GitLab

* 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

Fix dispatch where we have a parameter group with multiple
combinations of types
Optionally apply weight decay after momentum

Incorrect types used in a few places

Only support the 4 specific cases we care about
Remove more general set of switch statements

Fuse in fp16 gradient -> fp32 convert
Additional option fp16 weight copy written out

Initial implementation, all fp32
Tested against torch.optim.sgd

Allow multi-tensor unscale to handle FP16 output, so it can also be used for copy-scatter. Rename some options.

Added kernel to support sync BN for channel last tensor

adjusted kernel config for better perf.
removed divergence in welford warp reduction.

* test passes

* notes

* Using C++-side flatten and unflatten functions

* Adding csrc

* Persistent synchronization event so it doesn't need to be created and destroyed each time

* Interop with parameter flattening in SSD

* Added deterministic option to imagenet main.py

* Adding options to split gradient averaging and allreduce in pure fp32

* Fixing allreduce_maybe_retain call

* Fixing allreduce_fallback

* Also sync active_i_buckets from rank 0

* Making retain_allreduce_buffers compatible with/orthogonal to delay_allreduce=True|False

* Correcting syntax error, now all seems to work with SSD

* Optional cpp extension build

* Add mixed precision adam optimizer (#59)

* Add FusedAdam Optimizer to Apex that places all the math into a cuda kernel.

* Added fixes to fused_adam to get it to work with network.

* wip work on python interface for adam with options

* fix dispatch for halfs, add python options to handle optional half gradients and params

* cleanup, get rid of grid-stride loop