GitLab

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: xiny <xiny@nvidia.com>

Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>

Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-25-242.ap-northeast-1.compute.internal>

Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

* cleanup unused cmake options

* disable BUILD_TORCH for cugraph

* resolve comments

* Enable AVX by default

* Fix linting errors

* Fix win64 build (libxsmm not linked)

Libxsmm on Win64 is not linked, should be disabled by default

* Fix clang format issues

* Change lower supported cpu version to LIBXSMM_X86_AVX2

Change lower supported cpu version to LIBXSMM_X86_AVX2 to address https://github.com/dmlc/dgl/issues/3459

 issue

* Fix unit test

Remove assumption that libxsmm is enabled in the config by default (only true for intel CPUs with AVX2 instructions)

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Co-authored-by: Ubuntu <ubuntu@ip-172-31-15-137.us-west-2.compute.internal>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>

* expose GeneratePermutation

* add sparse_all_to_all_push

* add sparse_all_to_all_pull

* add unit test

* handle world_size=1

* remove python nccl wrapper

* remove the nccl dependency

* use pinned memory to speedup D2H copy

* fix lint

* resolve comments

* fix lint

* fix ut

* resolve comments

* Fix ubuntu 22 build

* Add one more flag

This reverts commit a5e31391.

* Set USE_LIBXSMM default to OFF.

* update

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Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* enable dgl sparse build in default

* disable sparse build on cugraph

* update

* initial commit

* Update build.sh

* [Sparse] Support SpMM and SDDMM

* Add SDDMMAutoGrad

* Rename SpMMImpl and SDDMMImpl and move them to dgl_sparse/src/matmul.h

* Update comments

* Update

* Minor fix

* Revert "[Sparse] Add SpMM and SDDMM. (#4999)"

This reverts commit 15365d78.

* lint

* [Sparse] Add SpMM and SDDMM

* Update

* Add CSR and CSC SpMM tests

* replace dgl PRNG with pcg32

* remove pcg submodule, add a simple implementation

* replace pcg32 with std::mt19937_64

* fix include order

* change RandomEngine to pcg32

* Remove custom pcg32 implementation, use the submodule provided by the original author.

* minor bug

* move include for linting

* include pcg for tests too
Co-authored-by: Hongzhi (Steve), Chen <chenhongzhi.nkcs@gmail.com>

* adding LABOR sampling

* add ladies and pladies samplers

* fix compile error after rebase

* add reference for ladies sampler

* Improve ladies implementation.

* weighted labor sampling initial implementation draft
fix indentation and small bug in ladies script

* importance_sampling currently doesn't work with weights

* fix weighted importance sampling

* move labor example into its own folder

* lint fixes

* Improve documentation

* remove examples from the main PR

* fix linting by not using c++17 features

* fix documentation of labor_sampler.py

* update documentation for labor.py

* reformat the labor.py file with black

* fix linting errors

* replace exception use with if

* fix typo in error comment

* fixing win64 build for ci

* fixing weighted implementation, works now.

* fix bug in the weighted case and importance_sampling==0

* address part of the reviews

* remove unused code paths from cuda

* remove unused code path from cpu side

* remove extra features of labor making use of random seed.

* fix exclude_edges bug

* remove pcg and seed logic from cpu implementation, seed logic should still work for cuda.

* minor style change

* refactor CPU implementation, take out the importance_sampling probability computation into a function.

* improve CUDAWorkspaceAllocator

* refactor importance_sampling part out to a function

* minor optimization

* fix linting issue

* Revert "remove pcg and seed logic from cpu implementation, seed logic should still work for cuda."

This reverts commit c250e07ac6d7e13f57e79e8a2c2f098d777378c2.

* Revert "remove extra features of labor making use of random seed."

This reverts commit 7f99034353080308f4783f27d9a08bea343fb796.

* fix the documentation

* disable NIDs

* improve the documentation in the code

* use the stream argument in pcg32 instead of skipping ahead t times, can discard the use of hashmap now since it is faster this way.

* fix linting issue

* address another round of reviews

* further optimize CPU LABOR sampling implementation

* fix linting error

* update the comment

* reformat

* rename and rephrase comment

* fix formatting according to new linting specs

* fix compile error due to renaming, fix linting.

* lint

* rename DGLHeteroGraph to DGLGraph to match master

* replace other occurrences of DGLHeteroGraph to DGLGraph
Co-authored-by: Muhammed Fatih BALIN <m.f.balin@gmail.com>
Co-authored-by: Kaan Sancak <kaansnck@gmail.com>
Co-authored-by: Quan Gan <coin2028@hotmail.com>

* [Sparse] Add sparse matrix C++ implementation

* Add documentation

* Update

* Minor fix

* Move Python code to dgl/mock_sparse2

* Move headers to include

* lint

* Update

* Add dgl_sparse directory

* Move src code to dgl_sparse

* Add __init__.py in tests to avoid naming conflict

* Add dgl sparse so in Jenkinsfile

* Complete docstring & SparseMatrix basic op

* lint

* Disable win tests

* add bf16 specializations

* remove SWITCH_BITS

* enable amp for bf16

* remove SWITCH_BITS for cpu kernels

* enbale bf16 based on CUDART

* fix compiling for sm<80

* fix cpu build

* enable unit tests

* update doc

* disable test for CUDA < 11.0

* address comments

* address comments

* disable warning for tensorpipe

* fix warning

* enable lint check for cuh files

* resolve comments

* remove third_part/cub

* remove from third_party
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: Xin Yao <xiny@nvidia.com>

* Remove async_transferer

* remove test

* Remove AsyncTransferer
Co-authored-by: Xin Yao <xiny@nvidia.com>
Co-authored-by: Xin Yao <yaox12@outlook.com>

* add weighted sampling without replacement (A-Chao)

* improve Algorithm A-Chao with block-wise prefix sum

* correctly fill out_idxs

* implement weighted sampling with replacement

* small fix

* merge host-side code of weighted/uniform sampling

* enable unit tests for cuda weighted sampling

* move thrust/cub wrapper to the cmake file

* update docs accordingly

* fix linting

* fix linting

* fix unit test

* Bump external CUB/Thrust versions

* Fix code style and update description of algorithm design

* [Feature] GPU support weighted graph neighbor sampling
commit by pengqirong(OPPO)

* merge pengqirong's implementation

* revert the change to cub and thrust

* fix linting

* use DeviceSegmentedSort for better performance

* add more comments

* add necessary notes

* add necessary notes

* resolve some comments

* define THRUST_CUB_WRAPPED_NAMESPACE

* fix doc
Co-authored-by: 彭齐荣 <657017034@qq.com>

* [BugFix] fix build issue on mac OS

* refine

* [Dist] enable USE_EPOLL in default

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue on windows

* fix build issue

* [DistTest] add basic pipeline for dist test across machines

* move launch remote cmd to separate file

* add test for rpc

* fix function naming rule
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

With the emergence of new ISA (like ARM and RISCV) retaining USE_AVX ON default makes the default build instructions fail. Fundamentally DGL does not require the use of AVX for functional working. AVX is mainly needed when to enable optimization. So proposal is to default turn it off and then later during build instructions, folks with AVX capabilities can enable with 
`cmake .. -DUSE_AVX=ON`
Co-authored-by: Zihao Ye <expye@outlook.com>