* 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
* [Feature] Exclude edges in sample_neighbors
Extending sample_neighbors and sample_frontier
API to support exclude_edges parameter.
exclude_edges support tensor and dict data
Feature enable excluding certain edges
during neighborhood sampling
Exclude_edges contains EID's of edges
which will be excluded
during neighbor picking for seed nodes.
Added test case for heterograph and homograph
RFC issue id: 2944
* compatibility
* fix
* fix
Co-authored-by: Quan Gan <coin2028@hotmail.com>
* [CPU, Parallel] Rewriting omp pragmas with parallel_for
* [CPU, Parallel] Decrease number of calls to task function
* c[CPU, Parallel] Modify calls to new interface of parallel_for
* 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>
* [CPU Parallel] implementation of parallel_for with grain size
* Create parallel_for.h
* Update parallel_for.h
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>
Quan Gan