mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-17 19:40:04 +00:00
Merge flatmm Operator with universal gemm (#2434)
* Initial commit
* Adding new tile partitioner to flatmm
* intermediate changes
* debugging kernels
* Updating flatmm example to universal gemm example
* updated flatmm kernel to run via gemmKernel
* update universal gemm to incorporate flatmm
* debug
* Fix flatmm call
* Fixing other kernels and tests for API changes
* clang formatted
* fixing gemm tests
* added test for flatmm and simplify kernel arguments
* adding flatmm test
* fix test for flatmm
* simplify gemm kernel with flatmm
* remove flatmm related files
* addressing review comments and code clean up
* resolving empty file
* resolving empty file
* clang formatted
* addressing review comments
* enable persistent kernel for flatmm
* reverted the removed files for flatmm
* reverted the removed files for flatmm
* changed flatmm to weightPReshuffle; removed the _1 added in teh faltmm example
* some more renames
* clang formatted
[ROCm/composable_kernel commit: d239b91fd5]
This commit is contained in:
Khushbu Agarwal
@@ -1,5 +1,6 @@
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add_subdirectory(image_to_column)
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add_subdirectory(gemm)
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add_subdirectory(gemm_weight_preshuffle)
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add_subdirectory(batched_gemm)
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add_subdirectory(grouped_gemm)
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add_subdirectory(gemm_multi_d)
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@@ -22,9 +22,10 @@ using Intrawave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
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ck_tile::GemmPipelineScheduler::Intrawave>;
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using Interwave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
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ck_tile::GemmPipelineScheduler::Interwave>;
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using Mem = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Mem>;
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using CompV3 = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::CompV3>;
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using CompV4 = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::CompV4>;
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using Mem = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Mem>;
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using CompV3 = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::CompV3>;
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using CompV4 = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::CompV4>;
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using Persistent = std::true_type;
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using NonPersistent = std::false_type;
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@@ -90,7 +90,7 @@ class TestCkTileGemmPipeline : public ::testing::Test
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ck_tile::tuple_element_or_default_t<Tuple, 9, std::false_type>::value;
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// TODO: expose tile size through test t-param ?
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template <bool PadM, bool PadN, bool PadK>
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template <bool PadM, bool PadN, bool PadK, bool Preshuffle>
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void invoke_gemm(const ck_tile::GemmHostArgs</*NumDTensor = 0*/>& args,
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const ck_tile::stream_config& s)
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{
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@@ -107,9 +107,10 @@ class TestCkTileGemmPipeline : public ::testing::Test
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constexpr ck_tile::index_t N_Warp_Tile = 32;
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constexpr ck_tile::index_t K_Warp_Tile = 16;
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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr bool preshuffle = Preshuffle;
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constexpr bool DoubleSmemBuffer = (PipelineType == GemmPipelineType::CompV4) ? true : false;
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@@ -131,7 +132,9 @@ class TestCkTileGemmPipeline : public ::testing::Test
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using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
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static constexpr bool StructuredSparsity = false;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
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static constexpr bool NumWaveGroup = 1;
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
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kPadN,
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kPadK,
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DoubleSmemBuffer,
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@@ -140,7 +143,9 @@ class TestCkTileGemmPipeline : public ::testing::Test
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CLayout,
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TransposeC,
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StructuredSparsity,
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Persistent>;
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Persistent,
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NumWaveGroup,
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preshuffle>;
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using GemmPipelineProblem =
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
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@@ -261,7 +266,7 @@ class TestCkTileGemmPipeline : public ::testing::Test
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}
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}
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template <bool PadM = true, bool PadN = true, bool PadK = true>
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template <bool PadM = true, bool PadN = true, bool PadK = true, bool Preshuffle = false>
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void Run(const int M,
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const int N,
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const int K,
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@@ -271,11 +276,11 @@ class TestCkTileGemmPipeline : public ::testing::Test
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{
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for(auto kb : k_batches_)
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{
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RunSingle<PadM, PadN, PadK>(M, N, K, StrideA, StrideB, StrideC, kb);
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RunSingle<PadM, PadN, PadK, Preshuffle>(M, N, K, StrideA, StrideB, StrideC, kb);
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}
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}
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template <bool PadM, bool PadN, bool PadK>
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template <bool PadM, bool PadN, bool PadK, bool Preshuffle>
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void RunSingle(const int M,
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const int N,
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const int K,
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@@ -352,7 +357,7 @@ class TestCkTileGemmPipeline : public ::testing::Test
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args.stride_B = stride_B;
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args.stride_E = stride_C;
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invoke_gemm<PadM, PadN, PadK>(args, ck_tile::stream_config{nullptr, false});
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invoke_gemm<PadM, PadN, PadK, Preshuffle>(args, ck_tile::stream_config{nullptr, false});
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c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
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bool pass = true;
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22
test/ck_tile/gemm_weight_preshuffle/CMakeLists.txt
Normal file
22
test/ck_tile/gemm_weight_preshuffle/CMakeLists.txt
Normal file
@@ -0,0 +1,22 @@
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# Currently ck_tile_gemm is only built on gfx94/gfx95
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set(EXAMPLE_GEMM_COMPILE_OPTIONS)
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if(CK_USE_OCP_FP8)
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list(APPEND EXAMPLE_GEMM_COMPILE_OPTIONS -DCK_TILE_USE_OCP_FP8)
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endif()
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set(EXAMPLE_GEMM_COMPILE_COMPUTE_V4_OPTIONS)
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if(CK_USE_OCP_FP8)
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list(APPEND EXAMPLE_GEMM_COMPILE_COMPUTE_V4_OPTIONS -DCK_TILE_USE_OCP_FP8)
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endif()
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list(APPEND EXAMPLE_GEMM_COMPILE_COMPUTE_V4_OPTIONS
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-mllvm
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-enable-noalias-to-md-conversion=0
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)
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if(GPU_TARGETS MATCHES "gfx94" OR GPU_TARGETS MATCHES "gfx95")
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add_gtest_executable(test_ck_tile_gemm_pipeline_wp test_gemm_pipeline_wp.cpp)
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target_compile_options(test_ck_tile_gemm_pipeline_wp PRIVATE ${EXAMPLE_GEMM_COMPILE_OPTIONS})
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else()
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message(DEBUG "Skipping ck_tile_gemm tests for current target")
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endif()
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@@ -0,0 +1,32 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <tuple>
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#include <type_traits>
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#include "gtest/gtest.h"
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#include "ck_tile/host.hpp"
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#include "test_gemm_pipeline_util.hpp"
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using F16 = ck_tile::half_t;
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using F32 = float;
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using F8 = ck_tile::fp8_t;
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using Row = ck_tile::tensor_layout::gemm::RowMajor;
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using Col = ck_tile::tensor_layout::gemm::ColumnMajor;
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using Default = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
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ck_tile::GemmPipelineScheduler::Default>;
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using WeightPreshuffle =
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ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::WeightPreshuffle>;
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// clang-format off
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using KernelTypesWeightPreshuffle = ::testing::Types<
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std::tuple< Row, Col, Row, F16, F16, F32, F16, Default, WeightPreshuffle>,
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std::tuple< Row, Col, Row, F8, F8, F32, F16, Default, WeightPreshuffle>
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>;
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// clang-format on
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21
test/ck_tile/gemm_weight_preshuffle/test_gemm_pipeline_ut_cases.inc
Executable file
21
test/ck_tile/gemm_weight_preshuffle/test_gemm_pipeline_ut_cases.inc
Executable file
@@ -0,0 +1,21 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#ifndef TEST_GEMM_PIPELINE_UT_CASES_INC
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#define TEST_GEMM_PIPELINE_UT_CASES_INC
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TYPED_TEST(TEST_SUITE_NAME, GemmPreshuffle)
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{
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constexpr int M = 2048;
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constexpr int N = 4096;
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constexpr int K = 5120;
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constexpr bool PadM = false;
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constexpr bool PadN = false;
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constexpr bool PadK = false;
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constexpr bool Preshuffle = true;
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this->template Run<PadM, PadN, PadK, Preshuffle>(M, N, K);
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}
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#endif
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384
test/ck_tile/gemm_weight_preshuffle/test_gemm_pipeline_util.hpp
Normal file
384
test/ck_tile/gemm_weight_preshuffle/test_gemm_pipeline_util.hpp
Normal file
@@ -0,0 +1,384 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <sstream>
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#include <gtest/gtest.h>
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#include "ck_tile/core.hpp"
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#include "ck_tile/host.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/epilogue.hpp"
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#include "ck_tile/ops/gemm.hpp"
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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const float max_accumulated_value)
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{
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using ComputeType =
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std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
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// Calculate thresholds
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const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
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ck_tile::integer_divide_ceil(K, kbatch));
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const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
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max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
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// Calculate error due to split_k accumulation
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const auto rtol_split_k =
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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enum struct GemmPipelineType
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{
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WeightPreshuffle
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};
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template <GemmPipelineType PT, typename Problem>
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struct GemmPipelineTypeSelector;
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template <typename Problem>
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struct GemmPipelineTypeSelector<GemmPipelineType::WeightPreshuffle, Problem>
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{
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using base_pipeline = ck_tile::BaseWeightPreshufflePipelineAGmemBGmemCRegV1<Problem>;
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using pipeline = ck_tile::WeightPreshufflePipelineAGmemBGmemCRegV1<Problem>;
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static constexpr auto GetName() { return "GemmPipelineAgBgCrWeightPreshuffle"; }
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};
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template <typename Datatype>
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struct config
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{
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static constexpr ck_tile::index_t M_Tile = 128;
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static constexpr ck_tile::index_t N_Tile = 128;
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static constexpr ck_tile::index_t K_Tile = 128 / sizeof(Datatype);
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static constexpr ck_tile::index_t M_Warp = 1;
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static constexpr ck_tile::index_t N_Warp = 4;
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static constexpr ck_tile::index_t K_Warp = 1;
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static constexpr ck_tile::index_t M_Warp_Tile = 32;
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static constexpr ck_tile::index_t N_Warp_Tile = 32;
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static constexpr ck_tile::index_t K_Warp_Tile = sizeof(Datatype) == 2 ? 16 : 32;
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};
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template <typename Tuple>
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class TestCkTileGemmPipeline : public ::testing::Test
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{
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protected:
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using ALayout = std::tuple_element_t<0, Tuple>;
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using BLayout = std::tuple_element_t<1, Tuple>;
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using CLayout = std::tuple_element_t<2, Tuple>;
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using ADataType = std::tuple_element_t<3, Tuple>;
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using BDataType = std::tuple_element_t<4, Tuple>;
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using AccDataType = std::tuple_element_t<5, Tuple>;
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using CDataType = std::tuple_element_t<6, Tuple>;
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static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
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static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;
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using DsLayout = ck_tile::tuple<>;
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using DsDataType = ck_tile::tuple<>;
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using GemmConfig = config<ADataType>;
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static constexpr bool Persistent =
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ck_tile::tuple_element_or_default_t<Tuple, 9, std::false_type>::value;
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// TODO: expose tile size through test t-param ?
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template <bool PadM, bool PadN, bool PadK, bool Preshuffle>
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void invoke_gemm(const ck_tile::GemmHostArgs</*NumDTensor = 0*/>& args,
|
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const ck_tile::stream_config& s)
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{
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// TODO: This should be parameterized in tests
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// constexpr ck_tile::index_t M_Tile = 128;
|
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// constexpr ck_tile::index_t N_Tile = 128;
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// constexpr ck_tile::index_t K_Tile = 128;
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|
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// constexpr ck_tile::index_t M_Warp = 1;
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// constexpr ck_tile::index_t N_Warp = 4;
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// constexpr ck_tile::index_t K_Warp = 1;
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// constexpr ck_tile::index_t M_Warp_Tile = 32;
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// constexpr ck_tile::index_t N_Warp_Tile = 32;
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// constexpr ck_tile::index_t K_Warp_Tile = sizeof(ADataType) == 2 ? 16 : 32;
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constexpr bool kPadM = PadM;
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constexpr bool kPadN = PadN;
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constexpr bool kPadK = PadK;
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constexpr bool preshuffle = Preshuffle;
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|
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constexpr bool DoubleSmemBuffer = false;
|
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|
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// TODO: For now - but this should also be a test parameter
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constexpr bool TransposeC = false;
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|
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constexpr int kBlockPerCu = 2;
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constexpr ck_tile::index_t TileParitionerGroupNum = 8;
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constexpr ck_tile::index_t TileParitionerM01 = 4;
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// ===============================================
|
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using GemmShape = ck_tile::TileGemmShape<
|
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ck_tile::sequence<GemmConfig::M_Tile, GemmConfig::N_Tile, GemmConfig::K_Tile>,
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ck_tile::sequence<GemmConfig::M_Warp, GemmConfig::N_Warp, GemmConfig::K_Warp>,
|
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ck_tile::sequence<GemmConfig::M_Warp_Tile,
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GemmConfig::N_Warp_Tile,
|
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GemmConfig::K_Warp_Tile>>;
|
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using TilePartitioner = ck_tile::
|
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GemmSpatiallyLocalTilePartitioner<GemmShape, TileParitionerGroupNum, TileParitionerM01>;
|
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|
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using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
|
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static constexpr bool StructuredSparsity = false;
|
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static constexpr bool NumWaveGroup = 1;
|
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|
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using GemmUniversalTraits = ck_tile::TileGemmUniversalTraits<kPadM,
|
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kPadN,
|
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kPadK,
|
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DoubleSmemBuffer,
|
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ALayout,
|
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BLayout,
|
||||
CLayout,
|
||||
TransposeC,
|
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StructuredSparsity,
|
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Persistent,
|
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NumWaveGroup,
|
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preshuffle>;
|
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|
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using GemmPipelineProblem =
|
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ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>;
|
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|
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using BaseGemmPipeline =
|
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typename GemmPipelineTypeSelector<PipelineType, GemmPipelineProblem>::base_pipeline;
|
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|
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const ck_tile::index_t k_grain = args.k_batch * GemmConfig::K_Tile;
|
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const ck_tile::index_t K_split = (args.K + k_grain - 1) / k_grain * GemmConfig::K_Tile;
|
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const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(K_split);
|
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const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
|
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const ck_tile::TailNumber tail_num = BaseGemmPipeline::GetBlockLoopTailNum(num_loop);
|
||||
|
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const auto Run = [&](const auto has_hot_loop_,
|
||||
const auto tail_number_,
|
||||
const auto memory_operation_) {
|
||||
constexpr bool has_hot_loop_v = has_hot_loop_.value;
|
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constexpr auto tail_number_v = tail_number_.value;
|
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constexpr auto memory_operation = memory_operation_.value;
|
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|
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using UniversalGemmProblem = ck_tile::UniversalGemmPipelineProblem<ADataType,
|
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BDataType,
|
||||
AccDataType,
|
||||
GemmShape,
|
||||
GemmUniversalTraits,
|
||||
Scheduler,
|
||||
has_hot_loop_v,
|
||||
tail_number_v>;
|
||||
|
||||
using GemmPipeline =
|
||||
typename GemmPipelineTypeSelector<PipelineType, UniversalGemmProblem>::pipeline;
|
||||
|
||||
using GemmEpilogue = ck_tile::CShuffleEpilogue<
|
||||
ck_tile::CShuffleEpilogueProblem<ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
AccDataType,
|
||||
CDataType,
|
||||
DsLayout,
|
||||
CLayout,
|
||||
ck_tile::element_wise::PassThrough,
|
||||
GemmPipeline::BlockSize,
|
||||
TilePartitioner::MPerBlock,
|
||||
TilePartitioner::NPerBlock,
|
||||
GemmConfig::M_Warp,
|
||||
GemmConfig::N_Warp,
|
||||
GemmConfig::M_Warp_Tile,
|
||||
GemmConfig::N_Warp_Tile,
|
||||
GemmConfig::K_Warp_Tile,
|
||||
UniversalGemmProblem::TransposeC,
|
||||
memory_operation>>;
|
||||
|
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using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
|
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auto kargs = Kernel::MakeKernelArgs(args);
|
||||
|
||||
dim3 grids;
|
||||
if constexpr(Persistent)
|
||||
{
|
||||
grids = Kernel::MaxOccupancyGridSize(s);
|
||||
}
|
||||
else
|
||||
{
|
||||
grids = Kernel::GridSize(args.M, args.N, args.k_batch);
|
||||
}
|
||||
constexpr dim3 blocks = Kernel::BlockSize();
|
||||
|
||||
if(!Kernel::IsSupportedArgument(kargs))
|
||||
{
|
||||
throw std::runtime_error("Wrong! Arguments not supported! Skipping gemm!\n");
|
||||
}
|
||||
|
||||
if(s.log_level_ > 0)
|
||||
{
|
||||
std::cout << "Launching kernel with args:"
|
||||
<< " grid: {" << grids.x << ", " << grids.y << ", " << grids.z << "}"
|
||||
<< ", blocks: {" << blocks.x << ", " << blocks.y << ", " << blocks.z
|
||||
<< "}" << std::endl;
|
||||
}
|
||||
|
||||
ck_tile::launch_kernel(
|
||||
s, ck_tile::make_kernel<blocks.x, kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
|
||||
};
|
||||
|
||||
const auto RunSplitk = [&](const auto has_hot_loop_, const auto tail_number_) {
|
||||
if(args.k_batch == 1)
|
||||
{
|
||||
Run(has_hot_loop_,
|
||||
tail_number_,
|
||||
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::set>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
Run(has_hot_loop_,
|
||||
tail_number_,
|
||||
ck_tile::integral_constant<ck_tile::memory_operation_enum,
|
||||
ck_tile::memory_operation_enum::atomic_add>{});
|
||||
}
|
||||
};
|
||||
|
||||
BaseGemmPipeline::TailHandler(RunSplitk, has_hot_loop, tail_num);
|
||||
}
|
||||
|
||||
public:
|
||||
std::vector<int> k_batches_;
|
||||
|
||||
void SetUp() override
|
||||
{
|
||||
|
||||
// Otherwise, use k_batch = 1 and 2
|
||||
k_batches_ = {1};
|
||||
}
|
||||
|
||||
template <bool PadM = true, bool PadN = true, bool PadK = true, bool Preshuffle = false>
|
||||
void Run(const int M,
|
||||
const int N,
|
||||
const int K,
|
||||
const int StrideA = 0,
|
||||
const int StrideB = 0,
|
||||
const int StrideC = 0)
|
||||
{
|
||||
for(auto kb : k_batches_)
|
||||
{
|
||||
RunSingle<PadM, PadN, PadK, Preshuffle>(M, N, K, StrideA, StrideB, StrideC, kb);
|
||||
}
|
||||
}
|
||||
|
||||
template <bool PadM, bool PadN, bool PadK, bool Preshuffle>
|
||||
void RunSingle(const int M,
|
||||
const int N,
|
||||
const int K,
|
||||
const int StrideA,
|
||||
const int StrideB,
|
||||
const int StrideC,
|
||||
int kbatch = 1)
|
||||
{
|
||||
using namespace ck_tile::literals;
|
||||
|
||||
auto f_host_tensor_descriptor = [](std::size_t row,
|
||||
std::size_t col,
|
||||
std::size_t stride,
|
||||
auto layout) {
|
||||
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});
|
||||
}
|
||||
};
|
||||
|
||||
auto f_get_default_stride =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(stride == 0)
|
||||
{
|
||||
// give a chance if stride is zero, return a default packed stride
|
||||
if constexpr(std::is_same_v<decltype(layout),
|
||||
ck_tile::tensor_layout::gemm::RowMajor>)
|
||||
{
|
||||
return col;
|
||||
}
|
||||
else
|
||||
{
|
||||
return row;
|
||||
}
|
||||
}
|
||||
else
|
||||
return stride;
|
||||
};
|
||||
|
||||
std::size_t stride_A = f_get_default_stride(M, K, StrideA, ALayout{});
|
||||
std::size_t stride_B = f_get_default_stride(K, N, StrideB, BLayout{});
|
||||
std::size_t stride_C = f_get_default_stride(M, N, StrideC, CLayout{});
|
||||
|
||||
ck_tile::HostTensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, stride_A, ALayout{}));
|
||||
ck_tile::HostTensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, stride_B, BLayout{}));
|
||||
ck_tile::HostTensor<CDataType> c_m_n_dev_result(
|
||||
f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
|
||||
|
||||
ck_tile::FillUniformDistributionIntegerValue<ADataType>{-5, 5}(a_m_k);
|
||||
ck_tile::FillUniformDistributionIntegerValue<BDataType>{-5, 5}(b_k_n);
|
||||
|
||||
ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
|
||||
ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());
|
||||
|
||||
constexpr int divisor = GemmConfig::N_Warp_Tile == 32 ? 2 : 4;
|
||||
ck_tile::HostTensor<BDataType> t_view({N / GemmConfig::N_Warp_Tile,
|
||||
GemmConfig::N_Warp_Tile,
|
||||
K / GemmConfig::K_Warp_Tile,
|
||||
divisor,
|
||||
GemmConfig::K_Warp_Tile / divisor});
|
||||
|
||||
std::copy(b_k_n.begin(), b_k_n.end(), t_view.begin());
|
||||
ck_tile::HostTensor<BDataType> b_shuffle_host =
|
||||
ck_tile::reference_permute(t_view, {0, 2, 3, 1, 4});
|
||||
|
||||
a_m_k_dev_buf.ToDevice(a_m_k.data());
|
||||
b_k_n_dev_buf.ToDevice(b_shuffle_host.data());
|
||||
c_m_n_dev_buf.SetZero();
|
||||
c_m_n_dev_result.SetZero();
|
||||
|
||||
ck_tile::GemmHostArgs</*NumDTensor = 0*/> args;
|
||||
args.a_ptr = a_m_k_dev_buf.GetDeviceBuffer();
|
||||
args.b_ptr = b_k_n_dev_buf.GetDeviceBuffer();
|
||||
args.e_ptr = c_m_n_dev_buf.GetDeviceBuffer();
|
||||
args.k_batch = kbatch;
|
||||
args.M = M;
|
||||
args.N = N;
|
||||
args.K = K;
|
||||
args.stride_A = stride_A;
|
||||
args.stride_B = stride_B;
|
||||
args.stride_E = stride_C;
|
||||
|
||||
invoke_gemm<PadM, PadN, PadK, Preshuffle>(args, ck_tile::stream_config{nullptr, false});
|
||||
|
||||
c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
|
||||
bool pass = true;
|
||||
|
||||
ck_tile::HostTensor<CDataType> c_m_n_host_ref(
|
||||
f_host_tensor_descriptor(M, N, stride_C, CLayout{}));
|
||||
c_m_n_host_ref.SetZero();
|
||||
|
||||
ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
|
||||
a_m_k, b_k_n, c_m_n_host_ref);
|
||||
|
||||
const float max_accumulated_value =
|
||||
*std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
|
||||
const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
|
||||
K, kbatch, max_accumulated_value);
|
||||
pass = ck_tile::check_err(c_m_n_dev_result,
|
||||
c_m_n_host_ref,
|
||||
"Error: Incorrect results!",
|
||||
rtol_atol.at(ck_tile::number<0>{}),
|
||||
rtol_atol.at(ck_tile::number<1>{}));
|
||||
EXPECT_TRUE(pass);
|
||||
}
|
||||
};
|
||||
@@ -0,0 +1,16 @@
|
||||
#include "test_gemm_pipeline_kernel_types.hpp"
|
||||
#include "test_gemm_pipeline_util.hpp"
|
||||
#include "gtest/gtest.h"
|
||||
|
||||
template <typename T>
|
||||
class TestCkTileGemmPipelineWeightPreshuffle : public TestCkTileGemmPipeline<T>
|
||||
{
|
||||
};
|
||||
|
||||
#define TEST_SUITE_NAME TestCkTileGemmPipelineWeightPreshuffle
|
||||
|
||||
TYPED_TEST_SUITE(TestCkTileGemmPipelineWeightPreshuffle, KernelTypesWeightPreshuffle);
|
||||
|
||||
#include "test_gemm_pipeline_ut_cases.inc"
|
||||
|
||||
#undef TEST_SUITE_NAME
|
||||
Reference in New Issue
Block a user