* 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>
* 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
* 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>
* Allocate tensors in DGL's current stream
* make tensoradaptor stream-aware
* replace TAemtpy with cpu allocator
* fix typo
* try fix cpu allocation
* clean header
* redirect AllocDataSpace as well
* resolve comments
* * Added missing specializations for `__half` of `DLDataTypeTraits`, `IndexSelect`, `Full`, `Scatter_`, `CSRGetData`, `CSRMM`, `CSRSum`, `IndexSelectCPUFromGPU`
* Fixed casting issue in `_LinearSearchKernel` that was preventing it from supporting `__half`
* Added `#if`'d out specializations of `CSRGEMM`, `CSRGEAM`, and `Xgeam`, which would require functions that aren't currently provided by cublas
* * Added more specific error messages for unimplemented FP16 specializations of Xgeam, CSRGEMM, and CSRGEAM
* * Added missing instantiation of DLDataTypeTraits<__half>::dtype
* * Fixed linter error
* Added clearer comment explaining why the cast to long long is necessary
* * Worked around a compile error in some particular setup, where __half can't be constructed on the host side
* * Fixed linter formatting errors
* * Changes to comments as recommended
* * Made recommended changes to logging errors in FP16 specializations
* Also changed the existing Xgeam function for unsupported data types from LOG(INFO) to LOG(FATAL)
* Explicitly unpin tensoradapter allocated arrays
* Undo unrelated change
* Add unit test
* update unit test
* add pinned_by_dgl flag to NDArray::Container
* use dgl.ndarray for holding the pinning status
* update multi-gpu uva inference
* reinterpret cast NDArray::Container* to DLTensor* in MoveAsDLTensor
* update unpin column and examples
* add unit test for unpin column
Co-authored-by: Dominique LaSalle <dlasalle@nvidia.com>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
* huuuuge update
* remove
* lint
* lint
* fix
* what happened to nccl
* update multi-gpu unsupervised graphsage example
* replace most of the dgl.mp.process with torch.mp.spawn
* update if condition for use_uva case
* update user guide
* address comments
* incorporating suggestions from @jermainewang
* oops
* fix tutorial to pass CI
* oops
* fix again
Co-authored-by: Xin Yao <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
* XPU_SWITCH for kDLCPUPinned
* a rough version ready for testing
* eliminate extra context parameter for pin/unpin
* update train_sampling
* fix linting
* fix typo
* multi-gpu uva sampling case
* disable new format materialization for pinned graphs
* update python doc for pin_memory_
* fix unit test
* UVA sampling for link prediction
* dispatch most csr ops
* update graphsage example to combine uva sampling and UnifiedTensor
* update graphsage example to combine uva sampling and UnifiedTensor
* update graphsage example to combine uva sampling and UnifiedTensor
* update doc
* update examples
* change unitgraph and heterograph's PinMemory to in-place
* update examples for multi-gpu uva sampling
* update doc
* fix linting
* fix cpu build
* fix is_pinned for DistGraph
* fix is_pinned for DistGraph
* update graphsage unsupervised example
* update doc for gpu sampling
* update some check for sampling device switching
* fix linting
* adapt for new dataloader
* fix linting
* fix
* fix some name issue
* adjust device check
* add unit test for uva sampling & fix some zero_copy bug
* fix linting
* update num_threads in graphsage examples
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.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>
* Pass the std:min argument's type, to avoid the compilation error.
* Update parallel_for.h
* Update negative_sampling.cc
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* [Feature] enable create/set/free cuda stream for internal use
* add unit test
* fix unit test failure on mxnet and tf
* refactor stream wrapper
* fix lint error
* fix lint error
Quan (Andy) Gan