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* enable deprecated fused adam optimizer

* enable deprecated fused lamb

* enable xentropy extension

* add warpsize 32 for nv and 64 for amd

* update compiler arguments

* update the syncwarp conditions

* update syncwarp condition

* enable deprecated fused adam optimizer

* enable deprecated fused lamb

* reset the compiler arguments

* syntax error

* aligning the compiler arguments

* enable mlp cuda

* add setup changes and tests

* skip the unit tests

* updated conditions for empty array

* removed hip platform conditions

* Enable sync batchnorm

* enable syncbn properly

* update the unit tests

* update tests

* update conditions for welford_merge_element

* updated conditions based on comments.

Co-authored-by: Michael Carilli <mcarilli@nvidia.com>

Fuses dropout and softmax in backward pass, add bias support to CPP MHA, add additive mask support, separate Q/K/V parameters (#854)
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* 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>

* disable ninja for multihead_attn

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

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

* 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 f...

* add WAR for pip>=19.3.1

* remove pipmain, use extras_require instead

* move previous fused_adam and fp16_optimizer to contrib

* make build contrib.fused_adam optional

* change build option name

* remove unnecessary try import

* Pushing for build tests

* Contrib files

* Removing deprecated checks

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

* First draft, for discussion

* Fix mistakes in LAMB equations

* Add loop over chunk

* Bug fix

* Bug fix

* Bug fix

* Undo bug fix

* Bug fix

* Add multi tensor LAMB optimizer to setup.py

* Rename step_size to learning_rate

* Fix compilation errors