GitLab

* update pytorch version for benchmarks

* reduce golden data precision check

* add license file headers for all files

* fix lint

* [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

* [refactor]Remove unused variables and refactor common configurations

* move helper function to call site

* fixed lint errors

* fix lint errors

* fix lint errors

* fix lint errors

* fix import order

* format files

* remove unused imports

* fix lint errors

* fix lint errors

* refactor common utilities

* address PR comments

* sorted imports

* add space

* modify comment

* added doc strings and addressed PR comments.

* addressed PR comments

* added another comment to clarify.

* fixing lint errors

* addressed PR comments

* addressed PR comments

* fixed typos

* initialize var

* rename seq_pred to lm

* fix lint errors

* move datasets and models into separate folders

* add the folders created

* fix lint errors

* create golden config to stats mapping

* add common batching for both synthetic and real data

* fixed lint errors

* enable real pipe benchmakrs with new golden data

* reduce seq len to avoid OOM

* updated golden data

* add logging

* add golden data

* add golden data

* fix lint errors

* add doc string

* remove unused class

* add seq len and batch size to the config

* remove commented out line

* address comments

* rename imports

* refactor common logic in dataloaders

* add golden configs

* lint changes

* merge latest changes

* lint errors

* address PR comments

* initial refactoring

* lint fixes

* fix lint errors

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

* removing a dead call since ShardedDDP, small speedup
* unrelated, but filling in the changelog
* another nit

* 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

* adding a shard-aware GradScaler wrap, credits to Sean Naren for the idea
* adding stubs & explanations in the documentation

add a clip gradients util, equivalent to torch's but aware of the sharded states. Add a corresponding unit test

* now works on a machine without cuda, easier to debug and quick test

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

* Some ease of use in the benchmark tool, add a debug option

* switching to MNIST
* updating the reference values, should be good to go
* download dataset once for all processes

* Minor, ease of life to debug and makes it possible to test a host of models with the same code

* adding a cpu option
* adjust the reference loss

* bugfix
* adjust default non-regression loss, not all_reduced now

More realistic benchmarks, comparing apples to apples. DDP/OSS+DDP/OSS+SDP

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

- small benchmark refactor, only one for all backends and ddp
- deterministic, enforce alignment with pytorch ddp

* Broadcasting grad-enabled tensors is forbidden in Gloo, because this is not differentiable. Workaround

* Doc extensions to some APIs
* FIx the benchmark and tutorial

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

Changes the structure of the returned state dict with respect to the param_groups to make it closer to what a vanilla optimizer would return (un-shard them). Shard again when loading

* Aligning the optimizer state dict with what PyTorch expects

* Adding a check on the dict keys, ensure that `state` and `param_groups` are there

* after installing the specific isort, black and all, one liner to please the linter..

* Adding some measurement of the memory consumption while training + checkpointing

* mandatory lintfix commit

* brainfart, reset the memory use counter at the beginning of the training in case two of them are run in a row

* move reset stats call, hotfix

* move the optimizer to rmsprop, more stateful and still used in CV

* trying to figure out a sigsev in circleci

* Aligning the optimizer state dict with what PyTorch expects

* Adding a check on the dict keys, ensure that `state` and `param_groups` are there

* after installing the specific isort, black and all, one liner to please the linter..

* initial commit, dummy training loop, pure pytorch but not DDP

* probably slightly broken, but rough DDP benchmark run

* adding the torchvision requirement for testing

* brainfart

* reduce the loss, do something slightly distributed

* Some cleanup, distributing the training on two GPUs

* some cleanup + adding a vanilla run, still not good to go

* less silly defaults, gtg for a start I think

* smaller batch to fit the smaller gpus used in the circleci rigs

* Adding some options for the benchmark, and regression testing

* [test] set torch seed for Adam tests (#49)

Set the torch seed for tests. xfail mixed precision and memory-efficient mixed-precision state_dict tests due to their states being cast to FP16 and back to FP32 during load_state_dict.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

* linting, I really need to automate this isort insanity
Co-authored-by: Jun Ru Anderson <33384298+andersonic@users.noreply.github.com>
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>