* [WIP] GEMM StreamK w/ Fused Epilogue
* Adds Gemm Streamk with Fused Epilogue kernel level struct.
* Mostly based on Gemm with Fused Epilogue,
* Requires a new epilogue
* Work in progress
* [WIP] StreamK support for GemmUniversalWithBroadcast
* Just based off of how StreamK is allowed in GemmUniversal
* Untested and a work in progress
* Minor fixes
* [WIP] It compiles!
It is almost certainly incorrect, but we're past getting the templates
to match, so checkpointing.
* Correction to reference kernel
* Fix typo
* Added MSE measurement
* Switch back to reference kernel + host for loop
Still WIP. Now we're getting even a larger MSE, but it's both on
basic Split-K and Stream-K.
* Fix typos
* Fix broadcast vector + requested changes
* Comment typo
* Small int option and more
* Fix incorrect condition on source needed
* Requested changes
* I think I got it?
* Bias vector should be stride 0
* Two source added!
* Typos
* Merge examples
* Bring back vector row offset
Just to ensure consistency with universal gemm with fused epilogue
* Base arguments and params structs for StreamK
* StreamK epilogue with broadcast now inherits the original
* undo params_streamk_base.h
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Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Relax stream K gemm alignment constraints
The current alignment requirements are too strict. Make them identical
to the checks for the regular universal gemm.
* Revert "Relax stream K gemm alignment constraints"
This reverts commit 31e80a250e2b0ac4bda2e4b437b39dc5bcd5e845.
* Relax stream K gemm alignment constraints
The current alignment requirements are too strict. Make them identical
to the checks for the regular universal gemm.
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* add two missing files
* fix bunch of bugs of gemm-reducek fusion and add a device interface
* small changes
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Removed trivial copy constructors on parameter classes to enable device-side launch of CUTLASS kernels
* Added SFINAE to the `TensorRef(NonConstTensorRef const&)` constructor to avoid making it a copy-constructor for device code
* std => platform
* fix affine2
* really fix affine2
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Fix the build of cutlass/gemm/device/gemm_array.h and add a demo for GemmArray
* Add a reference to GemmArray to the docs
Co-authored-by: Ivan Komarov <dfyz@yandex-team.ru>
CUTLASS 2.7
Mainloop fusion for GEMM: summation over A or B
Strided DGRAD (optimized iterators)
Half-precision GELU_taylor activation functions
Use these when accumulation and epilogue compute types are all cutlass::half_t
Tuning and bug fixes to fused GEMM + GEMM example
Support for smaller than 128b aligned Convolutions: see examples
Caching of results to accelerate Convolution unit tests
Can be enabled or disabled by running cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF
Corrections and bug fixes reported by the CUTLASS community
Thank you for filing these issues!
authored-by: Haicheng Wu haichengw@nvidia.com, Manish Gupta manigupta@nvidia.com, Dustyn Blasig dblasig@nvidia.com, Andrew Kerr akerr@nvidia.com
ANIKET SHIVAM