- clang 1.14 complains about missing function from a host call:
cutlass/include/cute/arch/util.hpp:106:32: error: no matching function for call to '__cvta_generic_to_shared'
return static_cast<uint32_t>(__cvta_generic_to_shared(ptr));
- fixes this by defining CUTE_HOST_DEVICE for clang as well
Signed-off-by: Janusz Lisiecki <jlisiecki@nvidia.com>
Currently, the `LinearCombinationClamp` header file is not standalone,
and must have the definition of `cutlass::epilogue::thread::ScaleType`
already available when it is `#include`d.
* 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
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Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Enable shared memory intrinsics and ldmatrix PTX on Clang.
This commit adds preprocessor checks to enable the shared memory
intrinsics `__cvta_generic_to_shared` and `__nvvm_get_smem_pointer`, as
well as the `ldmatrix` PTX instructions, on Clang. Preventing these
intrinsics from being used is a significant latency regression on Clang.
* refine the macro
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Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
* Changes to iterators to support s8 gemm with f16 outputs
* should work
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Co-authored-by: Sujan Gonugondla <gsujan@amaon.com>
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
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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
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Co-authored-by: danthe3rd <danthe3rd>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
