GitLab

* [BugFix] fix in/out degree computation

* add unit tests

* add word_ids and simplify

* simplify

* add word_ids to be removed later

* remove word_ids

* seems to work

* tweak

* transpose word_z

* add word_ids example

* check api compatibility

* improve compatibility

* update doc

* tweak verbose

* restore word_z layout; tweak

* tweak

* tweak doc

* word_cT

* use log_weight and some other tweaks

* rewrite README

* update equations

* rewrite for clarity and pass tests

* tweak

* bugfix import

* fix unit test

* fix mult to be the same as old versions

* tweak

* could be a bugfix

* 0/0=nan

* add doc_subgraph utility function

* minor cache optimization

* minor cache tweak

* add environmental variable to trade cache speed for memory

* update README

* tweak

* add sparse update pass unit test

* simplify sparse update

* improve low-memory efficiency

* tweak

* add sample expectation scores to allow resampling

* simplify

* update comment

* avoid edge cases

* bugfix pred scores

* simplify

* add save function
Co-authored-by: Yifei Ma <yifeim@amazon.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

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

Remove self-loops and duplicate edges before ParMETIS and restore when converting to DGLGraph (#3472)

* save self-loops and duplicated edges separately.

* [BugFix] sort graph by dgl.ETYPE

* fix bugs in verify script

* fix verify logic

* refine README
Co-authored-by: Da Zheng <zhengda1936@gmail.com>

* Based on issue #3436. Improving _SegmentCopyKernel s GPU utilization by switching to nonzero based thread assignment

* fixing lint issues

* Update cub for cuda 11.5 compatibility (#3468)

* fixing type mismatch

* tx guaranteed to be smaller than nnz. Hence removing last check

* minor: updating comment

* adding three unit tests for csr slice method to cover some corner cases
Co-authored-by: Abdurrahman Yasar <ayasar@nvidia.com>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

Co-authored-by: Rhett Ying <85214957+Rhett-Ying@users.noreply.github.com>

* link prediction tutorial

* add performance tip

* Update L2_large_link_prediction.py

* enable unit tests for dataloader on the GPU

* fix compatibility

* copyright

* fix linting
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>

* relabel gpu

* unittest for ralebl_ on the GPU

* finish Relabel_ for the GPU

* copyright

* re-enable the unittest for edge_subgrah on the GPU

* fix unittest for tensorflow

* use a fixed number of threads
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>

* Update

* Fix

* Fix

* Update

* Update

* Update

* Fix CI

* Fix

* Fix

* Fix

* Update

* Update

* Update

* Fix

* Fix

* Fix for TF

* [Model][Core] GATv2

* lint

* gatv2conv.py

* lint

* lint

* style and docs

* lint

* gatv2conv fix
Co-authored-by: Shaked Brody shakedbr@campus.technion.ac.il <shakedbr@tangerine.cslcs.technion.ac.il>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* added nn pytorch egatconv

* aligned with test build

* aligned with test build

* fixed wihite spaces

* fixed wihite spaces

* fixed wihite spaces

* added missing egatconv in imports

* added indentation in forward

* GATConv based implementation

* removed **kw_args

* added dgl relative imports

* PR corrections

* added DGL Error to EGATConv imports

* Update test_nn.py
Co-authored-by: Argusmocny <k.kaminski@cent.uw.edu.pl>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

* gpu compact graph template

* cuda compact graph draft

* fix typo

* compact graphs

* pass unit test but fail in training

* example using EdgeDataLoader on the GPU

* refactor cuda_compact_graph and cuda_to_block

* update training scripts

* fix linting

* fix linting

* fix exclude_edges for the GPU

* add --data-cpu & fix copyright

* remove duplicate paper

* Update README.md

* parallelize CSRRowSlice()

* use parallel_for for the second loop
Co-authored-by: nv-dlasalle <63612878+nv-dlasalle@users.noreply.github.com>
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

* try to avoid memory leak

* try to avoid memory leak

* avoid memory leak with no hope

* Revert "avoid memory leak with no hope"

This reverts commit c77befe9479f46758e744642f66dd209b50eef7d.

* no message

* Update sparse.py

* Update tensor.py
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>

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

* Add pytorch-direct version

* remove

* add documentation for UnifiedTensor

* Revert "add documentation for UnifiedTensor"

This reverts commit 63ba42644d4aba197c1cb4ea4b85fa1bc43b8849.

* add boundary check for UVM IndexSelect

* relocate boundary check index kernels to cuda

* fix function name

* fix indexkernel in nccl api

* fix argument ordering

* simplify code

* Add a comment for the uvm version
Co-authored-by: shhssdm <shhssdm@gmail.com>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* 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>