GitLab

Use global gradient clipping in FusedLAMB & add option for using NVLAMB

Signed-off-by: asears <asears@users.noreply.github.com>

Reversible fused adam with mt support

* Changes to make xentropysoftmax load/store vectorized when possible:
Increase default ILP so that each thread handle 16 Bytes data in one step
Make thread load/store longest vector possible
Make unroll case handle adjacent data instead of strided, so same order compare to vector case

* Add shift for not aligned case. Remove less than 16 bytes aligned access

* modify MTA axpby for wider load/store

* Make scale/axpby/l2/adam/lamb multi_tensor uses wider load

* update fused bias relu backward kernel

* adding support for not require first layer dgrad

* fix bug: wrong layer in requires grad

* add infrastructure for optional bias and activation, currently only support no bias and no relu

* make bias and relu optional separately

* add sigmoid activation option

Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* add CUDAGenerator guard

* fix generator_flag

* add guards for gen pointer/ref issue

* change mutex_ to mutex()

* add check_generator
Co-authored-by: pbialecki <pbialecki@nvidia.com>

The LARC optimizer wraps an underlying optimizer and then needs to be passed
to amp.initialize for mixed precision. There were 3 different crashes happening
in this situation, fix all of them and add a unit test.

I don't know if the 'LARC' in sys.modules check ever worked. In my setup, the
entry in sys.modules is 'apex.parallel.LARC'. Checking if the variable is
defined seems more reliable though.