mirror of
https://github.com/ROCm/composable_kernel.git
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140 lines
5.1 KiB
C++
140 lines
5.1 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "profiler/profile_gemm_add_relu_impl.hpp"
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#include "profiler_operation_registry.hpp"
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#define OP_NAME "gemm_add_relu"
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#define OP_DESC "GEMM+Add+ReLU"
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using INT8 = int8_t;
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using BF16 = ck::bhalf_t;
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int profile_gemm_add_relu(int argc, char* argv[])
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{
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enum struct MatrixLayout
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{
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MK_KN_MN_MN, // 0
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MK_NK_MN_MN, // 1
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KM_KN_MN_MN, // 2
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KM_NK_MN_MN, // 3
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};
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enum struct MatrixDataType
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{
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F16_INT8_F16_F16, // 0
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BF16_INT8_BF16_BF16, // 1
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};
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if(argc != 15)
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{
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// clang-format off
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printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
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printf("arg2: data type (0: f16&i8 1: bf16&i8)\n");
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printf("arg3: matrix layout (0: E[m, n] = ReLU(A[m, k] * B[k, n] + D0[m, n]);\n");
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printf(" 1: E[m, n] = ReLU(A[m, k] * B[n, k] + D0[m, n]);\n");
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printf(" 2: E[m, n] = ReLU(A[k, m] * B[k, n] + D0[m, n]);\n");
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printf(" 3: E[m, n] = ReLU(A[k, m] * B[n, k] + D0[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 14: M, N, K, StrideA, StrideB, StrideD0, StrideE\n");
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// clang-format on
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exit(1);
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}
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const auto data_type = static_cast<MatrixDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<MatrixLayout>(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 M = std::stoi(argv[8]);
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const int N = std::stoi(argv[9]);
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const int K = std::stoi(argv[10]);
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const int StrideA = std::stoi(argv[11]);
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const int StrideB = std::stoi(argv[12]);
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const int StrideD0 = std::stoi(argv[13]);
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const int StrideE = std::stoi(argv[14]);
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using F16 = ck::half_t;
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using F32 = float;
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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 acc_type,
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auto d0_type,
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auto e_type,
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auto a_layout,
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auto b_layout,
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auto d0_layout,
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auto e_layout) {
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using ADataType = decltype(a_type);
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using BDataType = decltype(b_type);
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using AccDataType = decltype(acc_type);
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using D0DataType = decltype(d0_type);
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using EDataType = decltype(e_type);
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using ALayout = decltype(a_layout);
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using BLayout = decltype(b_layout);
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using D0Layout = decltype(d0_layout);
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using ELayout = decltype(e_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 DefaultStrideD0 = ck::is_same_v<D0Layout, Row> ? N : M;
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const int DefaultStrideE = ck::is_same_v<ELayout, Row> ? N : M;
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bool pass = ck::profiler::profile_gemm_add_relu_impl<ADataType,
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BDataType,
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AccDataType,
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D0DataType,
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EDataType,
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ALayout,
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BLayout,
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D0Layout,
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ELayout>(
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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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(StrideD0 < 0) ? DefaultStrideD0 : StrideD0,
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(StrideE < 0) ? DefaultStrideE : StrideE);
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return pass ? 0 : 1;
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};
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if(data_type == MatrixDataType::F16_INT8_F16_F16 && layout == MatrixLayout::MK_KN_MN_MN)
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{
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return profile(F16{}, INT8{}, F32{}, F16{}, F16{}, Row{}, Row{}, Row{}, Row{});
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}
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else if(data_type == MatrixDataType::BF16_INT8_BF16_BF16 && layout == MatrixLayout::MK_KN_MN_MN)
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{
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return profile(BF16{}, INT8{}, F32{}, BF16{}, BF16{}, Row{}, Row{}, Row{}, Row{});
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}
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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_add_relu);
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