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https://github.com/ROCm/composable_kernel.git
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* chore(copyright): update copyright header for tile_engine directory * chore(copyright): update copyright header for script directory * chore(copyright): update copyright header for test_data directory * chore(copyright): update copyright header for python directory * chore(copyright): update copyright header for profiler directory
371 lines
13 KiB
C++
371 lines
13 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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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_tensor_rearrange_impl.hpp"
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#include "profiler_operation_registry.hpp"
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namespace {
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enum struct RearrangeOp
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{
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ImageToColumn, // 0
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ColumnToImage, // 1
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};
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enum struct ConvLayout
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{
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GNHWC, // 0
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NHWGC, // 1
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};
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enum struct DataType
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{
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F32_F32, // 0
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F16_F16, // 1
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BF16_BF16, // 2
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INT8_INT8, // 3
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};
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#define OP_NAME "conv_tensor_rearrange"
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#define OP_DESC "Conv Tensor Rearrange"
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static void print_helper_msg()
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{
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std::cout
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// clang-format off
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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[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],\n"
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<< " 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])\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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<< "arg8: operation type (0: ImageToColumn, 1: ColumnToImage)\n"
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<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
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// clang-format on
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}
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} // namespace
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int profile_conv_tensor_rearrange(int argc, char* argv[])
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{
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// 9 for control, 1 for num_dim_spatial
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if(argc < 10)
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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<DataType>(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 auto rearrange_op = static_cast<RearrangeOp>(std::stoi(argv[8]));
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const int num_dim_spatial = std::stoi(argv[9]);
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// 9 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial
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if(argc != 9 + 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, 10, 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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using INT8 = int8_t;
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using namespace ck::tensor_layout::convolution;
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using namespace ck::conv_tensor_rearrange_op;
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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 in_type,
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auto out_type,
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auto rearrange_op_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 InDataType = decltype(in_type);
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using OutDataType = decltype(out_type);
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using Op = decltype(rearrange_op_type);
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bool pass = ck::profiler::
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profile_conv_tensor_rearrange_impl<NDimSpatial, InLayout, InDataType, OutDataType, Op>(
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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(rearrange_op == RearrangeOp::ImageToColumn)
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{
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if(layout == ConvLayout::GNHWC)
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{
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if(num_dim_spatial == 1)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I1, GNWC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I1, GNWC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I1, GNWC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I1, GNWC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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else if(num_dim_spatial == 2)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I2, GNHWC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I2, GNHWC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I2, GNHWC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I2, GNHWC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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else if(num_dim_spatial == 3)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I3, GNDHWC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I3, GNDHWC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I3, GNDHWC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I3, GNDHWC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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}
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else if(layout == ConvLayout::NHWGC)
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{
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if(num_dim_spatial == 1)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I1, NWGC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I1, NWGC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I1, NWGC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I1, NWGC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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else if(num_dim_spatial == 2)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I2, NHWGC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I2, NHWGC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I2, NHWGC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I2, NHWGC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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else if(num_dim_spatial == 3)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I3, NDHWGC{}, F32{}, F32{}, ImageToColumn{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I3, NDHWGC{}, F16{}, F16{}, ImageToColumn{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I3, NDHWGC{}, BF16{}, BF16{}, ImageToColumn{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I3, NDHWGC{}, INT8{}, INT8{}, ImageToColumn{});
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}
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}
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}
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}
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else if(rearrange_op == RearrangeOp::ColumnToImage)
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{
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if(layout == ConvLayout::GNHWC)
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{
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if(num_dim_spatial == 1)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I1, GNWC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I1, GNWC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I1, GNWC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I1, GNWC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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else if(num_dim_spatial == 2)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I2, GNHWC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I2, GNHWC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I2, GNHWC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I2, GNHWC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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else if(num_dim_spatial == 3)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I3, GNDHWC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I3, GNDHWC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I3, GNDHWC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I3, GNDHWC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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}
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else if(layout == ConvLayout::NHWGC)
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{
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if(num_dim_spatial == 1)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I1, NWGC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I1, NWGC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I1, NWGC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I1, NWGC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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else if(num_dim_spatial == 2)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I2, NHWGC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I2, NHWGC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I2, NHWGC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I2, NHWGC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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else if(num_dim_spatial == 3)
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{
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if(data_type == DataType::F32_F32)
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{
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return profile(I3, NDHWGC{}, F32{}, F32{}, ColumnToImage{});
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}
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else if(data_type == DataType::F16_F16)
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{
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return profile(I3, NDHWGC{}, F16{}, F16{}, ColumnToImage{});
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}
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else if(data_type == DataType::BF16_BF16)
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{
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return profile(I3, NDHWGC{}, BF16{}, BF16{}, ColumnToImage{});
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}
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else if(data_type == DataType::INT8_INT8)
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{
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return profile(I3, NDHWGC{}, INT8{}, INT8{}, ColumnToImage{});
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}
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}
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}
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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_tensor_rearrange);
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