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Co-authored-by: Piotr Bialecki <pbialecki@nvidia.com>
Co-authored-by: Eddie Yan <eddiey@nvidia.com>
Co-authored-by: Rishi Puri <riship@nvidia.com>
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* support for fused dense layer with cublasLt, fusion in both fprop and bprop

* fix typo causing syntax error

* add fused GEMM+gelu+GEMM modue

* fix typo for workspace size

* update cublas check for 11600

* add tests for fused dense layer

* fix CUDA 10.x path

* safer guard around CUBLAS constants, remove unreferenced variable

* more guard changes

* guard against cublas version instead of cuda
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* fuse norm into scale

* add fused norm into dlamb
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

* support for fused dense layer with cublasLt, fusion in both fprop and bprop

* fix typo causing syntax error

* add fused GEMM+gelu+GEMM modue

* fix typo for workspace size

* update cublas check for 11600

* add tests for fused dense layer

* fix CUDA 10.x path
Co-authored-by: Sukru Eryilmaz <seryilmaz@computelab-dgx1v-32.nvidia.com>

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

* don't create cublasLt handle, fix zero block size case

* cleanup

* initial cublaslt support

* 64 bit input

* add license headers

* cleanup

* remove license
Co-authored-by: pbialecki <pbialecki@nvidia.com>

* Add unit tests for fused-novograd

* Fix: tensors should reside on the same device

* Fix: Cudastream should be called on the same device on which the tensors reside on. Found this during debugging fused novograd multi-device unit test

* fixed issues mentioned in the comments

This reverts commit bdd481d1.

- incorrect use of __shfl_down
- fix warp size assumptions
- update unit tests to exit on failure

use __launch_bounds__(1024) for multi_tensor_apply, re-enable skipped tests

* fix warp size in WARP_SHFL* in layernorm

* enable fused_layer_norm tests on ROCm

In this PR, we mainly tried to optimize the performance of Syncatchnorm and also fixed one potential issue in the welford_parallel kernel implementation.

For performance improvement, we batched the mean/var/count all_gather communication together and sent it once in the forward path
We also batch the all_reduce in backward path
We add the contiguous call on the input of welford_parallel kernel.
If there is any standard perf benchmark, I would be happy to run it.

* enable mlp cuda

* add setup changes and tests

* skip the unit tests

* updated conditions for empty array

* removed hip platform conditions

* add device guards to the optimizers

* add untracked file

* set deviceGuard in multi_tensor_apply

* address review comments; fix lamb

* indent

* typo

* Enable sync batchnorm

* enable syncbn properly

* update the unit tests

* update tests

* update conditions for welford_merge_element

* updated conditions based on comments.

* [sync BN]

support non-uniform batch size across process group.

TODO: test should be added once cleaned up.

* updating unit tests

* new unit tests for different inputs

* cleaning