* 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>
* 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
* 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>
* implement pin_memory/unpin_memory/is_pinned for dgl.graph
* update python docstring
* update c++ docstring
* add test
* fix the broken UnifiedTensor
* eliminate extra context parameter for pin/unpin
* fix linting
* fix typo
* disable new format materialization for pinned graphs
* update python doc for pin_memory_
* fix unit test
* update doc
* change unitgraph and heterograph's PinMemory to in-place
* update comments for NDArray's PinMemory_ and PinData
* update doc
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
* 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>
* start
* coo csr union partition
* lint
* lint
* lint
* Add matrix->data transform
* update
* Fix window compile
* Add CSR support for DisjointPartition
* lint
* Fix
* Use IdArray Op
* Concat ready
* Fix and all pass
* resolve comments
* Add union COO C++ test
* Add C++ test for csr
* lint
* triger
* Update include
* Fix merge
* test
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
* add cuda utils; change g.to; add g.device
* split array.h into several headers
* cuda index select
* file
* three cuda kernels
* add cuda elementwise arith and several others
* cuda CSRIsNonZero
* fix lint
* lint
* lint
* fix bug in changing ctx to property
* address comments
* remove unused codes
* address comments
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