GitLab

* 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.

* Persistent LayerNorm: Multi-CTA Rewrite

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

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>

- passing include directories to `CUDAExtension`'s `include_dirs` argument
- removing `-I/path/to/dir` arguments from `extra_compile_args`

* 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>

* Added support for fused ReLU and dropout into transducer joint

* Reorganized code selection path in transducer joint fwd
* Added support for fused ReLU+dropout into transducer joint

* Vectorize transducer loss backward with fused softmax (#3)

* Nanz/transducer loss (#4)

* Vectorize transducer loss backward with fused softmax

* Added a predicate to avoid potential IMA

* Nanz/transducer loss (#5)

* Vectorize transducer loss backward with fused softmax

* Added a predicate to avoid potentional IMA

* Added more predicates to avoid IMAs

* Updated documentations for newly added features.

* Fixed a error in transducer.py

* initial commit for adding fast bottleneck

* sync cudnn-frontend module
Co-authored-by: pbialecki <pbialecki@nvidia.com>

* Initial check-in of the transducer extension.

* Added more comments to help explain the code

* Corrected minor typos

* 1. Renamed variable in tests to match the extension
2. Disabled ninja build option

DistributedFusedAdam Model Parallelism Support (Megatron)
Co-authored-by: Kexin Yu <kexiny@nvidia.com>
Co-authored-by: Kexin Yu <kexinznzn›@gmail.com>

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

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>

* 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...