GitLab

* Make index_mul_2d extension backward compatible for Atomic header include

* Typo
Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com>

* Remove redundant import's and enable ninja for MHA extension

* Remove redundant CUDAExtension import's

* Enable --transducer extension for ROCm

* Enable --transducer unit tests for ROCm

* Skip some failing tests in test_transducer_joint.py

* Skip test_transducer_joint_pack for transducer extension

* Keep transducer extension CUDA-compatible

Co-authored-by: Han Bao <hbao@nvidia.com>

* remove pyprof

* remove reparameterization

* remove pyprof test

* clean up

* Make rocblas_gemm_flags_fp16_alt_impl backward-compat for new naming

* Use BACKWARD_PASS_GUARD_CLASS to prevent lengthy if-statement

* guard

* update

* remove unnecessary version guard

* runtime version guard

* cosmetic

* skip tests appropriately

* bump version

* add guard

* fix the cond

* Add setup_simple.py for debugging the compiling issue of scaled_masked_softmax_cuda

* Comment out CUDA-specific implementations

* Resolve filename collision of *cpp files with to-hipify code and *cu files

* Faster `--fast_multihead_attn` build (#1245)

* merge .so files

* odr

* fix build

* update import

* apply psf/black with max line length of 120

* update

* fix

* update

* build fixed again but undefined symbol again

* fix 2, still layer norm grad is undefined

* remove unused cpp files

* without layer_norm.cuh, import works

* import fast_multihead_attn works...

but why? Was unnecessary `#include "layer_norm.cuh"` was the culprit
causing .shared objects not to be able to link `HostApplyLayerNorm` and
`HostLayerNormGradient`?

* clean up layer norm

* Fix some bugs
Co-authored-by: Masaki Kozuki <mkozuki@nvidia.com>

Make rocblas_gemm_flags_fp16_alt_impl in MHA and MLP backward compatible with old PyTorch versions (#74)

* First attempt to make rocblas flag backward compatible

* Fix some bugs

* Fix some bugs

* Make rocblas_gemm_flags_fp16_alt_impl in MHA backward compatible with old PyTorch versions

* Add groupbn extension unit tests for ROCm

* Fix some bugs

* Enabled Conv-Bias-ReLU fusion

The following modules are enabled using cuDNN runtime fusion:
1) Conv-Bias-ReLU (+backward)
2) Conv-Bias (+backward)
3) Conv-Bias-Mask-ReLU (+backward)

* Casts cleanup and autocast in unittest

- Remove redundant dtype casts
- Simulate the usage in the unittest by using torch.cuda.amp.autocast
Co-authored-by: Masaki Kozuki <mkozuki@nvidia.com>

* Fixed save_for_backward
Co-authored-by: Masaki Kozuki <mkozuki@nvidia.com>
Co-authored-by: root <root@luna-0277.selene.nvidia.com>

Take-over of #1097

* Add fast CUDA focal loss implementation.

* Enable fast math for CUDA focal loss.

* Correct typo.

* replace deprecated macros

* Add fast CUDA focal loss implementation.

* Enable fast math for CUDA focal loss.

* Correct typo.

* replace deprecated macros

* TORCH_CUDA_CHECK -> AT_CUDA_CHECK

The former is defined in torch/csrc/profiler/cuda.cpp so it's not available usually.
The latter however is defined in ATen/cuda/Exceptions.h as an alias of C10_CUDA_CHECK.

* add test

* clean up

* guard for torchvision
Co-authored-by: Wil Kong <alpha0422@gmail.com>

* fuse grad accumulation w/ weight grad
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* fp32 training path

* not using *args, **kwargs

* backward: moved the tensor dimension cnversion
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* move files to csrc/megatron

* fix fp32 path

* fix typo

* add  to  in order to select the correct custom extension

* fix typo

* comment on import guard

* update test: enable gradient_accumulation_fusion

* 86

* remove redundant call of `test_column_parallel_linear`
Co-authored-by: Sangkug Lym <slym@nvidia.com>

* Add the permutation related support as the extension for asp lib.

* [Fix] Track the permutation sequence for progressive channel swap strategy.

* Fix the corner case that one layer is not sparse, but need to apply permutation due to its siblings.

* Fix the deprecated functions in ASP unit tests.

* Fix the sparsity info typo in ASP lib.

* [Enhancement] Set the identical random seed for all GPUs to make sure the same results generated in permutation search.

* Update the README.md with identical random seed setting and NeurIPS info.

* Integrate the Pybind11 enhancement of permutation search into ASP lib.

* apply formatter & remove duplicate func def

* dry CUDA_HOME None check

* `--threads 4`

* merge .so files

* odr

* fix build

* update import

* apply psf/black with max line length of 120

* update

* fix

* update

* build fixed again but undefined symbol again

* fix 2, still layer norm grad is undefined

* remove unused cpp files

* without layer_norm.cuh, import works

* import fast_multihead_attn works...

but why? Was unnecessary `#include "layer_norm.cuh"` was the culprit
causing .shared objects not to be able to link `HostApplyLayerNorm` and
`HostLayerNormGradient`?

* clean up layer norm

* Add fused mixed precision lamb optimizer.

* Fix device usage in constructor.

* Fix sending param_group tensor state to device.

* Remove unneeded device set.

* Add fused mixed precision lamb optimizer.

* Fix device usage in constructor.

* Fix sending param_group tensor state to device.

* Remove unneeded device set.

* Use --cuda_ext flag to build all supported extensions

* Don't remove --cuda_ext since it'll be needed to build other extensions

* Need to clear all cmdline args so setup.py doesn't complain

Co-authored-by: Jeff Daily <jeff.daily@amd.com>

* Persistent LayerNorm: Multi-CTA Rewrite

* autocast support
Co-authored-by: Young-Jun Ko <youngjun.ko@gmail.com>

1) multihead_attn
2) xentropy
3) fused_adam and distributed_fused_adam