GitLab

* [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

* better capture the errors

* debugging

* fix pyenv command

* add universe repo

* update to cuda 11 for 171

* add a test file, improved the checking script

* [test] FSDP: add the failing test for #421

* skip on 1.5

* better skipping

* Update tests/nn/data_parallel/test_fsdp_grad_scaler.py
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* [fix] FSDP corner case of all params at in the children

* lint

* fix

* tradeoff

* fix doc build

* review comments

* [feat]: add summon_full_params context mgr

* fix

* fix

* addressed comments

* fixed the state_dict copy

* lint

* bring back a fix from FSDP, may help a few existing users

* [test]: add peak mem in checkpoint test

* more debugging

* new test

* more fix

* better collection of debug in case of future failures

* update the comment

* typo

* comment

* clarify

* better wording

* POC, testing against the DDP comm hook when available
* docs, adding a reference to DDP's compress hook
* updating changelog, prep for v0.1.8 release

* [bug]: not all CUDA memory is freed when model is deleted

* fixed memory leak

- without this, peak memory will be high when more than one model
  is trained (i.e. first model leave staff around pushing up the
  peak memory when the second model runs)

* addressed comments

* fix

* changelog

Recent work by [Microsoft](https://arxiv.org/abs/1910.02054) and [Google](https://arxiv.org/abs/2004.13336

) has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new **`FullyShardedDataParallel` (FSDP)** wrapper, which is a drop-in replacement for PyTorch's `DistributedDataParallel` (DDP) wrapper.

Compared to PyTorch DDP:
* FSDP shards parameters (FP16 + FP32) and optimizer state across data parallel GPUs
* FSDP with `reshard_after_forward=False` has the same communication cost as PyTorch DDP and is similar to ZeRO-2
* FSDP with `reshard_after_forward=True` increases total communication by 50% and is similar to ZeRO-3:
    * all-gather parameters at start of forward pass and start of backward pass
    * reduce-scatter grads at end of backward pass
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* test with and without buckets for all the shardedDDP unit tests
* parametrize all the things
* refactoring, adding even more  combinations at times
* handle hosts not having cuda

* Adding multiple groups support to ShardedDDP + unit test
* adding gloo to the backends tested for multiple groups

* [fix] ShardedDDP train/eval modes
* Update CHANGELOG.md

* initial implementation, with unit test and assert
* added changelog and better debug string

* Better unit testing
* Make it possible to refresh the DDP assumptions when the model has changed. Make it optional so that you save some time
* Enabling accumulation tests

* Add fairscale.utils.containers
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>

* Add fairscale.nn.misc.checkpoint_activations
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
Co-authored-by: Min Xu <24926999+min-xu-ai@users.noreply.github.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Adding a proper ddp parity / AMP unit test, overdue
* catch non-AMP pytorch

* adding the .to(device) support + unit testing
* doc update

* no idea about the root issue, but it proved to be fairly narrowed (gloo+cpu+python3.8+no cuda installed) so I guess that's out of scope for fairscale

* Add AMPnet implementation (clean version)

* Move ampnet to experimental

* Move stuff around pipeline

* Address review comments and fix pre-commit errors

* Refactor and modify delegate functionality

* Modify header in pipe.py

* working around broken mypy

* minor, but ease of life, one less papercut

* 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

* fix typo, backend for CPU test

* Add function to add handle for sync BN
* Add test to ensure batch norm handles have been added

* catching properly a given test failing if not enough gpus