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CK-Tile Grouped GEMM refactor and post PR fixes (#1756)

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* Grouped gemm simple code refactor

* Offset invoker

* Invoke generic Run, and replace name of parrtitioner variable

* Tests fix type

* Removed namespaces

* Add template param to avoid implicit cast

* Remove generic function

* Constant value

* underline enum to int16_t

* Generalize partitioner function

* Remove whitespaces

* Rename function

* Using support

* Clang-format

* Clang-format

* Fn-partitioner description fn

* Typo

* Typo 2

* Better description

* Better description

* Refactor after review

* Use ctr instead of set fn

* Inovke ctr and typo

* Comments

* Remove unnecessary comment

* Review, remove modulo

[ROCm/composable_kernel commit: 3c93d3c444]
This commit is contained in:
Mateusz Ozga
2025-01-21 21:06:10 +01:00
committed by GitHub
parent dde428cdf9
commit 91b54b683e
17 changed files with 363 additions and 388 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
example/ck_tile/03_gemm/gemm_basic.cpp
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@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <hip/hip_runtime.h>
@@ -49,7 +49,7 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<CodegenGemmShape>;
using TilePartitioner = ck_tile::GemmTile2DPartitioner<CodegenGemmShape>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
@@ -61,8 +61,8 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;

2
example/ck_tile/03_gemm/universal_gemm.cpp
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@@ -56,7 +56,7 @@ float gemm_calc(const ck_tile::GemmHostArgs& args, const ck_tile::stream_config&
ck_tile::TileGemmShape<ck_tile::sequence<M_Tile, N_Tile, K_Tile>,
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<GemmShape>;
using TilePartitioner = ck_tile::GemmTile2DPartitioner<GemmShape>;
using GemmEpilogue = ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>;

8
example/ck_tile/16_batched_gemm/batched_gemm.cpp
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@@ -1,5 +1,5 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include <hip/hip_runtime.h>
@@ -51,7 +51,7 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
ck_tile::sequence<M_Warp, N_Warp, K_Warp>,
ck_tile::sequence<M_Warp_Tile, N_Warp_Tile, K_Warp_Tile>>;
using TilePartitioner = ck_tile::GemmTilePartitioner<CodegenGemmShape>;
using TilePartitioner = ck_tile::GemmTile2DPartitioner<CodegenGemmShape>;
using GemmEpilogue = std::conditional_t<
CShuffleEpilogue,
@@ -63,8 +63,8 @@ float batched_gemm(const ck_tile::BatchedGemmHostArgs& args, const ck_tile::stre
kOutputRank,
1,
0,
TilePartitioner::kM,
TilePartitioner::kN>>,
TilePartitioner::MPerBlock,
TilePartitioner::NPerBlock>>,
ck_tile::Default2DEpilogue<
ck_tile::Default2DEpilogueProblem<AccDataType, CDataType, kPadM, kPadN>>>;

3
example/ck_tile/17_grouped_gemm/grouped_gemm.cpp
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@@ -15,7 +15,6 @@
#include "ck_tile/ops/gemm.hpp"
#include "ck_tile/host.hpp"
#include "grouped_gemm.hpp"
#include "utils.hpp"
namespace {
@@ -102,7 +101,7 @@ using Kernel = ck_tile::GroupedGemmKernel<TilePartitioner,
GemmEpilogue<CLayout>>;
}; // namespace
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs)
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs)
{
return ::Kernel<std::nullptr_t, std::nullptr_t, std::nullptr_t>::GetWorkSpaceSize(gemm_descs);
}

8
example/ck_tile/17_grouped_gemm/grouped_gemm.hpp
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@@ -52,8 +52,8 @@ auto create_args(int argc, char* argv[])
return std::make_tuple(result, arg_parser);
}
std::size_t GetWorkspaceSize(const std::vector<grouped_gemm_kargs>& gemm_descs);
std::size_t get_workspace_size(const std::vector<grouped_gemm_kargs>& gemm_descs);
float grouped_gemm_calc(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);
float grouped_gemm(const std::vector<grouped_gemm_kargs>& gemm_descs,
const ck_tile::stream_config& s,
void* p_workspace_);

20
example/ck_tile/17_grouped_gemm/run_grouped_gemm_example.inc
View File

@@ -31,7 +31,7 @@ float invoke_gemm(int n_warmup,
{
ck_tile::DeviceMem gemm_workspace;
gemm_workspace.Realloc(GetWorkspaceSize(args));
gemm_workspace.Realloc(get_workspace_size(args));
float ave_time = grouped_gemm<ALayout, BLayout, CLayout>(
args,
@@ -128,16 +128,16 @@ int run_grouped_gemm_example_with_layouts(int argc,
const ck_tile::index_t N = Ns[i];
const ck_tile::index_t K = Ks[i];
stride_As[i] = f_get_default_stride(M, N, stride_As[i], a_layout);
stride_Bs[i] = f_get_default_stride(K, N, stride_Bs[i], b_layout);
stride_Cs[i] = f_get_default_stride(M, N, stride_Cs[i], CLayout{});
stride_As[i] = ck_tile::get_default_stride(M, N, stride_As[i], a_layout);
stride_Bs[i] = ck_tile::get_default_stride(K, N, stride_Bs[i], b_layout);
stride_Cs[i] = ck_tile::get_default_stride(M, N, stride_Cs[i], CLayout{});
a_m_k_tensors.push_back(
ck_tile::HostTensor<ADataType>(f_host_tensor_descriptor(M, K, stride_As[i], a_layout)));
b_k_n_tensors.push_back(
ck_tile::HostTensor<BDataType>(f_host_tensor_descriptor(K, N, stride_Bs[i], b_layout)));
a_m_k_tensors.push_back(ck_tile::HostTensor<ADataType>(
ck_tile::host_tensor_descriptor(M, K, stride_As[i], a_layout)));
b_k_n_tensors.push_back(ck_tile::HostTensor<BDataType>(
ck_tile::host_tensor_descriptor(K, N, stride_Bs[i], b_layout)));
c_m_n_tensors.push_back(ck_tile::HostTensor<CDataType>(
f_host_tensor_descriptor(M, N, stride_Cs[i], CLayout{})));
ck_tile::host_tensor_descriptor(M, N, stride_Cs[i], CLayout{})));
std::cout << "gemm[" << i << "]"
<< " a_m_k: " << a_m_k_tensors[i].mDesc << " b_k_n: " << b_k_n_tensors[i].mDesc
@@ -178,7 +178,7 @@ int run_grouped_gemm_example_with_layouts(int argc,
for(int i = 0; i < group_count; ++i)
{
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
f_host_tensor_descriptor(Ms[i], Ns[i], stride_Cs[i], CLayout{}));
ck_tile::host_tensor_descriptor(Ms[i], Ns[i], stride_Cs[i], CLayout{}));
c_m_n_host_ref.SetZero();
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
a_m_k_tensors[i], b_k_n_tensors[i], c_m_n_host_ref);

38
example/ck_tile/17_grouped_gemm/utils.hpp
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@@ -1,38 +0,0 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
template <typename TLayout>
constexpr auto
f_host_tensor_descriptor(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
using namespace ck_tile::literals;
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return ck_tile::HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return ck_tile::HostTensorDescriptor({row, col}, {1_uz, stride});
}
}
template <typename TLayout>
constexpr auto
f_get_default_stride(std::size_t row, std::size_t col, std::size_t stride, TLayout layout)
{
if(stride == 0)
{
if constexpr(std::is_same_v<decltype(layout), ck_tile::tensor_layout::gemm::RowMajor>)
{
return col;
}
else
{
return row;
}
}
else
return stride;
}