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* 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

* shortest dist

* update

* address issues

* update

* fix indent

* lint

* fix indent

* [Misc] clang-format auto fix.

* manual

* manual
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* [Misc] clang-format auto fix.

* fix

* manual
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* replace

* more

* file

* change
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* [Misc] clang-format auto fix.

* manual
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* [Dist] deprecate etype and always use canonical etype for partition and load

* enable canonical etypes in dist part pipeline

* resolve rebase conflicts

* fix lint

* fix test failure

* throw exception if outdated part config is loaded

* refine

* refine

* revert unnecessary change

* fix typo

* edgewise mpops

* formatting

* black formatting

* isort

* fix lint

* remove typing module; address comments

* fix gpu ut; rename test file

* remove dependecy of load_partition_book in change tool

* fix issue

* fix issue
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* [Misc] clang-format auto fix.

* manual

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* fix
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* [Misc] clang-format auto fix.

* fix
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* get device pointers

* change if condition to IsPinned

* Use black for formatting

* limit line width to 80 characters.

* Use a backslash instead of directly concatenating

* file structure adjustment.

* file structure adjustment(2)

* codes for citation2

* format slight adjustment

* adjust format in models.py

* now it runs normally for all datasets.

* add comments; adjust code order.

* adjust indenting.
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* [Misc] clang-format auto fix.

* manual
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* clang-format

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* manul

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* Add files via upload

This file will contain several utility modules for Graph Transformer. DegreeEncoder has been implemented in this file now.

* Update graph_transformer.py

* Update nn-pytorch.rst

Add graph_transformer.DegreeEncoder in Utility Modules

* Update test_nn.py

* Update __init__.py

* Update graph_transformer.py

* Update test_nn.py

* Update graph_transformer.py

fix unused import

* Update graph_transformer.py

add module doc-string

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Signed-off-by: Serge Panev <spanev@nvidia.com>
Signed-off-by: Serge Panev <spanev@nvidia.com>

* add save/load for distributed  optimizer
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Merging this PR to the master branch

* add a learned laplacian positional encoder

* leverage black to beautify the python code

* refine according to dongyu's comments
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* sample neighbors with masks

* oops

* refactor again

* remove

* remove debug code

* rename macro

* address comments

* more stuff

* remove

* fix

* try fix unit test

* oops

* fix test

* oops

* change name

* rename a lot of stuff

* oops

* ugh

* misc fixes

* lint

* address a lot of comments

* lint

* lint

* fix

* that was silly

* fix

* fix

* fix

* oops

* sample neighbors with masks

* oops

* refactor again

* remove

* remove debug code

* rename macro

* address comments

* address comment

* address comments

* rename a lot of stuff

* oops

* Update distributed-preprocessing.rst

* Another typo for the writes edge type

* Use black for formatting

* limit line width to 80 characters.

* Use a backslash instead of directly concatenating

* file structure adjustment.

* file structure adjustment(2)

* codes for citation2

* format slight adjustment

* adjust format in models.py

* now it runs normally for all datasets.
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* [Model] Heterogeneous graph support for GNNExplainer (#1)

* add HeteroGNNExplainer

* GNNExplainer for heterogeenous graph

* fix typo

* variable name cleanup

* added HeteroGNNExplainer test

* added doc indexing for HeteroGNNExplainer

* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update python/dgl/nn/pytorch/explain/gnnexplainer.py
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* Update gnnexplainer.py

Change DGLHeteroGraph to DGLGraph, and specified parameter inputs

* Added ntype parameter to the explainer_node call

* responding to @mufeili's comment regarding restoring empty lines at appriopiate places to be consistent with existing practices

* responding to @mufeili's comment regarding restoring empty lines at appriopiate places that were missed in the last commit

* docstring comments added based on @mufeili suggestions

* indorporated @mufeili requested changes related to docstring model declaration.

* example model and test_nn.py added for explain_graphs

* explain_nodes fixed and fixed the way hetero num nodes and edges are handled

* white spaces removed

* lint issues fixed

* explain_graph model updated

* explain nodes model updated

* minor fixes related to gpu compatability

* cuda support added

* simplify WIP

* _init_masks for ennexplainer updated to match heterographs

* Update

* model simplified and docstring comments updated

* nits: docstring udpated

* lint check issues updated

* lint check updated

* soem formatting updated

* disabling int32 testing for GNNExplainer

* Update
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* Update

* Update

* Update

* Update

* Update test_transform.py
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* [Dist] fix etype issue in dist part pipeline

* add comments

* Update from master (#4584)

* [Example][Refactor] Refactor graphsage multigpu and full-graph example (#4430)

* Add refactors for multi-gpu and full-graph example

* Fix format

* Update

* Update

* Update

* [Cleanup] Remove async_transferer (#4505)

* Remove async_transferer

* remove test

* Remove AsyncTransferer
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* [Cleanup] Remove duplicate entries of CUB submodule   (issue# 4395) (#4499)

* remove third_part/cub

* remove from third_party
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* [Bug] Enable turn on/off libxsmm at runtime (#4455)

* enable turn on/off libxsmm at runtime by adding a global config and related API
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* [Feature] Unify the cuda stream used in core library (#4480)

* 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>

* [Feature] Added exclude_self and output_batch to knn graph construction (Issues #4323 #4316) (#4389)

* * Added "exclude_self" and "output_batch" options to knn_graph and segmented_knn_graph
* Updated out-of-date comments on remove_edges and remove_self_loop, since they now preserve batch information

* * Changed defaults on new knn_graph and segmented_knn_graph function parameters, for compatibility; pytorch/test_geometry.py was failing

* * Added test to ensure dgl.remove_self_loop function correctly updates batch information

* * Added new knn_graph and segmented_knn_graph parameters to dgl.nn.KNNGraph and dgl.nn.SegmentedKNNGraph

* * Formatting

* * Oops, I missed the one in segmented_knn_graph when I fixed the similar thing in knn_graph

* * Fixed edge case handling when invalid k specified, since it still needs to be handled consistently for tests to pass
* Fixed context of batch info, since it must match the context of the input position data for remove_self_loop to succeed

* * Fixed batch info resulting from knn_graph when output_batch is true, for case of 3D input tensor, representing multiple segments

* * Added testing of new exclude_self and output_batch parameters on knn_graph and segmented_knn_graph, and their wrappers, KNNGraph and SegmentedKNNGraph, into the test_knn_cuda test

* * Added doc comments for new parameters

* * Added correct handling for uncommon case of k or more coincident points when excluding self edges in knn_graph and segmented_knn_graph
* Added test cases for more than k coincident points

* * Updated doc comments for output_batch parameters for clarity

* * Linter formatting fixes

* * Extracted out common function for test_knn_cpu and test_knn_cuda, to add the new test cases to test_knn_cpu

* * Rewording in doc comments

* * Removed output_batch parameter from knn_graph and segmented_knn_graph, in favour of always setting the batch information, except in knn_graph if x is a 2D tensor
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* [CI] only known devs are authorized to trigger CI (#4518)

* [CI] only known devs are authorized to trigger CI

* fix if author is null

* add comments

* [Readability] Auto fix setup.py and update-version.py (#4446)

* Auto fix update-version

* Auto fix setup.py

* Auto fix update-version

* Auto fix setup.py

* [Doc] Change random.py to random_partition.py in guide on distributed partition pipeline (#4438)

* Update distributed-preprocessing.rst

* Update
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* fix unpinning when tensoradaptor is not available (#4450)

* [Doc] fix print issue in tutorial (#4459)

* [Example][Refactor] Refactor RGCN example (#4327)

* Refactor full graph entity classification

* Refactor rgcn with sampling

* README update

* Update

* Results update

* Respect default setting of self_loop=false in entity.py

* Update

* Update README

* Update for multi-gpu

* Update

* [doc] fix invalid link in user guide (#4468)

* [Example] directional_GSN for ogbg-molpcba (#4405)

* version-1

* version-2

* version-3

* update examples/README

* Update .gitignore

* update performance in README, delete scripts

* 1st approving review

* 2nd approving review
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* Clarify the message name, which is 'm'. (#4462)
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* [Refactor] Auto fix view.py. (#4461)
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* [Example] SEAL for OGBL (#4291)

* [Example] SEAL for OGBL

* update index

* update

* fix readme typo

* add seal sampler

* modify set ops

* prefetch

* efficiency test

* update

* optimize

* fix ScatterAdd dtype issue

* update sampler style

* update
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* [CI] use https instead of http (#4488)

* [BugFix] fix crash due to incorrect dtype in dgl.to_block() (#4487)

* [BugFix] fix crash due to incorrect dtype in dgl.to_block()

* fix test failure in TF

* [Feature] Make TensorAdapter Stream Aware (#4472)

* 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

* [Build][Doc] Specify the sphinx version (#4465)
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* reformat

* reformat

* Auto fix update-version

* Auto fix setup.py

* reformat

* reformat
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* Move mock version of dgl_sparse library to DGL main repo (#4524)

* init

* Add api doc for sparse library

* support op btwn matrices with differnt sparsity

* Fixed docstring

* addresses comments

* lint check

* change keyword format to fmt
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* [DistPart] expose timeout config for process group (#4532)

* [DistPart] expose timeout config for process group

* refine code

* Update tools/distpartitioning/data_proc_pipeline.py
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* [Feature] Import PyTorch's CUDA stream management (#4503)

* 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

* [examples]educe memory consumption (#4558)

* [examples]educe memory consumption

* reffine help message

* refine

* [Feature][REVIEW] Enable DGL cugaph nightly CI  (#4525)

* Added cugraph nightly scripts

* Removed nvcr.io//nvidia/pytorch:22.04-py3 reference
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* Revert "[Feature][REVIEW] Enable DGL cugaph nightly CI  (#4525)" (#4563)

This reverts commit ec171c64

.

* [Misc] Add flake8 lint workflow. (#4566)

* Add pyproject.toml for autopep8.

* Add pyproject.toml for autopep8.

* Add flake8 annotation in workflow.

* remove

* add

* clean up
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* [Misc] Try use official pylint workflow. (#4568)

* polish update_version

* update pylint workflow.

* add

* revert.
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* [CI] refine stage logic (#4565)

* [CI] refine stage logic

* refine

* refine

* remove (#4570)
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* Add Pylint workflow for flake8. (#4571)

* remove

* Add pylint.
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* [Misc] Update the python version in Pylint workflow for flake8. (#4572)

* remove

* Add pylint.

* Change the python version for pylint.
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* Update pylint. (#4574)
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* [Misc] Use another workflow. (#4575)

* Update pylint.

* Use another workflow.
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* Update pylint. (#4576)
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* Update pylint.yml

* Update pylint.yml

* Delete pylint.yml

* [Misc]Add pyproject.toml for autopep8 & black. (#4543)

* Add pyproject.toml for autopep8.

* Add pyproject.toml for autopep8.
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* [Feature] Bump DLPack to v0.7 and decouple DLPack from the core library (#4454)

* rename `DLContext` to `DGLContext`

* rename `kDLGPU` to `kDLCUDA`

* replace DLTensor with DGLArray

* fix linting

* Unify DGLType and DLDataType to DGLDataType

* Fix FFI

* rename DLDeviceType to DGLDeviceType

* decouple dlpack from the core library

* fix bug

* fix lint

* fix merge

* fix build

* address comments

* rename dl_converter to dlpack_convert

* remove redundant comments
Co-authored-by: Chang Liu <chang.liu@utexas.edu>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
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* [Deprecation] Dataset Attributes (#4546)

* Update

* CI

* CI

* Update
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* [Example] Bug Fix (#4665)

* Update

* CI

* CI

* Update

* Update
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* Update

* Update (#4724)
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* [Dist] reduce startup overhead: enable to save in specified formats

* [Dist] reduce startup overhead: sort partitions when generating

* sort csc/csr only whenmultiple etypes

* refine

* Add device support in hetero_rgcn
Signed-off-by: Xue, Chendi <chendi.xue@intel.com>

* use num_workers instead of hard code and enable cpu_affinity for pytorch > 1.12
Signed-off-by: Xue, Chendi <chendi.xue@intel.com>

* Remove hard code num_workers and use dgl version to check if cpu_affinity is supported
Signed-off-by: Xue, Chendi <chendi.xue@intel.com>

* Remove specified dl_cores and computer_cores

Add error print if num_workers are miss set
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* expected num_workers should be less than num_physical_cores
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* Update examples/pytorch/ogb/ogbn-mag/hetero_rgcn.py
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* Remove dgl version and num_workers print
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* add comment to explain is_support_affinity

* Fix typo
Signed-off-by: Xue, Chendi <chendi.xue@intel.com>
Signed-off-by: Xue, Chendi <chendi.xue@intel.com>
Signed-off-by: Chendi Xue <chendi.xue@intel.com>
Co-authored-by: Chang Liu <chang.liu@utexas.edu>

This reverts commit 9962b7bd.

* add a standalone tool for change etypes to canonical etypes in part config

Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

* [Feature] enable to save graphs in multiple formats

* use StreamWithCount to accomodate formats

* fix lint

* fix dynamic cast issue

* fix lint

* refine docstring and code naming

* update docstring

* [Dist] decouple num_chunks and num_parts for graphs with edge feature

* fix test failure

* iosrt

* sort
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>