* fix.
* fix.
* fix.
* fix.
* Fix test
* Deprecate old DistEmbedding impl, use synchronized embedding impl
* Basic imple of heterogeneous on homogenenous sampling
* make pass
* Pass C++ test
* Add python test code
* lint
* lint
* Add MultiLayerEtypeNeighborSampler
* Add unitest for single machine dataloader
* Add dist dataloader test for edge type sampler
* Fix lint
* fix
* support for per etype sample
* Fix some bug and enable distributed training with per edge sample
* fix
* Now distributed training works
* turn off some mxnet
* turn off mxnet for some dist test
* fix
* upd
* upd according to the comments
* Fix
* Fix test and now distributed works.
* upd
* upd
* Fix
* Fix bug
* remove dead code.
* upd
* Fix
* upd
* Fix
Co-authored-by: Ubuntu <ubuntu@ip-172-31-71-112.ec2.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-2-66.ec2.internal>
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
* add output device for dataloading
* Update dataloader
* Get sampler device from dataloader
* Fix line length
* Update examples
* Fix to_block GPU for empty relation types
* Handle the case where the DistGraph has None for the underlying graph
Co-authored-by: Da Zheng <zhengda1936@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>
* [Bugfix] add default value for BlockSampler
* [Doc] modify user_guide description about MultiLayerDropoutSample
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
There is a small typo in python/dgl/dataloading/async_transferer.py.
Should read `transfer` rather than `tranfer`.
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Disable copying for anywhere but the GPU
* Remove unused import and remove references to transferring from the GPU from the docs
* Skip gpu test in cpu mode
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Add async transferer class
* Add async ndarray copy interface
* Add python bindings
* Fix comment
* Add python class
* Fix linting issues
* Add python unit test
* Update python interface
* move async_transferer to cuda only directory
* Fix linting issue
* Move out of contrib
* Add doc strings
* Move test compute from backend
* Update comment
* Fix test naming
* Fix argument usage
* Wrap/unwrap backend parameters
* Move to dataloading
* Move to 'dataloading'
* Make GPU/CPU compatible
* Fix unit tests
* Add docs
* Use only backend interface for datamovement in unit test
* rename get_data_size.
* remove g from DistTensor.
* remove g from DistEmbedding.
* clean up API of graph partition book.
* fix DistGraph
* fix lint.
* collect all part policies.
* fix.
* fix.
* support distributed sampler.
* remove partition.py
* clean commit
* oops forgot the most important files
* use einsum
* copy feature from frontier to block
* Revert "copy feature from frontier to block"
This reverts commit 5224ec963eb6a3ef1b6ab74d8ecbd44e4e42f285.
* temp fix
* unit test
* fix
* revert jtnn
* lint
* fix win64
* docstring fixes and doc indexing
* revert einsum in sparse bidecoder
* fix some examples
* lint
* fix due to some tediousness in remove_edges
* addresses comments
* fix
* more jtnn fixes
* fix
Quan (Andy) Gan