mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 17:00:18 +00:00
* initial stream-k implementation with example * fix unexpected change in err * improve a little bit performance by reorganize pipeline. * improve perf a little bit by swizzle block idx * add profiler * update example * fix spelling * shrink karg for streamk * support dynamic buffer using memory coherence glc_slc bit from template * control memory coherence while construct dynamic buffer * update reduction for streamk(not ready yet) * Add template parameter to make_dynamic_buffer to support amd_buffer coherence setting * fix build issue * fix several bug * now result is correct, everything works (but has scratch) * remove scratch by manually reset coordinate * update device code * fix a bug in final reduce * fix something in example * update async memset * fix enum as camel case * modify coherence enum name * clean code and use atomic streamk by default * remove unused var * throw exception if have empty pointer * fix format * fix CI warning * fix type in init * modify CI error * filter out on gfx10+ * restore changed example code --------- Co-authored-by: Qianfeng Zhang <Qianfeng.Zhang@amd.com>
156 lines
5.6 KiB
C++
156 lines
5.6 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/profile_gemm_streamk_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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};
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#define OP_NAME "gemm_streamk"
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#define OP_DESC "StreamK GEMM"
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int profile_gemm_streamk(int argc, char* argv[])
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{
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if(argc < 14)
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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)\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: num_sk_blocks (optional)\n");
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exit(1);
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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 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 StrideC = std::stoi(argv[13]);
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const uint32_t NumSKBlocks =
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argc >= 15 ? static_cast<uint32_t>(std::stoul(std::string(argv[14]))) : 0xffffffff;
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using F32 = float;
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using F16 = ck::half_t;
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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 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 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_streamk_impl<ADataType,
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BDataType,
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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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NumSKBlocks);
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return pass ? 0 : 1;
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};
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if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(F32{}, F32{}, F32{}, F32{}, Row{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::MK_NK_MN)
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{
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return profile(F32{}, F32{}, F32{}, F32{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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return profile(F32{}, F32{}, F32{}, F32{}, Col{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F32_F32_F32 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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return profile(F32{}, F32{}, F32{}, F32{}, Col{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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return profile(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{}, F32{}, F16{}, Row{}, Col{}, Row{});
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_KN_MN)
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{
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return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Row{}, Row{});
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}
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else if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::KM_NK_MN)
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{
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return profile(F16{}, F16{}, F32{}, F16{}, Col{}, Col{}, 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_streamk);
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