GitLab

Bug fix

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.

add additional loop for lists of params in FP16_Optimizer's load_state_dict