* Remove unused variables
* Qualify calls to make_fragment_? from templated base class.
Fixes clang build error.
* Add missing `#include <cstdio>`
* Various changes to fix clang compile errors.
* More changes to fix clang build.
Remaining issues:
- `params` initializer of `CollectiveEpilogue`.
- `ops` initializer of `Sm90VisitorImplBase`.
- `__usAtomicCAS` needs to be added to clang upstream.
* Fix remaining clang build issues.
* Qualify `cute::rank()` calls.
* Qualify some more calls that are otherwise ambiguous between `cute` and `std` namespace.
* Double-escape special registers in inline asm.
* small change
---------
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Release 3.3.0
Adds support for mixed precision GEMMs On Hopper and Ampere
Adds support for < 16B aligned GEMMs on Hopper
Enhancements to EVT
Enhancements to Python interface
Enhancements to Sub-byte type handling in CuTe
Several other bug-fixes and performance improvements.
* minor doc update
* [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
---------
Co-authored-by: Ali Hassani <ahassanijr@gmail.com>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* added support of b2b bmm
* fixed arguments and params structures
* added batch_count argument
* removed SplitKSerial and added new test case with b2b bmm
* fixed support of Kbatched and added new test case with batch stride
* added batch support for bias and scale
* make test
* small changes
---------
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Fix MHA kernel
Summary:
ATT
Test Plan:
Reviewers:
Subscribers:
Tasks:
Tags:
* Extend DualGemm to support batched mode (#5)
Following the GemmUniversalMode::kBatched implementation, batched mode is added to the DualGemm (under examples/45_dual_gemm). DualGemmMode::kBatched and SplitKSerial are not compatible: Status::kErrorInvalidProblem is returned if both are set.
* Decouple LayoutB0 and LayoutB1 in DualGemm
The DualGemm template assumed the same layout, LayoutB, for both right operand matrices B0 and B1. This is problematic if the layout of the two matrices is different. In particular, this may be the case when one of the matrices is row-major, while the other is a (column) vector that has to be broadcasted in column-major with zero stride (e.g., as {B1.device_data(), 0}) for the DualGemm implementation to be able to process B0 and B1 simultaneously.
In this commit, LayoutB0 and LayoutB1 are decoupled throughout the DualGemm code (device, kernel, and mma). Additionally, the batch strides of B0 and B1 are also decoupled to accommodate the column vector B1 case described above.
* Remove comment as no longer relevant
* Revert Fix MHA kernel
---------
Co-authored-by: mikeiovine <mikeiovine@fb.com>
* xFormer updates to fMHA FW
* Convert format to BMHK for '41_fused_multi_head_attention_fixed_seqlen'
* Add missing files
* Remove xFormers specific code
* Update fused_multihead_attention_fixed_seqlen.cu
* rebase and solve conflicts
* remove white space
---------
Co-authored-by: danthe3rd <danthe3rd>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
