// SPDX-License-Identifier: MIT // Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved. #include #include #include #include #include #include "profiler/profile_contraction_impl.hpp" #include "profiler/profile_contraction_utils.hpp" #include "profiler_operation_registry.hpp" #define OP_NAME "contraction_scale" #define OP_DESC "CONTRACTION+Scale" static void print_helper_msg() { std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" << "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + " "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" << " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + " "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" << " 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + " "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" << " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + " "D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n" << "arg5: verification (0: no; 1: yes)\n" << "arg6: initialization (0: no init; 1: integer value; 2: decimal " << "value)\n" << "arg7: print tensor value (0: no; 1: yes)\n" << "arg8: time kernel (0: no, 1: yes)\n" << "arg9: alpha\n" << "arg10 to 15: M0, M1, N0, N1, K0, K1\n" << "arg16 to 31: Strides for A, B, D and E (skip for default)\n" << std::endl; } int profile_contraction_scale(int argc, char* argv[]) { const bool default_strides = argc == 16; if(argc != 32 && argc != 16) { print_helper_msg(); exit(1); } const auto data_type = static_cast(std::stoi(argv[2])); const auto compute_data_type = static_cast(std::stoi(argv[3])); const auto layout = static_cast(std::stoi(argv[4])); const bool do_verification = std::stoi(argv[5]); const ck::index_t init_method = std::stoi(argv[6]); const bool do_log = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[8]); const float alpha = std::stof(argv[9]); std::vector M; std::vector N; std::vector K; const ck::index_t dims_arg_num = 10; collect_index_params(argv, M, dims_arg_num, 2); collect_index_params(argv, N, dims_arg_num + 2, 2); collect_index_params(argv, K, dims_arg_num + 4, 2); std::vector StridesA; std::vector StridesB; std::vector StridesE; std::vector StridesD; if(!default_strides) { collect_index_params(argv, StridesA, dims_arg_num + 6, 4); collect_index_params(argv, StridesB, dims_arg_num + 10, 4); collect_index_params(argv, StridesE, dims_arg_num + 14, 4); collect_index_params(argv, StridesD, dims_arg_num + 18, 4); } using F16 = ck::half_t; using BF16 = ck::bhalf_t; using F32 = float; using F64 = double; auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) { using ALayout = decltype(a_layout); using BLayout = decltype(b_layout); using CDELayout = decltype(cde_layout); using DataType = decltype(type); using ComputeDataType = decltype(compute_type); if(default_strides) { assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]}); assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]}); assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]}); assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]}); } bool pass = ck::profiler::profile_contraction_impl, Scale>(do_verification, init_method, do_log, time_kernel, Scale{alpha}, M, N, K, StridesA, StridesB, StridesE, StridesD); return pass; }; auto run_profile_for_datatype = [&](auto type, auto compute_type) { if(layout == ContractionMatrixLayout::MK_KN_MN_MN) { return profile(Row{}, Row{}, Row{}, type, compute_type); } else if(layout == ContractionMatrixLayout::MK_NK_MN_MN) { return profile(Row{}, Col{}, Row{}, type, compute_type); } else if(layout == ContractionMatrixLayout::KM_KN_MN_MN) { return profile(Col{}, Row{}, Row{}, type, compute_type); } else if(layout == ContractionMatrixLayout::KM_NK_MN_MN) { return profile(Col{}, Col{}, Row{}, type, compute_type); } return false; }; if(data_type == ContractionDataType::F32_F32_F32_F32) { if(compute_data_type == ContractionComputeDataType::F32) { return run_profile_for_datatype(F32{}, F32{}); } else if(compute_data_type == ContractionComputeDataType::F16) { return run_profile_for_datatype(F32{}, F16{}); } else if(compute_data_type == ContractionComputeDataType::BF16) { return run_profile_for_datatype(F32{}, BF16{}); } else { std::cout << "Incorrect combination of data type and compute data type." << std::endl; return 1; } } else if(data_type == ContractionDataType::F64_F64_F64_F64) { if(compute_data_type == ContractionComputeDataType::F64) { return run_profile_for_datatype(F64{}, F64{}); } else if(compute_data_type == ContractionComputeDataType::F32) { return run_profile_for_datatype(F64{}, F32{}); } else { std::cout << "Incorrect combination of data type and compute data type." << std::endl; return 1; } } else if(data_type == ContractionDataType::F16_F16_F16_F16) { if(compute_data_type == ContractionComputeDataType::F32) { return run_profile_for_datatype(F16{}, F32{}); } else { std::cout << "Incorrect combination of data type and compute data type." << std::endl; return 1; } } else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16) { if(compute_data_type == ContractionComputeDataType::F32) { return run_profile_for_datatype(BF16{}, F32{}); } else { std::cout << "Incorrect combination of data type and compute data type." << std::endl; return 1; } } return 1; } REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale);