GitLab

* move import of amp_C to __init__()

* make fp16/32 separate lists to support mixed param types, disable double test

* make zero_grad consistent between adam/novograd/lamb

* add state_dict, load_state_dict

* add test_restoring, test_loss_scale_decrease

* disable amp outputs for checkpoint tests

* add test for amp.state_dict, cleanup

* add state_dict patch, add test

* fixed testing, cleanup

* add readme for checkpointing

* add docs to source/amp

* add review changes to doc

FusedSGD now work as before
FusedAdam now work with o1/o2, no longer fuse scaling and casting
Removed special backend handling for FusedAdam
Moved and updated test for FusedAdam into run_optimizers
Removed legacy tests for optimizers.FP16_optimizer and FusedAdam in run_mixed_adam

Co-authored-by: Aditya Agrawal <aditya.iitb@gmail.com>
Co-authored-by: Marek Kolodziej <mkolod@gmail.com>

* Bug fix for non-affine layer-norm + add backward unit test

* clean up tests and add tests for a large batch

fixing empty return from python implementation
  adding proper test to verify functional correctness for python implementation

fixing empty return from python implementation
  adding proper test to verify functional correctness for python implementation

* Existing tests passing, still need to add per-tensor tests

* Test is passing, still need to measure performance

* ILP for l2norm functor

* Support add_param_group

* syntax

* Test added and passing

* remove unused tens tensor in example/imagenet/main_amp.py

* remove unused tens tensor in deprecated examples and tests/L1

* Refactor to allow more flexible treatment of multiple optimizers/models/losses

* Adding _process_optimizers.py

* Created L0 tests (now passing).

* fix: minor print typo (#234)

* make L1 results easier to read

* L0 multiple model/optimizer/loss test fleshed out

* Adding test that master params remain synced across distributed processes

* Docstring updates

* Docstring updates

* Adding Torch + bare-metal nvcc version check and container build tests

* Putting a canary in the coalmine

* canary proved elusive

* Trying direct setup.py install

* this should work

* Removing canary

* hopefully this works