Skip to content

GitLab

Commit fcfe70f9 authored by illsilin's avatar illsilin
Browse files

Merge branch 'develop' into lwpck-1010

parents 4c683df4 9de63596
Hide whitespace changes
Inline Side-by-side
Showing with 528 additions and 182 deletions
docs/Contributors_Guide.rst
View file @ fcfe70f9
......@@ -17,7 +17,7 @@ Getting started
`Composable Kernel User Guide <https://rocm.docs.amd.com/projects/composable_kernel/en/latest/>`_.
It provides insight into the core concepts, environment configuration, and steps to obtain or
build the library. You can also find some of this information in the
`README file <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/README.md>`_
`README file <https://github.com/ROCm/composable_kernel/blob/develop/README.md>`_
on the project's GitHub page.
#. **Additional reading:** The blog post `AMD Composable Kernel library: efficient fused kernels for AI apps with just a few lines of code <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_ provides a deeper understanding of the CK library and showcases its performance capabilities.
<https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_
......@@ -33,7 +33,7 @@ You can make an impact by reporting issues or proposing code enhancements throug
Reporting issues
----------------
Use `Github issues <https://github.com/ROCmSoftwarePlatform/composable_kernel/issues>`_
Use `Github issues <https://github.com/ROCm/composable_kernel/issues>`_
to track public bugs and enhancement requests.
If you encounter an issue with the library, please check if the problem has already been
......@@ -68,7 +68,7 @@ Creating Pull Requests
----------------------
You can submit `Pull Requests (PR) on GitHub
<https://github.com/ROCmSoftwarePlatform/composable_kernel/pulls>`_.
<https://github.com/ROCm/composable_kernel/pulls>`_.
All contributors are required to develop their changes on a separate branch and then create a
pull request to merge their changes into the `develop` branch, which is the default
......@@ -89,7 +89,7 @@ When submitting a Pull Request you should:
the project's root directory. We leverage `pre-commit` to run `clang-format` automatically. We
highly recommend contributors utilize this method to maintain consistent code formatting.
Instructions on setting up `pre-commit` can be found in the project's
`README file <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/README.md>`_
`README file <https://github.com/ROCm/composable_kernel/blob/develop/README.md>`_
* Link your PR to any related issues:
......
docs/conf.py
View file @ fcfe70f9
......@@ -45,3 +45,5 @@ for sphinx_var in ROCmDocs.SPHINX_VARS:
extensions += ['sphinxcontrib.bibtex']
bibtex_bibfiles = ['refs.bib']
cpp_id_attributes = ["__global__", "__device__", "__host__"]
docs/dockerhub.rst
View file @ fcfe70f9
......@@ -36,9 +36,9 @@ What is inside the image?
The docker images have everything you need for running CK including:
* `ROCm <https://www.amd.com/en/graphics/servers-solutions-rocm>`_
* `ROCm <https://rocm.docs.amd.com/en/latest/index.html>`_
* `CMake <https://cmake.org/getting-started/>`_
* `Compiler <https://github.com/RadeonOpenCompute/llvm-project>`_
* `Compiler <https://github.com/ROCm/llvm-project>`_
* `Composable Kernel library <https://github.com/ROCm/composable_kernel>`_
Running the docker container
......@@ -97,5 +97,5 @@ Editing the docker image
=======================
If you want to customize the docker image, edit the
`Dockerfile <https://github.com/ROCmSoftwarePlatform/composable_kernel/blob/develop/Dockerfile>`_
`Dockerfile <https://github.com/ROCm/composable_kernel/blob/develop/Dockerfile>`_
from the GitHub repository to suit your needs.
docs/sphinx/requirements.in
View file @ fcfe70f9
rocm-docs-core==0.33.2
rocm-docs-core==0.35.1
sphinxcontrib-bibtex==2.6.2
docs/sphinx/requirements.txt
View file @ fcfe70f9
......@@ -113,7 +113,7 @@ requests==2.31.0
# via
# pygithub
# sphinx
rocm-docs-core==0.33.2
rocm-docs-core==0.35.1
# via -r requirements.in
six==1.16.0
# via
......
docs/tutorial_hello_world.rst
View file @ fcfe70f9
......@@ -32,7 +32,7 @@ CK library acceleration features are based on:
If you need more technical details and benchmarking results read the following
`blog post <https://community.amd.com/t5/instinct-accelerators/amd-composable-kernel-library-efficient-fused-kernels-for-ai/ba-p/553224>`_.
To download the library visit the `composable_kernel repository <https://github.com/ROCmSoftwarePlatform/composable_kernel>`_.
To download the library visit the `composable_kernel repository <https://github.com/ROCm/composable_kernel>`_.
Hardware targets
================
......@@ -58,7 +58,7 @@ This tutorial is based on the use of docker images as explained in :ref:`docker-
.. note::
You can also `install ROCm <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/>`_ on your system, clone the `Composable Kernel repository <https://github.com/ROCmSoftwarePlatform/composable_kernel.git>`_ on GitHub, and use that to build and run the examples using the commands described below.
You can also `install ROCm <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/>`_ on your system, clone the `Composable Kernel repository <https://github.com/ROCm/composable_kernel.git>`_ on GitHub, and use that to build and run the examples using the commands described below.
Both the docker container and GitHub repository include the Composable Kernel library. Navigate to the library::
......
docs/wrapper.rst
View file @ fcfe70f9
......@@ -12,10 +12,6 @@ Wrapper
Description
-------------------------------------
.. note::
The wrapper is under development and its functionality is limited.
The CK library provides a lightweight wrapper for more complex operations implemented in
the library.
......@@ -54,39 +50,45 @@ Output::
2 6 10 14 18 22 26 30
Tutorials:
* `GEMM tutorial <https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/README.md>`_
Advanced examples:
* `Image to column <https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/wrapper_img2col.cpp>`_
* `Basic gemm <https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/wrapper_basic_gemm.cpp>`_
* `Optimized gemm <https://github.com/ROCm/composable_kernel/blob/develop/client_example/25_wrapper/wrapper_optimized_gemm.cpp>`_
-------------------------------------
Layout
-------------------------------------
.. doxygenstruct:: ck::wrapper::Layout
.. doxygenstruct:: Layout
-------------------------------------
Layout helpers
-------------------------------------
.. doxygenfile:: layout_utils.hpp
.. doxygenfile:: include/ck/wrapper/utils/layout_utils.hpp
-------------------------------------
Tensor
-------------------------------------
.. doxygenstruct:: ck::wrapper::Tensor
.. doxygenstruct:: Tensor
-------------------------------------
Tensor helpers
-------------------------------------
.. doxygenfile:: tensor_utils.hpp
.. doxygenfile:: include/ck/wrapper/utils/tensor_utils.hpp
.. doxygenfile:: tensor_partition.hpp
.. doxygenfile:: include/ck/wrapper/utils/tensor_partition.hpp
-------------------------------------
Operations
-------------------------------------
.. doxygenfile:: copy.hpp
.. doxygenfile:: gemm.hpp
.. doxygenfile:: include/ck/wrapper/operations/copy.hpp
.. doxygenfile:: include/ck/wrapper/operations/gemm.hpp
example/01_gemm/CMakeLists.txt
View file @ fcfe70f9
......@@ -53,12 +53,6 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp64)
add_example_executable(example_gemm_xdl_streamk gemm_xdl_streamk.cpp)
add_example_executable(example_gemm_xdl_fp8 gemm_xdl_fp8.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8)
add_example_executable(example_gemm_xdl_fp8_bf8 gemm_xdl_fp8_bf8.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_bf8)
list(APPEND gpu_list gfx90a gfx940 gfx941 gfx942 gfx950)
set(target 0)
foreach(gpu IN LISTS GPU_TARGETS)
......@@ -72,5 +66,12 @@ foreach(gpu IN LISTS GPU_TARGETS)
endif()
endforeach()
add_example_executable(example_gemm_xdl_fp8 gemm_xdl_fp8.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8)
add_example_executable(example_gemm_xdl_fp8_bf8 gemm_xdl_fp8_bf8.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_bf8)
add_example_executable(example_gemm_xdl_fp16_fp8 gemm_xdl_fp16_fp8.cpp)
add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8)
example/01_gemm/common.hpp
View file @ fcfe70f9
......@@ -49,7 +49,7 @@ struct ProblemSizeStreamK final
struct ExecutionConfig final
{
bool do_verification = true;
int init_method = 1;
int init_method = 2;
bool time_kernel = false;
};
......
example/01_gemm/gemm_wmma_fp16.cpp
View file @ fcfe70f9
......@@ -19,15 +19,50 @@ using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmWmma_CShuffle
// ######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer|MRepeat|NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN|MWmmaPerWave|NWmmaPerWave| _MBlock_MWaveMPerWmma| ScalarPerVector|
// ######| | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerWmma| _NWaveNPerWmma|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmMNKPadding, 256, 128, 256, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 1, 1, S<1, 32, 1, 8>, 8, 1>;
< ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
CDataType,
AccDataType,
CShuffleDataType,
AElementOp,
BElementOp,
CElementOp,
GemmDefault,
1, // Prefetch stage
128, // BlockSize
64, // MPerBlock
128, // NPerBlock
64, // KPerBlock
8, // K1
16, // MPerWmma
16, // NPerWmma
2, // M-Repeat // M-PerWmma / M-Repeat = M-Wave
4, // N-Repeat // N-PerWmma / N-Repeat = N-Wave
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
1, // C shuffle (M Repeat) Per store
1, // C shuffle (N Repeat) Per store
S<1, 32, 1, 4>,
8>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
......
example/01_gemm/gemm_xdl_fp16_fp8.cpp
View file @ fcfe70f9
......@@ -33,8 +33,14 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, LoopSched, PipelineVer, ComputeType>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
CDataType,
AccDataType,
AElementOp,
BElementOp,
CElementOp,
ComputeType>;
#include "run_gemm_example.inc"
......
example/01_gemm/gemm_xdl_fp8.cpp
View file @ fcfe70f9
......@@ -20,14 +20,18 @@ using BElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto LoopSched = ck::make_default_loop_scheduler();
static constexpr auto PipelineVer = ck::PipelineVersion::v1;
using ComputeTypeA = ck::f8_t;
using ComputeTypeB = ck::f8_t;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
// ######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 64, 16, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 64, 1, 4>, 8>;
// ######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Loop| Pipeline| Compute| Compute|
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Scheduler| Version| TypeA| TypeB|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| | | | |
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 64, 16, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 64, 1, 4>, 8, LoopSched, PipelineVer, ComputeTypeA, ComputeTypeB>;
// clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host::
......
example/01_gemm/gemm_xdl_fp8_bf8.cpp
View file @ fcfe70f9
......@@ -27,10 +27,10 @@ using ComputeTypeB = ck::bf8_t;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
// ######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
// ######| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| Loop| Pipeline| Compute| Compute|
// ######| | | | Type| Type| Type| Type| DataType| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector| Scheduler| Version| TypeA| TypeB|
// ######| | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| | | | |
// ######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, CLayout, ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, AElementOp, BElementOp, CElementOp, GemmDefault, 1, 256, 256, 128, 64, 16, 16, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 64, 1, 4>, 8, LoopSched, PipelineVer, ComputeTypeA, ComputeTypeB>;
// clang-format on
......
example/01_gemm/run_gemm_example.inc
View file @ fcfe70f9
......@@ -5,6 +5,88 @@
#include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp"
template <typename DataType>
inline __host__ __device__ constexpr double get_rtol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 1e-1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 1.5e-1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename DataType>
inline __host__ __device__ constexpr double get_atol()
{
if constexpr(std::is_same_v<DataType, float>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, double>)
{
return 1e-6;
}
else if constexpr(std::is_same_v<DataType, ck::half_t>)
{
return 1e-3;
}
else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
{
return 5e-2;
}
else if constexpr(std::is_same_v<DataType, int32_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, int8_t>)
{
return 1e-1;
}
else if constexpr(std::is_same_v<DataType, ck::f8_t>)
{
return 16.1; // 240 and 224 are acceptable
}
else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
{
return 8192.1; // 57344 and 49152 are acceptable
}
else
{
return 1e-3;
}
}
template <typename ProblemType>
bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
{
......@@ -68,9 +150,25 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
break;
default:
case 2:
ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistribution<BDataType>{-1.f, 1.f}(b_k_n);
break;
case 3:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_k_n);
break;
case 4:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{1.f, 1.f}(b_k_n);
break;
case 5:
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-2.f, 2.f}(a_m_k);
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-2.f, 2.f}(b_k_n);
break;
default:
ck::utils::FillUniformDistribution<ADataType>{-0.1f, 0.1f}(a_m_k);
ck::utils::FillUniformDistribution<BDataType>{-0.1f, 0.1f}(b_k_n);
}
Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
......@@ -240,7 +338,11 @@ bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
#else
c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
return ck::utils::check_err(c_m_n_device_result,
c_m_n_host_result,
"Error: Incorrect results!",
get_rtol<CDataType>(),
get_atol<CDataType>());
#endif
}
......
example/02_gemm_bilinear/gemm_bilinear_wmma_fp16.cpp
View file @ fcfe70f9
......@@ -65,48 +65,49 @@ using CDEElementOp = AlphaBetaAdd;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
using DeviceOpInstance =
ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<ALayout,
BLayout,
ck::Tuple<DLayout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<DDataType>,
EDataType,
AccDataType,
CShuffleDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
256,
128,
256,
8,
8,
16,
16,
4,
4,
S<4, 64, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
1,
1,
S<1, 32, 1, 8>,
8>;
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<
ALayout,
BLayout,
ck::Tuple<DLayout>,
ELayout,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<DDataType>,
EDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
2, // Prefetch stage
128, // BlockSize
128, // MPerBlock
64, // NPerBlock
64, // KPerBlock
8, // K1
16, // MPerWmma
16, // NPerWmma
4, // M-Repeat // M-PerWmma / M-Repeat = M-Wave
2, // N-Repeat // N-PerWmma / N-Repeat = N-Wave
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
1, // C shuffle (M Repeat) Per store
1, // C shuffle (N Repeat) Per store
S<1, 32, 1, 4>,
8>;
int main(int argc, char* argv[])
{
......@@ -264,7 +265,7 @@ int main(int argc, char* argv[])
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
<< device_op.GetTypeString() << std::endl;
e_device_buf.FromDevice(e_m_n_device_result.mData.data());
......
example/02_gemm_bilinear/gemm_bilinear_wmma_int8.cpp
View file @ fcfe70f9
......@@ -55,7 +55,7 @@ using DDataType = I8;
using EDataType = I8;
using ALayout = Row;
using BLayout = Row;
using BLayout = Col;
using DLayout = Row;
using ELayout = Row;
......@@ -65,48 +65,49 @@ using CDEElementOp = AlphaBetaAdd;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceOpInstance =
ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<ALayout,
BLayout,
ck::Tuple<DLayout>,
ELayout,
ADataType,
BDataType,
ck::Tuple<DDataType>,
EDataType,
AccDataType,
CShuffleDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
32,
16,
16,
4,
16,
16,
16,
1,
1,
S<2, 16, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
16,
16,
1,
S<4, 1, 8>,
S<0, 2, 1>,
S<0, 2, 1>,
1,
16,
2,
1,
1,
1,
S<1, 16, 1, 2>,
8>;
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Wmma_CShuffle<
ALayout,
BLayout,
ck::Tuple<DLayout>,
ELayout,
ADataType,
BDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<DDataType>,
EDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
2, // Prefetch stage
128, // BlockSize
128, // MPerBlock
64, // NPerBlock
64, // KPerBlock
8, // K1
16, // MPerWmma
16, // NPerWmma
4, // M-Repeat // M-PerWmma / M-Repeat = M-Wave
2, // N-Repeat // N-PerWmma / N-Repeat = N-Wave
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
1, // C shuffle (M Repeat) Per store
1, // C shuffle (N Repeat) Per store
S<1, 32, 1, 4>,
8>;
int main(int argc, char* argv[])
{
......
example/29_batched_gemm_bias_e_permute/batched_gemm_bias_e_permute_wmma_fp16.cpp
View file @ fcfe70f9
......@@ -43,9 +43,10 @@ using AElementOp = ck::tensor_operation::element_wise::PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
using CDEElementOp = ck::tensor_operation::element_wise::Add;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
static constexpr auto ABSpec = ck::tensor_operation::device::TensorSpecialization::Packed;
static constexpr auto ASpec = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto BSpec = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto DESpec = ck::tensor_operation::device::TensorSpecialization::Default;
using DeviceOpInstanceKKNN =
......@@ -55,43 +56,44 @@ using DeviceOpInstanceKKNN =
NumDimK,
ADataType,
BDataType,
DsDataType,
EDataType,
AccDataType,
CShuffleDataType,
DsDataType,
EDataType,
AElementOp,
BElementOp,
CDEElementOp,
GemmSpec,
ABSpec,
ABSpec,
ASpec,
BSpec,
DESpec,
256,
1,
128,
256,
8,
8,
64,
64,
64,
4,
16,
16,
1,
4,
4,
S<4, 64, 1>,
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
4,
4,
true,
S<4, 64, 1>,
S<4, 32, 1>,
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
4,
4,
true,
1,
1,
S<1, 32, 1, 8>,
S<1, 64, 1, 2>,
8>;
using DeviceOpInstance = DeviceOpInstanceKKNN;
......@@ -251,21 +253,6 @@ int main(int argc, char* argv[])
ck::index_t K0 = 2048;
// A[G0, G1, M0, M1, K0]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
// B[G0, G1, N0, N1, K0]
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
// D[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
// E[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> e_gs_ms_ns_strides{
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
if(argc == 1)
{
// use default case
......@@ -276,13 +263,43 @@ int main(int argc, char* argv[])
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 11)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
G0 = std::stoi(argv[4]);
G1 = std::stoi(argv[5]);
M0 = std::stoi(argv[6]);
M1 = std::stoi(argv[7]);
N0 = std::stoi(argv[8]);
N1 = std::stoi(argv[9]);
K0 = std::stoi(argv[10]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4-10: G0, G1, M0, M1, N0, N1, K0\n");
exit(0);
}
// A[G0, G1, M0, M1, K0]
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, G1, M0, M1, K0};
std::vector<ck::index_t> a_gs_ms_ks_strides{G1 * M0 * M1 * K0, M0 * M1 * K0, M1 * K0, K0, 1};
// B[G0, G1, N0, N1, K0]
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, G1, N0, N1, K0};
std::vector<ck::index_t> b_gs_ns_ks_strides{G1 * N0 * N1 * K0, N0 * N1 * K0, N1 * K0, K0, 1};
// D[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> d_gs_ms_ns_strides{G1 * N0 * N1, N0 * N1, 0, 0, N1, 1};
// E[G0, G1, M0, N0, M1, N1]
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, G1, M0, M1, N0, N1};
std::vector<ck::index_t> e_gs_ms_ns_strides{
G1 * M0 * N0 * M1 * N1, M0 * N0 * M1 * N1, N0 * M1 * N1, N1, M1 * N1, 1};
Tensor<ADataType> a_gs_ms_ks(a_gs_ms_ks_lengths, a_gs_ms_ks_strides);
Tensor<BDataType> b_gs_ns_ks(b_gs_ns_ks_lengths, b_gs_ns_ks_strides);
Tensor<DDataType> d_gs_ms_ns(d_gs_ms_ns_lengths, d_gs_ms_ns_strides);
......
example/30_grouped_conv_fwd_multiple_d/run_grouped_conv_fwd_bias_relu_add_wmma_example.inc
View file @ fcfe70f9
......@@ -42,41 +42,42 @@ using DeviceConvFwdInstance =
OutputLayout<NDimSpatial>,
InKernelDataType,
WeiKernelDataType,
ck::Tuple<BiasKernelDataType, ResidualKernelDataType>,
OutKernelDataType,
AccDataType,
CShuffleDataType,
ck::Tuple<BiasKernelDataType, ResidualKernelDataType>,
OutKernelDataType,
InElementOp,
WeiElementOp,
OutElementOp,
ConvSpec, // ConvForwardSpecialization
GemmSpec, // GemmSpecialization
256, // BlockSize
128, // MPerBlock
128, // NPerBlock
4, // K0PerBlock
1, // Prefetch stage
128, // BlockSize
64, // MPerBlock
64, // NPerBlock
64, // KPerBlock
8, // K1
16, // MPerWMMA
16, // NPerWMMA
4, // MRepeat
2, // NRepeat
S<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
1, // NRepeat
S<4, 32, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // ABlockTransferSrcAccessOrder
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
true, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<4, 32, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
true, // BBlockLdsExtraN
4,
2,
S<1, 32, 1, 8>,
1,
1,
S<1, 16, 1, 8>,
8>;
template <ck::index_t NDimSpatial>
......@@ -277,9 +278,9 @@ bool run_grouped_conv_fwd_bias_relu_add_example(int argc, char* argv[])
switch(conv_param.num_dim_spatial_)
{
case 1: return run_grouped_conv_fwd_bias_relu_add<1>(config, conv_param);
// case 1: return run_grouped_conv_fwd_bias_relu_add<1>(config, conv_param);
case 2: return run_grouped_conv_fwd_bias_relu_add<2>(config, conv_param);
case 3: return run_grouped_conv_fwd_bias_relu_add<3>(config, conv_param);
// case 3: return run_grouped_conv_fwd_bias_relu_add<3>(config, conv_param);
}
return false;
......
example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt
View file @ fcfe70f9
if(GPU_TARGETS MATCHES "gfx11")
add_example_executable(example_batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16 batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16.cpp)
add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_wmma_fp16 batched_gemm_scale_softmax_gemm_permute_wmma_fp16.cpp)
add_example_executable(example_self_attention_forward_wmma_fp16 self_attention_forward_wmma_fp16.cpp)
add_example_executable(example_cross_attention_forward_wmma_fp16 cross_attention_forward_wmma_fp16.cpp)
add_example_executable(example_multi_query_attention_forward_wmma_fp16 multi_query_attention_forward_wmma_fp16.cpp)
add_example_executable(example_grouped_query_attention_forward_wmma_fp16 grouped_query_attention_forward_wmma_fp16.cpp)
endif()
add_custom_target(example_gemm_scale_softmax_gemm)
add_example_executable(example_batched_gemm_scale_softmax_gemm_xdl_fp16 batched_gemm_scale_softmax_gemm_xdl_fp16.cpp)
......@@ -20,4 +29,3 @@ add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_sc
add_example_executable(example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16 batched_gemm_scale_softmax_gemm_permute_xdl_bf16.cpp)
add_example_dependencies(example_gemm_scale_softmax_gemm example_batched_gemm_scale_softmax_gemm_permute_xdl_bf16)
example/32_batched_gemm_scale_softmax_gemm/batched_gemm_lower_triangle_scale_softmax_gemm_permute_wmma_fp16.cpp 0 → 100644
View file @ fcfe70f9
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
/*
Gemm + Softmax + Gemm fused operation. Computes C_g_m_n = Softmax(A_g_m_k * B0_g_k_l) * B1_g_l_n
|-----------------|
Gemm0
|-------------------------------------|
Gemm1
*/
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_gemm_softmax_gemm_permute_wmma_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = F16;
using B0DataType = F16;
using B1DataType = F16;
using Acc0DataType = F32;
using Acc1DataType = F32;
using CShuffleDataType = F32;
using CDataType = F16;
using Acc0BiasDataType = ck::Tuple<>;
using Acc1BiasDataType = ck::Tuple<>;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
using AElementOp = PassThrough;
using B0ElementOp = PassThrough;
using Acc0ElementOp = ck::tensor_operation::element_wise::Scale;
using B1ElementOp = PassThrough;
using CElementOp = PassThrough;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskOutUpperTriangle;
static constexpr auto TensorSpecA = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
using DeviceMHAFactory =
std::tuple<ck::tensor_operation::device::DeviceBatchedGemmSoftmaxGemmPermute_Wmma_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
Acc0BiasDataType,
Acc0DataType,
Acc1BiasDataType,
Acc1DataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
// Gemm 0
128, // MPerBlock
64, // LPerBlock
64, // KPerBlock
8, // AK1
8, // BK1
// Gemm 1
64, // NPerBlock
64, // LTilePerBlock
8, // L1
16, // MPerWMMA
16, // LPerWMMA
16, // NPerWMMA
// Per repeat = wave_m = wave_num, wave_n = 1
1, // MRepeat
4, // LRepeat
4, // NRepeat
S<4, 64, 1>, // ABlockTransfer MK -> K0 M K1
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // B0BlockTransfer LK -> K0 L K1
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 8, 8>, // B1BlockTransfer NL -> L0 N L1
S<0, 2, 1>,
S<0, 2, 1>,
1,
8,
1,
false,
1, // CShuffleMWmmaPerWavePerShuffle
2, // CShuffleNWmmaPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec> // MaskingSpecialization
>;
// Ref Gemm0: fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B0DataType,
Acc0DataType,
Acc1DataType,
AElementOp,
B0ElementOp,
Acc0ElementOp>;
// Ref Softmax: fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<Acc0DataType, ADataType, Acc0DataType>;
// Ref Gemm1: fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
B1DataType,
CDataType,
Acc1DataType,
AElementOp,
B1ElementOp,
CElementOp>;
#include "run_batched_gemm_scale_softmax_gemm_permute_wmma.inc"
int main(int argc, char* argv[]) { return run(argc, argv); }
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment