GitLab

* fix dropout scaling from p to 1/(1-p) (#816)
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* Improvements to apex.mlp (#804)

* 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

* enable wider load/store for multi_tensor_apply kernels (#763)

* modify MTA axpby for wider load/store

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

* Changes to make xentropysoftmax load/store vectorized when possible: (#725)

* 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...

* Initial commit to hipify all cuda code

* enable multi_tensor_apply extension

* added generatedFileCleaner to handle nested hip files

* 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 

The cuda kernel used by fused-adam was using the default stream
on the default device. The kernel needs use the same device as
the parameter tensor.

Fixed by using context manager to set correct default device. For
the use_mt case, raised an error. Alternatively, the use_mt
case could launch one kernel per cuda device.

The non-contrib version will also need to be fixed.
Co-authored-by: Mandeep Singh Baines <msb@fb.com>

add additional loop for lists of params when loading state_dict in apex.contrib.optimizers.FP16_Optimizer

* disable ninja for multihead_attn

* fix getCurrentStream in multihead_attn
Co-authored-by: pbialecki <pbialecki@nvidia.com>

* Do not unscale the gradients if loss scale equal to 1

* Disable unscaling loss scale == 1 only for static scaling

This reverts commit 92b3b9a9.

* NHWC support for multi tensor apply

* compilation fix for version<=1.4

* Adding C++ Multihead Attention implementation to contrib.

* Add reference test that at least works for forward.

* Remove CublasLt support from multihead attention.

* Add new Python version of self attention.

* Update python model of MHA with backward pass.

* Fixed Output Linear connection in MHA.

* Clean up compiles and add documentation to PySelfAttention.

* Add Encdec Python version of multihead attention.  Cleanup files.

* Tests for self and encdec multihead attention.

* Add reference pytorch implementation of attention with norm and add.

* Add cutlass branch definition.

* Add cutlass download to compile.

* Add norm/add tests.

* Add biases to pytorch python versions.

* Add tests and fix issues with python version of attention masking.

* Create README.md

* Update README.md

* Update README.md

* Update perf test parameters.

* Update README.md

* Update README.md

* Update README.md

* Add files via upload

* Update README.md

* Update README.md

* Update README.md

* Fix matmul1 output tensor size.  Fix tests that missed issue.

* Allow for Z dimensions of 64K and greater on batched GEMMs.

* remove redundant imports

* general cleanup, remove deprecated or unused functions

Actual flag is --opt_level and copy pasting the example results in an unrecognized arguments error.