* refactor the function and module of laplacian_pe
* add blank lines between design doc
* 1st approving review
* update the unittest
* fix lint issues
* fix trailing space
* update by mufei's comments and fix backend bugs
* del test file
Co-authored-by: rudongyu <ru_dongyu@outlook.com>
Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>
* Creating ParMETIS wrapper script to run parmetis using one script from user perspective
* Addressed all the CI comments from PR https://github.com/dmlc/dgl/pull/4529
* Addressing CI comments.
* Isort, and black changes.
* Replaced python with python3
* Replaced single quote with double quotes per suggestion.
* Removed print statement
* Addressing CI Commets.
* Addressing CI review comments.
* Addressing CI comments as per chime discussion with Rui
* CI Comments, Black and isort changes
* Align with code refactoring, black, isort and code review comments.
* Addressing CI review comments, and fixing merge issues with the master branch
* Updated with proper unit test skip decorator
* Added support for edge features.
* Added comments and removing unnecessary print statements.
* updated data_shuffle.py to remove compile error.
* Repaled python3 with python to match CI test framework.
* Removed unrelated files from the pull request.
* Isort changes.
* black changes on this file.
* Addressing CI review comments.
* Addressing CI comments.
* Removed duplicated and resolved merge conflict code.
* Addressing CI Comments from Rui.
* Addressing CI comments, and fixing merge issues.
* Addressing CI comments, code refactoring, isort and black
* [Sparse] Add SparseMatrix unittest and fix docstring problem
* Minor fix
* Update
* check permission
* rm future annonations
* Skip create_from_csr and create_from_csc tests because Pytorch 1.9.0 does not have torch.sparse_csr_tensor
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
* sddmm init
* SDDMM with N-D nonzero values
* drop support for vector shaped non zero elements
* address comments
* skip cpu test
* skip GPU test too
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* [Dist] enable to partition many chunks into less partitions via pipeline
* refine
* add meta file for num_parts, add more tests, refine docstring
* remove args.num_parts
* create pydantic class for partition metadata
* refine
* rename json file
* add set_stream
* add .record_stream for NDArray and HeteroGraph
* refactor dgl stream Python APIs
* test record_stream
* add unit test for record stream
* use pytorch's stream
* fix lint
* fix cpu build
* address comments
* address comments
* add record stream tests for dgl.graph
* record frames and update dataloder
* add docstring
* update frame
* add backend check for record_stream
* remove CUDAThreadEntry::stream
* record stream for newly created formats
* fix bug
* fix cpp test
* fix None c_void_p to c_handle
* [DistPart] expose timeout config for process group
* refine code
* Update tools/distpartitioning/data_proc_pipeline.py
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* * Added "exclude_self" and "output_batch" options to knn_graph and segmented_knn_graph
* Updated out-of-date comments on remove_edges and remove_self_loop, since they now preserve batch information
* * Changed defaults on new knn_graph and segmented_knn_graph function parameters, for compatibility; pytorch/test_geometry.py was failing
* * Added test to ensure dgl.remove_self_loop function correctly updates batch information
* * Added new knn_graph and segmented_knn_graph parameters to dgl.nn.KNNGraph and dgl.nn.SegmentedKNNGraph
* * Formatting
* * Oops, I missed the one in segmented_knn_graph when I fixed the similar thing in knn_graph
* * Fixed edge case handling when invalid k specified, since it still needs to be handled consistently for tests to pass
* Fixed context of batch info, since it must match the context of the input position data for remove_self_loop to succeed
* * Fixed batch info resulting from knn_graph when output_batch is true, for case of 3D input tensor, representing multiple segments
* * Added testing of new exclude_self and output_batch parameters on knn_graph and segmented_knn_graph, and their wrappers, KNNGraph and SegmentedKNNGraph, into the test_knn_cuda test
* * Added doc comments for new parameters
* * Added correct handling for uncommon case of k or more coincident points when excluding self edges in knn_graph and segmented_knn_graph
* Added test cases for more than k coincident points
* * Updated doc comments for output_batch parameters for clarity
* * Linter formatting fixes
* * Extracted out common function for test_knn_cpu and test_knn_cuda, to add the new test cases to test_knn_cpu
* * Rewording in doc comments
* * Removed output_batch parameter from knn_graph and segmented_knn_graph, in favour of always setting the batch information, except in knn_graph if x is a 2D tensor
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Use an internal cuda stream for CopyDataFromTo
* small fix white space
* Fix to compile
* Make stream optional in copydata for compile
* fix lint issue
* Update cub functions to use internal stream
* Lint check
* Update CopyTo/CopyFrom/CopyFromTo to use internal stream
* Address comments
* Fix backward CUDA stream
* Avoid overloading CopyFromTo()
* Minor comment update
* Overload copydatafromto in cuda device api
Co-authored-by: xiny <xiny@nvidia.com>
* rgcn-ogbn-mag
* Add link in README.md
* correct code-format,add the reset_parameters function to the HeteroEmbedding module
* add the annotation in hetero.py
* add a unit test
* modify format
* Update
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-50-143.us-west-2.compute.internal>
Rhett Ying