md new file mode 100644 index 00000000000..a5541109c4f --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholememory_implementation_details.md @@ -0,0 +1,58 @@ +# WholeMemory Implementation Details +As described in [WholeMemory Introduction](wholegraph_intro.md), there are two WholeMemory location and three +WholeMemory types. So there will be total six WholeMemory. + +| Type | CONTINUOUS | CONTINUOUS | CHUNKED | CHUNKED | DISTRIBUTED | DISTRIBUTED | +|:-------------:|:-----------:|:----------:|:---------:|:---------:|:-----------:|:-----------:| +| Location | DEVICE | HOST | DEVICE | HOST | DEVICE | HOST | +| Allocated by | EACH | FIRST | EACH | FIRST | EACH | EACH | +| Allocate API | Driver | Host | Runtime | Host | Runtime | Runtime | +| IPC Mapping | Unix fd | mmap | cudaIpc | mmap | No IPC map | No IPC map | + +For "Continuous" and "Chunked" types of WholeMemory, all memory is mapped to each GPU, +so these two types are all "Mapped" WholeMemory, in contrast to "Distributed" WholeMemory where all are not mapped. + +## WholeMemory Layout +Since the underlying memory of a single WholeMemory object may be on multiple GPU devices, the WholeGraph library will +partition data into these GPU devices. +The partition method guarantees that each GPU can access one continuous part of the entire memory. +Here "can access" means can directly access from CUDA kernels, but the memory doesn't have to be physically on that GPU. +For example,it can be on host memory or other GPU's device memory that can be access using P2P. +In that case the stored data has its own granularity that shouldn't be split. Data granularity can be specified while +creating WholeMemory. Then each data granularity can be considered as a block of data. + +The follow figure shows the layout of 15 data block over 4 GPUs. +![WholeMemory Layout](../imgs/general_wholememory.png) + +For WholeMemory Tensors, they can be 1D or 2D tensors. +For 1D tensor, data granularity is one element. For 2D tensor, data granularity is its 1D tensor. +The layout will be like this: +![WholeMemory Tensor Layout](../imgs/wholememory_tensor.png) + +## WholeMemory Allocation +As there are six types of WholeMemory, the allocation process of each type are as follows: + +### Device Continuous WholeMemory +For Device Continuous WholeMemory, first a range of virtual address space is reserved in each GPU, which covers the +entire memory range. Then a part of pyhsical memory is allocated in each GPU, as shown in the following figure. +![Device Continuous WholeMemory Allocation Step 1](../imgs/device_continuous_wholememory_step1.png) +After that, each GPU gathers all the memory handles from all GPUs, and maps them to the reserved address space. +![Device Continuous WholeMemory Allocation Step 2](../imgs/device_continuous_wholememory_step2.png) + +### Device Chunked WholeMemory +For Device Chunked WholeMemory, first each GPU allocates its own part of memory using CUDA runtime API, this will create +both a virtual address space and physical memory for its own memory. +![Device Chunked WholeMemory Allocation Step 1](../imgs/device_chunked_wholememory_step1.png) +Each GPU gathers the Ipc handle of memory from all other GPUs, and maps that into its own virtual address space. +![Device Chunked WholeMemory Allocation Step 2](../imgs/device_chunked_wholememory_step2.png) + +### Host Mapped WholeMemory +For Host, Continuous and Chunked are using the same method. First, rank and allocate the host physical and share that to all +ranks. +![Host Mapped WholeMemory Allocation Step 1](../imgs/host_mapped_wholememory_step1.png) +Then each rank registers that host memory to GPU address space. +![Host Mapped WholeMemory Allocation Step 2](../imgs/host_mapped_wholememory_step2.png) + +### Distributed WholeMemory +For Distributed WholeMemory, each GPU just malloc its own part of memory, no need to share to other GPUs. +![Distributed WholeMemory Allocation](../imgs/distributed_wholememory.png) diff --git a/docs/cugraph/source/wholegraph/basics/wholememory_intro.md b/docs/cugraph/source/wholegraph/basics/wholememory_intro.md new file mode 100644 index 00000000000..7209da9471c --- /dev/null +++ b/docs/cugraph/source/wholegraph/basics/wholememory_intro.md @@ -0,0 +1,123 @@ +## WholeMemory +WholeMemory can be regarded as a whole view of GPU memory. +WholeMemory exposes a handle to the memory instance no matter how the underlying data is stored across multiple GPUs. +WholeMemory assumes that a separate process is used to control each GPU. + +### WholeMemory Basics +To define WholeMemory, we need to specify the following: + +#### 1. Specify the set of GPU to handle the Memory + +As WholeMemory is owned by a set of GPUs, so the set of GPUs need to be specified. +This is done by creating [WholeMemory Communicator](#wholememory-communicator) and specify the WholeMemory Communicator +when creating WholeMemory. + +#### 2. Specify the location of the memory + +Although WholeMemory is owned by a set of GPUs, the memory itself can be located on host memory or on device memory. +So the location of the memory needs to be specified. Two types of location can be specified. + +- **Host memory**: will use pinned host memory as underlying storage. +- **Device memory**: will use GPU device memory as underlying storage. + +#### 3. Specify the address mapping mode of the memory + +As WholeMemory is owned by multiple GPUs, each GPU will access the whole memory space, so we need address mapping. +There are three types of address mapping modes (also known as WholeMemory types), they are: + +- **Continuous**: All memory from each GPU will be mapped into a single continuous memory address space for each GPU. + In this mode, each GPU can directly access the whole memory using a single pointer and offset, just like using normal + device memory. Software will see no difference. Hardware peer-to-peer access will handle the underlying communication. + +- **Chunked**: Memory from each GPU will be mapped into different memory chunks, one chunk for each GPU. + In this mode, direct access is also supported, but not using a single pointer. Software will see the chunked memory. + However, an abstract layer can hide this. + +- **Distributed**: Memory from other GPUs is not mapped into current GPU, so no direct access is supported. + To access memory of another GPU, explicit communication is needed. + +If you would like to know more details about WholeMemory locations and WholeMemory types, please refer to +[WholeMemory Implementation Details](wholememory_implementation_details.md) + +### WholeMemory Communicator +WholeMemory Communicator has two main purpose: + +- **Defines a set of GPUs which works together on WholeMemory.** WholeMemory Communicator is created by all GPUs that + wants to work together. A WholeMemory Communicator can be reused as long as the GPU set needed is the same. +- **Provides underlying communication channel needed by WholeMemory.** WholeMemory may need commuincator between GPUs + during the WholeMemory creation and some OPs on some types of WholeMemory. + +To Create WholeMemory Communicator, a WholeMemory Unique ID need to be created first, it is usually created by the first +GPU in the set of GPUs, and then broadcasted to all GPUs that want to work together. Then all GPUs in this communicator +will call WholeMemory Communicator creation function using this WholeMemory Unique ID, and the rank of current GPU as +well as all GPU count. + +### WholeMemory Granularity +As underlying storage may be physically partitioned into multiple GPUs, it is usually not wanted inside one single +user data block. To help with this, when creating WholeMemory, the granularity of data can be specified. Therefore +WholeMemory is considered as multiple blocks of the same granularity and will not get split inside the granularity. + +### WholeMemory Mapping +Since WholeMemory provides a whole view of memory to GPU, mapping is usually needed to access WholeMemory. +Different types of WholeMemory have different mapping methods supported as their names. +Some mappings supported include: +- All the WholeMemory types support mapping the memory range that local GPU is responsible for. + That is, each rank can directly access "Local" memory in all types of WholeMemory. + Here "Local" memory doesn't have to be on current GPU's memory, it can be on host memory or even maybe on other GPU, + but it is guaranteed to be directly accessed by current GPU. +- Chunked and Continuous WholeMemory also support Chunked mapping. That is, memory of all GPUs can be mapped into + current GPU, one continuous chunk for one GPU. Each chunk can be directly accessed by current GPU. But the memory of + different chunks are not guaranteed to be continuous. +- Continuous WholeMemory can be mapped into continuous memory space. That is, memory of all GPUs are mapped into a + single range of virtual memory, accessing different positions of this memory will physically access different + GPUs. This mapping will be handled by hardware (CPU pagetable or GPU pagetable). + +### Operations on WholeMemory +There are some operations that can be performed on WholeMemory. They are based on the mapping of WholeMemory. +#### Local Operation +As all WholeMemory supports mapping of local memory, so operation on local memory is supported. The operation can be +either read or write. Just use it as GPU memory of current device is OK. +#### Load / Store +To facilitate file operation, Load / Store WholeMemory from file or to file is supported. WholeMemory use raw binary +file format for disk operation. For Load, the input file can be single file or a list of files, if it is a list, they +will be logically concatenated together and then loaded. For store, each GPU stores its local memory to file, producing +a list of files. +#### Gather / Scatter +WholeMemory also supports Gather / Scatter operations, usually they operate on a +[WholeMemory Tensor](#wholememory-tensor). + +### WholeMemory Tensor +Compared to PyTorch, WholeMemory is like PyTorch Storage while WholeMemory Tensor is like PyTorch Tensor. +For now, WholeMemory supports only 1D and 2D tensor, or array and matrix. Only first dimension is partitioned. + +### WholeMemory Embedding +WholeMemory Embedding is just like 2D WholeMemory Tensor, with cache support and sparse optimizer support added. +#### Cache Support +WholeMemory Embedding supports cache. To create WholeMemory Embedding with cache, WholeMemory CachePolicy need first be +created. WholeMemoryCachePolicy can be created with following fields: +- **WholeMemory Communicator**: WholeMemory CachePolicy also need WholeMemory Communicator. + This WholeMemory Communicator defines the set of GPUs that cache the all the Embedding. + It can be the same as the WholeMemory Communicator used to create WholeMemory Embedding. +- **WholeMemory type**: WholeMemory CachePolicy uses WholeMemory type to specify the WholeMemory type of the cache. +- **WholeMemory location**: WholeMemory CachePolicy uses WholeMemory location to specify the location of the cache. +- **Access type**: Access type can be readonly or readwrite. +- **Cache ratio**: Specify how much memory the cache will use. This ratio is computed for each GPU set that caches the + whole embedding. + +There are two most commonly used caches. They are: +- **Device cached host memory**: When the WholeMemory Communicator for Cache Policy is the same as the WholeMemory + Communicator used to create WholeMemory Embedding, it means that cache has the same GPU set as WholeMemory Embedding. + So each GPU just cache its own part of raw Embedding. + Normally, when raw WholeMemory Embedding is located on host memory, and the cache is on device + memory, each GPU just caches its own part of host memory. +- **Local cached global memory**: The WholeMemory Communicator of WholeMemory CachePolicy can also be a subset of the + WholeMemory Communicator of WholeMemory Embedding. In this case, the subset of GPUs together cache all the embeddings. + Typically, raw WholeMemory Embedding is partitioned on different machine nodes, and we + want to cache some embeddings in local machine or local GPU, then the subset of GPUs can be all the GPUs on the local + machine. For local cached global memory supports just readonly. + +#### WholeMemory Embedding Sparse Optimizer +Another feature of WholeMemory Embedding is that WholeMemory Embedding supports embedding training. +To efficiently train large embedding tables, a sparse optimizer is needed. +The WholeMemory Embedding Sparse Optimizer can run on cached or non-cached WholeMemory Embedding. +Currently supported optimizers include SGD, Adam, RMSProp and AdaGrad. diff --git a/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png b/docs/cugraph/source/wholegraph/imgs/device_chunked_wholememory_step1.png new file mode 100644 index 0000000000000000000000000000000000000000..b8a0447e6fb9dde5691c987aa734a6ba4ae24663 GIT binary patch literal 23136 zcmeFZ2UOErw=WuWyZJVv=$7UdL~tuei-3T18#a0g)exjgml7aA01K#~6p`K(AxP*o z5Mscs)X*eC5F{uN0tASJ5<)2NAC&#=citWEoIB2akM6JdGYBcRuKoL-Z`c1N z+x)Lzgr7F!V1Y-=YXoI)1U^F^@yoV9+}VI=@Gfv_6Os%{{*Z|_A6=27pH^7BY-p-V 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zFb{6PcMtyiKXCX>sQX`*Bz`9TXW|C_|C+j*>l=I57*q0IF@3|IY~GSpx{-NZ|HXd+ DoB#6Y literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/wholegraph/index.rst b/docs/cugraph/source/wholegraph/index.rst new file mode 100644 index 00000000000..2a69544b4c9 --- /dev/null +++ b/docs/cugraph/source/wholegraph/index.rst @@ -0,0 +1,14 @@ +WholeGraph +========== +RAPIDS WholeGraph has following package: + +* pylibwholegraph: shared memory-based GPU-accelerated GNN training + + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + basics/index + installation/index + diff --git a/docs/cugraph/source/wholegraph/installation/container.md b/docs/cugraph/source/wholegraph/installation/container.md new file mode 100644 index 00000000000..3a2c627c56a --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/container.md @@ -0,0 +1,29 @@ +# Build Container for WholeGraph +To run WholeGraph or build WholeGraph from source, set up the environment first. +We recommend using Docker images. +For example, to build the WholeGraph base image from the NGC pytorch 22.10 image, you can follow `Dockerfile`: +```dockerfile +FROM nvcr.io/nvidia/pytorch:22.10-py3 + +RUN apt update && DEBIAN_FRONTEND=noninteractive apt install -y lsb-core software-properties-common wget libspdlog-dev + +#RUN remove old cmake to update +RUN conda remove --force -y cmake +RUN rm -rf /usr/local/bin/cmake && rm -rf /usr/local/lib/cmake && rm -rf /usr/lib/cmake + +RUN apt-key adv --fetch-keys https://apt.kitware.com/keys/kitware-archive-latest.asc && \ + export LSB_CODENAME=$(lsb_release -cs) && \ + apt-add-repository -y "deb https://apt.kitware.com/ubuntu/ ${LSB_CODENAME} main" && \ + apt update && apt install -y cmake + +# update py for pytest +RUN pip3 install -U py +RUN pip3 install Cython setuputils3 scikit-build nanobind pytest-forked pytest +``` + +To run GNN applications, you may also need cuGraphOps, DGL and/or PyG libraries to run the GNN layers. +You may refer to [DGL](https://www.dgl.ai/pages/start.html) or [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) +For example, to install DGL, you may need to add: +```dockerfile +RUN pip3 install dgl -f https://data.dgl.ai/wheels/cu118/repo.html +``` diff --git a/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md new file mode 100644 index 00000000000..5b2072b0523 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/getting_wholegraph.md @@ -0,0 +1,48 @@ + +# Getting the WholeGraph Packages + +Start by reading the [RAPIDS Instalation guide](https://docs.rapids.ai/install) +and checkout the [RAPIDS install selector](https://rapids.ai/start.html) for a pick list of install options. + + +There are 4 ways to get WholeGraph packages: +1. [Quick start with Docker Repo](#docker) +2. [Conda Installation](#conda) +3. [Pip Installation](#pip) +4. [Build from Source](./source_build.md) + + +
+ +## Docker +The RAPIDS Docker containers (as of Release 23.10) contain all RAPIDS packages, including WholeGraph, as well as all required supporting packages. To download a container, please see the [Docker Repository](https://hub.docker.com/r/rapidsai/rapidsai/), choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize all of the RAPIDS libraries. + +
+ + +## Conda +It is easy to install WholeGraph using conda. You can get a minimal conda installation with [Miniconda](https://conda.io/miniconda.html) or get the full installation with [Anaconda](https://www.anaconda.com/download). + +WholeGraph conda packages + * libwholegraph + * pylibwholegraph + +Replace the package name in the example below to the one you want to install. + + +Install and update WholeGraph using the conda command: + +```bash +conda install -c rapidsai -c conda-forge -c nvidia wholegraph cudatoolkit=11.8 +``` + +
+ +## PIP +wholegraph, and all of RAPIDS, is available via pip. + +``` +pip install wholegraph-cu11 --extra-index-url=https://pypi.nvidia.com +``` + +
diff --git a/docs/cugraph/source/wholegraph/installation/index.rst b/docs/cugraph/source/wholegraph/installation/index.rst new file mode 100644 index 00000000000..09f1cb44a24 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/index.rst @@ -0,0 +1,9 @@ +Installation +============ + +.. toctree:: + :maxdepth: 2 + + getting_wholegraph + container + source_build diff --git a/docs/cugraph/source/wholegraph/installation/source_build.md b/docs/cugraph/source/wholegraph/installation/source_build.md new file mode 100644 index 00000000000..c468048c351 --- /dev/null +++ b/docs/cugraph/source/wholegraph/installation/source_build.md @@ -0,0 +1,187 @@ +# Building from Source + +The following instructions are for users wishing to build wholegraph from source code. These instructions are tested on supported distributions of Linux,CUDA, +and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for a list of supported environments. +Other operating systems _might be_ compatible, but are not currently tested. + +The wholegraph package includes both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. +The C/C++ CUDA library is `libwholegraph` and the python library is `pylibwholegraph`. + +## Prerequisites + +__Compiler__: +* `gcc` version 11.0+ +* `nvcc` version 11.0+ +* `cmake` version 3.26.4+ + +__CUDA__: +* CUDA 11.8+ +* NVIDIA driver 450.80.02+ +* Pascal architecture or better + +You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). + +__Other Packages__: +* ninja +* nccl +* cython +* setuputils3 +* scikit-learn +* scikit-build +* nanobind>=0.2.0 + +## Building wholegraph +To install wholegraph from source, ensure the dependencies are met. + +### Clone Repo and Configure Conda Environment +__GIT clone a version of the repository__ + + ```bash + # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME + export WHOLEGRAPH_HOME=$(pwd)/wholegraph + + # Download the wholegraph repo - if you have a forked version, use that path here instead + git clone https://github.com/rapidsai/wholegraph.git $WHOLEGRAPH_HOME + + cd $WHOLEGRAPH_HOME + ``` + +__Create the conda development environment__ + +```bash +# create the conda environment (assuming in base `wholegraph` directory) + +# for CUDA 11.x +conda env create --name wholegraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml + +# activate the environment +conda activate wholegraph_dev + +# to deactivate an environment +conda deactivate +``` + + - The environment can be updated as development includes/changes the dependencies. To do so, run: + + +```bash + +# Where XXX is the CUDA version +conda env update --name wholegraph_dev --file conda/environments/all_cuda-XXX_arch-x86_64.yaml + +conda activate wholegraph_dev +``` + + +### Build and Install Using the `build.sh` Script +Using the `build.sh` script make compiling and installing wholegraph a +breeze. To build and install, simply do: + +```bash +$ cd $WHOLEGRAPH_HOME +$ ./build.sh clean +$ ./build.sh libwholegraph +$ ./build.sh pylibwholegraph +``` + +There are several other options available on the build script for advanced users. +`build.sh` options: +```bash +build.sh [ ...] [ ...] + where is: + clean - remove all existing build artifacts and configuration (start over). + uninstall - uninstall libwholegraph and pylibwholegraph from a prior build/install (see also -n) + libwholegraph - build the libwholegraph C++ library. + pylibwholegraph - build the pylibwholegraph Python package. + tests - build the C++ (OPG) tests. + benchmarks - build benchmarks. + docs - build the docs + and is: + -v - verbose build mode + -g - build for debug + -n - no install step + --allgpuarch - build for all supported GPU architectures + --cmake-args=\\\"\\\" - add arbitrary CMake arguments to any cmake call + --compile-cmd - only output compile commands (invoke CMake without build) + --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) + -h | --h[elp] - print this text + + default action (no args) is to build and install 'libwholegraph' then 'pylibwholegraph' targets + +examples: +$ ./build.sh clean # remove prior build artifacts (start over) +$ ./build.sh + +# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) +$ PARALLEL_LEVEL=4 ./build.sh libwholegraph + +Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +``` + + +## Building each section independently +### Build and Install the C++/CUDA `libwholegraph` Library +CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. + +This project uses cmake for building the C/C++ library. To configure cmake, run: + + ```bash + # Set the location to wholegraph in an environment variable WHOLEGRAPH_HOME + export WHOLEGRAPH_HOME=$(pwd)/wholegraph + + cd $WHOLEGRAPH_HOME + cd cpp # enter cpp directory + mkdir build # create build directory + cd build # enter the build directory + cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX + + # now build the code + make -j # "-j" starts multiple threads + make install # install the libraries + ``` +The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/wholegraph` respectively. + +### Building and installing the Python package + +Build and Install the Python packages to your Python path: + +```bash +cd $WHOLEGRAPH_HOME +cd python +cd pylibwholegraph +python setup.py build_ext --inplace +python setup.py install # install pylibwholegraph +``` + +## Run tests + +Run either the C++ or the Python tests with datasets + + - **Python tests with datasets** + + ```bash + cd $WHOLEGRAPH_HOME + cd python + pytest + ``` + + - **C++ stand alone tests** + + From the build directory : + + ```bash + # Run the tests + cd $WHOLEGRAPH_HOME + cd cpp/build + gtests/PARALLEL_UTILS_TESTS # this is an executable file + ``` + + +Note: This conda installation only applies to Linux and Python versions 3.8/3.10. + +## Creating documentation + +Python API documentation can be generated from _./docs/wholegraph directory_. Or through using "./build.sh docs" + +## Attribution +Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md From 332676e78bbbd209b77caf81ab756fa6bff2a07b Mon Sep 17 00:00:00 2001 From: Huiyu Xie Date: Tue, 21 Nov 2023 11:40:36 -0800 Subject: [PATCH 096/111] [DOC]: Fix invalid links and add materials to notebooks (#4002) I'm new to CuGraph repository and would like to contribute further in the future. So I start with minor tasks with docs to familiarize myself with the contribution process in this repository. Here is what I have done in this PR: - Fixed some invalid links in the documentation. - Corrected and added formulas related to centrality. - Added more references to the centrality to ensure consistency. Also, the images located at https://github.com/rapidsai/cugraph/tree/main/python/cugraph cannot be displayed because it is symbolic link to the repository's README file. But it's not a major issue. Authors: - Huiyu Xie (https://github.com/huiyuxie) - Ralph Liu (https://github.com/nv-rliu) - Naim (https://github.com/naimnv) - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4002 --- .../graph_support/algorithms/Centrality.md | 14 +++++++----- .../source/installation/getting_cugraph.md | 2 +- .../algorithms/centrality/Centrality.ipynb | 22 ++++++++++++++----- notebooks/algorithms/centrality/Degree.ipynb | 2 +- notebooks/algorithms/centrality/Katz.ipynb | 7 ------ readme_pages/CONTRIBUTING.md | 4 ++-- 6 files changed, 29 insertions(+), 22 deletions(-) diff --git a/docs/cugraph/source/graph_support/algorithms/Centrality.md b/docs/cugraph/source/graph_support/algorithms/Centrality.md index f82a1fe123b..8119e655236 100644 --- a/docs/cugraph/source/graph_support/algorithms/Centrality.md +++ b/docs/cugraph/source/graph_support/algorithms/Centrality.md @@ -15,13 +15,15 @@ But which vertices are most important? The answer depends on which measure/algor |Algorithm |Notebooks Containing |Description | | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | -|[Degree Centrality](./degree_centrality.html)| [Centrality](./Centrality.ipynb), [Degree](./Degree.ipynb) |Measure based on counting direct connections for each vertex| -|[Betweenness Centrality](./betweenness_centrality.html)| [Centrality](./Centrality.ipynb), [Betweenness](./Betweenness.ipynb) |Number of shortest paths through the vertex| -|[Eigenvector Centrality](./eigenvector_centrality.html)|[Centrality](./Centrality.ipynb), [Eigenvector](./Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| -|[Katz Centrality](./katz_centrality.html)|[Centrality](./Centrality.ipynb), [Katz](./Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | -|Pagerank|[Centrality](./Centrality.ipynb), [Pagerank](../../link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | +|[Degree Centrality](./degree_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Degree](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Degree.ipynb) |Measure based on counting direct connections for each vertex| +|[Betweenness Centrality](./betweenness_centrality.md)| [Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Betweenness](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Betweenness.ipynb) |Number of shortest paths through the vertex| +|[Eigenvector Centrality](./eigenvector_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Eigenvector](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Eigenvector.ipynb)|Measure of connectivity to other important vertices (which also have high connectivity) often referred to as the influence measure of a vertex| +|[Katz Centrality](./katz_centrality.md)|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Katz](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Katz.ipynb) |Similar to Eigenvector but has tweaks to measure more weakly connected graph | +|Pagerank|[Centrality](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/centrality/Centrality.ipynb), [Pagerank](https://github.com/rapidsai/cugraph/blob/main/notebooks/algorithms/link_analysis/Pagerank.ipynb) |Classified as both a link analysis and centrality measure by quantifying incoming links from central vertices. | + +[System Requirements](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md#requirements) + -[System Requirements](../../README.html#requirements) | Author Credit | Date | Update | cuGraph Version | Test Hardware | | --------------|------------|------------------|-----------------|----------------| diff --git a/docs/cugraph/source/installation/getting_cugraph.md b/docs/cugraph/source/installation/getting_cugraph.md index 509508c5283..625f2c64c27 100644 --- a/docs/cugraph/source/installation/getting_cugraph.md +++ b/docs/cugraph/source/installation/getting_cugraph.md @@ -9,7 +9,7 @@ There are 4 ways to get cuGraph packages: 1. [Quick start with Docker Repo](#docker) 2. [Conda Installation](#conda) 3. [Pip Installation](#pip) -4. [Build from Source](#SOURCE) +4. [Build from Source](./source_build.md)
diff --git a/notebooks/algorithms/centrality/Centrality.ipynb b/notebooks/algorithms/centrality/Centrality.ipynb index d19dd646b15..e470febb975 100644 --- a/notebooks/algorithms/centrality/Centrality.ipynb +++ b/notebooks/algorithms/centrality/Centrality.ipynb @@ -36,22 +36,34 @@ "__Degree Centrality__
\n", "Degree centrality is based on the notion that whoever has the most connections must be important. \n", "\n", - "$C_d(v) = \\frac{{\\text{{degree of vertex }} v}}{{\\text{{total number of vertices}} - 1}}$\n", - "\n", + "$C_{degree}(v) = \\frac{{\\text{degree of vertex} \\ v}}{{\\text{total number of vertices} - 1}}$\n", "\n", + "See:\n", + "* [Degree (graph theory) on Wikipedia](https://en.wikipedia.org/wiki/Degree_(graph_theory)) for more details on the algorithm.\n", + "* [Learn more about Degree Centrality](https://www.sci.unich.it/~francesc/teaching/network/degree.html)\n", "\n", - "___Closeness centrality – coming soon___
\n", + "__Closeness Centrality__
\n", "Closeness is a measure of the shortest path to every other node in the graph. A node that is close to every other node, can reach over other node in the fewest number of hops, means that it has greater influence on the network versus a node that is not close.\n", "\n", + "$C_{closeness}(v)=\\frac{n-1}{\\sum_{t} d(v,t)}$\n", + "\n", + "See:\n", + "* [Closeness Centrality on Wikipedia](https://en.wikipedia.org/wiki/Closeness_centrality) for more details on the algorithm.\n", + "* [Learn more about Closeness Centrality](https://www.sci.unich.it/~francesc/teaching/network/closeness.html)\n", + "\n", "__Betweenness Centrality__
\n", "Betweenness is a measure of the number of shortest paths that cross through a node, or over an edge. A node with high betweenness means that it had a greater influence on the flow of information. \n", "\n", "Betweenness centrality of a node 𝑣 is the sum of the fraction of all-pairs shortest paths that pass through 𝑣\n", "\n", - "$C_{betweenness}=\\sum_{s \\neq v \\neq t} \\frac{\\sigma_{st}(v)}{\\sigma_{st}}$\n", + "$C_{betweenness}(v)=\\sum_{s \\neq v \\neq t} \\frac{\\sigma_{st}(v)}{\\sigma_{st}}$\n", "\n", "To speedup runtime of betweenness centrailty, the metric can be computed on a limited number of nodes (randomly selected) and then used to estimate the other scores. For this example, the graphs are relatively small (under 5,000 nodes) so betweenness on every node will be computed.\n", "\n", + "See:\n", + "* [Betweenness Centrality on Wikipedia](https://en.wikipedia.org/wiki/Betweenness_centrality) for more details on the algorithm.\n", + "* [Learn more about Betweenness Centrality](https://www.sci.unich.it/~francesc/teaching/network/betweeness.html)\n", + "\n", "__Katz Centrality__
\n", "Katz is a variant of degree centrality and of eigenvector centrality. \n", "Katz centrality is a measure of the relative importance of a node within the graph based on measuring the influence across the total number of walks between vertex pairs.\n", @@ -60,7 +72,7 @@ "\n", "\n", "See:\n", - "* [Katz on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm.\n", + "* [Katz Centrality on Wikipedia](https://en.wikipedia.org/wiki/Katz_centrality) for more details on the algorithm.\n", "* [Learn more about Katz Centrality](https://www.sci.unich.it/~francesc/teaching/network/katz.html)\n", "\n", "__Eigenvector Centrality__
\n", diff --git a/notebooks/algorithms/centrality/Degree.ipynb b/notebooks/algorithms/centrality/Degree.ipynb index e7535420b65..5a5213a904f 100644 --- a/notebooks/algorithms/centrality/Degree.ipynb +++ b/notebooks/algorithms/centrality/Degree.ipynb @@ -27,7 +27,7 @@ "\n", "See [Degree Centrality on Wikipedia](https://en.wikipedia.org/wiki/Degree_centrality) for more details on the algorithm.\n", "\n", - "$C_d(v) = \\frac{{\\text{{degree of vertex }} v}}{{\\text{{number of vertices in graph}} - 1}}$" + "$C_d(v) = \\frac{{\\text{degree of vertex } \\ v}}{{\\text{number of vertices in graph} - 1}}$" ] }, { diff --git a/notebooks/algorithms/centrality/Katz.ipynb b/notebooks/algorithms/centrality/Katz.ipynb index c94a14bb14a..08ee42df788 100755 --- a/notebooks/algorithms/centrality/Katz.ipynb +++ b/notebooks/algorithms/centrality/Katz.ipynb @@ -333,13 +333,6 @@ "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n", "___" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/readme_pages/CONTRIBUTING.md b/readme_pages/CONTRIBUTING.md index 4b736b25155..ffe1ef1831b 100644 --- a/readme_pages/CONTRIBUTING.md +++ b/readme_pages/CONTRIBUTING.md @@ -68,7 +68,7 @@ If you need more context on a particular issue, please ask. # So you want to contribute code **TL;DR General Development Process** -1. Read the documentation on [building from source](./SOURCEBUILD.md) to learn how to setup, and validate, the development environment +1. Read the documentation on [building from source](../docs/cugraph/source/installation/source_build.md) to learn how to setup, and validate, the development environment 2. Read the RAPIDS [Code of Conduct](https://docs.rapids.ai/resources/conduct/) 3. Find or submit an issue to work on (include a comment that you are working issue) 4. Fork the cuGraph [repo](#fork) and Code (make sure to add unit tests)! @@ -99,7 +99,7 @@ The RAPIDS cuGraph repo cannot directly be modified. Contributions must come in ```git clone https://github.com//cugraph.git``` -Read the section on [building cuGraph from source](./SOURCEBUILD.md) to validate that the environment is correct. +Read the section on [building cuGraph from source](../docs/cugraph/source/installation/source_build.md) to validate that the environment is correct. **Pro Tip** add an upstream remote repository so that you can keep your forked repo in sync ```git remote add upstream https://github.com/rapidsai/cugraph.git``` From f2df81d27fbf72c569c850504fd055196899b40c Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Tue, 21 Nov 2023 20:44:54 -0600 Subject: [PATCH 097/111] nx-cugraph: add `eigenvector_centrality`, `katz_centrality`, `hits`, `pagerank` (#3968) Add `eigenvector_centrality`, `katz_centrality`, and `hits`. I may add pagerank next. Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) - Naim (https://github.com/naimnv) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3968 --- python/nx-cugraph/_nx_cugraph/__init__.py | 21 ++++ python/nx-cugraph/lint.yaml | 2 +- .../nx_cugraph/algorithms/__init__.py | 10 +- .../algorithms/centrality/__init__.py | 2 + .../algorithms/centrality/eigenvector.py | 64 ++++++++++ .../nx_cugraph/algorithms/centrality/katz.py | 100 ++++++++++++++++ .../algorithms/community/louvain.py | 34 ++++-- .../algorithms/link_analysis/__init__.py | 14 +++ .../algorithms/link_analysis/hits_alg.py | 81 +++++++++++++ .../algorithms/link_analysis/pagerank_alg.py | 112 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/classes/graph.py | 50 ++++---- python/nx-cugraph/nx_cugraph/interface.py | 21 +++- .../nx_cugraph/tests/test_ktruss.py | 2 +- python/nx-cugraph/nx_cugraph/utils/misc.py | 34 +++++- 14 files changed, 509 insertions(+), 38 deletions(-) create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py index 910db1bc379..ef5f8f3fc23 100644 --- a/python/nx-cugraph/_nx_cugraph/__init__.py +++ b/python/nx-cugraph/_nx_cugraph/__init__.py @@ -47,12 +47,14 @@ "diamond_graph", "dodecahedral_graph", "edge_betweenness_centrality", + "eigenvector_centrality", "empty_graph", "florentine_families_graph", "from_pandas_edgelist", "from_scipy_sparse_array", "frucht_graph", "heawood_graph", + "hits", "house_graph", "house_x_graph", "icosahedral_graph", @@ -62,6 +64,7 @@ "isolates", "k_truss", "karate_club_graph", + "katz_centrality", "krackhardt_kite_graph", "ladder_graph", "les_miserables_graph", @@ -75,6 +78,7 @@ "number_of_selfloops", "octahedral_graph", "out_degree_centrality", + "pagerank", "pappus_graph", "path_graph", "petersen_graph", @@ -96,19 +100,36 @@ # BEGIN: extra_docstrings "betweenness_centrality": "`weight` parameter is not yet supported.", "edge_betweenness_centrality": "`weight` parameter is not yet supported.", + "eigenvector_centrality": "`nstart` parameter is not used, but it is checked for validity.", "from_pandas_edgelist": "cudf.DataFrame inputs also supported.", "k_truss": ( "Currently raises `NotImplementedError` for graphs with more than one connected\n" "component when k >= 3. We expect to fix this soon." ), + "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.", "louvain_communities": "`seed` parameter is currently ignored.", + "pagerank": "`dangling` parameter is not supported, but it is checked for validity.", # END: extra_docstrings }, "extra_parameters": { # BEGIN: extra_parameters + "eigenvector_centrality": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, + "hits": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + 'weight : string or None, optional (default="weight")': "The edge attribute to use as the edge weight.", + }, + "katz_centrality": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, "louvain_communities": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", "max_level : int, optional": "Upper limit of the number of macro-iterations (max: 500).", }, + "pagerank": { + "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.", + }, # END: extra_parameters }, } diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml index 01a806e6162..a94aa9f0448 100644 --- a/python/nx-cugraph/lint.yaml +++ b/python/nx-cugraph/lint.yaml @@ -53,7 +53,7 @@ repos: rev: v0.1.3 hooks: - id: ruff - args: [--fix-only, --show-fixes] + args: [--fix-only, --show-fixes] # --unsafe-fixes] - repo: https://github.com/PyCQA/flake8 rev: 6.1.0 hooks: diff --git a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py index 32cd6f31a47..63841b15bd5 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py @@ -10,10 +10,18 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -from . import bipartite, centrality, community, components, shortest_paths +from . import ( + bipartite, + centrality, + community, + components, + shortest_paths, + link_analysis, +) from .bipartite import complete_bipartite_graph from .centrality import * from .components import * from .core import * from .isolate import * from .shortest_paths import * +from .link_analysis import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py index af91f227843..496dc6aff81 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/__init__.py @@ -12,3 +12,5 @@ # limitations under the License. from .betweenness import * from .degree_alg import * +from .eigenvector import * +from .katz import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py new file mode 100644 index 00000000000..c0f02a6258e --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py @@ -0,0 +1,64 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + networkx_algorithm, + not_implemented_for, +) + +__all__ = ["eigenvector_centrality"] + + +@not_implemented_for("multigraph") +@networkx_algorithm(extra_params=_dtype_param) +def eigenvector_centrality( + G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None +): + """`nstart` parameter is not used, but it is checked for validity.""" + G = _to_graph(G, weight, np.float32) + if len(G) == 0: + raise nx.NetworkXPointlessConcept( + "cannot compute centrality for the null graph" + ) + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + # Check if given nstart is valid even though we don't use it + nstart = G._dict_to_nodearray(nstart, dtype=dtype) + if (nstart == 0).all(): + raise nx.NetworkXError("initial vector cannot have all zero values") + if nstart.sum() == 0: + raise ZeroDivisionError + # nstart /= total # Uncomment (and assign total) when nstart is used below + try: + node_ids, values = plc.eigenvector_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + epsilon=tol, + max_iterations=max_iter, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return G._nodearrays_to_dict(node_ids, values) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py new file mode 100644 index 00000000000..b61b811b8fa --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py @@ -0,0 +1,100 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + networkx_algorithm, + not_implemented_for, +) + +__all__ = ["katz_centrality"] + + +@not_implemented_for("multigraph") +@networkx_algorithm(extra_params=_dtype_param) +def katz_centrality( + G, + alpha=0.1, + beta=1.0, + max_iter=1000, + tol=1.0e-6, + nstart=None, + normalized=True, + weight=None, + *, + dtype=None, +): + """`nstart` isn't used (but is checked), and `normalized=False` is not supported.""" + if not normalized: + # Redundant with the `_can_run` check below when being dispatched by NetworkX, + # but we raise here in case this funcion is called directly. + raise NotImplementedError("normalized=False is not supported.") + G = _to_graph(G, weight, np.float32) + if (N := len(G)) == 0: + return {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + # Check if given nstart is valid even though we don't use it + nstart = G._dict_to_nodearray(nstart, 0, dtype) + b = bs = None + try: + b = float(beta) + except (TypeError, ValueError) as exc: + try: + bs = G._dict_to_nodearray(beta, dtype=dtype) + b = 1.0 # float value must be given to PLC (and will be ignored) + except (KeyError, ValueError): + raise nx.NetworkXError( + "beta dictionary must have a value for every node" + ) from exc + try: + node_ids, values = plc.katz_centrality( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + betas=bs, + alpha=alpha, + beta=b, + epsilon=N * tol, + max_iterations=max_iter, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return G._nodearrays_to_dict(node_ids, values) + + +@katz_centrality._can_run +def _( + G, + alpha=0.1, + beta=1.0, + max_iter=1000, + tol=1.0e-6, + nstart=None, + normalized=True, + weight=None, + *, + dtype=None, +): + return normalized diff --git a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py index 45a3429d2ee..936d837dacd 100644 --- a/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py +++ b/python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py @@ -16,6 +16,7 @@ from nx_cugraph.convert import _to_undirected_graph from nx_cugraph.utils import ( + _dtype_param, _groupby, _seed_to_int, networkx_algorithm, @@ -32,11 +33,19 @@ extra_params={ "max_level : int, optional": ( "Upper limit of the number of macro-iterations (max: 500)." - ) + ), + **_dtype_param, } ) def louvain_communities( - G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, ): """`seed` parameter is currently ignored.""" # NetworkX allows both directed and undirected, but cugraph only allows undirected. @@ -54,20 +63,20 @@ def louvain_communities( stacklevel=2, ) max_level = 500 - vertices, clusters, modularity = plc.louvain( + node_ids, clusters, modularity = plc.louvain( resource_handle=plc.ResourceHandle(), - graph=G._get_plc_graph(), + graph=G._get_plc_graph(weight, 1, dtype), max_level=max_level, # TODO: add this parameter to NetworkX threshold=threshold, resolution=resolution, do_expensive_check=False, ) - groups = _groupby(clusters, vertices, groups_are_canonical=True) - rv = [set(G._nodearray_to_list(node_ids)) for node_ids in groups.values()] - # TODO: PLC doesn't handle isolated vertices yet, so this is a temporary fix + groups = _groupby(clusters, node_ids, groups_are_canonical=True) + rv = [set(G._nodearray_to_list(ids)) for ids in groups.values()] + # TODO: PLC doesn't handle isolated node_ids yet, so this is a temporary fix isolates = _isolates(G) if isolates.size > 0: - isolates = isolates[isolates > vertices.max()] + isolates = isolates[isolates > node_ids.max()] if isolates.size > 0: rv.extend({node} for node in G._nodearray_to_list(isolates)) return rv @@ -75,7 +84,14 @@ def louvain_communities( @louvain_communities._can_run def _( - G, weight="weight", resolution=1, threshold=0.0000001, seed=None, *, max_level=None + G, + weight="weight", + resolution=1, + threshold=0.0000001, + seed=None, + *, + max_level=None, + dtype=None, ): # NetworkX allows both directed and undirected, but cugraph only allows undirected. return not G.is_directed() diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py new file mode 100644 index 00000000000..a68d6940d02 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/__init__.py @@ -0,0 +1,14 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from .hits_alg import * +from .pagerank_alg import * diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py new file mode 100644 index 00000000000..1c8a47c24b1 --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py @@ -0,0 +1,81 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + index_dtype, + networkx_algorithm, +) + +__all__ = ["hits"] + + +@networkx_algorithm( + extra_params={ + 'weight : string or None, optional (default="weight")': ( + "The edge attribute to use as the edge weight." + ), + **_dtype_param, + } +) +def hits( + G, + max_iter=100, + tol=1.0e-8, + nstart=None, + normalized=True, + *, + weight="weight", + dtype=None, +): + G = _to_graph(G, weight, np.float32) + if (N := len(G)) == 0: + return {}, {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + nstart = G._dict_to_nodearray(nstart, 0, dtype) + if max_iter <= 0: + if nx.__version__[:3] <= "3.2": + raise ValueError("`maxiter` must be a positive integer.") + raise nx.PowerIterationFailedConvergence(max_iter) + try: + node_ids, hubs, authorities = plc.hits( + resource_handle=plc.ResourceHandle(), + graph=G._get_plc_graph(weight, 1, dtype, store_transposed=True), + tol=tol, + initial_hubs_guess_vertices=None + if nstart is None + else cp.arange(N, dtype=index_dtype), + initial_hubs_guess_values=nstart, + max_iter=max_iter, + normalized=normalized, + do_expensive_check=False, + ) + except RuntimeError as exc: + # Errors from PLC are sometimes a little scary and not very helpful + raise nx.PowerIterationFailedConvergence(max_iter) from exc + return ( + G._nodearrays_to_dict(node_ids, hubs), + G._nodearrays_to_dict(node_ids, authorities), + ) diff --git a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py new file mode 100644 index 00000000000..63f6e89c33a --- /dev/null +++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py @@ -0,0 +1,112 @@ +# Copyright (c) 2023, NVIDIA CORPORATION. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import cupy as cp +import networkx as nx +import numpy as np +import pylibcugraph as plc + +from nx_cugraph.convert import _to_graph +from nx_cugraph.utils import ( + _dtype_param, + _get_float_dtype, + index_dtype, + networkx_algorithm, +) + +__all__ = ["pagerank"] + + +@networkx_algorithm(extra_params=_dtype_param) +def pagerank( + G, + alpha=0.85, + personalization=None, + max_iter=100, + tol=1.0e-6, + nstart=None, + weight="weight", + dangling=None, + *, + dtype=None, +): + """`dangling` parameter is not supported, but it is checked for validity.""" + G = _to_graph(G, weight, 1, np.float32) + if (N := len(G)) == 0: + return {} + if dtype is not None: + dtype = _get_float_dtype(dtype) + elif weight in G.edge_values: + dtype = _get_float_dtype(G.edge_values[weight].dtype) + else: + dtype = np.float32 + if nstart is not None: + nstart = G._dict_to_nodearray(nstart, 0, dtype=dtype) + if (total := nstart.sum()) == 0: + raise ZeroDivisionError + nstart /= total + if personalization is not None: + personalization = G._dict_to_nodearray(personalization, 0, dtype=dtype) + if (total := personalization.sum()) == 0: + raise ZeroDivisionError + personalization /= total + if dangling is not None: + # Check if given dangling is valid even though we don't use it + dangling = G._dict_to_nodearray(dangling, 0) # Check validity + if dangling.sum() == 0: + raise ZeroDivisionError + if (G._out_degrees_array() == 0).any(): + raise NotImplementedError("custom dangling weights is not supported") + if max_iter <= 0: + raise nx.PowerIterationFailedConvergence(max_iter) + kwargs = { + "resource_handle": plc.ResourceHandle(), + "graph": G._get_plc_graph(weight, 1, dtype, store_transposed=True), + "precomputed_vertex_out_weight_vertices": None, + "precomputed_vertex_out_weight_sums": None, + "initial_guess_vertices": None + if nstart is None + else cp.arange(N, dtype=index_dtype), + "initial_guess_values": nstart, + "alpha": alpha, + "epsilon": N * tol, + "max_iterations": max_iter, + "do_expensive_check": False, + "fail_on_nonconvergence": False, + } + if personalization is None: + node_ids, values, is_converged = plc.pagerank(**kwargs) + else: + node_ids, values, is_converged = plc.personalized_pagerank( + personalization_vertices=cp.arange(N, dtype=index_dtype), # Why? + personalization_values=personalization, + **kwargs, + ) + if not is_converged: + raise nx.PowerIterationFailedConvergence(max_iter) + return G._nodearrays_to_dict(node_ids, values) + + +@pagerank._can_run +def pagerank( + G, + alpha=0.85, + personalization=None, + max_iter=100, + tol=1.0e-6, + nstart=None, + weight="weight", + dangling=None, + *, + dtype=None, +): + return dangling is None diff --git a/python/nx-cugraph/nx_cugraph/classes/graph.py b/python/nx-cugraph/nx_cugraph/classes/graph.py index fea318e036e..e32f93d8bfe 100644 --- a/python/nx-cugraph/nx_cugraph/classes/graph.py +++ b/python/nx-cugraph/nx_cugraph/classes/graph.py @@ -79,7 +79,7 @@ class Graph: np.dtype(np.uint32): np.dtype(np.float64), np.dtype(np.uint64): np.dtype(np.float64), # raise if x > 2**53 # other - np.dtype(np.bool_): np.dtype(np.float16), + np.dtype(np.bool_): np.dtype(np.float32), np.dtype(np.float16): np.dtype(np.float32), } _plc_allowed_edge_types: ClassVar[set[np.dtype]] = { @@ -562,12 +562,13 @@ def _get_plc_graph( switch_indices: bool = False, edge_array: cp.ndarray[EdgeValue] | None = None, ): - if edge_array is not None: + if edge_array is not None or edge_attr is None: pass - elif edge_attr is None: - edge_array = None elif edge_attr not in self.edge_values: - raise KeyError("Graph has no edge attribute {edge_attr!r}") + if edge_default is None: + raise KeyError("Graph has no edge attribute {edge_attr!r}") + # If we were given a default edge value, then it's probably okay to + # use None for the edge_array if we don't have this edge attribute. elif edge_attr not in self.edge_masks: edge_array = self.edge_values[edge_attr] elif not self.edge_masks[edge_attr].all(): @@ -685,6 +686,9 @@ def _degrees_array(self): degrees += cp.bincount(self.dst_indices, minlength=self._N) return degrees + _in_degrees_array = _degrees_array + _out_degrees_array = _degrees_array + # Data conversions def _nodeiter_to_iter(self, node_ids: Iterable[IndexValue]) -> Iterable[NodeKey]: """Convert an iterable of node IDs to an iterable of node keys.""" @@ -748,20 +752,22 @@ def _dict_to_nodearrays( values = cp.fromiter(d.values(), dtype) return node_ids, values - # def _dict_to_nodearray( - # self, - # d: dict[NodeKey, NodeValue] | cp.ndarray[NodeValue], - # default: NodeValue | None = None, - # dtype: Dtype | None = None, - # ) -> cp.ndarray[NodeValue]: - # if isinstance(d, cp.ndarray): - # if d.shape[0] != len(self): - # raise ValueError - # return d - # if default is None: - # val_iter = map(d.__getitem__, self) - # else: - # val_iter = (d.get(node, default) for node in self) - # if dtype is None: - # return cp.array(list(val_iter)) - # return cp.fromiter(val_iter, dtype) + def _dict_to_nodearray( + self, + d: dict[NodeKey, NodeValue] | cp.ndarray[NodeValue], + default: NodeValue | None = None, + dtype: Dtype | None = None, + ) -> cp.ndarray[NodeValue]: + if isinstance(d, cp.ndarray): + if d.shape[0] != len(self): + raise ValueError + if dtype is not None and d.dtype != dtype: + return d.astype(dtype) + return d + if default is None: + val_iter = map(d.__getitem__, self) + else: + val_iter = (d.get(node, default) for node in self) + if dtype is None: + return cp.array(list(val_iter)) + return cp.fromiter(val_iter, dtype) diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py index 8903fdc541e..be6b3596030 100644 --- a/python/nx-cugraph/nx_cugraph/interface.py +++ b/python/nx-cugraph/nx_cugraph/interface.py @@ -72,12 +72,29 @@ def key(testpath): from packaging.version import parse nxver = parse(nx.__version__) - if nxver.major == 3 and nxver.minor in {0, 1}: + + if nxver.major == 3 and nxver.minor <= 2: + # Networkx versions prior to 3.2.1 have tests written to expect + # sp.sparse.linalg.ArpackNoConvergence exceptions raised on no + # convergence in HITS. Newer versions since the merge of + # https://github.com/networkx/networkx/pull/7084 expect + # nx.PowerIterationFailedConvergence, which is what nx_cugraph.hits + # raises, so we mark them as xfail for previous versions of NX. + xfail.update( + { + key( + "test_hits.py:TestHITS.test_hits_not_convergent" + ): "nx_cugraph.hits raises updated exceptions not caught in " + "these tests", + } + ) + + if nxver.major == 3 and nxver.minor <= 1: # MAINT: networkx 3.0, 3.1 # NetworkX 3.2 added the ability to "fallback to nx" if backend algorithms # raise NotImplementedError or `can_run` returns False. The tests below # exercise behavior we have not implemented yet, so we mark them as xfail - # for previous versions of NetworkX. + # for previous versions of NX. xfail.update( { key( diff --git a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py index a3e4cee3124..92fe2360688 100644 --- a/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py +++ b/python/nx-cugraph/nx_cugraph/tests/test_ktruss.py @@ -22,7 +22,7 @@ def test_k_truss(get_graph): Gnx = get_graph() Gcg = nxcg.from_networkx(Gnx, preserve_all_attrs=True) - for k in range(10): + for k in range(6): Hnx = nx.k_truss(Gnx, k) Hcg = nxcg.k_truss(Gcg, k) assert nx.utils.graphs_equal(Hnx, nxcg.to_networkx(Hcg)) diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py index 26f023bdcec..e303375918d 100644 --- a/python/nx-cugraph/nx_cugraph/utils/misc.py +++ b/python/nx-cugraph/nx_cugraph/utils/misc.py @@ -16,11 +16,14 @@ import operator as op import sys from random import Random -from typing import SupportsIndex +from typing import TYPE_CHECKING, SupportsIndex import cupy as cp import numpy as np +if TYPE_CHECKING: + from ..typing import Dtype + try: from itertools import pairwise # Python >=3.10 except ImportError: @@ -33,11 +36,26 @@ def pairwise(it): prev = cur -__all__ = ["index_dtype", "_groupby", "_seed_to_int", "_get_int_dtype"] +__all__ = [ + "index_dtype", + "_groupby", + "_seed_to_int", + "_get_int_dtype", + "_get_float_dtype", + "_dtype_param", +] # This may switch to np.uint32 at some point index_dtype = np.int32 +# To add to `extra_params=` of `networkx_algorithm` +_dtype_param = { + "dtype : dtype or None, optional": ( + "The data type (np.float32, np.float64, or None) to use for the edge weights " + "in the algorithm. If None, then dtype is determined by the edge values." + ), +} + def _groupby( groups: cp.ndarray, values: cp.ndarray, groups_are_canonical: bool = False @@ -144,3 +162,15 @@ def _get_int_dtype( return np.dtype(dtype_string) except TypeError as exc: raise ValueError("Value is too large to store as integer: {val}") from exc + + +def _get_float_dtype(dtype: Dtype): + """Promote dtype to float32 or float64 as appropriate.""" + if dtype is None: + return np.dtype(np.float32) + rv = np.promote_types(dtype, np.float32) + if np.float32 != rv != np.float64: + raise TypeError( + f"Dtype {dtype} cannot be safely promoted to float32 or float64" + ) + return rv From 4b1618b7c6e474420e8ad07cefebb45064476475 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 22 Nov 2023 12:12:50 -0600 Subject: [PATCH 098/111] Adds missing connected_components algo to table. (#4019) --- python/nx-cugraph/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 273a6112d77..f6a9aac1088 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -101,6 +101,7 @@ supported in nx-cugraph. | balanced_cut_clustering | ? | | betweenness_centrality | 23.10 | | bfs | ? | +| connected_components | 23.12 | | core_number | ? | | degree_centrality | 23.12 | | ecg | ? | From 3116eed763acad5808bd355d66406a3a630947f6 Mon Sep 17 00:00:00 2001 From: Joseph Nke <76006812+jnke2016@users.noreply.github.com> Date: Sun, 26 Nov 2023 21:24:19 -0600 Subject: [PATCH 099/111] Moves more MG graph ETL to libcugraph and re-enables MG tests in CI (#3941) This PR includes changes that moves some of the MG graph etl steps (such as computing number of edges) to libcugraph to reduce the amount of dask overhead involved in graph creation. Those ETL steps were also responsible for various dask-related transient errors that caused us to temporarily disable MG testing in CI. These changes allow us to re-enable MG testing in CI, so this PR includes that update too. Authors: - Joseph Nke (https://github.com/jnke2016) - Chuck Hastings (https://github.com/ChuckHastings) - Naim (https://github.com/naimnv) - Vibhu Jawa (https://github.com/VibhuJawa) - Rick Ratzel (https://github.com/rlratzel) Approvers: - Jake Awe (https://github.com/AyodeAwe) - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/3941 --- ci/test_python.sh | 2 +- ci/test_wheel.sh | 2 +- cpp/include/cugraph_c/graph.h | 3 +- .../cugraph/cugraph/dask/community/egonet.py | 14 +- .../dask/community/induced_subgraph.py | 19 +- .../cugraph/cugraph/dask/community/leiden.py | 16 +- .../cugraph/cugraph/dask/community/louvain.py | 16 +- .../cugraph/dask/link_analysis/pagerank.py | 17 +- .../cugraph/dask/sampling/random_walks.py | 18 +- python/cugraph/cugraph/dask/traversal/bfs.py | 15 +- .../simpleDistributedGraph.py | 130 +++++---- .../cugraph/cugraph/structure/symmetrize.py | 6 +- python/cugraph/cugraph/testing/mg_utils.py | 2 + python/cugraph/cugraph/tests/conftest.py | 7 +- .../tests/link_analysis/test_hits_mg.py | 2 +- .../cugraph/tests/structure/test_graph_mg.py | 12 +- .../pylibcugraph/_cugraph_c/graph.pxd | 62 +++- .../analyze_clustering_edge_cut.pyx | 2 +- .../analyze_clustering_modularity.pyx | 2 +- .../analyze_clustering_ratio_cut.pyx | 2 +- .../pylibcugraph/balanced_cut_clustering.pyx | 2 +- python/pylibcugraph/pylibcugraph/ecg.pyx | 2 +- .../edge_betweenness_centrality.pyx | 2 +- python/pylibcugraph/pylibcugraph/egonet.pyx | 2 +- .../pylibcugraph/eigenvector_centrality.pyx | 4 +- python/pylibcugraph/pylibcugraph/graphs.pxd | 6 +- python/pylibcugraph/pylibcugraph/graphs.pyx | 267 ++++++++++++------ .../pylibcugraph/induced_subgraph.pyx | 2 +- .../pylibcugraph/k_truss_subgraph.pyx | 2 +- python/pylibcugraph/pylibcugraph/leiden.pyx | 2 +- python/pylibcugraph/pylibcugraph/louvain.pyx | 2 +- python/pylibcugraph/pylibcugraph/node2vec.pyx | 2 +- python/pylibcugraph/pylibcugraph/pagerank.pyx | 2 +- .../pylibcugraph/personalized_pagerank.pyx | 2 +- .../spectral_modularity_maximization.pyx | 2 +- python/pylibcugraph/pylibcugraph/sssp.pyx | 4 +- .../pylibcugraph/tests/conftest.py | 12 +- .../tests/test_eigenvector_centrality.py | 12 +- .../pylibcugraph/tests/test_graph_sg.py | 25 +- .../tests/test_katz_centrality.py | 12 +- .../pylibcugraph/tests/test_louvain.py | 20 +- .../pylibcugraph/tests/test_node2vec.py | 22 +- .../pylibcugraph/tests/test_pagerank.py | 2 +- .../pylibcugraph/tests/test_sssp.py | 2 +- .../pylibcugraph/tests/test_triangle_count.py | 22 +- .../tests/test_uniform_neighbor_sample.py | 30 +- .../pylibcugraph/tests/test_utils.py | 2 +- .../pylibcugraph/uniform_random_walks.pyx | 2 +- .../weakly_connected_components.pyx | 2 +- 49 files changed, 521 insertions(+), 298 deletions(-) diff --git a/ci/test_python.sh b/ci/test_python.sh index 273d3c93482..d6e92e8d1a5 100755 --- a/ci/test_python.sh +++ b/ci/test_python.sh @@ -79,7 +79,7 @@ pytest \ --cov=cugraph \ --cov-report=xml:"${RAPIDS_COVERAGE_DIR}/cugraph-coverage.xml" \ --cov-report=term \ - -k "not _mg" \ + -k "not test_property_graph_mg" \ tests popd diff --git a/ci/test_wheel.sh b/ci/test_wheel.sh index 28f59f0209e..428efd4ed21 100755 --- a/ci/test_wheel.sh +++ b/ci/test_wheel.sh @@ -26,5 +26,5 @@ else DASK_DISTRIBUTED__SCHEDULER__WORKER_TTL="1000s" \ DASK_DISTRIBUTED__COMM__TIMEOUTS__CONNECT="1000s" \ DASK_CUDA_WAIT_WORKERS_MIN_TIMEOUT="1000s" \ - python -m pytest -k "not _mg" ./python/${package_name}/${python_package_name}/tests + python -m pytest ./python/${package_name}/${python_package_name}/tests fi diff --git a/cpp/include/cugraph_c/graph.h b/cpp/include/cugraph_c/graph.h index 88176a9c1b6..00fce0493a3 100644 --- a/cpp/include/cugraph_c/graph.h +++ b/cpp/include/cugraph_c/graph.h @@ -103,7 +103,8 @@ cugraph_error_code_t cugraph_sg_graph_create( * Note that setting this flag will arbitrarily select one instance of a multi edge to be the * edge that survives. If the edges have properties that should be honored (e.g. sum the weights, - * or take the maximum weight), the caller should do that on not rely on this flag. + * or take the maximum weight), the caller should remove specific edges themselves and not rely + * on this flag. * @param [in] do_expensive_check If true, do expensive checks to validate the input data * is consistent with software assumptions. If false bypass these checks. * @param [out] graph A pointer to the graph object diff --git a/python/cugraph/cugraph/dask/community/egonet.py b/python/cugraph/cugraph/dask/community/egonet.py index 06f5d5b9a79..e49d4777cef 100644 --- a/python/cugraph/cugraph/dask/community/egonet.py +++ b/python/cugraph/cugraph/dask/community/egonet.py @@ -18,7 +18,9 @@ import cugraph.dask.comms.comms as Comms import dask_cudf import cudf -from cugraph.dask.common.input_utils import get_distributed_data +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) from pylibcugraph import ResourceHandle, ego_graph as pylibcugraph_ego_graph @@ -135,11 +137,7 @@ def ego_graph(input_graph, n, radius=1, center=True): n = dask_cudf.from_cudf(n, npartitions=min(input_graph._npartitions, len(n))) n = n.astype(n_type) - n = get_distributed_data(n) - wait(n) - - n = n.worker_to_parts - + n = persist_dask_df_equal_parts_per_worker(n, client, return_type="dict") do_expensive_check = False result = [ @@ -147,13 +145,13 @@ def ego_graph(input_graph, n, radius=1, center=True): _call_ego_graph, Comms.get_session_id(), input_graph._plc_graph[w], - n[w][0], + n_[0] if n_ else cudf.Series(dtype=n_type), radius, do_expensive_check, workers=[w], allow_other_workers=False, ) - for w in Comms.get_workers() + for w, n_ in n.items() ] wait(result) diff --git a/python/cugraph/cugraph/dask/community/induced_subgraph.py b/python/cugraph/cugraph/dask/community/induced_subgraph.py index 5d902f667a4..d079bcaf653 100644 --- a/python/cugraph/cugraph/dask/community/induced_subgraph.py +++ b/python/cugraph/cugraph/dask/community/induced_subgraph.py @@ -19,7 +19,9 @@ import dask_cudf import cudf import cupy as cp -from cugraph.dask.common.input_utils import get_distributed_data +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) from typing import Union, Tuple from pylibcugraph import ( @@ -154,15 +156,12 @@ def induced_subgraph( vertices_type = input_graph.input_df.dtypes[0] if isinstance(vertices, (cudf.Series, cudf.DataFrame)): - vertices = dask_cudf.from_cudf( - vertices, npartitions=min(input_graph._npartitions, len(vertices)) - ) + vertices = dask_cudf.from_cudf(vertices, npartitions=input_graph._npartitions) vertices = vertices.astype(vertices_type) - vertices = get_distributed_data(vertices) - wait(vertices) - - vertices = vertices.worker_to_parts + vertices = persist_dask_df_equal_parts_per_worker( + vertices, client, return_type="dict" + ) do_expensive_check = False @@ -171,13 +170,13 @@ def induced_subgraph( _call_induced_subgraph, Comms.get_session_id(), input_graph._plc_graph[w], - vertices[w][0], + vertices_[0] if vertices_ else cudf.Series(dtype=vertices_type), offsets, do_expensive_check, workers=[w], allow_other_workers=False, ) - for w in Comms.get_workers() + for w, vertices_ in vertices.items() ] wait(result) diff --git a/python/cugraph/cugraph/dask/community/leiden.py b/python/cugraph/cugraph/dask/community/leiden.py index 67bd0876ce6..10a266ed519 100644 --- a/python/cugraph/cugraph/dask/community/leiden.py +++ b/python/cugraph/cugraph/dask/community/leiden.py @@ -125,9 +125,19 @@ def leiden( Examples -------- - >>> from cugraph.datasets import karate - >>> G = karate.get_graph(fetch=True) - >>> parts, modularity_score = cugraph.leiden(G) + >>> import cugraph.dask as dcg + >>> import dask_cudf + >>> # ... Init a DASK Cluster + >>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html + >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/.. + >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv") + >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv", + ... chunksize=chunksize, delimiter=" ", + ... names=["src", "dst", "value"], + ... dtype=["int32", "int32", "float32"]) + >>> dg = cugraph.Graph() + >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst') + >>> parts, modularity_score = dcg.leiden(dg) """ diff --git a/python/cugraph/cugraph/dask/community/louvain.py b/python/cugraph/cugraph/dask/community/louvain.py index 1b091817a1a..e83d41811ea 100644 --- a/python/cugraph/cugraph/dask/community/louvain.py +++ b/python/cugraph/cugraph/dask/community/louvain.py @@ -129,9 +129,19 @@ def louvain( Examples -------- - >>> from cugraph.datasets import karate - >>> G = karate.get_graph(fetch=True) - >>> parts = cugraph.louvain(G) + >>> import cugraph.dask as dcg + >>> import dask_cudf + >>> # ... Init a DASK Cluster + >>> # see https://docs.rapids.ai/api/cugraph/stable/dask-cugraph.html + >>> # Download dataset from https://github.com/rapidsai/cugraph/datasets/.. + >>> chunksize = dcg.get_chunksize(datasets_path / "karate.csv") + >>> ddf = dask_cudf.read_csv(datasets_path / "karate.csv", + ... chunksize=chunksize, delimiter=" ", + ... names=["src", "dst", "value"], + ... dtype=["int32", "int32", "float32"]) + >>> dg = cugraph.Graph() + >>> dg.from_dask_cudf_edgelist(ddf, source='src', destination='dst') + >>> parts, modularity_score = dcg.louvain(dg) """ diff --git a/python/cugraph/cugraph/dask/link_analysis/pagerank.py b/python/cugraph/cugraph/dask/link_analysis/pagerank.py index 2dfd25fa522..1dffb3cba78 100644 --- a/python/cugraph/cugraph/dask/link_analysis/pagerank.py +++ b/python/cugraph/cugraph/dask/link_analysis/pagerank.py @@ -28,7 +28,9 @@ ) import cugraph.dask.comms.comms as Comms -from cugraph.dask.common.input_utils import get_distributed_data +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) from cugraph.exceptions import FailedToConvergeError @@ -352,7 +354,14 @@ def pagerank( personalization, npartitions=len(Comms.get_workers()) ) - data_prsztn = get_distributed_data(personalization_ddf) + data_prsztn = persist_dask_df_equal_parts_per_worker( + personalization_ddf, client, return_type="dict" + ) + + empty_df = cudf.DataFrame(columns=list(personalization_ddf.columns)) + empty_df = empty_df.astype( + dict(zip(personalization_ddf.columns, personalization_ddf.dtypes)) + ) result = [ client.submit( @@ -361,7 +370,7 @@ def pagerank( input_graph._plc_graph[w], precomputed_vertex_out_weight_vertices, precomputed_vertex_out_weight_sums, - data_personalization[0], + data_personalization[0] if data_personalization else empty_df, initial_guess_vertices, initial_guess_values, alpha, @@ -372,7 +381,7 @@ def pagerank( workers=[w], allow_other_workers=False, ) - for w, data_personalization in data_prsztn.worker_to_parts.items() + for w, data_personalization in data_prsztn.items() ] else: result = [ diff --git a/python/cugraph/cugraph/dask/sampling/random_walks.py b/python/cugraph/cugraph/dask/sampling/random_walks.py index 993544ac45c..bb9baf2c92c 100644 --- a/python/cugraph/cugraph/dask/sampling/random_walks.py +++ b/python/cugraph/cugraph/dask/sampling/random_walks.py @@ -16,6 +16,9 @@ import dask_cudf import cudf import operator as op +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) from pylibcugraph import ResourceHandle @@ -24,7 +27,6 @@ ) from cugraph.dask.comms import comms as Comms -from cugraph.dask.common.input_utils import get_distributed_data def convert_to_cudf(cp_paths, number_map=None, is_vertex_paths=False): @@ -104,7 +106,7 @@ def random_walks( max_path_length : int The maximum path length """ - + client = default_client() if isinstance(start_vertices, int): start_vertices = [start_vertices] @@ -126,23 +128,21 @@ def random_walks( start_vertices, npartitions=min(input_graph._npartitions, len(start_vertices)) ) start_vertices = start_vertices.astype(start_vertices_type) - start_vertices = get_distributed_data(start_vertices) - wait(start_vertices) - start_vertices = start_vertices.worker_to_parts - - client = default_client() + start_vertices = persist_dask_df_equal_parts_per_worker( + start_vertices, client, return_type="dict" + ) result = [ client.submit( _call_plc_uniform_random_walks, Comms.get_session_id(), input_graph._plc_graph[w], - start_vertices[w][0], + start_v[0] if start_v else cudf.Series(dtype=start_vertices_type), max_depth, workers=[w], allow_other_workers=False, ) - for w in Comms.get_workers() + for w, start_v in start_vertices.items() ] wait(result) diff --git a/python/cugraph/cugraph/dask/traversal/bfs.py b/python/cugraph/cugraph/dask/traversal/bfs.py index cf467aaa18f..412fd851ad6 100644 --- a/python/cugraph/cugraph/dask/traversal/bfs.py +++ b/python/cugraph/cugraph/dask/traversal/bfs.py @@ -16,7 +16,9 @@ from pylibcugraph import ResourceHandle, bfs as pylibcugraph_bfs from dask.distributed import wait, default_client -from cugraph.dask.common.input_utils import get_distributed_data +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) import cugraph.dask.comms.comms as Comms import cudf import dask_cudf @@ -159,8 +161,13 @@ def bfs(input_graph, start, depth_limit=None, return_distances=True, check_start tmp_col_names = None start = input_graph.lookup_internal_vertex_id(start, tmp_col_names) + vertex_dtype = start.dtype # if the edgelist was renumbered, update + # the vertex type accordingly + + data_start = persist_dask_df_equal_parts_per_worker( + start, client, return_type="dict" + ) - data_start = get_distributed_data(start) do_expensive_check = False # FIXME: Why is 'direction_optimizing' not part of the python cugraph API # and why is it set to 'False' by default @@ -171,7 +178,7 @@ def bfs(input_graph, start, depth_limit=None, return_distances=True, check_start _call_plc_bfs, Comms.get_session_id(), input_graph._plc_graph[w], - st[0], + st[0] if st else cudf.Series(dtype=vertex_dtype), depth_limit, direction_optimizing, return_distances, @@ -179,7 +186,7 @@ def bfs(input_graph, start, depth_limit=None, return_distances=True, check_start workers=[w], allow_other_workers=False, ) - for w, st in data_start.worker_to_parts.items() + for w, st in data_start.items() ] wait(cupy_result) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index 935d0c597d4..f666900b226 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -36,8 +36,8 @@ from cugraph.structure.symmetrize import symmetrize from cugraph.dask.common.part_utils import ( get_persisted_df_worker_map, - get_length_of_parts, persist_dask_df_equal_parts_per_worker, + _chunk_lst, ) from cugraph.dask import get_n_workers import cugraph.dask.comms.comms as Comms @@ -81,6 +81,10 @@ def __init__(self, properties): self.destination_columns = None self.weight_column = None self.vertex_columns = None + self.vertex_type = None + self.weight_type = None + self.edge_id_type = None + self.edge_type_id_type = None def _make_plc_graph( sID, @@ -89,51 +93,69 @@ def _make_plc_graph( src_col_name, dst_col_name, store_transposed, - num_edges, + vertex_type, + weight_type, + edge_id_type, + edge_type_id, ): - weights = None edge_ids = None edge_types = None - if simpleDistributedGraphImpl.edgeWeightCol in edata_x[0]: - weights = _get_column_from_ls_dfs( - edata_x, simpleDistributedGraphImpl.edgeWeightCol - ) - if weights.dtype == "int32": - weights = weights.astype("float32") - elif weights.dtype == "int64": - weights = weights.astype("float64") - - if simpleDistributedGraphImpl.edgeIdCol in edata_x[0]: - edge_ids = _get_column_from_ls_dfs( - edata_x, simpleDistributedGraphImpl.edgeIdCol - ) - if edata_x[0][src_col_name].dtype == "int64" and edge_ids.dtype != "int64": - edge_ids = edge_ids.astype("int64") + num_arrays = len(edata_x) + if weight_type is not None: + weights = [ + edata_x[i][simpleDistributedGraphImpl.edgeWeightCol] + for i in range(num_arrays) + ] + if weight_type == "int32": + weights = [w_array.astype("float32") for w_array in weights] + elif weight_type == "int64": + weights = [w_array.astype("float64") for w_array in weights] + + if edge_id_type is not None: + edge_ids = [ + edata_x[i][simpleDistributedGraphImpl.edgeIdCol] + for i in range(num_arrays) + ] + if vertex_type == "int64" and edge_id_type != "int64": + edge_ids = [e_id_array.astype("int64") for e_id_array in edge_ids] warnings.warn( - f"Vertex type is int64 but edge id type is {edge_ids.dtype}" + f"Vertex type is int64 but edge id type is {edge_ids[0].dtype}" ", automatically casting edge id type to int64. " "This may cause extra memory usage. Consider passing" " a int64 list of edge ids instead." ) - if simpleDistributedGraphImpl.edgeTypeCol in edata_x[0]: - edge_types = _get_column_from_ls_dfs( - edata_x, simpleDistributedGraphImpl.edgeTypeCol - ) + if edge_type_id is not None: + edge_types = [ + edata_x[i][simpleDistributedGraphImpl.edgeTypeCol] + for i in range(num_arrays) + ] - return MGGraph( + src_array = [edata_x[i][src_col_name] for i in range(num_arrays)] + dst_array = [edata_x[i][dst_col_name] for i in range(num_arrays)] + plc_graph = MGGraph( resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), graph_properties=graph_props, - src_array=_get_column_from_ls_dfs(edata_x, src_col_name), - dst_array=_get_column_from_ls_dfs(edata_x, dst_col_name), - weight_array=weights, - edge_id_array=edge_ids, - edge_type_array=edge_types, + src_array=src_array if src_array else cudf.Series(dtype=vertex_type), + dst_array=dst_array if dst_array else cudf.Series(dtype=vertex_type), + weight_array=weights + if weights + else ([cudf.Series(dtype=weight_type)] if weight_type else None), + edge_id_array=edge_ids + if edge_ids + else ([cudf.Series(dtype=edge_id_type)] if edge_id_type else None), + edge_type_array=edge_types + if edge_types + else ([cudf.Series(dtype=edge_type_id)] if edge_type_id else None), + num_arrays=num_arrays, store_transposed=store_transposed, - num_edges=num_edges, do_expensive_check=False, ) + del edata_x + gc.collect() + + return plc_graph # Functions def __from_edgelist( @@ -182,7 +204,6 @@ def __from_edgelist( workers = _client.scheduler_info()["workers"] # Repartition to 2 partitions per GPU for memory efficient process input_ddf = input_ddf.repartition(npartitions=len(workers) * 2) - input_ddf = input_ddf.map_partitions(lambda df: df.copy()) # The dataframe will be symmetrized iff the graph is undirected # otherwise, the inital dataframe will be returned if edge_attr is not None: @@ -314,19 +335,25 @@ def __from_edgelist( dst_col_name = self.renumber_map.renumbered_dst_col_name ddf = self.edgelist.edgelist_df + + # Get the edgelist dtypes + self.vertex_type = ddf[src_col_name].dtype + if simpleDistributedGraphImpl.edgeWeightCol in ddf.columns: + self.weight_type = ddf[simpleDistributedGraphImpl.edgeWeightCol].dtype + if simpleDistributedGraphImpl.edgeIdCol in ddf.columns: + self.edge_id_type = ddf[simpleDistributedGraphImpl.edgeIdCol].dtype + if simpleDistributedGraphImpl.edgeTypeCol in ddf.columns: + self.edge_type_id_type = ddf[simpleDistributedGraphImpl.edgeTypeCol].dtype + graph_props = GraphProperties( is_multigraph=self.properties.multi_edge, is_symmetric=not self.properties.directed, ) ddf = ddf.repartition(npartitions=len(workers) * 2) - persisted_keys_d = persist_dask_df_equal_parts_per_worker( - ddf, _client, return_type="dict" - ) - del ddf - length_of_parts = get_length_of_parts(persisted_keys_d, _client) - num_edges = sum( - [item for sublist in length_of_parts.values() for item in sublist] - ) + ddf_keys = ddf.to_delayed() + workers = _client.scheduler_info()["workers"].keys() + ddf_keys_ls = _chunk_lst(ddf_keys, len(workers)) + delayed_tasks_d = { w: delayed(simpleDistributedGraphImpl._make_plc_graph)( Comms.get_session_id(), @@ -335,9 +362,12 @@ def __from_edgelist( src_col_name, dst_col_name, store_transposed, - num_edges, + self.vertex_type, + self.weight_type, + self.edge_id_type, + self.edge_type_id_type, ) - for w, edata in persisted_keys_d.items() + for w, edata in zip(workers, ddf_keys_ls) } self._plc_graph = { w: _client.compute( @@ -346,8 +376,9 @@ def __from_edgelist( for w, delayed_task in delayed_tasks_d.items() } wait(list(self._plc_graph.values())) - del persisted_keys_d + del ddf_keys del delayed_tasks_d + gc.collect() _client.run(gc.collect) @property @@ -1189,18 +1220,3 @@ def vertex_column_size(self): @property def _npartitions(self) -> int: return len(self._plc_graph) - - -def _get_column_from_ls_dfs(lst_df, col_name): - """ - This function concatenates the column - and drops it from the input list - """ - len_df = sum([len(df) for df in lst_df]) - if len_df == 0: - return lst_df[0][col_name] - output_col = cudf.concat([df[col_name] for df in lst_df], ignore_index=True) - for df in lst_df: - df.drop(columns=[col_name], inplace=True) - gc.collect() - return output_col diff --git a/python/cugraph/cugraph/structure/symmetrize.py b/python/cugraph/cugraph/structure/symmetrize.py index 4c00e68344d..b324ff65834 100644 --- a/python/cugraph/cugraph/structure/symmetrize.py +++ b/python/cugraph/cugraph/structure/symmetrize.py @@ -299,10 +299,8 @@ def _memory_efficient_drop_duplicates(ddf, vertex_col_name, num_workers): Drop duplicate edges from the input dataframe. """ # drop duplicates has a 5x+ overhead - # and does not seem to be working as expected - # TODO: Triage an MRE ddf = ddf.reset_index(drop=True).repartition(npartitions=num_workers * 2) - ddf = ddf.groupby(by=[*vertex_col_name], as_index=False).min( - split_out=num_workers * 2 + ddf = ddf.drop_duplicates( + subset=[*vertex_col_name], ignore_index=True, split_out=num_workers * 2 ) return ddf diff --git a/python/cugraph/cugraph/testing/mg_utils.py b/python/cugraph/cugraph/testing/mg_utils.py index bd165ba3db5..32854652f05 100644 --- a/python/cugraph/cugraph/testing/mg_utils.py +++ b/python/cugraph/cugraph/testing/mg_utils.py @@ -33,6 +33,7 @@ def start_dask_client( rmm_pool_size=None, dask_worker_devices=None, jit_unspill=False, + worker_class=None, device_memory_limit=0.8, ): """ @@ -141,6 +142,7 @@ def start_dask_client( rmm_async=rmm_async, CUDA_VISIBLE_DEVICES=dask_worker_devices, jit_unspill=jit_unspill, + worker_class=worker_class, device_memory_limit=device_memory_limit, ) client = Client(cluster) diff --git a/python/cugraph/cugraph/tests/conftest.py b/python/cugraph/cugraph/tests/conftest.py index 916e445cfdb..cb5755128eb 100644 --- a/python/cugraph/cugraph/tests/conftest.py +++ b/python/cugraph/cugraph/tests/conftest.py @@ -20,6 +20,9 @@ import os import tempfile +# Avoid timeout during shutdown +from dask_cuda.utils_test import IncreasedCloseTimeoutNanny + # module-wide fixtures @@ -40,7 +43,9 @@ def dask_client(): # start_dask_client will check for the SCHEDULER_FILE and # DASK_WORKER_DEVICES env vars and use them when creating a client if # set. start_dask_client will also initialize the Comms singleton. - dask_client, dask_cluster = start_dask_client() + dask_client, dask_cluster = start_dask_client( + worker_class=IncreasedCloseTimeoutNanny + ) yield dask_client diff --git a/python/cugraph/cugraph/tests/link_analysis/test_hits_mg.py b/python/cugraph/cugraph/tests/link_analysis/test_hits_mg.py index 5590eb17401..73ec13c674c 100644 --- a/python/cugraph/cugraph/tests/link_analysis/test_hits_mg.py +++ b/python/cugraph/cugraph/tests/link_analysis/test_hits_mg.py @@ -45,7 +45,7 @@ def setup_function(): fixture_params = gen_fixture_params_product( (datasets, "graph_file"), ([50], "max_iter"), - ([1.0e-6], "tol"), + ([1.0e-4], "tol"), # FIXME: Temporarily lower tolerance (IS_DIRECTED, "directed"), ) diff --git a/python/cugraph/cugraph/tests/structure/test_graph_mg.py b/python/cugraph/cugraph/tests/structure/test_graph_mg.py index 3024e50402a..7837916ae53 100644 --- a/python/cugraph/cugraph/tests/structure/test_graph_mg.py +++ b/python/cugraph/cugraph/tests/structure/test_graph_mg.py @@ -30,6 +30,9 @@ from cugraph.dask.traversal.bfs import convert_to_cudf from cugraph.dask.common.input_utils import get_distributed_data from pylibcugraph.testing.utils import gen_fixture_params_product +from cugraph.dask.common.part_utils import ( + persist_dask_df_equal_parts_per_worker, +) # ============================================================================= @@ -141,10 +144,13 @@ def test_create_mg_graph(dask_client, input_combo): assert len(G._plc_graph) == len(dask_client.has_what()) start = dask_cudf.from_cudf(cudf.Series([1], dtype="int32"), len(G._plc_graph)) + vertex_dtype = start.dtype if G.renumbered: start = G.lookup_internal_vertex_id(start, None) - data_start = get_distributed_data(start) + data_start = persist_dask_df_equal_parts_per_worker( + start, dask_client, return_type="dict" + ) res = [ dask_client.submit( @@ -159,10 +165,10 @@ def test_create_mg_graph(dask_client, input_combo): ), Comms.get_session_id(), G._plc_graph[w], - data_start.worker_to_parts[w][0], + st[0] if st else cudf.Series(dtype=vertex_dtype), workers=[w], ) - for w in Comms.get_workers() + for w, st in data_start.items() ] wait(res) diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph.pxd index 590c5679264..28a9f5a3be5 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph.pxd @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -51,12 +51,38 @@ cdef extern from "cugraph_c/graph.h": bool_t check, cugraph_graph_t** graph, cugraph_error_t** error) + + # Supports isolated vertices + cdef cugraph_error_code_t \ + cugraph_graph_create_sg( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* vertices, + const cugraph_type_erased_device_array_view_t* src, + const cugraph_type_erased_device_array_view_t* dst, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_types, + bool_t store_transposed, + bool_t renumber, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t check, + cugraph_graph_t** graph, + cugraph_error_t** error) # This may get renamed to cugraph_graph_free() cdef void \ cugraph_sg_graph_free( cugraph_graph_t* graph ) + + # FIXME: Might want to delete 'cugraph_sg_graph_free' and replace + # 'cugraph_mg_graph_free' by 'cugraph_graph_free' + cdef void \ + cugraph_graph_free( + cugraph_graph_t* graph + ) cdef cugraph_error_code_t \ cugraph_mg_graph_create( @@ -96,6 +122,22 @@ cdef extern from "cugraph_c/graph.h": cugraph_error_t** error ) + cdef cugraph_error_code_t \ + cugraph_graph_create_sg_from_csr( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t* offsets, + const cugraph_type_erased_device_array_view_t* indices, + const cugraph_type_erased_device_array_view_t* weights, + const cugraph_type_erased_device_array_view_t* edge_ids, + const cugraph_type_erased_device_array_view_t* edge_type_ids, + bool_t store_transposed, + bool_t renumber, + bool_t check, + cugraph_graph_t** graph, + cugraph_error_t** error + ) + cdef void \ cugraph_sg_graph_free( cugraph_graph_t* graph @@ -117,6 +159,24 @@ cdef extern from "cugraph_c/graph.h": cugraph_error_t** error ) + cdef cugraph_error_code_t \ + cugraph_graph_create_mg( + const cugraph_resource_handle_t* handle, + const cugraph_graph_properties_t* properties, + const cugraph_type_erased_device_array_view_t** vertices, + const cugraph_type_erased_device_array_view_t** src, + const cugraph_type_erased_device_array_view_t** dst, + const cugraph_type_erased_device_array_view_t** weights, + const cugraph_type_erased_device_array_view_t** edge_ids, + const cugraph_type_erased_device_array_view_t** edge_type_ids, + bool_t store_transposed, + size_t num_arrays, + bool_t drop_self_loops, + bool_t drop_multi_edges, + bool_t do_expensive_check, + cugraph_graph_t** graph, + cugraph_error_t** error) + cdef void \ cugraph_mg_graph_free( cugraph_graph_t* graph diff --git a/python/pylibcugraph/pylibcugraph/analyze_clustering_edge_cut.pyx b/python/pylibcugraph/pylibcugraph/analyze_clustering_edge_cut.pyx index 60613f27a0d..3370e71f469 100644 --- a/python/pylibcugraph/pylibcugraph/analyze_clustering_edge_cut.pyx +++ b/python/pylibcugraph/pylibcugraph/analyze_clustering_edge_cut.pyx @@ -86,7 +86,7 @@ def analyze_clustering_edge_cut(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertex, cluster) = pylibcugraph.spectral_modularity_maximization( ... resource_handle, G, num_clusters=5, num_eigen_vects=2, evs_tolerance=0.00001 diff --git a/python/pylibcugraph/pylibcugraph/analyze_clustering_modularity.pyx b/python/pylibcugraph/pylibcugraph/analyze_clustering_modularity.pyx index 76ba48f52b7..2e7c1d2f649 100644 --- a/python/pylibcugraph/pylibcugraph/analyze_clustering_modularity.pyx +++ b/python/pylibcugraph/pylibcugraph/analyze_clustering_modularity.pyx @@ -87,7 +87,7 @@ def analyze_clustering_modularity(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertex, cluster) = pylibcugraph.spectral_modularity_maximization( ... resource_handle, G, num_clusters=5, num_eigen_vects=2, evs_tolerance=0.00001 diff --git a/python/pylibcugraph/pylibcugraph/analyze_clustering_ratio_cut.pyx b/python/pylibcugraph/pylibcugraph/analyze_clustering_ratio_cut.pyx index 39b317e107d..c06f870d048 100644 --- a/python/pylibcugraph/pylibcugraph/analyze_clustering_ratio_cut.pyx +++ b/python/pylibcugraph/pylibcugraph/analyze_clustering_ratio_cut.pyx @@ -86,7 +86,7 @@ def analyze_clustering_ratio_cut(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertex, cluster) = pylibcugraph.spectral_modularity_maximization( ... resource_handle, G, num_clusters=5, num_eigen_vects=2, evs_tolerance=0.00001 diff --git a/python/pylibcugraph/pylibcugraph/balanced_cut_clustering.pyx b/python/pylibcugraph/pylibcugraph/balanced_cut_clustering.pyx index 5a61f9e0dd7..a1a5c8182eb 100644 --- a/python/pylibcugraph/pylibcugraph/balanced_cut_clustering.pyx +++ b/python/pylibcugraph/pylibcugraph/balanced_cut_clustering.pyx @@ -109,7 +109,7 @@ def balanced_cut_clustering(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, clusters) = pylibcugraph.balanced_cut_clustering( ... resource_handle, G, num_clusters=5, num_eigen_vects=2, evs_tolerance=0.00001 diff --git a/python/pylibcugraph/pylibcugraph/ecg.pyx b/python/pylibcugraph/pylibcugraph/ecg.pyx index c5c1fe2eda7..4188aaa213e 100644 --- a/python/pylibcugraph/pylibcugraph/ecg.pyx +++ b/python/pylibcugraph/pylibcugraph/ecg.pyx @@ -101,7 +101,7 @@ def ecg(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, clusters) = pylibcugraph.ecg(resource_handle, G) # FIXME: Check this docstring example diff --git a/python/pylibcugraph/pylibcugraph/edge_betweenness_centrality.pyx b/python/pylibcugraph/pylibcugraph/edge_betweenness_centrality.pyx index c88c9fe8a67..e1dae1ff10a 100644 --- a/python/pylibcugraph/pylibcugraph/edge_betweenness_centrality.pyx +++ b/python/pylibcugraph/pylibcugraph/edge_betweenness_centrality.pyx @@ -180,7 +180,7 @@ def edge_betweenness_centrality(ResourceHandle resource_handle, cdef cugraph_type_erased_device_array_view_t* values_ptr = \ cugraph_edge_centrality_result_get_values(result_ptr) - if graph.edge_id_view_ptr is NULL: + if graph.edge_id_view_ptr is NULL and graph.edge_id_view_ptr_ptr is NULL: cupy_edge_ids = None else: edge_ids_ptr = cugraph_edge_centrality_result_get_edge_ids(result_ptr) diff --git a/python/pylibcugraph/pylibcugraph/egonet.pyx b/python/pylibcugraph/pylibcugraph/egonet.pyx index d011d946e46..e7237cc3ba4 100644 --- a/python/pylibcugraph/pylibcugraph/egonet.pyx +++ b/python/pylibcugraph/pylibcugraph/egonet.pyx @@ -101,7 +101,7 @@ def ego_graph(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) >>> (sources, destinations, edge_weights, subgraph_offsets) = ... pylibcugraph.ego_graph(resource_handle, G, source_vertices, 2, False) diff --git a/python/pylibcugraph/pylibcugraph/eigenvector_centrality.pyx b/python/pylibcugraph/pylibcugraph/eigenvector_centrality.pyx index 88612c242e2..568f072ee3d 100644 --- a/python/pylibcugraph/pylibcugraph/eigenvector_centrality.pyx +++ b/python/pylibcugraph/pylibcugraph/eigenvector_centrality.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -97,7 +97,7 @@ def eigenvector_centrality(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, values) = pylibcugraph.eigenvector_centrality( resource_handle, G, 1e-6, 1000, False) diff --git a/python/pylibcugraph/pylibcugraph/graphs.pxd b/python/pylibcugraph/pylibcugraph/graphs.pxd index a2df44ba26e..dac69e0ad04 100644 --- a/python/pylibcugraph/pylibcugraph/graphs.pxd +++ b/python/pylibcugraph/pylibcugraph/graphs.pxd @@ -25,11 +25,13 @@ from pylibcugraph._cugraph_c.graph cimport ( cdef class _GPUGraph: cdef cugraph_graph_t* c_graph_ptr cdef cugraph_type_erased_device_array_view_t* edge_id_view_ptr - cdef cugraph_type_erased_device_array_view_t* weights_view_ptr + cdef cugraph_type_erased_device_array_view_t** edge_id_view_ptr_ptr + cdef cugraph_type_erased_device_array_view_t* weights_view_ptr + cdef cugraph_type_erased_device_array_view_t** weights_view_ptr_ptr cdef class SGGraph(_GPUGraph): pass cdef class MGGraph(_GPUGraph): - pass + pass diff --git a/python/pylibcugraph/pylibcugraph/graphs.pyx b/python/pylibcugraph/pylibcugraph/graphs.pyx index 33a8a09c6f4..b3065fa0684 100644 --- a/python/pylibcugraph/pylibcugraph/graphs.pyx +++ b/python/pylibcugraph/pylibcugraph/graphs.pyx @@ -18,16 +18,20 @@ from pylibcugraph._cugraph_c.error cimport ( cugraph_error_code_t, cugraph_error_t, ) +from cython.operator cimport dereference as deref from pylibcugraph._cugraph_c.array cimport ( cugraph_type_erased_device_array_view_t, cugraph_type_erased_device_array_view_free, ) from pylibcugraph._cugraph_c.graph cimport ( - cugraph_sg_graph_create, - cugraph_mg_graph_create, - cugraph_sg_graph_create_from_csr, - cugraph_sg_graph_free, - cugraph_mg_graph_free, + cugraph_graph_create_sg, + cugraph_graph_create_mg, + cugraph_sg_graph_create_from_csr, #FIXME: Remove this once + # 'cugraph_graph_create_sg_from_csr' is exposed + cugraph_graph_create_sg_from_csr, + cugraph_sg_graph_free, #FIXME: Remove this + cugraph_graph_free, + cugraph_mg_graph_free, #FIXME: Remove this ) from pylibcugraph.resource_handle cimport ( ResourceHandle, @@ -38,8 +42,11 @@ from pylibcugraph.graph_properties cimport ( from pylibcugraph.utils cimport ( assert_success, assert_CAI_type, + get_c_type_from_numpy_type, create_cugraph_type_erased_device_array_view_from_py_obj, ) +from libc.stdlib cimport malloc + cdef class SGGraph(_GPUGraph): @@ -70,6 +77,9 @@ cdef class SGGraph(_GPUGraph): CSR format. In the case of a COO, The order of the array corresponds to the ordering of the src_offset_array, where the ith item in src_offset_array and the ith item in dst_index_array define the ith edge of the graph. + + vertices_array : device array type + Device array containing the isolated vertices of the graph. weight_array : device array type Device array containing the weight values of each directed edge. The @@ -105,6 +115,12 @@ cdef class SGGraph(_GPUGraph): COO: arrays represent src_array and dst_array CSR: arrays represent offset_array and index_array + drop_self_loops : bool, optional (default='False') + If true, drop any self loops that exist in the provided edge list. + + drop_multi_edges: bool, optional (default='False') + If true, drop any multi edges that exist in the provided edge list + Examples --------- >>> import pylibcugraph, cupy, numpy @@ -116,7 +132,7 @@ cdef class SGGraph(_GPUGraph): >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) """ @@ -131,7 +147,10 @@ cdef class SGGraph(_GPUGraph): do_expensive_check=False, edge_id_array=None, edge_type_array=None, - input_array_format="COO"): + input_array_format="COO", + vertices_array=None, + drop_self_loops=False, + drop_multi_edges=False): # FIXME: add tests for these if not(isinstance(store_transposed, (int, bool))): @@ -145,13 +164,13 @@ cdef class SGGraph(_GPUGraph): f"{type(do_expensive_check)}") assert_CAI_type(src_or_offset_array, "src_or_offset_array") assert_CAI_type(dst_or_index_array, "dst_or_index_array") + assert_CAI_type(vertices_array, "vertices_array", True) assert_CAI_type(weight_array, "weight_array", True) - if edge_id_array is not None: - assert_CAI_type(edge_id_array, "edge_id_array") - if edge_type_array is not None: - assert_CAI_type(edge_type_array, "edge_type_array") + assert_CAI_type(edge_id_array, "edge_id_array", True) + assert_CAI_type(edge_type_array, "edge_type_array", True) - # FIXME: assert that src_or_offset_array and dst_or_index_array have the same type + # FIXME: assert that src_or_offset_array and dst_or_index_array have + # the same type cdef cugraph_error_t* error_ptr cdef cugraph_error_code_t error_code @@ -159,31 +178,31 @@ cdef class SGGraph(_GPUGraph): cdef cugraph_type_erased_device_array_view_t* srcs_or_offsets_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( src_or_offset_array - ) - + ) cdef cugraph_type_erased_device_array_view_t* dsts_or_indices_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( dst_or_index_array ) - - + cdef cugraph_type_erased_device_array_view_t* vertices_view_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + vertices_array + ) self.weights_view_ptr = create_cugraph_type_erased_device_array_view_from_py_obj( weight_array ) - self.edge_id_view_ptr = create_cugraph_type_erased_device_array_view_from_py_obj( edge_id_array - ) - + ) cdef cugraph_type_erased_device_array_view_t* edge_type_view_ptr = \ create_cugraph_type_erased_device_array_view_from_py_obj( edge_type_array ) if input_array_format == "COO": - error_code = cugraph_sg_graph_create( + error_code = cugraph_graph_create_sg( resource_handle.c_resource_handle_ptr, &(graph_properties.c_graph_properties), + vertices_view_ptr, srcs_or_offsets_view_ptr, dsts_or_indices_view_ptr, self.weights_view_ptr, @@ -191,12 +210,13 @@ cdef class SGGraph(_GPUGraph): edge_type_view_ptr, store_transposed, renumber, + drop_self_loops, + drop_multi_edges, do_expensive_check, &(self.c_graph_ptr), &error_ptr) - assert_success(error_code, error_ptr, - "cugraph_sg_graph_create()") + "cugraph_graph_create_sg()") elif input_array_format == "CSR": error_code = cugraph_sg_graph_create_from_csr( @@ -209,6 +229,8 @@ cdef class SGGraph(_GPUGraph): edge_type_view_ptr, store_transposed, renumber, + # drop_self_loops, #FIXME: Not supported yet + # drop_multi_edges, #FIXME: Not supported yet do_expensive_check, &(self.c_graph_ptr), &error_ptr) @@ -223,7 +245,8 @@ cdef class SGGraph(_GPUGraph): cugraph_type_erased_device_array_view_free(srcs_or_offsets_view_ptr) cugraph_type_erased_device_array_view_free(dsts_or_indices_view_ptr) - cugraph_type_erased_device_array_view_free(self.weights_view_ptr) + if self.weights_view_ptr is not NULL: + cugraph_type_erased_device_array_view_free(self.weights_view_ptr) if self.edge_id_view_ptr is not NULL: cugraph_type_erased_device_array_view_free(self.edge_id_view_ptr) if edge_type_view_ptr is not NULL: @@ -259,6 +282,9 @@ cdef class MGGraph(_GPUGraph): each directed edge. The order of the array corresponds to the ordering of the src_array, where the ith item in src_array and the ith item in dst_array define the ith edge of the graph. + + vertices_array : device array type + Device array containing the isolated vertices of the graph. weight_array : device array type Device array containing the weight values of each directed edge. The @@ -270,8 +296,10 @@ cdef class MGGraph(_GPUGraph): Set to True if the graph should be transposed. This is required for some algorithms, such as pagerank. - num_edges : int - Number of edges + num_arrays : size_t + Number of arrays. + + If provided, all list of device arrays should be of the same size. do_expensive_check : bool If True, performs more extensive tests on the inputs to ensure @@ -286,6 +314,12 @@ cdef class MGGraph(_GPUGraph): Device array containing the edge types of each directed edge. Must match the ordering of the src/dst/edge_id arrays. Optional (may be null). If provided, edge_id_array must be provided. + + drop_self_loops : bool, optional (default='False') + If true, drop any self loops that exist in the provided edge list. + + drop_multi_edges: bool, optional (default='False') + If true, drop any multi edges that exist in the provided edge list """ def __cinit__(self, ResourceHandle resource_handle, @@ -294,85 +328,156 @@ cdef class MGGraph(_GPUGraph): dst_array, weight_array=None, store_transposed=False, - num_edges=-1, - do_expensive_check=False, + do_expensive_check=False, # default to False edge_id_array=None, - edge_type_array=None): + edge_type_array=None, + vertices_array=None, + size_t num_arrays=1, # default value to not break users + drop_self_loops=False, + drop_multi_edges=False): - # FIXME: add tests for these if not(isinstance(store_transposed, (int, bool))): raise TypeError("expected int or bool for store_transposed, got " f"{type(store_transposed)}") - if not(isinstance(num_edges, (int))): - raise TypeError("expected int for num_edges, got " - f"{type(num_edges)}") - if num_edges < 0: - raise TypeError("num_edges must be > 0") + if not(isinstance(do_expensive_check, (int, bool))): raise TypeError("expected int or bool for do_expensive_check, got " f"{type(do_expensive_check)}") - assert_CAI_type(src_array, "src_array") - assert_CAI_type(dst_array, "dst_array") - assert_CAI_type(weight_array, "weight_array", True) - - assert_CAI_type(edge_id_array, "edge_id_array", True) - if edge_id_array is not None and len(edge_id_array) != len(src_array): - raise ValueError('Edge id array must be same length as edgelist') - - assert_CAI_type(edge_type_array, "edge_type_array", True) - if edge_type_array is not None and len(edge_type_array) != len(src_array): - raise ValueError('Edge type array must be same length as edgelist') - - # FIXME: assert that src_array and dst_array have the same type cdef cugraph_error_t* error_ptr cdef cugraph_error_code_t error_code - cdef cugraph_type_erased_device_array_view_t* srcs_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - src_array - ) - cdef cugraph_type_erased_device_array_view_t* dsts_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - dst_array - ) - self.weights_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - weight_array - ) - self.edge_id_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - edge_id_array - ) - cdef cugraph_type_erased_device_array_view_t* edge_type_view_ptr = \ - create_cugraph_type_erased_device_array_view_from_py_obj( - edge_type_array - ) - error_code = cugraph_mg_graph_create( + if not isinstance(src_array, list): + src_array = [src_array] + if not any(src_array): + src_array = src_array * num_arrays + + if not isinstance(dst_array, list): + dst_array = [dst_array] + if not any(dst_array): + dst_array = dst_array * num_arrays + + if not isinstance(weight_array, list): + weight_array = [weight_array] + if not any(weight_array): + weight_array = weight_array * num_arrays + + if not isinstance(edge_id_array, list): + edge_id_array = [edge_id_array] + if not any(edge_id_array): + edge_id_array = edge_id_array * num_arrays + + if not isinstance(edge_type_array, list): + edge_type_array = [edge_type_array] + if not any(edge_type_array): + edge_type_array = edge_type_array * num_arrays + + if not isinstance(vertices_array, list): + vertices_array = [vertices_array] + if not any(vertices_array): + vertices_array = vertices_array * num_arrays + + cdef cugraph_type_erased_device_array_view_t** srcs_view_ptr_ptr = NULL + cdef cugraph_type_erased_device_array_view_t** dsts_view_ptr_ptr = NULL + cdef cugraph_type_erased_device_array_view_t** vertices_view_ptr_ptr = NULL + cdef cugraph_type_erased_device_array_view_t** edge_type_view_ptr_ptr = NULL + + for i in range(num_arrays): + if do_expensive_check: + assert_CAI_type(src_array[i], "src_array") + assert_CAI_type(dst_array[i], "dst_array") + assert_CAI_type(weight_array[i], "weight_array", True) + assert_CAI_type(vertices_array[i], "vertices_array", True) + + assert_CAI_type(edge_id_array[i], "edge_id_array", True) + + if edge_id_array is not None and len(edge_id_array[i]) != len(src_array[i]): + raise ValueError('Edge id array must be same length as edgelist') + + assert_CAI_type(edge_type_array[i], "edge_type_array", True) + if edge_type_array[i] is not None and len(edge_type_array[i]) != len(src_array[i]): + raise ValueError('Edge type array must be same length as edgelist') + + if src_array[i] is not None: + if i == 0: + srcs_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + srcs_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(src_array[i]) + + if dst_array[i] is not None: + if i == 0: + dsts_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + dsts_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(dst_array[i]) + + if vertices_array[i] is not None: + if i == 0: + vertices_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + vertices_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(vertices_array[i]) + + if weight_array[i] is not None: + if i == 0: + self.weights_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + self.weights_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(weight_array[i]) + + if edge_id_array[i] is not None: + if i == 0: + self.edge_id_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + self.edge_id_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(edge_id_array[i]) + + if edge_type_array[i] is not None: + if i == 0: + edge_type_view_ptr_ptr = \ + malloc( + num_arrays * sizeof(cugraph_type_erased_device_array_view_t*)) + edge_type_view_ptr_ptr[i] = \ + create_cugraph_type_erased_device_array_view_from_py_obj(edge_type_array[i]) + + error_code = cugraph_graph_create_mg( resource_handle.c_resource_handle_ptr, &(graph_properties.c_graph_properties), - srcs_view_ptr, - dsts_view_ptr, - self.weights_view_ptr, - self.edge_id_view_ptr, - edge_type_view_ptr, + vertices_view_ptr_ptr, + srcs_view_ptr_ptr, + dsts_view_ptr_ptr, + self.weights_view_ptr_ptr, + self.edge_id_view_ptr_ptr, + edge_type_view_ptr_ptr, store_transposed, - num_edges, + num_arrays, do_expensive_check, + drop_self_loops, + drop_multi_edges, &(self.c_graph_ptr), &error_ptr) assert_success(error_code, error_ptr, "cugraph_mg_graph_create()") - cugraph_type_erased_device_array_view_free(srcs_view_ptr) - cugraph_type_erased_device_array_view_free(dsts_view_ptr) - cugraph_type_erased_device_array_view_free(self.weights_view_ptr) - if self.edge_id_view_ptr is not NULL: - cugraph_type_erased_device_array_view_free(self.edge_id_view_ptr) - if edge_type_view_ptr is not NULL: - cugraph_type_erased_device_array_view_free(edge_type_view_ptr) + for i in range(num_arrays): + cugraph_type_erased_device_array_view_free(srcs_view_ptr_ptr[i]) + cugraph_type_erased_device_array_view_free(dsts_view_ptr_ptr[i]) + if vertices_view_ptr_ptr is not NULL: + cugraph_type_erased_device_array_view_free(vertices_view_ptr_ptr[i]) + if self.weights_view_ptr_ptr is not NULL: + cugraph_type_erased_device_array_view_free(self.weights_view_ptr_ptr[i]) + if self.edge_id_view_ptr_ptr is not NULL: + cugraph_type_erased_device_array_view_free(self.edge_id_view_ptr_ptr[i]) + if edge_type_view_ptr_ptr is not NULL: + cugraph_type_erased_device_array_view_free(edge_type_view_ptr_ptr[i]) def __dealloc__(self): if self.c_graph_ptr is not NULL: diff --git a/python/pylibcugraph/pylibcugraph/induced_subgraph.pyx b/python/pylibcugraph/pylibcugraph/induced_subgraph.pyx index aab36d3d5e0..99b89ec2a58 100644 --- a/python/pylibcugraph/pylibcugraph/induced_subgraph.pyx +++ b/python/pylibcugraph/pylibcugraph/induced_subgraph.pyx @@ -98,7 +98,7 @@ def induced_subgraph(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) >>> (sources, destinations, edge_weights, subgraph_offsets) = ... pylibcugraph.induced_subgraph( diff --git a/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx b/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx index cc91e76dd55..2c22c618249 100644 --- a/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx +++ b/python/pylibcugraph/pylibcugraph/k_truss_subgraph.pyx @@ -96,7 +96,7 @@ def k_truss_subgraph(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) >>> (sources, destinations, edge_weights, subgraph_offsets) = ... pylibcugraph.k_truss_subgraph(resource_handle, G, k, False) diff --git a/python/pylibcugraph/pylibcugraph/leiden.pyx b/python/pylibcugraph/pylibcugraph/leiden.pyx index 87286234f16..04f8887551c 100644 --- a/python/pylibcugraph/pylibcugraph/leiden.pyx +++ b/python/pylibcugraph/pylibcugraph/leiden.pyx @@ -116,7 +116,7 @@ def leiden(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, clusters, modularity) = pylibcugraph.Leiden( resource_handle, G, 100, 1., False) diff --git a/python/pylibcugraph/pylibcugraph/louvain.pyx b/python/pylibcugraph/pylibcugraph/louvain.pyx index eca569d7da1..58f4f10bc18 100644 --- a/python/pylibcugraph/pylibcugraph/louvain.pyx +++ b/python/pylibcugraph/pylibcugraph/louvain.pyx @@ -103,7 +103,7 @@ def louvain(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, clusters, modularity) = pylibcugraph.louvain( resource_handle, G, 100, 1e-7, 1., False) diff --git a/python/pylibcugraph/pylibcugraph/node2vec.pyx b/python/pylibcugraph/pylibcugraph/node2vec.pyx index d0ab3f22b00..5d83fc46c3c 100644 --- a/python/pylibcugraph/pylibcugraph/node2vec.pyx +++ b/python/pylibcugraph/pylibcugraph/node2vec.pyx @@ -115,7 +115,7 @@ def node2vec(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) >>> (paths, weights, sizes) = pylibcugraph.node2vec( ... resource_handle, G, seeds, 3, True, 1.0, 1.0) diff --git a/python/pylibcugraph/pylibcugraph/pagerank.pyx b/python/pylibcugraph/pylibcugraph/pagerank.pyx index f831d844338..9fec1328bbf 100644 --- a/python/pylibcugraph/pylibcugraph/pagerank.pyx +++ b/python/pylibcugraph/pylibcugraph/pagerank.pyx @@ -154,7 +154,7 @@ def pagerank(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, pageranks) = pylibcugraph.pagerank( ... resource_handle, G, None, None, None, None, alpha=0.85, diff --git a/python/pylibcugraph/pylibcugraph/personalized_pagerank.pyx b/python/pylibcugraph/pylibcugraph/personalized_pagerank.pyx index 79ef80be549..85addffa694 100644 --- a/python/pylibcugraph/pylibcugraph/personalized_pagerank.pyx +++ b/python/pylibcugraph/pylibcugraph/personalized_pagerank.pyx @@ -161,7 +161,7 @@ def personalized_pagerank(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, pageranks) = pylibcugraph.personalized_pagerank( ... resource_handle, G, None, None, None, None, alpha=0.85, diff --git a/python/pylibcugraph/pylibcugraph/spectral_modularity_maximization.pyx b/python/pylibcugraph/pylibcugraph/spectral_modularity_maximization.pyx index c74b1f0db41..fa01714744d 100644 --- a/python/pylibcugraph/pylibcugraph/spectral_modularity_maximization.pyx +++ b/python/pylibcugraph/pylibcugraph/spectral_modularity_maximization.pyx @@ -109,7 +109,7 @@ def spectral_modularity_maximization(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=True, renumber=False, do_expensive_check=False) >>> (vertices, clusters) = pylibcugraph.spectral_modularity_maximization( ... resource_handle, G, num_clusters=5, num_eigen_vects=2, evs_tolerance=0.00001 diff --git a/python/pylibcugraph/pylibcugraph/sssp.pyx b/python/pylibcugraph/pylibcugraph/sssp.pyx index b2cd829cb2e..56765c4a1b8 100644 --- a/python/pylibcugraph/pylibcugraph/sssp.pyx +++ b/python/pylibcugraph/pylibcugraph/sssp.pyx @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -109,7 +109,7 @@ def sssp(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=False, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=False, do_expensive_check=False) >>> (vertices, distances, predecessors) = pylibcugraph.sssp( ... resource_handle, G, source=1, cutoff=999, diff --git a/python/pylibcugraph/pylibcugraph/tests/conftest.py b/python/pylibcugraph/pylibcugraph/tests/conftest.py index a7fcbfdb42a..228147a6e9f 100644 --- a/python/pylibcugraph/pylibcugraph/tests/conftest.py +++ b/python/pylibcugraph/pylibcugraph/tests/conftest.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021-2022, NVIDIA CORPORATION. +# Copyright (c) 2021-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -135,11 +135,11 @@ def create_SGGraph(device_srcs, device_dsts, device_weights, transposed=False): graph_props = GraphProperties(is_symmetric=False, is_multigraph=False) g = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=transposed, renumber=False, do_expensive_check=False, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_eigenvector_centrality.py b/python/pylibcugraph/pylibcugraph/tests/test_eigenvector_centrality.py index b4ff29f31c4..551dd58bdd6 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_eigenvector_centrality.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_eigenvector_centrality.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -56,11 +56,11 @@ def _generic_eigenvector_test( resource_handle = ResourceHandle() graph_props = GraphProperties(is_symmetric=False, is_multigraph=False) G = SGGraph( - resource_handle, - graph_props, - src_arr, - dst_arr, - wgt_arr, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=src_arr, + dst_or_index_array=dst_arr, + weight_array=wgt_arr, store_transposed=False, renumber=False, do_expensive_check=True, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_graph_sg.py b/python/pylibcugraph/pylibcugraph/tests/test_graph_sg.py index 4ebb6f1895e..b555a9a16bb 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_graph_sg.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_graph_sg.py @@ -85,11 +85,11 @@ def test_sg_graph(graph_data): if is_valid: g = SGGraph( # noqa:F841 - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=False, do_expensive_check=False, @@ -100,11 +100,11 @@ def test_sg_graph(graph_data): else: with pytest.raises(ValueError): SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=False, do_expensive_check=False, @@ -130,7 +130,6 @@ def test_SGGraph_create_from_cudf(): SGGraph, ) - print("get edgelist...", end="", flush=True) edgelist = cudf.DataFrame( { "src": [0, 1, 2], @@ -139,10 +138,6 @@ def test_SGGraph_create_from_cudf(): } ) - print("edgelist = ", edgelist) - print("done", flush=True) - print("create Graph...", end="", flush=True) - graph_props = GraphProperties(is_multigraph=False, is_symmetric=False) plc_graph = SGGraph( diff --git a/python/pylibcugraph/pylibcugraph/tests/test_katz_centrality.py b/python/pylibcugraph/pylibcugraph/tests/test_katz_centrality.py index d12f90426fa..9550d3be481 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_katz_centrality.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_katz_centrality.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -53,11 +53,11 @@ def _generic_katz_test( resource_handle = ResourceHandle() graph_props = GraphProperties(is_symmetric=False, is_multigraph=False) G = SGGraph( - resource_handle, - graph_props, - src_arr, - dst_arr, - wgt_arr, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=src_arr, + dst_or_index_array=dst_arr, + weight_array=wgt_arr, store_transposed=False, renumber=False, do_expensive_check=True, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_louvain.py b/python/pylibcugraph/pylibcugraph/tests/test_louvain.py index adea5e01f15..620c50f8412 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_louvain.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_louvain.py @@ -81,11 +81,11 @@ def test_sg_louvain_cupy(): resolution = 1.0 sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=True, do_expensive_check=False, @@ -135,11 +135,11 @@ def test_sg_louvain_cudf(): resolution = 1.0 sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=True, do_expensive_check=False, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_node2vec.py b/python/pylibcugraph/pylibcugraph/tests/test_node2vec.py index 0e400a5306c..fb303ce8047 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_node2vec.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_node2vec.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -94,11 +94,11 @@ def _run_node2vec( resource_handle = ResourceHandle() graph_props = GraphProperties(is_symmetric=False, is_multigraph=False) G = SGGraph( - resource_handle, - graph_props, - src_arr, - dst_arr, - wgt_arr, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=src_arr, + dst_or_index_array=dst_arr, + weight_array=wgt_arr, store_transposed=False, renumber=renumbered, do_expensive_check=True, @@ -795,11 +795,11 @@ def test_node2vec_renumber_cupy(graph_file, renumber): resource_handle = ResourceHandle() graph_props = GraphProperties(is_symmetric=False, is_multigraph=False) G = SGGraph( - resource_handle, - graph_props, - src_arr, - dst_arr, - wgt_arr, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=src_arr, + dst_or_index_array=dst_arr, + weight_array=wgt_arr, store_transposed=False, renumber=renumber, do_expensive_check=True, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_pagerank.py b/python/pylibcugraph/pylibcugraph/tests/test_pagerank.py index 56c4878324f..2a313a33f83 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_pagerank.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_pagerank.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/python/pylibcugraph/pylibcugraph/tests/test_sssp.py b/python/pylibcugraph/pylibcugraph/tests/test_sssp.py index ab46af4ff55..6ffbab76ae2 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_sssp.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_sssp.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/python/pylibcugraph/pylibcugraph/tests/test_triangle_count.py b/python/pylibcugraph/pylibcugraph/tests/test_triangle_count.py index aa0d5cd35f5..1862f94ac26 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_triangle_count.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_triangle_count.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at @@ -81,11 +81,11 @@ def test_sg_triangle_count_cupy(): start_list = None sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=True, do_expensive_check=False, @@ -131,11 +131,11 @@ def test_sg_triangle_count_cudf(): start_list = None sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=False, renumber=True, do_expensive_check=False, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py b/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py index ac04635edcf..ffa90731483 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_uniform_neighbor_sample.py @@ -95,11 +95,11 @@ def test_neighborhood_sampling_cupy( num_edges = max(len(device_srcs), len(device_dsts)) sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=store_transposed, renumber=renumber, do_expensive_check=False, @@ -153,11 +153,11 @@ def test_neighborhood_sampling_cudf( num_edges = max(len(device_srcs), len(device_dsts)) sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=store_transposed, renumber=renumber, do_expensive_check=False, @@ -203,11 +203,11 @@ def test_neighborhood_sampling_large_sg_graph(gpubenchmark): fanout_vals = np.asarray([1, 2], dtype=np.int32) sg = SGGraph( - resource_handle, - graph_props, - device_srcs, - device_dsts, - device_weights, + resource_handle=resource_handle, + graph_properties=graph_props, + src_or_offset_array=device_srcs, + dst_or_index_array=device_dsts, + weight_array=device_weights, store_transposed=True, renumber=False, do_expensive_check=False, diff --git a/python/pylibcugraph/pylibcugraph/tests/test_utils.py b/python/pylibcugraph/pylibcugraph/tests/test_utils.py index 036a62b9c1e..64947c21b74 100644 --- a/python/pylibcugraph/pylibcugraph/tests/test_utils.py +++ b/python/pylibcugraph/pylibcugraph/tests/test_utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2022, NVIDIA CORPORATION. +# Copyright (c) 2022-2023, NVIDIA CORPORATION. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at diff --git a/python/pylibcugraph/pylibcugraph/uniform_random_walks.pyx b/python/pylibcugraph/pylibcugraph/uniform_random_walks.pyx index 1570523beb8..677695f93a9 100644 --- a/python/pylibcugraph/pylibcugraph/uniform_random_walks.pyx +++ b/python/pylibcugraph/pylibcugraph/uniform_random_walks.pyx @@ -116,7 +116,7 @@ def uniform_random_walks(ResourceHandle resource_handle, cdef cugraph_type_erased_device_array_view_t* path_ptr = \ cugraph_random_walk_result_get_paths(result_ptr) - if input_graph.weights_view_ptr is NULL: + if input_graph.weights_view_ptr is NULL and input_graph.weights_view_ptr_ptr is NULL: cupy_weights = None else: weights_ptr = cugraph_random_walk_result_get_weights(result_ptr) diff --git a/python/pylibcugraph/pylibcugraph/weakly_connected_components.pyx b/python/pylibcugraph/pylibcugraph/weakly_connected_components.pyx index 7cc0d8ab4c1..240c374353d 100644 --- a/python/pylibcugraph/pylibcugraph/weakly_connected_components.pyx +++ b/python/pylibcugraph/pylibcugraph/weakly_connected_components.pyx @@ -129,7 +129,7 @@ def weakly_connected_components(ResourceHandle resource_handle, >>> graph_props = pylibcugraph.GraphProperties( ... is_symmetric=True, is_multigraph=False) >>> G = pylibcugraph.SGGraph( - ... resource_handle, graph_props, srcs, dsts, weights, + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, ... store_transposed=False, renumber=True, do_expensive_check=False) >>> (vertices, labels) = weakly_connected_components( ... resource_handle, G, None, None, None, None, False) From 0a297526042460a16c48a1aca06e2dcec58fcfe7 Mon Sep 17 00:00:00 2001 From: Rick Ratzel Date: Sun, 26 Nov 2023 21:32:47 -0600 Subject: [PATCH 100/111] Updates version for 24.02 --- build.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build.sh b/build.sh index eef19046d85..fa7a4f6f363 100755 --- a/build.sh +++ b/build.sh @@ -18,7 +18,7 @@ ARGS=$* # script, and that this script resides in the repo dir! REPODIR=$(cd $(dirname $0); pwd) -RAPIDS_VERSION=23.12 +RAPIDS_VERSION=24.02 # Valid args to this script (all possible targets and options) - only one per line VALIDARGS=" From 1e446c4da80dc3e023c9765094c4507d1334ea45 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Mon, 27 Nov 2023 20:35:39 -0500 Subject: [PATCH 101/111] Correct defect found in DLFW testing (#4021) In MG tests, we had 2 different coding approaches, and one of the paths had a defect. This makes all of the coding consistent and corrects the bug. Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/4021 --- cpp/src/c_api/graph_mg.cpp | 6 +- cpp/tests/c_api/mg_test_utils.cpp | 110 ++++++++++++------------------ 2 files changed, 46 insertions(+), 70 deletions(-) diff --git a/cpp/src/c_api/graph_mg.cpp b/cpp/src/c_api/graph_mg.cpp index 5413949e3a3..326022a3fa9 100644 --- a/cpp/src/c_api/graph_mg.cpp +++ b/cpp/src/c_api/graph_mg.cpp @@ -523,9 +523,9 @@ extern "C" cugraph_error_code_t cugraph_mg_graph_create( NULL, &src, &dst, - &weights, - &edge_ids, - &edge_type_ids, + (weights == nullptr) ? nullptr : &weights, + (edge_ids == nullptr) ? nullptr : &edge_ids, + (edge_type_ids == nullptr) ? nullptr : &edge_type_ids, store_transposed, 1, FALSE, diff --git a/cpp/tests/c_api/mg_test_utils.cpp b/cpp/tests/c_api/mg_test_utils.cpp index 15df613ae05..6eec436e77d 100644 --- a/cpp/tests/c_api/mg_test_utils.cpp +++ b/cpp/tests/c_api/mg_test_utils.cpp @@ -158,30 +158,22 @@ extern "C" int create_mg_test_graph(const cugraph_resource_handle_t* handle, rank = cugraph_resource_handle_get_rank(handle); - if (rank == 0) { - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + size_t original_num_edges = num_edges; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + if (rank != 0) num_edges = 0; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } else { - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); - ret_code = cugraph_type_erased_device_array_create(handle, 0, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); src_view = cugraph_type_erased_device_array_view(src); dst_view = cugraph_type_erased_device_array_view(dst); @@ -207,7 +199,7 @@ extern "C" int create_mg_test_graph(const cugraph_resource_handle_t* handle, NULL, NULL, store_transposed, - num_edges, + original_num_edges, // UNUSED FALSE, p_graph, ret_error); @@ -260,30 +252,22 @@ extern "C" int create_mg_test_graph_double(const cugraph_resource_handle_t* hand rank = cugraph_resource_handle_get_rank(handle); - if (rank == 0) { - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + size_t original_num_edges = num_edges; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + if (rank != 0) num_edges = 0; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } else { - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); - ret_code = cugraph_type_erased_device_array_create(handle, 0, weight_tid, &wgt, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, weight_tid, &wgt, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); src_view = cugraph_type_erased_device_array_view(src); dst_view = cugraph_type_erased_device_array_view(dst); @@ -309,7 +293,7 @@ extern "C" int create_mg_test_graph_double(const cugraph_resource_handle_t* hand NULL, NULL, store_transposed, - num_edges, + original_num_edges, // UNUSED FALSE, p_graph, ret_error); @@ -357,30 +341,22 @@ extern "C" int create_mg_test_graph_with_edge_ids(const cugraph_resource_handle_ rank = cugraph_resource_handle_get_rank(handle); - if (rank == 0) { - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + size_t original_num_edges = num_edges; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + if (rank != 0) num_edges = 0; - ret_code = - cugraph_type_erased_device_array_create(handle, num_edges, edge_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "idx create failed."); - } else { - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &src, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, cugraph_error_message(*ret_error)); - ret_code = cugraph_type_erased_device_array_create(handle, 0, vertex_tid, &dst, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &dst, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "dst create failed."); - ret_code = cugraph_type_erased_device_array_create(handle, 0, edge_tid, &idx, ret_error); - TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "wgt create failed."); - } + ret_code = + cugraph_type_erased_device_array_create(handle, num_edges, edge_tid, &idx, ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "idx create failed."); src_view = cugraph_type_erased_device_array_view(src); dst_view = cugraph_type_erased_device_array_view(dst); @@ -406,7 +382,7 @@ extern "C" int create_mg_test_graph_with_edge_ids(const cugraph_resource_handle_ idx_view, NULL, store_transposed, - num_edges, + original_num_edges, // UNUSED FALSE, p_graph, ret_error); @@ -464,7 +440,7 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl size_t original_num_edges = num_edges; - if (rank == 0) num_edges = 0; + if (rank != 0) num_edges = 0; ret_code = cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); @@ -529,7 +505,7 @@ extern "C" int create_mg_test_graph_with_properties(const cugraph_resource_handl idx_view, type_view, store_transposed, - original_num_edges, + original_num_edges, // UNUSED FALSE, p_graph, ret_error); @@ -593,7 +569,7 @@ int create_mg_test_graph_new(const cugraph_resource_handle_t* handle, size_t original_num_edges = num_edges; if (rank != 0) num_edges = 0; - + ret_code = cugraph_type_erased_device_array_create(handle, num_edges, vertex_tid, &src, ret_error); TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "src create failed."); @@ -658,7 +634,7 @@ int create_mg_test_graph_new(const cugraph_resource_handle_t* handle, edge_id_view, edge_type_view, store_transposed, - renumber, + original_num_edges, // UNUSED FALSE, graph, ret_error); From 6041ab9446ffc4625a5da10f70b1a4e00003f98e Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 29 Nov 2023 15:23:00 -0600 Subject: [PATCH 102/111] Removes unsupported `setup.py` calls, cleans up text (#4024) closes #4008 * Removes unsupported `setup.py` calls, which resulted in issue #4008 . The document now describes how to use `build.sh`, which is more consistent with docs for other RAPIDS libs. * Removes `build.sh` output from docs in favor of documenting how to use `--help` to eliminate the doc being out-of-date with the script. * Removes extra wording in places and cleans up various sections. Authors: - Rick Ratzel (https://github.com/rlratzel) Approvers: - Don Acosta (https://github.com/acostadon) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4024 --- .../source/installation/source_build.md | 195 ++++++------------ 1 file changed, 65 insertions(+), 130 deletions(-) diff --git a/docs/cugraph/source/installation/source_build.md b/docs/cugraph/source/installation/source_build.md index f5ee0741da6..1a129d45295 100644 --- a/docs/cugraph/source/installation/source_build.md +++ b/docs/cugraph/source/installation/source_build.md @@ -1,53 +1,46 @@ # Building from Source -The following instructions are for users wishing to build cuGraph from source code. These instructions are tested on supported distributions of Linux, CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) for list of supported environments. Other operating systems _might be_ compatible, but are not currently tested. - -The cuGraph package include both a C/C++ CUDA portion and a python portion. Both libraries need to be installed in order for cuGraph to operate correctly. +These instructions are tested on supported versions/distributions of Linux, +CUDA, and Python - See [RAPIDS Getting Started](https://rapids.ai/start.html) +for the list of supported environments. Other environments _might be_ +compatible, but are not currently tested. ## Prerequisites -__Compiler:__ +__Compilers:__ * `gcc` version 9.3+ -* `nvcc` version 11.0+ -* `cmake` version 3.20.1+ +* `nvcc` version 11.5+ __CUDA:__ -* CUDA 11.0+ +* CUDA 11.2+ * NVIDIA driver 450.80.02+ * Pascal architecture or better -You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads). - -__Packages:__ -* `cmake` version 3.20.1+ -* `libcugraphops` (version matching source branch version, eg. `23.10`) - -You can obtain `libcugraphops` using `conda`/`mamba` from the `nvidia` channel, or using `pip` with the `--extra-index-url=https://pypi.nvidia.com` option. See the [RAPIDS docs](https://docs.rapids.ai/install#environment) for more details. - -## Building cuGraph -To install cuGraph from source, ensure the dependencies are met. +Further details and download links for these prerequisites are available on the +[RAPIDS System Requirements page](https://docs.rapids.ai/install#system-req). +## Setting up the development environment -### Clone Repo and Configure Conda Environment -__GIT clone a version of the repository__ - - ```bash - # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME - export CUGRAPH_HOME=$(pwd)/cugraph - - # Download the cuGraph repo - if you have a folked version, use that path here instead - git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME +### Clone the repository: +```bash +CUGRAPH_HOME=$(pwd)/cugraph +git clone https://github.com/rapidsai/cugraph.git $CUGRAPH_HOME +cd $CUGRAPH_HOME +``` - cd $CUGRAPH_HOME - ``` +### Create the conda environment -__Create the conda development environment__ +Using conda is the easiest way to install both the build and runtime +dependencies for cugraph. While it is possible to build and run cugraph without +conda, the required packages occasionally change, making it difficult to +document here. The best way to see the current dependencies needed for a build +and run environment is to examine the list of packages in the [conda +environment YAML +files](https://github.com/rapidsai/cugraph/blob/main/conda/environments). ```bash -# create the conda environment (assuming in base `cugraph` directory) - # for CUDA 11.x -conda env create --name cugraph_dev --file conda/environments/all_cuda-118_arch-x86_64.yaml +conda env create --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml # activate the environment conda activate cugraph_dev @@ -56,101 +49,53 @@ conda activate cugraph_dev conda deactivate ``` - - The environment can be updated as development includes/changes the dependencies. To do so, run: - +The environment can be updated as cugraph adds/removes/updates its dependencies. To do so, run: ```bash - -# Where XXX is the CUDA 11 version -conda env update --name cugraph_dev --file conda/environments/cugraph_dev_cuda11.XXX.yml - +# for CUDA 11.x +conda env update --name cugraph_dev --file $CUGRAPH_HOME/conda/environments/all_cuda-118_arch-x86_64.yaml conda activate cugraph_dev ``` +### Build and Install -### Build and Install Using the `build.sh` Script -Using the `build.sh` script make compiling and installing cuGraph a breeze. To build and install, simply do: +#### Build and install using `build.sh` +Using the `build.sh` script, located in the `$CUGRAPH_HOME` directory, is the +recommended way to build and install the cugraph libraries. By default, +`build.sh` will build and install a predefined set of targets +(packages/libraries), but can also accept a list of targets to build. -```bash -$ cd $CUGRAPH_HOME -$ ./build.sh clean -$ ./build.sh libcugraph -$ ./build.sh cugraph -``` +For example, to build only the cugraph C++ library (`libcugraph`) and the +high-level python library (`cugraph`) without building the C++ test binaries, +run this command: -There are several other options available on the build script for advanced users. -`build.sh` options: ```bash -build.sh [ ...] [ ...] - where is: - clean - remove all existing build artifacts and configuration (start over) - uninstall - uninstall libcugraph and cugraph from a prior build/install (see also -n) - libcugraph - build libcugraph.so and SG test binaries - libcugraph_etl - build libcugraph_etl.so and SG test binaries - pylibcugraph - build the pylibcugraph Python package - cugraph - build the cugraph Python package - nx-cugraph - build the nx-cugraph Python package - cugraph-service - build the cugraph-service_client and cugraph-service_server Python package - cpp-mgtests - build libcugraph and libcugraph_etl MG tests. Builds MPI communicator, adding MPI as a dependency. - cugraph-dgl - build the cugraph-dgl extensions for DGL - cugraph-pyg - build the cugraph-dgl extensions for PyG - docs - build the docs - and is: - -v - verbose build mode - -g - build for debug - -n - do not install after a successful build - --pydevelop - use setup.py develop instead of install - --allgpuarch - build for all supported GPU architectures - --skip_cpp_tests - do not build the SG test binaries as part of the libcugraph and libcugraph_etl targets - --without_cugraphops - do not build algos that require cugraph-ops - --cmake_default_generator - use the default cmake generator instead of ninja - --clean - clean an individual target (note: to do a complete rebuild, use the clean target described above) - -h - print this text - - default action (no args) is to build and install 'libcugraph' then 'libcugraph_etl' then 'pylibcugraph' then 'cugraph' then 'cugraph-service' targets - -examples: -$ ./build.sh clean # remove prior build artifacts (start over) -$ ./build.sh libcugraph -v # compile and install libcugraph with verbose output -$ ./build.sh libcugraph -g # compile and install libcugraph for debug -$ ./build.sh libcugraph -n # compile libcugraph but do not install - -# make parallelism options can also be defined: Example build jobs using 4 threads (make -j4) -$ PARALLEL_LEVEL=4 ./build.sh libcugraph - -Note that the libraries will be installed to the location set in `$PREFIX` if set (i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. +$ cd $CUGRAPH_HOME +$ ./build.sh libcugraph pylibcugraph cugraph --skip_cpp_tests ``` +There are several other options available on the build script for advanced +users. Refer to the output of `--help` for details. -## Building each section independently -#### Build and Install the C++/CUDA `libcugraph` Library -CMake depends on the `nvcc` executable being on your path or defined in `$CUDACXX`. - -This project uses cmake for building the C/C++ library. To configure cmake, run: - - ```bash - # Set the localtion to cuGraph in an environment variable CUGRAPH_HOME - export CUGRAPH_HOME=$(pwd)/cugraph - - cd $CUGRAPH_HOME - cd cpp # enter cpp directory - mkdir build # create build directory - cd build # enter the build directory - cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX - - # now build the code - make -j # "-j" starts multiple threads - make install # install the libraries - ``` -The default installation locations are `$CMAKE_INSTALL_PREFIX/lib` and `$CMAKE_INSTALL_PREFIX/include/cugraph` respectively. +Note that libraries will be installed to the location set in `$PREFIX` if set +(i.e. `export PREFIX=/install/path`), otherwise to `$CONDA_PREFIX`. #### Updating the RAFT branch -`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and there are times when it might be desirable to build against a different RAFT branch, such as when working on new features that might span both RAFT and cuGraph. +`libcugraph` uses the [RAFT](https://github.com/rapidsai/raft) library and +there are times when it might be desirable to build against a different RAFT +branch, such as when working on new features that might span both RAFT and +cuGraph. -For local development, the `CPM_raft_SOURCE=` option can be passed to the `cmake` command to enable `libcugraph` to use the local RAFT branch. +For local development, the `CPM_raft_SOURCE=` option can +be passed to the `cmake` command to enable `libcugraph` to use the local RAFT +branch. The `build.sh` script calls `cmake` to build the C/C++ targets, but +developers can call `cmake` directly in order to pass it options like those +described here. Refer to the `build.sh` script to see how to call `cmake` and +other commands directly. -To have CI test a `cugraph` pull request against a different RAFT branch, modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: +To have CI test a `cugraph` pull request against a different RAFT branch, +modify the bottom of the `cpp/cmake/thirdparty/get_raft.cmake` file as follows: ```cmake # Change pinned tag and fork here to test a commit in CI @@ -167,24 +112,10 @@ find_and_configure_raft(VERSION ${CUGRAPH_MIN_VERSION_raft} ) ``` -When the above change is pushed to a pull request, the continuous integration servers will use the specified RAFT branch to run the cuGraph tests. After the changes in the RAFT branch are merged to the release branch, remember to revert the `get_raft.cmake` file back to the original cuGraph branch. - -### Building and installing the Python package - -2) Install the Python packages to your Python path: - -```bash -cd $CUGRAPH_HOME -cd python -cd pylibcugraph -python setup.py build_ext --inplace -python setup.py install # install pylibcugraph -cd ../cugraph -python setup.py build_ext --inplace -python setup.py install # install cugraph python bindings - -``` - +When the above change is pushed to a pull request, the continuous integration +servers will use the specified RAFT branch to run the cuGraph tests. After the +changes in the RAFT branch are merged to the release branch, remember to revert +the `get_raft.cmake` file back to the original cuGraph branch. ## Run tests @@ -240,7 +171,10 @@ Note: This conda installation only applies to Linux and Python versions 3.8/3.10 ### (OPTIONAL) Set environment variable on activation -It is possible to configure the conda environment to set environmental variables on activation. Providing instructions to set PATH to include the CUDA toolkit bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be helpful. +It is possible to configure the conda environment to set environment variables +on activation. Providing instructions to set PATH to include the CUDA toolkit +bin directory and LD_LIBRARY_PATH to include the CUDA lib64 directory will be +helpful. ```bash cd ~/anaconda3/envs/cugraph_dev @@ -271,7 +205,8 @@ unset LD_LIBRARY_PATH ## Creating documentation -Python API documentation can be generated from _./docs/cugraph directory_. Or through using "./build.sh docs" +Python API documentation can be generated from _./docs/cugraph directory_. Or +through using "./build.sh docs" ## Attribution Portions adopted from https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md From 5eaae7d6543de708a21545f8fc236cedfb05f018 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Thu, 30 Nov 2023 13:03:19 -0800 Subject: [PATCH 103/111] Update count_if_e, transform_reduce_e, and transform_e to support edge masking (#4001) Update transform_e to support graphs with edge masking. This is necessary for K-truss. Authors: - Seunghwa Kang (https://github.com/seunghwak) - Naim (https://github.com/naimnv) Approvers: - Naim (https://github.com/naimnv) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/4001 --- cpp/include/cugraph/graph_view.hpp | 54 +++--- cpp/include/cugraph/utilities/misc_utils.cuh | 1 + cpp/src/prims/count_if_e.cuh | 2 - cpp/src/prims/fill_edge_property.cuh | 73 +++++++- cpp/src/prims/transform_e.cuh | 123 ++++++++---- cpp/src/prims/transform_reduce_e.cuh | 177 +++++++++++------- .../prims/update_edge_src_dst_property.cuh | 8 - cpp/src/structure/graph_view_impl.cuh | 24 +-- cpp/tests/prims/mg_count_if_e.cu | 47 +++-- ...r_v_pair_transform_dst_nbr_intersection.cu | 63 +------ ...er_v_random_select_transform_outgoing_e.cu | 5 +- cpp/tests/prims/mg_transform_e.cu | 24 ++- cpp/tests/prims/mg_transform_reduce_e.cu | 25 ++- cpp/tests/prims/property_generator.cuh | 37 +++- 14 files changed, 404 insertions(+), 259 deletions(-) diff --git a/cpp/include/cugraph/graph_view.hpp b/cpp/include/cugraph/graph_view.hpp index f30a8b7e2af..d79d4635c54 100644 --- a/cpp/include/cugraph/graph_view.hpp +++ b/cpp/include/cugraph/graph_view.hpp @@ -268,7 +268,11 @@ class graph_base_t { properties_(properties){}; vertex_t number_of_vertices() const { return number_of_vertices_; } - edge_t number_of_edges() const { return number_of_edges_; } + edge_t number_of_edges() const + { + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); + return number_of_edges_; + } template std::enable_if_t::value, bool> is_valid_vertex(vertex_type v) const @@ -285,6 +289,20 @@ class graph_base_t { bool is_symmetric() const { return properties_.is_symmetric; } bool is_multigraph() const { return properties_.is_multigraph; } + void attach_edge_mask(edge_property_view_t edge_mask_view) + { + edge_mask_view_ = edge_mask_view; + } + + void clear_edge_mask() { edge_mask_view_ = std::nullopt; } + + bool has_edge_mask() const { return edge_mask_view_.has_value(); } + + std::optional> edge_mask_view() const + { + return edge_mask_view_; + } + protected: raft::handle_t const* handle_ptr() const { return handle_ptr_; }; graph_properties_t graph_properties() const { return properties_; } @@ -296,6 +314,8 @@ class graph_base_t { edge_t number_of_edges_{0}; graph_properties_t properties_{}; + + std::optional> edge_mask_view_{std::nullopt}; }; } // namespace detail @@ -731,20 +751,6 @@ class graph_view_t edge_mask_view) - { - edge_mask_view_ = edge_mask_view; - } - - void clear_edge_mask() { edge_mask_view_ = std::nullopt; } - - bool has_edge_mask() const { return edge_mask_view_.has_value(); } - - std::optional> edge_mask_view() const - { - return edge_mask_view_; - } - private: std::vector edge_partition_offsets_{}; std::vector edge_partition_indices_{}; @@ -790,8 +796,6 @@ class graph_view_t>, std::optional /* dummy */> local_sorted_unique_edge_dst_vertex_partition_offsets_{std::nullopt}; - - std::optional> edge_mask_view_{std::nullopt}; }; // single-GPU version @@ -1012,28 +1016,12 @@ class graph_view_t edge_mask_view) - { - edge_mask_view_ = edge_mask_view; - } - - void clear_edge_mask() { edge_mask_view_ = std::nullopt; } - - bool has_edge_mask() const { return edge_mask_view_.has_value(); } - - std::optional> edge_mask_view() const - { - return edge_mask_view_; - } - private: edge_t const* offsets_{nullptr}; vertex_t const* indices_{nullptr}; // segment offsets based on vertex degree, relevant only if vertex IDs are renumbered std::optional> segment_offsets_{std::nullopt}; - - std::optional> edge_mask_view_{std::nullopt}; }; } // namespace cugraph diff --git a/cpp/include/cugraph/utilities/misc_utils.cuh b/cpp/include/cugraph/utilities/misc_utils.cuh index a62e8ce85ec..28e2853727f 100644 --- a/cpp/include/cugraph/utilities/misc_utils.cuh +++ b/cpp/include/cugraph/utilities/misc_utils.cuh @@ -19,6 +19,7 @@ #include #include +#include #include #include #include diff --git a/cpp/src/prims/count_if_e.cuh b/cpp/src/prims/count_if_e.cuh index f6e4bc9bead..9cff4f5eceb 100644 --- a/cpp/src/prims/count_if_e.cuh +++ b/cpp/src/prims/count_if_e.cuh @@ -74,8 +74,6 @@ typename GraphViewType::edge_type count_if_e(raft::handle_t const& handle, using vertex_t = typename GraphViewType::vertex_type; using edge_t = typename GraphViewType::edge_type; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } diff --git a/cpp/src/prims/fill_edge_property.cuh b/cpp/src/prims/fill_edge_property.cuh index d446944b65b..e6875576044 100644 --- a/cpp/src/prims/fill_edge_property.cuh +++ b/cpp/src/prims/fill_edge_property.cuh @@ -15,6 +15,7 @@ */ #pragma once +#include #include #include #include @@ -23,6 +24,7 @@ #include #include +#include #include @@ -38,21 +40,78 @@ void fill_edge_property(raft::handle_t const& handle, { static_assert(std::is_same_v); + using edge_t = typename GraphViewType::edge_type; + + auto edge_mask_view = graph_view.edge_mask_view(); + auto value_firsts = edge_property_output.value_firsts(); auto edge_counts = edge_property_output.edge_counts(); for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; + if constexpr (cugraph::has_packed_bool_element< std::remove_reference_t, T>()) { static_assert(std::is_arithmetic_v, "unimplemented for thrust::tuple types."); auto packed_input = input ? packed_bool_full_mask() : packed_bool_empty_mask(); - thrust::fill_n(handle.get_thrust_policy(), - value_firsts[i], - packed_bool_size(static_cast(edge_counts[i])), - packed_input); + auto rem = edge_counts[i] % packed_bools_per_word(); + if (edge_partition_e_mask) { + auto input_first = + thrust::make_zip_iterator(value_firsts[i], (*edge_partition_e_mask).value_first()); + thrust::transform(handle.get_thrust_policy(), + input_first, + input_first + packed_bool_size(static_cast(edge_counts[i] - rem)), + value_firsts[i], + [packed_input] __device__(thrust::tuple pair) { + auto old_value = thrust::get<0>(pair); + auto mask = thrust::get<1>(pair); + return (old_value & ~mask) | (packed_input & mask); + }); + if (rem > 0) { + thrust::transform( + handle.get_thrust_policy(), + input_first + packed_bool_size(static_cast(edge_counts[i] - rem)), + input_first + packed_bool_size(static_cast(edge_counts[i])), + value_firsts[i] + packed_bool_size(static_cast(edge_counts[i] - rem)), + [packed_input, rem] __device__(thrust::tuple pair) { + auto old_value = thrust::get<0>(pair); + auto mask = thrust::get<1>(pair); + return ((old_value & ~mask) | (packed_input & mask)) & packed_bool_partial_mask(rem); + }); + } + } else { + thrust::fill_n(handle.get_thrust_policy(), + value_firsts[i], + packed_bool_size(static_cast(edge_counts[i] - rem)), + packed_input); + if (rem > 0) { + thrust::fill_n( + handle.get_thrust_policy(), + value_firsts[i] + packed_bool_size(static_cast(edge_counts[i] - rem)), + 1, + packed_input & packed_bool_partial_mask(rem)); + } + } } else { - thrust::fill_n( - handle.get_thrust_policy(), value_firsts[i], static_cast(edge_counts[i]), input); + if (edge_partition_e_mask) { + thrust::transform_if(handle.get_thrust_policy(), + thrust::make_constant_iterator(input), + thrust::make_constant_iterator(input) + edge_counts[i], + thrust::make_counting_iterator(edge_t{0}), + value_firsts[i], + thrust::identity{}, + [edge_partition_e_mask = *edge_partition_e_mask] __device__(edge_t i) { + return edge_partition_e_mask.get(i); + }); + } else { + thrust::fill_n( + handle.get_thrust_policy(), value_firsts[i], static_cast(edge_counts[i]), input); + } } } } @@ -79,8 +138,6 @@ void fill_edge_property(raft::handle_t const& handle, edge_property_t& edge_property_output, bool do_expensive_check = false) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } diff --git a/cpp/src/prims/transform_e.cuh b/cpp/src/prims/transform_e.cuh index edacdc8a970..c6623621d24 100644 --- a/cpp/src/prims/transform_e.cuh +++ b/cpp/src/prims/transform_e.cuh @@ -16,10 +16,12 @@ #pragma once #include +#include #include #include #include #include +#include #include #include @@ -44,6 +46,7 @@ template __global__ void transform_e_packed_bool( @@ -53,6 +56,7 @@ __global__ void transform_e_packed_bool( EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + thrust::optional edge_partition_e_mask, EdgePartitionEdgeValueOutputWrapper edge_partition_e_value_output, EdgeOp e_op) { @@ -68,11 +72,14 @@ __global__ void transform_e_packed_bool( auto num_edges = edge_partition.number_of_edges(); while (idx < static_cast(packed_bool_size(num_edges))) { + auto edge_mask = packed_bool_full_mask(); + if (edge_partition_e_mask) { edge_mask = *((*edge_partition_e_mask).value_first() + idx); } + auto local_edge_idx = idx * static_cast(packed_bools_per_word()) + static_cast(lane_id); - uint32_t mask{0}; int predicate{0}; - if (local_edge_idx < num_edges) { + + if ((local_edge_idx < num_edges) && (edge_mask & packed_bool_mask(lane_id))) { auto major_idx = edge_partition.major_idx_from_local_edge_idx_nocheck(local_edge_idx); auto major = edge_partition.major_from_major_idx_nocheck(major_idx); auto major_offset = edge_partition.major_offset_from_major_nocheck(major); @@ -91,8 +98,15 @@ __global__ void transform_e_packed_bool( ? int{1} : int{0}; } - mask = __ballot_sync(uint32_t{0xffffffff}, predicate); - if (lane_id == 0) { *(edge_partition_e_value_output.value_first() + idx) = mask; } + uint32_t new_val = __ballot_sync(uint32_t{0xffffffff}, predicate); + if (lane_id == 0) { + if (edge_mask == packed_bool_full_mask()) { + *(edge_partition_e_value_output.value_first() + idx) = new_val; + } else { + auto old_val = *(edge_partition_e_value_output.value_first() + idx); + *(edge_partition_e_value_output.value_first() + idx) = (old_val & ~edge_mask) | new_val; + } + } idx += static_cast(gridDim.x * (blockDim.x / raft::warp_size())); } @@ -178,12 +192,18 @@ void transform_e(raft::handle_t const& handle, typename EdgeValueOutputWrapper::value_iterator, typename EdgeValueOutputWrapper::value_type>; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); + auto edge_mask_view = graph_view.edge_mask_view(); for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; edge_partition_src_input_device_view_t edge_partition_src_value_input{}; edge_partition_dst_input_device_view_t edge_partition_dst_value_input{}; @@ -214,35 +234,40 @@ void transform_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, edge_partition_e_value_output, e_op); } } else { - thrust::transform( + thrust::for_each( handle.get_thrust_policy(), thrust::make_counting_iterator(edge_t{0}), thrust::make_counting_iterator(num_edges), - edge_partition_e_value_output.value_first(), [e_op, edge_partition, edge_partition_src_value_input, edge_partition_dst_value_input, - edge_partition_e_value_input] __device__(edge_t i) { - auto major_idx = edge_partition.major_idx_from_local_edge_idx_nocheck(i); - auto major = edge_partition.major_from_major_idx_nocheck(major_idx); - auto major_offset = edge_partition.major_offset_from_major_nocheck(major); - auto minor = *(edge_partition.indices() + i); - auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); - - auto src = GraphViewType::is_storage_transposed ? minor : major; - auto dst = GraphViewType::is_storage_transposed ? major : minor; - auto src_offset = GraphViewType::is_storage_transposed ? minor_offset : major_offset; - auto dst_offset = GraphViewType::is_storage_transposed ? major_offset : minor_offset; - return e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(i)); + edge_partition_e_value_input, + edge_partition_e_mask, + edge_partition_e_value_output] __device__(edge_t i) { + if (!edge_partition_e_mask || (*edge_partition_e_mask).get(i)) { + auto major_idx = edge_partition.major_idx_from_local_edge_idx_nocheck(i); + auto major = edge_partition.major_from_major_idx_nocheck(major_idx); + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto minor = *(edge_partition.indices() + i); + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + + auto src = GraphViewType::is_storage_transposed ? minor : major; + auto dst = GraphViewType::is_storage_transposed ? major : minor; + auto src_offset = GraphViewType::is_storage_transposed ? minor_offset : major_offset; + auto dst_offset = GraphViewType::is_storage_transposed ? major_offset : minor_offset; + auto e_op_result = e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(i)); + edge_partition_e_value_output.set(i, e_op_result); + } }); } } @@ -336,14 +361,12 @@ void transform_e(raft::handle_t const& handle, typename EdgeValueOutputWrapper::value_iterator, typename EdgeValueOutputWrapper::value_type>; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - auto major_first = GraphViewType::is_storage_transposed ? edge_list.dst_begin() : edge_list.src_begin(); auto minor_first = GraphViewType::is_storage_transposed ? edge_list.src_begin() : edge_list.dst_begin(); - auto edge_first = thrust::make_zip_iterator(thrust::make_tuple(major_first, minor_first)); + auto edge_first = thrust::make_zip_iterator(major_first, minor_first); if (do_expensive_check) { CUGRAPH_EXPECTS( @@ -382,10 +405,18 @@ void transform_e(raft::handle_t const& handle, edge_partition_offsets.back() = edge_list.size(); } + auto edge_mask_view = graph_view.edge_mask_view(); + for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; if (do_expensive_check) { CUGRAPH_EXPECTS( @@ -393,7 +424,8 @@ void transform_e(raft::handle_t const& handle, handle.get_thrust_policy(), edge_first + edge_partition_offsets[i], edge_first + edge_partition_offsets[i + 1], - [edge_partition] __device__(thrust::tuple edge) { + [edge_partition, + edge_partition_e_mask] __device__(thrust::tuple edge) { auto major = thrust::get<0>(edge); auto minor = thrust::get<1>(edge); vertex_t major_idx{}; @@ -416,8 +448,19 @@ void transform_e(raft::handle_t const& handle, edge_t edge_offset{}; edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(major_idx); - auto it = thrust::lower_bound(thrust::seq, indices, indices + local_degree, minor); - return *it != minor; + auto lower_it = + thrust::lower_bound(thrust::seq, indices, indices + local_degree, minor); + if (*lower_it != minor) { return true; } + if (edge_partition_e_mask) { + auto upper_it = + thrust::upper_bound(thrust::seq, lower_it, indices + local_degree, minor); + if (detail::count_set_bits((*edge_partition_e_mask).value_first(), + edge_offset + thrust::distance(indices, lower_it), + thrust::distance(lower_it, upper_it)) == 0) { + return true; + } + } + return false; }) == 0, "Invalid input arguments: edge_list contains edges that do not exist in the input graph."); } @@ -446,6 +489,7 @@ void transform_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, edge_partition_e_value_output] __device__(thrust::tuple edge) { auto major = thrust::get<0>(edge); auto minor = thrust::get<1>(edge); @@ -469,7 +513,7 @@ void transform_e(raft::handle_t const& handle, edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(major_idx); auto lower_it = thrust::lower_bound(thrust::seq, indices, indices + local_degree, minor); - auto upper_it = thrust::upper_bound(thrust::seq, indices, indices + local_degree, minor); + auto upper_it = thrust::upper_bound(thrust::seq, lower_it, indices + local_degree, minor); auto src = GraphViewType::is_storage_transposed ? minor : major; auto dst = GraphViewType::is_storage_transposed ? major : minor; @@ -478,14 +522,17 @@ void transform_e(raft::handle_t const& handle, for (auto it = lower_it; it != upper_it; ++it) { assert(*it == minor); - auto e_op_result = - e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(edge_offset + thrust::distance(indices, it))); - edge_partition_e_value_output.set(edge_offset + thrust::distance(indices, it), - e_op_result); + if (!edge_partition_e_mask || + ((*edge_partition_e_mask).get(edge_offset + thrust::distance(indices, it)))) { + auto e_op_result = + e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(edge_offset + thrust::distance(indices, it))); + edge_partition_e_value_output.set(edge_offset + thrust::distance(indices, it), + e_op_result); + } } }); } diff --git a/cpp/src/prims/transform_reduce_e.cuh b/cpp/src/prims/transform_reduce_e.cuh index 9c23f3fca18..483ab64dcd9 100644 --- a/cpp/src/prims/transform_reduce_e.cuh +++ b/cpp/src/prims/transform_reduce_e.cuh @@ -56,6 +56,7 @@ template __global__ void transform_reduce_e_hypersparse( @@ -65,6 +66,7 @@ __global__ void transform_reduce_e_hypersparse( EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + thrust::optional edge_partition_e_mask, ResultIterator result_iter /* size 1 */, EdgeOp e_op) { @@ -101,24 +103,31 @@ __global__ void transform_reduce_e_hypersparse( &edge_partition_src_value_input, &edge_partition_dst_value_input, &edge_partition_e_value_input, + &edge_partition_e_mask, &e_op, major, indices, edge_offset] __device__(auto i) { - auto major_offset = edge_partition.major_offset_from_major_nocheck(major); - auto minor = indices[i]; - auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); - auto src = GraphViewType::is_storage_transposed ? minor : major; - auto dst = GraphViewType::is_storage_transposed ? major : minor; - auto src_offset = - GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); - auto dst_offset = - GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; - return e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(edge_offset + i)); + if (!edge_partition_e_mask || (*edge_partition_e_mask).get(edge_offset + i)) { + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto minor = indices[i]; + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + auto src = GraphViewType::is_storage_transposed ? minor : major; + auto dst = GraphViewType::is_storage_transposed ? major : minor; + auto src_offset = GraphViewType::is_storage_transposed + ? minor_offset + : static_cast(major_offset); + auto dst_offset = GraphViewType::is_storage_transposed + ? static_cast(major_offset) + : minor_offset; + return e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(edge_offset + i)); + } else { + return e_op_result_t{}; + } }, e_op_result_t{}, edge_property_add); @@ -135,6 +144,7 @@ template __global__ void transform_reduce_e_low_degree( @@ -146,6 +156,7 @@ __global__ void transform_reduce_e_low_degree( EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + thrust::optional edge_partition_e_mask, ResultIterator result_iter /* size 1 */, EdgeOp e_op) { @@ -177,27 +188,34 @@ __global__ void transform_reduce_e_low_degree( &edge_partition_src_value_input, &edge_partition_dst_value_input, &edge_partition_e_value_input, + &edge_partition_e_mask, &e_op, major_offset, indices, edge_offset] __device__(auto i) { - auto minor = indices[i]; - auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); - auto src = GraphViewType::is_storage_transposed - ? minor - : edge_partition.major_from_major_offset_nocheck(major_offset); - auto dst = GraphViewType::is_storage_transposed - ? edge_partition.major_from_major_offset_nocheck(major_offset) - : minor; - auto src_offset = - GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); - auto dst_offset = - GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; - return e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(edge_offset + i)); + if (!edge_partition_e_mask || (*edge_partition_e_mask).get(edge_offset + i)) { + auto minor = indices[i]; + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + auto src = GraphViewType::is_storage_transposed + ? minor + : edge_partition.major_from_major_offset_nocheck(major_offset); + auto dst = GraphViewType::is_storage_transposed + ? edge_partition.major_from_major_offset_nocheck(major_offset) + : minor; + auto src_offset = GraphViewType::is_storage_transposed + ? minor_offset + : static_cast(major_offset); + auto dst_offset = GraphViewType::is_storage_transposed + ? static_cast(major_offset) + : minor_offset; + return e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(edge_offset + i)); + } else { + return e_op_result_t{}; + } }, e_op_result_t{}, edge_property_add); @@ -214,6 +232,7 @@ template __global__ void transform_reduce_e_mid_degree( @@ -225,6 +244,7 @@ __global__ void transform_reduce_e_mid_degree( EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + thrust::optional edge_partition_e_mask, ResultIterator result_iter /* size 1 */, EdgeOp e_op) { @@ -250,24 +270,26 @@ __global__ void transform_reduce_e_mid_degree( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(major_offset); for (edge_t i = lane_id; i < local_degree; i += raft::warp_size()) { - auto minor = indices[i]; - auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); - auto src = GraphViewType::is_storage_transposed - ? minor - : edge_partition.major_from_major_offset_nocheck(major_offset); - auto dst = GraphViewType::is_storage_transposed - ? edge_partition.major_from_major_offset_nocheck(major_offset) - : minor; - auto src_offset = - GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); - auto dst_offset = - GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; - auto e_op_result = e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(edge_offset + i)); - e_op_result_sum = edge_property_add(e_op_result_sum, e_op_result); + if (!edge_partition_e_mask || (*edge_partition_e_mask).get(edge_offset + i)) { + auto minor = indices[i]; + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + auto src = GraphViewType::is_storage_transposed + ? minor + : edge_partition.major_from_major_offset_nocheck(major_offset); + auto dst = GraphViewType::is_storage_transposed + ? edge_partition.major_from_major_offset_nocheck(major_offset) + : minor; + auto src_offset = + GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); + auto dst_offset = + GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; + auto e_op_result = e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(edge_offset + i)); + e_op_result_sum = edge_property_add(e_op_result_sum, e_op_result); + } } idx += gridDim.x * (blockDim.x / raft::warp_size()); } @@ -280,6 +302,7 @@ template __global__ void transform_reduce_e_high_degree( @@ -291,6 +314,7 @@ __global__ void transform_reduce_e_high_degree( EdgePartitionSrcValueInputWrapper edge_partition_src_value_input, EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, + thrust::optional edge_partition_e_mask, ResultIterator result_iter /* size 1 */, EdgeOp e_op) { @@ -313,24 +337,26 @@ __global__ void transform_reduce_e_high_degree( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(major_offset); for (edge_t i = threadIdx.x; i < local_degree; i += blockDim.x) { - auto minor = indices[i]; - auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); - auto src = GraphViewType::is_storage_transposed - ? minor - : edge_partition.major_from_major_offset_nocheck(major_offset); - auto dst = GraphViewType::is_storage_transposed - ? edge_partition.major_from_major_offset_nocheck(major_offset) - : minor; - auto src_offset = - GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); - auto dst_offset = - GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; - auto e_op_result = e_op(src, - dst, - edge_partition_src_value_input.get(src_offset), - edge_partition_dst_value_input.get(dst_offset), - edge_partition_e_value_input.get(edge_offset + i)); - e_op_result_sum = edge_property_add(e_op_result_sum, e_op_result); + if (!edge_partition_e_mask || (*edge_partition_e_mask).get(edge_offset + i)) { + auto minor = indices[i]; + auto minor_offset = edge_partition.minor_offset_from_minor_nocheck(minor); + auto src = GraphViewType::is_storage_transposed + ? minor + : edge_partition.major_from_major_offset_nocheck(major_offset); + auto dst = GraphViewType::is_storage_transposed + ? edge_partition.major_from_major_offset_nocheck(major_offset) + : minor; + auto src_offset = + GraphViewType::is_storage_transposed ? minor_offset : static_cast(major_offset); + auto dst_offset = + GraphViewType::is_storage_transposed ? static_cast(major_offset) : minor_offset; + auto e_op_result = e_op(src, + dst, + edge_partition_src_value_input.get(src_offset), + edge_partition_dst_value_input.get(dst_offset), + edge_partition_e_value_input.get(edge_offset + i)); + e_op_result_sum = edge_property_add(e_op_result_sum, e_op_result); + } } idx += gridDim.x; } @@ -417,8 +443,6 @@ T transform_reduce_e(raft::handle_t const& handle, typename EdgeValueInputWrapper::value_iterator, typename EdgeValueInputWrapper::value_type>>; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } @@ -431,10 +455,18 @@ T transform_reduce_e(raft::handle_t const& handle, get_dataframe_buffer_begin(result_buffer) + 1, T{}); + auto edge_mask_view = graph_view.edge_mask_view(); + for (size_t i = 0; i < graph_view.number_of_local_edge_partitions(); ++i) { auto edge_partition = edge_partition_device_view_t( graph_view.local_edge_partition_view(i)); + auto edge_partition_e_mask = + edge_mask_view + ? thrust::make_optional< + detail::edge_partition_edge_property_device_view_t>( + *edge_mask_view, i) + : thrust::nullopt; edge_partition_src_input_device_view_t edge_partition_src_value_input{}; edge_partition_dst_input_device_view_t edge_partition_dst_value_input{}; @@ -467,6 +499,7 @@ T transform_reduce_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(result_buffer), e_op); } @@ -482,6 +515,7 @@ T transform_reduce_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(result_buffer), e_op); } @@ -497,6 +531,7 @@ T transform_reduce_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(result_buffer), e_op); } @@ -510,6 +545,7 @@ T transform_reduce_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(result_buffer), e_op); } @@ -527,6 +563,7 @@ T transform_reduce_e(raft::handle_t const& handle, edge_partition_src_value_input, edge_partition_dst_value_input, edge_partition_e_value_input, + edge_partition_e_mask, get_dataframe_buffer_begin(result_buffer), e_op); } @@ -601,8 +638,6 @@ auto transform_reduce_e(raft::handle_t const& handle, edge_op_result_type::type; static_assert(!std::is_same_v); - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } diff --git a/cpp/src/prims/update_edge_src_dst_property.cuh b/cpp/src/prims/update_edge_src_dst_property.cuh index 2d72a075ca5..b8621e122c6 100644 --- a/cpp/src/prims/update_edge_src_dst_property.cuh +++ b/cpp/src/prims/update_edge_src_dst_property.cuh @@ -866,8 +866,6 @@ void update_edge_src_property( edge_src_property_output, bool do_expensive_check = false) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } @@ -917,8 +915,6 @@ void update_edge_src_property( edge_src_property_output, bool do_expensive_check = false) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { auto num_invalids = thrust::count_if( handle.get_thrust_policy(), @@ -985,8 +981,6 @@ void update_edge_dst_property( edge_dst_property_output, bool do_expensive_check = false) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } @@ -1036,8 +1030,6 @@ void update_edge_dst_property( edge_dst_property_output, bool do_expensive_check = false) { - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { auto num_invalids = thrust::count_if( handle.get_thrust_policy(), diff --git a/cpp/src/structure/graph_view_impl.cuh b/cpp/src/structure/graph_view_impl.cuh index 64a8a3212b3..37a553dcdbd 100644 --- a/cpp/src/structure/graph_view_impl.cuh +++ b/cpp/src/structure/graph_view_impl.cuh @@ -548,7 +548,7 @@ graph_view_tpartition_, this->edge_partition_segment_offsets_); } else { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); return compute_minor_degrees(handle, *this); } } @@ -566,7 +566,7 @@ graph_view_tlocal_vertex_partition_range_size()); } else { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); return compute_minor_degrees(handle, *this); } } @@ -577,7 +577,7 @@ graph_view_thas_edge_mask()), "unimplemented."); return compute_minor_degrees(handle, *this); } else { return compute_major_degrees(handle, @@ -598,7 +598,7 @@ graph_view_thas_edge_mask()), "unimplemented."); return compute_minor_degrees(handle, *this); } else { return compute_major_degrees( @@ -614,7 +614,7 @@ template >:: compute_max_in_degree(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); auto in_degrees = compute_in_degrees(handle); auto it = thrust::max_element(handle.get_thrust_policy(), in_degrees.begin(), in_degrees.end()); @@ -632,7 +632,7 @@ template >:: compute_max_in_degree(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); auto in_degrees = compute_in_degrees(handle); auto it = thrust::max_element(handle.get_thrust_policy(), in_degrees.begin(), in_degrees.end()); @@ -646,7 +646,7 @@ template >:: compute_max_out_degree(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); auto out_degrees = compute_out_degrees(handle); auto it = thrust::max_element(handle.get_thrust_policy(), out_degrees.begin(), out_degrees.end()); @@ -664,7 +664,7 @@ template >:: compute_max_out_degree(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); auto out_degrees = compute_out_degrees(handle); auto it = thrust::max_element(handle.get_thrust_policy(), out_degrees.begin(), out_degrees.end()); @@ -678,7 +678,7 @@ template >:: count_self_loops(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); return count_if_e( handle, @@ -693,7 +693,7 @@ template >:: count_self_loops(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); return count_if_e( handle, @@ -708,7 +708,7 @@ template >:: count_multi_edges(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); if (!this->is_multigraph()) { return edge_t{0}; } @@ -728,7 +728,7 @@ template >:: count_multi_edges(raft::handle_t const& handle) const { - CUGRAPH_EXPECTS(!has_edge_mask(), "unimplemented."); + CUGRAPH_EXPECTS(!(this->has_edge_mask()), "unimplemented."); if (!this->is_multigraph()) { return edge_t{0}; } diff --git a/cpp/tests/prims/mg_count_if_e.cu b/cpp/tests/prims/mg_count_if_e.cu index 449aa728d87..03bf8ae0ae5 100644 --- a/cpp/tests/prims/mg_count_if_e.cu +++ b/cpp/tests/prims/mg_count_if_e.cu @@ -53,8 +53,9 @@ #include struct Prims_Usecase { - bool check_correctness{true}; bool test_weighted{false}; + bool edge_masking{false}; + bool check_correctness{true}; }; template @@ -102,6 +103,13 @@ class Tests_MGCountIfE auto mg_graph_view = mg_graph.view(); + std::optional> edge_mask{std::nullopt}; + if (prims_usecase.edge_masking) { + edge_mask = + cugraph::test::generate::edge_property(*handle_, mg_graph_view, 2); + mg_graph_view.attach_edge_mask((*edge_mask).view()); + } + // 2. run MG count_if_e const int hash_bin_count = 5; @@ -148,19 +156,19 @@ class Tests_MGCountIfE (*mg_renumber_map).size()), false); - auto sg_graph_view = sg_graph.view(); + if (handle_->get_comms().get_rank() == 0) { + auto sg_graph_view = sg_graph.view(); - auto sg_vertex_prop = cugraph::test::generate::vertex_property( - *handle_, - thrust::make_counting_iterator(sg_graph_view.local_vertex_partition_range_first()), - thrust::make_counting_iterator(sg_graph_view.local_vertex_partition_range_last()), - hash_bin_count); - auto sg_src_prop = cugraph::test::generate::src_property( - *handle_, sg_graph_view, sg_vertex_prop); - auto sg_dst_prop = cugraph::test::generate::dst_property( - *handle_, sg_graph_view, sg_vertex_prop); + auto sg_vertex_prop = cugraph::test::generate::vertex_property( + *handle_, + thrust::make_counting_iterator(sg_graph_view.local_vertex_partition_range_first()), + thrust::make_counting_iterator(sg_graph_view.local_vertex_partition_range_last()), + hash_bin_count); + auto sg_src_prop = cugraph::test::generate::src_property( + *handle_, sg_graph_view, sg_vertex_prop); + auto sg_dst_prop = cugraph::test::generate::dst_property( + *handle_, sg_graph_view, sg_vertex_prop); - if (handle_->get_comms().get_rank() == 0) { auto expected_result = count_if_e( *handle_, sg_graph_view, @@ -312,7 +320,10 @@ INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGCountIfE_File, ::testing::Combine( - ::testing::Values(Prims_Usecase{true}), + ::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx"), cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), @@ -320,7 +331,10 @@ INSTANTIATE_TEST_SUITE_P( INSTANTIATE_TEST_SUITE_P(rmat_small_test, Tests_MGCountIfE_Rmat, - ::testing::Combine(::testing::Values(Prims_Usecase{true}), + ::testing::Combine(::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::Rmat_Usecase( 10, 16, 0.57, 0.19, 0.19, 0, false, false)))); @@ -332,7 +346,10 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGCountIfE_Rmat, ::testing::Combine( - ::testing::Values(Prims_Usecase{false}), + ::testing::Values(Prims_Usecase{false, false, false}, + Prims_Usecase{false, true, false}, + Prims_Usecase{true, false, false}, + Prims_Usecase{true, true, false}), ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); CUGRAPH_MG_TEST_PROGRAM_MAIN() diff --git a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu index a3edb1f6372..ac73c446d89 100644 --- a/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu +++ b/cpp/tests/prims/mg_per_v_pair_transform_dst_nbr_intersection.cu @@ -14,6 +14,8 @@ * limitations under the License. */ +#include "property_generator.cuh" + #include #include #include @@ -116,29 +118,8 @@ class Tests_MGPerVPairTransformDstNbrIntersection std::optional> edge_mask{std::nullopt}; if (prims_usecase.edge_masking) { - cugraph::edge_src_property_t edge_src_renumber_map( - *handle_, mg_graph_view); - cugraph::edge_dst_property_t edge_dst_renumber_map( - *handle_, mg_graph_view); - cugraph::update_edge_src_property( - *handle_, mg_graph_view, (*mg_renumber_map).begin(), edge_src_renumber_map); - cugraph::update_edge_dst_property( - *handle_, mg_graph_view, (*mg_renumber_map).begin(), edge_dst_renumber_map); - - edge_mask = cugraph::edge_property_t(*handle_, mg_graph_view); - - cugraph::transform_e( - *handle_, - mg_graph_view, - edge_src_renumber_map.view(), - edge_dst_renumber_map.view(), - cugraph::edge_dummy_property_t{}.view(), - [] __device__(auto src, auto dst, auto src_property, auto dst_property, thrust::nullopt_t) { - return ((src_property % 2 == 0) && (dst_property % 2 == 0)) - ? false - : true; // mask out the edges with even unrenumbered src & dst vertex IDs - }, - (*edge_mask).mutable_view()); + edge_mask = + cugraph::test::generate::edge_property(*handle_, mg_graph_view, 2); mg_graph_view.attach_edge_mask((*edge_mask).view()); } @@ -257,42 +238,6 @@ class Tests_MGPerVPairTransformDstNbrIntersection if (handle_->get_comms().get_rank() == 0) { auto sg_graph_view = sg_graph.view(); - if (prims_usecase.edge_masking) { - rmm::device_uvector srcs(0, handle_->get_stream()); - rmm::device_uvector dsts(0, handle_->get_stream()); - std::tie(srcs, dsts, std::ignore, std::ignore) = - cugraph::decompress_to_edgelist( - *handle_, sg_graph_view, std::nullopt, std::nullopt, std::nullopt); - auto edge_first = thrust::make_zip_iterator(srcs.begin(), dsts.begin()); - srcs.resize(thrust::distance(edge_first, - thrust::remove_if(handle_->get_thrust_policy(), - edge_first, - edge_first + srcs.size(), - [] __device__(auto pair) { - return (thrust::get<0>(pair) % 2 == 0) && - (thrust::get<1>(pair) % 2 == 0); - })), - handle_->get_stream()); - dsts.resize(srcs.size(), handle_->get_stream()); - rmm::device_uvector vertices(sg_graph_view.number_of_vertices(), - handle_->get_stream()); - thrust::sequence( - handle_->get_thrust_policy(), vertices.begin(), vertices.end(), vertex_t{0}); - std::tie(sg_graph, std::ignore, std::ignore, std::ignore, std::ignore) = cugraph:: - create_graph_from_edgelist( - *handle_, - std::move(vertices), - std::move(srcs), - std::move(dsts), - std::nullopt, - std::nullopt, - std::nullopt, - cugraph::graph_properties_t{sg_graph_view.is_symmetric(), - sg_graph_view.is_multigraph()}, - false); - sg_graph_view = sg_graph.view(); - } - auto sg_result_buffer = cugraph::allocate_dataframe_buffer>( cugraph::size_dataframe_buffer(mg_aggregate_vertex_pair_buffer), handle_->get_stream()); auto sg_out_degrees = sg_graph_view.compute_out_degrees(*handle_); diff --git a/cpp/tests/prims/mg_per_v_random_select_transform_outgoing_e.cu b/cpp/tests/prims/mg_per_v_random_select_transform_outgoing_e.cu index eb6a8fd5cb6..2b9e9aafa3f 100644 --- a/cpp/tests/prims/mg_per_v_random_select_transform_outgoing_e.cu +++ b/cpp/tests/prims/mg_per_v_random_select_transform_outgoing_e.cu @@ -324,8 +324,9 @@ class Tests_MGPerVRandomSelectTransformOutgoingE with_replacement = prims_usecase.with_replacement, invalid_value = invalid_value ? thrust::make_optional(*invalid_value) : thrust::nullopt, - property_transform = cugraph::test::detail::property_transform{ - hash_bin_count}] __device__(size_t i) { + property_transform = + cugraph::test::detail::vertex_property_transform{ + hash_bin_count}] __device__(size_t i) { auto v = *(frontier_vertex_first + i); // check sample_offsets diff --git a/cpp/tests/prims/mg_transform_e.cu b/cpp/tests/prims/mg_transform_e.cu index 24deaad810a..e9be80f1f7d 100644 --- a/cpp/tests/prims/mg_transform_e.cu +++ b/cpp/tests/prims/mg_transform_e.cu @@ -52,6 +52,7 @@ struct Prims_Usecase { bool use_edgelist{false}; + bool edge_masking{false}; bool check_correctness{true}; }; @@ -100,6 +101,13 @@ class Tests_MGTransformE auto mg_graph_view = mg_graph.view(); + std::optional> edge_mask{std::nullopt}; + if (prims_usecase.edge_masking) { + edge_mask = + cugraph::test::generate::edge_property(*handle_, mg_graph_view, 2); + mg_graph_view.attach_edge_mask((*edge_mask).view()); + } + // 2. run MG transform_e const int hash_bin_count = 5; @@ -439,7 +447,10 @@ INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGTransformE_File, ::testing::Combine( - ::testing::Values(Prims_Usecase{false, true}, Prims_Usecase{true, true}), + ::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx"), cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), @@ -447,8 +458,10 @@ INSTANTIATE_TEST_SUITE_P( INSTANTIATE_TEST_SUITE_P(rmat_small_test, Tests_MGTransformE_Rmat, - ::testing::Combine(::testing::Values(Prims_Usecase{false, true}, - Prims_Usecase{true, true}), + ::testing::Combine(::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::Rmat_Usecase( 10, 16, 0.57, 0.19, 0.19, 0, false, false)))); @@ -460,7 +473,10 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGTransformE_Rmat, ::testing::Combine( - ::testing::Values(Prims_Usecase{false, false}, Prims_Usecase{true, false}), + ::testing::Values(Prims_Usecase{false, false, false}, + Prims_Usecase{false, true, false}, + Prims_Usecase{true, false, false}, + Prims_Usecase{true, true, false}), ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); CUGRAPH_MG_TEST_PROGRAM_MAIN() diff --git a/cpp/tests/prims/mg_transform_reduce_e.cu b/cpp/tests/prims/mg_transform_reduce_e.cu index 79aa3da54df..c4ae11ab7c9 100644 --- a/cpp/tests/prims/mg_transform_reduce_e.cu +++ b/cpp/tests/prims/mg_transform_reduce_e.cu @@ -91,8 +91,9 @@ struct result_compare> { }; struct Prims_Usecase { - bool check_correctness{true}; bool test_weighted{false}; + bool edge_masking{false}; + bool check_correctness{true}; }; template @@ -141,6 +142,13 @@ class Tests_MGTransformReduceE auto mg_graph_view = mg_graph.view(); + std::optional> edge_mask{std::nullopt}; + if (prims_usecase.edge_masking) { + edge_mask = + cugraph::test::generate::edge_property(*handle_, mg_graph_view, 2); + mg_graph_view.attach_edge_mask((*edge_mask).view()); + } + // 2. run MG transform reduce const int hash_bin_count = 5; @@ -365,7 +373,10 @@ INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGTransformReduceE_File, ::testing::Combine( - ::testing::Values(Prims_Usecase{true}), + ::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx"), cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), @@ -373,7 +384,10 @@ INSTANTIATE_TEST_SUITE_P( INSTANTIATE_TEST_SUITE_P(rmat_small_test, Tests_MGTransformReduceE_Rmat, - ::testing::Combine(::testing::Values(Prims_Usecase{true}), + ::testing::Combine(::testing::Values(Prims_Usecase{false, false, true}, + Prims_Usecase{false, true, true}, + Prims_Usecase{true, false, true}, + Prims_Usecase{true, true, true}), ::testing::Values(cugraph::test::Rmat_Usecase( 10, 16, 0.57, 0.19, 0.19, 0, false, false)))); @@ -385,7 +399,10 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGTransformReduceE_Rmat, ::testing::Combine( - ::testing::Values(Prims_Usecase{false}), + ::testing::Values(Prims_Usecase{false, false, false}, + Prims_Usecase{false, true, false}, + Prims_Usecase{true, false, false}, + Prims_Usecase{true, true, false}), ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); CUGRAPH_MG_TEST_PROGRAM_MAIN() diff --git a/cpp/tests/prims/property_generator.cuh b/cpp/tests/prims/property_generator.cuh index e7264cd276f..680455eda79 100644 --- a/cpp/tests/prims/property_generator.cuh +++ b/cpp/tests/prims/property_generator.cuh @@ -15,6 +15,7 @@ */ #pragma once +#include #include #include @@ -61,7 +62,7 @@ __host__ __device__ auto make_property_value(T val) } template -struct property_transform { +struct vertex_property_transform { int32_t mod{}; constexpr __device__ property_t operator()(vertex_t v) const @@ -73,6 +74,20 @@ struct property_transform { } }; +template +struct edge_property_transform { + int32_t mod{}; + + constexpr __device__ property_t operator()( + vertex_t src, vertex_t dst, thrust::nullopt_t, thrust::nullopt_t, thrust::nullopt_t) const + { + static_assert(cugraph::is_thrust_tuple_of_arithmetic::value || + std::is_arithmetic_v); + cuco::detail::MurmurHash3_32 hash_func{}; + return make_property_value(hash_func(src + dst) % mod); + } +}; + } // namespace detail template @@ -96,7 +111,7 @@ struct generate { labels.begin(), labels.end(), cugraph::get_dataframe_buffer_begin(data), - detail::property_transform{hash_bin_count}); + detail::vertex_property_transform{hash_bin_count}); return data; } @@ -111,7 +126,7 @@ struct generate { begin, end, cugraph::get_dataframe_buffer_begin(data), - detail::property_transform{hash_bin_count}); + detail::vertex_property_transform{hash_bin_count}); return data; } @@ -138,6 +153,22 @@ struct generate { handle, graph_view, cugraph::get_dataframe_buffer_begin(property), output_property); return output_property; } + + template + static auto edge_property(raft::handle_t const& handle, + graph_view_type const& graph_view, + int32_t hash_bin_count) + { + auto output_property = cugraph::edge_property_t(handle, graph_view); + cugraph::transform_e(handle, + graph_view, + cugraph::edge_src_dummy_property_t{}.view(), + cugraph::edge_dst_dummy_property_t{}.view(), + cugraph::edge_dummy_property_t{}.view(), + detail::edge_property_transform{hash_bin_count}, + output_property.mutable_view()); + return output_property; + } }; } // namespace test From c6aa9814bff8902d3efae1574fb9d1de633f1b24 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Fri, 1 Dec 2023 14:03:44 -0500 Subject: [PATCH 104/111] Mtmg updates for rmm (#4031) Discovered RAII capabilities in RMM while reviewing issues related to MTMG testing. This PR modifies the MTMG implementation to use the RAII capabilities for setting the device id temporarily to another device. Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/4031 --- .../mtmg/detail/device_shared_wrapper.hpp | 8 +++---- cpp/include/cugraph/mtmg/handle.hpp | 21 +++++++------------ cpp/include/cugraph/mtmg/instance_manager.hpp | 10 ++------- cpp/include/cugraph/mtmg/resource_manager.hpp | 11 +++------- cpp/src/structure/detail/structure_utils.cuh | 1 + cpp/tests/mtmg/threaded_test.cu | 21 ++++++++++++++++--- 6 files changed, 35 insertions(+), 37 deletions(-) diff --git a/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp index c4cacb401af..3e4b2513a8d 100644 --- a/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp +++ b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp @@ -57,10 +57,10 @@ class device_shared_wrapper_t { { std::lock_guard lock(lock_); - auto pos = objects_.find(handle.get_local_rank()); + auto pos = objects_.find(handle.get_rank()); CUGRAPH_EXPECTS(pos == objects_.end(), "Cannot overwrite wrapped object"); - objects_.insert(std::make_pair(handle.get_local_rank(), std::move(obj))); + objects_.insert(std::make_pair(handle.get_rank(), std::move(obj))); } /** @@ -90,7 +90,7 @@ class device_shared_wrapper_t { { std::lock_guard lock(lock_); - auto pos = objects_.find(handle.get_local_rank()); + auto pos = objects_.find(handle.get_rank()); CUGRAPH_EXPECTS(pos != objects_.end(), "Uninitialized wrapped object"); return pos->second; @@ -106,7 +106,7 @@ class device_shared_wrapper_t { { std::lock_guard lock(lock_); - auto pos = objects_.find(handle.get_local_rank()); + auto pos = objects_.find(handle.get_rank()); CUGRAPH_EXPECTS(pos != objects_.end(), "Uninitialized wrapped object"); diff --git a/cpp/include/cugraph/mtmg/handle.hpp b/cpp/include/cugraph/mtmg/handle.hpp index 6223de1781d..0b02091a3cc 100644 --- a/cpp/include/cugraph/mtmg/handle.hpp +++ b/cpp/include/cugraph/mtmg/handle.hpp @@ -32,18 +32,19 @@ namespace mtmg { * */ class handle_t { + handle_t(handle_t const&) = delete; + handle_t operator=(handle_t const&) = delete; + public: /** * @brief Constructor * * @param raft_handle Raft handle for the resources * @param thread_rank Rank for this thread + * @param device_id Device id for the device this handle operates on */ - handle_t(raft::handle_t const& raft_handle, int thread_rank, size_t device_id) - : raft_handle_(raft_handle), - thread_rank_(thread_rank), - local_rank_(raft_handle.get_comms().get_rank()), // FIXME: update for multi-node - device_id_(device_id) + handle_t(raft::handle_t const& raft_handle, int thread_rank, rmm::cuda_device_id device_id) + : raft_handle_(raft_handle), thread_rank_(thread_rank), device_id_raii_(device_id) { } @@ -118,18 +119,10 @@ class handle_t { */ int get_rank() const { return raft_handle_.get_comms().get_rank(); } - /** - * @brief Get local gpu rank - * - * @return local gpu rank - */ - int get_local_rank() const { return local_rank_; } - private: raft::handle_t const& raft_handle_; int thread_rank_; - int local_rank_; - size_t device_id_; + rmm::cuda_set_device_raii device_id_raii_; }; } // namespace mtmg diff --git a/cpp/include/cugraph/mtmg/instance_manager.hpp b/cpp/include/cugraph/mtmg/instance_manager.hpp index f819a5a0abe..f60063c4101 100644 --- a/cpp/include/cugraph/mtmg/instance_manager.hpp +++ b/cpp/include/cugraph/mtmg/instance_manager.hpp @@ -47,15 +47,10 @@ class instance_manager_t { ~instance_manager_t() { - int current_device{}; - RAFT_CUDA_TRY(cudaGetDevice(¤t_device)); - for (size_t i = 0; i < nccl_comms_.size(); ++i) { - RAFT_CUDA_TRY(cudaSetDevice(device_ids_[i].value())); + rmm::cuda_set_device_raii local_set_device(device_ids_[i]); RAFT_NCCL_TRY(ncclCommDestroy(*nccl_comms_[i])); } - - RAFT_CUDA_TRY(cudaSetDevice(current_device)); } /** @@ -75,8 +70,7 @@ class instance_manager_t { int gpu_id = local_id % raft_handle_.size(); int thread_id = local_id / raft_handle_.size(); - RAFT_CUDA_TRY(cudaSetDevice(device_ids_[gpu_id].value())); - return handle_t(*raft_handle_[gpu_id], thread_id, static_cast(gpu_id)); + return handle_t(*raft_handle_[gpu_id], thread_id, device_ids_[gpu_id]); } /** diff --git a/cpp/include/cugraph/mtmg/resource_manager.hpp b/cpp/include/cugraph/mtmg/resource_manager.hpp index 127944cf7ba..bc312c9ae77 100644 --- a/cpp/include/cugraph/mtmg/resource_manager.hpp +++ b/cpp/include/cugraph/mtmg/resource_manager.hpp @@ -89,7 +89,7 @@ class resource_manager_t { local_rank_map_.insert(std::pair(global_rank, local_device_id)); - RAFT_CUDA_TRY(cudaSetDevice(local_device_id.value())); + rmm::cuda_set_device_raii local_set_device(local_device_id); // FIXME: There is a bug in the cuda_memory_resource that results in a Hang. // using the pool resource as a work-around. @@ -182,14 +182,12 @@ class resource_manager_t { --gpu_row_comm_size; } - int current_device{}; - RAFT_CUDA_TRY(cudaGetDevice(¤t_device)); RAFT_NCCL_TRY(ncclGroupStart()); for (size_t i = 0; i < local_ranks_to_include.size(); ++i) { int rank = local_ranks_to_include[i]; auto pos = local_rank_map_.find(rank); - RAFT_CUDA_TRY(cudaSetDevice(pos->second.value())); + rmm::cuda_set_device_raii local_set_device(pos->second); nccl_comms.push_back(std::make_unique()); handles.push_back( @@ -204,7 +202,6 @@ class resource_manager_t { handles[i].get(), *nccl_comms[i], ranks_to_include.size(), rank); } RAFT_NCCL_TRY(ncclGroupEnd()); - RAFT_CUDA_TRY(cudaSetDevice(current_device)); std::vector running_threads; @@ -217,9 +214,7 @@ class resource_manager_t { &device_ids, &nccl_comms, &handles]() { - int rank = local_ranks_to_include[idx]; - RAFT_CUDA_TRY(cudaSetDevice(device_ids[idx].value())); - + rmm::cuda_set_device_raii local_set_device(device_ids[idx]); cugraph::partition_manager::init_subcomm(*handles[idx], gpu_row_comm_size); }); } diff --git a/cpp/src/structure/detail/structure_utils.cuh b/cpp/src/structure/detail/structure_utils.cuh index c49b62e4543..7630d5855a0 100644 --- a/cpp/src/structure/detail/structure_utils.cuh +++ b/cpp/src/structure/detail/structure_utils.cuh @@ -524,6 +524,7 @@ std::tuple> mark_entries(raft::handle_t co return word; }); + // FIXME: use detail::count_set_bits size_t bit_count = thrust::transform_reduce( handle.get_thrust_policy(), marked_entries.begin(), diff --git a/cpp/tests/mtmg/threaded_test.cu b/cpp/tests/mtmg/threaded_test.cu index bc4d8cfef6a..1a6a17eaa18 100644 --- a/cpp/tests/mtmg/threaded_test.cu +++ b/cpp/tests/mtmg/threaded_test.cu @@ -155,10 +155,25 @@ class Tests_Multithreaded input_usecase.template construct_edgelist( handle, multithreaded_usecase.test_weighted, false, false); + rmm::device_uvector d_unique_vertices(2 * d_src_v.size(), handle.get_stream()); + thrust::copy( + handle.get_thrust_policy(), d_src_v.begin(), d_src_v.end(), d_unique_vertices.begin()); + thrust::copy(handle.get_thrust_policy(), + d_dst_v.begin(), + d_dst_v.end(), + d_unique_vertices.begin() + d_src_v.size()); + thrust::sort(handle.get_thrust_policy(), d_unique_vertices.begin(), d_unique_vertices.end()); + + d_unique_vertices.resize(thrust::distance(d_unique_vertices.begin(), + thrust::unique(handle.get_thrust_policy(), + d_unique_vertices.begin(), + d_unique_vertices.end())), + handle.get_stream()); + auto h_src_v = cugraph::test::to_host(handle, d_src_v); auto h_dst_v = cugraph::test::to_host(handle, d_dst_v); auto h_weights_v = cugraph::test::to_host(handle, d_weights_v); - auto unique_vertices = cugraph::test::to_host(handle, d_vertices_v); + auto unique_vertices = cugraph::test::to_host(handle, d_unique_vertices); // Load edgelist from different threads. We'll use more threads than GPUs here for (int i = 0; i < num_threads; ++i) { @@ -293,13 +308,13 @@ class Tests_Multithreaded num_threads]() { auto thread_handle = instance_manager->get_handle(); - auto number_of_vertices = unique_vertices->size(); + auto number_of_vertices = unique_vertices.size(); std::vector my_vertex_list; my_vertex_list.reserve((number_of_vertices + num_threads - 1) / num_threads); for (size_t j = i; j < number_of_vertices; j += num_threads) { - my_vertex_list.push_back((*unique_vertices)[j]); + my_vertex_list.push_back(unique_vertices[j]); } rmm::device_uvector d_my_vertex_list(my_vertex_list.size(), From 90fec7123eea3b8e04b6c1141d10e30a25acbe3e Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Fri, 1 Dec 2023 18:43:58 -0800 Subject: [PATCH 105/111] Fix % 0 bug in MG_SELECT_RANDOM_VERTICES test (#4034) `% 0` throws an exception (`mg_graph_view.local_vertex_partition_range_size()` & `num_of_elements_in_given_set` can be 0). This PR fixes the bug. Authors: - Seunghwa Kang (https://github.com/seunghwak) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/4034 --- cpp/tests/structure/mg_select_random_vertices_test.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cpp/tests/structure/mg_select_random_vertices_test.cpp b/cpp/tests/structure/mg_select_random_vertices_test.cpp index 79c50301922..e49e1ebcb99 100644 --- a/cpp/tests/structure/mg_select_random_vertices_test.cpp +++ b/cpp/tests/structure/mg_select_random_vertices_test.cpp @@ -90,7 +90,7 @@ class Tests_MGSelectRandomVertices std::iota( h_given_set.begin(), h_given_set.end(), mg_graph_view.local_vertex_partition_range_first()); std::shuffle(h_given_set.begin(), h_given_set.end(), std::mt19937{std::random_device{}()}); - h_given_set.resize(std::rand() % mg_graph_view.local_vertex_partition_range_size() + 1); + h_given_set.resize(std::rand() % (mg_graph_view.local_vertex_partition_range_size() + 1)); // Compute size of the distributed vertex set int num_of_elements_in_given_set = static_cast(h_given_set.size()); @@ -105,7 +105,7 @@ class Tests_MGSelectRandomVertices size_t select_count = num_of_elements_in_given_set > select_random_vertices_usecase.select_count ? select_random_vertices_usecase.select_count - : std::rand() % num_of_elements_in_given_set + 1; + : std::rand() % (num_of_elements_in_given_set + 1); for (int idx = 0; idx < with_replacement_flags.size(); idx++) { bool with_replacement = with_replacement_flags[idx]; From 3fc3e98ba260083bdc5c0b52f41139c6180bb074 Mon Sep 17 00:00:00 2001 From: Ray Douglass <3107146+raydouglass@users.noreply.github.com> Date: Mon, 4 Dec 2023 14:09:23 -0500 Subject: [PATCH 106/111] Pin actions/labeler to v4 [skip ci] (#4038) RAPIDS repos are using the `main` branch of https://github.com/actions/labeler which recently introduced [breaking changes](https://github.com/actions/labeler/releases/tag/v5.0.0). This PR pins to the latest v4 release of the labeler action until we can evaluate the changes required for v5. This PR also moves the labeler workflow to the correct location in the repo. Authors: - Ray Douglass (https://github.com/raydouglass) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) --- {github => .github}/workflows/labeler.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename {github => .github}/workflows/labeler.yml (83%) diff --git a/github/workflows/labeler.yml b/.github/workflows/labeler.yml similarity index 83% rename from github/workflows/labeler.yml rename to .github/workflows/labeler.yml index 23956a02fbd..31e78f82a62 100644 --- a/github/workflows/labeler.yml +++ b/.github/workflows/labeler.yml @@ -6,6 +6,6 @@ jobs: triage: runs-on: ubuntu-latest steps: - - uses: actions/labeler@main + - uses: actions/labeler@v4 with: repo-token: "${{ secrets.GITHUB_TOKEN }}" From 32eaa5e97e5c26ebf5c8ca04faee51316eee75e8 Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Tue, 5 Dec 2023 12:34:30 -0500 Subject: [PATCH 107/111] Clean up self-loop and multi-edge removal logic (#4032) There are mask utilities that perform some of the functions that were implemented to do this cleanup. Use the mask utilities instead of replicating functionality. Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/4032 --- cpp/src/structure/detail/structure_utils.cuh | 33 ++--- cpp/src/structure/remove_multi_edges_impl.cuh | 53 ++++---- cpp/src/structure/remove_self_loops_impl.cuh | 36 ++--- cpp/tests/community/triangle_count_test.cpp | 4 +- cpp/tests/utilities/test_graphs.hpp | 20 ++- cpp/tests/utilities/thrust_wrapper.cu | 126 ------------------ cpp/tests/utilities/thrust_wrapper.hpp | 13 -- 7 files changed, 73 insertions(+), 212 deletions(-) diff --git a/cpp/src/structure/detail/structure_utils.cuh b/cpp/src/structure/detail/structure_utils.cuh index 7630d5855a0..f0f729bce18 100644 --- a/cpp/src/structure/detail/structure_utils.cuh +++ b/cpp/src/structure/detail/structure_utils.cuh @@ -20,6 +20,7 @@ #include #include #include +#include #include #include @@ -524,35 +525,21 @@ std::tuple> mark_entries(raft::handle_t co return word; }); - // FIXME: use detail::count_set_bits - size_t bit_count = thrust::transform_reduce( - handle.get_thrust_policy(), - marked_entries.begin(), - marked_entries.end(), - [] __device__(auto word) { return __popc(word); }, - size_t{0}, - thrust::plus()); + size_t bit_count = detail::count_set_bits(handle, marked_entries.begin(), num_entries); return std::make_tuple(bit_count, std::move(marked_entries)); } template -rmm::device_uvector remove_flagged_elements(raft::handle_t const& handle, - rmm::device_uvector&& vector, - raft::device_span remove_flags, - size_t remove_count) +rmm::device_uvector keep_flagged_elements(raft::handle_t const& handle, + rmm::device_uvector&& vector, + raft::device_span keep_flags, + size_t keep_count) { - rmm::device_uvector result(vector.size() - remove_count, handle.get_stream()); - - thrust::copy_if( - handle.get_thrust_policy(), - thrust::make_counting_iterator(size_t{0}), - thrust::make_counting_iterator(vector.size()), - thrust::make_transform_output_iterator(result.begin(), - indirection_t{vector.data()}), - [remove_flags] __device__(size_t i) { - return !(remove_flags[cugraph::packed_bool_offset(i)] & cugraph::packed_bool_mask(i)); - }); + rmm::device_uvector result(keep_count, handle.get_stream()); + + detail::copy_if_mask_set( + handle, vector.begin(), vector.end(), keep_flags.begin(), result.begin()); return result; } diff --git a/cpp/src/structure/remove_multi_edges_impl.cuh b/cpp/src/structure/remove_multi_edges_impl.cuh index ab6b1fba8eb..fdd3059f874 100644 --- a/cpp/src/structure/remove_multi_edges_impl.cuh +++ b/cpp/src/structure/remove_multi_edges_impl.cuh @@ -254,50 +254,47 @@ remove_multi_edges(raft::handle_t const& handle, } } - auto [multi_edge_count, multi_edges_to_delete] = - detail::mark_entries(handle, - edgelist_srcs.size(), - [d_edgelist_srcs = edgelist_srcs.data(), - d_edgelist_dsts = edgelist_dsts.data()] __device__(auto idx) { - return (idx > 0) && (d_edgelist_srcs[idx - 1] == d_edgelist_srcs[idx]) && - (d_edgelist_dsts[idx - 1] == d_edgelist_dsts[idx]); - }); - - if (multi_edge_count > 0) { - edgelist_srcs = detail::remove_flagged_elements( + auto [keep_count, keep_flags] = detail::mark_entries( + handle, + edgelist_srcs.size(), + [d_edgelist_srcs = edgelist_srcs.data(), + d_edgelist_dsts = edgelist_dsts.data()] __device__(auto idx) { + return !((idx > 0) && (d_edgelist_srcs[idx - 1] == d_edgelist_srcs[idx]) && + (d_edgelist_dsts[idx - 1] == d_edgelist_dsts[idx])); + }); + + if (keep_count < edgelist_srcs.size()) { + edgelist_srcs = detail::keep_flagged_elements( handle, std::move(edgelist_srcs), - raft::device_span{multi_edges_to_delete.data(), multi_edges_to_delete.size()}, - multi_edge_count); - edgelist_dsts = detail::remove_flagged_elements( + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); + edgelist_dsts = detail::keep_flagged_elements( handle, std::move(edgelist_dsts), - raft::device_span{multi_edges_to_delete.data(), multi_edges_to_delete.size()}, - multi_edge_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_weights) - edgelist_weights = detail::remove_flagged_elements( + edgelist_weights = detail::keep_flagged_elements( handle, std::move(*edgelist_weights), - raft::device_span{multi_edges_to_delete.data(), - multi_edges_to_delete.size()}, - multi_edge_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_edge_ids) - edgelist_edge_ids = detail::remove_flagged_elements( + edgelist_edge_ids = detail::keep_flagged_elements( handle, std::move(*edgelist_edge_ids), - raft::device_span{multi_edges_to_delete.data(), - multi_edges_to_delete.size()}, - multi_edge_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_edge_types) - edgelist_edge_types = detail::remove_flagged_elements( + edgelist_edge_types = detail::keep_flagged_elements( handle, std::move(*edgelist_edge_types), - raft::device_span{multi_edges_to_delete.data(), - multi_edges_to_delete.size()}, - multi_edge_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); } return std::make_tuple(std::move(edgelist_srcs), diff --git a/cpp/src/structure/remove_self_loops_impl.cuh b/cpp/src/structure/remove_self_loops_impl.cuh index 161ffeae28e..dafe26cd1c5 100644 --- a/cpp/src/structure/remove_self_loops_impl.cuh +++ b/cpp/src/structure/remove_self_loops_impl.cuh @@ -44,44 +44,44 @@ remove_self_loops(raft::handle_t const& handle, std::optional>&& edgelist_edge_ids, std::optional>&& edgelist_edge_types) { - auto [self_loop_count, self_loops_to_delete] = + auto [keep_count, keep_flags] = detail::mark_entries(handle, edgelist_srcs.size(), [d_srcs = edgelist_srcs.data(), d_dsts = edgelist_dsts.data()] __device__( - size_t i) { return d_srcs[i] == d_dsts[i]; }); + size_t i) { return d_srcs[i] != d_dsts[i]; }); - if (self_loop_count > 0) { - edgelist_srcs = detail::remove_flagged_elements( + if (keep_count < edgelist_srcs.size()) { + edgelist_srcs = detail::keep_flagged_elements( handle, std::move(edgelist_srcs), - raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, - self_loop_count); - edgelist_dsts = detail::remove_flagged_elements( + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); + edgelist_dsts = detail::keep_flagged_elements( handle, std::move(edgelist_dsts), - raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, - self_loop_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_weights) - edgelist_weights = detail::remove_flagged_elements( + edgelist_weights = detail::keep_flagged_elements( handle, std::move(*edgelist_weights), - raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, - self_loop_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_edge_ids) - edgelist_edge_ids = detail::remove_flagged_elements( + edgelist_edge_ids = detail::keep_flagged_elements( handle, std::move(*edgelist_edge_ids), - raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, - self_loop_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); if (edgelist_edge_types) - edgelist_edge_types = detail::remove_flagged_elements( + edgelist_edge_types = detail::keep_flagged_elements( handle, std::move(*edgelist_edge_types), - raft::device_span{self_loops_to_delete.data(), self_loops_to_delete.size()}, - self_loop_count); + raft::device_span{keep_flags.data(), keep_flags.size()}, + keep_count); } return std::make_tuple(std::move(edgelist_srcs), diff --git a/cpp/tests/community/triangle_count_test.cpp b/cpp/tests/community/triangle_count_test.cpp index 836bab59457..592924c3c47 100644 --- a/cpp/tests/community/triangle_count_test.cpp +++ b/cpp/tests/community/triangle_count_test.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2022, NVIDIA CORPORATION. + * Copyright (c) 2022-2023, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -232,7 +232,7 @@ class Tests_TriangleCount for (size_t i = 0; i < h_cugraph_vertices.size(); ++i) { auto v = h_cugraph_vertices[i]; auto count = h_cugraph_triangle_counts[i]; - ASSERT_TRUE(count == h_reference_triangle_counts[v]) + ASSERT_EQ(count, h_reference_triangle_counts[v]) << "Triangle count values do not match with the reference values."; } } diff --git a/cpp/tests/utilities/test_graphs.hpp b/cpp/tests/utilities/test_graphs.hpp index 16c9d3ed145..8cc87b26f1d 100644 --- a/cpp/tests/utilities/test_graphs.hpp +++ b/cpp/tests/utilities/test_graphs.hpp @@ -621,9 +621,25 @@ construct_graph(raft::handle_t const& handle, CUGRAPH_EXPECTS(d_src_v.size() <= static_cast(std::numeric_limits::max()), "Invalid template parameter: edge_t overflow."); - if (drop_self_loops) { remove_self_loops(handle, d_src_v, d_dst_v, d_weights_v); } + if (drop_self_loops) { + std::tie(d_src_v, d_dst_v, d_weights_v, std::ignore, std::ignore) = + cugraph::remove_self_loops(handle, + std::move(d_src_v), + std::move(d_dst_v), + std::move(d_weights_v), + std::nullopt, + std::nullopt); + } - if (drop_multi_edges) { sort_and_remove_multi_edges(handle, d_src_v, d_dst_v, d_weights_v); } + if (drop_multi_edges) { + std::tie(d_src_v, d_dst_v, d_weights_v, std::ignore, std::ignore) = + cugraph::remove_multi_edges(handle, + std::move(d_src_v), + std::move(d_dst_v), + std::move(d_weights_v), + std::nullopt, + std::nullopt); + } graph_t graph(handle); std::optional< diff --git a/cpp/tests/utilities/thrust_wrapper.cu b/cpp/tests/utilities/thrust_wrapper.cu index cb7e6f1bd66..2daf250b4a2 100644 --- a/cpp/tests/utilities/thrust_wrapper.cu +++ b/cpp/tests/utilities/thrust_wrapper.cu @@ -206,131 +206,5 @@ template void populate_vertex_ids(raft::handle_t const& handle, rmm::device_uvector& d_vertices_v, int64_t vertex_id_offset); -template -void remove_self_loops(raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */) -{ - if (d_weight_v) { - auto edge_first = thrust::make_zip_iterator( - thrust::make_tuple(d_src_v.begin(), d_dst_v.begin(), (*d_weight_v).begin())); - d_src_v.resize( - thrust::distance(edge_first, - thrust::remove_if( - handle.get_thrust_policy(), - edge_first, - edge_first + d_src_v.size(), - [] __device__(auto e) { return thrust::get<0>(e) == thrust::get<1>(e); })), - handle.get_stream()); - d_dst_v.resize(d_src_v.size(), handle.get_stream()); - (*d_weight_v).resize(d_src_v.size(), handle.get_stream()); - } else { - auto edge_first = - thrust::make_zip_iterator(thrust::make_tuple(d_src_v.begin(), d_dst_v.begin())); - d_src_v.resize( - thrust::distance(edge_first, - thrust::remove_if( - handle.get_thrust_policy(), - edge_first, - edge_first + d_src_v.size(), - [] __device__(auto e) { return thrust::get<0>(e) == thrust::get<1>(e); })), - handle.get_stream()); - d_dst_v.resize(d_src_v.size(), handle.get_stream()); - } - - d_src_v.shrink_to_fit(handle.get_stream()); - d_dst_v.shrink_to_fit(handle.get_stream()); - if (d_weight_v) { (*d_weight_v).shrink_to_fit(handle.get_stream()); } -} - -template void remove_self_loops( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void remove_self_loops( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void remove_self_loops( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void remove_self_loops( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template -void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */) -{ - if (d_weight_v) { - auto edge_first = thrust::make_zip_iterator( - thrust::make_tuple(d_src_v.begin(), d_dst_v.begin(), (*d_weight_v).begin())); - thrust::sort(handle.get_thrust_policy(), edge_first, edge_first + d_src_v.size()); - d_src_v.resize( - thrust::distance(edge_first, - thrust::unique(handle.get_thrust_policy(), - edge_first, - edge_first + d_src_v.size(), - [] __device__(auto lhs, auto rhs) { - return (thrust::get<0>(lhs) == thrust::get<0>(rhs)) && - (thrust::get<1>(lhs) == thrust::get<1>(rhs)); - })), - handle.get_stream()); - d_dst_v.resize(d_src_v.size(), handle.get_stream()); - (*d_weight_v).resize(d_src_v.size(), handle.get_stream()); - } else { - auto edge_first = - thrust::make_zip_iterator(thrust::make_tuple(d_src_v.begin(), d_dst_v.begin())); - thrust::sort(handle.get_thrust_policy(), edge_first, edge_first + d_src_v.size()); - d_src_v.resize( - thrust::distance( - edge_first, - thrust::unique(handle.get_thrust_policy(), edge_first, edge_first + d_src_v.size())), - handle.get_stream()); - d_dst_v.resize(d_src_v.size(), handle.get_stream()); - } - - d_src_v.shrink_to_fit(handle.get_stream()); - d_dst_v.shrink_to_fit(handle.get_stream()); - if (d_weight_v) { (*d_weight_v).shrink_to_fit(handle.get_stream()); } -} - -template void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - } // namespace test } // namespace cugraph diff --git a/cpp/tests/utilities/thrust_wrapper.hpp b/cpp/tests/utilities/thrust_wrapper.hpp index eead4dc268f..fb82d781198 100644 --- a/cpp/tests/utilities/thrust_wrapper.hpp +++ b/cpp/tests/utilities/thrust_wrapper.hpp @@ -46,18 +46,5 @@ void populate_vertex_ids(raft::handle_t const& handle, rmm::device_uvector& d_vertices_v /* [INOUT] */, vertex_t vertex_id_offset); -template -void remove_self_loops(raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - -template -void sort_and_remove_multi_edges( - raft::handle_t const& handle, - rmm::device_uvector& d_src_v /* [INOUT] */, - rmm::device_uvector& d_dst_v /* [INOUT] */, - std::optional>& d_weight_v /* [INOUT] */); - } // namespace test } // namespace cugraph From 2ec94b310ae854678e3c39a687b6cf34bd702c1b Mon Sep 17 00:00:00 2001 From: Ray Douglass Date: Wed, 6 Dec 2023 09:58:26 -0500 Subject: [PATCH 108/111] Update Changelog [skip ci] --- CHANGELOG.md | 81 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 81 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 33a5b2bc5e7..d165cd7efc4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,84 @@ +# cuGraph 23.12.00 (6 Dec 2023) + +## 🚨 Breaking Changes + +- [BUG] Restore the original default order of CSR, which does not reverse edges in cuGraph-PyG ([#3980](https://github.com/rapidsai/cugraph/pull/3980)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- `Resultset` and `Dataset` Refactors ([#3957](https://github.com/rapidsai/cugraph/pull/3957)) [@nv-rliu](https://github.com/nv-rliu) +- Moves more MG graph ETL to libcugraph and re-enables MG tests in CI ([#3941](https://github.com/rapidsai/cugraph/pull/3941)) [@jnke2016](https://github.com/jnke2016) + +## 🐛 Bug Fixes + +- Pin actions/labeler to v4 ([#4038](https://github.com/rapidsai/cugraph/pull/4038)) [@raydouglass](https://github.com/raydouglass) +- Find rmm before cuco ([#4011](https://github.com/rapidsai/cugraph/pull/4011)) [@vyasr](https://github.com/vyasr) +- Pin to minor versions of packages outside the cuGraph repository. ([#4004](https://github.com/rapidsai/cugraph/pull/4004)) [@bdice](https://github.com/bdice) +- Move MTMG_TEST to MG tests block ([#3993](https://github.com/rapidsai/cugraph/pull/3993)) [@naimnv](https://github.com/naimnv) +- Fix Leiden refinement phase ([#3990](https://github.com/rapidsai/cugraph/pull/3990)) [@naimnv](https://github.com/naimnv) +- [BUG] Fix Graph Construction From Pandas in cuGraph-PyG ([#3985](https://github.com/rapidsai/cugraph/pull/3985)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- [BUG] Restore the original default order of CSR, which does not reverse edges in cuGraph-PyG ([#3980](https://github.com/rapidsai/cugraph/pull/3980)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- Fix eigenvector testing and HITS testing discrepancies ([#3979](https://github.com/rapidsai/cugraph/pull/3979)) [@ChuckHastings](https://github.com/ChuckHastings) +- [BUG] Fix Incorrect Edge Index, Directory Selection in cuGraph-PyG Loader ([#3978](https://github.com/rapidsai/cugraph/pull/3978)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- [BUG] Check if Dask has quit to avoid throwing an exception and triggering a segfault on ddp exit ([#3961](https://github.com/rapidsai/cugraph/pull/3961)) [@alexbarghi-nv](https://github.com/alexbarghi-nv) +- nx-cugraph: xfail test_louvain.py:test_threshold in Python 3.9 ([#3944](https://github.com/rapidsai/cugraph/pull/3944)) [@eriknw](https://github.com/eriknw) + +## 📖 Documentation + +- [DOC]: Fix invalid links and add materials to notebooks ([#4002](https://github.com/rapidsai/cugraph/pull/4002)) [@huiyuxie](https://github.com/huiyuxie) +- Update Broken Links in README.md ([#3924](https://github.com/rapidsai/cugraph/pull/3924)) [@nv-rliu](https://github.com/nv-rliu) + +## 🚀 New Features + +- Implement the transform_e primitive (to update property values for all edges) ([#3917](https://github.com/rapidsai/cugraph/pull/3917)) [@seunghwak](https://github.com/seunghwak) +- Update the neighbor intersection primitive to support edge masking. 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([#3928](https://github.com/rapidsai/cugraph/pull/3928)) [@bdice](https://github.com/bdice) +- Fix an issue occurring in the cuGraph-DGL example for "mixed" mode. ([#3927](https://github.com/rapidsai/cugraph/pull/3927)) [@drivanov](https://github.com/drivanov) +- Updating Docs ([#3923](https://github.com/rapidsai/cugraph/pull/3923)) [@BradReesWork](https://github.com/BradReesWork) +- Forward-merge branch-23.10 to branch-23.12 ([#3919](https://github.com/rapidsai/cugraph/pull/3919)) [@nv-rliu](https://github.com/nv-rliu) +- new build all option ([#3916](https://github.com/rapidsai/cugraph/pull/3916)) [@BradReesWork](https://github.com/BradReesWork) +- Silence spurious compiler warnings ([#3913](https://github.com/rapidsai/cugraph/pull/3913)) [@seunghwak](https://github.com/seunghwak) +- Link wholegrah and cugraphops XML docs ([#3906](https://github.com/rapidsai/cugraph/pull/3906)) [@AyodeAwe](https://github.com/AyodeAwe) +- Updates to 23.12 ([#3905](https://github.com/rapidsai/cugraph/pull/3905)) [@raydouglass](https://github.com/raydouglass) +- Forward-merge branch-23.10 to branch-23.12 ([#3904](https://github.com/rapidsai/cugraph/pull/3904)) [@GPUtester](https://github.com/GPUtester) +- Build CUDA 12.0 ARM conda packages. ([#3903](https://github.com/rapidsai/cugraph/pull/3903)) [@bdice](https://github.com/bdice) +- Merge branch-23.10 into branch-23.12 ([#3898](https://github.com/rapidsai/cugraph/pull/3898)) [@rlratzel](https://github.com/rlratzel) +- Some MTMG code cleanup and small optimizations ([#3894](https://github.com/rapidsai/cugraph/pull/3894)) [@ChuckHastings](https://github.com/ChuckHastings) +- Enable parallel mode ([#3875](https://github.com/rapidsai/cugraph/pull/3875)) [@jnke2016](https://github.com/jnke2016) +- Adds benchmarks for `nx-cugraph` ([#3854](https://github.com/rapidsai/cugraph/pull/3854)) [@rlratzel](https://github.com/rlratzel) +- Add nx-cugraph notebook for showing accelerated networkX APIs ([#3830](https://github.com/rapidsai/cugraph/pull/3830)) [@betochimas](https://github.com/betochimas) + # cuGraph 23.10.00 (11 Oct 2023) ## 🚨 Breaking Changes From 783e4dc0d4ae06f16e469cb63883c66a224aab73 Mon Sep 17 00:00:00 2001 From: AJ Schmidt Date: Wed, 6 Dec 2023 10:06:20 -0500 Subject: [PATCH 109/111] Prevent automatic labeler from adding `Label Checker` labels (#4048) This PR prevents the `doc` label from being automatically added to PRs since it can interfere with the [Label Checker](https://docs.rapids.ai/resources/label-checker/) check. [skip ci] --- .github/labeler.yml | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/.github/labeler.yml b/.github/labeler.yml index c589fda6099..368bf328b99 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -9,17 +9,6 @@ python: benchmarks: - 'benchmarks/**' -doc: - - 'docs/**' - - '**/*.md' - - 'datasets/**' - - 'notebooks/**' - - '**/*.txt' - - '**/*.rst' - - '**/*.ipynb' - - '**/*.pdf' - - '**/*.png' - datasets: - 'datasets/**' From f80480dae7aca9c419a7cdf9c6e90a77e056695f Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Wed, 6 Dec 2023 11:29:47 -0500 Subject: [PATCH 110/111] Add support for Louvain to MTMG (#4033) Added vertex result instantiation for `vertex_t` which is necessary to handle Louvain results. Added an MTMG test for Louvain to demonstrate how to use Louvain in MTMG. Closes https://github.com/rapidsai/graph_dl/issues/330 Authors: - Chuck Hastings (https://github.com/ChuckHastings) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Joseph Nke (https://github.com/jnke2016) - Naim (https://github.com/naimnv) URL: https://github.com/rapidsai/cugraph/pull/4033 --- .../mtmg/detail/device_shared_wrapper.hpp | 1 - cpp/include/cugraph/mtmg/graph_view.hpp | 23 +- .../cugraph/mtmg/vertex_result_view.hpp | 5 +- cpp/src/mtmg/vertex_result.cu | 89 ++- cpp/tests/CMakeLists.txt | 13 +- cpp/tests/mtmg/multi_node_threaded_test.cu | 3 +- cpp/tests/mtmg/threaded_test.cu | 3 +- cpp/tests/mtmg/threaded_test_louvain.cu | 511 ++++++++++++++++++ 8 files changed, 617 insertions(+), 31 deletions(-) create mode 100644 cpp/tests/mtmg/threaded_test_louvain.cu diff --git a/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp index 3e4b2513a8d..5fbe7bc9f01 100644 --- a/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp +++ b/cpp/include/cugraph/mtmg/detail/device_shared_wrapper.hpp @@ -79,7 +79,6 @@ class device_shared_wrapper_t { objects_.insert(std::make_pair(local_rank, std::move(obj))); } - public: /** * @brief Get reference to an object for a particular thread * diff --git a/cpp/include/cugraph/mtmg/graph_view.hpp b/cpp/include/cugraph/mtmg/graph_view.hpp index 94347e016ea..8e202ab4904 100644 --- a/cpp/include/cugraph/mtmg/graph_view.hpp +++ b/cpp/include/cugraph/mtmg/graph_view.hpp @@ -27,8 +27,27 @@ namespace mtmg { * @brief Graph view for each GPU */ template -using graph_view_t = detail::device_shared_wrapper_t< - cugraph::graph_view_t>; +class graph_view_t : public detail::device_shared_wrapper_t< + cugraph::graph_view_t> { + public: + /** + * @brief Get the vertex_partition_view for this graph + */ + vertex_partition_view_t get_vertex_partition_view( + cugraph::mtmg::handle_t const& handle) const + { + return this->get(handle).local_vertex_partition_view(); + } + + /** + * @brief Get the vertex_partition_view for this graph + */ + std::vector get_vertex_partition_range_lasts( + cugraph::mtmg::handle_t const& handle) const + { + return this->get(handle).vertex_partition_range_lasts(); + } +}; } // namespace mtmg } // namespace cugraph diff --git a/cpp/include/cugraph/mtmg/vertex_result_view.hpp b/cpp/include/cugraph/mtmg/vertex_result_view.hpp index a349bb95333..42b80cea62f 100644 --- a/cpp/include/cugraph/mtmg/vertex_result_view.hpp +++ b/cpp/include/cugraph/mtmg/vertex_result_view.hpp @@ -39,11 +39,12 @@ class vertex_result_view_t : public detail::device_shared_device_span_t + template rmm::device_uvector gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + cugraph::vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); }; diff --git a/cpp/src/mtmg/vertex_result.cu b/cpp/src/mtmg/vertex_result.cu index 97fcd291c87..5b1825656ff 100644 --- a/cpp/src/mtmg/vertex_result.cu +++ b/cpp/src/mtmg/vertex_result.cu @@ -27,15 +27,14 @@ namespace cugraph { namespace mtmg { template -template +template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view) { - auto this_gpu_graph_view = graph_view.get(handle); - rmm::device_uvector local_vertices(vertices.size(), handle.get_stream()); rmm::device_uvector vertex_gpu_ids(vertices.size(), handle.get_stream()); rmm::device_uvector vertex_pos(vertices.size(), handle.get_stream()); @@ -47,11 +46,11 @@ rmm::device_uvector vertex_result_view_t::gather( cugraph::detail::sequence_fill( handle.get_stream(), vertex_pos.data(), vertex_pos.size(), size_t{0}); - rmm::device_uvector d_vertex_partition_range_lasts( - this_gpu_graph_view.vertex_partition_range_lasts().size(), handle.get_stream()); + rmm::device_uvector d_vertex_partition_range_lasts(vertex_partition_range_lasts.size(), + handle.get_stream()); raft::update_device(d_vertex_partition_range_lasts.data(), - this_gpu_graph_view.vertex_partition_range_lasts().data(), - this_gpu_graph_view.vertex_partition_range_lasts().size(), + vertex_partition_range_lasts.data(), + vertex_partition_range_lasts.size(), handle.get_stream()); if (renumber_map_view) { @@ -60,8 +59,8 @@ rmm::device_uvector vertex_result_view_t::gather( local_vertices.data(), local_vertices.size(), renumber_map_view->get(handle).data(), - this_gpu_graph_view.local_vertex_partition_range_first(), - this_gpu_graph_view.local_vertex_partition_range_last()); + vertex_partition_view.local_vertex_partition_range_first(), + vertex_partition_view.local_vertex_partition_range_last()); } auto const major_comm_size = @@ -89,8 +88,8 @@ rmm::device_uvector vertex_result_view_t::gather( auto& wrapped = this->get(handle); - auto vertex_partition = vertex_partition_device_view_t( - this_gpu_graph_view.local_vertex_partition_view()); + auto vertex_partition = + vertex_partition_device_view_t(vertex_partition_view); auto iter = thrust::make_transform_iterator(local_vertices.begin(), [vertex_partition] __device__(auto v) { @@ -130,37 +129,85 @@ rmm::device_uvector vertex_result_view_t::gather( template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); -template rmm::device_uvector vertex_result_view_t::gather( +template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); -template rmm::device_uvector vertex_result_view_t::gather( +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); -template rmm::device_uvector vertex_result_view_t::gather( +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( + handle_t const& handle, + raft::device_span vertices, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, + std::optional>& renumber_map_view); + +template rmm::device_uvector vertex_result_view_t::gather( handle_t const& handle, raft::device_span vertices, - cugraph::mtmg::graph_view_t const& graph_view, + std::vector const& vertex_partition_range_lasts, + vertex_partition_view_t vertex_partition_view, std::optional>& renumber_map_view); } // namespace mtmg diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 6530a25d178..d9c88bc179e 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -738,9 +738,16 @@ if (BUILD_CUGRAPH_MTMG_TESTS) # - MTMG tests ------------------------------------------------------------------------- ConfigureTest(MTMG_TEST mtmg/threaded_test.cu) target_link_libraries(MTMG_TEST - PRIVATE - UCP::UCP - ) + PRIVATE + UCP::UCP + ) + + ConfigureTest(MTMG_LOUVAIN_TEST mtmg/threaded_test_louvain.cu) + target_link_libraries(MTMG_LOUVAIN_TEST + PRIVATE + cugraphmgtestutil + UCP::UCP + ) if(BUILD_CUGRAPH_MG_TESTS) ############################################################################################### diff --git a/cpp/tests/mtmg/multi_node_threaded_test.cu b/cpp/tests/mtmg/multi_node_threaded_test.cu index e5a7de07781..17aed4fdecf 100644 --- a/cpp/tests/mtmg/multi_node_threaded_test.cu +++ b/cpp/tests/mtmg/multi_node_threaded_test.cu @@ -311,7 +311,8 @@ class Tests_Multithreaded auto d_my_pageranks = pageranks_view.gather( thread_handle, raft::device_span{d_my_vertex_list.data(), d_my_vertex_list.size()}, - graph_view, + graph_view.get_vertex_partition_range_lasts(thread_handle), + graph_view.get_vertex_partition_view(thread_handle), renumber_map_view); std::vector my_pageranks(d_my_pageranks.size()); diff --git a/cpp/tests/mtmg/threaded_test.cu b/cpp/tests/mtmg/threaded_test.cu index 1a6a17eaa18..a5df0199cac 100644 --- a/cpp/tests/mtmg/threaded_test.cu +++ b/cpp/tests/mtmg/threaded_test.cu @@ -327,7 +327,8 @@ class Tests_Multithreaded auto d_my_pageranks = pageranks_view.gather( thread_handle, raft::device_span{d_my_vertex_list.data(), d_my_vertex_list.size()}, - graph_view, + graph_view.get_vertex_partition_range_lasts(thread_handle), + graph_view.get_vertex_partition_view(thread_handle), renumber_map_view); std::vector my_pageranks(d_my_pageranks.size()); diff --git a/cpp/tests/mtmg/threaded_test_louvain.cu b/cpp/tests/mtmg/threaded_test_louvain.cu new file mode 100644 index 00000000000..c1395037646 --- /dev/null +++ b/cpp/tests/mtmg/threaded_test_louvain.cu @@ -0,0 +1,511 @@ +/* + * Copyright (c) 2023, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include + +#include + +#include + +#include +#include + +struct Multithreaded_Usecase { + bool test_weighted{false}; + bool check_correctness{true}; +}; + +template +class Tests_Multithreaded + : public ::testing::TestWithParam> { + public: + Tests_Multithreaded() {} + + static void SetUpTestCase() {} + static void TearDownTestCase() {} + + virtual void SetUp() {} + virtual void TearDown() {} + + std::vector get_gpu_list() + { + int num_gpus_per_node{1}; + RAFT_CUDA_TRY(cudaGetDeviceCount(&num_gpus_per_node)); + + std::vector gpu_list(num_gpus_per_node); + std::iota(gpu_list.begin(), gpu_list.end(), 0); + + return gpu_list; + } + + template + void run_current_test( + std::tuple const& param, + std::vector gpu_list) + { + using edge_type_t = int32_t; + + constexpr bool renumber = true; + constexpr bool do_expensive_check = false; + + auto [multithreaded_usecase, input_usecase] = param; + + raft::handle_t handle{}; + + size_t max_level{1}; // Louvain is non-deterministic in MG if max_leve > 1 + weight_t threshold{1e-6}; + weight_t resolution{1}; + + size_t device_buffer_size{64 * 1024 * 1024}; + size_t thread_buffer_size{4 * 1024 * 1024}; + + int num_gpus = gpu_list.size(); + int num_threads = num_gpus * 4; + + cugraph::mtmg::resource_manager_t resource_manager; + + std::for_each(gpu_list.begin(), gpu_list.end(), [&resource_manager](int gpu_id) { + resource_manager.register_local_gpu(gpu_id, rmm::cuda_device_id{gpu_id}); + }); + + ncclUniqueId instance_manager_id; + ncclGetUniqueId(&instance_manager_id); + + auto instance_manager = resource_manager.create_instance_manager( + resource_manager.registered_ranks(), instance_manager_id); + + cugraph::mtmg::edgelist_t edgelist; + cugraph::mtmg::graph_t graph; + cugraph::mtmg::graph_view_t graph_view; + cugraph::mtmg::vertex_result_t louvain_clusters; + std::optional> renumber_map = + std::make_optional>(); + + auto edge_weights = multithreaded_usecase.test_weighted + ? std::make_optional, + weight_t>>() + : std::nullopt; + + // + // Simulate graph creation by spawning threads to walk through the + // local COO and add edges + // + std::vector running_threads; + + // Initialize shared edgelist object, one per GPU + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &edgelist, + device_buffer_size, + use_weight = true, + use_edge_id = false, + use_edge_type = false]() { + auto thread_handle = instance_manager->get_handle(); + + edgelist.set(thread_handle, device_buffer_size, use_weight, use_edge_id, use_edge_type); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + // Load SG edge list + auto [d_src_v, d_dst_v, d_weights_v, d_vertices_v, is_symmetric] = + input_usecase.template construct_edgelist( + handle, multithreaded_usecase.test_weighted, false, false); + + rmm::device_uvector d_unique_vertices(2 * d_src_v.size(), handle.get_stream()); + thrust::copy( + handle.get_thrust_policy(), d_src_v.begin(), d_src_v.end(), d_unique_vertices.begin()); + thrust::copy(handle.get_thrust_policy(), + d_dst_v.begin(), + d_dst_v.end(), + d_unique_vertices.begin() + d_src_v.size()); + thrust::sort(handle.get_thrust_policy(), d_unique_vertices.begin(), d_unique_vertices.end()); + + d_unique_vertices.resize(thrust::distance(d_unique_vertices.begin(), + thrust::unique(handle.get_thrust_policy(), + d_unique_vertices.begin(), + d_unique_vertices.end())), + handle.get_stream()); + + auto h_src_v = cugraph::test::to_host(handle, d_src_v); + auto h_dst_v = cugraph::test::to_host(handle, d_dst_v); + auto h_weights_v = cugraph::test::to_host(handle, d_weights_v); + auto unique_vertices = cugraph::test::to_host(handle, d_unique_vertices); + + // Load edgelist from different threads. We'll use more threads than GPUs here + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back([&instance_manager, + thread_buffer_size, + &edgelist, + &h_src_v, + &h_dst_v, + &h_weights_v, + i, + num_threads]() { + auto thread_handle = instance_manager->get_handle(); + cugraph::mtmg::per_thread_edgelist_t + per_thread_edgelist(edgelist.get(thread_handle), thread_buffer_size); + + for (size_t j = i; j < h_src_v.size(); j += num_threads) { + per_thread_edgelist.append( + thread_handle, + h_src_v[j], + h_dst_v[j], + h_weights_v ? std::make_optional((*h_weights_v)[j]) : std::nullopt, + std::nullopt, + std::nullopt); + } + + per_thread_edgelist.flush(thread_handle); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph, + &edge_weights, + &edgelist, + &renumber_map, + is_symmetric = is_symmetric, + renumber, + do_expensive_check]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + std::optional, + edge_t>> + edge_ids{std::nullopt}; + std::optional, + int32_t>> + edge_types{std::nullopt}; + + edgelist.finalize_buffer(thread_handle); + edgelist.consolidate_and_shuffle(thread_handle, false); + + cugraph::mtmg:: + create_graph_from_edgelist( + thread_handle, + edgelist, + cugraph::graph_properties_t{is_symmetric, true}, + renumber, + graph, + edge_weights, + edge_ids, + edge_types, + renumber_map, + do_expensive_check); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + graph_view = graph.view(); + auto renumber_map_view = renumber_map ? std::make_optional(renumber_map->view()) : std::nullopt; + + weight_t modularity{0}; + + for (int i = 0; i < num_threads; ++i) { + running_threads.emplace_back([&instance_manager, + &graph_view, + &edge_weights, + &louvain_clusters, + &modularity, + &renumber_map, + max_level, + threshold, + resolution]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_thread_rank() > 0) return; + + rmm::device_uvector local_louvain_clusters( + graph_view.get(thread_handle).local_vertex_partition_range_size(), + thread_handle.get_stream()); + + std::tie(std::ignore, modularity) = cugraph::louvain( + thread_handle.raft_handle(), + graph_view.get(thread_handle), + edge_weights ? std::make_optional(edge_weights->get(thread_handle).view()) : std::nullopt, + local_louvain_clusters.data(), + max_level, + threshold, + resolution); + + louvain_clusters.set(thread_handle, std::move(local_louvain_clusters)); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + std::vector, std::vector>> computed_clusters_v; + std::mutex computed_clusters_lock{}; + + auto louvain_clusters_view = louvain_clusters.view(); + std::vector h_renumber_map; + + // Load computed_clusters_v from different threads. + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back([&instance_manager, + &graph_view, + &renumber_map_view, + &louvain_clusters_view, + &computed_clusters_lock, + &computed_clusters_v, + &h_src_v, + &h_dst_v, + &h_weights_v, + &h_renumber_map, + &unique_vertices, + i, + num_threads]() { + auto thread_handle = instance_manager->get_handle(); + + auto number_of_vertices = unique_vertices.size(); + + std::vector my_vertex_list; + my_vertex_list.reserve((number_of_vertices + num_threads - 1) / num_threads); + + for (size_t j = i; j < number_of_vertices; j += num_threads) { + my_vertex_list.push_back(unique_vertices[j]); + } + + rmm::device_uvector d_my_vertex_list(my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + raft::update_device(d_my_vertex_list.data(), + my_vertex_list.data(), + my_vertex_list.size(), + thread_handle.raft_handle().get_stream()); + + auto d_my_clusters = louvain_clusters_view.gather( + thread_handle, + raft::device_span{d_my_vertex_list.data(), d_my_vertex_list.size()}, + graph_view.get_vertex_partition_range_lasts(thread_handle), + graph_view.get_vertex_partition_view(thread_handle), + renumber_map_view); + + std::vector my_clusters(d_my_clusters.size()); + raft::update_host(my_clusters.data(), + d_my_clusters.data(), + d_my_clusters.size(), + thread_handle.raft_handle().get_stream()); + + { + std::lock_guard lock(computed_clusters_lock); + computed_clusters_v.push_back( + std::make_tuple(std::move(my_vertex_list), std::move(my_clusters))); + } + + h_renumber_map = cugraph::test::to_host( + thread_handle.raft_handle(), + cugraph::test::device_allgatherv(thread_handle.raft_handle(), + renumber_map_view->get(thread_handle))); + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + if (multithreaded_usecase.check_correctness) { + // Want to compare the results in computed_clusters_v with SG results + cugraph::graph_t sg_graph(handle); + std::optional< + cugraph::edge_property_t, weight_t>> + sg_edge_weights{std::nullopt}; + + for (int i = 0; i < num_gpus; ++i) { + running_threads.emplace_back( + [&instance_manager, &graph_view, &edge_weights, &sg_graph, &sg_edge_weights]() { + auto thread_handle = instance_manager->get_handle(); + + if (thread_handle.get_rank() == 0) { + std::tie(sg_graph, sg_edge_weights, std::ignore) = + cugraph::test::mg_graph_to_sg_graph( + thread_handle.raft_handle(), + graph_view.get(thread_handle), + edge_weights ? std::make_optional(edge_weights->get(thread_handle).view()) + : std::nullopt, + std::optional>{std::nullopt}, + false); // create an SG graph with MG graph vertex IDs + } else { + cugraph::test::mg_graph_to_sg_graph( + thread_handle.raft_handle(), + graph_view.get(thread_handle), + edge_weights ? std::make_optional(edge_weights->get(thread_handle).view()) + : std::nullopt, + std::optional>{std::nullopt}, + false); // create an SG graph with MG graph vertex IDs + } + }); + } + + // Wait for CPU threads to complete + std::for_each(running_threads.begin(), running_threads.end(), [](auto& t) { t.join(); }); + running_threads.resize(0); + instance_manager->reset_threads(); + + rmm::device_uvector sg_clusters(sg_graph.number_of_vertices(), handle.get_stream()); + weight_t modularity; + + std::tie(std::ignore, modularity) = cugraph::louvain( + handle, + sg_graph.view(), + sg_edge_weights ? std::make_optional(sg_edge_weights->view()) : std::nullopt, + sg_clusters.data(), + max_level, + threshold, + resolution); + + auto h_sg_clusters = cugraph::test::to_host(handle, sg_clusters); + std::map h_cluster_map; + std::map h_cluster_reverse_map; + + std::for_each( + computed_clusters_v.begin(), + computed_clusters_v.end(), + [&h_sg_clusters, &h_cluster_map, &h_renumber_map, &h_cluster_reverse_map](auto t1) { + std::for_each( + thrust::make_zip_iterator(std::get<0>(t1).begin(), std::get<1>(t1).begin()), + thrust::make_zip_iterator(std::get<0>(t1).end(), std::get<1>(t1).end()), + [&h_sg_clusters, &h_cluster_map, &h_renumber_map, &h_cluster_reverse_map](auto t2) { + vertex_t v = thrust::get<0>(t2); + vertex_t c = thrust::get<1>(t2); + + auto pos = std::find(h_renumber_map.begin(), h_renumber_map.end(), v); + auto offset = std::distance(h_renumber_map.begin(), pos); + + auto cluster_pos = h_cluster_map.find(c); + if (cluster_pos == h_cluster_map.end()) { + auto reverse_pos = h_cluster_reverse_map.find(h_sg_clusters[offset]); + + ASSERT_TRUE(reverse_pos != h_cluster_map.end()) << "two different cluster mappings"; + + h_cluster_map.insert(std::make_pair(c, h_sg_clusters[offset])); + h_cluster_reverse_map.insert(std::make_pair(h_sg_clusters[offset], c)); + } else { + ASSERT_EQ(cluster_pos->second, h_sg_clusters[offset]) + << "vertex " << v << ", offset = " << offset + << ", SG cluster = " << h_sg_clusters[offset] << ", mtmg cluster = " << c + << ", mapped value = " << cluster_pos->second; + } + }); + }); + } + } +}; + +using Tests_Multithreaded_File = Tests_Multithreaded; +using Tests_Multithreaded_Rmat = Tests_Multithreaded; + +// FIXME: add tests for type combinations +TEST_P(Tests_Multithreaded_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam()), std::vector{{0, 1}}); +} + +TEST_P(Tests_Multithreaded_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam()), std::vector{{0, 1}}); +} + +INSTANTIATE_TEST_SUITE_P(file_test, + Tests_Multithreaded_File, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{true, true}), + ::testing::Values(cugraph::test::File_Usecase("karate.csv"), + cugraph::test::File_Usecase("dolphins.csv")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_Multithreaded_Rmat, + ::testing::Combine( + // enable correctness checks + ::testing::Values(Multithreaded_Usecase{true, true}), + //::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + ::testing::Values(cugraph::test::Rmat_Usecase(5, 8, 0.57, 0.19, 0.19, 0, false, false)))); + +INSTANTIATE_TEST_SUITE_P( + file_benchmark_test, /* note that the test filename can be overridden in benchmarking (with + --gtest_filter to select only the file_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one File_Usecase that differ only in filename + (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_File, + ::testing::Combine( + // disable correctness checks + ::testing::Values(Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_benchmark_test, /* note that scale & edge factor can be overridden in benchmarking (with + --gtest_filter to select only the rmat_benchmark_test with a specific + vertex & edge type combination) by command line arguments and do not + include more than one Rmat_Usecase that differ only in scale or edge + factor (to avoid running same benchmarks more than once) */ + Tests_Multithreaded_Rmat, + ::testing::Combine( + // disable correctness checks for large graphs + ::testing::Values(Multithreaded_Usecase{true, false}), + ::testing::Values(cugraph::test::Rmat_Usecase(10, 16, 0.57, 0.19, 0.19, 0, false, false)))); + +CUGRAPH_TEST_PROGRAM_MAIN() From a5718c66aa5e72a7d91b4b3a073736f195352736 Mon Sep 17 00:00:00 2001 From: Vibhu Jawa Date: Wed, 6 Dec 2023 08:52:39 -0800 Subject: [PATCH 111/111] Add a barrier before cugraph Graph creation (#4046) This PR introduces a short term fix for https://github.com/rapidsai/cugraph/issues/4037 . CC: @jnke2016 , @rlratzel Authors: - Vibhu Jawa (https://github.com/VibhuJawa) Approvers: - Rick Ratzel (https://github.com/rlratzel) - Joseph Nke (https://github.com/jnke2016) URL: https://github.com/rapidsai/cugraph/pull/4046 --- .../cugraph/cugraph/dask/common/mg_utils.py | 7 +++++- .../simpleDistributedGraph.py | 22 +++++++++---------- 2 files changed, 16 insertions(+), 13 deletions(-) diff --git a/python/cugraph/cugraph/dask/common/mg_utils.py b/python/cugraph/cugraph/dask/common/mg_utils.py index 6acda48c9da..b04f293dc0e 100644 --- a/python/cugraph/cugraph/dask/common/mg_utils.py +++ b/python/cugraph/cugraph/dask/common/mg_utils.py @@ -12,7 +12,7 @@ # limitations under the License. import os - +import gc import numba.cuda @@ -68,3 +68,8 @@ def get_visible_devices(): else: visible_devices = _visible_devices.strip().split(",") return visible_devices + + +def run_gc_on_dask_cluster(client): + gc.collect() + client.run(gc.collect) diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index f666900b226..319435575cc 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -37,8 +37,8 @@ from cugraph.dask.common.part_utils import ( get_persisted_df_worker_map, persist_dask_df_equal_parts_per_worker, - _chunk_lst, ) +from cugraph.dask.common.mg_utils import run_gc_on_dask_cluster from cugraph.dask import get_n_workers import cugraph.dask.comms.comms as Comms @@ -171,7 +171,6 @@ def __from_edgelist( store_transposed=False, legacy_renum_only=False, ): - if not isinstance(input_ddf, dask_cudf.DataFrame): raise TypeError("input should be a dask_cudf dataFrame") @@ -275,7 +274,6 @@ def __from_edgelist( ) value_col = None else: - source_col, dest_col, value_col = symmetrize( input_ddf, source, @@ -350,9 +348,11 @@ def __from_edgelist( is_symmetric=not self.properties.directed, ) ddf = ddf.repartition(npartitions=len(workers) * 2) - ddf_keys = ddf.to_delayed() workers = _client.scheduler_info()["workers"].keys() - ddf_keys_ls = _chunk_lst(ddf_keys, len(workers)) + persisted_keys_d = persist_dask_df_equal_parts_per_worker( + ddf, _client, return_type="dict" + ) + del ddf delayed_tasks_d = { w: delayed(simpleDistributedGraphImpl._make_plc_graph)( @@ -367,19 +367,19 @@ def __from_edgelist( self.edge_id_type, self.edge_type_id_type, ) - for w, edata in zip(workers, ddf_keys_ls) + for w, edata in persisted_keys_d.items() } + del persisted_keys_d self._plc_graph = { w: _client.compute( delayed_task, workers=w, allow_other_workers=False, pure=False ) for w, delayed_task in delayed_tasks_d.items() } - wait(list(self._plc_graph.values())) - del ddf_keys del delayed_tasks_d - gc.collect() - _client.run(gc.collect) + run_gc_on_dask_cluster(_client) + wait(list(self._plc_graph.values())) + run_gc_on_dask_cluster(_client) @property def renumbered(self): @@ -945,7 +945,6 @@ def convert_to_cudf(cp_arrays: cp.ndarray) -> cudf.Series: def _call_plc_select_random_vertices( mg_graph_x, sID: bytes, random_state: int, num_vertices: int ) -> cudf.Series: - cp_arrays = pylibcugraph_select_random_vertices( graph=mg_graph_x, resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), @@ -961,7 +960,6 @@ def _mg_call_plc_select_random_vertices( random_state: int, num_vertices: int, ) -> dask_cudf.Series: - result = [ client.submit( _call_plc_select_random_vertices,

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