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https://github.com/ROCm/composable_kernel.git
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* refactor * start * add device gemm file * add BatchStrideD0 * add stridd0 * add gridwise file * add d0 parameters to gridwise gemm * add c layout transformer * add d0 threadwise copy * init kernel * init kernel * regular code * nm desc put to out * kernel parameter can not use reference * host add bias+gelu * run right for bias+gelu * change AddFastGelu into another file * interface add d1 bias parameters * add d1 parameter to argument * add d1 parameter to gridwise * first all code,not verify * gelu change to relu and GetElementSpaceSize bug * add instance * start add to ckprofiler * ckprofiler finish code * change input parameter for ckProfiler * fix host bias+gelu bug * show help for ckProfiler * fix bug for lunch kernel ignore parametes * add pad and fix about bug * mutiple d0 * add dynamic d0_element_op * change profiler and instance to mutiple d0 * example have 2 d0 * remove some comments not using * change 2 d0 have self parameters * change d element_op name * change class name(multiple_d) * fix bug * fix bug that don't find file * update profiler * refactor * update profiler * clean * revert example change * add gon layout * optimize parameter for gno * add gon to gemm+gemm * change helping input parameters * change to GemmPadder_v2 * using ForEach * fix gb_per_sec Co-authored-by: Chao Liu <lc.roy86@gmail.com> Co-authored-by: ltqin <letaoqin@amd.com>
182 lines
6.3 KiB
C++
182 lines
6.3 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, 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/include/profile_batched_gemm_gemm_impl.hpp"
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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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int profile_batched_gemm_gemm(int argc, char* argv[])
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{
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enum struct GemmMatrixLayout
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{
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MK_NK_NO_MO, // 0
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MK_NK_ON_MO, // 0
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};
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enum struct GemmDataType
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{
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F32_F32_F32_F32, // 0
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F16_F16_F16_F16, // 1
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};
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GemmDataType data_type = GemmDataType::F16_F16_F16_F16;
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GemmMatrixLayout layout = GemmMatrixLayout::MK_NK_NO_MO;
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bool do_verification = true;
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int init_method = 1;
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bool do_log = 0;
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bool time_kernel = false;
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// GEMM shape
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t K = 64;
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ck::index_t O = 128;
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ck::index_t BatchCount = 4;
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ck::index_t StrideA0 = -1;
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ck::index_t StrideB0 = -1;
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ck::index_t StrideB1 = -1;
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ck::index_t StrideE1 = -1;
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ck::index_t BatchStrideA0 = -1;
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ck::index_t BatchStrideB0 = -1;
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ck::index_t BatchStrideB1 = -1;
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ck::index_t BatchStrideE1 = -1;
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if(argc == 8)
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{
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data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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do_verification = std::stoi(argv[4]);
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init_method = std::stoi(argv[5]);
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do_log = std::stoi(argv[6]);
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time_kernel = std::stoi(argv[7]);
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}
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else if(argc == 13)
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{
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data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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do_verification = std::stoi(argv[4]);
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init_method = std::stoi(argv[5]);
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do_log = std::stoi(argv[6]);
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time_kernel = std::stoi(argv[7]);
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M = std::stoi(argv[8]);
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N = std::stoi(argv[9]);
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K = std::stoi(argv[10]);
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O = std::stoi(argv[11]);
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BatchCount = std::stoi(argv[12]);
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}
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else if(argc == 21)
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{
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data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
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layout = static_cast<GemmMatrixLayout>(std::stoi(argv[3]));
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do_verification = std::stoi(argv[4]);
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init_method = std::stoi(argv[5]);
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do_log = std::stoi(argv[6]);
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time_kernel = std::stoi(argv[7]);
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M = std::stoi(argv[8]);
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N = std::stoi(argv[9]);
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K = std::stoi(argv[10]);
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O = std::stoi(argv[11]);
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BatchCount = std::stoi(argv[12]);
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StrideA0 = std::stoi(argv[13]);
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StrideB0 = std::stoi(argv[14]);
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StrideB1 = std::stoi(argv[15]);
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StrideE1 = std::stoi(argv[16]);
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BatchStrideA0 = std::stoi(argv[17]);
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BatchStrideB0 = std::stoi(argv[18]);
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BatchStrideB1 = std::stoi(argv[19]);
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BatchStrideE1 = std::stoi(argv[20]);
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}
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else
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{
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printf("arg1: tensor operation (batched_gemm_gemm: Batched_GEMM+Gemm)\n");
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printf("arg2: data type (1: fp16)\n");
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printf("arg3: matrix layout (0: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[n, o] + D1[m, o] "
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"= E1[m, o]; 1: Relu(A0[m, k] * B0[n, k] + D0[m, n]) * B1[o, n] + D1[m, o] = E1[m, "
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"o];)\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 12: M, N, K, O, Batch\n");
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printf("arg13 to 16: StrideA0, StrideB0, StrideB1, StrideE1\n");
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printf("arg17 to 20: BatchStrideA0, BatchStrideB0, BatchStrideB1, BatchStrideE1 \n");
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exit(1);
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}
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if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_NO_MO)
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{
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ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
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F16, // B0DataType,
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F16, // B1DataType,
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F16, // E1DataType,
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Row, // A0Layout,
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Col, // B0Layout,
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Row, // B1Layout,
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Row> // E1Layout,
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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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O,
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BatchCount,
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StrideA0,
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StrideB0,
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StrideB1,
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StrideE1,
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BatchStrideA0,
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BatchStrideB0,
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BatchStrideB1,
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BatchStrideE1);
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}
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else if(data_type == GemmDataType::F16_F16_F16_F16 && layout == GemmMatrixLayout::MK_NK_ON_MO)
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{
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ck::profiler::profile_batched_gemm_gemm_impl<F16, // A0DataType,
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F16, // B0DataType,
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F16, // B1DataType,
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F16, // E1DataType,
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Row, // A0Layout,
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Col, // B0Layout,
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Col, // B1Layout,
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Row> // E1Layout,
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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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O,
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BatchCount,
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StrideA0,
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StrideB0,
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StrideB1,
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StrideE1,
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BatchStrideA0,
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BatchStrideB0,
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BatchStrideB1,
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BatchStrideE1);
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}
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else
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{
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throw std::runtime_error("wrong! this data_type & layout is not implemented");
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}
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return 0;
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}
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