GitLab

* flat params all along, way simpler
* updating the docstring

fix a broken earlier commit, only worked for the first step

* minor

* minor

cache this iterator, easy speed up

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

It is not currently being used so we can simplify the interface
by removing it.

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

* precise skip, only if agent has only cpu

* 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>

* [feat] Add AdaScaleWrapper

- This enables a different API for wrapping an optimizer with AdaScale.
- This also enables AdaScale to be wrapped by OSS.
- However, OSS wrapping AdaScale results in different optimization,
  which future research will be needed to study its effects.

testing: add unit tests.

* addressed comment: typo

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

* adding a test to prove the inter operability with upstream pytorch
* updating the changelog
* eager state pruning
* pytorch 1.5 compat

* 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

authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* adding fairseq's gha
* adding py3.9, removing submodules
* yaml linting

* [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

* [test]: test adascale with oss

* minor fix

* add a small comment

* refactor: moved find_tensor_by_shape

* refactor: move test golden data into its own module

* refactor: simplied the train function

* refactor: added comments as suggested

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

* removing the torch util altogether, broken on 1.7.1

* docfix

* fix for torch dist broadcast failing on dummy object

* fix broken link

* remove link

* add direct link

* add direct link

* add direct link

* add direct link

* add direct link

* add direct link
Co-authored-by: Anjali Sridhar <anj@devfair0443.h2.fair>

* [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

* [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>

* 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