mirror of
https://github.com/ROCm/composable_kernel.git
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* experiment with config file * experiment with version.h config * add more info to version.h * minor updates * minor updates * fix case where DTYPE is not used * large amount of files but minor changes * remove white space * minor changes to add more MACROs * fix cmakedefine01 * fix issue with CK internal conflict * fix define and define value * fix clang-format * fix formatting issue * experiment with cmake * clang format v12 to be consistent with miopen * avoid clang-format for config file
199 lines
6.7 KiB
C++
199 lines
6.7 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, 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_conv_bwd_data_impl.hpp"
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#include "profiler_operation_registry.hpp"
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namespace {
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enum struct ConvLayout
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{
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NCHW_KCYX_NKHW, // 0
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NHWC_KYXC_NHWK, // 1
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};
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enum struct ConvDataType
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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 "conv_bwd_data"
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#define OP_DESC "Convolution Backward Data"
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static void print_helper_msg()
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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: Input fp32, Weight fp32, Output fp32\n"
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<< " 1: Input fp16, Weight fp16, Output fp16\n"
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<< " 2: Input bf16, Weight bf16, Output bf16\n"
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<< " 3: Input int8, Weight int8, Output int8)\n"
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<< "arg3: tensor layout (0: Input[N, C, Hi, Wi], Weight[K, C, Y, X], Output[N, K, Ho, Wo]\n"
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<< " 1: Input[N, Hi, Wi, C], Weight[K, Y, X, C], Output[N, Ho, Wo, "
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"K])\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: no, 1: yes)\n"
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<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
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}
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} // namespace
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int profile_conv_bwd_data(int argc, char* argv[])
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{
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// 8 for control, 1 for num_dim_spatial
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if(argc < 9)
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{
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print_helper_msg();
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return 1;
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}
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const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<ConvLayout>(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 num_dim_spatial = std::stoi(argv[8]);
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// 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
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if(argc != 8 + 1 + 4 + 6 * num_dim_spatial)
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{
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print_helper_msg();
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return 1;
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}
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const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 9, argv);
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using F32 = float;
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using F16 = ck::half_t;
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using BF16 = ck::bhalf_t;
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#ifdef CK_ENABLE_INT8
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using INT8 = int8_t;
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#endif
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using NWC = ck::tensor_layout::convolution::NWC;
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using NHWC = ck::tensor_layout::convolution::NHWC;
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using NDHWC = ck::tensor_layout::convolution::NDHWC;
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using KXC = ck::tensor_layout::convolution::KXC;
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using KYXC = ck::tensor_layout::convolution::KYXC;
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using KZYXC = ck::tensor_layout::convolution::KZYXC;
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using NWK = ck::tensor_layout::convolution::NWK;
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using NHWK = ck::tensor_layout::convolution::NHWK;
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using NDHWK = ck::tensor_layout::convolution::NDHWK;
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constexpr auto I1 = ck::Number<1>{};
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constexpr auto I2 = ck::Number<2>{};
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constexpr auto I3 = ck::Number<3>{};
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auto profile = [&](auto num_dim_spatial_tmp,
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auto in_layout,
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auto wei_layout,
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auto out_layout,
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auto in_type,
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auto wei_type,
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auto out_type) {
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constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
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using InLayout = decltype(in_layout);
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using WeiLayout = decltype(wei_layout);
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using OutLayout = decltype(out_layout);
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using InDataType = decltype(in_type);
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using WeiDataType = decltype(wei_type);
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using OutDataType = decltype(out_type);
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bool pass = ck::profiler::profile_conv_bwd_data_impl<NDimSpatial,
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InLayout,
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WeiLayout,
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OutLayout,
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InDataType,
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WeiDataType,
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OutDataType>(
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do_verification, init_method, do_log, time_kernel, params);
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return pass ? 0 : 1;
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};
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if(num_dim_spatial == 1 && layout == ConvLayout::NHWC_KYXC_NHWK)
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{
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if(data_type == ConvDataType::F32_F32_F32)
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{
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return profile(I1, NWC{}, KXC{}, NWK{}, F32{}, F32{}, F32{});
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}
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else if(data_type == ConvDataType::F16_F16_F16)
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{
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return profile(I1, NWC{}, KXC{}, NWK{}, F16{}, F16{}, F16{});
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}
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else if(data_type == ConvDataType::BF16_BF16_BF16)
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{
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return profile(I1, NWC{}, KXC{}, NWK{}, BF16{}, BF16{}, BF16{});
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}
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#ifdef CK_ENABLE_INT8
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I1, NWC{}, KXC{}, NWK{}, INT8{}, INT8{}, INT8{});
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}
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#endif
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}
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else if(num_dim_spatial == 2 && layout == ConvLayout::NHWC_KYXC_NHWK)
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{
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if(data_type == ConvDataType::F32_F32_F32)
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{
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return profile(I2, NHWC{}, KYXC{}, NHWK{}, F32{}, F32{}, F32{});
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}
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else if(data_type == ConvDataType::F16_F16_F16)
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{
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return profile(I2, NHWC{}, KYXC{}, NHWK{}, F16{}, F16{}, F16{});
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}
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else if(data_type == ConvDataType::BF16_BF16_BF16)
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{
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return profile(I2, NHWC{}, KYXC{}, NHWK{}, BF16{}, BF16{}, BF16{});
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}
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#ifdef CK_ENABLE_INT8
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I2, NHWC{}, KYXC{}, NHWK{}, INT8{}, INT8{}, INT8{});
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}
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#endif
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}
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else if(num_dim_spatial == 3 && layout == ConvLayout::NHWC_KYXC_NHWK)
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{
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if(data_type == ConvDataType::F32_F32_F32)
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{
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return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, F32{}, F32{}, F32{});
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}
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else if(data_type == ConvDataType::F16_F16_F16)
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{
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return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, F16{}, F16{}, F16{});
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}
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else if(data_type == ConvDataType::BF16_BF16_BF16)
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{
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return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, BF16{}, BF16{}, BF16{});
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}
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#ifdef CK_ENABLE_INT8
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I3, NDHWC{}, KZYXC{}, NDHWK{}, INT8{}, INT8{}, INT8{});
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}
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#endif
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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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REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_conv_bwd_data);
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