GitLab

* update pytorch version for benchmarks

* reduce golden data precision check

* update python version for cpu tess

* run CPU tests with updated PyTorch version

* update nightly and test PyTorch versions

* skip failing multiprocess pipe test

* always skip test

* always skip test

* always skip test

* lint error

* skip unsupported versions

* improve skip message

* lint errors

* update master branch to main

* added FAQ about updating the branch from master to main

* fixed some false positive correction

* added what is new section

* fixed the quoted code area

* added release what is new section

* added a step in release.md

* fixed a word

[cleanup] CI test updates; mypy cleanup; partial broadcast_object cleanup; pre-commit documentation (#744)

* changelog; mypy; oss cleanup

* more broadcast_object cleanup in FSDP

* one more mypy fix

* retire pytorch 1.6 from circleci, add new lightly, add 1.8 LTS and 1.9 stable release

* update torch version for LTS

* minor fixes

* update cache key

* trying newer gpu VMs

* bump the cache

* update to gpu.medium, which should be 2 GPUs

* update nightly version

* add pre-commit instruction

* fixed CHANGELOG after merging

* updated to newer nightly

* retained the older broadcast function for older GPUs for oss.py

* fixed a bug

* added a comment

* fixing a test for pytorch 1.10

* testing a fix

* Update fairscale/optim/oss.py

* Update CONTRIBUTING.md
Co-authored-by: Min Xu <min.xu.public@gmail.com>

* Update torch to 1.9.0.dev20210614+cu102

* Update config.yml

* Update config.yml

* Update setup.py

* Update config.yml

* Update config.yml

* Update config.yml

* Update config.yml

* [test] fixing 1.9 nightly install

* update cache version so that we don't keep reinstall
Co-authored-by: Min Xu <min.xu.public@gmail.com>

[fix] Revert change that removed the option to run OffloadModel with out activation checkpointing. (#608)

* revert change made

* add tests and revert sync shard changes

* add tests

* remove file checked in by error

* inine var

* fix lint errors

* add checkpoint activation

* fix mypy

* use a bigger model

* modify tests for now

* resolve conflicts
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* add model

* add offload regression benchmarks

* add golden data

* remove mp pipe benchmark

* fix lint

* remove rank

* add check for model type

* lint errors

NCCL all_to_all is now supported in PyTorch (since v1.8.0)

Fixes: #548

* codedcov testing

* codecov testnig

* more changes for uploading cov

* fix invalid config

* fix invalid config

* modify name

* fix config
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* [hotfix]: fix a bug in CI command

* debug

* debug

* bump cache ver

* fix

* eq

* check

* bump

* addressed comment

* [chore] move a test script

* add a shortcut for installing

* more skipping

* keep apt-get part

* [chores]: CI py39 on GPU and more efficiency

* add test list files

* fix

* add test list files

* split benchmark run into 2 runs

* fix 1.8 version and balance benchmarks

* fix

* fix

* fix

* fix

* recording tests

* py39 install fix

* test again

* move tests

* reorg tests

* skip tests for torch 1.8 due to an upstream bug

* removed __init__.py from tests since it confuses pytest

* Revert "removed __init__.py from tests since it confuses pytest"

This reverts commit 7e156ba33dfaa5ed052031780613ec0cb57a45b0.

* don't include __init__ in file list

* notes on __init__.py and added missing ones

* fixed mypy in a test file

* balance test runtime

* better pip install

* balance more

* pip fix

* balance

* balance more, all test should finish within 20m now

* minor license update

* trying cu102

* more doc and addressed Ben's comments

* debugging

* debugging...

* restoring the regression test, adding a test of the for_each optims
* fix the regression test on circleci
* removing unused flags

* mp cleanup

* round of multiprocess refactoring

* test golden run

* print cuda stats

* fix lint errors

* enable multiprocess pipe benchmarks

* set world size to be available gpus

* more changes

* use synthetic loaders for intermediate pipeline stages

* merged master

* fix for the devices property

* dataloader fix

* modify rank check

* print wps stats

* enable verification

* fix logging

* fix flag name

* fix flag name

* check for rank

* fix indent

* pass args

* pass args

* modify golden data

* remove unused print messsage

* fix lint errors

* add comments

* fix benchmarks
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* [test]: test with py39 + torch 1.8 nightly

* version fix

* more fix

* fix version function for nightly version

* fix torch_pg build

* invalidate cache

* separate benchmark requirements

* comment

* fixed mypy

* fixed a test

Also, we can save time by only running unittests once instead of
twice (with and without coverage).

* [test] cover python 3.7 to 3.9 on CPU

- covering common python versions on CPU tests
- added doc build test

* add doc build test

* skipping failing tests on py39

* catching doc build warnings

* add doc build to py38 and py39

* minor fix

* fix doc build for adascale

* removed dead code

* fix the skipping

* skip unit test for py39

* add failing example

* no more py39 skipping the tests

* tentatively fixing the cpu version of circleci jobs, now pipe tests are the last ones standing
* fixing oss backcompat, trying to fix rpc in old pytorch also
* fixing the file based init in torch 1.5

* adding the pytest timeout plugin to properly root out hanging tests
* removing redundant code, slightly more reasonable timeout, works on single cuda
* finding the root bug for some of the cpu hangs, rpc init
* propagating all the rpc init test changes to the pipe and model parallel tests

- tighter regression detection, based on the best case vs. worst case
- still run all configurations, useful for comparisons but not a target

* keep two torch 1.7 profiles to save cuda 10.1 testing

* testing median and MAD

* synchronize on kernels to make sure that we're measuring the actual completion time

* adjusting the circleci threshold, not that the speed has regressed but because we measure proper cuda execution time

* rewrite using autograd and Variable execution queue to make the reduce automatic
* share buckets with OSS to remove duplication
* some speed still likely on the table since the speed vs. bucketing does not match expectations, could be a follow up

* oss benchmark: add an --amp option
* add a circleCI test