[Homo Optimization] Optimization on GSF

docs/source/configuration/configuration-run.rst

@@ -381,20 +381,20 @@ Classification and Regression Task
 
 Node Classification/Regression Specific
 .........................................
-- **target_ntype**: (**Required**) The node type for prediction.
+- **target_ntype**: The node type for prediction.
 
     - Yaml: ``target_ntype: movie``
     - Argument: ``--target-ntype movie``
-    - Default value: This parameter must be provided by user.
+    - Default value: For heterogeneous input graph, this parameter must be provided by the user. If not provided, GraphStorm will assume the input graph is a homogeneous graph and set ``target_ntype`` to "_N".
 
 Edge Classification/Regression Specific
 ..........................................
-- **target_etype**: (**Required**) The list of canonical edge types that will be added as a training target in edge classification/regression tasks, for example ``--train-etype query,clicks,asin`` or ``--train-etype query,clicks,asin query,search,asin``. A canonical edge type should be formatted as `src_node_type,relation_type,dst_node_type`. Currently, GraphStorm only supports single task edge classification/regression, i.e., it only accepts one canonical edge type.
+- **target_etype**: The list of canonical edge types that will be added as training targets in edge classification/regression tasks, for example ``--train-etype query,clicks,asin`` or ``--train-etype query,clicks,asin query,search,asin``. A canonical edge type should be formatted as `src_node_type,relation_type,dst_node_type`. Currently, GraphStorm only supports single task edge classification/regression, i.e., it only accepts one canonical edge type.
 
     - Yaml: ``target_etype:``
            | ``- query,clicks,asin``
     - Argument: ``--target-etype query,clicks,asin``
-    - Default value: This parameter must be provided by user.
+    - Default value: For heterogeneous input graph, this parameter must be provided by the user. If not provided, GraphStorm will assume the input graph is a homogeneous graph and set ``target_etype`` to ("_N", "_E", "_N").
 - **remove_target_edge_type**: When set to true, GraphStorm removes target_etype in message passing, i.e., any edge with target_etype will not be sampled during training and inference.
 
     - Yaml: ``remove_target_edge_type: false``

python/graphstorm/config/argument.py

@@ -25,6 +25,7 @@
 import yaml
 import torch as th
 import torch.nn.functional as F
+from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE
 
 from .config import BUILTIN_GNN_ENCODER
 from .config import BUILTIN_ENCODER
@@ -1573,9 +1574,12 @@ def target_ntype(self):
         """ The node type for prediction
         """
         # pylint: disable=no-member
-        assert hasattr(self, "_target_ntype"), \
-            "Must provide the target ntype through target_ntype"
-        return self._target_ntype
+        if hasattr(self, "_target_ntype"):
+            return self._target_ntype
+        else:
+            logging.warning("There is not target ntype provided, "
+                            "will treat the input graph as a homogeneous graph")
+            return DEFAULT_NTYPE
 
     @property
     def eval_target_ntype(self):
@@ -1648,8 +1652,10 @@ def target_etype(self):
             classification/regression. Support multiple tasks when needed.
         """
         # pylint: disable=no-member
-        assert hasattr(self, "_target_etype"), \
-            "Edge classification task needs a target etype"
+        if not hasattr(self, "_target_etype"):
+            logging.warning("There is not target etype provided, "
+                            "will treat the input graph as a homogeneous graph")
+            return [DEFAULT_ETYPE]
         assert isinstance(self._target_etype, list), \
             "target_etype must be a list in format: " \
             "[\"query,clicks,asin\", \"query,search,asin\"]."

python/graphstorm/dataloading/dataset.py

@@ -23,6 +23,7 @@
 
 import torch as th
 import dgl
+from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE
 from torch.utils.data import Dataset
 import pandas as pd
 
@@ -554,6 +555,15 @@ def __init__(self, graph_name, part_config, train_etypes, eval_etypes=None,
                                                  lm_feat_ntypes=lm_feat_ntypes,
                                                  lm_feat_etypes=lm_feat_etypes)
 
+        if self._train_etypes == [DEFAULT_ETYPE]:
+            # DGL Graph edge type is not canonical. It is just list[str].
+            assert self._g.ntypes == [DEFAULT_NTYPE] and \
+                   self._g.etypes == [DEFAULT_ETYPE[1]], \
+                f"It is required to be a homogeneous graph when target_etype is not provided " \
+                f"or is set to {DEFAULT_ETYPE} on edge tasks, expect node type " \
+                f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \
+                f"but get {self._g.ntypes} and {self._g.etypes}"
+
     def prepare_data(self, g):
         """
         Prepare the training, validation and testing edge set.
@@ -731,6 +741,14 @@ def __init__(self, graph_name, part_config, eval_etypes,
                                                  decoder_edge_feat,
                                                  lm_feat_ntypes=lm_feat_ntypes,
                                                  lm_feat_etypes=lm_feat_etypes)
+        if self._eval_etypes == [DEFAULT_ETYPE]:
+            # DGL Graph edge type is not canonical. It is just list[str].
+            assert self._g.ntypes == [DEFAULT_NTYPE] and \
+                   self._g.etypes == [DEFAULT_ETYPE[1]], \
+                f"It is required to be a homogeneous graph when target_etype is not provided " \
+                f"or is set to {DEFAULT_ETYPE} on edge tasks, expect node type " \
+                f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \
+                f"but get {self._g.ntypes} and {self._g.etypes}"
 
     def prepare_data(self, g):
         """ Prepare the testing edge set if any
@@ -916,7 +934,6 @@ def __init__(self, graph_name, part_config, train_ntypes, eval_ntypes=None,
         assert isinstance(train_ntypes, list), \
                 "prediction ntypes for training has to be a string or a list of strings."
         self._train_ntypes = train_ntypes
-
         if eval_ntypes is not None:
             if isinstance(eval_ntypes, str):
                 eval_ntypes = [eval_ntypes]
@@ -932,6 +949,14 @@ def __init__(self, graph_name, part_config, train_ntypes, eval_ntypes=None,
                                                  edge_feat_field=edge_feat_field,
                                                  lm_feat_ntypes=lm_feat_ntypes,
                                                  lm_feat_etypes=lm_feat_etypes)
+        if self._train_ntypes == [DEFAULT_NTYPE]:
+            # DGL Graph edge type is not canonical. It is just list[str].
+            assert self._g.ntypes == [DEFAULT_NTYPE] and \
+                   self._g.etypes == [DEFAULT_ETYPE[1]], \
+                f"It is required to be a homogeneous graph when target_ntype is not provided " \
+                f"or is set to {DEFAULT_NTYPE} on node tasks, expect node type " \
+                f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \
+                f"but get {self._g.ntypes} and {self._g.etypes}"
 
     def prepare_data(self, g):
         pb = g.get_partition_book()
@@ -1072,6 +1097,15 @@ def __init__(self, graph_name, part_config, eval_ntypes,
                                                  lm_feat_ntypes=lm_feat_ntypes,
                                                  lm_feat_etypes=lm_feat_etypes)
 
+        if self._eval_ntypes == [DEFAULT_NTYPE]:
+            # DGL Graph edge type is not canonical. It is just list[str].
+            assert self._g.ntypes == [DEFAULT_NTYPE] and \
+                   self._g.etypes == [DEFAULT_ETYPE[1]], \
+                f"It is required to be a homogeneous graph when target_ntype is not provided " \
+                f"or is set to {DEFAULT_NTYPE} on node tasks, expect node type " \
+                f"to be {[DEFAULT_NTYPE]} and edge type to be {[DEFAULT_ETYPE[1]]}, " \
+                f"but get {self._g.ntypes} and {self._g.etypes}"
+
     def prepare_data(self, g):
         """
         Prepare the testing node set if any

tests/end2end-tests/data_process/homogeneous_test.sh

@@ -45,6 +45,25 @@ echo "********* Test Node Classification on GConstruct Homogeneous Graph with re
 python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc.yaml --target-ntype _N
 error_and_exit $?
 
-echo "********* Test Edge Classification on GConstruct Homogeneous Graph with reverse edge ********"
+echo "********* Test Edge Classification on GConstruct Homogeneous Graph with reverse edge********"
 python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec.yaml --target-etype _N,_E,_N
-error_and_exit $?
+error_and_exit $?
+
+echo "********* Test Node Classification with homogeneous graph optimization********"
+python3 -m graphstorm.run.gs_node_classification --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_homogeneous.yaml --save-model-path /tmp/homogeneous_node_model
+error_and_exit $?
+
+echo "********* Test Node Classification with homogeneous graph optimization doing inference********"
+python3 -m graphstorm.run.gs_node_classification --inference --workspace $GS_HOME/training_scripts/gsgnn_np/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_nc_homogeneous.yaml --restore-model-path /tmp/homogeneous_node_model/epoch-2
+error_and_exit $?
+
+echo "********* Test Edge Classification with homogeneous graph optimization********"
+python3 -m graphstorm.run.gs_edge_classification --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec_homogeneous.yaml --save-model-path /tmp/homogeneous_edge_model
+error_and_exit $?
+
+echo "********* Test Edge Classification with homogeneous graph optimization doing inference********"
+python3 -m graphstorm.run.gs_edge_classification --inference --workspace $GS_HOME/training_scripts/gsgnn_ep/ --num-trainers $NUM_TRAINERS --num-servers 1 --num-samplers 0 --part-config /tmp/movielen_100k_train_val_1p_4t_homogeneous_rev/movie-lens-100k.json --ip-config ip_list.txt --ssh-port 2222 --cf ml_ec_homogeneous.yaml --restore-model-path /tmp/homogeneous_edge_model/epoch-2
+error_and_exit $?
+
+rm -rf /tmp/homogeneous_node_model
+rm -rf /tmp/homogeneous_edge_model

tests/unit-tests/data_utils.py

@@ -343,6 +343,92 @@ def generate_dummy_homo_graph(size='tiny', gen_mask=True):
 
     return hetero_graph
 
+def generate_dummy_homogeneous_failure_graph(size='tiny', gen_mask=True, type='node'):
+    """
+    generate a dummy homogeneous graph for failure case.
+
+    In a homogeneous graph, the correct node type is defined as ["_N"], and the correct edge type is [("_N", "_E", "_N")].
+    Any deviation from this specification implies an invalid input for a homogeneous graph. This function is designed
+    to create test cases that intentionally fail for homogeneous graph inputs. For type="node", it will produce a graph
+    with the correct node type ["_N"] but with an altered edge type set as [("_N", "_E", "_N"), ("_N", "fake_E", "_N")].
+    Conversely, for type="edge", the function generates a graph with an incorrect node type ["_N", "fake_N"] while
+    maintaining the correct edge type [("_N", "_E", "_N")]. The unit test is expected to identify and flag errors
+    in both these scenarios.
+
+    Parameters
+    ----------
+    size: the size of dummy graph data, could be one of tiny, small, medium, large, and largest
+    type: task type to generate failure case
+
+    :return:
+    hg: a homogeneous graph in one node type and one edge type.
+    """
+    size_dict = {
+        'tiny': 1e+2,
+        'small': 1e+4,
+        'medium': 1e+6,
+        'large': 1e+8,
+        'largest': 1e+10
+    }
+
+    data_size = int(size_dict[size])
+
+    if type == 'node':
+        ntype = "_N"
+        etype = ("_N", "fake_E", "_N")
+
+        num_nodes_dict = {
+            ntype: data_size,
+        }
+    else:
+        ntype = "_N"
+        etype = ("_N", "_E", "_N")
+        num_nodes_dict = {
+            ntype: data_size,
+            "fake_N": data_size
+        }
+
+    edges = {
+        etype: (th.randint(data_size, (2 * data_size,)),
+                             th.randint(data_size, (2 * data_size,)))
+    }
+
+    hetero_graph = dgl.heterograph(edges, num_nodes_dict=num_nodes_dict)
+
+    # set node and edge features
+    node_feat = {ntype: th.randn(data_size, 2)}
+
+    edge_feat = {etype: th.randn(2 * data_size, 2)}
+
+    hetero_graph.nodes[ntype].data['feat'] = node_feat[ntype]
+    hetero_graph.nodes[ntype].data['label'] = th.randint(10, (hetero_graph.number_of_nodes(ntype), ))
+
+    hetero_graph.edges[etype].data['feat'] = edge_feat[etype]
+    hetero_graph.edges[etype].data['label'] = th.randint(10, (hetero_graph.number_of_edges(etype), ))
+
+    # set train/val/test masks for nodes and edges
+    if gen_mask:
+        target_ntype = [ntype]
+        target_etype = [etype]
+
+        node_train_mask = generate_mask([0,1], data_size)
+        node_val_mask = generate_mask([2,3], data_size)
+        node_test_mask = generate_mask([4,5], data_size)
+
+        edge_train_mask = generate_mask([0,1], 2 * data_size)
+        edge_val_mask = generate_mask([2,3], 2 * data_size)
+        edge_test_mask = generate_mask([4,5], 2 * data_size)
+
+        hetero_graph.nodes[target_ntype[0]].data['train_mask'] = node_train_mask
+        hetero_graph.nodes[target_ntype[0]].data['val_mask'] = node_val_mask
+        hetero_graph.nodes[target_ntype[0]].data['test_mask'] = node_test_mask
+
+        hetero_graph.edges[target_etype[0]].data['train_mask'] = edge_train_mask
+        hetero_graph.edges[target_etype[0]].data['val_mask'] = edge_val_mask
+        hetero_graph.edges[target_etype[0]].data['test_mask'] = edge_test_mask
+
+    return hetero_graph
+
 
 def partion_and_load_distributed_graph(hetero_graph, dirname, graph_name='dummy'):
     """
@@ -431,6 +517,28 @@ def generate_dummy_dist_graph_multi_target_ntypes(dirname, size='tiny', graph_na
     return partion_and_load_distributed_graph(hetero_graph=hetero_graph, dirname=dirname,
                                               graph_name=graph_name)
 
+
+def generate_dummy_dist_graph_homogeneous_failure_graph(dirname, size='tiny', graph_name='dummy',
+                                                 gen_mask=True, type='node'):
+    """
+    Generate a dummy DGL distributed graph with the given size
+    Parameters
+    ----------
+    dirname : the directory where the graph will be partitioned and stored.
+    size: the size of dummy graph data, could be one of tiny, small, medium, large, and largest
+    graph_name: string as a name
+    type: task type to generate failure case
+
+    Returns
+    -------
+    dist_graph: a DGL distributed graph
+    part_config : the path of the partition configuration file.
+    type:
+    """
+    hetero_graph = generate_dummy_homogeneous_failure_graph(size=size, gen_mask=gen_mask, type=type)
+    return partion_and_load_distributed_graph(hetero_graph=hetero_graph, dirname=dirname,
+                                              graph_name=graph_name)
+
 def load_lm_graph(part_config):
     with open(part_config) as f:
         part_metadata = json.load(f)

tests/unit-tests/test_config.py

@@ -21,6 +21,7 @@
 import math
 import tempfile
 from argparse import Namespace
+from dgl.distributed.constants import DEFAULT_NTYPE, DEFAULT_ETYPE
 
 import dgl
 import torch as th
@@ -613,7 +614,7 @@ def test_node_class_info():
         create_node_class_config(Path(tmpdirname), 'node_class_test')
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'node_class_test_default.yaml'), local_rank=0)
         config = GSConfig(args)
-        check_failure(config, "target_ntype")
+        assert config.target_ntype == DEFAULT_NTYPE
         check_failure(config, "label_field")
         assert config.multilabel == False
         assert config.multilabel_weights == None
@@ -748,7 +749,7 @@ def test_node_regress_info():
         create_node_regress_config(Path(tmpdirname), 'node_regress_test')
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'node_regress_test_default.yaml'), local_rank=0)
         config = GSConfig(args)
-        check_failure(config, "target_ntype")
+        assert config.target_ntype == DEFAULT_NTYPE
         check_failure(config, "label_field")
         assert len(config.eval_metric) == 1
         assert config.eval_metric[0] == "rmse"
@@ -840,7 +841,7 @@ def test_edge_class_info():
         create_edge_class_config(Path(tmpdirname), 'edge_class_test')
         args = Namespace(yaml_config_file=os.path.join(Path(tmpdirname), 'edge_class_test_default.yaml'), local_rank=0)
         config = GSConfig(args)
-        check_failure(config, "target_etype")
+        assert config.target_etype == [DEFAULT_ETYPE]
         assert config.decoder_type == "DenseBiDecoder"
         assert config.num_decoder_basis == 2
         assert config.remove_target_edge_type == True