* Disable pinning non-contiguous memory
* Prevent views from being converted for write
* Fix linting
* Add unit tests
* Improve error message for users
* Switch to pytest function
* exclude mxnet and tensorflow from inplace pinning
* Add skip
* Restrict to pytorch backend
* Use backend to retrieve device
* Fix capitalization in decorator
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
* fix uva sampling with features
* fix
* add is_listlike function to distinguish strings from sequence
* fix
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
* WIP: TypedLinear and new RelGraphConv
* wip
* further simplify RGCN
* a bunch of tweak for performance; add basic cpu support
* update on segmm
* wip: segment.cu
* new backward kernel works
* fix a bunch of bugs in kernel; leave idx_a for future
* add nn test for typed_linear
* rgcn nn test
* bugfix in corner case; update RGCN README
* doc
* fix cpp lint
* fix lint
* fix ut
* wip: hgtconv; presorted flag for rgcn
* hgt code and ut; WIP: some fix on reorder graph
* better typed linear init
* fix ut
* fix lint; add docstring
* 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>
* [Feature] enable async transfer in NodeDataLoader for homograph
* fix lint issues
* fix device choose when creating stream
* fix test on cpu only machine
* fix pin_memory config
* support homo only
* avoid creating stream in each step and sync via event
* fix lint
* enable graph copy on non-default stream
* fix lint
* refine arg description
* fix conflicts
* [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
* Add pytorch-direct version
* remove
* Add multi-gpu unified tensor test for pytorch
* relocate verification step to each process
* reduce number of workers
* add parameter
Co-authored-by: shhssdm <shhssdm@gmail.com>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
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>
* csr and csc creation
* fix
* fix
* fixes to adj transpose
* fine
* raise error if indptr did not match number of nodes
* fix
* huh?
* oh
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
* Use shared memory for grad sync when NCCL is not avaliable as PyTorch distributed backend.
Fix small bugs and update unitests
* Fix bug
* update test
* update test
* Fix unitest
* Fix unitest
* Fix test
* Fix
* simple update
Co-authored-by: Ubuntu <ubuntu@ip-172-31-24-212.ec2.internal>
* add unit test
* Extend NDArrayPartition object
* Add method for setting embedding, and improve documentation
* Sync before returning
* Use name unique to sparse embedding class to avoid delete
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* 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>
Quan (Andy) Gan