GitLab

* 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

* 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

* minor, but ease of life, one less papercut

* 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

[OSS] Getting rid of the "should bucket" hash table, just use a list + non trainable params fix (#259)

* Getting rid of the "should bucket" hash table, just use a list
Properly handle all params, with or without requires_grad

* make sure that this case is unit tested

* unit test checking ddp and sharded_ddp equivalence, reproducing the issue that Sean spotted
* fixing the issue, not counting requests in flight properly
* adding a multiple optimizers case

* proper unit testing, but no other solution than disabling bucketing for now, couple of options tested do not work

* 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

Same bucketing strategy for OSS and SDP:
sort everything ahead of time, per rank and per size, smaller tensors first. Bucket the smallest elements in a fixed buffer, send async, then send all the others async, and get back to the bucket. Once done then scatter the contents if needed

- adding the buffer broadcast option
- minor cleanup in shardedDDP

- rename oss_ddp to ShardedDataParallel
- some refactoring
- ShardedDataParallel owns the sharded optimizer, exposed if need be
- some small perf bumps

- added train(mode) method to be aware of eval mode

Co-authored-by: Min Xu <m1n@fb.com>