mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-15 02:27:57 +00:00
* Fixed cmake errors related to gemm_bilinear. Previously, if the above flags are set, cmake build fails: GPU_TARGETS="gfx1100;gfx1201" -D DTYPES="fp16;bf16;fp8" * Fixed cmake build errors related to test_fp8 * Updates to support mixed precision * Adding support for RRR, F8xF16xF16 gemm_universal_wmma - wip * Added support for F8xF16xF16 to gemm_wmma_universal * Added support for F16xF8xF16 to gemm_wmma_universal * Added support for BF16xI4xBF16 to gemm_wmma_universal * Added support for F16xI4xF16 to gemm_wmma_universal * Fixed IsSupportedArgument to check ComputeTypeA, ComputeTypeB instead of ADataType, BDataType * Added missing test class for FP16_KM_NK * Pre-commit hooks fixes * Added padding instances for f16xf16xf16 * Fixed cmake errors related to gemm_bilinear. Previously, if the above flags are set, cmake build fails: GPU_TARGETS="gfx1100;gfx1201" -D DTYPES="fp16;bf16;fp8" * Fixed cmake build errors related to test_fp8 * Ammending changes for adding support for padding instances for f16xf16xf16 * Fixes for padding instances for f16xf16xf16 * Added padding instances for bf16xbf16, f8xf8 * Added packed instances for bf16xi4xbf16 * Added padding instances for f8xf16xf16 * Added padding instances for f16xf8xf16, f16xi4xf16 * Fixed typos for bf16xbf16xbf16 padding instances * Fixed typos for padded instances * Added tests for fp16, KM_KN and KM_NK * Padding not supported for when BDataType is pk_i4_t. Added fix for correct check and removed padding instances. * Fixed typos * Updated the set of tests for FP16 * Updated the set of tests for FP16 * Fix typo * Moved f16xi4 test under the correct data layout group * example for gemm_universal_bf16 * Adding examples for gemm_wmma instances * Added the missing parameters * Fixed review comments and added executable to cmakeLists * Fixing clang format * Fixing build erros * Fixed compilation failure. * Modified some code as per gemm_universal_examples * Fixed the gemm specialization error * Fixed the build errors. * Fix strides of a/b_thread_desc The descriptors are larger than needed (even though the compiler don't alloc registers for unused values). * Load in M/NRepeat dims with thread copy's slice instead of a loop * Clone BlockwiseGemmXdlops_pipeline_v1 for WMMA implementation * Implement Intrawave and Interwave variants of pipeline v1 * Add instances for Interwave and Intrawave v1 * Add instances with ABlockLdsExtraM and BBlockLdsExtraN = 0 * Remove instances that are too slow (mostly because of register spilling) * Add a workaround for fp8/bf8->f32 packed conversion issue * Add instances for Interwave and Intrawave v1 * Enable profiling of mixed precision with f8 and int4 on WMMA * Fix segfault in profiler when B is pk_i4_t b_device_buf's size in bytes is larger than b_k_n_permute so b_device_buf.ToDevice reads out-of-bounds. * Remove instances that are too slow (mostly because of register spilling) * Add missing add_device_gemm_wmma_universal_f8_f8_bf16 declarations * Add test case for bf16_i4 * Add missing Regular tests * Add test_gemm_universal_xdl/wmma_fp16 to REGRESSION_TESTS They take more than 30 seconds * Fix a bug that fp16_i4 validation passes only with PermuteB A permutation required by conversion from pk_i4_t to half_t does not depend on PermuteB, they can be used independently. * Use PermuteB with f16_i4 in most instances (as xdl) Some instances use PermuteB = false for checking correctness. See also the previous commit. * Fix cache flushing for pk_i4 * Add mixed precision examples * Disable all tests and instances with f8 on gfx11 Even though f8_f16 and f16_f8 don't require f8 WMMA instructions, gfx11 still lacks hardware instructions for fast f8->f32 conversion. * Add FP16 KM_NK and KM_KN test suites for XDL These tests were added to common .inc for better testing of WMMA instances * Fix int8 DTYPES check for gemm_bilinear --------- Co-authored-by: Anca Hamuraru <anca@streamhpc.com> Co-authored-by: Apoorva Kalyani <apoorva@streamhpc.com>
231 lines
8.4 KiB
C++
231 lines
8.4 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2023-2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <initializer_list>
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#include <iostream>
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#include <numeric>
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#include "profiler/profile_gemm_universal_impl.hpp"
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#include "profiler_operation_registry.hpp"
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enum struct GemmMatrixLayout
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{
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MK_KN_MN, // 0
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MK_NK_MN, // 1
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KM_KN_MN, // 2
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KM_NK_MN, // 3
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};
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enum struct GemmDataType
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{
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F32_F32_F32, // 0
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F16_F16_F16, // 1
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BF16_BF16_BF16, // 2
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INT8_INT8_INT8, // 3
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F8_F16_F16, // 4
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F16_F8_F16, // 5
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F16_F16_F16_F8, // 6
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F8_F8_BF16, // 7
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F16_I4_F16, // 8
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BF16_I4_BF16, // 9
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};
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#define OP_NAME "gemm_universal"
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#define OP_DESC "Universal GEMM"
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int profile_gemm_universal(int argc, char* argv[])
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{
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if(argc != 15 && argc != 18)
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{
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printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
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printf("arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: "
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"f16->f8; 7: f8->bf16, "
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"comp f8; 8: f16@i4; 9: bf16@i4\n");
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printf("arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n");
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printf(" 1: A[m, k] * B[n, k] = C[m, n];\n");
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printf(" 2: A[k, m] * B[k, n] = C[m, n];\n");
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printf(" 3: A[k, m] * B[n, k] = C[m, n])\n");
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printf("arg4: verification (0: no; 1: yes)\n");
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printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
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printf("arg6: print tensor value (0: no; 1: yes)\n");
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printf("arg7: time kernel (0=no, 1=yes)\n");
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printf("arg8 to 13: M, N, K, StrideA, StrideB, StrideC\n");
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printf("arg14: split k into mulitiple batch\n");
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printf("optional:\n");
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printf("arg15: number of warm-up cycles (default 1)\n");
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printf("arg16: number of iterations (default 10)\n");
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printf("arg17: memory for rotating buffer (default 0, size in MB)\n");
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exit(1);
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}
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int M;
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int N;
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int StrideA;
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int StrideB;
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// Analyze the unsupported matrix shapes, switch the M and N number
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if(std::stoi(argv[9]) % 8 != 0 && std::stoi(argv[8]) % 8 == 0)
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{
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M = std::stoi(argv[9]);
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StrideA = std::stoi(argv[12]);
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N = std::stoi(argv[8]);
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StrideB = std::stoi(argv[11]);
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}
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else
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{
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M = std::stoi(argv[8]);
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StrideA = std::stoi(argv[11]);
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N = std::stoi(argv[9]);
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StrideB = std::stoi(argv[12]);
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}
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const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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const bool do_verification = std::stoi(argv[4]);
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const int init_method = std::stoi(argv[5]);
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const bool do_log = std::stoi(argv[6]);
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const bool time_kernel = std::stoi(argv[7]);
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const int K = std::stoi(argv[10]);
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const int StrideC = std::stoi(argv[13]);
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const int KBatch = std::stoi(argv[14]);
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int n_warmup = 1;
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int n_iter = 10;
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uint64_t rotating = 0;
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if(argc == 18)
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{
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n_warmup = std::stoi(argv[15]);
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n_iter = std::stoi(argv[16]);
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rotating = std::stoull(argv[17]) * 1024 * 1024;
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}
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using F32 = float;
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using F16 = ck::half_t;
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using BF16 = ck::bhalf_t;
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#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94) || defined(CK_USE_WMMA_FP8)
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using F8 = ck::f8_t;
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using I4 = ck::pk_i4_t;
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#endif
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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auto profile = [&](auto a_type,
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auto b_type,
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auto comp_type,
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auto acc_type,
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auto c_type,
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auto a_layout,
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auto b_layout,
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auto c_layout) {
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using ADataType = decltype(a_type);
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using BDataType = decltype(b_type);
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using ComputeDataType = decltype(comp_type);
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using AccDataType = decltype(acc_type);
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using CDataType = decltype(c_type);
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using ALayout = decltype(a_layout);
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using BLayout = decltype(b_layout);
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using CLayout = decltype(c_layout);
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const int DefaultStrideA = ck::is_same_v<ALayout, Row> ? K : M;
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const int DefaultStrideB = ck::is_same_v<BLayout, Row> ? N : K;
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const int DefaultStrideC = ck::is_same_v<CLayout, Row> ? N : M;
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bool pass = ck::profiler::profile_gemm_universal_impl<ADataType,
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BDataType,
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ComputeDataType,
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AccDataType,
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CDataType,
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ALayout,
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BLayout,
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CLayout>(
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do_verification,
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init_method,
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do_log,
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time_kernel,
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M,
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N,
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K,
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(StrideA < 0) ? DefaultStrideA : StrideA,
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(StrideB < 0) ? DefaultStrideB : StrideB,
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(StrideC < 0) ? DefaultStrideC : StrideC,
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KBatch,
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n_warmup,
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n_iter,
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rotating);
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return pass ? 0 : 1;
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};
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if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(F16{}, F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F16{}, F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
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}
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#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94) || defined(CK_USE_WMMA_FP8)
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else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(F16{}, F8{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F16_F8_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F16{}, F8{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(F8{}, F16{}, F16{}, F32{}, F16{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F8_F16_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F8{}, F16{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
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}
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#endif
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Col{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::BF16_BF16_BF16 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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return profile(BF16{}, BF16{}, BF16{}, F32{}, BF16{}, Col{}, Row{}, Row{});
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}
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#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94) || defined(CK_USE_WMMA_FP8)
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else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F8_F8_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F8{}, F8{}, F8{}, F32{}, BF16{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::F16_I4_F16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F16{}, I4{}, F16{}, F32{}, F16{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::BF16_I4_BF16 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(BF16{}, I4{}, BF16{}, F32{}, BF16{}, Row{}, Col{}, Row{});
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}
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#endif
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else
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{
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std::cout << "this data_type & layout is not implemented" << std::endl;
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return 1;
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}
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}
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REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_gemm_universal);
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