GitLab

* Use ==/!= to compare constant literals (str, bytes, int, float, tuple)

Avoid Syntax Warnings on Python >= 3.8

$ `python3`
```
>>> "" == ""
True
>>> "" is ""
<stdin>:1: SyntaxWarning: "is" with a literal. Did you mean "=="?
True
```

* Use ==/!= to compare constant literals (str, bytes, int, float, tuple)

[PyTorch][Bugfix] Use uint8 instead of bool in pytorch to be compatible with nightly version (#3406)

* Use uint8 instead of bool in pytorch

* Handle type aliases

* Fix syntax error
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

* [Bugfix] fix a compile error for Debug-BuildType on Windows Platform

When using CMakeLists.txt to build the "Debug" BuildType on the Windows Platform, it has three compile errors (C4716) in the file "dgl\src\runtime\shared_mem.cc":

'dgl::runtime::SharedMemory::CreateNew': must return a value
'dgl::runtime::SharedMemory::Open': must return a value
'dgl::runtime::SharedMemory::Exist': must return a value

* [Bugfix] cmake error "cannot find load file" when DGL as a sub_directory on Linux

When using DGL as a subdirectory in a CMake Project, the "CMAKE_SOURCE_DIR" here will return the parent cmake scope dir, which is not a expected dir.
Maybe it is better to use "CMAKE_CURRENT_SOURCE_DIR" to set "GKLIB_PATH".

* [Bugfix] cmd cmake error when DGL as a subdirectory

When DGL as a subdirectory of another project, the WORKING_DIRECTORY of "add_custom_command" will be incorrect at the line 255 of "CMakeLists.txt", such that making a cmake "setlocal" error.

* Update README.md

* Update README.md

Co-authored-by: Israt Nisa <nisisrat@amazon.com>

* The start of experiments of Jiahang Li on GraphSAINT.

* a nightly build

* a nightly build

Check the basic pipeline of codes. Next to check the details of samplers , GCN layer (forward propagation) and loss (backward propagation)

* a night build

* Implement GraphSAINT with torch.dataloader

There're still some bugs with sampling in training procedure

* Test validity

Succeed in testing validity on ppi_node experiments without testing other setup.
1. Online sampling on ppi_node experiments performs perfectly.
2. Sampling speed is a bit slow because the operations on [dgl.subgraphs], next step is to improve this part by putting the conversion into parallelism
3. Figuring out why offline+online sampling method performs bad, which does not make sense
4. Doing experiments on other setup

* Implement saint with torch.dataloader

Use torch.dataloader to speed up saint sampling with experiments. Except experiments on too large dataset Amazon, we've done some experiments on other four datasets including ppi, flickr, reddit and yelp. Preliminary experimental results show consumed time and metrics reach not bad level. Next step is to employ more accurate profiler which is the line_profiler to test consumed period, and adjust num_workers to speed up sampling procedures on same certain datasets faster.

* a nightly build

* Update .gitignore

* reorganize codes

Reorganize some codes and comments.

* a nightly build

* Update .gitignore

* fix bugs

Fix bugs about why fully offline sampling and author's version don't work

* reorganize files and codes

Reorganize files and codes then do some experiments to test the performance of offline sampling and online sampling

* do some experiments and update README

* a nightly build

* a nightly build

* Update README.md

* delete unnecessary files

* Update README.md

* a nightly update

1. handle directory named 'graphsaintdata'
2. control graph shift between gpu and cpu related to large dataset ('amazon')
3. remove parameter 'train'
4. refine annotations of the sampler
5. update README.md including updating dataset info, dependencies info, etc

* a nightly update

explain config differences in TEST part
remove a sampling time variant
make 'online' an argument
change 'norm' to 'sampler'
explain parameters in README.md

* Update README.md

* a nightly build

* make online an argument
* refine README.md
* refine codes of `collate_fn` in sampler.py, in training phase only return one subgraph, no need to check if the number of subgraphs larger than 1

* Update sampler.py

check the problem on flickr is about overfitting.

* a nightly update

Fix the overfitting problem of `flickr` dataset. We need to restrict the number of subgraphs (also the number of iterations) used in each epoch of training phase. Or it might overfit when validating at the end of each epoch. The method to limit the number is a formula specified by the author.

* Set up a new flag `full` specifying if the number of subgraphs used in training phase equals to that of pre-sampled subgraphs

* Modify codes and annotations related the new flag

* Add a new parameter called `node_budget` in the base class `SAINTSampler` to compute the specific formula

* set `gpu` as a command line argument

* Update README.md

* Finish the experiments on Flickr, which is done after adding new flag `full`

* a nightly update

* use half of edges in the original graph to do sampling
* test dgl.random.choice with or without replacement with half of edges
~ next is to test what if put the calculating probability part out of __getitem__ can speed up sampling and try to implement sampling method of author

* employ cython to implement edge sampling for per edge

* employ cython to implement edge sampling for per edge
* doing experiments to test consumed time and performance
** the consumed time decreased to approximately 480s, the performance decrease about 5 points.
* deprecate cython implementation

* Revert "employ cython to implement edge sampling for per edge"

* This reverts commit 4ba4f092
* Deprecate cython implementation
* Reserve half-edges mechanism

* a nightly update

* delete unnecessary annotations
Co-authored-by: Mufei Li <mufeili1996@gmail.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

Co-authored-by: JingchengYu94 <jingchengyu94@gmail.com>

[Distributed] Allow user to pass-in extra env parameters when launching a distributed training task. (#3375)

* Allow user to pass-in extra env parameters when launching a distributed training task.

* Update

* upd
Co-authored-by: xiangsx <xiangsx@ip-10-3-59-214.eu-west-1.compute.internal>

* graceful c++ exception in OpenMP

* credits

* add test
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

* [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>

* Added md5sum for the large dataset files

md5sum helps in validating the correctness of large dataset files once downloaded. 

Refer: https://github.com/snap-stanford/ogb/issues/253

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

[Performance][Feature] Add `src_nodes` paramter to `to_block()` to avoid cost running unique() when available. (#2973)

* Add lhs_nodes are paremeter to to_block

* Update unit test

* Switch to simplified node conversion

* Switch lhs_nodes to be in/out parameter

* Update docs
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* put PYTHONPATH in server launch

* remove prints
Co-authored-by: xiangsx <xiangsx@ip-10-3-59-214.eu-west-1.compute.internal>

* [Performance] improve coo2csr space complexity when row is not sorted

* [Perf] replace std::vector<> by NDArray

* keep both impl of unsorted coo to csr and choose according to graph density dynamically

* refine criteria to choose btw Unsorted algos
Co-authored-by: Ubuntu <ubuntu@ip-172-31-34-27.us-west-2.compute.internal>

* Fix #3291

* update

* fix

* Unit key

* Fix
Co-authored-by: Ubuntu <ubuntu@ip-172-31-2-66.ec2.internal>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

* Fixes bug #3312

* Fixing lint errors
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* publish pct

* add train_cls

* add readme

* update opt for point transformer

* update the example index

* update for comments
Co-authored-by: Tong He <hetong007@gmail.com>

* Update model.py

fix typo

* Update main.py

fix autoscale

* Update README.md
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-2-66.ec2.internal>

* update.

* update.

* fix.
Co-authored-by: Zheng <dzzhen@88665a0acb11.ant.amazon.com>

* fix start_idx

* fix the bug when cuda > 0
Co-authored-by: Tong He <hetong007@gmail.com>

* rgcn with new heterograph API

* apply_edge() forward for multi relation

* undoing changes from rgcn-hetero

* backward apply_edge(copy_u) added

* unittest for apply_edge(copy_e)

* Compatible with new PRs

* resolving conflict with master

* Bringing back change after resolving conflict

* minor

* minor
Co-authored-by: Israt Nisa <nisisrat@amazon.com>

* Added binary builtinMsgFunc forward() for heterograph

* Added backward for u_op_v

* Supports all binary builtin forward

* Supports binary message funcs with reduce func sum

* lint check

* removed import torch from unittest

* enabled GPU test

* lint check

* Fixed docstrings

* rename func get_hs_id

* edited comment
Co-authored-by: Israt Nisa <nisisrat@amazon.com>

* remove

* remove

* fix

* remove

* remove

* [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