GitLab

* catching properly a given test failing if not enough gpus

* file based dist init
* nicer handling of broken world sizes vs. number of available GPUs, do not break but warn out

[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

* fallback on internal pytorch numbering

* 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

Adds support for:
* Reused layers (e.g. for weight sharing)
* Lazily-constructed layers
* Single-process control via PipeRPCWrapper
* PipelineStyle.AsyncScheudle, which lays the foundation for asynchronous pipeline work by introducing an event loop for each rank/worker to process either activations or gradients as they arrive

Also added examples for multi-process and PipeRPCWrapper

- fixed typing
- make it run less often to reduce CI time

testing: run it in a loop make sure it is run in the right frequency.

The expert annotation is used by clip_grads and DDP.

Currently only implemented for a single process and expert.

* Add unittest for checkpoint & DDP

- this change adds test cases to reproduce the error with checkpoint & DDP
- mandeep mentioned that there is also deadlock in this case, but this
  change doesn't cover that.
- we cover cases where weight sharing is OK
- however, same module multiple checkpoint or find_unused_parameters are
  both not OK

* added norm checks

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

Adds support for distributing pipeline stages across multiple processes (and therefore multiple machines)
* Adds a style argument to the Pipe constructor, defaulting to PipelineStyle.SingleProcess, but also supporting PipelineStyle.MultiProcess
* Added support for lazy construction of modules (see lazy_construction for an example)
* Added two implementations of inter-process communication: one based on rpc with globally visible queues, one based on send/recv
* Copied all the relevant tests from tests/pipe to tests/pipe_process and modified them to exercise PipelineStyle.MultiProcess

- 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>