From af84356bea6d256b5a8844537697a050984deb9c Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 12 Mar 2024 09:18:49 -0400 Subject: [PATCH 1/9] Starting work on blog links and nx_cugraph docs (#4160) This PR starts to address updating cuGraph docs to reflect recent blogs and work on nx_cugraph as a backend for NetworkX Authors: - Don Acosta (https://github.com/acostadon) Approvers: - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4160 --- docs/cugraph/source/api_docs/index.rst | 2 +- docs/cugraph/source/basics/nx_transition.rst | 42 ++--- .../source/graph_support/property_graph.md | 2 +- docs/cugraph/source/images/ancestors.png | Bin 0 -> 14156 bytes docs/cugraph/source/images/bfs_tree.png | Bin 0 -> 13763 bytes docs/cugraph/source/images/conn_component.png | Bin 0 -> 13053 bytes docs/cugraph/source/images/descendents.png | Bin 0 -> 13639 bytes docs/cugraph/source/images/k_truss.png | Bin 0 -> 13389 bytes docs/cugraph/source/images/katz.png | Bin 0 -> 13558 bytes docs/cugraph/source/images/pagerank.png | Bin 0 -> 13243 bytes docs/cugraph/source/images/sssp.png | Bin 0 -> 14542 bytes docs/cugraph/source/images/wcc.png | Bin 0 -> 14519 bytes docs/cugraph/source/index.rst | 1 + docs/cugraph/source/nx_cugraph/index.rst | 9 + docs/cugraph/source/nx_cugraph/nx_cugraph.md | 165 ++++++++++++++++++ .../source/tutorials/community_resources.md | 2 + .../source/tutorials/cugraph_blogs.rst | 15 ++ 17 files changed, 208 insertions(+), 30 deletions(-) create mode 100644 docs/cugraph/source/images/ancestors.png create mode 100644 docs/cugraph/source/images/bfs_tree.png create mode 100644 docs/cugraph/source/images/conn_component.png create mode 100644 docs/cugraph/source/images/descendents.png create mode 100644 docs/cugraph/source/images/k_truss.png create mode 100644 docs/cugraph/source/images/katz.png create mode 100644 docs/cugraph/source/images/pagerank.png create mode 100644 docs/cugraph/source/images/sssp.png create mode 100644 docs/cugraph/source/images/wcc.png create mode 100644 docs/cugraph/source/nx_cugraph/index.rst create mode 100644 docs/cugraph/source/nx_cugraph/nx_cugraph.md diff --git a/docs/cugraph/source/api_docs/index.rst b/docs/cugraph/source/api_docs/index.rst index 1b907165a39..d76902772fb 100644 --- a/docs/cugraph/source/api_docs/index.rst +++ b/docs/cugraph/source/api_docs/index.rst @@ -15,7 +15,7 @@ Core Graph API Documentation cugraph_c/index.rst cugraph_cpp/index.rst -Graph Nerual Networks API Documentation +Graph Neural Networks API Documentation --------------------------------------- .. toctree:: diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst index 9849865814d..07c2ad26ffa 100644 --- a/docs/cugraph/source/basics/nx_transition.rst +++ b/docs/cugraph/source/basics/nx_transition.rst @@ -1,30 +1,20 @@ ************************************** -NetworkX Compatibility and Transition +NetworkX by calling cuGraph Algorithms ************************************** + *Note: this is a work in progress and will be updatred and changed as we better flesh out compatibility issues* -One of the goals of RAPIDS cuGraph is to mimic the NetworkX API to simplify -the transition to accelerated GPU data science. However, graph analysis, -also called network science, like most other data science workflow, is more -than just running an algorithm. Graph data requires cleaning and prep (ETL) -and then the construction of a graph object; that is all before the execution -of a graph algorithm. RAPIDS and cuGraph allow a portion or the complete -analytic workflow to be accelerated. To achieve the maximum amount of -acceleration, we encourage fully replacing existing code with cuGraph. -But sometimes it is easier to replace just a portion. - -Last Update -########### +Latest Update +############# -Last Update: Oct 14th, 2020 -Release: 0.16 +Last Update: March 7th, 2024 +Release: 24.04 -Information on `NetworkX `_ - -This transition guide in an expansion of the Medium Blog on `NetworkX Compatibility -`_ +**CuGraph is now a registered backend for networkX. This is described in the following blog: +`Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics +`_ Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms @@ -33,12 +23,11 @@ Easy Path – Use NetworkX Graph Objects, Accelerated Algorithms Rather than updating all of your existing code, simply update the calls to graph algorithms by replacing the module name. This allows all the complicated ETL code to be unchanged while still seeing significate performance +improvements. Again this will be deprecated since networkX dispatching to nx_cugraph +has many advantages. + improvements. -In the following example, the cuGraph module is being imported as “cnx”. -While module can be assigned any name can be used, we picked cnx to reduce -the amount of text to be changed. The text highlighted in yellow indicates -changes. .. image:: ../images/Nx_Cg_1.png :width: 600 @@ -49,9 +38,6 @@ input and match the NetworkX API list of arguments. Currently, cuGraph accepts both NetworkX Graph and DiGraph objects. We will be adding support for Bipartite graph and Multigraph over the next few releases. -| - - Differences in Algorithms ########################## @@ -169,8 +155,8 @@ Graph Building ############## The biggest difference between NetworkX and cuGraph is with how Graph objects -are built. NetworkX, for the most part, stores graph data in a dictionary. -That structure allows easy insertion of new records. Consider the following +are built. NetworkX, for the most part, stores graph data in a dictionary. +That structure allows easy insertion of new records. 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literal 0 HcmV?d00001 diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index b18a79d3396..9ea9e4d65cf 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -46,6 +46,7 @@ the docs and links :caption: Contents: basics/index + nx_cugraph/index installation/index tutorials/index graph_support/index diff --git a/docs/cugraph/source/nx_cugraph/index.rst b/docs/cugraph/source/nx_cugraph/index.rst new file mode 100644 index 00000000000..ef6f51601ab --- /dev/null +++ b/docs/cugraph/source/nx_cugraph/index.rst @@ -0,0 +1,9 @@ +=============================== +nxCugraph as a NetworkX Backend +=============================== + + +.. toctree:: + :maxdepth: 2 + + nx_cugraph.md diff --git a/docs/cugraph/source/nx_cugraph/nx_cugraph.md b/docs/cugraph/source/nx_cugraph/nx_cugraph.md new file mode 100644 index 00000000000..8d497e3a1d7 --- /dev/null +++ b/docs/cugraph/source/nx_cugraph/nx_cugraph.md @@ -0,0 +1,165 @@ +### nx_cugraph + + +Whereas previous versions of cuGraph have included mechanisms to make it +trivial to plug in cuGraph algorithm calls. Beginning with version 24.02, nx-cuGraph +is now a [networkX backend](). +The user now need only [install nx-cugraph]() +to experience GPU speedups. + +Lets look at some examples of algorithm speedups comparing CPU based NetworkX to dispatched versions run on GPU with nx_cugraph. + +Each chart has three measurements. +* NX - running the algorithm natively with networkX on CPU. +* nx-cugraph - running with GPU accelerated networkX achieved by simply calling the cugraph backend. This pays the overhead of building the GPU resident object for each algorithm called. This achieves significant improvement but stil isn't compleltely optimum. +* nx-cugraph (preconvert) - This is a bit more complicated since it involves building (precomputing) the GPU resident graph ahead and reusing it for each algorithm. + + +![Ancestors](../images/ancestors.png) +![BFS Tree](../images/bfs_tree.png) +![Connected Components](../images/conn_component.png) +![Descendents](../images/descendents.png) +![Katz](../images/katz.png) +![Pagerank](../images/pagerank.png) +![Single Source Shortest Path](../images/sssp.png) +![Weakly Connected Components](../images/wcc.png) + + +The following algorithms are supported and automatically dispatched to nx-cuGraph for acceleration. + +#### Algorithms +``` +bipartite + ├─ basic + │ └─ is_bipartite + └─ generators + └─ complete_bipartite_graph +centrality + ├─ betweenness + │ ├─ betweenness_centrality + │ └─ edge_betweenness_centrality + ├─ degree_alg + │ ├─ degree_centrality + │ ├─ in_degree_centrality + │ └─ out_degree_centrality + ├─ eigenvector + │ └─ eigenvector_centrality + └─ katz + └─ katz_centrality +cluster + ├─ average_clustering + ├─ clustering + ├─ transitivity + └─ triangles +community + └─ louvain + └─ louvain_communities +components + ├─ connected + │ ├─ connected_components + │ ├─ is_connected + │ ├─ node_connected_component + │ └─ number_connected_components + └─ weakly_connected + ├─ is_weakly_connected + ├─ number_weakly_connected_components + └─ weakly_connected_components +core + ├─ core_number + └─ k_truss +dag + ├─ ancestors + └─ descendants +isolate + ├─ is_isolate + ├─ isolates + └─ number_of_isolates +link_analysis + ├─ hits_alg + │ └─ hits + └─ pagerank_alg + └─ pagerank +operators + └─ unary + ├─ complement + └─ reverse +reciprocity + ├─ overall_reciprocity + └─ reciprocity +shortest_paths + └─ unweighted + ├─ single_source_shortest_path_length + └─ single_target_shortest_path_length +traversal + └─ breadth_first_search + ├─ bfs_edges + ├─ bfs_layers + ├─ bfs_predecessors + ├─ bfs_successors + ├─ bfs_tree + ├─ descendants_at_distance + └─ generic_bfs_edges +tree + └─ recognition + ├─ is_arborescence + ├─ is_branching + ├─ is_forest + └─ is_tree +``` + +#### Generators +``` +classic + ├─ 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 +community + └─ caveman_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 + ├─ pappus_graph + ├─ petersen_graph + ├─ sedgewick_maze_graph + ├─ tetrahedral_graph + ├─ truncated_cube_graph + ├─ truncated_tetrahedron_graph + └─ tutte_graph +social + ├─ davis_southern_women_graph + ├─ florentine_families_graph + ├─ karate_club_graph + └─ les_miserables_graph +``` + +#### Other + +``` +convert_matrix + ├─ from_pandas_edgelist + └─ from_scipy_sparse_array +``` diff --git a/docs/cugraph/source/tutorials/community_resources.md b/docs/cugraph/source/tutorials/community_resources.md index 1c4362393d1..975f11965de 100644 --- a/docs/cugraph/source/tutorials/community_resources.md +++ b/docs/cugraph/source/tutorials/community_resources.md @@ -1,2 +1,4 @@ # Commmunity Resources [Rapids Community Repository](https://github.com/rapidsai-community/notebooks-contrib) +[RAPIDS Containers on Docker Hub](https://catalog.ngc.nvidia.com/containers) +[RAPIDS PyTorch Container in Docker](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pyg) diff --git a/docs/cugraph/source/tutorials/cugraph_blogs.rst b/docs/cugraph/source/tutorials/cugraph_blogs.rst index 373e846f6c3..3665f425e3f 100644 --- a/docs/cugraph/source/tutorials/cugraph_blogs.rst +++ b/docs/cugraph/source/tutorials/cugraph_blogs.rst @@ -9,6 +9,17 @@ Here, we've selected just a few that are of particular interest to cuGraph users Blogs & Conferences ==================== +2024 +------ +Coming Soon + +2023 +------ + * `Intro to Graph Neural Networks with cuGraph-DGL `_ + * `GTC 2023 Ask the Experts Q&A `_ + * `Accelerating NetworkX on NVIDIA GPUs for High Performance Graph Analytics `_ + * `Introduction to Graph Neural Networks with NVIDIA cuGraph-DGL `_ + * `Supercharge Graph Analytics at Scale with GPU-CPU Fusion for 100x Performance `_ 2022 ------ * `GTC: State of cuGraph (video & slides) `_ @@ -50,6 +61,8 @@ Media Academic Papers =============== + * Seunghwa Kang, Chuck Hastings, Joe Eaton, Brad Rees `cuGraph C++ primitives: vertex/edge-centric building blocks for parallel graph computing `_ + * Alex Fender, Brad Rees, Joe Eaton (2022) `Massive Graph Analytics `_ Bader, D. (Editor) CRC Press * S Kang, A. Fender, J. Eaton, B. Rees:`Computing PageRank Scores of Web Crawl Data Using DGX A100 Clusters`. In IEEE HPEC, Sep. 2020 @@ -58,6 +71,8 @@ Academic Papers * Richardson, B., Rees, B., Drabas, T., Oldridge, E., Bader, D. A., & Allen, R. (2020, August). Accelerating and Expanding End-to-End Data Science Workflows with DL/ML Interoperability Using RAPIDS. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3503-3504). + * A Gondhalekar, P Sathre, W Feng `Hybrid CPU-GPU Implementation of Edge-Connected Jaccard Similarity in Graph Datasets `_ + Other Blogs ======================== From 6171bd93eee9b7c761162e913edfb597756a5ae9 Mon Sep 17 00:00:00 2001 From: Don Acosta <97529984+acostadon@users.noreply.github.com> Date: Tue, 12 Mar 2024 09:20:03 -0400 Subject: [PATCH 2/9] Added a weighted example to the jaccard notebook (#4222) Added Dining Prefs example to show weighted jaccard. Authors: - Don Acosta (https://github.com/acostadon) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Brad Rees (https://github.com/BradReesWork) URL: https://github.com/rapidsai/cugraph/pull/4222 --- .../link_prediction/Jaccard-Similarity.ipynb | 420 +++++------------- notebooks/img/dorm_data_diagram.png | Bin 0 -> 21289 bytes 2 files changed, 116 insertions(+), 304 deletions(-) create mode 100644 notebooks/img/dorm_data_diagram.png diff --git a/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb b/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb index 86bb4d17c22..9f62fd4f421 100755 --- a/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb +++ b/notebooks/algorithms/link_prediction/Jaccard-Similarity.ipynb @@ -8,12 +8,7 @@ "# Jaccard Similarity\n", "----\n", "\n", - "In this notebook we will explore the Jaccard vertex similarity metrics available in cuGraph.\n", - "\n", - "cuGraph supports Jaccard similarity for both unweighted and weighted graphs, but this notebook \n", - "will demonstrate Jaccard similarity only on unweighted graphs. A future update will include an \n", - "example using a graph with edge weights, where the weights are used to influence the Jaccard \n", - "similarity coefficients." + "In this notebook we will explore the Jaccard vertex similarity metrics available in cuGraph." ] }, { @@ -23,48 +18,30 @@ "source": [ "## Introduction\n", "\n", - "The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection \n", - "divided by the volume of their union, where the sets used are the sets of neighboring vertices for each \n", - "vertex.\n", - "\n", - "The neighbors of a vertex, _v_, is defined as the set, _U_, of vertices connected by way of an edge to vertex v, or _N(v) = {U} where v ∈ V and ∀ u ∈ U ∃ edge(v,u)∈ E_.\n", + "The Jaccard similarity between two sets is defined as the ratio of the volume of their intersection divided by the volume of their union. \n", "\n", - "If we then let set __A__ be the set of neighbors for vertex _a_, and set __B__ be the set of neighbors for vertex _b_, then the Jaccard Similarity for the vertex pair _(a, b)_ can be expressed as\n", + "The Jaccard Similarity can then be expressed as\n", "\n", "$\\text{Jaccard similarity} = \\frac{|A \\cap B|}{|A \\cup B|}$\n", "\n", "\n", - "cuGraph's Jaccard function will, by default, compute the Jaccard similarity coefficient for every pair of \n", - "vertices in the two-hop neighborhood for every vertex.\n", - "\n", - "```df = cugraph.jaccard(G, vertex_pair=None)```\n", - "\n", - "Parameters:\n", + "To compute the Jaccard similarity between all pairs of vertices connected by an edge in cuGraph use:
\n", + "__df = cugraph.jaccard(G)__\n", "\n", " G: A cugraph.Graph object\n", "\n", - " vertex_pair: cudf.DataFrame, optional (default=None)\n", - " A GPU dataframe consisting of two columns representing pairs of\n", - " vertices. If provided, the jaccard coefficient is computed for the\n", - " given vertex pairs. If the vertex_pair is not provided then the\n", - " current implementation computes the jaccard coefficient for all\n", - " adjacent vertices in the graph.\n", - "\n", "Returns:\n", "\n", " df: cudf.DataFrame with three columns:\n", " df[\"first\"]: The first vertex id of each pair.\n", " df[\"second\"]: The second vertex id of each pair.\n", " df[\"jaccard_coeff\"]: The jaccard coefficient computed between the vertex pairs.\n", - "\n", - "To limit the computation to specific vertex pairs, including those not in the same two-hop \n", - "neighborhood, pass a `vertex_pair` value (see example below).\n", + "
\n", "\n", "__References__ \n", "- https://research.nvidia.com/publication/2017-11_Parallel-Jaccard-and \n", "\n", "__Additional Reading__ \n", - "- [Intro to Graph Analysis using cuGraph: Similarity Algorithms](https://medium.com/rapids-ai/intro-to-graph-analysis-using-cugraph-similarity-algorithms-64fa923791ac)\n", "- [Wikipedia: Jaccard](https://en.wikipedia.org/wiki/Jaccard_index)\n" ] }, @@ -94,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "scrolled": true }, @@ -119,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -170,189 +147,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

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" - ], - "text/plain": [ - " first second jaccard_coeff\n", - "0 16 33 0.0" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "cugraph.jaccard(G, cudf.DataFrame([(16, 33)]))" ] @@ -443,19 +192,75 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "We can use the `cudf.DataFrame` argument to pass in any number of specific vertex pairs \n", - "to compute the similarity for, regardless of whether or not they're included by default. \n", - "This is useful to limit the computation and result size when only specific vertex \n", - "similarities are needed." + "---\n", + "# Now we look at weighted Jaccard!\n", + "\n", + "A full explanation of the weighted jaccard is found [here](https://en.wikipedia.org/wiki/Jaccard_index#Weighted_Jaccard_similarity_and_distance).\n", + "\n", + "The Dining Preferences data set is a staple of smallest scale social network analysis.\n", + "The data represents the first (weight = 1) and second (weight = 2) dining partner preference from a survey done in a small school dormitory.\n", + "\n", + "This data originated in social network publication by J.L. Moreno\n", + "\n", + "Reference: J. L. Moreno (1960). The Sociometry Reader. The Free Press, Glencoe, Illinois, pg.35\n", + "\n", + "\n", + "Here is a visualization of the dataset\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First pull in the dining preferences data set and load it into a cuGraph." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import the dining preferences dataset from cugraph's examples\n", + "from cugraph.datasets import dining_prefs\n", + "# load the graph making sure to not ignore the weights\n", + "G = dining_prefs.get_graph(download=True, store_transposed=True, ignore_weights=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do the calculations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# calculate both the unweighted and weighted Jaccard\n", + "jaccard_coeffs = cugraph.jaccard(G)\n", + "jaccard_weighted = cugraph.jaccard(G, use_weight=True)\n", + "# rename the weighted results\n", + "jaccard_weighted = jaccard_weighted.rename(columns={'jaccard_coeff' : 'weighted_jaccard' })" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Join the results dataframes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -482,46 +287,68 @@ " first\n", " second\n", " jaccard_coeff\n", + " weighted_jaccard\n", " \n", " \n", " \n", " \n", " 0\n", - " 16\n", - " 33\n", - " 0.000000\n", + " Lena\n", + " Marion\n", + " 0.125000\n", + " 0.076923\n", " \n", " \n", " 1\n", - " 32\n", - " 33\n", - " 0.526316\n", + " Lena\n", + " Adele\n", + " 0.142857\n", + " 0.090909\n", " \n", " \n", " 2\n", - " 0\n", - " 23\n", - " 0.000000\n", + " Lena\n", + " Ellen\n", + " 0.166667\n", + " 0.100000\n", + " \n", + " \n", + " 3\n", + " Lena\n", + " Louise\n", + " 0.200000\n", + " 0.111111\n", + " \n", + " \n", + " 4\n", + " Louise\n", + " Eva\n", + " 0.111111\n", + " 0.076923\n", " \n", " \n", "\n", "" ], "text/plain": [ - " first second jaccard_coeff\n", - "0 16 33 0.000000\n", - "1 32 33 0.526316\n", - "2 0 23 0.000000" + " first second jaccard_coeff weighted_jaccard\n", + "0 Lena Marion 0.125000 0.076923\n", + "1 Lena Adele 0.142857 0.090909\n", + "2 Lena Ellen 0.166667 0.100000\n", + "3 Lena Louise 0.200000 0.111111\n", + "4 Louise Eva 0.111111 0.076923" ] }, - "execution_count": 7, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "pairs = cudf.DataFrame([(16, 33), (32, 33), (0, 23)])\n", - "cugraph.jaccard(G, pairs)" + "# Merge the two results together joining on the vertices pairs\n", + "jaccard_merged = jaccard_coeffs.merge(jaccard_weighted, on=['first','second'], how='left')\n", + "jaccard_merged.sort_values('weighted_jaccard',ascending=False)\n", + "jaccard_merged.head()" ] }, { @@ -539,21 +366,6 @@ "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", "___" ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Revision History\n", - "\n", - "| Author | Date | Update | cuGraph Version | Test Hardware |\n", - "| --------------|------------|------------------|-----------------|---------------------------|\n", - "| Brad Rees | 10/14/2019 | created | 0.14 | GV100 32 GB, CUDA 10.2 |\n", - "| Don Acosta | 07/20/2022 | tested/updated | 22.08 nightly | DGX Tesla V100, CUDA 11.5 |\n", - "| Ralph Liu | 06/29/2023 | updated | 23.08 nightly | DGX Tesla V100, CUDA 12.0 |\n", - "| Rick Ratzel | 02/23/2024 | tested/updated | 24.04 nightly | DGX Tesla V100, CUDA 12.0 |" - ] } ], "metadata": { diff --git a/notebooks/img/dorm_data_diagram.png b/notebooks/img/dorm_data_diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..e0780c9c8a32bc7c81c2a1e0c80f2a056acbf36b GIT binary patch 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<43019056+aMahanna@users.noreply.github.com> Date: Tue, 12 Mar 2024 12:40:08 -0400 Subject: [PATCH 3/9] [docs] fix GNN typo (#4196) Authors: - Anthony Mahanna (https://github.com/aMahanna) - Alex Barghi (https://github.com/alexbarghi-nv) - Ralph Liu (https://github.com/nv-rliu) - Brad Rees (https://github.com/BradReesWork) Approvers: - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/4196 From e4b58ec04e4add07578ce779977835cfe28f516a Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Tue, 12 Mar 2024 19:00:31 +0100 Subject: [PATCH 4/9] Make external vertex and edge shuffling function public (#4227) Make vertex and edge shuffling function public Authors: - Naim (https://github.com/naimnv) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Seunghwa Kang (https://github.com/seunghwak) URL: https://github.com/rapidsai/cugraph/pull/4227 --- cpp/CMakeLists.txt | 4 +- cpp/include/cugraph/graph_functions.hpp | 63 +++++++++++ .../shuffle_vertex_pairs.cu | 102 ++++++++++++++++++ .../{detail => utilities}/shuffle_vertices.cu | 70 ++++++++++++ 4 files changed, 237 insertions(+), 2 deletions(-) rename cpp/src/{detail => utilities}/shuffle_vertex_pairs.cu (80%) rename cpp/src/{detail => utilities}/shuffle_vertices.cu (73%) diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index a3392627fb8..3131404712f 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -186,9 +186,9 @@ endif() # which should give us a better parallel schedule. set(CUGRAPH_SOURCES - src/detail/shuffle_vertices.cu + src/utilities/shuffle_vertices.cu src/detail/permute_range.cu - src/detail/shuffle_vertex_pairs.cu + src/utilities/shuffle_vertex_pairs.cu src/detail/collect_local_vertex_values.cu src/detail/groupby_and_count.cu src/detail/collect_comm_wrapper.cu diff --git a/cpp/include/cugraph/graph_functions.hpp b/cpp/include/cugraph/graph_functions.hpp index 90425f86bef..6d4470e8251 100644 --- a/cpp/include/cugraph/graph_functions.hpp +++ b/cpp/include/cugraph/graph_functions.hpp @@ -1052,4 +1052,67 @@ remove_multi_edges(raft::handle_t const& handle, std::optional>&& edgelist_edge_types, bool keep_min_value_edge = false); +/** + * @brief Shuffle external vertex ids to the proper GPU. + * + * @tparam vertex_t Type of vertex identifiers. 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 vertices List of vertex ids + * @return Vector of vertex ids mapped to this GPU. + */ +template +rmm::device_uvector shuffle_external_vertices(raft::handle_t const& handle, + rmm::device_uvector&& vertices); + +/** + * @brief Shuffle external vertex ids and values to the proper GPU. + * + * @tparam vertex_t Type of vertex identifiers. Needs to be an integral type. + * @tparam value_t Type of values. currently supported types are int32_t, + * int64_t, size_t, float and double. + * + * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and + * handles to various CUDA libraries) to run graph algorithms. + * @param vertices List of vertex ids + * @param values List of values + * @return Tuple of vectors storing vertex ids and values mapped to this GPU. + */ +template +std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +/** + * @brief Shuffle external edges to the proper 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. + * + * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and + * handles to various CUDA libraries) to run graph algorithms. + * @param edge_srcs List of source vertex ids + * @param edge_dsts List of destination vertex ids + * @param edge_weights Optional list of edge weights + * @param edge_ids Optional list of edge ids + * @param edge_types Optional list of edge types + * @return Tuple of vectors storing edge sources, destinations, optional weights, + * optional edge ids, optional edge types mapped to this GPU. + */ +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& edge_srcs, + rmm::device_uvector&& edge_dsts, + std::optional>&& edge_weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + } // namespace cugraph diff --git a/cpp/src/detail/shuffle_vertex_pairs.cu b/cpp/src/utilities/shuffle_vertex_pairs.cu similarity index 80% rename from cpp/src/detail/shuffle_vertex_pairs.cu rename to cpp/src/utilities/shuffle_vertex_pairs.cu index 33a7834f5ff..b473796aa9d 100644 --- a/cpp/src/detail/shuffle_vertex_pairs.cu +++ b/cpp/src/utilities/shuffle_vertex_pairs.cu @@ -519,4 +519,106 @@ shuffle_int_vertex_pairs_with_values_to_local_gpu_by_edge_partitioning( std::vector const& vertex_partition_range_lasts); } // namespace detail + +template +std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& edge_srcs, + rmm::device_uvector&& edge_dsts, + std::optional>&& edge_weights, + std::optional>&& edge_ids, + std::optional>&& edge_types) +{ + auto& comm = handle.get_comms(); + auto const comm_size = comm.get_size(); + auto& major_comm = handle.get_subcomm(cugraph::partition_manager::major_comm_name()); + auto const major_comm_size = major_comm.get_size(); + auto& minor_comm = handle.get_subcomm(cugraph::partition_manager::minor_comm_name()); + auto const minor_comm_size = minor_comm.get_size(); + + return detail::shuffle_ext_vertex_pairs_with_values_to_local_gpu_by_edge_partitioning( + handle, + std::move(edge_srcs), + std::move(edge_dsts), + std::move(edge_weights), + std::move(edge_ids), + std::move(edge_types)); +} + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + +template std::tuple, + rmm::device_uvector, + std::optional>, + std::optional>, + std::optional>> +shuffle_external_edges(raft::handle_t const& handle, + rmm::device_uvector&& majors, + rmm::device_uvector&& minors, + std::optional>&& weights, + std::optional>&& edge_ids, + std::optional>&& edge_types); + } // namespace cugraph diff --git a/cpp/src/detail/shuffle_vertices.cu b/cpp/src/utilities/shuffle_vertices.cu similarity index 73% rename from cpp/src/detail/shuffle_vertices.cu rename to cpp/src/utilities/shuffle_vertices.cu index be6875f1073..b396201f509 100644 --- a/cpp/src/detail/shuffle_vertices.cu +++ b/cpp/src/utilities/shuffle_vertices.cu @@ -249,4 +249,74 @@ shuffle_ext_vertex_value_pairs_to_local_gpu_by_vertex_partitioning( rmm::device_uvector&& values); } // namespace detail + +template +std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values) +{ + return detail::shuffle_ext_vertex_value_pairs_to_local_gpu_by_vertex_partitioning( + handle, std::move(vertices), std::move(values)); +} + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template std::tuple, rmm::device_uvector> +shuffle_external_vertex_value_pairs(raft::handle_t const& handle, + rmm::device_uvector&& vertices, + rmm::device_uvector&& values); + +template +rmm::device_uvector shuffle_external_vertices(raft::handle_t const& handle, + rmm::device_uvector&& vertices) +{ + return detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning(handle, + std::move(vertices)); +} + +template rmm::device_uvector shuffle_external_vertices( + raft::handle_t const& handle, rmm::device_uvector&& d_vertices); + +template rmm::device_uvector shuffle_external_vertices( + raft::handle_t const& handle, rmm::device_uvector&& d_vertices); + } // namespace cugraph From a4eab99f8f0ff50aab980dfab1fffa68e8983942 Mon Sep 17 00:00:00 2001 From: Naim <110031745+naimnv@users.noreply.github.com> Date: Tue, 12 Mar 2024 19:01:20 +0100 Subject: [PATCH 5/9] Graph coloring and MIS (#4211) This PR implements - MNMG Graph Coloring - MNMG MIS for general graphs Closes #4230 Closes #4231 Authors: - Naim (https://github.com/naimnv) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Chuck Hastings (https://github.com/ChuckHastings) URL: https://github.com/rapidsai/cugraph/pull/4211 --- cpp/CMakeLists.txt | 8 +- cpp/include/cugraph/algorithms.hpp | 30 +- .../detail/maximal_independent_moves.cuh | 313 ++++++++++++++++++ .../maximal_independent_moves.hpp} | 6 +- .../detail/maximal_independent_moves_mg.cu | 38 +++ .../detail/maximal_independent_moves_sg.cu | 37 +++ cpp/src/community/detail/refine_impl.cuh | 6 +- .../detail => components}/mis_impl.cuh | 37 ++- .../detail => components}/mis_mg.cu | 8 +- .../detail => components}/mis_sg.cu | 7 +- cpp/src/components/vertex_coloring_impl.cuh | 150 +++++++++ cpp/src/components/vertex_coloring_mg.cu | 35 ++ cpp/src/components/vertex_coloring_sg.cu | 35 ++ cpp/tests/CMakeLists.txt | 20 +- .../{community => components}/mg_mis_test.cu | 19 +- .../components/mg_vertex_coloring_test.cu | 275 +++++++++++++++ cpp/tests/components/mis_test.cu | 240 ++++++++++++++ cpp/tests/components/vertex_coloring_test.cu | 244 ++++++++++++++ 18 files changed, 1468 insertions(+), 40 deletions(-) create mode 100644 cpp/src/community/detail/maximal_independent_moves.cuh rename cpp/src/community/{mis.hpp => detail/maximal_independent_moves.hpp} (91%) create mode 100644 cpp/src/community/detail/maximal_independent_moves_mg.cu create mode 100644 cpp/src/community/detail/maximal_independent_moves_sg.cu rename cpp/src/{community/detail => components}/mis_impl.cuh (90%) rename cpp/src/{community/detail => components}/mis_mg.cu (83%) rename cpp/src/{community/detail => components}/mis_sg.cu (82%) create mode 100644 cpp/src/components/vertex_coloring_impl.cuh create mode 100644 cpp/src/components/vertex_coloring_mg.cu create mode 100644 cpp/src/components/vertex_coloring_sg.cu rename cpp/tests/{community => components}/mg_mis_test.cu (95%) create mode 100644 cpp/tests/components/mg_vertex_coloring_test.cu create mode 100644 cpp/tests/components/mis_test.cu create mode 100644 cpp/tests/components/vertex_coloring_test.cu diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index 3131404712f..b12403710ab 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -197,8 +197,8 @@ set(CUGRAPH_SOURCES src/community/detail/common_methods_sg.cu src/community/detail/refine_sg.cu src/community/detail/refine_mg.cu - src/community/detail/mis_sg.cu - src/community/detail/mis_mg.cu + src/community/detail/maximal_independent_moves_sg.cu + src/community/detail/maximal_independent_moves_mg.cu src/detail/utility_wrappers.cu src/structure/graph_view_mg.cu src/structure/remove_self_loops.cu @@ -295,6 +295,10 @@ set(CUGRAPH_SOURCES src/tree/legacy/mst.cu src/components/weakly_connected_components_sg.cu src/components/weakly_connected_components_mg.cu + src/components/mis_sg.cu + src/components/mis_mg.cu + src/components/vertex_coloring_sg.cu + src/components/vertex_coloring_mg.cu src/structure/create_graph_from_edgelist_sg.cu src/structure/create_graph_from_edgelist_mg.cu src/structure/symmetrize_edgelist_sg.cu diff --git a/cpp/include/cugraph/algorithms.hpp b/cpp/include/cugraph/algorithms.hpp index 5c29604a5a7..1471d340cec 100644 --- a/cpp/include/cugraph/algorithms.hpp +++ b/cpp/include/cugraph/algorithms.hpp @@ -2340,15 +2340,41 @@ std::tuple, rmm::device_uvector> k_hop_nbr * handles to various CUDA libraries) to run graph algorithms. * @param graph_view Graph view object. * @param rng_state The RngState instance holding pseudo-random number generator state. - * @return A device vector containing vertices found in the maximal independent set + * @return A device vector containing vertices in the maximal independent set. */ - template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, graph_view_t const& graph_view, raft::random::RngState& rng_state); +/* + * @brief Find a Greedy Vertex Coloring + * + * A vertex coloring is an assignment of colors or labels to each vertex of a graph so that + * no two adjacent vertices have the same color or label. Finding the minimum number of colors + * needed to color the vertices of a graph is an NP-hard problem and therefore for practical + * use cases greedy coloring is used. Here we provide an implementation of greedy vertex + * coloring based on maximal independent set. + * See + * https://research.nvidia.com/sites/default/files/pubs/2015-05_Parallel-Graph-Coloring/nvr-2015-001.pdf + * for further information. + * + * @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 multi_gpu Flag indicating whether template instantiation should target single-GPU (false) + * @param handle RAFT handle object to encapsulate resources (e.g. CUDA stream, communicator, and + * handles to various CUDA libraries) to run graph algorithms. + * @param graph_view Graph view object. + * @param rng_state The RngState instance holding pseudo-random number generator state. + * @return A device vector containing color for each vertex. + */ +template +rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + } // namespace cugraph /** diff --git a/cpp/src/community/detail/maximal_independent_moves.cuh b/cpp/src/community/detail/maximal_independent_moves.cuh new file mode 100644 index 00000000000..82d20a04203 --- /dev/null +++ b/cpp/src/community/detail/maximal_independent_moves.cuh @@ -0,0 +1,313 @@ + +/* + * Copyright (c) 2023-2024, 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 "maximal_independent_moves.hpp" +#include "prims/fill_edge_src_dst_property.cuh" +#include "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" +#include "prims/update_edge_src_dst_property.cuh" + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +namespace cugraph { + +namespace detail { + +template +rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + raft::random::RngState& rng_state) +{ + using GraphViewType = cugraph::graph_view_t; + + vertex_t local_vtx_partitoin_size = graph_view.local_vertex_partition_range_size(); + + rmm::device_uvector remaining_vertices(local_vtx_partitoin_size, handle.get_stream()); + + auto vertex_begin = + thrust::make_counting_iterator(graph_view.local_vertex_partition_range_first()); + auto vertex_end = thrust::make_counting_iterator(graph_view.local_vertex_partition_range_last()); + + // Compute out-degree + auto out_degrees = graph_view.compute_out_degrees(handle); + + // Only vertices with non-zero out-degree are possible can move + remaining_vertices.resize( + thrust::distance(remaining_vertices.begin(), + thrust::copy_if(handle.get_thrust_policy(), + vertex_begin, + vertex_end, + out_degrees.begin(), + remaining_vertices.begin(), + [] __device__(auto deg) { return deg > 0; })), + handle.get_stream()); + + // Set ID of each vertex as its rank + rmm::device_uvector ranks(local_vtx_partitoin_size, handle.get_stream()); + thrust::copy(handle.get_thrust_policy(), vertex_begin, vertex_end, ranks.begin()); + + // Set ranks of zero out-degree vetices to std::numeric_limits::lowest() + thrust::transform_if(handle.get_thrust_policy(), + out_degrees.begin(), + out_degrees.end(), + ranks.begin(), + cuda::proclaim_return_type( + [] __device__(auto) { return std::numeric_limits::lowest(); }), + [] __device__(auto deg) { return deg == 0; }); + + out_degrees.resize(0, handle.get_stream()); + out_degrees.shrink_to_fit(handle.get_stream()); + + size_t loop_counter = 0; + while (true) { + loop_counter++; + + // Copy ranks into temporary vector to begin with + + rmm::device_uvector temporary_ranks(local_vtx_partitoin_size, handle.get_stream()); + thrust::copy(handle.get_thrust_policy(), ranks.begin(), ranks.end(), temporary_ranks.begin()); + + // Select a random set of candidate vertices + + vertex_t nr_remaining_vertices_to_check = remaining_vertices.size(); + if (multi_gpu) { + nr_remaining_vertices_to_check = host_scalar_allreduce(handle.get_comms(), + nr_remaining_vertices_to_check, + raft::comms::op_t::SUM, + handle.get_stream()); + } + + vertex_t nr_candidates = (nr_remaining_vertices_to_check < 1024) + ? nr_remaining_vertices_to_check + : std::min(static_cast((0.50 + 0.25 * loop_counter) * + nr_remaining_vertices_to_check), + nr_remaining_vertices_to_check); + + // FIXME: Can we improve performance here? + // FIXME: if(nr_remaining_vertices_to_check < 1024), may avoid calling select_random_vertices + auto d_sampled_vertices = + cugraph::select_random_vertices(handle, + graph_view, + std::make_optional(raft::device_span{ + remaining_vertices.data(), remaining_vertices.size()}), + rng_state, + nr_candidates, + false, + true); + + rmm::device_uvector non_candidate_vertices( + remaining_vertices.size() - d_sampled_vertices.size(), handle.get_stream()); + + thrust::set_difference(handle.get_thrust_policy(), + remaining_vertices.begin(), + remaining_vertices.end(), + d_sampled_vertices.begin(), + d_sampled_vertices.end(), + non_candidate_vertices.begin()); + + // Set temporary ranks of non-candidate vertices to std::numeric_limits::lowest() + thrust::for_each( + handle.get_thrust_policy(), + non_candidate_vertices.begin(), + non_candidate_vertices.end(), + [temporary_ranks = + raft::device_span(temporary_ranks.data(), temporary_ranks.size()), + v_first = graph_view.local_vertex_partition_range_first()] __device__(auto v) { + // + // if rank of a non-candidate vertex is not std::numeric_limits::max() (i.e. the + // vertex is not already in MIS), set it to std::numeric_limits::lowest() + // + auto v_offset = v - v_first; + if (temporary_ranks[v_offset] < std::numeric_limits::max()) { + temporary_ranks[v_offset] = std::numeric_limits::lowest(); + } + }); + + // Caches for ranks + edge_src_property_t src_rank_cache(handle); + edge_dst_property_t dst_rank_cache(handle); + + // Update rank caches with temporary ranks + if constexpr (multi_gpu) { + src_rank_cache = edge_src_property_t(handle, graph_view); + dst_rank_cache = edge_dst_property_t(handle, graph_view); + update_edge_src_property(handle, graph_view, temporary_ranks.begin(), src_rank_cache); + update_edge_dst_property(handle, graph_view, temporary_ranks.begin(), dst_rank_cache); + } + + // + // Find maximum rank outgoing neighbor for each vertex + // + + rmm::device_uvector max_outgoing_ranks(local_vtx_partitoin_size, handle.get_stream()); + + per_v_transform_reduce_outgoing_e( + handle, + graph_view, + multi_gpu + ? src_rank_cache.view() + : detail::edge_major_property_view_t(temporary_ranks.data()), + multi_gpu ? dst_rank_cache.view() + : detail::edge_minor_property_view_t( + temporary_ranks.data(), vertex_t{0}), + edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_rank, auto dst_rank, auto wt) { return dst_rank; }, + std::numeric_limits::lowest(), + cugraph::reduce_op::maximum{}, + max_outgoing_ranks.begin()); + + // + // Find maximum rank incoming neighbor for each vertex + // + + rmm::device_uvector max_incoming_ranks(local_vtx_partitoin_size, handle.get_stream()); + + per_v_transform_reduce_incoming_e( + handle, + graph_view, + multi_gpu + ? src_rank_cache.view() + : detail::edge_major_property_view_t(temporary_ranks.data()), + multi_gpu ? dst_rank_cache.view() + : detail::edge_minor_property_view_t( + temporary_ranks.data(), vertex_t{0}), + edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_rank, auto dst_rank, auto wt) { return src_rank; }, + std::numeric_limits::lowest(), + cugraph::reduce_op::maximum{}, + max_incoming_ranks.begin()); + + temporary_ranks.resize(0, handle.get_stream()); + temporary_ranks.shrink_to_fit(handle.get_stream()); + + // + // Compute max of outgoing and incoming neighbors + // + thrust::transform(handle.get_thrust_policy(), + max_incoming_ranks.begin(), + max_incoming_ranks.end(), + max_outgoing_ranks.begin(), + max_outgoing_ranks.begin(), + thrust::maximum()); + + max_incoming_ranks.resize(0, handle.get_stream()); + max_incoming_ranks.shrink_to_fit(handle.get_stream()); + + // + // If the max neighbor of a vertex is already in MIS (i.e. has rank + // std::numeric_limits::max()), discard it, otherwise, + // include the vertex if it has larger rank than its maximum rank neighbor + // + auto last = thrust::remove_if( + handle.get_thrust_policy(), + d_sampled_vertices.begin(), + d_sampled_vertices.end(), + [max_rank_neighbor_first = max_outgoing_ranks.begin(), + ranks = raft::device_span(ranks.data(), ranks.size()), + v_first = graph_view.local_vertex_partition_range_first()] __device__(auto v) { + auto v_offset = v - v_first; + auto max_neighbor_rank = *(max_rank_neighbor_first + v_offset); + auto rank_of_v = ranks[v_offset]; + + if (max_neighbor_rank >= std::numeric_limits::max()) { + // Maximum rank neighbor is alreay in MIS + // Discard current vertex by setting its rank to + // std::numeric_limits::lowest() + ranks[v_offset] = std::numeric_limits::lowest(); + return true; + } + + if (rank_of_v >= max_neighbor_rank) { + // Include v and set its rank to std::numeric_limits::max() + ranks[v_offset] = std::numeric_limits::max(); + return true; + } + return false; + }); + + max_outgoing_ranks.resize(0, handle.get_stream()); + max_outgoing_ranks.shrink_to_fit(handle.get_stream()); + + d_sampled_vertices.resize(thrust::distance(d_sampled_vertices.begin(), last), + handle.get_stream()); + d_sampled_vertices.shrink_to_fit(handle.get_stream()); + + remaining_vertices.resize(non_candidate_vertices.size() + d_sampled_vertices.size(), + handle.get_stream()); + remaining_vertices.shrink_to_fit(handle.get_stream()); + + // merge non-candidate and remaining candidate vertices + thrust::merge(handle.get_thrust_policy(), + non_candidate_vertices.begin(), + non_candidate_vertices.end(), + d_sampled_vertices.begin(), + d_sampled_vertices.end(), + remaining_vertices.begin()); + + nr_remaining_vertices_to_check = remaining_vertices.size(); + if (multi_gpu) { + nr_remaining_vertices_to_check = host_scalar_allreduce(handle.get_comms(), + nr_remaining_vertices_to_check, + raft::comms::op_t::SUM, + handle.get_stream()); + } + + if (nr_remaining_vertices_to_check == 0) { break; } + } + + // Count number of vertices included in MIS + + vertex_t nr_vertices_included_in_mis = thrust::count_if( + handle.get_thrust_policy(), ranks.begin(), ranks.end(), [] __device__(auto v_rank) { + return v_rank >= std::numeric_limits::max(); + }); + + // Build MIS and return + rmm::device_uvector mis(nr_vertices_included_in_mis, handle.get_stream()); + thrust::copy_if( + handle.get_thrust_policy(), + vertex_begin, + vertex_end, + ranks.begin(), + mis.begin(), + [] __device__(auto v_rank) { return v_rank >= std::numeric_limits::max(); }); + + ranks.resize(0, handle.get_stream()); + ranks.shrink_to_fit(handle.get_stream()); + return mis; +} +} // namespace detail + +} // namespace cugraph diff --git a/cpp/src/community/mis.hpp b/cpp/src/community/detail/maximal_independent_moves.hpp similarity index 91% rename from cpp/src/community/mis.hpp rename to cpp/src/community/detail/maximal_independent_moves.hpp index 83c0d9775f9..b5588b11ef7 100644 --- a/cpp/src/community/mis.hpp +++ b/cpp/src/community/detail/maximal_independent_moves.hpp @@ -23,9 +23,13 @@ #include namespace cugraph { +namespace detail { + template -rmm::device_uvector maximal_independent_set( +rmm::device_uvector maximal_independent_moves( raft::handle_t const& handle, graph_view_t const& graph_view, raft::random::RngState& rng_state); + +} // namespace detail } // namespace cugraph diff --git a/cpp/src/community/detail/maximal_independent_moves_mg.cu b/cpp/src/community/detail/maximal_independent_moves_mg.cu new file mode 100644 index 00000000000..577253cdf58 --- /dev/null +++ b/cpp/src/community/detail/maximal_independent_moves_mg.cu @@ -0,0 +1,38 @@ +/* + * Copyright (c) 2023-2024, 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 "maximal_independent_moves.cuh" + +namespace cugraph { +namespace detail { + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +} // namespace detail + +} // namespace cugraph diff --git a/cpp/src/community/detail/maximal_independent_moves_sg.cu b/cpp/src/community/detail/maximal_independent_moves_sg.cu new file mode 100644 index 00000000000..18527c1ce48 --- /dev/null +++ b/cpp/src/community/detail/maximal_independent_moves_sg.cu @@ -0,0 +1,37 @@ +/* + * Copyright (c) 2023-2024, 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 "maximal_independent_moves.cuh" + +namespace cugraph { +namespace detail { + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector maximal_independent_moves( + raft::handle_t const& handle, + graph_view_t const& decision_graph_view, + raft::random::RngState& rng_state); + +} // namespace detail +} // namespace cugraph diff --git a/cpp/src/community/detail/refine_impl.cuh b/cpp/src/community/detail/refine_impl.cuh index b767ce7d8bb..ef34ad90584 100644 --- a/cpp/src/community/detail/refine_impl.cuh +++ b/cpp/src/community/detail/refine_impl.cuh @@ -16,8 +16,8 @@ #pragma once #include "common_methods.hpp" -#include "community/mis.hpp" #include "detail/graph_partition_utils.cuh" +#include "maximal_independent_moves.hpp" #include "prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh" #include "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" #include "prims/reduce_op.cuh" @@ -660,8 +660,8 @@ refine_clustering( // Determine a set of moves using MIS of the decision_graph // - auto vertices_in_mis = - maximal_independent_set(handle, decision_graph_view, rng_state); + auto vertices_in_mis = maximal_independent_moves( + handle, decision_graph_view, rng_state); rmm::device_uvector numbering_indices((*renumber_map).size(), handle.get_stream()); detail::sequence_fill(handle.get_stream(), diff --git a/cpp/src/community/detail/mis_impl.cuh b/cpp/src/components/mis_impl.cuh similarity index 90% rename from cpp/src/community/detail/mis_impl.cuh rename to cpp/src/components/mis_impl.cuh index 217d64eb1c1..550edf9807a 100644 --- a/cpp/src/community/detail/mis_impl.cuh +++ b/cpp/src/components/mis_impl.cuh @@ -16,11 +16,12 @@ */ #pragma once -#include "community/mis.hpp" +#include "prims/fill_edge_property.cuh" #include "prims/fill_edge_src_dst_property.cuh" #include "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" #include "prims/update_edge_src_dst_property.cuh" +#include #include #include #include @@ -60,36 +61,46 @@ rmm::device_uvector maximal_independent_set( thrust::make_counting_iterator(graph_view.local_vertex_partition_range_first()); auto vertex_end = thrust::make_counting_iterator(graph_view.local_vertex_partition_range_last()); - // Compute out-degree auto out_degrees = graph_view.compute_out_degrees(handle); + auto in_degrees = graph_view.compute_in_degrees(handle); - // Vertices with non-zero out-degree are possible candidates for MIS. + // Vertices with degree zero are always part of MIS remaining_vertices.resize( thrust::distance(remaining_vertices.begin(), thrust::copy_if(handle.get_thrust_policy(), vertex_begin, vertex_end, - out_degrees.begin(), + thrust::make_zip_iterator( + thrust::make_tuple(out_degrees.begin(), in_degrees.begin())), remaining_vertices.begin(), - [] __device__(auto deg) { return deg > 0; })), + [] __device__(auto out_deg_and_in_deg) { + return !((thrust::get<0>(out_deg_and_in_deg) == 0) && + (thrust::get<1>(out_deg_and_in_deg) == 0)); + })), handle.get_stream()); // Set ID of each vertex as its rank rmm::device_uvector ranks(local_vtx_partitoin_size, handle.get_stream()); thrust::copy(handle.get_thrust_policy(), vertex_begin, vertex_end, ranks.begin()); - // Set ranks of zero out-degree vetices to std::numeric_limits::lowest() - thrust::transform_if(handle.get_thrust_policy(), - out_degrees.begin(), - out_degrees.end(), - ranks.begin(), - cuda::proclaim_return_type( - [] __device__(auto) { return std::numeric_limits::lowest(); }), - [] __device__(auto deg) { return deg == 0; }); + // Set ranks of zero degree vetices to std::numeric_limits::max() + thrust::transform_if( + handle.get_thrust_policy(), + thrust::make_zip_iterator(thrust::make_tuple(out_degrees.begin(), in_degrees.begin())), + thrust::make_zip_iterator(thrust::make_tuple(out_degrees.end(), in_degrees.end())), + ranks.begin(), + cuda::proclaim_return_type( + [] __device__(auto) { return std::numeric_limits::max(); }), + [] __device__(auto in_out_degree) { + return (thrust::get<0>(in_out_degree) == 0) && (thrust::get<1>(in_out_degree) == 0); + }); out_degrees.resize(0, handle.get_stream()); out_degrees.shrink_to_fit(handle.get_stream()); + in_degrees.resize(0, handle.get_stream()); + in_degrees.shrink_to_fit(handle.get_stream()); + size_t loop_counter = 0; while (true) { loop_counter++; diff --git a/cpp/src/community/detail/mis_mg.cu b/cpp/src/components/mis_mg.cu similarity index 83% rename from cpp/src/community/detail/mis_mg.cu rename to cpp/src/components/mis_mg.cu index 0fc5eeb63c0..2418b38dd0b 100644 --- a/cpp/src/community/detail/mis_mg.cu +++ b/cpp/src/components/mis_mg.cu @@ -14,21 +14,21 @@ * limitations under the License. */ #include "mis_impl.cuh" - namespace cugraph { + template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); } // namespace cugraph diff --git a/cpp/src/community/detail/mis_sg.cu b/cpp/src/components/mis_sg.cu similarity index 82% rename from cpp/src/community/detail/mis_sg.cu rename to cpp/src/components/mis_sg.cu index 8a20b31d157..fea4c4f2765 100644 --- a/cpp/src/community/detail/mis_sg.cu +++ b/cpp/src/components/mis_sg.cu @@ -16,19 +16,20 @@ #include "mis_impl.cuh" namespace cugraph { + template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); template rmm::device_uvector maximal_independent_set( raft::handle_t const& handle, - graph_view_t const& decision_graph_view, + graph_view_t const& graph_view, raft::random::RngState& rng_state); } // namespace cugraph diff --git a/cpp/src/components/vertex_coloring_impl.cuh b/cpp/src/components/vertex_coloring_impl.cuh new file mode 100644 index 00000000000..ce445ab3809 --- /dev/null +++ b/cpp/src/components/vertex_coloring_impl.cuh @@ -0,0 +1,150 @@ +/* + * Copyright (c) 2024, 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 "prims/fill_edge_property.cuh" +#include "prims/transform_e.cuh" +#include "prims/update_edge_src_dst_property.cuh" + +#include + +#include +#include + +namespace cugraph { + +namespace detail { + +template +rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + cugraph::graph_view_t const& graph_view, + raft::random::RngState& rng_state) +{ + using graph_view_t = cugraph::graph_view_t; + graph_view_t current_graph_view(graph_view); + + // edge mask + cugraph::edge_property_t edge_masks_even(handle, current_graph_view); + cugraph::fill_edge_property(handle, current_graph_view, bool{false}, edge_masks_even); + + cugraph::edge_property_t edge_masks_odd(handle, current_graph_view); + cugraph::fill_edge_property(handle, current_graph_view, bool{false}, edge_masks_odd); + + cugraph::transform_e( + handle, + current_graph_view, + edge_src_dummy_property_t{}.view(), + edge_dst_dummy_property_t{}.view(), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, thrust::nullopt_t, thrust::nullopt_t, thrust::nullopt_t) { + return !(src == dst); // mask out self-loop + }, + edge_masks_even.mutable_view()); + + current_graph_view.attach_edge_mask(edge_masks_even.view()); + + // device vector to store colors of vertices + rmm::device_uvector colors = rmm::device_uvector( + current_graph_view.local_vertex_partition_range_size(), handle.get_stream()); + thrust::fill( + handle.get_thrust_policy(), colors.begin(), colors.end(), std::numeric_limits::max()); + + vertex_t color_id = 0; + while (true) { + auto mis = cugraph::maximal_independent_set( + handle, current_graph_view, rng_state); + + using flag_t = uint8_t; + rmm::device_uvector is_vertex_in_mis = rmm::device_uvector( + current_graph_view.local_vertex_partition_range_size(), handle.get_stream()); + thrust::fill(handle.get_thrust_policy(), is_vertex_in_mis.begin(), is_vertex_in_mis.end(), 0); + + thrust::for_each( + handle.get_thrust_policy(), + mis.begin(), + mis.end(), + [color_id, + colors = colors.data(), + is_vertex_in_mis = is_vertex_in_mis.data(), + v_first = current_graph_view.local_vertex_partition_range_first()] __device__(vertex_t v) { + auto v_offset = v - v_first; + is_vertex_in_mis[v_offset] = flag_t{1}; + vertex_t initial_color_id = colors[v_offset]; + colors[v_offset] = (color_id < initial_color_id) ? color_id : initial_color_id; + }); + + if (current_graph_view.compute_number_of_edges(handle) == 0) { break; } + + cugraph::edge_src_property_t src_mis_flags(handle, current_graph_view); + cugraph::edge_dst_property_t dst_mis_flags(handle, current_graph_view); + + cugraph::update_edge_src_property( + handle, current_graph_view, is_vertex_in_mis.begin(), src_mis_flags); + + cugraph::update_edge_dst_property( + handle, current_graph_view, is_vertex_in_mis.begin(), dst_mis_flags); + + if (color_id % 2 == 0) { + cugraph::transform_e( + handle, + current_graph_view, + src_mis_flags.view(), + dst_mis_flags.view(), + cugraph::edge_dummy_property_t{}.view(), + [color_id] __device__( + auto src, auto dst, auto is_src_in_mis, auto is_dst_in_mis, thrust::nullopt_t) { + return !((is_src_in_mis == uint8_t{true}) || (is_dst_in_mis == uint8_t{true})); + }, + edge_masks_odd.mutable_view()); + + if (current_graph_view.has_edge_mask()) current_graph_view.clear_edge_mask(); + cugraph::fill_edge_property(handle, current_graph_view, bool{false}, edge_masks_even); + current_graph_view.attach_edge_mask(edge_masks_odd.view()); + } else { + cugraph::transform_e( + handle, + current_graph_view, + src_mis_flags.view(), + dst_mis_flags.view(), + cugraph::edge_dummy_property_t{}.view(), + [color_id] __device__( + auto src, auto dst, auto is_src_in_mis, auto is_dst_in_mis, thrust::nullopt_t) { + return !((is_src_in_mis == uint8_t{true}) || (is_dst_in_mis == uint8_t{true})); + }, + edge_masks_even.mutable_view()); + + if (current_graph_view.has_edge_mask()) current_graph_view.clear_edge_mask(); + cugraph::fill_edge_property(handle, current_graph_view, bool{false}, edge_masks_odd); + current_graph_view.attach_edge_mask(edge_masks_even.view()); + } + + color_id++; + } + return colors; +} +} // namespace detail + +template +rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state) +{ + return detail::vertex_coloring(handle, graph_view, rng_state); +} + +} // namespace cugraph diff --git a/cpp/src/components/vertex_coloring_mg.cu b/cpp/src/components/vertex_coloring_mg.cu new file mode 100644 index 00000000000..8f87e8bd534 --- /dev/null +++ b/cpp/src/components/vertex_coloring_mg.cu @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2024, 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 "vertex_coloring_impl.cuh" + +namespace cugraph { + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +} // namespace cugraph diff --git a/cpp/src/components/vertex_coloring_sg.cu b/cpp/src/components/vertex_coloring_sg.cu new file mode 100644 index 00000000000..427bc0b2c81 --- /dev/null +++ b/cpp/src/components/vertex_coloring_sg.cu @@ -0,0 +1,35 @@ +/* + * Copyright (c) 2024, 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 "vertex_coloring_impl.cuh" + +namespace cugraph { + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +template rmm::device_uvector vertex_coloring( + raft::handle_t const& handle, + graph_view_t const& graph_view, + raft::random::RngState& rng_state); + +} // namespace cugraph diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index 46a895536ef..af0dffcbf65 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -372,6 +372,14 @@ ConfigureTest(EDGE_BETWEENNESS_CENTRALITY_TEST centrality/edge_betweenness_centr # - WEAKLY CONNECTED COMPONENTS tests ------------------------------------------------------------- ConfigureTest(WEAKLY_CONNECTED_COMPONENTS_TEST components/weakly_connected_components_test.cpp) +############################################################################################### +# - MIS tests ------------------------------------------------------------------------------ +ConfigureTest(MIS_TEST components/mis_test.cu) + +############################################################################################### +# - VERTEX COLORING tests ------------------------------------------------------------------- +ConfigureTest(VERTEX_COLORING_TEST components/vertex_coloring_test.cu) + ################################################################################################### # - SIMILARITY tests ------------------------------------------------------------------------------ ConfigureTest(SIMILARITY_TEST link_prediction/similarity_test.cu) @@ -535,10 +543,6 @@ if(BUILD_CUGRAPH_MG_TESTS) # - MG ECG tests -------------------------------------------------------------------------- ConfigureTestMG(MG_ECG_TEST community/mg_ecg_test.cpp) - ############################################################################################### - # - MG MIS tests ------------------------------------------------------------------------------ - ConfigureTestMG(MG_MIS_TEST community/mg_mis_test.cu) - ############################################################################################### # - MG SELECT RANDOM VERTICES tests ----------------------------------------------------------- ConfigureTestMG(MG_SELECT_RANDOM_VERTICES structure/mg_select_random_vertices_test.cpp) @@ -552,6 +556,14 @@ if(BUILD_CUGRAPH_MG_TESTS) ConfigureTestMG(MG_WEAKLY_CONNECTED_COMPONENTS_TEST components/mg_weakly_connected_components_test.cpp) + ############################################################################################### + # - MG MIS tests ------------------------------------------------------------------------------ + ConfigureTestMG(MG_MIS_TEST components/mg_mis_test.cu) + + ############################################################################################### + # - MG VERTEX COLORING tests ------------------------------------------------------------------- + ConfigureTestMG(MG_VERTEX_COLORING_TEST components/mg_vertex_coloring_test.cu) + ############################################################################################### # - MG Core Number tests ---------------------------------------------------------------------- ConfigureTestMG(MG_CORE_NUMBER_TEST cores/mg_core_number_test.cpp) diff --git a/cpp/tests/community/mg_mis_test.cu b/cpp/tests/components/mg_mis_test.cu similarity index 95% rename from cpp/tests/community/mg_mis_test.cu rename to cpp/tests/components/mg_mis_test.cu index 1240cf812f9..04c346b0f00 100644 --- a/cpp/tests/community/mg_mis_test.cu +++ b/cpp/tests/components/mg_mis_test.cu @@ -245,18 +245,20 @@ TEST_P(Tests_MGMaximalIndependentSet_Rmat, CheckInt64Int64FloatFloat) override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); } +bool constexpr check_correctness = false; INSTANTIATE_TEST_SUITE_P( file_test, Tests_MGMaximalIndependentSet_File, - ::testing::Combine(::testing::Values(MaximalIndependentSet_Usecase{false}, - MaximalIndependentSet_Usecase{false}), + ::testing::Combine(::testing::Values(MaximalIndependentSet_Usecase{check_correctness}, + MaximalIndependentSet_Usecase{check_correctness}), ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); -INSTANTIATE_TEST_SUITE_P(rmat_small_test, - Tests_MGMaximalIndependentSet_Rmat, - ::testing::Combine(::testing::Values(MaximalIndependentSet_Usecase{false}), - ::testing::Values(cugraph::test::Rmat_Usecase( - 3, 4, 0.57, 0.19, 0.19, 0, true, false)))); +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_MGMaximalIndependentSet_Rmat, + ::testing::Combine( + ::testing::Values(MaximalIndependentSet_Usecase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(3, 4, 0.57, 0.19, 0.19, 0, true, false)))); INSTANTIATE_TEST_SUITE_P( rmat_benchmark_test, /* note that scale & edge factor can be overridden in benchmarking (with @@ -266,7 +268,8 @@ INSTANTIATE_TEST_SUITE_P( factor (to avoid running same benchmarks more than once) */ Tests_MGMaximalIndependentSet_Rmat, ::testing::Combine( - ::testing::Values(MaximalIndependentSet_Usecase{false}, MaximalIndependentSet_Usecase{false}), + ::testing::Values(MaximalIndependentSet_Usecase{check_correctness}, + MaximalIndependentSet_Usecase{check_correctness}), ::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/components/mg_vertex_coloring_test.cu b/cpp/tests/components/mg_vertex_coloring_test.cu new file mode 100644 index 00000000000..ce4dadaa786 --- /dev/null +++ b/cpp/tests/components/mg_vertex_coloring_test.cu @@ -0,0 +1,275 @@ +/* + * Copyright (c) 2024, 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 "prims/fill_edge_src_dst_property.cuh" +#include "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" +#include "prims/property_generator.cuh" +#include "prims/reduce_op.cuh" +#include "prims/transform_reduce_e.cuh" +#include "prims/update_edge_src_dst_property.cuh" +#include "utilities/base_fixture.hpp" +#include "utilities/test_graphs.hpp" +#include "utilities/test_utilities.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include +#include +#include + +struct GraphColoring_UseCase { + bool check_correctness{true}; +}; + +template +class Tests_MGGraphColoring + : public ::testing::TestWithParam> { + public: + Tests_MGGraphColoring() {} + + static void SetUpTestCase() { handle_ = cugraph::test::initialize_mg_handle(); } + static void TearDownTestCase() { handle_.reset(); } + + virtual void SetUp() {} + virtual void TearDown() {} + + template + void run_current_test(std::tuple const& param) + { + auto [coloring_usecase, input_usecase] = param; + + HighResTimer hr_timer{}; + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + handle_->get_comms().barrier(); + hr_timer.start("MG Construct graph"); + } + + constexpr bool multi_gpu = true; + + auto [mg_graph, mg_edge_weights, mg_renumber_map] = + cugraph::test::construct_graph( + *handle_, input_usecase, false, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + auto mg_graph_view = mg_graph.view(); + auto mg_edge_weight_view = + mg_edge_weights ? std::make_optional((*mg_edge_weights).view()) : std::nullopt; + + raft::random::RngState rng_state(multi_gpu ? handle_->get_comms().get_rank() : 0); + auto d_colors = + cugraph::vertex_coloring(*handle_, mg_graph_view, rng_state); + + // Test Graph Coloring + + if (coloring_usecase.check_correctness) { + std::vector h_colors(d_colors.size()); + raft::update_host(h_colors.data(), d_colors.data(), d_colors.size(), handle_->get_stream()); + + std::for_each(h_colors.begin(), + h_colors.end(), + [num_vertices = mg_graph_view.number_of_vertices()](vertex_t color_id) { + ASSERT_TRUE(color_id <= num_vertices); + }); + + using GraphViewType = cugraph::graph_view_t; + cugraph::edge_src_property_t src_color_cache(*handle_); + cugraph::edge_dst_property_t dst_color_cache(*handle_); + + if constexpr (multi_gpu) { + src_color_cache = + cugraph::edge_src_property_t(*handle_, mg_graph_view); + dst_color_cache = + cugraph::edge_dst_property_t(*handle_, mg_graph_view); + update_edge_src_property(*handle_, mg_graph_view, d_colors.begin(), src_color_cache); + update_edge_dst_property(*handle_, mg_graph_view, d_colors.begin(), dst_color_cache); + } + + rmm::device_uvector d_color_conflicts( + mg_graph_view.local_vertex_partition_range_size(), handle_->get_stream()); + + per_v_transform_reduce_outgoing_e( + *handle_, + mg_graph_view, + multi_gpu + ? src_color_cache.view() + : cugraph::detail::edge_major_property_view_t(d_colors.data()), + multi_gpu ? dst_color_cache.view() + : cugraph::detail::edge_minor_property_view_t( + d_colors.data(), vertex_t{0}), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_color, auto dst_color, thrust::nullopt_t) { + if ((src != dst) && (src_color == dst_color)) { + return uint8_t{1}; + } else { + return uint8_t{0}; + } + }, + uint8_t{0}, + cugraph::reduce_op::maximum{}, + d_color_conflicts.begin()); + + std::vector h_color_conflicts(d_color_conflicts.size()); + raft::update_host(h_color_conflicts.data(), + d_color_conflicts.data(), + d_color_conflicts.size(), + handle_->get_stream()); + + std::vector h_vertices_in_this_proces((*mg_renumber_map).size()); + + raft::update_host(h_vertices_in_this_proces.data(), + (*mg_renumber_map).data(), + (*mg_renumber_map).size(), + handle_->get_stream()); + handle_->sync_stream(); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + size_t nr_conflicts = cugraph::transform_reduce_e( + *handle_, + mg_graph_view, + multi_gpu ? src_color_cache.view() + : cugraph::detail::edge_major_property_view_t( + d_colors.begin()), + multi_gpu ? dst_color_cache.view() + : cugraph::detail::edge_minor_property_view_t( + d_colors.begin(), vertex_t{0}), + cugraph::edge_dummy_property_t{}.view(), + [renumber_map = (*mg_renumber_map).data()] __device__( + auto src, auto dst, auto src_color, auto dst_color, thrust::nullopt_t) { + if ((src != dst) && (src_color == dst_color)) { + return vertex_t{1}; + } else { + return vertex_t{0}; + } + }, + vertex_t{0}); + + ASSERT_TRUE(nr_conflicts == edge_t{0}) + << "adjacent vertices can't have same color." << std::endl; + + { + thrust::for_each( + thrust::host, + thrust::make_zip_iterator(thrust::make_tuple( + h_colors.begin(), h_vertices_in_this_proces.begin(), h_color_conflicts.begin())), + thrust::make_zip_iterator(thrust::make_tuple( + h_colors.end(), h_vertices_in_this_proces.end(), h_color_conflicts.end())), + [](auto color_vetex_and_conflict_flag) { + auto color = thrust::get<0>(color_vetex_and_conflict_flag); + auto v = thrust::get<1>(color_vetex_and_conflict_flag); + auto conflict_flag = thrust::get<2>(color_vetex_and_conflict_flag); + ASSERT_TRUE(conflict_flag == 0) + << v << " got same color as one of its neighbor" << std::endl; + }); + } + } + } + + private: + static std::unique_ptr handle_; +}; + +template +std::unique_ptr Tests_MGGraphColoring::handle_ = nullptr; + +using Tests_MGGraphColoring_File = Tests_MGGraphColoring; +using Tests_MGGraphColoring_Rmat = Tests_MGGraphColoring; + +TEST_P(Tests_MGGraphColoring_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_MGGraphColoring_File, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_MGGraphColoring_File, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_MGGraphColoring_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_MGGraphColoring_Rmat, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_MGGraphColoring_Rmat, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +bool constexpr check_correctness = false; + +INSTANTIATE_TEST_SUITE_P( + file_test, + Tests_MGGraphColoring_File, + ::testing::Combine(::testing::Values(GraphColoring_UseCase{check_correctness}, + GraphColoring_UseCase{check_correctness}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_MGGraphColoring_Rmat, + ::testing::Combine( + ::testing::Values(GraphColoring_UseCase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(3, 4, 0.57, 0.19, 0.19, 0, true, false)))); + +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_MGGraphColoring_Rmat, + ::testing::Combine( + ::testing::Values(GraphColoring_UseCase{check_correctness}, + GraphColoring_UseCase{check_correctness}), + ::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/components/mis_test.cu b/cpp/tests/components/mis_test.cu new file mode 100644 index 00000000000..f3bdd3d0e8b --- /dev/null +++ b/cpp/tests/components/mis_test.cu @@ -0,0 +1,240 @@ +/* + * Copyright (c) 2024, 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 "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" +#include "prims/reduce_op.cuh" +#include "utilities/base_fixture.hpp" +#include "utilities/test_graphs.hpp" +#include "utilities/test_utilities.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include +#include +#include + +struct MaximalIndependentSet_Usecase { + bool check_correctness{true}; +}; + +template +class Tests_SGMaximalIndependentSet + : public ::testing::TestWithParam> { + public: + Tests_SGMaximalIndependentSet() {} + + static void SetUpTestCase() {} + static void TearDownTestCase() {} + + virtual void SetUp() {} + virtual void TearDown() {} + + template + void run_current_test(std::tuple const& param) + { + auto [mis_usecase, input_usecase] = param; + + raft::handle_t handle{}; + HighResTimer hr_timer{}; + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + hr_timer.start("Construct graph"); + } + + constexpr bool multi_gpu = false; + + auto [sg_graph, sg_edge_weights, sg_renumber_map] = + cugraph::test::construct_graph( + handle, input_usecase, false, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + auto sg_graph_view = sg_graph.view(); + auto sg_edge_weight_view = + sg_edge_weights ? std::make_optional((*sg_edge_weights).view()) : std::nullopt; + + raft::random::RngState rng_state(0); + auto d_mis = cugraph::maximal_independent_set( + handle, sg_graph_view, rng_state); + + // Test MIS + if (mis_usecase.check_correctness) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + std::vector h_mis(d_mis.size()); + raft::update_host(h_mis.data(), d_mis.data(), d_mis.size(), handle.get_stream()); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + auto vertex_first = sg_graph_view.local_vertex_partition_range_first(); + auto vertex_last = sg_graph_view.local_vertex_partition_range_last(); + + std::for_each(h_mis.begin(), h_mis.end(), [vertex_first, vertex_last](vertex_t v) { + ASSERT_TRUE((v >= vertex_first) && (v < vertex_last)); + }); + + // If a vertex is included in MIS, then none of its neighbor should be + + vertex_t local_vtx_partitoin_size = sg_graph_view.local_vertex_partition_range_size(); + rmm::device_uvector d_total_outgoing_nbrs_included_mis(local_vtx_partitoin_size, + handle.get_stream()); + + rmm::device_uvector inclusiong_flags(local_vtx_partitoin_size, handle.get_stream()); + + thrust::uninitialized_fill( + handle.get_thrust_policy(), inclusiong_flags.begin(), inclusiong_flags.end(), vertex_t{0}); + + thrust::for_each( + handle.get_thrust_policy(), + d_mis.begin(), + d_mis.end(), + [inclusiong_flags = + raft::device_span(inclusiong_flags.data(), inclusiong_flags.size()), + v_first = sg_graph_view.local_vertex_partition_range_first()] __device__(auto v) { + auto v_offset = v - v_first; + inclusiong_flags[v_offset] = vertex_t{1}; + }); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + per_v_transform_reduce_outgoing_e( + handle, + sg_graph_view, + cugraph::detail::edge_major_property_view_t( + inclusiong_flags.data()), + cugraph::detail::edge_minor_property_view_t( + inclusiong_flags.data(), vertex_t{0}), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_included, auto dst_included, auto wt) { + return (src == dst) ? 0 : dst_included; + }, + vertex_t{0}, + cugraph::reduce_op::plus{}, + d_total_outgoing_nbrs_included_mis.begin()); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + std::vector h_total_outgoing_nbrs_included_mis( + d_total_outgoing_nbrs_included_mis.size()); + raft::update_host(h_total_outgoing_nbrs_included_mis.data(), + d_total_outgoing_nbrs_included_mis.data(), + d_total_outgoing_nbrs_included_mis.size(), + handle.get_stream()); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + { + auto vertex_first = sg_graph_view.local_vertex_partition_range_first(); + auto vertex_last = sg_graph_view.local_vertex_partition_range_last(); + + std::for_each(h_mis.begin(), + h_mis.end(), + [vertex_first, vertex_last, &h_total_outgoing_nbrs_included_mis](vertex_t v) { + ASSERT_TRUE((v >= vertex_first) && (v < vertex_last)) + << v << " is not within vertex parition range" << std::endl; + + ASSERT_TRUE(h_total_outgoing_nbrs_included_mis[v - vertex_first] == 0) + << v << "'s neighbor is included in MIS" << std::endl; + }); + } + } + } +}; + +using Tests_SGMaximalIndependentSet_File = + Tests_SGMaximalIndependentSet; +using Tests_SGMaximalIndependentSet_Rmat = + Tests_SGMaximalIndependentSet; + +TEST_P(Tests_SGMaximalIndependentSet_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGMaximalIndependentSet_File, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGMaximalIndependentSet_File, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGMaximalIndependentSet_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGMaximalIndependentSet_Rmat, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGMaximalIndependentSet_Rmat, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +bool constexpr check_correctness = false; +INSTANTIATE_TEST_SUITE_P( + file_test, + Tests_SGMaximalIndependentSet_File, + ::testing::Combine(::testing::Values(MaximalIndependentSet_Usecase{check_correctness}, + MaximalIndependentSet_Usecase{check_correctness}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_SGMaximalIndependentSet_Rmat, + ::testing::Combine( + ::testing::Values(MaximalIndependentSet_Usecase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(3, 4, 0.57, 0.19, 0.19, 0, true, false)))); + +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_SGMaximalIndependentSet_Rmat, + ::testing::Combine( + ::testing::Values(MaximalIndependentSet_Usecase{check_correctness}, + MaximalIndependentSet_Usecase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); + +CUGRAPH_TEST_PROGRAM_MAIN() diff --git a/cpp/tests/components/vertex_coloring_test.cu b/cpp/tests/components/vertex_coloring_test.cu new file mode 100644 index 00000000000..27a0c5013bd --- /dev/null +++ b/cpp/tests/components/vertex_coloring_test.cu @@ -0,0 +1,244 @@ +/* + * Copyright (c) 2024, 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 "prims/per_v_transform_reduce_incoming_outgoing_e.cuh" +#include "prims/reduce_op.cuh" +#include "prims/transform_reduce_e.cuh" +#include "utilities/base_fixture.hpp" +#include "utilities/test_graphs.hpp" +#include "utilities/test_utilities.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include +#include +#include + +struct GraphColoring_UseCase { + bool check_correctness{true}; +}; + +template +class Tests_SGGraphColoring + : public ::testing::TestWithParam> { + public: + Tests_SGGraphColoring() {} + + static void SetUpTestCase() {} + static void TearDownTestCase() {} + + virtual void SetUp() {} + virtual void TearDown() {} + + template + void run_current_test(std::tuple const& param) + { + auto [coloring_usecase, input_usecase] = param; + + raft::handle_t handle{}; + HighResTimer hr_timer{}; + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + hr_timer.start("Construct graph"); + } + + constexpr bool multi_gpu = false; + + auto [sg_graph, sg_edge_weights, sg_renumber_map] = + cugraph::test::construct_graph( + handle, input_usecase, false, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + auto sg_graph_view = sg_graph.view(); + auto sg_edge_weight_view = + sg_edge_weights ? std::make_optional((*sg_edge_weights).view()) : std::nullopt; + + raft::random::RngState rng_state(0); + auto d_colors = + cugraph::vertex_coloring(handle, sg_graph_view, rng_state); + + // Test Graph Coloring + + if (coloring_usecase.check_correctness) { + std::vector h_colors(d_colors.size()); + raft::update_host(h_colors.data(), d_colors.data(), d_colors.size(), handle.get_stream()); + + std::for_each(h_colors.begin(), + h_colors.end(), + [num_vertices = sg_graph_view.number_of_vertices()](vertex_t color_id) { + ASSERT_TRUE(color_id <= num_vertices); + }); + + rmm::device_uvector d_color_conflict_flags( + sg_graph_view.local_vertex_partition_range_size(), handle.get_stream()); + + per_v_transform_reduce_outgoing_e( + handle, + sg_graph_view, + cugraph::detail::edge_major_property_view_t(d_colors.data()), + cugraph::detail::edge_minor_property_view_t(d_colors.data(), + vertex_t{0}), + cugraph::edge_dummy_property_t{}.view(), + [] __device__(auto src, auto dst, auto src_color, auto dst_color, thrust::nullopt_t) { + if ((src != dst) && (src_color == dst_color)) { + return uint8_t{1}; + } else { + return uint8_t{0}; + } + }, + uint8_t{0}, + cugraph::reduce_op::maximum{}, + d_color_conflict_flags.begin()); + + std::vector h_color_conflict_flags(d_color_conflict_flags.size()); + raft::update_host(h_color_conflict_flags.data(), + d_color_conflict_flags.data(), + d_color_conflict_flags.size(), + handle.get_stream()); + + std::vector h_vertices_in_this_proces((*sg_renumber_map).size()); + + raft::update_host(h_vertices_in_this_proces.data(), + (*sg_renumber_map).data(), + (*sg_renumber_map).size(), + handle.get_stream()); + handle.sync_stream(); + + RAFT_CUDA_TRY(cudaDeviceSynchronize()); + + edge_t nr_conflicts = cugraph::transform_reduce_e( + handle, + sg_graph_view, + cugraph::detail::edge_major_property_view_t(d_colors.begin()), + cugraph::detail::edge_minor_property_view_t(d_colors.begin(), + vertex_t{0}), + cugraph::edge_dummy_property_t{}.view(), + [renumber_map = (*sg_renumber_map).data()] __device__( + auto src, auto dst, auto src_color, auto dst_color, thrust::nullopt_t) { + if ((src != dst) && (src_color == dst_color)) { + return vertex_t{1}; + } else { + return vertex_t{0}; + } + }, + vertex_t{0}); + + ASSERT_TRUE(nr_conflicts == edge_t{0}) + << "adjacent vertices can't have same color." << std::endl; + + if (nr_conflicts >= 0) { + thrust::for_each( + thrust::host, + thrust::make_zip_iterator(thrust::make_tuple( + h_colors.begin(), h_vertices_in_this_proces.begin(), h_color_conflict_flags.begin())), + thrust::make_zip_iterator(thrust::make_tuple( + h_colors.end(), h_vertices_in_this_proces.end(), h_color_conflict_flags.end())), + [](auto color_vetex_and_conflict_flag) { + auto color = thrust::get<0>(color_vetex_and_conflict_flag); + auto v = thrust::get<1>(color_vetex_and_conflict_flag); + auto conflict_flag = thrust::get<2>(color_vetex_and_conflict_flag); + ASSERT_TRUE(conflict_flag == 0) + << v << " got same color as one of its neighbor" << std::endl; + }); + } + } + } +}; + +using Tests_SGGraphColoring_File = Tests_SGGraphColoring; +using Tests_SGGraphColoring_Rmat = Tests_SGGraphColoring; + +TEST_P(Tests_SGGraphColoring_File, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGGraphColoring_File, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGGraphColoring_File, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_File_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGGraphColoring_Rmat, CheckInt32Int32FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGGraphColoring_Rmat, CheckInt32Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +TEST_P(Tests_SGGraphColoring_Rmat, CheckInt64Int64FloatFloat) +{ + run_current_test( + override_Rmat_Usecase_with_cmd_line_arguments(GetParam())); +} + +bool constexpr check_correctness = false; + +INSTANTIATE_TEST_SUITE_P( + file_test, + Tests_SGGraphColoring_File, + ::testing::Combine(::testing::Values(GraphColoring_UseCase{check_correctness}, + GraphColoring_UseCase{check_correctness}), + ::testing::Values(cugraph::test::File_Usecase("test/datasets/karate.mtx")))); + +INSTANTIATE_TEST_SUITE_P( + rmat_small_test, + Tests_SGGraphColoring_Rmat, + ::testing::Combine( + ::testing::Values(GraphColoring_UseCase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(3, 4, 0.57, 0.19, 0.19, 0, true, false)))); + +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_SGGraphColoring_Rmat, + ::testing::Combine( + ::testing::Values(GraphColoring_UseCase{check_correctness}, + GraphColoring_UseCase{check_correctness}), + ::testing::Values(cugraph::test::Rmat_Usecase(20, 32, 0.57, 0.19, 0.19, 0, false, false)))); + +CUGRAPH_TEST_PROGRAM_MAIN() From fdc6aa5acde918e50b6f6f365cc1ec03fcfb18ec Mon Sep 17 00:00:00 2001 From: Chuck Hastings <45364586+ChuckHastings@users.noreply.github.com> Date: Tue, 12 Mar 2024 19:01:26 -0400 Subject: [PATCH 6/9] Add degrees to C API (#4212) Add new method `cugraph_degrees` to the C API to compute and return the degrees of vertices. Closes #4171 Authors: - Chuck Hastings (https://github.com/ChuckHastings) - Joseph Nke (https://github.com/jnke2016) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Rick Ratzel (https://github.com/rlratzel) URL: https://github.com/rapidsai/cugraph/pull/4212 --- cpp/CMakeLists.txt | 2 + cpp/include/cugraph_c/graph_functions.h | 112 +++++ cpp/src/c_api/abstract_functor.hpp | 2 +- cpp/src/c_api/degrees.cu | 225 ++++++++++ cpp/src/c_api/degrees_result.cpp | 63 +++ cpp/src/c_api/degrees_result.hpp | 32 ++ cpp/tests/CMakeLists.txt | 2 + cpp/tests/c_api/c_test_utils.h | 4 +- cpp/tests/c_api/degrees_test.c | 387 +++++++++++++++++ cpp/tests/c_api/mg_degrees_test.c | 407 ++++++++++++++++++ cpp/tests/c_api/test_utils.cpp | 14 +- .../simpleDistributedGraph.py | 307 +++++++------ .../graph_implementation/simpleGraph.py | 198 +++++---- .../centrality/test_degree_centrality_mg.py | 4 +- .../pylibcugraph/pylibcugraph/CMakeLists.txt | 3 +- python/pylibcugraph/pylibcugraph/__init__.py | 2 + .../_cugraph_c/graph_functions.pxd | 55 +++ python/pylibcugraph/pylibcugraph/degrees.pyx | 307 +++++++++++++ 18 files changed, 1905 insertions(+), 221 deletions(-) create mode 100644 cpp/src/c_api/degrees.cu create mode 100644 cpp/src/c_api/degrees_result.cpp create mode 100644 cpp/src/c_api/degrees_result.hpp create mode 100644 cpp/tests/c_api/degrees_test.c create mode 100644 cpp/tests/c_api/mg_degrees_test.c create mode 100644 python/pylibcugraph/pylibcugraph/degrees.pyx diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt index b12403710ab..88908ef70ce 100644 --- a/cpp/CMakeLists.txt +++ b/cpp/CMakeLists.txt @@ -415,6 +415,8 @@ endif() add_library(cugraph_c src/c_api/resource_handle.cpp src/c_api/array.cpp + src/c_api/degrees.cu + src/c_api/degrees_result.cpp src/c_api/error.cpp src/c_api/graph_sg.cpp src/c_api/graph_mg.cpp diff --git a/cpp/include/cugraph_c/graph_functions.h b/cpp/include/cugraph_c/graph_functions.h index 8fe1ea0b958..94b06189796 100644 --- a/cpp/include/cugraph_c/graph_functions.h +++ b/cpp/include/cugraph_c/graph_functions.h @@ -229,6 +229,118 @@ cugraph_error_code_t cugraph_allgather(const cugraph_resource_handle_t* handle, cugraph_induced_subgraph_result_t** result, cugraph_error_t** error); +/** + * @brief Opaque degree result type + */ +typedef struct { + int32_t align_; +} cugraph_degrees_result_t; + +/** + * @brief Compute in degrees + * + * Compute the in degrees for the vertices in the graph. + * + * @param [in] handle Handle for accessing resources. + * @param [in] graph Pointer to graph + * @param [in] source_vertices Device array of vertices we want to compute in degrees for. + * @param [in] do_expensive_check A flag to run expensive checks for input arguments (if set to + * true) + * @param [out] result Opaque pointer to degrees 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_in_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error); + +/** + * @brief Compute out degrees + * + * Compute the out degrees for the vertices in the graph. + * + * @param [in] handle Handle for accessing resources. + * @param [in] graph Pointer to graph + * @param [in] source_vertices Device array of vertices we want to compute out degrees for. + * @param [in] do_expensive_check A flag to run expensive checks for input arguments (if set to + * true) + * @param [out] result Opaque pointer to degrees 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_out_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error); + +/** + * @brief Compute degrees + * + * Compute the degrees for the vertices in the graph. + * + * @param [in] handle Handle for accessing resources. + * @param [in] graph Pointer to graph + * @param [in] source_vertices Device array of vertices we want to compute degrees for. + * @param [in] do_expensive_check A flag to run expensive checks for input arguments (if set to + * true) + * @param [out] result Opaque pointer to degrees 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_degrees(const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error); + +/** + * @brief Get the vertex ids + * + * @param [in] degrees_result Opaque pointer to degree result + * @return type erased array view of vertex ids + */ +cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_vertices( + cugraph_degrees_result_t* degrees_result); + +/** + * @brief Get the in degrees + * + * @param [in] degrees_result Opaque pointer to degree result + * @return type erased array view of vertex ids + */ +cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_in_degrees( + cugraph_degrees_result_t* degrees_result); + +/** + * @brief Get the out degrees + * + * If the graph is symmetric, in degrees and out degrees will be equal (and + * will be stored in the same memory). + * + * @param [in] degrees_result Opaque pointer to degree result + * @return type erased array view of vertex ids + */ +cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_out_degrees( + cugraph_degrees_result_t* degrees_result); + +/** + * @brief Free degree result + * + * @param [in] degrees_result Opaque pointer to degree result + */ +void cugraph_degrees_result_free(cugraph_degrees_result_t* degrees_result); + #ifdef __cplusplus } #endif diff --git a/cpp/src/c_api/abstract_functor.hpp b/cpp/src/c_api/abstract_functor.hpp index 219b1256065..8d3ed11341f 100644 --- a/cpp/src/c_api/abstract_functor.hpp +++ b/cpp/src/c_api/abstract_functor.hpp @@ -27,7 +27,7 @@ namespace c_api { struct abstract_functor { // Move to abstract functor... make operator a void, add cugraph_graph_t * result to functor // try that with instantiation questions - std::unique_ptr error_{std::make_unique("")}; + std::unique_ptr error_ = {std::make_unique("")}; cugraph_error_code_t error_code_{CUGRAPH_SUCCESS}; void unsupported() diff --git a/cpp/src/c_api/degrees.cu b/cpp/src/c_api/degrees.cu new file mode 100644 index 00000000000..d6481efa905 --- /dev/null +++ b/cpp/src/c_api/degrees.cu @@ -0,0 +1,225 @@ +/* + * Copyright (c) 2024, 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_api/abstract_functor.hpp" +#include "c_api/degrees_result.hpp" +#include "c_api/graph.hpp" +#include "c_api/resource_handle.hpp" +#include "c_api/utils.hpp" + +#include + +#include +#include +#include +#include +#include + +#include + +#include + +namespace { + +struct degrees_functor : public cugraph::c_api::abstract_functor { + raft::handle_t const& handle_; + cugraph::c_api::cugraph_graph_t* graph_{}; + cugraph::c_api::cugraph_type_erased_device_array_view_t const* source_vertices_; + bool in_degrees_{false}; + bool out_degrees_{false}; + bool do_expensive_check_{false}; + cugraph::c_api::cugraph_degrees_result_t* result_{}; + + degrees_functor(cugraph_resource_handle_t const* handle, + cugraph_graph_t* graph, + ::cugraph_type_erased_device_array_view_t const* source_vertices, + bool in_degrees, + bool out_degrees, + bool do_expensive_check) + : abstract_functor(), + handle_(*reinterpret_cast(handle)->handle_), + graph_(reinterpret_cast(graph)), + source_vertices_( + reinterpret_cast( + source_vertices)), + in_degrees_{in_degrees}, + out_degrees_{out_degrees}, + do_expensive_check_(do_expensive_check) + { + } + + template + void operator()() + { + // FIXME: Think about how to handle SG vice MG + if constexpr (!cugraph::is_candidate::value) { + unsupported(); + } else { + auto graph = + reinterpret_cast*>( + graph_->graph_); + + auto graph_view = graph->view(); + + auto number_map = reinterpret_cast*>(graph_->number_map_); + + std::optional> in_degrees{std::nullopt}; + std::optional> out_degrees{std::nullopt}; + + if (in_degrees_ && out_degrees_ && graph_view.is_symmetric()) { + in_degrees = store_transposed ? graph_view.compute_in_degrees(handle_) + : graph_view.compute_out_degrees(handle_); + // out_degrees will be extracted from in_degrees in the result + } else { + if (in_degrees_) in_degrees = graph_view.compute_in_degrees(handle_); + + if (out_degrees_) out_degrees = graph_view.compute_out_degrees(handle_); + } + + rmm::device_uvector vertex_ids(0, handle_.get_stream()); + + if (source_vertices_) { + // FIXME: Would be more efficient if graph_view.compute_*_degrees could take a vertex + // subset + vertex_ids.resize(source_vertices_->size_, handle_.get_stream()); + raft::copy(vertex_ids.data(), + source_vertices_->as_type(), + vertex_ids.size(), + handle_.get_stream()); + + if constexpr (multi_gpu) { + vertex_ids = cugraph::detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning( + handle_, std::move(vertex_ids)); + } + + cugraph::renumber_ext_vertices( + handle_, + vertex_ids.data(), + vertex_ids.size(), + number_map->data(), + graph_view.local_vertex_partition_range_first(), + graph_view.local_vertex_partition_range_last(), + do_expensive_check_); + + auto vertex_partition = cugraph::vertex_partition_device_view_t( + graph_view.local_vertex_partition_view()); + + auto vertices_iter = thrust::make_transform_iterator( + vertex_ids.begin(), + cuda::proclaim_return_type([vertex_partition] __device__(auto v) { + return vertex_partition.local_vertex_partition_offset_from_vertex_nocheck(v); + })); + + if (in_degrees && out_degrees) { + rmm::device_uvector tmp_in_degrees(vertex_ids.size(), handle_.get_stream()); + rmm::device_uvector tmp_out_degrees(vertex_ids.size(), handle_.get_stream()); + thrust::gather( + handle_.get_thrust_policy(), + vertices_iter, + vertices_iter + vertex_ids.size(), + thrust::make_zip_iterator(in_degrees->begin(), out_degrees->begin()), + thrust::make_zip_iterator(tmp_in_degrees.begin(), tmp_out_degrees.begin())); + in_degrees = std::move(tmp_in_degrees); + out_degrees = std::move(tmp_out_degrees); + } else if (in_degrees) { + rmm::device_uvector tmp_in_degrees(vertex_ids.size(), handle_.get_stream()); + thrust::gather(handle_.get_thrust_policy(), + vertices_iter, + vertices_iter + vertex_ids.size(), + in_degrees->begin(), + tmp_in_degrees.begin()); + in_degrees = std::move(tmp_in_degrees); + } else { + rmm::device_uvector tmp_out_degrees(vertex_ids.size(), handle_.get_stream()); + thrust::gather(handle_.get_thrust_policy(), + vertices_iter, + vertices_iter + vertex_ids.size(), + out_degrees->begin(), + tmp_out_degrees.begin()); + out_degrees = std::move(tmp_out_degrees); + } + + cugraph::unrenumber_local_int_vertices( + handle_, + vertex_ids.data(), + vertex_ids.size(), + number_map->data(), + graph_view.local_vertex_partition_range_first(), + graph_view.local_vertex_partition_range_last(), + do_expensive_check_); + } else { + vertex_ids.resize(graph_view.local_vertex_partition_range_size(), handle_.get_stream()); + raft::copy(vertex_ids.data(), number_map->data(), vertex_ids.size(), handle_.get_stream()); + } + + result_ = new cugraph::c_api::cugraph_degrees_result_t{ + graph_view.is_symmetric(), + new cugraph::c_api::cugraph_type_erased_device_array_t(vertex_ids, graph_->vertex_type_), + in_degrees + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*in_degrees, graph_->edge_type_) + : nullptr, + out_degrees + ? new cugraph::c_api::cugraph_type_erased_device_array_t(*out_degrees, graph_->edge_type_) + : nullptr}; + } + } +}; + +} // namespace + +extern "C" cugraph_error_code_t cugraph_in_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error) +{ + degrees_functor functor(handle, graph, source_vertices, true, false, do_expensive_check); + + return cugraph::c_api::run_algorithm(graph, functor, result, error); +} + +extern "C" cugraph_error_code_t cugraph_out_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error) +{ + degrees_functor functor(handle, graph, source_vertices, false, true, do_expensive_check); + + return cugraph::c_api::run_algorithm(graph, functor, result, error); +} + +extern "C" cugraph_error_code_t cugraph_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error) +{ + degrees_functor functor(handle, graph, source_vertices, true, true, do_expensive_check); + + return cugraph::c_api::run_algorithm(graph, functor, result, error); +} diff --git a/cpp/src/c_api/degrees_result.cpp b/cpp/src/c_api/degrees_result.cpp new file mode 100644 index 00000000000..a4649e36d05 --- /dev/null +++ b/cpp/src/c_api/degrees_result.cpp @@ -0,0 +1,63 @@ +/* + * Copyright (c) 2024, 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_api/degrees_result.hpp" + +#include + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_vertices( + cugraph_degrees_result_t* degrees_result) +{ + auto internal_pointer = + reinterpret_cast(degrees_result); + return reinterpret_cast( + internal_pointer->vertex_ids_->view()); +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_in_degrees( + cugraph_degrees_result_t* degrees_result) +{ + auto internal_pointer = + reinterpret_cast(degrees_result); + return internal_pointer->in_degrees_ == nullptr + ? nullptr + : reinterpret_cast( + internal_pointer->in_degrees_->view()); +} + +extern "C" cugraph_type_erased_device_array_view_t* cugraph_degrees_result_get_out_degrees( + cugraph_degrees_result_t* degrees_result) +{ + auto internal_pointer = + reinterpret_cast(degrees_result); + return internal_pointer->out_degrees_ != nullptr + ? reinterpret_cast( + internal_pointer->out_degrees_->view()) + : internal_pointer->is_symmetric + ? reinterpret_cast( + internal_pointer->in_degrees_->view()) + : nullptr; +} + +extern "C" void cugraph_degrees_result_free(cugraph_degrees_result_t* degrees_result) +{ + auto internal_pointer = + reinterpret_cast(degrees_result); + delete internal_pointer->vertex_ids_; + delete internal_pointer->in_degrees_; + delete internal_pointer->out_degrees_; + delete internal_pointer; +} diff --git a/cpp/src/c_api/degrees_result.hpp b/cpp/src/c_api/degrees_result.hpp new file mode 100644 index 00000000000..c6e9bffa5a1 --- /dev/null +++ b/cpp/src/c_api/degrees_result.hpp @@ -0,0 +1,32 @@ +/* + * Copyright (c) 2024, 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 "c_api/array.hpp" + +namespace cugraph { +namespace c_api { + +struct cugraph_degrees_result_t { + bool is_symmetric{false}; + cugraph_type_erased_device_array_t* vertex_ids_{}; + cugraph_type_erased_device_array_t* in_degrees_{}; + cugraph_type_erased_device_array_t* out_degrees_{}; +}; + +} // namespace c_api +} // namespace cugraph diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index af0dffcbf65..c84711e1a69 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -696,6 +696,7 @@ if(BUILD_CUGRAPH_MG_TESTS) ConfigureCTestMG(MG_CAPI_SIMILARITY_TEST c_api/mg_similarity_test.c) ConfigureCTestMG(MG_CAPI_K_CORE_TEST c_api/mg_k_core_test.c) ConfigureCTestMG(MG_CAPI_INDUCED_SUBGRAPH_TEST c_api/mg_induced_subgraph_test.c) + ConfigureCTestMG(MG_CAPI_DEGREES c_api/mg_degrees_test.c) ConfigureCTestMG(MG_CAPI_EGONET_TEST c_api/mg_egonet_test.c) ConfigureCTestMG(MG_CAPI_TWO_HOP_NEIGHBORS_TEST c_api/mg_two_hop_neighbors_test.c) @@ -764,6 +765,7 @@ ConfigureCTest(CAPI_CORE_NUMBER_TEST c_api/core_number_test.c) ConfigureCTest(CAPI_SIMILARITY_TEST c_api/similarity_test.c) ConfigureCTest(CAPI_K_CORE_TEST c_api/k_core_test.c) ConfigureCTest(CAPI_INDUCED_SUBGRAPH_TEST c_api/induced_subgraph_test.c) +ConfigureCTest(CAPI_DEGREES c_api/degrees_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) diff --git a/cpp/tests/c_api/c_test_utils.h b/cpp/tests/c_api/c_test_utils.h index ab9fbeccd4b..fbbf6333ee3 100644 --- a/cpp/tests/c_api/c_test_utils.h +++ b/cpp/tests/c_api/c_test_utils.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2023, NVIDIA CORPORATION. + * Copyright (c) 2021-2024, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -101,6 +101,8 @@ int create_sg_test_graph(const cugraph_resource_handle_t* handle, cugraph_graph_t** graph, cugraph_error_t** ret_error); +size_t cugraph_size_t_allreduce(const cugraph_resource_handle_t* handle, size_t value); + #ifdef __cplusplus } #endif diff --git a/cpp/tests/c_api/degrees_test.c b/cpp/tests/c_api/degrees_test.c new file mode 100644 index 00000000000..10a038b323b --- /dev/null +++ b/cpp/tests/c_api/degrees_test.c @@ -0,0 +1,387 @@ +/* + * Copyright (c) 2024, 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; + +/* + * Simple check of creating a graph from a COO on device memory. + */ +int generic_degrees_test(vertex_t* h_src, + vertex_t* h_dst, + weight_t* h_wgt, + size_t num_vertices, + size_t num_edges, + vertex_t* h_vertices, + size_t num_vertices_to_compute, + bool_t in_degrees, + bool_t out_degrees, + bool_t store_transposed, + bool_t is_symmetric, + edge_t *h_in_degrees, + edge_t *h_out_degrees) +{ + int test_ret_value = 0; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + + cugraph_resource_handle_t* handle = NULL; + cugraph_graph_t* graph = NULL; + cugraph_degrees_result_t* result = NULL; + + handle = cugraph_create_resource_handle(NULL); + TEST_ASSERT(test_ret_value, handle != NULL, "resource handle creation failed."); + + ret_code = create_test_graph( + handle, h_src, h_dst, h_wgt, num_edges, store_transposed, FALSE, is_symmetric, &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)); + + if (h_vertices == NULL) { + if (in_degrees && out_degrees) { + ret_code = cugraph_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } else if (in_degrees) { + ret_code = cugraph_in_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } else { + ret_code = cugraph_out_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } + + TEST_ASSERT( + test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_extract_degrees failed."); + } else { + cugraph_type_erased_device_array_t* vertices = NULL; + cugraph_type_erased_device_array_view_t* vertices_view = NULL; + + ret_code = + cugraph_type_erased_device_array_create(handle, num_vertices_to_compute, INT32, &vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "seeds create failed."); + + vertices_view = cugraph_type_erased_device_array_view(vertices); + + 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, "src copy_from_host failed."); + + if (in_degrees && out_degrees) { + ret_code = cugraph_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } else if (in_degrees) { + ret_code = cugraph_in_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } else { + ret_code = cugraph_out_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } + + TEST_ASSERT( + test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_extract_degrees failed."); + } + + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* result_in_degrees; + cugraph_type_erased_device_array_view_t* result_out_degrees; + + result_vertices = cugraph_degrees_result_get_vertices(result); + result_in_degrees = cugraph_degrees_result_get_in_degrees(result); + result_out_degrees = cugraph_degrees_result_get_out_degrees(result); + + size_t num_result_vertices = cugraph_type_erased_device_array_view_size(result_vertices); + + vertex_t h_result_vertices[num_result_vertices]; + edge_t h_result_in_degrees[num_result_vertices]; + edge_t h_result_out_degrees[num_result_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."); + + if (result_in_degrees != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_in_degrees, result_in_degrees, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } + + if (result_out_degrees != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_out_degrees, result_out_degrees, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } + + if (h_vertices != NULL) { + TEST_ASSERT(test_ret_value, num_result_vertices == num_vertices_to_compute, "results not the same size"); + } else { + TEST_ASSERT(test_ret_value, num_result_vertices == num_vertices, "results not the same size"); + } + + for (size_t i = 0; (i < num_result_vertices) && (test_ret_value == 0); ++i) { + if (h_in_degrees != NULL) { + TEST_ASSERT(test_ret_value, h_result_in_degrees[i] == h_in_degrees[h_result_vertices[i]], "in degree did not match"); + } + + if (h_out_degrees != NULL) { + TEST_ASSERT(test_ret_value, h_result_out_degrees[i] == h_out_degrees[h_result_vertices[i]], "out degree did not match"); + } + } + + cugraph_degrees_result_free(result); + cugraph_graph_free(graph); + cugraph_error_free(ret_error); + + return test_ret_value; +} + +int test_degrees() +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_in_degrees[] = {1, 2, 0, 2, 1, 2}; + vertex_t h_out_degrees[] = {1, 2, 3, 1, 1, 0}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + TRUE, + FALSE, + FALSE, + h_in_degrees, + h_out_degrees); +} + +int test_degrees_symmetric() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + + 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_in_degrees[] = {2, 4, 3, 3, 2, 2}; + vertex_t h_out_degrees[] = {2, 4, 3, 3, 2, 2}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + TRUE, + FALSE, + TRUE, + h_in_degrees, + h_out_degrees); +} + +int test_in_degrees() +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_in_degrees[] = {1, 2, 0, 2, 1, 2}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + FALSE, + FALSE, + TRUE, + h_in_degrees, + NULL); +} + +int test_out_degrees() +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_out_degrees[] = {1, 2, 3, 1, 1, 0}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + FALSE, + TRUE, + FALSE, + TRUE, + NULL, + h_out_degrees); +} + +int test_degrees_subset() +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 0, 2, -1, 2}; + vertex_t h_out_degrees[] = {-1, -1, 3, 1, -1, 0}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + TRUE, + FALSE, + FALSE, + h_in_degrees, + h_out_degrees); +} + +int test_degrees_symmetric_subset() +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 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_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 3, 3, -1, 2}; + vertex_t h_out_degrees[] = {-1, -1, 3, 3, -1, 2}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + TRUE, + FALSE, + TRUE, + h_in_degrees, + h_out_degrees); +} + +int test_in_degrees_subset() +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 0, 2, -1, 2}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + FALSE, + FALSE, + TRUE, + h_in_degrees, + NULL); +} + +int test_out_degrees_subset() +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_out_degrees[] = {-1, -1, 3, 1, -1, 0}; + + return generic_degrees_test(h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + FALSE, + TRUE, + FALSE, + TRUE, + NULL, + h_out_degrees); +} + +/******************************************************************************/ + +int main(int argc, char** argv) +{ + int result = 0; + result |= RUN_TEST(test_degrees); + result |= RUN_TEST(test_degrees_symmetric); + result |= RUN_TEST(test_in_degrees); + result |= RUN_TEST(test_out_degrees); + result |= RUN_TEST(test_degrees_subset); + result |= RUN_TEST(test_degrees_symmetric_subset); + result |= RUN_TEST(test_in_degrees_subset); + result |= RUN_TEST(test_out_degrees_subset); + return result; +} diff --git a/cpp/tests/c_api/mg_degrees_test.c b/cpp/tests/c_api/mg_degrees_test.c new file mode 100644 index 00000000000..3312dd4f5bb --- /dev/null +++ b/cpp/tests/c_api/mg_degrees_test.c @@ -0,0 +1,407 @@ +/* + * Copyright (c) 2024, 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 "mg_test_utils.h" /* RUN_TEST */ + +#include +#include + +#include + +typedef int32_t vertex_t; +typedef int32_t edge_t; +typedef float weight_t; + +/* + * Simple check of creating a graph from a COO on device memory. + */ +int generic_degrees_test(const cugraph_resource_handle_t* handle, + vertex_t* h_src, + vertex_t* h_dst, + weight_t* h_wgt, + size_t num_vertices, + size_t num_edges, + vertex_t* h_vertices, + size_t num_vertices_to_compute, + bool_t in_degrees, + bool_t out_degrees, + bool_t store_transposed, + bool_t is_symmetric, + edge_t* h_in_degrees, + edge_t* h_out_degrees) +{ + int test_ret_value = 0; + + cugraph_error_code_t ret_code = CUGRAPH_SUCCESS; + cugraph_error_t* ret_error; + + cugraph_graph_t* graph = NULL; + cugraph_degrees_result_t* result = NULL; + + ret_code = create_mg_test_graph( + handle, h_src, h_dst, h_wgt, num_edges, store_transposed, is_symmetric, &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)); + + if (h_vertices == NULL) { + if (in_degrees && out_degrees) { + ret_code = cugraph_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } else if (in_degrees) { + ret_code = cugraph_in_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } else { + ret_code = cugraph_out_degrees( + handle, graph, NULL, FALSE, &result, &ret_error); + } + + TEST_ASSERT( + test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_extract_degrees failed."); + } else { + cugraph_type_erased_device_array_t* vertices = NULL; + cugraph_type_erased_device_array_view_t* vertices_view = NULL; + + int rank = cugraph_resource_handle_get_rank(handle); + + size_t num_to_allocate = 0; + if (rank == 0) num_to_allocate = num_vertices_to_compute; + + ret_code = + cugraph_type_erased_device_array_create(handle, num_to_allocate, INT32, &vertices, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "seeds create failed."); + + vertices_view = cugraph_type_erased_device_array_view(vertices); + + 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, "src copy_from_host failed."); + + if (in_degrees && out_degrees) { + ret_code = cugraph_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } else if (in_degrees) { + ret_code = cugraph_in_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } else { + ret_code = cugraph_out_degrees( + handle, graph, vertices_view, FALSE, &result, &ret_error); + } + + TEST_ASSERT( + test_ret_value, ret_code == CUGRAPH_SUCCESS, "cugraph_extract_degrees failed."); + } + + cugraph_type_erased_device_array_view_t* result_vertices; + cugraph_type_erased_device_array_view_t* result_in_degrees; + cugraph_type_erased_device_array_view_t* result_out_degrees; + + result_vertices = cugraph_degrees_result_get_vertices(result); + result_in_degrees = cugraph_degrees_result_get_in_degrees(result); + result_out_degrees = cugraph_degrees_result_get_out_degrees(result); + + size_t num_result_vertices = cugraph_type_erased_device_array_view_size(result_vertices); + + vertex_t h_result_vertices[num_result_vertices]; + edge_t h_result_in_degrees[num_result_vertices]; + edge_t h_result_out_degrees[num_result_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."); + + if (result_in_degrees != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_in_degrees, result_in_degrees, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } + + if (result_out_degrees != NULL) { + ret_code = cugraph_type_erased_device_array_view_copy_to_host( + handle, (byte_t*)h_result_out_degrees, result_out_degrees, &ret_error); + TEST_ASSERT(test_ret_value, ret_code == CUGRAPH_SUCCESS, "copy_to_host failed."); + } + + if (h_vertices != NULL) { + size_t xxx = cugraph_size_t_allreduce(handle, num_result_vertices); + TEST_ASSERT(test_ret_value, cugraph_size_t_allreduce(handle, num_result_vertices) == num_vertices_to_compute, "results not the same size"); + } else { + size_t xxx = cugraph_size_t_allreduce(handle, num_result_vertices); + TEST_ASSERT(test_ret_value, cugraph_size_t_allreduce(handle, num_result_vertices) == num_vertices, "results not the same size"); + } + + for (size_t i = 0; (i < num_result_vertices) && (test_ret_value == 0); ++i) { + if (h_in_degrees != NULL) { + TEST_ASSERT(test_ret_value, h_result_in_degrees[i] == h_in_degrees[h_result_vertices[i]], "in degree did not match"); + } + + if (h_out_degrees != NULL) { + TEST_ASSERT(test_ret_value, h_result_out_degrees[i] == h_out_degrees[h_result_vertices[i]], "out degree did not match"); + } + } + + cugraph_degrees_result_free(result); + cugraph_graph_free(graph); + cugraph_error_free(ret_error); + return test_ret_value; +} + +int test_degrees(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_in_degrees[] = {1, 2, 0, 2, 1, 2}; + vertex_t h_out_degrees[] = {1, 2, 3, 1, 1, 0}; + + // Pagerank wants store_transposed = TRUE + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + TRUE, + TRUE, + FALSE, + h_in_degrees, + h_out_degrees); +} + +int test_degrees_symmetric(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 16; + size_t num_vertices = 6; + + 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_in_degrees[] = {2, 4, 3, 3, 2, 2}; + vertex_t h_out_degrees[] = {2, 4, 3, 3, 2, 2}; + + // Pagerank wants store_transposed = TRUE + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + TRUE, + TRUE, + TRUE, + h_in_degrees, + h_out_degrees); +} + +int test_in_degrees(const cugraph_resource_handle_t *handle) +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_in_degrees[] = {1, 2, 0, 2, 1, 2}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + TRUE, + FALSE, + FALSE, + TRUE, + h_in_degrees, + NULL); +} + +int test_out_degrees(const cugraph_resource_handle_t *handle) +{ + size_t num_edges = 8; + size_t num_vertices = 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}; + vertex_t h_out_degrees[] = {1, 2, 3, 1, 1, 0}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + NULL, + 0, + FALSE, + TRUE, + FALSE, + TRUE, + NULL, + h_out_degrees); +} + +int test_degrees_subset(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 0, 2, -1, 2}; + vertex_t h_out_degrees[] = {-1, -1, 3, 1, -1, 0}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + TRUE, + FALSE, + FALSE, + h_in_degrees, + h_out_degrees); +} + +int test_degrees_symmetric_subset(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 16; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 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_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 3, 3, -1, 2}; + vertex_t h_out_degrees[] = {-1, -1, 3, 3, -1, 2}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + TRUE, + FALSE, + TRUE, + h_in_degrees, + h_out_degrees); +} + +int test_in_degrees_subset(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_in_degrees[] = {-1, -1, 0, 2, -1, 2}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + TRUE, + FALSE, + FALSE, + TRUE, + h_in_degrees, + NULL); +} + +int test_out_degrees_subset(const cugraph_resource_handle_t* handle) +{ + size_t num_edges = 8; + size_t num_vertices = 6; + size_t num_vertices_to_compute = 3; + + 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}; + vertex_t h_vertices[] = {2, 3, 5}; + vertex_t h_out_degrees[] = {-1, -1, 3, 1, -1, 0}; + + return generic_degrees_test(handle, + h_src, + h_dst, + h_wgt, + num_vertices, + num_edges, + h_vertices, + num_vertices_to_compute, + FALSE, + TRUE, + FALSE, + TRUE, + NULL, + h_out_degrees); +} + +/******************************************************************************/ + +int main(int argc, char** argv) +{ + void* raft_handle = create_mg_raft_handle(argc, argv); + cugraph_resource_handle_t* handle = cugraph_create_resource_handle(raft_handle); + + int result = 0; + result |= RUN_MG_TEST(test_degrees, handle); + result |= RUN_MG_TEST(test_degrees_symmetric, handle); + result |= RUN_MG_TEST(test_in_degrees, handle); + result |= RUN_MG_TEST(test_out_degrees, handle); + result |= RUN_MG_TEST(test_degrees_subset, handle); + result |= RUN_MG_TEST(test_degrees_symmetric_subset, handle); + result |= RUN_MG_TEST(test_in_degrees_subset, handle); + result |= RUN_MG_TEST(test_out_degrees_subset, handle); + + cugraph_free_resource_handle(handle); + free_mg_raft_handle(raft_handle); + + return result; +} diff --git a/cpp/tests/c_api/test_utils.cpp b/cpp/tests/c_api/test_utils.cpp index e37cc4555dd..3013cbb7cc6 100644 --- a/cpp/tests/c_api/test_utils.cpp +++ b/cpp/tests/c_api/test_utils.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2021-2023, NVIDIA CORPORATION. + * Copyright (c) 2021-2024, NVIDIA CORPORATION. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. @@ -15,6 +15,9 @@ */ #include "c_test_utils.h" +#include "c_api/resource_handle.hpp" + +#include #include @@ -388,3 +391,12 @@ int create_sg_test_graph(const cugraph_resource_handle_t* handle, return test_ret_value; } + +extern "C" size_t cugraph_size_t_allreduce(const cugraph_resource_handle_t* handle, size_t value) +{ + auto internal_handle = reinterpret_cast(handle); + return cugraph::host_scalar_allreduce(internal_handle->handle_->get_comms(), + value, + raft::comms::op_t::SUM, + internal_handle->handle_->get_stream()); +} diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py index cdf1e937e67..0ef5eaf1b9e 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py @@ -12,7 +12,7 @@ # limitations under the License. import gc -from typing import Union +from typing import Union, Iterable import warnings import cudf @@ -28,10 +28,11 @@ GraphProperties, get_two_hop_neighbors as pylibcugraph_get_two_hop_neighbors, select_random_vertices as pylibcugraph_select_random_vertices, + degrees as pylibcugraph_degrees, + in_degrees as pylibcugraph_in_degrees, + out_degrees as pylibcugraph_out_degrees, ) -from cugraph.structure import graph_primtypes_wrapper -from cugraph.structure.graph_primtypes_wrapper import Direction from cugraph.structure.number_map import NumberMap from cugraph.structure.symmetrize import symmetrize from cugraph.dask.common.part_utils import ( @@ -536,7 +537,158 @@ def number_of_edges(self, directed_edges=False): raise RuntimeError("Graph is Empty") return self.properties.edge_count - def in_degree(self, vertex_subset=None): + def degrees_function( + self, + vertex_subset: Union[cudf.Series, dask_cudf.Series, Iterable] = None, + degree_type: str = "in_degree", + ) -> dask_cudf.DataFrame: + """ + Compute vertex in-degree, out-degree, degree and degrees. + + 1) Vertex in-degree is the number of edges pointing into the vertex. + 2) Vertex out-degree is the number of edges pointing out from the vertex. + 3) Vertex degree, is the total number of edges incident to a vertex + (both in and out edges) + 4) Vertex degrees computes vertex in-degree and out-degree. + + By default, this method computes vertex in-degree, out-degree, degree + or degrees for the entire set of vertices. If vertex_subset is provided, + this method optionally filters out all but those listed in + vertex_subset. + + Parameters + ---------- + vertex_subset : cudf.Series or dask_cudf.Series, iterable container, optional + A container of vertices for displaying corresponding in-degree. + If not set, degrees are computed for the entire set of vertices. + + degree_type : str (default='in_degree') + + Returns + ------- + df : dask_cudf.DataFrame + GPU DataFrame of size N (the default) or the size of the given + vertices (vertex_subset) containing the in_degree, out_degrees, + degree or degrees. The ordering is relative to the adjacency list, + or that given by the specified vertex_subset. + + Examples + -------- + >>> M = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ', + ... dtype=['int32', 'int32', 'float32'], header=None) + >>> G = cugraph.Graph() + >>> G.from_cudf_edgelist(M, '0', '1') + >>> df = G.degrees_function([0,9,12], "in_degree") + + """ + _client = default_client() + + def _call_plc_degrees_function( + sID: bytes, mg_graph_x, source_vertices: cudf.Series, degree_type: str + ) -> cp.array: + + if degree_type == "in_degree": + results = pylibcugraph_in_degrees( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + graph=mg_graph_x, + source_vertices=source_vertices, + do_expensive_check=False, + ) + elif degree_type == "out_degree": + results = pylibcugraph_out_degrees( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + graph=mg_graph_x, + source_vertices=source_vertices, + do_expensive_check=False, + ) + elif degree_type in ["degree", "degrees"]: + results = pylibcugraph_degrees( + resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()), + graph=mg_graph_x, + source_vertices=source_vertices, + do_expensive_check=False, + ) + else: + raise ValueError( + "Incorrect degree type passed, valid values are ", + "'in_degree', 'out_degree', 'degree' and 'degrees' ", + f"got '{degree_type}'", + ) + + return results + + if isinstance(vertex_subset, int): + vertex_subset = [vertex_subset] + + if isinstance(vertex_subset, list): + vertex_subset = cudf.Series(vertex_subset) + + if vertex_subset is not None: + if self.renumbered: + vertex_subset = self.renumber_map.to_internal_vertex_id(vertex_subset) + vertex_subset_type = self.edgelist.edgelist_df.dtypes.iloc[0] + else: + vertex_subset_type = self.input_df.dtypes.iloc[0] + + vertex_subset = vertex_subset.astype(vertex_subset_type) + + cupy_result = [ + _client.submit( + _call_plc_degrees_function, + Comms.get_session_id(), + self._plc_graph[w], + vertex_subset, + degree_type, + workers=[w], + allow_other_workers=False, + ) + for w in Comms.get_workers() + ] + + wait(cupy_result) + + def convert_to_cudf(cp_arrays: cp.ndarray, degree_type: bool) -> cudf.DataFrame: + """ + Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper + """ + df = cudf.DataFrame() + df["vertex"] = cp_arrays[0] + if degree_type in ["in_degree", "out_degree"]: + df["degree"] = cp_arrays[1] + # degree_type must be either 'degree' or 'degrees' + else: + if degree_type == "degrees": + df["in_degree"] = cp_arrays[1] + df["out_degree"] = cp_arrays[2] + else: + df["degree"] = cp_arrays[1] + cp_arrays[2] + return df + + cudf_result = [ + _client.submit( + convert_to_cudf, + cp_arrays, + degree_type, + workers=_client.who_has(cp_arrays)[cp_arrays.key], + ) + for cp_arrays in cupy_result + ] + + wait(cudf_result) + ddf = dask_cudf.from_delayed(cudf_result).persist() + wait(ddf) + + # Wait until the inactive futures are released + wait([(r.release(), c_r.release()) for r, c_r in zip(cupy_result, cudf_result)]) + + if self.properties.renumbered: + ddf = self.renumber_map.unrenumber(ddf, "vertex") + + return ddf + + def in_degree( + self, vertex_subset: Union[cudf.Series, dask_cudf.Series, Iterable] = None + ) -> dask_cudf.DataFrame: """ Compute vertex in-degree. Vertex in-degree is the number of edges pointing into the vertex. By default, this method computes vertex @@ -572,61 +724,11 @@ def in_degree(self, vertex_subset=None): >>> df = G.in_degree([0,9,12]) """ - src_col_name = self.source_columns - dst_col_name = self.destination_columns - - # select only the vertex columns - if not isinstance(src_col_name, list) and not isinstance(dst_col_name, list): - vertex_col_names = [src_col_name] + [dst_col_name] - - df = self.input_df[vertex_col_names] - df = df.drop(columns=src_col_name) - - nodes = self.nodes() - if isinstance(nodes, dask_cudf.Series): - nodes = nodes.to_frame() - - if not isinstance(dst_col_name, list): - df = df.rename(columns={dst_col_name: "vertex"}) - dst_col_name = "vertex" - - vertex_col_names = df.columns - nodes.columns = vertex_col_names - - df["degree"] = 1 - - # FIXME: leverage the C++ in_degree for optimal performance - in_degree = ( - df.groupby(dst_col_name) - .degree.count(split_out=df.npartitions) - .reset_index() - ) - - # Add vertices with zero in_degree - in_degree = nodes.merge(in_degree, how="outer").fillna(0) - - # Convert vertex_subset to dataframe. - if vertex_subset is not None: - if not isinstance(vertex_subset, (dask_cudf.DataFrame, cudf.DataFrame)): - if isinstance(vertex_subset, dask_cudf.Series): - vertex_subset = vertex_subset.to_frame() - else: - df = cudf.DataFrame() - if isinstance(vertex_subset, (cudf.Series, list)): - df["vertex"] = vertex_subset - vertex_subset = df - if isinstance(vertex_subset, (dask_cudf.DataFrame, cudf.DataFrame)): - vertex_subset.columns = vertex_col_names - in_degree = in_degree.merge(vertex_subset, how="inner") - else: - raise TypeError( - f"Expected type are: cudf, dask_cudf objects, " - f"iterable container, got " - f"{type(vertex_subset)}" - ) - return in_degree + return self.degrees_function(vertex_subset, "in_degree") - def out_degree(self, vertex_subset=None): + def out_degree( + self, vertex_subset: Union[cudf.Series, dask_cudf.Series, Iterable] = None + ) -> dask_cudf.DataFrame: """ Compute vertex out-degree. Vertex out-degree is the number of edges pointing out from the vertex. By default, this method computes vertex @@ -662,62 +764,11 @@ def out_degree(self, vertex_subset=None): >>> df = G.out_degree([0,9,12]) """ - src_col_name = self.source_columns - dst_col_name = self.destination_columns - - # select only the vertex columns - if not isinstance(src_col_name, list) and not isinstance(dst_col_name, list): - vertex_col_names = [src_col_name] + [dst_col_name] - - df = self.input_df[vertex_col_names] - df = df.drop(columns=dst_col_name) - - nodes = self.nodes() - if isinstance(nodes, dask_cudf.Series): - nodes = nodes.to_frame() - - if not isinstance(src_col_name, list): - df = df.rename(columns={src_col_name: "vertex"}) - src_col_name = "vertex" - - vertex_col_names = df.columns - - nodes.columns = vertex_col_names - - df["degree"] = 1 - # leverage the C++ out_degree for optimal performance - out_degree = ( - df.groupby(src_col_name) - .degree.count(split_out=df.npartitions) - .reset_index() - ) - - # Add vertices with zero out_degree - out_degree = nodes.merge(out_degree, how="outer").fillna(0) - - # Convert vertex_subset to dataframe. - if vertex_subset is not None: - if not isinstance(vertex_subset, (dask_cudf.DataFrame, cudf.DataFrame)): - if isinstance(vertex_subset, dask_cudf.Series): - vertex_subset = vertex_subset.to_frame() - else: - df = cudf.DataFrame() - if isinstance(vertex_subset, (cudf.Series, list)): - df["vertex"] = vertex_subset - vertex_subset = df - if isinstance(vertex_subset, (dask_cudf.DataFrame, cudf.DataFrame)): - vertex_subset.columns = vertex_col_names - out_degree = out_degree.merge(vertex_subset, how="inner") - else: - raise TypeError( - f"Expected type are: cudf, dask_cudf objects, " - f"iterable container, got " - f"{type(vertex_subset)}" - ) + return self.degrees_function(vertex_subset, "out_degree") - return out_degree - - def degree(self, vertex_subset=None): + def degree( + self, vertex_subset: Union[cudf.Series, dask_cudf.Series, Iterable] = None + ) -> dask_cudf.DataFrame: """ Compute vertex degree, which is the total number of edges incident to a vertex (both in and out edges). By default, this method computes @@ -754,18 +805,12 @@ def degree(self, vertex_subset=None): """ - vertex_in_degree = self.in_degree(vertex_subset) - vertex_out_degree = self.out_degree(vertex_subset) - # FIXME: leverage the C++ degree for optimal performance - vertex_degree = dask_cudf.concat([vertex_in_degree, vertex_out_degree]) - vertex_degree = vertex_degree.groupby(["vertex"], as_index=False).sum( - split_out=self.input_df.npartitions - ) - - return vertex_degree + return self.degrees_function(vertex_subset, "degree") # FIXME: vertex_subset could be a DataFrame for multi-column vertices - def degrees(self, vertex_subset=None): + def degrees( + self, vertex_subset: Union[cudf.Series, dask_cudf.Series, Iterable] = None + ) -> dask_cudf.DataFrame: """ Compute vertex in-degree and out-degree. By default, this method computes vertex degrees for the entire set of vertices. If @@ -802,21 +847,7 @@ def degrees(self, vertex_subset=None): >>> df = G.degrees([0,9,12]) """ - raise NotImplementedError("Not supported for distributed graph") - - def _degree(self, vertex_subset, direction=Direction.ALL): - vertex_col, degree_col = graph_primtypes_wrapper._mg_degree(self, direction) - df = cudf.DataFrame() - df["vertex"] = vertex_col - df["degree"] = degree_col - - if self.renumbered is True: - df = self.renumber_map.unrenumber(df, "vertex") - - if vertex_subset is not None: - df = df[df["vertex"].isin(vertex_subset)] - - return df + return self.degrees_function(vertex_subset, "degrees") def get_two_hop_neighbors(self, start_vertices=None): """ diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py index 121a4c6245a..99934e02b10 100644 --- a/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py +++ b/python/cugraph/cugraph/structure/graph_implementation/simpleGraph.py @@ -12,7 +12,6 @@ # limitations under the License. from cugraph.structure import graph_primtypes_wrapper -from cugraph.structure.graph_primtypes_wrapper import Direction from cugraph.structure.symmetrize import symmetrize from cugraph.structure.number_map import NumberMap import cugraph.dask.common.mg_utils as mg_utils @@ -23,10 +22,13 @@ import numpy as np import warnings from cugraph.dask.structure import replication -from typing import Union, Dict +from typing import Union, Dict, Iterable from pylibcugraph import ( get_two_hop_neighbors as pylibcugraph_get_two_hop_neighbors, select_random_vertices as pylibcugraph_select_random_vertices, + degrees as pylibcugraph_degrees, + in_degrees as pylibcugraph_in_degrees, + out_degrees as pylibcugraph_out_degrees, ) from pylibcugraph import ( @@ -854,7 +856,111 @@ def number_of_edges(self, directed_edges=False): raise ValueError("Graph is Empty") return self.properties.edge_count - def in_degree(self, vertex_subset=None): + def degrees_function( + self, + vertex_subset: Union[cudf.Series, Iterable] = None, + degree_type: str = "in_degree", + ) -> cudf.DataFrame: + """ + Compute vertex in-degree, out-degree, degree and degrees. + + 1) Vertex in-degree is the number of edges pointing into the vertex. + 2) Vertex out-degree is the number of edges pointing out from the vertex. + 3) Vertex degree, is the total number of edges incident to a vertex + (both in and out edges) + 4) Vertex degrees computes vertex in-degree and out-degree. + + By default, this method computes vertex in-degree, out-degree, degree + or degrees for the entire set of vertices. If vertex_subset is provided, + this method optionally filters out all but those listed in + vertex_subset. + + Parameters + ---------- + vertex_subset : cudf.Series or iterable container, optional + A container of vertices for displaying corresponding in-degree. + If not set, degrees are computed for the entire set of vertices. + + degree_type : str (default='in_degree') + + Returns + ------- + df : cudf.DataFrame + GPU DataFrame of size N (the default) or the size of the given + vertices (vertex_subset) containing the in_degree, out_degrees, + degree or degrees. The ordering is relative to the adjacency list, + or that given by the specified vertex_subset. + + Examples + -------- + >>> M = cudf.read_csv(datasets_path / 'karate.csv', delimiter=' ', + ... dtype=['int32', 'int32', 'float32'], header=None) + >>> G = cugraph.Graph() + >>> G.from_cudf_edgelist(M, '0', '1') + >>> df = G.degrees_function([0,9,12], "in_degree") + + """ + if vertex_subset is not None: + if not isinstance(vertex_subset, cudf.Series): + vertex_subset = cudf.Series(vertex_subset) + if self.properties.renumbered is True: + vertex_subset = self.renumber_map.to_internal_vertex_id( + vertex_subset + ) + vertex_subset_type = self.edgelist.edgelist_df.dtypes.iloc[0] + else: + vertex_subset_type = self.input_df.dtypes.iloc[0] + + vertex_subset = vertex_subset.astype(vertex_subset_type) + + do_expensive_check = False + df = cudf.DataFrame() + vertex = None + + if degree_type == "in_degree": + vertex, in_degrees = pylibcugraph_in_degrees( + resource_handle=ResourceHandle(), + graph=self._plc_graph, + source_vertices=vertex_subset, + do_expensive_check=do_expensive_check, + ) + df["degree"] = in_degrees + elif degree_type == "out_degree": + vertex, out_degrees = pylibcugraph_out_degrees( + resource_handle=ResourceHandle(), + graph=self._plc_graph, + source_vertices=vertex_subset, + do_expensive_check=do_expensive_check, + ) + df["degree"] = out_degrees + elif degree_type in ["degree", "degrees"]: + vertex, in_degrees, out_degrees = pylibcugraph_degrees( + resource_handle=ResourceHandle(), + graph=self._plc_graph, + source_vertices=vertex_subset, + do_expensive_check=do_expensive_check, + ) + if degree_type == "degrees": + df["in_degree"] = in_degrees + df["out_degree"] = out_degrees + + else: + df["degree"] = in_degrees + out_degrees + else: + raise ValueError( + "Incorrect degree type passed, valid values are ", + "'in_degree', 'out_degree', 'degree' and 'degrees' ", + f"got '{degree_type}'", + ) + df["vertex"] = vertex + if self.properties.renumbered is True: + df = self.renumber_map.unrenumber(df, "vertex") + + return df + + def in_degree( + self, vertex_subset: Union[cudf.Series, Iterable] = None + ) -> cudf.DataFrame: """ Compute vertex in-degree. Vertex in-degree is the number of edges pointing into the vertex. By default, this method computes vertex @@ -892,11 +998,11 @@ def in_degree(self, vertex_subset=None): >>> df = G.in_degree([0,9,12]) """ - in_degree = self._degree(vertex_subset, direction=Direction.IN) - - return in_degree + return self.degrees_function(vertex_subset, "in_degree") - def out_degree(self, vertex_subset=None): + def out_degree( + self, vertex_subset: Union[cudf.Series, Iterable] = None + ) -> cudf.DataFrame: """ Compute vertex out-degree. Vertex out-degree is the number of edges pointing out from the vertex. By default, this method computes vertex @@ -934,10 +1040,11 @@ def out_degree(self, vertex_subset=None): >>> df = G.out_degree([0,9,12]) """ - out_degree = self._degree(vertex_subset, direction=Direction.OUT) - return out_degree + return self.degrees_function(vertex_subset, "out_degree") - def degree(self, vertex_subset=None): + def degree( + self, vertex_subset: Union[cudf.Series, Iterable] = None + ) -> cudf.DataFrame: """ Compute vertex degree, which is the total number of edges incident to a vertex (both in and out edges). By default, this method computes @@ -976,10 +1083,12 @@ def degree(self, vertex_subset=None): >>> subset_df = G.degree([0,9,12]) """ - return self._degree(vertex_subset) + return self.degrees_function(vertex_subset, "degree") # FIXME: vertex_subset could be a DataFrame for multi-column vertices - def degrees(self, vertex_subset=None): + def degrees( + self, vertex_subset: Union[cudf.Series, Iterable] = None + ) -> cudf.DataFrame: """ Compute vertex in-degree and out-degree. By default, this method computes vertex degrees for the entire set of vertices. If @@ -1019,70 +1128,7 @@ def degrees(self, vertex_subset=None): >>> df = G.degrees([0,9,12]) """ - ( - vertex_col, - in_degree_col, - out_degree_col, - ) = graph_primtypes_wrapper._degrees(self) - - df = cudf.DataFrame() - df["vertex"] = vertex_col - df["in_degree"] = in_degree_col - df["out_degree"] = out_degree_col - - if self.properties.renumbered: - # Get the internal vertex IDs - nodes = self.renumber_map.df_internal_to_external["id"] - else: - nodes = self.nodes() - # If the vertex IDs are not contiguous, remove results for the - # isolated vertices - df = df[df["vertex"].isin(nodes.to_cupy())] - - if vertex_subset is not None: - if not isinstance(vertex_subset, cudf.Series): - vertex_subset = cudf.Series(vertex_subset) - if self.properties.renumbered: - vertex_subset = self.renumber_map.to_internal_vertex_id( - vertex_subset - ) - vertex_subset = vertex_subset.to_cupy() - df = df[df["vertex"].isin(vertex_subset)] - - if self.properties.renumbered: - df = self.renumber_map.unrenumber(df, "vertex") - - return df - - def _degree(self, vertex_subset, direction=Direction.ALL): - vertex_col, degree_col = graph_primtypes_wrapper._degree(self, direction) - df = cudf.DataFrame() - df["vertex"] = vertex_col - df["degree"] = degree_col - - if self.properties.renumbered: - # Get the internal vertex IDs - nodes = self.renumber_map.df_internal_to_external["id"] - else: - nodes = self.nodes() - # If the vertex IDs are not contiguous, remove results for the - # isolated vertices - df = df[df["vertex"].isin(nodes.to_cupy())] - - if vertex_subset is not None: - if not isinstance(vertex_subset, cudf.Series): - vertex_subset = cudf.Series(vertex_subset) - if self.properties.renumbered: - vertex_subset = self.renumber_map.to_internal_vertex_id( - vertex_subset - ) - vertex_subset = vertex_subset.to_cupy() - df = df[df["vertex"].isin(vertex_subset)] - - if self.properties.renumbered: - df = self.renumber_map.unrenumber(df, "vertex") - - return df + return self.degrees_function(vertex_subset, "degrees") def _make_plc_graph( self, diff --git a/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py b/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py index a46f4b9463b..1bef1e0872b 100644 --- a/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py +++ b/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py @@ -1,4 +1,4 @@ -# Copyright (c) 2018-2023, NVIDIA CORPORATION. +# Copyright (c) 2018-2024, 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 @@ -18,7 +18,6 @@ import cudf import dask_cudf import cugraph -from cugraph.dask.common.mg_utils import is_single_gpu from cugraph.testing.utils import RAPIDS_DATASET_ROOT_DIR_PATH from cudf.testing import assert_series_equal @@ -41,7 +40,6 @@ def setup_function(): @pytest.mark.mg -@pytest.mark.skipif(is_single_gpu(), reason="skipping MG testing on Single GPU system") @pytest.mark.parametrize("directed", IS_DIRECTED) @pytest.mark.parametrize("data_file", DATA_PATH) def test_dask_mg_degree(dask_client, directed, data_file): diff --git a/python/pylibcugraph/pylibcugraph/CMakeLists.txt b/python/pylibcugraph/pylibcugraph/CMakeLists.txt index c2e22fc1ff7..7cc90145949 100644 --- a/python/pylibcugraph/pylibcugraph/CMakeLists.txt +++ b/python/pylibcugraph/pylibcugraph/CMakeLists.txt @@ -1,5 +1,5 @@ # ============================================================================= -# Copyright (c) 2022-2023, NVIDIA CORPORATION. +# Copyright (c) 2022-2024, 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 @@ -57,6 +57,7 @@ set(cython_sources utils.pyx weakly_connected_components.pyx replicate_edgelist.pyx + degrees.pyx ) set(linked_libraries cugraph::cugraph;cugraph::cugraph_c) diff --git a/python/pylibcugraph/pylibcugraph/__init__.py b/python/pylibcugraph/pylibcugraph/__init__.py index ab518e24cae..dcdef05e106 100644 --- a/python/pylibcugraph/pylibcugraph/__init__.py +++ b/python/pylibcugraph/pylibcugraph/__init__.py @@ -95,6 +95,8 @@ from pylibcugraph.sorensen_coefficients import sorensen_coefficients +from pylibcugraph.degrees import in_degrees, out_degrees, degrees + from pylibcugraph import exceptions diff --git a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd index 90bc041e5f0..6f1ac1f640b 100644 --- a/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd +++ b/python/pylibcugraph/pylibcugraph/_cugraph_c/graph_functions.pxd @@ -182,3 +182,58 @@ cdef extern from "cugraph_c/graph_functions.h": cugraph_induced_subgraph_result_t** result, cugraph_error_t** error ) + + ########################################################################### + # degrees + ctypedef struct cugraph_degrees_result_t: + pass + + cdef cugraph_error_code_t \ + cugraph_in_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error + ) + + cdef cugraph_error_code_t \ + cugraph_out_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error + ) + + cdef cugraph_error_code_t \ + cugraph_degrees( + const cugraph_resource_handle_t* handle, + cugraph_graph_t* graph, + const cugraph_type_erased_device_array_view_t* source_vertices, + bool_t do_expensive_check, + cugraph_degrees_result_t** result, + cugraph_error_t** error + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_degrees_result_get_vertices( + cugraph_degrees_result_t* degrees_result + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_degrees_result_get_in_degrees( + cugraph_degrees_result_t* degrees_result + ) + + cdef cugraph_type_erased_device_array_view_t* \ + cugraph_degrees_result_get_out_degrees( + cugraph_degrees_result_t* degrees_result + ) + + cdef void \ + cugraph_degrees_result_free( + cugraph_degrees_result_t* degrees_result + ) diff --git a/python/pylibcugraph/pylibcugraph/degrees.pyx b/python/pylibcugraph/pylibcugraph/degrees.pyx new file mode 100644 index 00000000000..7818da441bd --- /dev/null +++ b/python/pylibcugraph/pylibcugraph/degrees.pyx @@ -0,0 +1,307 @@ +# Copyright (c) 2024, 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 libc.stdint cimport uintptr_t + +from pylibcugraph._cugraph_c.resource_handle cimport ( + bool_t, + data_type_id_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_degrees_result_t, + cugraph_degrees, + cugraph_in_degrees, + cugraph_out_degrees, + cugraph_degrees_result_get_vertices, + cugraph_degrees_result_get_in_degrees, + cugraph_degrees_result_get_out_degrees, + cugraph_degrees_result_free, +) +from pylibcugraph.resource_handle cimport ( + ResourceHandle, +) +from pylibcugraph.graphs cimport ( + _GPUGraph, +) +from pylibcugraph.utils cimport ( + assert_success, + copy_to_cupy_array, + assert_CAI_type, + create_cugraph_type_erased_device_array_view_from_py_obj, +) + + +def in_degrees(ResourceHandle resource_handle, + _GPUGraph graph, + source_vertices, + bool_t do_expensive_check): + """ + Compute the in degrees for the nodes of the graph. + + Parameters + ---------- + resource_handle : ResourceHandle + Handle to the underlying device resources needed for referencing data + and running algorithms. + + graph : SGGraph or MGGraph + The input graph, for either Single or Multi-GPU operations. + + source_vertices : cupy array + The nodes for which we will compute degrees. + + do_expensive_check : bool_t + A flag to run expensive checks for input arguments if True. + + Returns + ------- + A tuple of device arrays, where the first item in the tuple is a device + array containing the vertices, the second item in the tuple is a device + array containing the in degrees for the vertices. + + Examples + -------- + >>> import pylibcugraph, cupy, numpy + >>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32) + >>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32) + >>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32) + >>> resource_handle = pylibcugraph.ResourceHandle() + >>> graph_props = pylibcugraph.GraphProperties( + ... is_symmetric=False, is_multigraph=False) + >>> G = pylibcugraph.SGGraph( + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, + ... store_transposed=True, renumber=False, do_expensive_check=False) + >>> (vertices, in_degrees) = pylibcugraph.in_degrees( + resource_handle, G, None, False) + + """ + + 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_degrees_result_t* result_ptr + cdef cugraph_error_code_t error_code + cdef cugraph_error_t* error_ptr + + assert_CAI_type(source_vertices, "source_vertices", True) + + cdef cugraph_type_erased_device_array_view_t* \ + source_vertices_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + source_vertices) + + error_code = cugraph_in_degrees(c_resource_handle_ptr, + c_graph_ptr, + source_vertices_ptr, + do_expensive_check, + &result_ptr, + &error_ptr) + assert_success(error_code, error_ptr, "cugraph_in_degrees") + + # Extract individual device array pointers from result and copy to cupy + # arrays for returning. + cdef cugraph_type_erased_device_array_view_t* vertices_ptr = \ + cugraph_degrees_result_get_vertices(result_ptr) + cdef cugraph_type_erased_device_array_view_t* in_degrees_ptr = \ + cugraph_degrees_result_get_in_degrees(result_ptr) + + cupy_vertices = copy_to_cupy_array(c_resource_handle_ptr, vertices_ptr) + cupy_in_degrees = copy_to_cupy_array(c_resource_handle_ptr, in_degrees_ptr) + + cugraph_degrees_result_free(result_ptr) + + return (cupy_vertices, cupy_in_degrees) + +def out_degrees(ResourceHandle resource_handle, + _GPUGraph graph, + source_vertices, + bool_t do_expensive_check): + """ + Compute the out degrees for the nodes of the graph. + + Parameters + ---------- + resource_handle : ResourceHandle + Handle to the underlying device resources needed for referencing data + and running algorithms. + + graph : SGGraph or MGGraph + The input graph, for either Single or Multi-GPU operations. + + source_vertices : cupy array + The nodes for which we will compute degrees. + + do_expensive_check : bool_t + A flag to run expensive checks for input arguments if True. + + Returns + ------- + A tuple of device arrays, where the first item in the tuple is a device + array containing the vertices, the second item in the tuple is a device + array containing the out degrees for the vertices. + + Examples + -------- + >>> import pylibcugraph, cupy, numpy + >>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32) + >>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32) + >>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32) + >>> resource_handle = pylibcugraph.ResourceHandle() + >>> graph_props = pylibcugraph.GraphProperties( + ... is_symmetric=False, is_multigraph=False) + >>> G = pylibcugraph.SGGraph( + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, + ... store_transposed=True, renumber=False, do_expensive_check=False) + >>> (vertices, out_degrees) = pylibcugraph.out_degrees( + resource_handle, G, None, False) + + """ + + 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_degrees_result_t* result_ptr + cdef cugraph_error_code_t error_code + cdef cugraph_error_t* error_ptr + + assert_CAI_type(source_vertices, "source_vertices", True) + + cdef cugraph_type_erased_device_array_view_t* \ + source_vertices_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + source_vertices) + + error_code = cugraph_out_degrees(c_resource_handle_ptr, + c_graph_ptr, + source_vertices_ptr, + do_expensive_check, + &result_ptr, + &error_ptr) + assert_success(error_code, error_ptr, "cugraph_out_degrees") + + # Extract individual device array pointers from result and copy to cupy + # arrays for returning. + cdef cugraph_type_erased_device_array_view_t* vertices_ptr = \ + cugraph_degrees_result_get_vertices(result_ptr) + cdef cugraph_type_erased_device_array_view_t* out_degrees_ptr = \ + cugraph_degrees_result_get_out_degrees(result_ptr) + + cupy_vertices = copy_to_cupy_array(c_resource_handle_ptr, vertices_ptr) + cupy_out_degrees = copy_to_cupy_array(c_resource_handle_ptr, out_degrees_ptr) + + cugraph_degrees_result_free(result_ptr) + + return (cupy_vertices, cupy_out_degrees) + + +def degrees(ResourceHandle resource_handle, + _GPUGraph graph, + source_vertices, + bool_t do_expensive_check): + """ + Compute the degrees for the nodes of the graph. + + Parameters + ---------- + resource_handle : ResourceHandle + Handle to the underlying device resources needed for referencing data + and running algorithms. + + graph : SGGraph or MGGraph + The input graph, for either Single or Multi-GPU operations. + + source_vertices : cupy array + The nodes for which we will compute degrees. + + do_expensive_check : bool_t + A flag to run expensive checks for input arguments if True. + + Returns + ------- + A tuple of device arrays, where the first item in the tuple is a device + array containing the vertices, the second item in the tuple is a device + array containing the in degrees for the vertices, the third item in the + tuple is a device array containing the out degrees for the vertices. + + Examples + -------- + >>> import pylibcugraph, cupy, numpy + >>> srcs = cupy.asarray([0, 1, 2], dtype=numpy.int32) + >>> dsts = cupy.asarray([1, 2, 3], dtype=numpy.int32) + >>> weights = cupy.asarray([1.0, 1.0, 1.0], dtype=numpy.float32) + >>> resource_handle = pylibcugraph.ResourceHandle() + >>> graph_props = pylibcugraph.GraphProperties( + ... is_symmetric=False, is_multigraph=False) + >>> G = pylibcugraph.SGGraph( + ... resource_handle, graph_props, srcs, dsts, weight_array=weights, + ... store_transposed=True, renumber=False, do_expensive_check=False) + >>> (vertices, in_degrees, out_degrees) = pylibcugraph.degrees( + resource_handle, G, None, False) + + """ + + 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_degrees_result_t* result_ptr + cdef cugraph_error_code_t error_code + cdef cugraph_error_t* error_ptr + + assert_CAI_type(source_vertices, "source_vertices", True) + + cdef cugraph_type_erased_device_array_view_t* \ + source_vertices_ptr = \ + create_cugraph_type_erased_device_array_view_from_py_obj( + source_vertices) + + error_code = cugraph_degrees(c_resource_handle_ptr, + c_graph_ptr, + source_vertices_ptr, + do_expensive_check, + &result_ptr, + &error_ptr) + assert_success(error_code, error_ptr, "cugraph_degrees") + + # Extract individual device array pointers from result and copy to cupy + # arrays for returning. + cdef cugraph_type_erased_device_array_view_t* vertices_ptr = \ + cugraph_degrees_result_get_vertices(result_ptr) + cdef cugraph_type_erased_device_array_view_t* in_degrees_ptr = \ + cugraph_degrees_result_get_in_degrees(result_ptr) + cdef cugraph_type_erased_device_array_view_t* out_degrees_ptr = \ + cugraph_degrees_result_get_out_degrees(result_ptr) + + cupy_vertices = copy_to_cupy_array(c_resource_handle_ptr, vertices_ptr) + cupy_in_degrees = copy_to_cupy_array(c_resource_handle_ptr, in_degrees_ptr) + cupy_out_degrees = copy_to_cupy_array(c_resource_handle_ptr, out_degrees_ptr) + + cugraph_degrees_result_free(result_ptr) + + return (cupy_vertices, cupy_in_degrees, cupy_out_degrees) From 120e5b892d5396f10ceb5d957de01c8d74a2e781 Mon Sep 17 00:00:00 2001 From: Seunghwa Kang <45857425+seunghwak@users.noreply.github.com> Date: Tue, 12 Mar 2024 20:10:13 -0700 Subject: [PATCH 7/9] Enable edge masking in the remaining primitives (#4186) This PR will update all the remaining primitives to support edge masking. This PR pulls updates from https://github.com/rapidsai/cugraph/pull/4126 and better be reviewed/merged after PR https://github.com/rapidsai/cugraph/pull/4126. Authors: - Seunghwa Kang (https://github.com/seunghwak) 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/4186 --- cpp/include/cugraph/graph_view.hpp | 4 +- cpp/include/cugraph/utilities/misc_utils.cuh | 8 + cpp/src/prims/detail/nbr_intersection.cuh | 11 +- ...m_reduce_dst_key_aggregated_outgoing_e.cuh | 529 +++++++++++----- ...t_nbr_intersection_of_e_endpoints_by_v.cuh | 24 +- .../transform_reduce_e_by_src_dst_key.cuh | 292 +++++++-- cpp/tests/CMakeLists.txt | 33 +- ...rm_reduce_dst_key_aggregated_outgoing_e.cu | 599 ++++++++++++++++++ ..._v_transform_reduce_incoming_outgoing_e.cu | 84 +-- cpp/tests/prims/mg_reduce_v.cu | 47 +- ...st_nbr_intersection_of_e_endpoints_by_v.cu | 289 +++++++++ cpp/tests/prims/mg_transform_reduce_e.cu | 41 +- .../mg_transform_reduce_e_by_src_dst_key.cu | 495 +++++++++++++++ cpp/tests/prims/mg_transform_reduce_v.cu | 47 +- cpp/tests/prims/result_compare.cuh | 143 +++++ 15 files changed, 2189 insertions(+), 457 deletions(-) create mode 100644 cpp/tests/prims/mg_per_v_transform_reduce_dst_key_aggregated_outgoing_e.cu create mode 100644 cpp/tests/prims/mg_transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cu create mode 100644 cpp/tests/prims/mg_transform_reduce_e_by_src_dst_key.cu create mode 100644 cpp/tests/prims/result_compare.cuh diff --git a/cpp/include/cugraph/graph_view.hpp b/cpp/include/cugraph/graph_view.hpp index 3f3514179bf..cbb52ef3b1e 100644 --- a/cpp/include/cugraph/graph_view.hpp +++ b/cpp/include/cugraph/graph_view.hpp @@ -613,7 +613,7 @@ class graph_view_tnumber_of_vertices()); } - // FIXME: deprecated, replaced with copmute_number_of_edges (which works with or without edge + // FIXME: deprecated, replaced with compute_number_of_edges (which works with or without edge // masking) edge_t number_of_edges() const { diff --git a/cpp/include/cugraph/utilities/misc_utils.cuh b/cpp/include/cugraph/utilities/misc_utils.cuh index d3917a3e851..633dabe5b40 100644 --- a/cpp/include/cugraph/utilities/misc_utils.cuh +++ b/cpp/include/cugraph/utilities/misc_utils.cuh @@ -94,6 +94,14 @@ thrust::optional to_thrust_optional(std::optional val) return ret; } +template +std::optional to_std_optional(thrust::optional val) +{ + std::optional ret{std::nullopt}; + if (val) { ret = *val; } + return ret; +} + template rmm::device_uvector expand_sparse_offsets(raft::device_span offsets, idx_t base_idx, diff --git a/cpp/src/prims/detail/nbr_intersection.cuh b/cpp/src/prims/detail/nbr_intersection.cuh index e0a04eb59da..847c1db6937 100644 --- a/cpp/src/prims/detail/nbr_intersection.cuh +++ b/cpp/src/prims/detail/nbr_intersection.cuh @@ -50,6 +50,7 @@ #include #include #include +#include #include #include #include @@ -1232,9 +1233,11 @@ nbr_intersection(raft::handle_t const& handle, rx_v_pair_nbr_intersection_sizes.size() + 1, handle.get_stream()); rx_v_pair_nbr_intersection_offsets.set_element_to_zero_async(size_t{0}, handle.get_stream()); + auto size_first = thrust::make_transform_iterator( + rx_v_pair_nbr_intersection_sizes.begin(), cugraph::detail::typecast_t{}); thrust::inclusive_scan(handle.get_thrust_policy(), - rx_v_pair_nbr_intersection_sizes.begin(), - rx_v_pair_nbr_intersection_sizes.end(), + size_first, + size_first + rx_v_pair_nbr_intersection_sizes.size(), rx_v_pair_nbr_intersection_offsets.begin() + 1); rx_v_pair_nbr_intersection_indices.resize( @@ -1344,8 +1347,8 @@ nbr_intersection(raft::handle_t const& handle, } thrust::inclusive_scan(handle.get_thrust_policy(), - rx_v_pair_nbr_intersection_sizes.begin(), - rx_v_pair_nbr_intersection_sizes.end(), + size_first, + size_first + rx_v_pair_nbr_intersection_sizes.size(), rx_v_pair_nbr_intersection_offsets.begin() + 1); std::vector h_rx_v_pair_lasts(rx_v_pair_counts.size()); 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 3b25ae50773..5e4cd81513e 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 @@ -16,6 +16,7 @@ #pragma once #include "detail/graph_partition_utils.cuh" +#include "prims/detail/optional_dataframe_buffer.hpp" #include "prims/kv_store.cuh" #include "utilities/collect_comm.cuh" @@ -83,15 +84,23 @@ struct rebase_offset_t { // a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template -struct triplet_to_minor_comm_rank_t { +struct tuple_to_minor_comm_rank_t { compute_vertex_partition_id_from_ext_vertex_t key_func{}; int minor_comm_size{}; - __device__ int operator()( + template + __device__ std::enable_if_t, int> operator()( thrust::tuple val /* major, minor key, edge value */) const { return key_func(thrust::get<1>(val)) % minor_comm_size; } + + template + __device__ std::enable_if_t, int> operator()( + thrust::tuple val /* major, minor key */) const + { + return key_func(thrust::get<1>(val)) % minor_comm_size; + } }; // a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used @@ -106,6 +115,7 @@ struct pair_to_binary_partition_id_t { // a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used template - val /* major, minor key, aggregated edge value */) const + template + __device__ std::enable_if_t, e_op_result_t> + operator()(thrust::tuple + val /* major, minor key, aggregated edge value */) const { auto major = thrust::get<0>(val); auto minor_key = thrust::get<1>(val); @@ -131,6 +143,20 @@ struct call_key_aggregated_e_op_t { return key_aggregated_e_op( major, minor_key, major_val, edge_minor_key_value_map.find(minor_key), aggregated_edge_value); } + + template + __device__ std::enable_if_t, e_op_result_t> + operator()(thrust::tuple val /* major, minor key */) const + { + auto major = thrust::get<0>(val); + auto minor_key = thrust::get<1>(val); + auto major_val = edge_major_value_map + ? (*edge_major_value_map).find(major) + : edge_partition_major_value_input.get( + edge_partition.major_offset_from_major_nocheck(major)); + return key_aggregated_e_op( + major, minor_key, major_val, edge_minor_key_value_map.find(minor_key), thrust::nullopt); + } }; // a workaround for cudaErrorInvalidDeviceFunction error when device lambda is used @@ -182,9 +208,8 @@ struct reduce_with_init_t { * @tparam EdgeSrcValueInputWrapper Type of the wrapper for edge source property values. * @tparam EdgeValueInputWrapper Type of the wrapper for edge property values. * @tparam EdgeDstKeyInputWrapper Type of the wrapper for edge destination key values. - * @tparam VertexIterator Type of the iterator for keys in (key, value) pairs (key type should - * coincide with vertex type). - * @tparam ValueIterator Type of the iterator for values in (key, value) pairs. + * @tparam KVStoreViewType Type of the (key, value) store. Key type should coincide with vertex + * type. * @tparam KeyAggregatedEdgeOp Type of the quinary key-aggregated edge operator. * @tparam ReduceOp Type of the binary reduction operator. * @tparam T Type of the initial value for per-vertex reduction. @@ -204,15 +229,10 @@ struct reduce_with_init_t { * @param edge_dst_key_input Wrapper used to access destination input key values (for the edge * destinations assigned to this process in multi-GPU). Use cugraph::edge_dst_property_t::view(). * Use update_edge_dst_property to fill the wrapper. - * @param map_unique_key_first Iterator pointing to the first (inclusive) key in (key, value) pairs - * (assigned to this process in multi-GPU, `cugraph::detail::compute_gpu_id_from_ext_vertex_t` is - * used to map keys to processes). (Key, value) pairs may be provided by - * transform_reduce_by_src_key_e() or transform_reduce_by_dst_key_e(). - * @param map_unique_key_last Iterator pointing to the last (exclusive) key in (key, value) pairs - * (assigned to this process in multi-GPU). - * @param map_value_first Iterator pointing to the first (inclusive) value in (key, value) pairs - * (assigned to this process in multi-GPU). `map_value_last` (exclusive) is deduced as @p - * map_value_first + thrust::distance(@p map_unique_key_first, @p map_unique_key_last). + * @param kv_store_view view object of the (key, value) store (for the keys assigned to this process + * in multi-GPU). `cugraph::detail::compute_gpu_id_from_ext_vertex_t` is used to map keys to + * processes). (Key, value) pairs may be provided by transform_reduce_e_by_src_key() or + * transform_reduce_e_by_dst_key(). * @param key_aggregated_e_op Quinary operator takes 1) edge source, 2) key, 3) *(@p * edge_partition_src_value_input_first + i), 4) value for the key stored in the input (key, value) * pairs provided by @p map_unique_key_first, @p map_unique_key_last, and @p map_value_first @@ -263,8 +283,11 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( using edge_src_value_t = typename EdgeSrcValueInputWrapper::value_type; using edge_value_t = typename EdgeValueInputWrapper::value_type; using kv_pair_value_t = typename KVStoreViewType::value_type; + using optional_edge_value_buffer_value_type = + std::conditional_t, edge_value_t, void>; + static_assert( - std::is_arithmetic_v, + std::is_same_v || std::is_arithmetic_v, "Currently only scalar values are supported, should be extended to support thrust::tuple of " "arithmetic types and void (for dummy property values) to be consistent with other " "primitives."); // this will also require a custom edge value aggregation op. @@ -284,16 +307,15 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( detail::edge_partition_edge_dummy_property_device_view_t, detail::edge_partition_edge_property_device_view_t< edge_t, - typename EdgeValueInputWrapper::value_iterator>>; - - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); + typename EdgeValueInputWrapper::value_iterator, + typename EdgeValueInputWrapper::value_type>>; if (do_expensive_check) { /* currently, nothing to do */ } auto total_global_mem = handle.get_device_properties().totalGlobalMem; size_t element_size = sizeof(vertex_t) * 2; // major + minor keys - if constexpr (!std::is_same_v) { + if constexpr (!std::is_same_v) { static_assert(is_arithmetic_or_thrust_tuple_of_arithmetic::value); if constexpr (is_thrust_tuple_of_arithmetic::value) { element_size += sum_thrust_tuple_element_sizes(); @@ -317,24 +339,78 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( // 1. aggregate each vertex out-going edges based on keys and transform-reduce. + auto edge_mask_view = graph_view.edge_mask_view(); + rmm::device_uvector majors(0, handle.get_stream()); auto e_op_result_buffer = allocate_dataframe_buffer(0, handle.get_stream()); 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; auto edge_partition_src_value_input = edge_partition_src_input_device_view_t(edge_src_value_input, i); auto edge_partition_e_value_input = edge_partition_e_input_device_view_t(edge_value_input, i); - rmm::device_uvector tmp_majors(edge_partition.number_of_edges(), handle.get_stream()); + std::optional> offsets_with_mask{std::nullopt}; + if (edge_partition_e_mask) { + rmm::device_uvector degrees_with_mask(0, handle.get_stream()); + if (edge_partition.dcs_nzd_vertices()) { + auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); + + auto major_sparse_range_size = + (*segment_offsets)[detail::num_sparse_segments_per_vertex_partition]; + degrees_with_mask = rmm::device_uvector( + major_sparse_range_size + *(edge_partition.dcs_nzd_vertex_count()), handle.get_stream()); + auto major_first = thrust::make_transform_iterator( + thrust::make_counting_iterator(vertex_t{0}), + cuda::proclaim_return_type( + [major_sparse_range_size, + major_range_first = edge_partition.major_range_first(), + dcs_nzd_vertices = *(edge_partition.dcs_nzd_vertices())] __device__(vertex_t i) { + if (i < major_sparse_range_size) { // sparse + return major_range_first + i; + } else { // hypersparse + return *(dcs_nzd_vertices + (i - major_sparse_range_size)); + } + })); + degrees_with_mask = + edge_partition.compute_local_degrees_with_mask((*edge_partition_e_mask).value_first(), + major_first, + major_first + degrees_with_mask.size(), + handle.get_stream()); + } else { + degrees_with_mask = edge_partition.compute_local_degrees_with_mask( + (*edge_partition_e_mask).value_first(), + thrust::make_counting_iterator(edge_partition.major_range_first()), + thrust::make_counting_iterator(edge_partition.major_range_last()), + handle.get_stream()); + } + offsets_with_mask = + rmm::device_uvector(degrees_with_mask.size() + 1, handle.get_stream()); + (*offsets_with_mask).set_element_to_zero_async(0, handle.get_stream()); + thrust::inclusive_scan(handle.get_thrust_policy(), + degrees_with_mask.begin(), + degrees_with_mask.end(), + (*offsets_with_mask).begin() + 1); + } + + rmm::device_uvector tmp_majors( + edge_partition_e_mask ? (*offsets_with_mask).back_element(handle.get_stream()) + : edge_partition.number_of_edges(), + handle.get_stream()); rmm::device_uvector tmp_minor_keys(tmp_majors.size(), handle.get_stream()); - // FIXME: this doesn't work if edge_value_t is thrust::tuple or void - rmm::device_uvector tmp_key_aggregated_edge_values(tmp_majors.size(), - handle.get_stream()); + auto tmp_key_aggregated_edge_values = + detail::allocate_optional_dataframe_buffer( + tmp_majors.size(), handle.get_stream()); - if (edge_partition.number_of_edges() > 0) { + if (tmp_majors.size() > 0) { auto segment_offsets = graph_view.local_edge_partition_segment_offsets(i); detail::decompress_edge_partition_to_fill_edgelist_majors( handle, edge_partition, - std::nullopt, + detail::to_std_optional(edge_partition_e_mask), raft::device_span(tmp_majors.data(), tmp_majors.size()), segment_offsets); @@ -357,14 +433,14 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( static_cast(handle.get_device_properties().multiProcessorCount) * (1 << 20); auto [h_vertex_offsets, h_edge_offsets] = detail::compute_offset_aligned_element_chunks( handle, - raft::device_span{ - edge_partition.offsets(), - 1 + static_cast( - edge_partition.dcs_nzd_vertices() - ? (*segment_offsets)[detail::num_sparse_segments_per_vertex_partition] + - *(edge_partition.dcs_nzd_vertex_count()) - : edge_partition.major_range_size())}, - edge_partition.number_of_edges(), + raft::device_span( + offsets_with_mask ? (*offsets_with_mask).data() : edge_partition.offsets(), + (edge_partition.dcs_nzd_vertices() + ? (*segment_offsets)[detail::num_sparse_segments_per_vertex_partition] + + *(edge_partition.dcs_nzd_vertex_count()) + : edge_partition.major_range_size()) + + 1), + static_cast(tmp_majors.size()), approx_edges_to_sort_per_iteration); auto num_chunks = h_vertex_offsets.size() - 1; @@ -376,30 +452,69 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( rmm::device_uvector unreduced_majors(max_chunk_size, handle.get_stream()); rmm::device_uvector unreduced_minor_keys(unreduced_majors.size(), handle.get_stream()); - // FIXME: this doesn't work if edge_value_t is thrust::tuple or void - rmm::device_uvector unreduced_key_aggregated_edge_values( - unreduced_majors.size(), handle.get_stream()); + auto unreduced_key_aggregated_edge_values = + detail::allocate_optional_dataframe_buffer( + unreduced_majors.size(), handle.get_stream()); rmm::device_uvector d_tmp_storage(0, handle.get_stream()); size_t reduced_size{0}; for (size_t j = 0; j < num_chunks; ++j) { - thrust::copy(handle.get_thrust_policy(), - minor_key_first + h_edge_offsets[j], - minor_key_first + h_edge_offsets[j + 1], - tmp_minor_keys.begin() + h_edge_offsets[j]); + if (edge_partition_e_mask) { + std::array unmasked_ranges{}; + raft::update_host(unmasked_ranges.data(), + edge_partition.offsets() + h_vertex_offsets[j], + 1, + handle.get_stream()); + raft::update_host(unmasked_ranges.data() + 1, + edge_partition.offsets() + h_vertex_offsets[j + 1], + 1, + handle.get_stream()); + handle.sync_stream(); + if constexpr (!std::is_same_v) { + detail::copy_if_mask_set( + handle, + thrust::make_zip_iterator(minor_key_first, + edge_partition_e_value_input.value_first()) + + unmasked_ranges[0], + thrust::make_zip_iterator(minor_key_first, + edge_partition_e_value_input.value_first()) + + unmasked_ranges[1], + (*edge_partition_e_mask).value_first() + unmasked_ranges[0], + thrust::make_zip_iterator(tmp_minor_keys.begin(), + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values)) + + h_edge_offsets[j]); + } else { + detail::copy_if_mask_set(handle, + minor_key_first + unmasked_ranges[0], + minor_key_first + unmasked_ranges[1], + (*edge_partition_e_mask).value_first() + unmasked_ranges[0], + tmp_minor_keys.begin() + h_edge_offsets[j]); + } + } else { + thrust::copy(handle.get_thrust_policy(), + minor_key_first + h_edge_offsets[j], + minor_key_first + h_edge_offsets[j + 1], + tmp_minor_keys.begin() + h_edge_offsets[j]); + } size_t tmp_storage_bytes{0}; - auto offset_first = - thrust::make_transform_iterator(edge_partition.offsets() + h_vertex_offsets[j], - detail::rebase_offset_t{h_edge_offsets[j]}); - if constexpr (!std::is_same_v) { + auto offset_first = thrust::make_transform_iterator( + (offsets_with_mask ? (*offsets_with_mask).data() : edge_partition.offsets()) + + h_vertex_offsets[j], + detail::rebase_offset_t{h_edge_offsets[j]}); + if constexpr (!std::is_same_v) { cub::DeviceSegmentedSort::SortPairs( static_cast(nullptr), tmp_storage_bytes, tmp_minor_keys.begin() + h_edge_offsets[j], unreduced_minor_keys.begin(), - edge_partition_e_value_input.value_first() + h_edge_offsets[j], - unreduced_key_aggregated_edge_values.begin(), + (edge_partition_e_mask ? detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values) + : edge_partition_e_value_input.value_first()) + + h_edge_offsets[j], + detail::get_optional_dataframe_buffer_begin( + unreduced_key_aggregated_edge_values), h_edge_offsets[j + 1] - h_edge_offsets[j], h_vertex_offsets[j + 1] - h_vertex_offsets[j], offset_first, @@ -419,14 +534,18 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( if (tmp_storage_bytes > d_tmp_storage.size()) { d_tmp_storage = rmm::device_uvector(tmp_storage_bytes, handle.get_stream()); } - if constexpr (!std::is_same_v) { + if constexpr (!std::is_same_v) { cub::DeviceSegmentedSort::SortPairs( d_tmp_storage.data(), tmp_storage_bytes, tmp_minor_keys.begin() + h_edge_offsets[j], unreduced_minor_keys.begin(), - edge_partition_e_value_input.value_first() + h_edge_offsets[j], - unreduced_key_aggregated_edge_values.begin(), + (edge_partition_e_mask ? detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values) + : edge_partition_e_value_input.value_first()) + + h_edge_offsets[j], + detail::get_optional_dataframe_buffer_begin( + unreduced_key_aggregated_edge_values), h_edge_offsets[j + 1] - h_edge_offsets[j], h_vertex_offsets[j + 1] - h_vertex_offsets[j], offset_first, @@ -448,39 +567,44 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( tmp_majors.begin() + h_edge_offsets[j], tmp_majors.begin() + h_edge_offsets[j + 1], unreduced_majors.begin()); - auto input_key_first = thrust::make_zip_iterator( - thrust::make_tuple(unreduced_majors.begin(), unreduced_minor_keys.begin())); + auto input_key_first = + thrust::make_zip_iterator(unreduced_majors.begin(), unreduced_minor_keys.begin()); auto output_key_first = - thrust::make_zip_iterator(thrust::make_tuple(tmp_majors.begin(), tmp_minor_keys.begin())); - if constexpr (!std::is_same_v) { + thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin()); + if constexpr (!std::is_same_v) { reduced_size += thrust::distance(output_key_first + reduced_size, thrust::get<0>(thrust::reduce_by_key( handle.get_thrust_policy(), input_key_first, input_key_first + (h_edge_offsets[j + 1] - h_edge_offsets[j]), - unreduced_key_aggregated_edge_values.begin(), + detail::get_optional_dataframe_buffer_begin( + unreduced_key_aggregated_edge_values), output_key_first + reduced_size, - tmp_key_aggregated_edge_values.begin() + reduced_size))); + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values) + + reduced_size))); } else { - reduced_size += - thrust::distance(output_key_first + reduced_size, - thrust::get<0>(thrust::unique( - handle.get_thrust_policy(), - input_key_first, - input_key_first + (h_edge_offsets[j + 1] - h_edge_offsets[j]), - output_key_first + reduced_size))); + reduced_size += thrust::distance( + output_key_first + reduced_size, + thrust::copy_if( + handle.get_thrust_policy(), + input_key_first, + input_key_first + (h_edge_offsets[j + 1] - h_edge_offsets[j]), + thrust::make_counting_iterator(size_t{0}), + output_key_first + reduced_size, + cugraph::detail::is_first_in_run_t{input_key_first})); } } tmp_majors.resize(reduced_size, handle.get_stream()); tmp_minor_keys.resize(tmp_majors.size(), handle.get_stream()); - // FIXME: this doesn't work if edge_value_t is thrust::tuple or void - tmp_key_aggregated_edge_values.resize(tmp_majors.size(), handle.get_stream()); + detail::resize_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, tmp_majors.size(), handle.get_stream()); } tmp_majors.shrink_to_fit(handle.get_stream()); tmp_minor_keys.shrink_to_fit(handle.get_stream()); - // FIXME: this doesn't work if edge_value_t is thrust::tuple or void - tmp_key_aggregated_edge_values.shrink_to_fit(handle.get_stream()); + detail::shrink_to_fit_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, handle.get_stream()); std::unique_ptr< kv_store_t{ - detail::compute_vertex_partition_id_from_ext_vertex_t{comm_size}, - minor_comm_size}, - minor_comm_size, - mem_frugal_threshold, - handle.get_stream()); + rmm::device_uvector d_tx_value_counts(0, handle.get_stream()); + if constexpr (!std::is_same_v) { + auto triplet_first = + thrust::make_zip_iterator(tmp_majors.begin(), + tmp_minor_keys.begin(), + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values)); + d_tx_value_counts = cugraph::groupby_and_count( + triplet_first, + triplet_first + tmp_majors.size(), + detail::tuple_to_minor_comm_rank_t{ + detail::compute_vertex_partition_id_from_ext_vertex_t{comm_size}, + minor_comm_size}, + minor_comm_size, + mem_frugal_threshold, + handle.get_stream()); + } else { + auto pair_first = thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin()); + d_tx_value_counts = cugraph::groupby_and_count( + pair_first, + pair_first + tmp_majors.size(), + detail::tuple_to_minor_comm_rank_t{ + detail::compute_vertex_partition_id_from_ext_vertex_t{comm_size}, + minor_comm_size}, + minor_comm_size, + mem_frugal_threshold, + handle.get_stream()); + } std::vector h_tx_value_counts(d_tx_value_counts.size()); raft::update_host(h_tx_value_counts.data(), @@ -544,8 +684,7 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( thrust::copy( handle.get_thrust_policy(), tmp_majors.begin(), tmp_majors.end(), majors.begin()); - auto pair_first = - thrust::make_zip_iterator(thrust::make_tuple(minor_comm_ranks.begin(), majors.begin())); + auto pair_first = thrust::make_zip_iterator(minor_comm_ranks.begin(), majors.begin()); thrust::sort( handle.get_thrust_policy(), pair_first, pair_first + minor_comm_ranks.size()); auto unique_pair_last = thrust::unique( @@ -622,7 +761,9 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( rmm::device_uvector rx_majors(0, handle.get_stream()); rmm::device_uvector rx_minor_keys(0, handle.get_stream()); - rmm::device_uvector rx_key_aggregated_edge_values(0, handle.get_stream()); + auto rx_key_aggregated_edge_values = + detail::allocate_optional_dataframe_buffer( + 0, handle.get_stream()); auto mem_frugal_flag = host_scalar_allreduce(minor_comm, tmp_majors.size() > mem_frugal_threshold ? int{1} : int{0}, @@ -639,66 +780,120 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( tmp_minor_keys.resize(0, handle.get_stream()); tmp_minor_keys.shrink_to_fit(handle.get_stream()); - std::tie(rx_key_aggregated_edge_values, std::ignore) = - shuffle_values(minor_comm, - tmp_key_aggregated_edge_values.begin(), - h_tx_value_counts, - handle.get_stream()); - tmp_key_aggregated_edge_values.resize(0, handle.get_stream()); - tmp_key_aggregated_edge_values.shrink_to_fit(handle.get_stream()); + if constexpr (!std::is_same_v) { + std::tie(rx_key_aggregated_edge_values, std::ignore) = + shuffle_values(minor_comm, + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values), + h_tx_value_counts, + handle.get_stream()); + } + detail::resize_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, 0, handle.get_stream()); + detail::shrink_to_fit_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, handle.get_stream()); } else { - std::forward_as_tuple(std::tie(rx_majors, rx_minor_keys, rx_key_aggregated_edge_values), - std::ignore) = - shuffle_values(minor_comm, triplet_first, h_tx_value_counts, handle.get_stream()); + if constexpr (!std::is_same_v) { + auto triplet_first = + thrust::make_zip_iterator(tmp_majors.begin(), + tmp_minor_keys.begin(), + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values)); + std::forward_as_tuple(std::tie(rx_majors, rx_minor_keys, rx_key_aggregated_edge_values), + std::ignore) = + shuffle_values(minor_comm, triplet_first, h_tx_value_counts, handle.get_stream()); + } else { + auto pair_first = thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin()); + std::forward_as_tuple(std::tie(rx_majors, rx_minor_keys), std::ignore) = + shuffle_values(minor_comm, pair_first, h_tx_value_counts, handle.get_stream()); + } tmp_majors.resize(0, handle.get_stream()); tmp_majors.shrink_to_fit(handle.get_stream()); tmp_minor_keys.resize(0, handle.get_stream()); tmp_minor_keys.shrink_to_fit(handle.get_stream()); - tmp_key_aggregated_edge_values.resize(0, handle.get_stream()); - tmp_key_aggregated_edge_values.shrink_to_fit(handle.get_stream()); + detail::resize_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, 0, handle.get_stream()); + detail::shrink_to_fit_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, handle.get_stream()); } - auto key_pair_first = - thrust::make_zip_iterator(thrust::make_tuple(rx_majors.begin(), rx_minor_keys.begin())); - if (rx_majors.size() > mem_frugal_threshold) { // trade-off parallelism to lower peak memory - auto second_first = - detail::mem_frugal_partition(key_pair_first, - key_pair_first + rx_majors.size(), - rx_key_aggregated_edge_values.begin(), - detail::pair_to_binary_partition_id_t{}, - int{1}, - handle.get_stream()); - - thrust::sort_by_key(handle.get_thrust_policy(), - key_pair_first, - std::get<0>(second_first), - rx_key_aggregated_edge_values.begin()); - - thrust::sort_by_key(handle.get_thrust_policy(), - std::get<0>(second_first), - key_pair_first + rx_majors.size(), - std::get<1>(second_first)); + auto key_pair_first = thrust::make_zip_iterator(rx_majors.begin(), rx_minor_keys.begin()); + if constexpr (!std::is_same_v) { + if (rx_majors.size() > + mem_frugal_threshold) { // trade-off parallelism to lower peak memory + auto second_first = + detail::mem_frugal_partition(key_pair_first, + key_pair_first + rx_majors.size(), + detail::get_optional_dataframe_buffer_begin( + rx_key_aggregated_edge_values), + detail::pair_to_binary_partition_id_t{}, + int{1}, + handle.get_stream()); + + thrust::sort_by_key(handle.get_thrust_policy(), + key_pair_first, + std::get<0>(second_first), + detail::get_optional_dataframe_buffer_begin( + rx_key_aggregated_edge_values)); + + thrust::sort_by_key(handle.get_thrust_policy(), + std::get<0>(second_first), + key_pair_first + rx_majors.size(), + std::get<1>(second_first)); + } else { + thrust::sort_by_key(handle.get_thrust_policy(), + key_pair_first, + key_pair_first + rx_majors.size(), + detail::get_optional_dataframe_buffer_begin( + rx_key_aggregated_edge_values)); + } + + auto num_uniques = + thrust::count_if(handle.get_thrust_policy(), + thrust::make_counting_iterator(size_t{0}), + thrust::make_counting_iterator(rx_majors.size()), + detail::is_first_in_run_t{key_pair_first}); + tmp_majors.resize(num_uniques, handle.get_stream()); + tmp_minor_keys.resize(tmp_majors.size(), handle.get_stream()); + detail::resize_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, tmp_majors.size(), handle.get_stream()); + thrust::reduce_by_key( + handle.get_thrust_policy(), + key_pair_first, + key_pair_first + rx_majors.size(), + detail::get_optional_dataframe_buffer_begin(rx_key_aggregated_edge_values), + thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin()), + detail::get_optional_dataframe_buffer_begin( + tmp_key_aggregated_edge_values)); } else { - thrust::sort_by_key(handle.get_thrust_policy(), - key_pair_first, - key_pair_first + rx_majors.size(), - rx_key_aggregated_edge_values.begin()); + if (rx_majors.size() > + mem_frugal_threshold) { // trade-off parallelism to lower peak memory + auto second_first = + detail::mem_frugal_partition(key_pair_first, + key_pair_first + rx_majors.size(), + detail::pair_to_binary_partition_id_t{}, + int{1}, + handle.get_stream()); + + thrust::sort(handle.get_thrust_policy(), key_pair_first, second_first); + + thrust::sort(handle.get_thrust_policy(), second_first, key_pair_first + rx_majors.size()); + } else { + thrust::sort( + handle.get_thrust_policy(), key_pair_first, key_pair_first + rx_majors.size()); + } + + auto num_uniques = thrust::distance( + key_pair_first, + thrust::unique( + handle.get_thrust_policy(), key_pair_first, key_pair_first + rx_majors.size())); + tmp_majors.resize(num_uniques, handle.get_stream()); + tmp_minor_keys.resize(tmp_majors.size(), handle.get_stream()); + thrust::copy(handle.get_thrust_policy(), + key_pair_first, + key_pair_first + num_uniques, + thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin())); } - auto num_uniques = - thrust::count_if(handle.get_thrust_policy(), - thrust::make_counting_iterator(size_t{0}), - thrust::make_counting_iterator(rx_majors.size()), - detail::is_first_in_run_t{key_pair_first}); - tmp_majors.resize(num_uniques, handle.get_stream()); - tmp_minor_keys.resize(tmp_majors.size(), handle.get_stream()); - tmp_key_aggregated_edge_values.resize(tmp_majors.size(), handle.get_stream()); - thrust::reduce_by_key( - handle.get_thrust_policy(), - key_pair_first, - key_pair_first + rx_majors.size(), - rx_key_aggregated_edge_values.begin(), - thrust::make_zip_iterator(thrust::make_tuple(tmp_majors.begin(), tmp_minor_keys.begin())), - tmp_key_aggregated_edge_values.begin()); } std::unique_ptr> @@ -756,8 +951,6 @@ void per_v_transform_reduce_dst_key_aggregated_outgoing_e( auto tmp_e_op_result_buffer = allocate_dataframe_buffer(tmp_majors.size(), handle.get_stream()); - auto triplet_first = thrust::make_zip_iterator(thrust::make_tuple( - tmp_majors.begin(), tmp_minor_keys.begin(), tmp_key_aggregated_edge_values.begin())); auto major_value_map_device_view = (GraphViewType::is_multi_gpu && edge_src_value_input.keys()) ? thrust::make_optional> dst_key_value_map_device_view( GraphViewType::is_multi_gpu ? multi_gpu_minor_key_value_map_ptr->view() : kv_store_view); - thrust::transform(handle.get_thrust_policy(), - triplet_first, - triplet_first + tmp_majors.size(), - get_dataframe_buffer_begin(tmp_e_op_result_buffer), - detail::call_key_aggregated_e_op_t< - vertex_t, - edge_value_t, - decltype(edge_partition), - std::remove_reference_t, - edge_partition_src_input_device_view_t, - decltype(dst_key_value_map_device_view), - KeyAggregatedEdgeOp>{edge_partition, - major_value_map_device_view, - edge_partition_src_value_input, - dst_key_value_map_device_view, - key_aggregated_e_op}); + if constexpr (!std::is_same_v) { + auto triplet_first = thrust::make_zip_iterator( + tmp_majors.begin(), + tmp_minor_keys.begin(), + detail::get_optional_dataframe_buffer_begin(tmp_key_aggregated_edge_values)); + thrust::transform(handle.get_thrust_policy(), + triplet_first, + triplet_first + tmp_majors.size(), + get_dataframe_buffer_begin(tmp_e_op_result_buffer), + detail::call_key_aggregated_e_op_t< + vertex_t, + edge_value_t, + T, + decltype(edge_partition), + std::remove_reference_t, + edge_partition_src_input_device_view_t, + decltype(dst_key_value_map_device_view), + KeyAggregatedEdgeOp>{edge_partition, + major_value_map_device_view, + edge_partition_src_value_input, + dst_key_value_map_device_view, + key_aggregated_e_op}); + } else { + auto pair_first = thrust::make_zip_iterator(tmp_majors.begin(), tmp_minor_keys.begin()); + thrust::transform(handle.get_thrust_policy(), + pair_first, + pair_first + tmp_majors.size(), + get_dataframe_buffer_begin(tmp_e_op_result_buffer), + detail::call_key_aggregated_e_op_t< + vertex_t, + edge_value_t, + T, + decltype(edge_partition), + std::remove_reference_t, + edge_partition_src_input_device_view_t, + decltype(dst_key_value_map_device_view), + KeyAggregatedEdgeOp>{edge_partition, + major_value_map_device_view, + edge_partition_src_value_input, + dst_key_value_map_device_view, + key_aggregated_e_op}); + } if constexpr (GraphViewType::is_multi_gpu) { multi_gpu_minor_key_value_map_ptr.reset(); } tmp_minor_keys.resize(0, handle.get_stream()); tmp_minor_keys.shrink_to_fit(handle.get_stream()); - tmp_key_aggregated_edge_values.resize(0, handle.get_stream()); - tmp_key_aggregated_edge_values.shrink_to_fit(handle.get_stream()); + detail::resize_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, 0, handle.get_stream()); + detail::shrink_to_fit_optional_dataframe_buffer( + tmp_key_aggregated_edge_values, handle.get_stream()); { auto num_uniques = 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 b63b014ed05..244586e6d9e 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 @@ -19,6 +19,7 @@ #include "prims/detail/nbr_intersection.cuh" #include "prims/property_op_utils.cuh" +#include #include #include #include @@ -130,7 +131,9 @@ std::tuple, ValueBuffer> sort_and_reduce_by_vertic vertices.end(), get_dataframe_buffer_begin(value_buffer), reduced_vertices.begin(), - get_dataframe_buffer_begin(reduced_value_buffer)); + get_dataframe_buffer_begin(reduced_value_buffer), + thrust::equal_to{}, + property_op{}); vertices.resize(size_t{0}, handle.get_stream()); resize_dataframe_buffer(value_buffer, size_t{0}, handle.get_stream()); @@ -201,14 +204,14 @@ struct accumulate_vertex_property_t { * @param graph_view Non-owning graph object. * @param edge_src_value_input Wrapper used to access source input property values (for the edge * sources assigned to this process in multi-GPU). Use either cugraph::edge_src_property_t::view() - * (if @p e_op needs to access source property values) or cugraph::edge_src_dummy_property_t::view() - * (if @p e_op does not access source property values). Use update_edge_src_property to fill the - * wrapper. + * (if @p intersection_op needs to access source property values) or + * cugraph::edge_src_dummy_property_t::view() (if @p intersection_op does not access source property + * values). Use update_edge_src_property to fill the wrapper. * @param edge_dst_value_input Wrapper used to access destination input property values (for the * edge destinations assigned to this process in multi-GPU). Use either - * cugraph::edge_dst_property_t::view() (if @p e_op needs to access destination property values) or - * cugraph::edge_dst_dummy_property_t::view() (if @p e_op does not access destination property - * values). Use update_edge_dst_property to fill the wrapper. + * cugraph::edge_dst_property_t::view() (if @p intersection_op needs to access destination property + * values) or cugraph::edge_dst_dummy_property_t::view() (if @p intersection_op does not access + * destination property values). Use update_edge_dst_property to fill the wrapper. * @param intersection_op quinary operator takes edge source, edge destination, property values for * the source, property values for the destination, and a list of vertices in the intersection of * edge source & destination vertices' destination neighbors and returns a thrust::tuple of three @@ -260,8 +263,6 @@ void transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( typename EdgeDstValueInputWrapper::value_iterator, typename EdgeDstValueInputWrapper::value_type>>; - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do. } @@ -272,6 +273,7 @@ void transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( 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( @@ -484,7 +486,9 @@ void transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( merged_vertices.end(), get_dataframe_buffer_begin(merged_value_buffer), reduced_vertices.begin(), - get_dataframe_buffer_begin(reduced_value_buffer)); + get_dataframe_buffer_begin(reduced_value_buffer), + thrust::equal_to{}, + property_op{}); merged_vertices.resize(size_t{0}, handle.get_stream()); merged_vertices.shrink_to_fit(handle.get_stream()); resize_dataframe_buffer(merged_value_buffer, size_t{0}, handle.get_stream()); diff --git a/cpp/src/prims/transform_reduce_e_by_src_dst_key.cuh b/cpp/src/prims/transform_reduce_e_by_src_dst_key.cuh index eee0ed03d1c..00876012906 100644 --- a/cpp/src/prims/transform_reduce_e_by_src_dst_key.cuh +++ b/cpp/src/prims/transform_reduce_e_by_src_dst_key.cuh @@ -95,6 +95,7 @@ template __global__ static void transform_reduce_by_src_dst_key_hypersparse( @@ -105,6 +106,9 @@ __global__ static void transform_reduce_by_src_dst_key_hypersparse( EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, EdgePartitionSrcDstKeyInputWrapper edge_partition_src_dst_key_input, + EdgePartitionEdgeMaskWrapper edge_partition_e_mask, + thrust::optional> + edge_offsets_with_mask, EdgeOp e_op, typename GraphViewType::vertex_type* keys, ValueIterator value_iter) @@ -129,19 +133,42 @@ __global__ static void transform_reduce_by_src_dst_key_hypersparse( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(static_cast(major_idx)); - auto local_offset = edge_partition.local_offset(major_idx); - for (edge_t i = 0; i < local_degree; ++i) { - update_buffer_element(edge_partition, - major, - indices[i], - edge_offset + i, - edge_partition_src_value_input, - edge_partition_dst_value_input, - edge_partition_e_value_input, - edge_partition_src_dst_key_input, - e_op, - keys + local_offset + i, - value_iter + local_offset + i); + if (edge_partition_e_mask) { + auto major_offset = edge_partition.major_offset_from_major_nocheck(major); + auto edge_offset_with_mask = (*edge_offsets_with_mask)[major_offset]; + edge_t counter{0}; + for (edge_t i = 0; i < local_degree; ++i) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset_with_mask + counter, + value_iter + edge_offset_with_mask + counter); + ++counter; + } + } + } else { + for (edge_t i = 0; i < local_degree; ++i) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset + i, + value_iter + edge_offset + i); + } } idx += gridDim.x * blockDim.x; @@ -154,6 +181,7 @@ template __global__ static void transform_reduce_by_src_dst_key_low_degree( @@ -166,6 +194,9 @@ __global__ static void transform_reduce_by_src_dst_key_low_degree( EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, EdgePartitionSrcDstKeyInputWrapper edge_partition_src_dst_key_input, + EdgePartitionEdgeMaskWrapper edge_partition_e_mask, + thrust::optional> + edge_offsets_with_mask, EdgeOp e_op, typename GraphViewType::vertex_type* keys, ValueIterator value_iter) @@ -187,19 +218,41 @@ __global__ static void transform_reduce_by_src_dst_key_low_degree( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(static_cast(major_offset)); - auto local_offset = edge_partition.local_offset(major_offset); - for (edge_t i = 0; i < local_degree; ++i) { - update_buffer_element(edge_partition, - major, - indices[i], - edge_offset + i, - edge_partition_src_value_input, - edge_partition_dst_value_input, - edge_partition_e_value_input, - edge_partition_src_dst_key_input, - e_op, - keys + local_offset + i, - value_iter + local_offset + i); + if (edge_partition_e_mask) { + auto edge_offset_with_mask = (*edge_offsets_with_mask)[major_offset]; + edge_t counter{0}; + for (edge_t i = 0; i < local_degree; ++i) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset_with_mask + counter, + value_iter + edge_offset_with_mask + counter); + ++counter; + } + } + } else { + for (edge_t i = 0; i < local_degree; ++i) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset + i, + value_iter + edge_offset + i); + } } idx += gridDim.x * blockDim.x; @@ -212,6 +265,7 @@ template __global__ static void transform_reduce_by_src_dst_key_mid_degree( @@ -224,6 +278,9 @@ __global__ static void transform_reduce_by_src_dst_key_mid_degree( EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, EdgePartitionSrcDstKeyInputWrapper edge_partition_src_dst_key_input, + EdgePartitionEdgeMaskWrapper edge_partition_e_mask, + thrust::optional> + edge_offsets_with_mask, EdgeOp e_op, typename GraphViewType::vertex_type* keys, ValueIterator value_iter) @@ -238,6 +295,9 @@ __global__ static void transform_reduce_by_src_dst_key_mid_degree( static_cast(major_range_first - edge_partition.major_range_first()); size_t idx = static_cast(tid / raft::warp_size()); + using WarpScan = cub::WarpScan; + __shared__ typename WarpScan::TempStorage temp_storage; + while (idx < static_cast(major_range_last - major_range_first)) { auto major_offset = major_start_offset + idx; auto major = @@ -247,19 +307,49 @@ __global__ static void transform_reduce_by_src_dst_key_mid_degree( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(static_cast(major_offset)); - auto local_offset = edge_partition.local_offset(major_offset); - for (edge_t i = lane_id; i < local_degree; i += raft::warp_size()) { - update_buffer_element(edge_partition, - major, - indices[i], - edge_offset + i, - edge_partition_src_value_input, - edge_partition_dst_value_input, - edge_partition_e_value_input, - edge_partition_src_dst_key_input, - e_op, - keys + local_offset + i, - value_iter + local_offset + i); + if (edge_partition_e_mask) { + // FIXME: it might be faster to update in warp-sync way + auto edge_offset_with_mask = (*edge_offsets_with_mask)[major_offset]; + edge_t counter{0}; + for (edge_t i = lane_id; i < local_degree; i += raft::warp_size()) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { ++counter; } + } + edge_t offset_within_warp{}; + WarpScan(temp_storage).ExclusiveSum(counter, offset_within_warp); + edge_offset_with_mask += offset_within_warp; + counter = 0; + for (edge_t i = lane_id; i < local_degree; i += raft::warp_size()) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset_with_mask + counter, + value_iter + edge_offset_with_mask + counter); + ++counter; + } + } + } else { + for (edge_t i = lane_id; i < local_degree; i += raft::warp_size()) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset + i, + value_iter + edge_offset + i); + } } idx += gridDim.x * (blockDim.x / raft::warp_size()); @@ -272,6 +362,7 @@ template __global__ static void transform_reduce_by_src_dst_key_high_degree( @@ -284,6 +375,9 @@ __global__ static void transform_reduce_by_src_dst_key_high_degree( EdgePartitionDstValueInputWrapper edge_partition_dst_value_input, EdgePartitionEdgeValueInputWrapper edge_partition_e_value_input, EdgePartitionSrcDstKeyInputWrapper edge_partition_src_dst_key_input, + EdgePartitionEdgeMaskWrapper edge_partition_e_mask, + thrust::optional> + edge_offsets_with_mask, EdgeOp e_op, typename GraphViewType::vertex_type* keys, ValueIterator value_iter) @@ -295,6 +389,9 @@ __global__ static void transform_reduce_by_src_dst_key_high_degree( static_cast(major_range_first - edge_partition.major_range_first()); auto idx = static_cast(blockIdx.x); + using BlockScan = cub::BlockScan; + __shared__ typename BlockScan::TempStorage temp_storage; + while (idx < static_cast(major_range_last - major_range_first)) { auto major_offset = major_start_offset + idx; auto major = @@ -304,19 +401,49 @@ __global__ static void transform_reduce_by_src_dst_key_high_degree( edge_t local_degree{}; thrust::tie(indices, edge_offset, local_degree) = edge_partition.local_edges(static_cast(major_offset)); - auto local_offset = edge_partition.local_offset(major_offset); - for (edge_t i = threadIdx.x; i < local_degree; i += blockDim.x) { - update_buffer_element(edge_partition, - major, - indices[i], - edge_offset + i, - edge_partition_src_value_input, - edge_partition_dst_value_input, - edge_partition_e_value_input, - edge_partition_src_dst_key_input, - e_op, - keys + local_offset + i, - value_iter + local_offset + i); + if (edge_partition_e_mask) { + // FIXME: it might be faster to update in block-sync way + auto edge_offset_with_mask = (*edge_offsets_with_mask)[major_offset]; + edge_t counter{0}; + for (edge_t i = threadIdx.x; i < local_degree; i += blockDim.x) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { ++counter; } + } + edge_t offset_within_block{}; + BlockScan(temp_storage).ExclusiveSum(counter, offset_within_block); + edge_offset_with_mask += offset_within_block; + counter = 0; + for (edge_t i = threadIdx.x; i < local_degree; i += blockDim.x) { + if ((*edge_partition_e_mask).get(edge_offset + i)) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset_with_mask + counter, + value_iter + edge_offset_with_mask + counter); + ++counter; + } + } + } else { + for (edge_t i = threadIdx.x; i < local_degree; i += blockDim.x) { + update_buffer_element( + edge_partition, + major, + indices[i], + edge_offset + i, + edge_partition_src_value_input, + edge_partition_dst_value_input, + edge_partition_e_value_input, + edge_partition_src_dst_key_input, + e_op, + keys + edge_offset + i, + value_iter + edge_offset + i); + } } idx += gridDim.x; @@ -410,19 +537,41 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, typename EdgeSrcDstKeyInputWrapper::value_iterator, typename EdgeSrcDstKeyInputWrapper::value_type>; + auto edge_mask_view = graph_view.edge_mask_view(); + rmm::device_uvector keys(0, handle.get_stream()); auto value_buffer = allocate_dataframe_buffer(0, handle.get_stream()); 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 num_edges = edge_partition.number_of_edges(); - - rmm::device_uvector tmp_keys(num_edges, handle.get_stream()); + 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; + + rmm::device_uvector tmp_keys(0, handle.get_stream()); + std::optional> edge_offsets_with_mask{std::nullopt}; + if (edge_partition_e_mask) { + auto local_degrees = edge_partition.compute_local_degrees_with_mask( + (*edge_partition_e_mask).value_first(), handle.get_stream()); + edge_offsets_with_mask = + rmm::device_uvector(edge_partition.major_range_size() + 1, handle.get_stream()); + (*edge_offsets_with_mask).set_element_to_zero_async(0, handle.get_stream()); + thrust::inclusive_scan(handle.get_thrust_policy(), + local_degrees.begin(), + local_degrees.end(), + (*edge_offsets_with_mask).begin() + 1); + tmp_keys.resize((*edge_offsets_with_mask).back_element(handle.get_stream()), + handle.get_stream()); + } else { + tmp_keys.resize(edge_partition.number_of_edges(), handle.get_stream()); + } auto tmp_value_buffer = allocate_dataframe_buffer(tmp_keys.size(), handle.get_stream()); - if (num_edges > 0) { + if (tmp_keys.size() > 0) { 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) { @@ -467,6 +616,11 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, edge_partition_dst_value_input, edge_partition_e_value_input, edge_partition_src_dst_key_input, + edge_partition_e_mask, + edge_offsets_with_mask + ? thrust::make_optional>( + (*edge_offsets_with_mask).data(), (*edge_offsets_with_mask).size()) + : thrust::nullopt, e_op, tmp_keys.data(), get_dataframe_buffer_begin(tmp_value_buffer)); @@ -485,6 +639,11 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, edge_partition_dst_value_input, edge_partition_e_value_input, edge_partition_src_dst_key_input, + edge_partition_e_mask, + edge_offsets_with_mask + ? thrust::make_optional>( + (*edge_offsets_with_mask).data(), (*edge_offsets_with_mask).size()) + : thrust::nullopt, e_op, tmp_keys.data(), get_dataframe_buffer_begin(tmp_value_buffer)); @@ -503,6 +662,11 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, edge_partition_dst_value_input, edge_partition_e_value_input, edge_partition_src_dst_key_input, + edge_partition_e_mask, + edge_offsets_with_mask + ? thrust::make_optional>( + (*edge_offsets_with_mask).data(), (*edge_offsets_with_mask).size()) + : thrust::nullopt, e_op, tmp_keys.data(), get_dataframe_buffer_begin(tmp_value_buffer)); @@ -520,6 +684,11 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, edge_partition_dst_value_input, edge_partition_e_value_input, edge_partition_src_dst_key_input, + edge_partition_e_mask, + edge_offsets_with_mask + ? thrust::make_optional>( + (*edge_offsets_with_mask).data(), (*edge_offsets_with_mask).size()) + : thrust::nullopt, e_op, tmp_keys.data(), get_dataframe_buffer_begin(tmp_value_buffer)); @@ -539,6 +708,11 @@ transform_reduce_e_by_src_dst_key(raft::handle_t const& handle, edge_partition_dst_value_input, edge_partition_e_value_input, edge_partition_src_dst_key_input, + edge_partition_e_mask, + edge_offsets_with_mask + ? thrust::make_optional>( + (*edge_offsets_with_mask).data(), (*edge_offsets_with_mask).size()) + : thrust::nullopt, e_op, tmp_keys.data(), get_dataframe_buffer_begin(tmp_value_buffer)); @@ -682,8 +856,6 @@ auto transform_reduce_e_by_src_key(raft::handle_t const& handle, typename GraphViewType::vertex_type>::value); static_assert(ReduceOp::pure_function, "ReduceOp should be a pure function."); - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } @@ -772,8 +944,6 @@ auto transform_reduce_e_by_dst_key(raft::handle_t const& handle, typename GraphViewType::vertex_type>::value); static_assert(ReduceOp::pure_function, "ReduceOp should be a pure function."); - CUGRAPH_EXPECTS(!graph_view.has_edge_mask(), "unimplemented."); - if (do_expensive_check) { // currently, nothing to do } diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt index c84711e1a69..4d37c93326d 100644 --- a/cpp/tests/CMakeLists.txt +++ b/cpp/tests/CMakeLists.txt @@ -396,12 +396,10 @@ ConfigureTest(RANDOM_WALKS_TEST sampling/sg_random_walks_test.cpp) ################################################################################################### # - NBR SAMPLING tests ---------------------------------------------------------------------------- ConfigureTest(UNIFORM_NEIGHBOR_SAMPLING_TEST sampling/sg_uniform_neighbor_sampling.cu) -target_link_libraries(UNIFORM_NEIGHBOR_SAMPLING_TEST PRIVATE cuco::cuco) ################################################################################################### # - SAMPLING_POST_PROCESSING tests ---------------------------------------------------------------- ConfigureTest(SAMPLING_POST_PROCESSING_TEST sampling/sampling_post_processing_test.cu) -target_link_libraries(SAMPLING_POST_PROCESSING_TEST PRIVATE cuco::cuco) ################################################################################################### # - Renumber tests -------------------------------------------------------------------------------- @@ -583,78 +581,79 @@ if(BUILD_CUGRAPH_MG_TESTS) ############################################################################################### # - MG PRIMS COUNT_IF_V tests ----------------------------------------------------------------- ConfigureTestMG(MG_COUNT_IF_V_TEST prims/mg_count_if_v.cu) - target_link_libraries(MG_COUNT_IF_V_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS TRANSFORM_REDUCE_V_FRONTIER_OUTGOING_E_BY_DST tests ------------------------------ ConfigureTestMG(MG_TRANSFORM_REDUCE_V_FRONTIER_OUTGOING_E_BY_DST_TEST prims/mg_transform_reduce_v_frontier_outgoing_e_by_dst.cu) - target_link_libraries(MG_TRANSFORM_REDUCE_V_FRONTIER_OUTGOING_E_BY_DST_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS REDUCE_V tests ------------------------------------------------------------------- ConfigureTestMG(MG_REDUCE_V_TEST prims/mg_reduce_v.cu) - target_link_libraries(MG_REDUCE_V_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS TRANSFORM_REDUCE_V tests --------------------------------------------------------- ConfigureTestMG(MG_TRANSFORM_REDUCE_V_TEST prims/mg_transform_reduce_v.cu) - target_link_libraries(MG_TRANSFORM_REDUCE_V_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS TRANSFORM_REDUCE_E tests --------------------------------------------------------- ConfigureTestMG(MG_TRANSFORM_REDUCE_E_TEST prims/mg_transform_reduce_e.cu) - target_link_libraries(MG_TRANSFORM_REDUCE_E_TEST PRIVATE cuco::cuco) + + ############################################################################################### + # - MG PRIMS TRANSFORM_REDUCE_E _BY_SRC_DST_KEY tests ----------------------------------------- + ConfigureTestMG(MG_TRANSFORM_REDUCE_E_BY_SRC_DST_KEY_TEST + prims/mg_transform_reduce_e_by_src_dst_key.cu) ############################################################################################### # - MG PRIMS TRANSFORM_E tests ---------------------------------------------------------------- ConfigureTestMG(MG_TRANSFORM_E_TEST prims/mg_transform_e.cu) - target_link_libraries(MG_TRANSFORM_E_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS COUNT_IF_E tests ----------------------------------------------------------------- ConfigureTestMG(MG_COUNT_IF_E_TEST prims/mg_count_if_e.cu) - target_link_libraries(MG_COUNT_IF_E_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS PER_V_TRANSFORM_REDUCE_INCOMING_OUTGOING_E tests --------------------------------- ConfigureTestMG(MG_PER_V_TRANSFORM_REDUCE_INCOMING_OUTGOING_E_TEST prims/mg_per_v_transform_reduce_incoming_outgoing_e.cu) - target_link_libraries(MG_PER_V_TRANSFORM_REDUCE_INCOMING_OUTGOING_E_TEST PRIVATE cuco::cuco) + + ############################################################################################### + # - MG PRIMS PER_V_TRANSFORM_REDUCE_DST_KEY_AGGREGATED_OUTGOING_E tests ----------------------- + ConfigureTestMG(MG_PER_V_TRANSFORM_REDUCE_DST_KEY_AGGREGATED_OUTGOING_E_TEST + prims/mg_per_v_transform_reduce_dst_key_aggregated_outgoing_e.cu) ############################################################################################### # - MG PRIMS EXTRACT_TRANSFORM_E tests -------------------------------------------------------- ConfigureTestMG(MG_EXTRACT_TRANSFORM_E_TEST prims/mg_extract_transform_e.cu) - target_link_libraries(MG_EXTRACT_TRANSFORM_E_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS EXTRACT_TRANSFORM_V_FRONTIER_OUTGOING_E tests ------------------------------------ ConfigureTestMG(MG_EXTRACT_TRANSFORM_V_FRONTIER_OUTGOING_E_TEST prims/mg_extract_transform_v_frontier_outgoing_e.cu) - target_link_libraries(MG_EXTRACT_TRANSFORM_V_FRONTIER_OUTGOING_E_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS PER_V_RANDOM_SELECT_TRANSFORM_OUTGOING_E tests ----------------------------------- ConfigureTestMG(MG_PER_V_RANDOM_SELECT_TRANSFORM_OUTGOING_E_TEST prims/mg_per_v_random_select_transform_outgoing_e.cu) - target_link_libraries(MG_PER_V_RANDOM_SELECT_TRANSFORM_OUTGOING_E_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG PRIMS PER_V_PAIR_TRANSFORM_DST_NBR_INTERSECTION tests ---------------------------------- ConfigureTestMG(MG_PER_V_PAIR_TRANSFORM_DST_NBR_INTERSECTION_TEST prims/mg_per_v_pair_transform_dst_nbr_intersection.cu) - target_link_libraries(MG_PER_V_PAIR_TRANSFORM_DST_NBR_INTERSECTION_TEST PRIVATE cuco::cuco) + + ############################################################################################### + # - MG PRIMS TRANSFORM_REDUCE_DST_NBR_INTERSECTION OF_E_ENDPOINTS_BY_V tests ------------------ + ConfigureTestMG(MG_TRANSFORM_REDUCE_DST_NBR_INTERSECTION_BY_E_ENDPOINTS_BY_V_TEST + prims/mg_transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cu) ############################################################################################### # - MG PRIMS PER_V_PAIR_TRANSFORM_DST_NBR_WEIGHTED_INTERSECTION tests ------------------------- ConfigureTestMG(MG_PER_V_PAIR_TRANSFORM_DST_NBR_WEIGHTED_INTERSECTION_TEST - prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu) - target_link_libraries(MG_PER_V_PAIR_TRANSFORM_DST_NBR_WEIGHTED_INTERSECTION_TEST PRIVATE cuco::cuco) + prims/mg_per_v_pair_transform_dst_nbr_weighted_intersection.cu) ############################################################################################### # - MG NBR SAMPLING tests --------------------------------------------------------------------- ConfigureTestMG(MG_UNIFORM_NEIGHBOR_SAMPLING_TEST sampling/mg_uniform_neighbor_sampling.cu) - target_link_libraries(MG_UNIFORM_NEIGHBOR_SAMPLING_TEST PRIVATE cuco::cuco) ############################################################################################### # - MG RANDOM_WALKS tests --------------------------------------------------------------------- diff --git a/cpp/tests/prims/mg_per_v_transform_reduce_dst_key_aggregated_outgoing_e.cu b/cpp/tests/prims/mg_per_v_transform_reduce_dst_key_aggregated_outgoing_e.cu new file mode 100644 index 00000000000..af56807746a --- /dev/null +++ b/cpp/tests/prims/mg_per_v_transform_reduce_dst_key_aggregated_outgoing_e.cu @@ -0,0 +1,599 @@ +/* + * Copyright (c) 2021-2024, 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 "prims/per_v_transform_reduce_dst_key_aggregated_outgoing_e.cuh" +#include "prims/reduce_op.cuh" +#include "prims/update_edge_src_dst_property.cuh" +#include "property_generator.cuh" +#include "result_compare.cuh" +#include "utilities/base_fixture.hpp" +#include "utilities/device_comm_wrapper.hpp" +#include "utilities/mg_utilities.hpp" +#include "utilities/test_graphs.hpp" +#include "utilities/test_utilities.hpp" +#include "utilities/thrust_wrapper.hpp" + +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include +#include + +template +struct key_aggregated_e_op_t { + __device__ result_t operator()(vertex_t src, + vertex_t key, + result_t src_property, + result_t key_property, + edge_value_t edge_property) const + { + if (src_property < key_property) { + return src_property; + } else { + return key_property; + } + } +}; + +struct Prims_Usecase { + bool test_weighted{false}; + bool edge_masking{false}; + bool check_correctness{true}; +}; + +template +class Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE + : public ::testing::TestWithParam> { + public: + Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE() {} + + static void SetUpTestCase() { handle_ = cugraph::test::initialize_mg_handle(); } + + static void TearDownTestCase() { handle_.reset(); } + + virtual void SetUp() {} + virtual void TearDown() {} + + // Compare the results of per_v_transform_reduce_incoming|outgoing_e primitive + template + void run_current_test(Prims_Usecase const& prims_usecase, input_usecase_t const& input_usecase) + { + HighResTimer hr_timer{}; + + auto const comm_rank = handle_->get_comms().get_rank(); + + // 1. create MG graph + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG Construct graph"); + } + + auto [mg_graph, mg_edge_weights, mg_renumber_map] = + cugraph::test::construct_graph( + *handle_, input_usecase, prims_usecase.test_weighted, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + auto mg_graph_view = mg_graph.view(); + auto mg_edge_weight_view = + mg_edge_weights ? std::make_optional((*mg_edge_weights).view()) : std::nullopt; + + 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 per_v_transform_reduce_dst_key_aggregated_outgoing_e + + const int vertex_prop_hash_bin_count = 5; + const int key_hash_bin_count = 10; + const int key_prop_hash_bin_count = 20; + const int initial_value = 4; + + auto property_initial_value = + cugraph::test::generate::initial_value(initial_value); + + auto mg_vertex_prop = cugraph::test::generate::vertex_property( + *handle_, *mg_renumber_map, vertex_prop_hash_bin_count); + auto mg_src_prop = cugraph::test::generate::src_property( + *handle_, mg_graph_view, mg_vertex_prop); + + auto mg_vertex_key = cugraph::test::generate::vertex_property( + *handle_, *mg_renumber_map, key_hash_bin_count); + auto mg_dst_key = cugraph::test::generate::dst_property( + *handle_, mg_graph_view, mg_vertex_key); + + rmm::device_uvector mg_kv_store_keys(comm_rank == 0 ? key_hash_bin_count : int{0}, + handle_->get_stream()); + thrust::sequence( + handle_->get_thrust_policy(), mg_kv_store_keys.begin(), mg_kv_store_keys.end(), vertex_t{0}); + mg_kv_store_keys = cugraph::detail::shuffle_ext_vertices_to_local_gpu_by_vertex_partitioning( + *handle_, std::move(mg_kv_store_keys)); + auto mg_kv_store_values = cugraph::test::generate::vertex_property( + *handle_, mg_kv_store_keys, key_prop_hash_bin_count); + + static_assert(std::is_same_v || + std::is_same_v>); + result_t invalid_value{}; + if constexpr (std::is_same_v) { + invalid_value = std::numeric_limits::max(); + } else { + invalid_value = + thrust::make_tuple(std::numeric_limits::max(), std::numeric_limits::max()); + } + cugraph::kv_store_t mg_kv_store( + mg_kv_store_keys.begin(), + mg_kv_store_keys.end(), + cugraph::get_dataframe_buffer_begin(mg_kv_store_values), + cugraph::invalid_vertex_id::value, + invalid_value, + handle_->get_stream()); + + enum class reduction_type_t { PLUS, ELEMWISE_MIN, ELEMWISE_MAX }; + std::array reduction_types = { + reduction_type_t::PLUS, reduction_type_t::ELEMWISE_MIN, reduction_type_t::ELEMWISE_MAX}; + + std::vector(0, rmm::cuda_stream_view{}))> + mg_results{}; + mg_results.reserve(reduction_types.size()); + + for (size_t i = 0; i < reduction_types.size(); ++i) { + mg_results.push_back(cugraph::allocate_dataframe_buffer( + mg_graph_view.local_vertex_partition_range_size(), handle_->get_stream())); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG per_v_transform_reduce_outgoing_e"); + } + + switch (reduction_types[i]) { + case reduction_type_t::PLUS: + if (mg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + *mg_edge_weight_view, + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::plus{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::plus{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } + break; + case reduction_type_t::ELEMWISE_MIN: + if (mg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + *mg_edge_weight_view, + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_minimum{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_minimum{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } + break; + case reduction_type_t::ELEMWISE_MAX: + if (mg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + *mg_edge_weight_view, + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_maximum{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + mg_graph_view, + mg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + mg_dst_key.view(), + mg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_maximum{}, + cugraph::get_dataframe_buffer_begin(mg_results[i])); + } + break; + default: FAIL() << "should not be reached."; + } + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + } + + // 3. compare SG & MG results + + if (prims_usecase.check_correctness) { + cugraph::graph_t sg_graph(*handle_); + std::optional< + cugraph::edge_property_t, weight_t>> + sg_edge_weights{std::nullopt}; + std::tie(sg_graph, sg_edge_weights, std::ignore) = cugraph::test::mg_graph_to_sg_graph( + *handle_, + mg_graph_view, + std::optional>{std::nullopt}, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + false); + + for (size_t i = 0; i < reduction_types.size(); ++i) { + auto mg_aggregate_results = + cugraph::allocate_dataframe_buffer(0, handle_->get_stream()); + + static_assert(cugraph::is_arithmetic_or_thrust_tuple_of_arithmetic::value); + if constexpr (std::is_arithmetic_v) { + std::tie(std::ignore, mg_aggregate_results) = + cugraph::test::mg_vertex_property_values_to_sg_vertex_property_values( + *handle_, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + mg_graph_view.local_vertex_partition_range(), + std::optional>{std::nullopt}, + std::optional>{std::nullopt}, + raft::device_span(mg_results[i].data(), mg_results[i].size())); + } else { + std::tie(std::ignore, std::get<0>(mg_aggregate_results)) = + cugraph::test::mg_vertex_property_values_to_sg_vertex_property_values( + *handle_, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + mg_graph_view.local_vertex_partition_range(), + std::optional>{std::nullopt}, + std::optional>{std::nullopt}, + raft::device_span::type const>( + std::get<0>(mg_results[i]).data(), std::get<0>(mg_results[i]).size())); + + std::tie(std::ignore, std::get<1>(mg_aggregate_results)) = + cugraph::test::mg_vertex_property_values_to_sg_vertex_property_values( + *handle_, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + mg_graph_view.local_vertex_partition_range(), + std::optional>{std::nullopt}, + std::optional>{std::nullopt}, + raft::device_span::type const>( + std::get<1>(mg_results[i]).data(), std::get<1>(mg_results[i]).size())); + } + + if (handle_->get_comms().get_rank() == int{0}) { + auto sg_graph_view = sg_graph.view(); + auto sg_edge_weight_view = + sg_edge_weights ? std::make_optional((*sg_edge_weights).view()) : std::nullopt; + + 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()), + vertex_prop_hash_bin_count); + auto sg_src_prop = cugraph::test::generate::src_property( + *handle_, sg_graph_view, sg_vertex_prop); + + auto sg_vertex_key = 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()), + key_hash_bin_count); + auto sg_dst_key = cugraph::test::generate::dst_property( + *handle_, sg_graph_view, sg_vertex_key); + + rmm::device_uvector sg_kv_store_keys(key_hash_bin_count, handle_->get_stream()); + thrust::sequence(handle_->get_thrust_policy(), + sg_kv_store_keys.begin(), + sg_kv_store_keys.end(), + vertex_t{0}); + auto sg_kv_store_values = cugraph::test::generate::vertex_property( + *handle_, sg_kv_store_keys, key_prop_hash_bin_count); + + cugraph::kv_store_t sg_kv_store( + sg_kv_store_keys.begin(), + sg_kv_store_keys.end(), + cugraph::get_dataframe_buffer_begin(sg_kv_store_values), + cugraph::invalid_vertex_id::value, + invalid_value, + handle_->get_stream()); + + cugraph::test::vector_result_compare compare{*handle_}; + + auto global_result = cugraph::allocate_dataframe_buffer( + sg_graph_view.local_vertex_partition_range_size(), handle_->get_stream()); + + switch (reduction_types[i]) { + case reduction_type_t::PLUS: + if (sg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + *sg_edge_weight_view, + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::plus{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::plus{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } + break; + case reduction_type_t::ELEMWISE_MIN: + if (sg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + *sg_edge_weight_view, + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_minimum{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_minimum{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } + break; + case reduction_type_t::ELEMWISE_MAX: + if (sg_edge_weight_view) { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + *sg_edge_weight_view, + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_maximum{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } else { + per_v_transform_reduce_dst_key_aggregated_outgoing_e( + *handle_, + sg_graph_view, + sg_src_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + sg_dst_key.view(), + sg_kv_store.view(), + key_aggregated_e_op_t{}, + property_initial_value, + cugraph::reduce_op::elementwise_maximum{}, + cugraph::get_dataframe_buffer_begin(global_result)); + } + break; + default: FAIL() << "should not be reached."; + } + + ASSERT_TRUE(compare(mg_aggregate_results, global_result)); + } + } + } + } + + private: + static std::unique_ptr handle_; +}; + +template +std::unique_ptr + Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE::handle_ = nullptr; + +using Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_File = + Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE; +using Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat = + Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE; + +// FIXME: this tests do not build as cugrpah::kv_store_t has a build error when use_binary_search = +// false and value_t is thrust::tuple, this will be fixed in a separate PR +#if 0 +TEST_P(Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_File, + CheckInt32Int32FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test>(std::get<0>(param), + std::get<1>(param)); +} + +TEST_P(Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt32Int32FloatTupleIntFloatTransposeFalse) +{ + 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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt32Int64FloatTupleIntFloatTransposeFalse) +{ + 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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt64Int64FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} +#endif + +TEST_P(Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_File, + CheckInt32Int32FloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt32Int32FloatTransposeFalse) +{ + 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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt32Int64FloatTransposeFalse) +{ + 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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + CheckInt64Int64FloatTransposeFalse) +{ + 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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_File, + ::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::File_Usecase("test/datasets/karate.mtx"), + cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), + cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), + cugraph::test::File_Usecase("test/datasets/webbase-1M.mtx")))); + +INSTANTIATE_TEST_SUITE_P(rmat_small_test, + Tests_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + ::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)))); + +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_MGPerVTransformReduceDstKeyAggregatedOutgoingE_Rmat, + ::testing::Combine( + ::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_transform_reduce_incoming_outgoing_e.cu b/cpp/tests/prims/mg_per_v_transform_reduce_incoming_outgoing_e.cu index efab53f89e6..a459a677569 100644 --- a/cpp/tests/prims/mg_per_v_transform_reduce_incoming_outgoing_e.cu +++ b/cpp/tests/prims/mg_per_v_transform_reduce_incoming_outgoing_e.cu @@ -18,6 +18,7 @@ #include "prims/reduce_op.cuh" #include "prims/update_edge_src_dst_property.cuh" #include "property_generator.cuh" +#include "result_compare.cuh" #include "utilities/base_fixture.hpp" #include "utilities/device_comm_wrapper.hpp" #include "utilities/mg_utilities.hpp" @@ -72,83 +73,6 @@ struct e_op_t { } }; -template -__host__ __device__ bool compare_scalar(T val0, T val1, thrust::optional threshold_ratio) -{ - if (threshold_ratio) { - return std::abs(val0 - val1) <= (std::max(std::abs(val0), std::abs(val1)) * *threshold_ratio); - } else { - return val0 == val1; - } -} - -template -struct comparator { - static constexpr double threshold_ratio{1e-2}; - - __host__ __device__ bool operator()(T t0, T t1) const - { - static_assert(cugraph::is_arithmetic_or_thrust_tuple_of_arithmetic::value); - if constexpr (std::is_arithmetic_v) { - return compare_scalar( - t0, - t1, - std::is_floating_point_v ? thrust::optional{threshold_ratio} : thrust::nullopt); - } else { - auto val0 = thrust::get<0>(t0); - auto val1 = thrust::get<0>(t1); - auto passed = compare_scalar(val0, - val1, - std::is_floating_point_v - ? thrust::optional{threshold_ratio} - : thrust::nullopt); - if (!passed) return false; - - if constexpr (thrust::tuple_size::value >= 2) { - auto val0 = thrust::get<1>(t0); - auto val1 = thrust::get<1>(t1); - auto passed = compare_scalar(val0, - val1, - std::is_floating_point_v - ? thrust::optional{threshold_ratio} - : thrust::nullopt); - if (!passed) return false; - } - if constexpr (thrust::tuple_size::value >= 3) { - assert(false); // should not be reached. - } - return true; - } - } -}; - -struct result_compare { - const raft::handle_t& handle_; - result_compare(raft::handle_t const& handle) : handle_(handle) {} - - template - auto operator()(const std::tuple...>& t1, - const std::tuple...>& t2) - { - using type = thrust::tuple; - return equality_impl(t1, t2, std::make_index_sequence::value>()); - } - - template - auto operator()(const rmm::device_uvector& t1, const rmm::device_uvector& t2) - { - return thrust::equal( - handle_.get_thrust_policy(), t1.begin(), t1.end(), t2.begin(), comparator()); - } - - private: - template - auto equality_impl(T& t1, T& t2, std::index_sequence) - { - return (... && (result_compare::operator()(std::get(t1), std::get(t2)))); - } -}; - struct Prims_Usecase { bool test_weighted{false}; bool edge_masking{false}; @@ -440,7 +364,7 @@ class Tests_MGPerVTransformReduceIncomingOutgoingE *handle_, sg_graph_view, sg_vertex_prop); auto sg_dst_prop = cugraph::test::generate::dst_property( *handle_, sg_graph_view, sg_vertex_prop); - result_compare comp{*handle_}; + cugraph::test::vector_result_compare compare{*handle_}; auto global_in_result = cugraph::allocate_dataframe_buffer( sg_graph_view.local_vertex_partition_range_size(), handle_->get_stream()); @@ -528,8 +452,8 @@ class Tests_MGPerVTransformReduceIncomingOutgoingE default: FAIL() << "should not be reached."; } - ASSERT_TRUE(comp(mg_aggregate_in_results, global_in_result)); - ASSERT_TRUE(comp(mg_aggregate_out_results, global_out_result)); + ASSERT_TRUE(compare(mg_aggregate_in_results, global_in_result)); + ASSERT_TRUE(compare(mg_aggregate_out_results, global_out_result)); } } } diff --git a/cpp/tests/prims/mg_reduce_v.cu b/cpp/tests/prims/mg_reduce_v.cu index da3354b77d9..783e17b6d8f 100644 --- a/cpp/tests/prims/mg_reduce_v.cu +++ b/cpp/tests/prims/mg_reduce_v.cu @@ -17,6 +17,7 @@ #include "prims/property_op_utils.cuh" #include "prims/reduce_v.cuh" #include "property_generator.cuh" +#include "result_compare.cuh" #include "utilities/base_fixture.hpp" #include "utilities/device_comm_wrapper.hpp" #include "utilities/mg_utilities.hpp" @@ -49,50 +50,6 @@ #include -template -struct result_compare { - static constexpr double threshold_ratio{1e-2}; - constexpr auto operator()(const T& t1, const T& t2) - { - if constexpr (std::is_floating_point_v) { - bool passed = (t1 == t2) // when t1 == t2 == 0 - || - (std::abs(t1 - t2) < (std::max(std::abs(t1), std::abs(t2)) * threshold_ratio)); - return passed; - } - return t1 == t2; - } -}; - -template -struct result_compare> { - static constexpr double threshold_ratio{1e-3}; - - using Type = thrust::tuple; - constexpr auto operator()(const Type& t1, const Type& t2) - { - return equality_impl(t1, t2, std::make_index_sequence::value>()); - } - - private: - template - constexpr bool equal(T t1, T t2) - { - if constexpr (std::is_floating_point_v) { - bool passed = (t1 == t2) // when t1 == t2 == 0 - || - (std::abs(t1 - t2) < (std::max(std::abs(t1), std::abs(t2)) * threshold_ratio)); - return passed; - } - return t1 == t2; - } - template - constexpr auto equality_impl(T& t1, T& t2, std::index_sequence) - { - return (... && (equal(thrust::get(t1), thrust::get(t2)))); - } -}; - struct Prims_Usecase { bool check_correctness{true}; }; @@ -249,7 +206,7 @@ class Tests_MGReduceV break; default: FAIL() << "should not be reached."; } - result_compare compare{}; + cugraph::test::scalar_result_compare compare{}; ASSERT_TRUE(compare(expected_result, results[reduction_type])); } } diff --git a/cpp/tests/prims/mg_transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cu b/cpp/tests/prims/mg_transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cu new file mode 100644 index 00000000000..5fa37250e21 --- /dev/null +++ b/cpp/tests/prims/mg_transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v.cu @@ -0,0 +1,289 @@ +/* + * Copyright (c) 2024, 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 "property_generator.cuh" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include + +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +template +struct intersection_op_t { + __device__ thrust::tuple operator()( + vertex_t v0, + vertex_t v1, + edge_t v0_prop, + edge_t v1_prop, + raft::device_span intersection) const + { + return thrust::make_tuple( + v0_prop + v1_prop, v0_prop + v1_prop, static_cast(intersection.size())); + } +}; + +struct Prims_Usecase { + bool edge_masking{false}; + bool check_correctness{true}; +}; + +template +class Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV + : public ::testing::TestWithParam> { + public: + Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV() {} + + static void SetUpTestCase() { handle_ = cugraph::test::initialize_mg_handle(); } + + static void TearDownTestCase() { handle_.reset(); } + + virtual void SetUp() {} + virtual void TearDown() {} + + // Verify the results of transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v primitive + template + void run_current_test(Prims_Usecase const& prims_usecase, input_usecase_t const& input_usecase) + { + HighResTimer hr_timer{}; + + auto const comm_rank = handle_->get_comms().get_rank(); + auto const comm_size = handle_->get_comms().get_size(); + + // 1. create MG graph + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG Construct graph"); + } + + cugraph::graph_t mg_graph(*handle_); + std::optional> mg_renumber_map{std::nullopt}; + std::tie(mg_graph, std::ignore, mg_renumber_map) = + cugraph::test::construct_graph( + *handle_, input_usecase, false, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + 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_dst_nbr_intersection_of_e_endpoints_by_v primitive + + const int hash_bin_count = 5; + const int initial_value = 4; + + auto property_initial_value = + cugraph::test::generate::initial_value(initial_value); + + auto mg_vertex_prop = cugraph::test::generate::vertex_property( + *handle_, *mg_renumber_map, hash_bin_count); + auto mg_src_prop = cugraph::test::generate::src_property( + *handle_, mg_graph_view, mg_vertex_prop); + auto mg_dst_prop = cugraph::test::generate::dst_property( + *handle_, mg_graph_view, mg_vertex_prop); + + auto mg_result_buffer = rmm::device_uvector( + mg_graph_view.local_vertex_partition_range_size(), handle_->get_stream()); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v"); + } + + cugraph::transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + intersection_op_t{}, + property_initial_value, + mg_result_buffer.begin()); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + // 3. validate MG results + + if (prims_usecase.check_correctness) { + rmm::device_uvector mg_aggregate_result_buffer(0, handle_->get_stream()); + std::tie(std::ignore, mg_aggregate_result_buffer) = + cugraph::test::mg_vertex_property_values_to_sg_vertex_property_values( + *handle_, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + mg_graph_view.local_vertex_partition_range(), + std::optional>{std::nullopt}, + std::optional>{std::nullopt}, + raft::device_span(mg_result_buffer.data(), mg_result_buffer.size())); + + cugraph::graph_t sg_graph(*handle_); + std::tie(sg_graph, std::ignore, std::ignore) = cugraph::test::mg_graph_to_sg_graph( + *handle_, + mg_graph_view, + std::optional>{std::nullopt}, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + false); + + 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_result_buffer = cugraph::allocate_dataframe_buffer( + sg_graph_view.number_of_vertices(), handle_->get_stream()); + + cugraph::transform_reduce_dst_nbr_intersection_of_e_endpoints_by_v( + *handle_, + sg_graph_view, + sg_src_prop.view(), + sg_dst_prop.view(), + intersection_op_t{}, + property_initial_value, + sg_result_buffer.begin()); + + bool valid = thrust::equal(handle_->get_thrust_policy(), + mg_aggregate_result_buffer.begin(), + mg_aggregate_result_buffer.end(), + sg_result_buffer.begin()); + + ASSERT_TRUE(valid); + } + } + } + + private: + static std::unique_ptr handle_; +}; + +template +std::unique_ptr + Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV::handle_ = nullptr; + +using Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_File = + Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV; +using Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat = + Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV; + +TEST_P(Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_File, CheckInt32Int32Float) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat, CheckInt32Int32Float) +{ + 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_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat, CheckInt32Int64Float) +{ + 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_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat, CheckInt64Int64Float) +{ + 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_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_File, + ::testing::Combine( + ::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/netscience.mtx")))); + +INSTANTIATE_TEST_SUITE_P(rmat_small_test, + Tests_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat, + ::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)))); + +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_MGTransformReduceDstNbrIntersectionOfEEndpointsByV_Rmat, + ::testing::Combine( + ::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() diff --git a/cpp/tests/prims/mg_transform_reduce_e.cu b/cpp/tests/prims/mg_transform_reduce_e.cu index c8ce9fc3a47..53f37e83b30 100644 --- a/cpp/tests/prims/mg_transform_reduce_e.cu +++ b/cpp/tests/prims/mg_transform_reduce_e.cu @@ -17,6 +17,7 @@ #include "prims/transform_reduce_e.cuh" #include "prims/update_edge_src_dst_property.cuh" #include "property_generator.cuh" +#include "result_compare.cuh" #include "utilities/base_fixture.hpp" #include "utilities/device_comm_wrapper.hpp" #include "utilities/mg_utilities.hpp" @@ -52,44 +53,6 @@ #include -template -struct result_compare { - static constexpr double threshold_ratio{1e-3}; - constexpr auto operator()(const T& t1, const T& t2) - { - if constexpr (std::is_floating_point_v) { - return std::abs(t1 - t2) < (std::max(t1, t2) * threshold_ratio); - } - return t1 == t2; - } -}; - -template -struct result_compare> { - static constexpr double threshold_ratio{1e-3}; - - using type = thrust::tuple; - constexpr auto operator()(const type& t1, const type& t2) - { - return equality_impl(t1, t2, std::make_index_sequence::value>()); - } - - private: - template - constexpr bool equal(T t1, T t2) - { - if constexpr (std::is_floating_point_v) { - return std::abs(t1 - t2) < (std::max(t1, t2) * threshold_ratio); - } - return t1 == t2; - } - template - constexpr auto equality_impl(T& t1, T& t2, std::index_sequence) - { - return (... && (equal(thrust::get(t1), thrust::get(t2)))); - } -}; - struct Prims_Usecase { bool test_weighted{false}; bool edge_masking{false}; @@ -231,7 +194,7 @@ class Tests_MGTransformReduceE } }, property_initial_value); - result_compare compare{}; + cugraph::test::scalar_result_compare compare{}; ASSERT_TRUE(compare(expected_result, result)); } } diff --git a/cpp/tests/prims/mg_transform_reduce_e_by_src_dst_key.cu b/cpp/tests/prims/mg_transform_reduce_e_by_src_dst_key.cu new file mode 100644 index 00000000000..457e6b5ab93 --- /dev/null +++ b/cpp/tests/prims/mg_transform_reduce_e_by_src_dst_key.cu @@ -0,0 +1,495 @@ +/* + * Copyright (c) 2024, 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 "property_generator.cuh" +#include "result_compare.cuh" + +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include + +struct Prims_Usecase { + bool test_weighted{false}; + bool edge_masking{false}; + bool check_correctness{true}; +}; + +template +class Tests_MGTransformReduceEBySrcDstKey + : public ::testing::TestWithParam> { + public: + Tests_MGTransformReduceEBySrcDstKey() {} + + static void SetUpTestCase() { handle_ = cugraph::test::initialize_mg_handle(); } + + static void TearDownTestCase() { handle_.reset(); } + + virtual void SetUp() {} + virtual void TearDown() {} + + // Compare the results of transform_reduce_e_by_src|dst_key primitive + template + void run_current_test(Prims_Usecase const& prims_usecase, input_usecase_t const& input_usecase) + { + HighResTimer hr_timer{}; + + // 1. create MG graph + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG Construct graph"); + } + + cugraph::graph_t mg_graph(*handle_); + std::optional> mg_renumber_map{std::nullopt}; + std::tie(mg_graph, std::ignore, mg_renumber_map) = + cugraph::test::construct_graph( + *handle_, input_usecase, prims_usecase.test_weighted, true); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + 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; + const int initial_value = 4; + + auto property_initial_value = + cugraph::test::generate::initial_value(initial_value); + + auto mg_vertex_prop = cugraph::test::generate::vertex_property( + *handle_, *mg_renumber_map, hash_bin_count); + auto mg_src_prop = cugraph::test::generate::src_property( + *handle_, mg_graph_view, mg_vertex_prop); + auto mg_dst_prop = cugraph::test::generate::dst_property( + *handle_, mg_graph_view, mg_vertex_prop); + + auto mg_vertex_key = cugraph::test::generate::vertex_property( + *handle_, *mg_renumber_map, hash_bin_count); + auto mg_src_key = cugraph::test::generate::src_property( + *handle_, mg_graph_view, mg_vertex_key); + auto mg_dst_key = cugraph::test::generate::dst_property( + *handle_, mg_graph_view, mg_vertex_key); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG transform_reduce_e_by_src_key"); + } + + auto [by_src_keys, by_src_values] = transform_reduce_e_by_src_key( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + mg_src_key.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; + } + }, + property_initial_value, + cugraph::reduce_op::plus{}); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.start("MG transform_reduce_e_by_dst_key"); + } + + auto [by_dst_keys, by_dst_values] = transform_reduce_e_by_dst_key( + *handle_, + mg_graph_view, + mg_src_prop.view(), + mg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + mg_dst_key.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; + } + }, + property_initial_value, + cugraph::reduce_op::plus{}); + + if (cugraph::test::g_perf) { + RAFT_CUDA_TRY(cudaDeviceSynchronize()); // for consistent performance measurement + handle_->get_comms().barrier(); + hr_timer.stop(); + hr_timer.display_and_clear(std::cout); + } + + // 3. compare SG & MG results + + if (prims_usecase.check_correctness) { + auto mg_aggregate_by_src_keys = + cugraph::test::device_gatherv(*handle_, by_src_keys.data(), by_src_keys.size()); + auto mg_aggregate_by_src_values = + cugraph::allocate_dataframe_buffer(0, handle_->get_stream()); + if constexpr (std::is_arithmetic_v) { + mg_aggregate_by_src_values = + cugraph::test::device_gatherv(*handle_, by_src_values.data(), by_src_values.size()); + } else { + std::get<0>(mg_aggregate_by_src_values) = cugraph::test::device_gatherv( + *handle_, std::get<0>(by_src_values).data(), std::get<0>(by_src_values).size()); + std::get<1>(mg_aggregate_by_src_values) = cugraph::test::device_gatherv( + *handle_, std::get<1>(by_src_values).data(), std::get<1>(by_src_values).size()); + } + thrust::sort_by_key(handle_->get_thrust_policy(), + mg_aggregate_by_src_keys.begin(), + mg_aggregate_by_src_keys.end(), + cugraph::get_dataframe_buffer_begin(mg_aggregate_by_src_values)); + + auto mg_aggregate_by_dst_keys = + cugraph::test::device_gatherv(*handle_, by_dst_keys.data(), by_dst_keys.size()); + auto mg_aggregate_by_dst_values = + cugraph::allocate_dataframe_buffer(0, handle_->get_stream()); + if constexpr (std::is_arithmetic_v) { + mg_aggregate_by_dst_values = + cugraph::test::device_gatherv(*handle_, by_dst_values.data(), by_dst_values.size()); + } else { + std::get<0>(mg_aggregate_by_dst_values) = cugraph::test::device_gatherv( + *handle_, std::get<0>(by_dst_values).data(), std::get<0>(by_dst_values).size()); + std::get<1>(mg_aggregate_by_dst_values) = cugraph::test::device_gatherv( + *handle_, std::get<1>(by_dst_values).data(), std::get<1>(by_dst_values).size()); + } + thrust::sort_by_key(handle_->get_thrust_policy(), + mg_aggregate_by_dst_keys.begin(), + mg_aggregate_by_dst_keys.end(), + cugraph::get_dataframe_buffer_begin(mg_aggregate_by_dst_values)); + + cugraph::graph_t sg_graph(*handle_); + std::tie(sg_graph, std::ignore, std::ignore) = cugraph::test::mg_graph_to_sg_graph( + *handle_, + mg_graph_view, + std::optional>{std::nullopt}, + std::make_optional>((*mg_renumber_map).data(), + (*mg_renumber_map).size()), + false); + + 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_key = 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_key = cugraph::test::generate::src_property( + *handle_, sg_graph_view, sg_vertex_key); + auto sg_dst_key = cugraph::test::generate::dst_property( + *handle_, sg_graph_view, sg_vertex_key); + + auto [sg_by_src_keys, sg_by_src_values] = transform_reduce_e_by_src_key( + *handle_, + sg_graph_view, + sg_src_prop.view(), + sg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + sg_src_key.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; + } + }, + property_initial_value, + cugraph::reduce_op::plus{}); + thrust::sort_by_key(handle_->get_thrust_policy(), + sg_by_src_keys.begin(), + sg_by_src_keys.end(), + cugraph::get_dataframe_buffer_begin(sg_by_src_values)); + + auto [sg_by_dst_keys, sg_by_dst_values] = transform_reduce_e_by_dst_key( + *handle_, + sg_graph_view, + sg_src_prop.view(), + sg_dst_prop.view(), + cugraph::edge_dummy_property_t{}.view(), + sg_dst_key.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; + } + }, + property_initial_value, + cugraph::reduce_op::plus{}); + thrust::sort_by_key(handle_->get_thrust_policy(), + sg_by_dst_keys.begin(), + sg_by_dst_keys.end(), + cugraph::get_dataframe_buffer_begin(sg_by_dst_values)); + + cugraph::test::vector_result_compare compare{*handle_}; + + ASSERT_TRUE(compare(sg_by_src_keys, mg_aggregate_by_src_keys)); + ASSERT_TRUE(compare(sg_by_src_values, mg_aggregate_by_src_values)); + + ASSERT_TRUE(compare(sg_by_dst_keys, mg_aggregate_by_dst_keys)); + ASSERT_TRUE(compare(sg_by_dst_values, mg_aggregate_by_dst_values)); + } + } + } + + private: + static std::unique_ptr handle_; +}; + +template +std::unique_ptr Tests_MGTransformReduceEBySrcDstKey::handle_ = + nullptr; + +using Tests_MGTransformReduceEBySrcDstKey_File = + Tests_MGTransformReduceEBySrcDstKey; +using Tests_MGTransformReduceEBySrcDstKey_Rmat = + Tests_MGTransformReduceEBySrcDstKey; + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_File, CheckInt32Int32FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test, false>(std::get<0>(param), + std::get<1>(param)); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int32FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test, false>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int64FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test, false>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt64Int64FloatTupleIntFloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test, false>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_File, CheckInt32Int32FloatTupleIntFloatTransposeTrue) +{ + auto param = GetParam(); + run_current_test, true>(std::get<0>(param), + std::get<1>(param)); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int32FloatTupleIntFloatTransposeTrue) +{ + auto param = GetParam(); + run_current_test, true>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int64FloatTupleIntFloatTransposeTrue) +{ + auto param = GetParam(); + run_current_test, true>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt64Int64FloatTupleIntFloatTransposeTrue) +{ + auto param = GetParam(); + run_current_test, true>( + std::get<0>(param), + cugraph::test::override_Rmat_Usecase_with_cmd_line_arguments(std::get<1>(param))); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_File, CheckInt32Int32FloatTransposeFalse) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int32FloatTransposeFalse) +{ + 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_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int64FloatTransposeFalse) +{ + 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_MGTransformReduceEBySrcDstKey_Rmat, CheckInt64Int64FloatTransposeFalse) +{ + 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_MGTransformReduceEBySrcDstKey_File, CheckInt32Int32FloatTransposeTrue) +{ + auto param = GetParam(); + run_current_test(std::get<0>(param), std::get<1>(param)); +} + +TEST_P(Tests_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int32FloatTransposeTrue) +{ + 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_MGTransformReduceEBySrcDstKey_Rmat, CheckInt32Int64FloatTransposeTrue) +{ + 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_MGTransformReduceEBySrcDstKey_Rmat, CheckInt64Int64FloatTransposeTrue) +{ + 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_MGTransformReduceEBySrcDstKey_File, + ::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::File_Usecase("test/datasets/karate.mtx"), + cugraph::test::File_Usecase("test/datasets/web-Google.mtx"), + cugraph::test::File_Usecase("test/datasets/ljournal-2008.mtx"), + cugraph::test::File_Usecase("test/datasets/webbase-1M.mtx")))); + +INSTANTIATE_TEST_SUITE_P(rmat_small_test, + Tests_MGTransformReduceEBySrcDstKey_Rmat, + ::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)))); + +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_MGTransformReduceEBySrcDstKey_Rmat, + ::testing::Combine( + ::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_v.cu b/cpp/tests/prims/mg_transform_reduce_v.cu index c0d44bc94f1..c954f31d0f9 100644 --- a/cpp/tests/prims/mg_transform_reduce_v.cu +++ b/cpp/tests/prims/mg_transform_reduce_v.cu @@ -16,6 +16,7 @@ #include "prims/transform_reduce_v.cuh" #include "property_generator.cuh" +#include "result_compare.cuh" #include "utilities/base_fixture.hpp" #include "utilities/device_comm_wrapper.hpp" #include "utilities/mg_utilities.hpp" @@ -56,50 +57,6 @@ struct v_op_t { } }; -template -struct result_compare { - static constexpr double threshold_ratio{1e-3}; - constexpr auto operator()(const T& t1, const T& t2) - { - if constexpr (std::is_floating_point_v) { - bool passed = (t1 == t2) // when t1 == t2 == 0 - || - (std::abs(t1 - t2) < (std::max(std::abs(t1), std::abs(t2)) * threshold_ratio)); - return passed; - } - return t1 == t2; - } -}; - -template -struct result_compare> { - static constexpr double threshold_ratio{1e-3}; - - using Type = thrust::tuple; - constexpr auto operator()(const Type& t1, const Type& t2) - { - return equality_impl(t1, t2, std::make_index_sequence::value>()); - } - - private: - template - constexpr bool equal(T t1, T t2) - { - if constexpr (std::is_floating_point_v) { - bool passed = (t1 == t2) // when t1 == t2 == 0 - || - (std::abs(t1 - t2) < (std::max(std::abs(t1), std::abs(t2)) * threshold_ratio)); - return passed; - } - return t1 == t2; - } - template - constexpr auto equality_impl(T& t1, T& t2, std::index_sequence) - { - return (... && (equal(thrust::get(t1), thrust::get(t2)))); - } -}; - struct Prims_Usecase { bool check_correctness{true}; }; @@ -254,7 +211,7 @@ class Tests_MGTransformReduceV break; default: FAIL() << "should not be reached."; } - result_compare compare{}; + cugraph::test::scalar_result_compare compare{}; ASSERT_TRUE(compare(expected_result, results[reduction_type])); } } diff --git a/cpp/tests/prims/result_compare.cuh b/cpp/tests/prims/result_compare.cuh new file mode 100644 index 00000000000..5a1abb90e3c --- /dev/null +++ b/cpp/tests/prims/result_compare.cuh @@ -0,0 +1,143 @@ +/* + * Copyright (c) 2024, 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 test { + +namespace detail { + +template +__host__ __device__ bool compare_arithmetic_scalar(T val0, + T val1, + thrust::optional threshold_ratio) +{ + if (threshold_ratio) { + return std::abs(val0 - val1) <= (std::max(std::abs(val0), std::abs(val1)) * *threshold_ratio); + } else { + return val0 == val1; + } +} + +} // namespace detail + +template +struct comparator { + static constexpr double threshold_ratio{1e-2}; + + __host__ __device__ bool operator()(T t0, T t1) const + { + static_assert(cugraph::is_arithmetic_or_thrust_tuple_of_arithmetic::value); + if constexpr (std::is_arithmetic_v) { + return detail::compare_arithmetic_scalar( + t0, + t1, + std::is_floating_point_v ? thrust::optional{threshold_ratio} : thrust::nullopt); + } else { + auto val0 = thrust::get<0>(t0); + auto val1 = thrust::get<0>(t1); + auto passed = detail::compare_arithmetic_scalar( + val0, + val1, + std::is_floating_point_v ? thrust::optional{threshold_ratio} + : thrust::nullopt); + if (!passed) return false; + + if constexpr (thrust::tuple_size::value >= 2) { + auto val0 = thrust::get<1>(t0); + auto val1 = thrust::get<1>(t1); + auto passed = + detail::compare_arithmetic_scalar(val0, + val1, + std::is_floating_point_v + ? thrust::optional{threshold_ratio} + : thrust::nullopt); + if (!passed) return false; + } + if constexpr (thrust::tuple_size::value >= 3) { + assert(false); // should not be reached. + } + return true; + } + } +}; + +struct scalar_result_compare { + template + auto operator()(thrust::tuple t1, thrust::tuple t2) + { + using type = thrust::tuple; + return equality_impl(t1, t2, std::make_index_sequence::value>()); + } + + template + auto operator()(T t1, T t2) + { + comparator comp{}; + return comp(t1, t2); + } + + private: + template + auto equality_impl(T t1, T t2, std::index_sequence) + { + return (... && (scalar_result_compare::operator()(thrust::get(t1), thrust::get(t2)))); + } +}; + +struct vector_result_compare { + const raft::handle_t& handle_; + + vector_result_compare(raft::handle_t const& handle) : handle_(handle) {} + + template + auto operator()(std::tuple...> const& t1, + std::tuple...> const& t2) + { + using type = thrust::tuple; + return equality_impl(t1, t2, std::make_index_sequence::value>()); + } + + template + auto operator()(rmm::device_uvector const& t1, rmm::device_uvector const& t2) + { + return thrust::equal( + handle_.get_thrust_policy(), t1.begin(), t1.end(), t2.begin(), comparator()); + } + + private: + template + auto equality_impl(T& t1, T& t2, std::index_sequence) + { + return (... && (vector_result_compare::operator()(std::get(t1), std::get(t2)))); + } +}; + +} // namespace test +} // namespace cugraph From 6c88281b6a58bac05a2c29e5c9ab6b952d3a38c6 Mon Sep 17 00:00:00 2001 From: Rick Ratzel <3039903+rlratzel@users.noreply.github.com> Date: Wed, 13 Mar 2024 09:16:46 -0500 Subject: [PATCH 8/9] Adds nx-cugraph benchmarks for APIs added to prior releases that were never benchmarked (#4228) * Adds benchmark for nx-cugraph `pagerank` with a personalization dict (see results below) * Adds several placeholder benchmarks for other nx-cugraph APIs added to prior releases that were never benchmarked. ![image](https://github.com/rapidsai/cugraph/assets/3039903/4692e2a2-e14a-489d-84f7-772eda6fc316) Authors: - Rick Ratzel (https://github.com/rlratzel) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Erik Welch (https://github.com/eriknw) URL: https://github.com/rapidsai/cugraph/pull/4228 --- .../nx-cugraph/pytest-based/bench_algos.py | 112 ++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/benchmarks/nx-cugraph/pytest-based/bench_algos.py b/benchmarks/nx-cugraph/pytest-based/bench_algos.py index 97eb32e2aaa..3b085a9bfdb 100644 --- a/benchmarks/nx-cugraph/pytest-based/bench_algos.py +++ b/benchmarks/nx-cugraph/pytest-based/bench_algos.py @@ -242,6 +242,28 @@ def get_highest_degree_node(graph_obj): return max(degrees, key=lambda t: t[1])[0] +def build_personalization_dict(pagerank_dict): + """ + Returns a dictionary that can be used as the personalization value for a + call to nx.pagerank(). The pagerank_dict passed in is used as the initial + source of values for each node, and this function simply treats the list of + dict values as two halves (halves A and B) and swaps them so (most if not + all) nodes/keys are assigned a different value from the dictionary. + """ + num_half = len(pagerank_dict) // 2 + A_half_items = list(pagerank_dict.items())[:num_half] + B_half_items = list(pagerank_dict.items())[num_half:] + + # Support an odd number of items by initializing with B_half_items, which + # will always be one bigger if the number of items is odd. This will leave + # the one remainder (in the case of an odd number) unchanged. + pers_dict = dict(B_half_items) + pers_dict.update({A_half_items[i][0]: B_half_items[i][1] for i in range(num_half)}) + pers_dict.update({B_half_items[i][0]: A_half_items[i][1] for i in range(num_half)}) + + return pers_dict + + ################################################################################ # Benchmarks def bench_from_networkx(benchmark, graph_obj): @@ -431,6 +453,26 @@ def bench_pagerank(benchmark, graph_obj, backend_wrapper): assert type(result) is dict +def bench_pagerank_personalized(benchmark, graph_obj, backend_wrapper): + G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) + + # FIXME: This will run for every combination of inputs, even if the + # graph/dataset does not change. Ideally this is run once per + # graph/dataset. + pagerank_dict = nx.pagerank(G) + personalization_dict = build_personalization_dict(pagerank_dict) + + result = benchmark.pedantic( + target=backend_wrapper(nx.pagerank), + args=(G,), + kwargs={"personalization": personalization_dict}, + rounds=rounds, + iterations=iterations, + warmup_rounds=warmup_rounds, + ) + assert type(result) is dict + + def bench_single_source_shortest_path_length(benchmark, graph_obj, backend_wrapper): G = get_graph_obj_for_benchmark(graph_obj, backend_wrapper) node = get_highest_degree_node(graph_obj) @@ -804,3 +846,73 @@ def bench_weakly_connected_components(benchmark, graph_obj, backend_wrapper): warmup_rounds=warmup_rounds, ) assert type(result) is list + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_complete_bipartite_graph(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_connected_components(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_connected(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_node_connected_component(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_number_connected_components(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_isolate(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_isolates(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_number_of_isolates(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_complement(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_reverse(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_arborescence(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_branching(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_forest(benchmark, graph_obj, backend_wrapper): + pass + + +@pytest.mark.skip(reason="benchmark not implemented") +def bench_is_tree(benchmark, graph_obj, backend_wrapper): + pass From 6b28aefcabbe10a44de38bd1b3e54f3c717dd559 Mon Sep 17 00:00:00 2001 From: Erik Welch Date: Wed, 13 Mar 2024 09:18:37 -0500 Subject: [PATCH 9/9] nx-cugraph: add more shortest path algorithms (#4199) This begins by adding more unweighted shortest path algorithms. Next we'll do weighted via `sssp`, then generic. Note that there are some performance improvements that can be made: - add bidirectional search between source and target - for `bidirectional_shortest_path` and `has_path` - alternatively, perform `bfs` from `source` until `target` is reached - run `all_pairs*` in batched groups Authors: - Erik Welch (https://github.com/eriknw) - Ralph Liu (https://github.com/nv-rliu) Approvers: - Brad Rees (https://github.com/BradReesWork) - Rick Ratzel (https://github.com/rlratzel) - Don Acosta (https://github.com/acostadon) URL: https://github.com/rapidsai/cugraph/pull/4199 --- python/nx-cugraph/README.md | 25 +- python/nx-cugraph/_nx_cugraph/__init__.py | 52 +++- python/nx-cugraph/lint.yaml | 4 +- .../nx_cugraph/algorithms/__init__.py | 2 +- .../algorithms/bipartite/__init__.py | 1 - .../nx_cugraph/algorithms/bipartite/basic.py | 31 -- .../algorithms/centrality/eigenvector.py | 9 +- .../nx_cugraph/algorithms/centrality/katz.py | 9 +- .../algorithms/link_analysis/hits_alg.py | 9 +- .../algorithms/link_analysis/pagerank_alg.py | 7 +- .../algorithms/shortest_paths/__init__.py | 4 +- .../algorithms/shortest_paths/generic.py | 165 ++++++++++ .../algorithms/shortest_paths/unweighted.py | 174 ++++++++++- .../algorithms/shortest_paths/weighted.py | 286 ++++++++++++++++++ python/nx-cugraph/nx_cugraph/interface.py | 14 + python/nx-cugraph/nx_cugraph/utils/misc.py | 14 +- python/nx-cugraph/scripts/update_readme.py | 0 17 files changed, 723 insertions(+), 83 deletions(-) delete mode 100644 python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py create mode 100644 python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py mode change 100644 => 100755 python/nx-cugraph/scripts/update_readme.py diff --git a/python/nx-cugraph/README.md b/python/nx-cugraph/README.md index 8201dc34eb2..1bf310c8c88 100644 --- a/python/nx-cugraph/README.md +++ b/python/nx-cugraph/README.md @@ -95,8 +95,6 @@ Below is the list of algorithms that are currently supported in nx-cugraph. 
 bipartite
- ├─ basic
- │   └─ is_bipartite
  └─ generators
      └─ complete_bipartite_graph
 centrality
@@ -152,9 +150,26 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  ├─ overall_reciprocity
  └─ reciprocity
 shortest_paths
- └─ unweighted
-     ├─ single_source_shortest_path_length
-     └─ single_target_shortest_path_length
+ ├─ generic
+ │   ├─ has_path
+ │   ├─ shortest_path
+ │   └─ shortest_path_length
+ ├─ unweighted
+ │   ├─ all_pairs_shortest_path
+ │   ├─ all_pairs_shortest_path_length
+ │   ├─ bidirectional_shortest_path
+ │   ├─ single_source_shortest_path
+ │   ├─ single_source_shortest_path_length
+ │   ├─ single_target_shortest_path
+ │   └─ single_target_shortest_path_length
+ └─ weighted
+     ├─ all_pairs_bellman_ford_path
+     ├─ all_pairs_bellman_ford_path_length
+     ├─ bellman_ford_path
+     ├─ bellman_ford_path_length
+     ├─ single_source_bellman_ford
+     ├─ single_source_bellman_ford_path
+     └─ single_source_bellman_ford_path_length
 traversal
  └─ breadth_first_search
      ├─ bfs_edges
diff --git a/python/nx-cugraph/_nx_cugraph/__init__.py b/python/nx-cugraph/_nx_cugraph/__init__.py
index b2f13d25ff3..bc7f63fcd49 100644
--- a/python/nx-cugraph/_nx_cugraph/__init__.py
+++ b/python/nx-cugraph/_nx_cugraph/__init__.py
@@ -33,15 +33,22 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
+        "all_pairs_bellman_ford_path",
+        "all_pairs_bellman_ford_path_length",
+        "all_pairs_shortest_path",
+        "all_pairs_shortest_path_length",
         "ancestors",
         "average_clustering",
         "barbell_graph",
+        "bellman_ford_path",
+        "bellman_ford_path_length",
         "betweenness_centrality",
         "bfs_edges",
         "bfs_layers",
         "bfs_predecessors",
         "bfs_successors",
         "bfs_tree",
+        "bidirectional_shortest_path",
         "bull_graph",
         "caveman_graph",
         "chvatal_graph",
@@ -70,6 +77,7 @@
         "from_scipy_sparse_array",
         "frucht_graph",
         "generic_bfs_edges",
+        "has_path",
         "heawood_graph",
         "hits",
         "house_graph",
@@ -77,7 +85,6 @@
         "icosahedral_graph",
         "in_degree_centrality",
         "is_arborescence",
-        "is_bipartite",
         "is_branching",
         "is_connected",
         "is_forest",
@@ -110,7 +117,14 @@
         "reciprocity",
         "reverse",
         "sedgewick_maze_graph",
+        "shortest_path",
+        "shortest_path_length",
+        "single_source_bellman_ford",
+        "single_source_bellman_ford_path",
+        "single_source_bellman_ford_path_length",
+        "single_source_shortest_path",
         "single_source_shortest_path_length",
+        "single_target_shortest_path",
         "single_target_shortest_path_length",
         "star_graph",
         "tadpole_graph",
@@ -128,7 +142,11 @@
     },
     "additional_docs": {
         # BEGIN: additional_docs
+        "all_pairs_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "all_pairs_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "average_clustering": "Directed graphs and `weight` parameter are not yet supported.",
+        "bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
         "bfs_edges": "`sort_neighbors` parameter is not yet supported.",
         "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.",
@@ -147,11 +165,28 @@
         "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
         "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
         "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
+        "shortest_path": "Negative weights are not yet supported, and method is ununsed.",
+        "shortest_path_length": "Negative weights are not yet supported, and method is ununsed.",
+        "single_source_bellman_ford": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "transitivity": "Directed graphs are not yet supported.",
         # END: additional_docs
     },
     "additional_parameters": {
         # BEGIN: additional_parameters
+        "all_pairs_bellman_ford_path": {
+            "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.",
+        },
+        "all_pairs_bellman_ford_path_length": {
+            "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.",
+        },
+        "bellman_ford_path": {
+            "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.",
+        },
+        "bellman_ford_path_length": {
+            "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.",
+        },
         "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.",
         },
@@ -169,6 +204,21 @@
         "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.",
         },
+        "shortest_path": {
+            "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.",
+        },
+        "shortest_path_length": {
+            "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.",
+        },
+        "single_source_bellman_ford": {
+            "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.",
+        },
+        "single_source_bellman_ford_path": {
+            "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.",
+        },
+        "single_source_bellman_ford_path_length": {
+            "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: additional_parameters
     },
 }
diff --git a/python/nx-cugraph/lint.yaml b/python/nx-cugraph/lint.yaml
index fdd24861da7..3239fa151d9 100644
--- a/python/nx-cugraph/lint.yaml
+++ b/python/nx-cugraph/lint.yaml
@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     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 7aafa85f5b7..b4a10bcf0a1 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/__init__.py
@@ -22,7 +22,7 @@
     traversal,
     tree,
 )
-from .bipartite import complete_bipartite_graph, is_bipartite
+from .bipartite import complete_bipartite_graph
 from .centrality import *
 from .cluster import *
 from .components import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
index e028299c675..bfc7f1d4d42 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py
@@ -10,5 +10,4 @@
 # 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 .basic import *
 from .generators import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py b/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py
deleted file mode 100644
index 46c6b54075b..00000000000
--- a/python/nx-cugraph/nx_cugraph/algorithms/bipartite/basic.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (c) 2024, 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
-
-from nx_cugraph.algorithms.cluster import _triangles
-from nx_cugraph.convert import _to_graph
-from nx_cugraph.utils import networkx_algorithm
-
-__all__ = [
-    "is_bipartite",
-]
-
-
-@networkx_algorithm(version_added="24.02", _plc="triangle_count")
-def is_bipartite(G):
-    G = _to_graph(G)
-    # Counting triangles may not be the fastest way to do this, but it is simple.
-    node_ids, triangles, is_single_node = _triangles(
-        G, None, symmetrize="union" if G.is_directed() else None
-    )
-    return int(cp.count_nonzero(triangles)) == 0
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
index 65a8633667a..c32b6fbb708 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py
@@ -36,17 +36,12 @@ 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)
+    G = _to_graph(G, weight, 1, 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
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     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)
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
index 4a0684f72ee..1c6ed61703d 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py
@@ -49,15 +49,10 @@ def katz_centrality(
         # 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)
+    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
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     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)
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
index e61a931c069..e529b83ab1a 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py
@@ -46,15 +46,10 @@ def hits(
     weight="weight",
     dtype=None,
 ):
-    G = _to_graph(G, weight, np.float32)
+    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
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
     if max_iter <= 0:
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
index 40224e91d57..41203a2bc22 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py
@@ -48,12 +48,7 @@ def pagerank(
     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
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype=dtype)
         if (total := nstart.sum()) == 0:
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
index b7d6b742176..9d87389a98e 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -10,4 +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.
+from .generic import *
 from .unweighted import *
+from .weighted import *
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
new file mode 100644
index 00000000000..68dbbace93d
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/generic.py
@@ -0,0 +1,165 @@
+# Copyright (c) 2024, 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 nx_cugraph as nxcg
+from nx_cugraph.convert import _to_graph
+from nx_cugraph.utils import _dtype_param, _get_float_dtype, networkx_algorithm
+
+from .unweighted import _bfs
+from .weighted import _sssp
+
+__all__ = [
+    "shortest_path",
+    "shortest_path_length",
+    "has_path",
+]
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def has_path(G, source, target):
+    # TODO PERF: make faster in core
+    try:
+        nxcg.bidirectional_shortest_path(G, source, target)
+    except nx.NetworkXNoPath:
+        return False
+    return True
+
+
+@networkx_algorithm(
+    extra_params=_dtype_param, version_added="24.04", _plc={"bfs", "sssp"}
+)
+def shortest_path(
+    G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
+):
+    """Negative weights are not yet supported, and method is ununsed."""
+    if method not in {"dijkstra", "bellman-ford"}:
+        raise ValueError(f"method not supported: {method}")
+    if weight is None:
+        method = "unweighted"
+    if source is None:
+        if target is None:
+            # All pairs
+            if method == "unweighted":
+                paths = nxcg.all_pairs_shortest_path(G)
+            else:
+                # method == "dijkstra":
+                # method == 'bellman-ford':
+                paths = nxcg.all_pairs_bellman_ford_path(G, weight=weight, dtype=dtype)
+            if nx.__version__[:3] <= "3.4":
+                paths = dict(paths)
+        # To target
+        elif method == "unweighted":
+            paths = nxcg.single_target_shortest_path(G, target)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            # XXX: it seems weird that `reverse_path=True` is necessary here
+            G = _to_graph(G, weight, 1, np.float32)
+            dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+            paths = _sssp(
+                G, target, weight, return_type="path", dtype=dtype, reverse_path=True
+            )
+    elif target is None:
+        # From source
+        if method == "unweighted":
+            paths = nxcg.single_source_shortest_path(G, source)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            paths = nxcg.single_source_bellman_ford_path(
+                G, source, weight=weight, dtype=dtype
+            )
+    # From source to target
+    elif method == "unweighted":
+        paths = nxcg.bidirectional_shortest_path(G, source, target)
+    else:
+        # method == "dijkstra":
+        # method == 'bellman-ford':
+        paths = nxcg.bellman_ford_path(G, source, target, weight, dtype=dtype)
+    return paths
+
+
+@shortest_path._can_run
+def _(G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(
+    extra_params=_dtype_param, version_added="24.04", _plc={"bfs", "sssp"}
+)
+def shortest_path_length(
+    G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None
+):
+    """Negative weights are not yet supported, and method is ununsed."""
+    if method not in {"dijkstra", "bellman-ford"}:
+        raise ValueError(f"method not supported: {method}")
+    if weight is None:
+        method = "unweighted"
+    if source is None:
+        if target is None:
+            # All pairs
+            if method == "unweighted":
+                lengths = nxcg.all_pairs_shortest_path_length(G)
+            else:
+                # method == "dijkstra":
+                # method == 'bellman-ford':
+                lengths = nxcg.all_pairs_bellman_ford_path_length(
+                    G, weight=weight, dtype=dtype
+                )
+        # To target
+        elif method == "unweighted":
+            lengths = nxcg.single_target_shortest_path_length(G, target)
+            if nx.__version__[:3] <= "3.4":
+                lengths = dict(lengths)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            lengths = nxcg.single_source_bellman_ford_path_length(
+                G, target, weight=weight, dtype=dtype
+            )
+    elif target is None:
+        # From source
+        if method == "unweighted":
+            lengths = nxcg.single_source_shortest_path_length(G, source)
+        else:
+            # method == "dijkstra":
+            # method == 'bellman-ford':
+            lengths = dict(
+                nxcg.single_source_bellman_ford_path_length(
+                    G, source, weight=weight, dtype=dtype
+                )
+            )
+    # From source to target
+    elif method == "unweighted":
+        G = _to_graph(G)
+        lengths = _bfs(G, source, None, "Source", return_type="length", target=target)
+    else:
+        # method == "dijkstra":
+        # method == 'bellman-ford':
+        lengths = nxcg.bellman_ford_path_length(G, source, target, weight, dtype=dtype)
+    return lengths
+
+
+@shortest_path_length._can_run
+def _(G, source=None, target=None, weight=None, method="dijkstra", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
index 2012495953e..714289c5b4b 100644
--- a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py
@@ -10,33 +10,127 @@
 # 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 numpy as np
 import pylibcugraph as plc
 
 from nx_cugraph.convert import _to_graph
-from nx_cugraph.utils import index_dtype, networkx_algorithm
+from nx_cugraph.utils import _groupby, index_dtype, networkx_algorithm
+
+__all__ = [
+    "bidirectional_shortest_path",
+    "single_source_shortest_path",
+    "single_source_shortest_path_length",
+    "single_target_shortest_path",
+    "single_target_shortest_path_length",
+    "all_pairs_shortest_path",
+    "all_pairs_shortest_path_length",
+]
 
-__all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"]
+concat = itertools.chain.from_iterable
 
 
 @networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_source_shortest_path_length(G, source, cutoff=None):
-    return _single_shortest_path_length(G, source, cutoff, "Source")
+    G = _to_graph(G)
+    return _bfs(G, source, cutoff, "Source", return_type="length")
 
 
 @networkx_algorithm(version_added="23.12", _plc="bfs")
 def single_target_shortest_path_length(G, target, cutoff=None):
-    return _single_shortest_path_length(G, target, cutoff, "Target")
+    G = _to_graph(G)
+    rv = _bfs(G, target, cutoff, "Target", return_type="length")
+    if nx.__version__[:3] <= "3.4":
+        return iter(rv.items())
+    return rv
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def all_pairs_shortest_path_length(G, cutoff=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G)
+    for n in G:
+        yield (n, _bfs(G, n, cutoff, "Source", return_type="length"))
 
 
-def _single_shortest_path_length(G, source, cutoff, kind):
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def bidirectional_shortest_path(G, source, target):
+    # TODO PERF: do bidirectional traversal in core
     G = _to_graph(G)
+    if source not in G or target not in G:
+        raise nx.NodeNotFound(f"Either source {source} or target {target} is not in G")
+    return _bfs(G, source, None, "Source", return_type="path", target=target)
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def single_source_shortest_path(G, source, cutoff=None):
+    G = _to_graph(G)
+    return _bfs(G, source, cutoff, "Source", return_type="path")
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def single_target_shortest_path(G, target, cutoff=None):
+    G = _to_graph(G)
+    return _bfs(G, target, cutoff, "Target", return_type="path", reverse_path=True)
+
+
+@networkx_algorithm(version_added="24.04", _plc="bfs")
+def all_pairs_shortest_path(G, cutoff=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G)
+    for n in G:
+        yield (n, _bfs(G, n, cutoff, "Source", return_type="path"))
+
+
+def _bfs(
+    G, source, cutoff, kind, *, return_type, reverse_path=False, target=None, scale=None
+):
+    """BFS for unweighted shortest path algorithms.
+
+    Parameters
+    ----------
+    source: node label
+
+    cutoff: int, optional
+
+    kind: {"Source", "Target"}
+
+    return_type: {"length", "path", "length-path"}
+
+    reverse_path: bool
+
+    target: node label
+
+    scale: int or float, optional
+        The amount to scale the lengths
+    """
+    # DRY: _sssp in weighted.py has similar code
     if source not in G:
-        raise nx.NodeNotFound(f"{kind} {source} is not in G")
-    if G.src_indices.size == 0:
-        return {source: 0}
+        # Different message to pass networkx tests
+        if return_type == "length":
+            raise nx.NodeNotFound(f"{kind} {source} is not in G")
+        raise nx.NodeNotFound(f"{kind} {source} not in G")
+    if target is not None:
+        if source == target or cutoff is not None and cutoff <= 0:
+            if return_type == "path":
+                return [source]
+            if return_type == "length":
+                return 0
+            # return_type == "length-path"
+            return 0, [source]
+        if target not in G or G.src_indices.size == 0:
+            raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+    elif G.src_indices.size == 0 or cutoff is not None and cutoff <= 0:
+        if return_type == "path":
+            return {source: [source]}
+        if return_type == "length":
+            return {source: 0}
+        # return_type == "length-path"
+        return {source: 0}, {source: [source]}
+
     if cutoff is None:
         cutoff = -1
     src_index = source if G.key_to_id is None else G.key_to_id[source]
@@ -46,8 +140,68 @@ def _single_shortest_path_length(G, source, cutoff, kind):
         sources=cp.array([src_index], index_dtype),
         direction_optimizing=False,  # True for undirected only; what's recommended?
         depth_limit=cutoff,
-        compute_predecessors=False,
+        compute_predecessors=return_type != "length",
         do_expensive_check=False,
     )
     mask = distances != np.iinfo(distances.dtype).max
-    return G._nodearrays_to_dict(node_ids[mask], distances[mask])
+    node_ids = node_ids[mask]
+    if return_type != "path":
+        lengths = distances = distances[mask]
+        if scale is not None:
+            lengths = scale * lengths
+        lengths = G._nodearrays_to_dict(node_ids, lengths)
+        if target is not None:
+            if target not in lengths:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            lengths = lengths[target]
+    if return_type != "length":
+        if target is not None:
+            d = dict(zip(node_ids.tolist(), predecessors[mask].tolist()))
+            dst_index = target if G.key_to_id is None else G.key_to_id[target]
+            if dst_index not in d:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            cur = dst_index
+            paths = [dst_index]
+            while cur != src_index:
+                cur = d[cur]
+                paths.append(cur)
+            if (id_to_key := G.id_to_key) is not None:
+                if reverse_path:
+                    paths = [id_to_key[cur] for cur in paths]
+                else:
+                    paths = [id_to_key[cur] for cur in reversed(paths)]
+            elif not reverse_path:
+                paths.reverse()
+        else:
+            if return_type == "path":
+                distances = distances[mask]
+            groups = _groupby(distances, [predecessors[mask], node_ids])
+
+            # `pred_node_iter` does the equivalent as these nested for loops:
+            # for length in range(1, len(groups)):
+            #     preds, nodes = groups[length]
+            #     for pred, node in zip(preds.tolist(), nodes.tolist()):
+            if G.key_to_id is None:
+                pred_node_iter = concat(
+                    zip(*(x.tolist() for x in groups[length]))
+                    for length in range(1, len(groups))
+                )
+            else:
+                pred_node_iter = concat(
+                    zip(*(G._nodeiter_to_iter(x.tolist()) for x in groups[length]))
+                    for length in range(1, len(groups))
+                )
+            # Consider making utility functions for creating paths
+            paths = {source: [source]}
+            if reverse_path:
+                for pred, node in pred_node_iter:
+                    paths[node] = [node, *paths[pred]]
+            else:
+                for pred, node in pred_node_iter:
+                    paths[node] = [*paths[pred], node]
+    if return_type == "path":
+        return paths
+    if return_type == "length":
+        return lengths
+    # return_type == "length-path"
+    return lengths, paths
diff --git a/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
new file mode 100644
index 00000000000..32323dd45f3
--- /dev/null
+++ b/python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/weighted.py
@@ -0,0 +1,286 @@
+# Copyright (c) 2024, 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,
+    _groupby,
+    networkx_algorithm,
+)
+
+from .unweighted import _bfs
+
+__all__ = [
+    "bellman_ford_path",
+    "bellman_ford_path_length",
+    "single_source_bellman_ford",
+    "single_source_bellman_ford_path",
+    "single_source_bellman_ford_path_length",
+    "all_pairs_bellman_ford_path",
+    "all_pairs_bellman_ford_path_length",
+]
+
+
+def _add_doc(func):
+    func.__doc__ = (
+        "Negative cycles are not yet supported. ``NotImplementedError`` will be raised "
+        "if there are negative edge weights. We plan to support negative edge weights "
+        "soon. Also, callable ``weight`` argument is not supported."
+    )
+    return func
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path(G, source, target, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="path", dtype=dtype)
+
+
+@bellman_ford_path._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def bellman_ford_path_length(G, source, target, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="length", dtype=dtype)
+
+
+@bellman_ford_path_length._can_run
+def _(G, source, target, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford_path(G, source, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="path", dtype=dtype)
+
+
+@single_source_bellman_ford_path._can_run
+def _(G, source, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford_path_length(G, source, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, return_type="length", dtype=dtype)
+
+
+@single_source_bellman_ford_path_length._can_run
+def _(G, source, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def single_source_bellman_ford(G, source, target=None, weight="weight", *, dtype=None):
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    return _sssp(G, source, weight, target, return_type="length-path", dtype=dtype)
+
+
+@single_source_bellman_ford._can_run
+def _(G, source, target=None, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path_length(G, weight="weight", *, dtype=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    for n in G:
+        yield (n, _sssp(G, n, weight, return_type="length", dtype=dtype))
+
+
+@all_pairs_bellman_ford_path_length._can_run
+def _(G, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+@networkx_algorithm(extra_params=_dtype_param, version_added="24.04", _plc="sssp")
+@_add_doc
+def all_pairs_bellman_ford_path(G, weight="weight", *, dtype=None):
+    # TODO PERF: batched bfs to compute many at once
+    G = _to_graph(G, weight, 1, np.float32)
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
+    for n in G:
+        yield (n, _sssp(G, n, weight, return_type="path", dtype=dtype))
+
+
+@all_pairs_bellman_ford_path._can_run
+def _(G, weight="weight", *, dtype=None):
+    return (
+        weight is None
+        or not callable(weight)
+        and not nx.is_negatively_weighted(G, weight=weight)
+    )
+
+
+def _sssp(G, source, weight, target=None, *, return_type, dtype, reverse_path=False):
+    """SSSP for weighted shortest paths.
+
+    Parameters
+    ----------
+    return_type : {"length", "path", "length-path"}
+
+    """
+    # DRY: _bfs in unweighted.py has similar code
+    if source not in G:
+        raise nx.NodeNotFound(f"Node {source} not found in graph")
+    if target is not None:
+        if source == target:
+            if return_type == "path":
+                return [source]
+            if return_type == "length":
+                return 0
+            # return_type == "length-path"
+            return 0, [source]
+        if target not in G or G.src_indices.size == 0:
+            raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+    elif G.src_indices.size == 0:
+        if return_type == "path":
+            return {source: [source]}
+        if return_type == "length":
+            return {source: 0}
+        # return_type == "length-path"
+        return {source: 0}, {source: [source]}
+
+    if callable(weight):
+        raise NotImplementedError("callable `weight` argument is not supported")
+
+    if weight not in G.edge_values:
+        # No edge values, so use BFS instead
+        return _bfs(G, source, None, "Source", return_type=return_type, target=target)
+
+    # Check for negative values since we don't support negative cycles
+    edge_vals = G.edge_values[weight]
+    if weight in G.edge_masks:
+        edge_vals = edge_vals[G.edge_masks[weight]]
+    if (edge_vals < 0).any():
+        raise NotImplementedError("Negative edge weights not yet supported")
+    edge_val = edge_vals[0]
+    if (edge_vals == edge_val).all() and (
+        edge_vals.size == G.src_indices.size or edge_val == 1
+    ):
+        # Edge values are all the same, so use scaled BFS instead
+        return _bfs(
+            G,
+            source,
+            None,
+            "Source",
+            return_type=return_type,
+            target=target,
+            scale=edge_val,
+            reverse_path=reverse_path,
+        )
+
+    src_index = source if G.key_to_id is None else G.key_to_id[source]
+    node_ids, distances, predecessors = plc.sssp(
+        resource_handle=plc.ResourceHandle(),
+        graph=G._get_plc_graph(weight, 1, dtype),
+        source=src_index,
+        cutoff=np.inf,
+        compute_predecessors=True,  # TODO: False is not yet supported
+        # compute_predecessors=return_type != "length",
+        do_expensive_check=False,
+    )
+    mask = distances != np.finfo(distances.dtype).max
+    node_ids = node_ids[mask]
+    if return_type != "path":
+        lengths = G._nodearrays_to_dict(node_ids, distances[mask])
+        if target is not None:
+            if target not in lengths:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            lengths = lengths[target]
+    if return_type != "length":
+        if target is not None:
+            d = dict(zip(node_ids.tolist(), predecessors[mask].tolist()))
+            dst_index = target if G.key_to_id is None else G.key_to_id[target]
+            if dst_index not in d:
+                raise nx.NetworkXNoPath(f"Node {target} not reachable from {source}")
+            cur = dst_index
+            paths = [dst_index]
+            while cur != src_index:
+                cur = d[cur]
+                paths.append(cur)
+            if (id_to_key := G.id_to_key) is not None:
+                if reverse_path:
+                    paths = [id_to_key[cur] for cur in paths]
+                else:
+                    paths = [id_to_key[cur] for cur in reversed(paths)]
+            elif not reverse_path:
+                paths.reverse()
+        else:
+            groups = _groupby(predecessors[mask], node_ids)
+            if (id_to_key := G.id_to_key) is not None:
+                groups = {id_to_key[k]: v for k, v in groups.items() if k >= 0}
+            paths = {source: [source]}
+            preds = [source]
+            while preds:
+                pred = preds.pop()
+                pred_path = paths[pred]
+                nodes = G._nodearray_to_list(groups[pred])
+                if reverse_path:
+                    for node in nodes:
+                        paths[node] = [node, *pred_path]
+                else:
+                    for node in nodes:
+                        paths[node] = [*pred_path, node]
+                preds.extend(nodes & groups.keys())
+    if return_type == "path":
+        return paths
+    if return_type == "length":
+        return lengths
+    # return_type == "length-path"
+    return lengths, paths
diff --git a/python/nx-cugraph/nx_cugraph/interface.py b/python/nx-cugraph/nx_cugraph/interface.py
index d044ba6960d..0d893ac286b 100644
--- a/python/nx-cugraph/nx_cugraph/interface.py
+++ b/python/nx-cugraph/nx_cugraph/interface.py
@@ -67,6 +67,7 @@ def key(testpath):
         no_multigraph = "multigraphs not currently supported"
         louvain_different = "Louvain may be different due to RNG"
         no_string_dtype = "string edge values not currently supported"
+        sssp_path_different = "sssp may choose a different valid path"
 
         xfail = {
             # This is removed while strongly_connected_components() is not
@@ -77,6 +78,19 @@ def key(testpath):
             #     "test_strongly_connected.py:"
             #     "TestStronglyConnected.test_condensation_mapping_and_members"
             # ): "Strongly connected groups in different iteration order",
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_unweighted_diamond"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_weighted_diamond"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis.test_petersen_graph"
+            ): sssp_path_different,
+            key(
+                "test_cycles.py:TestMinimumCycleBasis."
+                "test_gh6787_and_edge_attribute_names"
+            ): sssp_path_different,
         }
 
         from packaging.version import parse
diff --git a/python/nx-cugraph/nx_cugraph/utils/misc.py b/python/nx-cugraph/nx_cugraph/utils/misc.py
index aa06d7fd29b..eab4b42c2cc 100644
--- a/python/nx-cugraph/nx_cugraph/utils/misc.py
+++ b/python/nx-cugraph/nx_cugraph/utils/misc.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2023, NVIDIA CORPORATION.
+# Copyright (c) 2023-2024, 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
@@ -22,7 +22,9 @@
 import numpy as np
 
 if TYPE_CHECKING:
-    from ..typing import Dtype
+    import nx_cugraph as nxcg
+
+    from ..typing import Dtype, EdgeKey
 
 try:
     from itertools import pairwise  # Python >=3.10
@@ -190,10 +192,14 @@ def _get_int_dtype(
         raise ValueError("Value is too large to store as integer: {val}") from exc
 
 
-def _get_float_dtype(dtype: Dtype):
+def _get_float_dtype(
+    dtype: Dtype, *, graph: nxcg.Graph | None = None, weight: EdgeKey | None = None
+):
     """Promote dtype to float32 or float64 as appropriate."""
     if dtype is None:
-        return np.dtype(np.float32)
+        if graph is None or weight not in graph.edge_values:
+            return np.dtype(np.float32)
+        dtype = graph.edge_values[weight].dtype
     rv = np.promote_types(dtype, np.float32)
     if np.float32 != rv != np.float64:
         raise TypeError(
diff --git a/python/nx-cugraph/scripts/update_readme.py b/python/nx-cugraph/scripts/update_readme.py
old mode 100644
new mode 100755