mirror of
https://github.com/ROCm/composable_kernel.git
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061ac0649c
* Few small fixes. * New GroupedGemm instances (BF16) * Unify and refactor GroupedGEMM device API. * Adapt changes to new API. * Adapt grouped gemm profiler. * Accept multiple kbatches for grouped gemm profiler. - delete obsolete two stage as it is now covered by grouped gemm * Update unit test for grouped gemm. * Fix thresholds for BF16 and F8. Unblock tests. * Fix few instances. * Multiple small fixes. * Adapt to new API, check dynamic casting. * Uncomment few data types in grouped gemm profiler. * Fix call to SetDeviceArgs. * Fix profile grouped gemm multiply tile loop. * Fix grouped gemm tile loop kernel args in client examples. * Review comments.
310 lines
18 KiB
C++
310 lines
18 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, 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_grouped_gemm_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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MK_KN_NM, // 4
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MK_NK_NM, // 5
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KM_KN_NM, // 6
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KM_NK_NM, // 7
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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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};
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#define OP_NAME "grouped_gemm"
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#define OP_DESC "Grouped GEMM"
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namespace {
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std::vector<int> argToIntArray(char* input)
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{
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std::vector<int> out;
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std::istringstream in(input);
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std::string item;
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while(std::getline(in, item, ','))
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{
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out.push_back(std::stoi(item));
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}
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return out;
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}
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int profile_grouped_gemm(int argc, char* argv[])
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{
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if(argc < 14)
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{
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std::cout
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<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
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<< "arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: fp8@fp6; 5: f16@f8)\n"
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<< "arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];\n"
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<< " 1: A[m, k] * B[n, k] = C[m, n];\n"
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<< " 2: A[k, m] * B[k, n] = C[m, n];\n"
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<< " 3: A[k, m] * B[n, k] = C[m, n])\n"
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<< "arg4: verification (0: no; 1: yes)\n"
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<< "arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n"
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<< "arg6: print tensor value (0: no; 1: yes)\n"
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<< "arg7: time kernel (0=n0, 1=yes)\n"
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<< "arg8 to 13: Ms, Ns, Ks, StrideAs, StrideBs, StrideCs (e.g., 256,256 128,128 64,64 "
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"64,64 64,64 128,128)\n"
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<< "arg15: kbatch values (default 1)\n"
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<< "optional:\n"
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<< "arg16: number of warm-up cycles (default 1)\n"
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<< "arg17: number of iterations (default 10)\n"
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<< std::endl;
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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 auto Ms = argToIntArray(argv[8]);
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const auto Ns = argToIntArray(argv[9]);
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const auto Ks = argToIntArray(argv[10]);
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const auto StrideAs = argToIntArray(argv[11]);
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const auto StrideBs = argToIntArray(argv[12]);
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const auto StrideCs = argToIntArray(argv[13]);
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const auto kbatches = argc >= 15 ? argToIntArray(argv[14]) : std::vector<int>{};
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int n_warmup = 1;
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int n_iter = 10;
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if(argc == 17)
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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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}
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if(data_type == GemmDataType::F16_F16_F16 && layout == GemmMatrixLayout::MK_KN_MN)
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{
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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::half_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::f8_t,
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ck::half_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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}
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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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ck::profiler::profile_grouped_gemm_impl<ck::half_t,
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ck::f8_t,
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ck::half_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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}
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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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ck::profiler::profile_grouped_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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float,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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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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ck::profiler::profile_grouped_gemm_impl<ck::bhalf_t,
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ck::bhalf_t,
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ck::bhalf_t,
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float,
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ck::tensor_layout::gemm::ColumnMajor,
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ck::tensor_layout::gemm::RowMajor,
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ck::tensor_layout::gemm::RowMajor>(do_verification,
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init_method,
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do_log,
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time_kernel,
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Ms,
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Ns,
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Ks,
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StrideAs,
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StrideBs,
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StrideCs,
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kbatches,
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n_warmup,
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n_iter);
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}
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else
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{
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throw std::runtime_error("wrong! this GEMM data_type & layout is not implemented");
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}
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return 0;
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}
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} // anonymous namespace
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REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_grouped_gemm);
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