* Split from NCCL PR
* Fix type in comment
* Expand documentation for sparse_all_to_all_push
* Restore previous behavior in example
* Re-work optimizer to use NCCL based on gradient location
* Allow for running with embedding on CPU but using NCCL for gradient exchange
* Optimize single partition case
* Fix pylint errors
* Add missing include
* fix gradient indexing
* Fix line continuation
* Migrate 'first_step'
* Skip tests without enough GPUs to run NCCL
* Improve empty tensor handling for pytorch 1.5
* Fix indentation
* Allow multiple NCCL communicator to coexist
* Improve handling of empty message
* Update python/dgl/nn/pytorch/sparse_emb.py
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* Update python/dgl/nn/pytorch/sparse_emb.py
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* Keepy empty tensor dimensionaless
* th.empty -> th.tensor
* Preserve shape for empty non-zero dimension tensors
* Use shared state, when embedding is shared
* Add support for gathering an embedding
* Fix typo
* Fix more typos
* Fix backend call
* Use NodeDataLoader to take advantage of ddp
* Update training script to share memory
* Only squeeze last dimension
* Better handle empty message
* Keep embedding on the target device GPU if dgl_sparse if false in RGCN example
* Fix typo in comment
* Add asserts
* Improve documentation in example
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* add two fraud dataset
* add comments and modify to heterogenous graph
* modify the node name based on datasets
* add assertion message
Co-authored-by: zhjwy9343 <6593865@qq.com>
* Split NCCL wrapper from sparse optimizer and sparse embedding
* Add more unit tests for single node nccl
* Fix unit test for tf
* Switch to device histogram
* Fix histgram issues
* Finish migration to histogram
* Handle cases with zero send/recieve data
* Start on partition object
* Get compiling
* Updates
* Add unit tests
* Switch to partition object
* Fix linting issues
* Rename partition file
* Add python doc
* Fix python assert and finish doxygen comments
* Remove stubs for range based partition to satisfy pylint
* Wrap unit test in GPU only
* Wrap explicit cuda call in ifdef
* Merge with partition.py
* update docstrings
* Cleanup partition_op
* Add Workspace object
* Switch to using workspace object
* Move last remainder based function out of nccl_api
* Add error messages
* Update docs with examples
* Fix linting erros
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* add bruteforce impl
* add support for bruteforce-sharemem
* modify python API
* add tests
* change file path
* change python API
* fix lint
* fix test
* also check worst_dist in the last few dim
* use heap and early-stop on CPU
* fix lint
* fix lint
* add device check
* use cuda function to determine max shared mem
* use cuda to determine block info
* add memory free for tmp var
* update doc-string and add dist option
* fix lint
* add more tests
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* test commit
* fixes
* oops
* add docs
* lint
* why does it say I have a trailing whitespace
* oh ok
* fixes
* why there's an invalid argument error
* address comments
* fix
* address comments
* Auto stash before rebase of "origin/pytorch-nn-working"
GCNII model added
* linting
* linting
* lint
* Frequency Adaptive gcn init comit
* Revert "Frequency Adaptive gcn init comit"
This reverts commit 86a80586ac0040497c1edfa0e80df719992dcc4a.
* Update python/dgl/nn/pytorch/conv/gcn2conv.py
modified docstring
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* added beta formula and changed param name
* fix docstring
* lint
* white space lint
* update docstring
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* docstring formula update
* added gcn2
* added GCN2Conv
* Update nn.pytorch.rst
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* add implementation of twirls
* format the code
* fix some format error, and ignore others
* fix format errors
* fix format errors
* expose unfolding & attention
* Update nn.pytorch.rst
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* adapt send_and_recv example
* lint fix
* Change title and example order
* Update heterograph.py
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* Add row/col sorted flags
* improve sorting paths
* Remove print statement
* Keep track of sorted matrices
* Remove sort check in to_block
* Improve CPU sorted COO->CSR
* Handle the zero edge case
* Remove omp default clause to work with MSVC
* Update comments on sorted COO->CSR cpu implementatoin
* Expose sorted to python interface
* Make check_sorted default to false for dgl.graph()
* remove check sorted; add utests
* remove check_sorted flag
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Start on uniform GPU sampling
* Save more work
* Get cu file compiling
* Update sampling
* More changes
* Get GPU sampling for uniform probabilities solved
* Fix batch tensor migration
* Fix
* update kernels
* expand blocking
* Undo testing change
* Cut down on sampling overhead
* Fix replacement
* Update unit tests
* Add option to gpu sample in graphsage
* Copy only csc to gpu
* Add ogbn support
* Fix linting
* Remove nvtx from sample
* Improve documentation and error checking
* Expand documentation
* Update assert checking
* delete extra space
* Use standard dataloader when dataset is a dictionary
* ogb -> ogbn
* Fix edge selection determinism
* Fix typos
* Remove nvtx
* Add comment for self.fanout_arrays and assert
* Fix linting
* Migrate to scalarbatcher
* Fix indentation
* Fix batcher
* Fix indexing
* Only use databatcher for GPU
* Convert to DGL NDArray to PyTorch Tensor
* Add optimization for PyTorch's F.tensor() for list of GPU tensors
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
* PPIDataset
* Revert "PPIDataset"
This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.
* Hide implementations that may cause confusions to the user
* fix number of classes in Amazon Cobuy datasets
* add four papers
* fix feature type of TU dataset
* fix readme
* remove commented codes
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
nv-dlasalle