// SPDX-License-Identifier: MIT // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved. #include #include #include #include #include "profiler/include/profile_conv_fwd_impl.hpp" namespace { enum struct ConvLayout { NCHW_KCYX_NKHW, // 0 NHWC_KYXC_NHWK, // 1 }; enum struct ConvDataType { F32_F32_F32, // 0 F16_F16_F16, // 1 BF16_BF16_BF16, // 2 INT8_INT8_INT8, // 3 }; static void print_helper_msg() { std::cout // clang-format-off << "arg1: tensor operation (conv_fwd: Convolution Forward)\n" << "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n" << " 1: Input fp16, Weight fp16, Output fp16\n" << " 2: Input bf16, Weight bf16, Output bf16\n" << " 3: Input int8, Weight int8, Output int8)\n" << "arg3: tensor layout (0: Input[N, C, Hi, Wi], Weight[K, C, Y, X], Output[N, K, Ho, Wo]\n" << " 1: Input[N, Hi, Wi, C], Weight[K, Y, X, C], Output[N, Ho, Wo, " "K])\n" << "arg4: verification (0: no, 1: yes)\n" << "arg5: initialization (0: no init, 1: integer value, 2: decimal value)\n" << "arg6: print tensor value (0: no; 1: yes)\n" << "arg7: time kernel (0: no, 1: yes)\n" << ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl; // clang-format-on } } // namespace int profile_conv_fwd(int argc, char* argv[]) { // 8 for control, 1 for num_dim_spatial if(argc < 9) { print_helper_msg(); return 1; } const auto data_type = static_cast(std::stoi(argv[2])); const auto layout = static_cast(std::stoi(argv[3])); const bool do_verification = std::stoi(argv[4]); const int init_method = std::stoi(argv[5]); const bool do_log = std::stoi(argv[6]); const bool time_kernel = std::stoi(argv[7]); const int num_dim_spatial = std::stoi(argv[8]); // 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial if(argc != 8 + 1 + 4 + 6 * num_dim_spatial) { print_helper_msg(); return 1; } const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv); using F32 = float; using F16 = ck::half_t; using BF16 = ck::bhalf_t; using INT8 = int8_t; using NWC = ck::tensor_layout::convolution::NWC; using NHWC = ck::tensor_layout::convolution::NHWC; using NDHWC = ck::tensor_layout::convolution::NDHWC; using KXC = ck::tensor_layout::convolution::KXC; using KYXC = ck::tensor_layout::convolution::KYXC; using KZYXC = ck::tensor_layout::convolution::KZYXC; using NWK = ck::tensor_layout::convolution::NWK; using NHWK = ck::tensor_layout::convolution::NHWK; using NDHWK = ck::tensor_layout::convolution::NDHWK; constexpr auto I1 = ck::Number<1>{}; constexpr auto I2 = ck::Number<2>{}; constexpr auto I3 = ck::Number<3>{}; auto profile = [&](auto num_dim_spatial_tmp, auto in_layout, auto wei_layout, auto out_layout, auto in_type, auto wei_type, auto out_type) { constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value; using InLayout = decltype(in_layout); using WeiLayout = decltype(wei_layout); using OutLayout = decltype(out_layout); using InDataType = decltype(in_type); using WeiDataType = decltype(wei_type); using OutDataType = decltype(out_type); bool pass = ck::profiler::profile_conv_fwd_impl( do_verification, init_method, do_log, time_kernel, params); return pass ? 0 : 1; }; if(num_dim_spatial == 1 && layout == ConvLayout::NHWC_KYXC_NHWK) { if(data_type == ConvDataType::F32_F32_F32) { return profile(I1, NWC{}, KXC{}, NWK{}, F32{}, F32{}, F32{}); } else if(data_type == ConvDataType::F16_F16_F16) { return profile(I1, NWC{}, KXC{}, NWK{}, F16{}, F16{}, F16{}); } else if(data_type == ConvDataType::BF16_BF16_BF16) { return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{}); } else if(data_type == ConvDataType::INT8_INT8_INT8) { return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{}); } } else if(num_dim_spatial == 2 && layout == ConvLayout::NHWC_KYXC_NHWK) { if(data_type == ConvDataType::F32_F32_F32) { return profile(I2, NHWC{}, KYXC{}, NHWK{}, F32{}, F32{}, F32{}); } else if(data_type == ConvDataType::F16_F16_F16) { return profile(I2, NHWC{}, KYXC{}, NHWK{}, F16{}, F16{}, F16{}); } else if(data_type == ConvDataType::BF16_BF16_BF16) { return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{}); } else if(data_type == ConvDataType::INT8_INT8_INT8) { return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{}); } } else if(num_dim_spatial == 3 && layout == ConvLayout::NHWC_KYXC_NHWK) { if(data_type == ConvDataType::F32_F32_F32) { return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, F32{}, F32{}, F32{}); } else if(data_type == ConvDataType::F16_F16_F16) { return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, F16{}, F16{}, F16{}); } else if(data_type == ConvDataType::BF16_BF16_BF16) { return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{}); } else if(data_type == ConvDataType::INT8_INT8_INT8) { return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{}); } } std::cout << "this data_type & layout is not implemented" << std::endl; return 1; }