mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 17:26:00 +00:00
4ec5c52a0c
* Support 64 bit indexing * Add new grouped conv fwd kernel for large tensors * Add instances large tensor * Fixes for transform conv to gemm * Fixes * fixes * Remove not needed instances * examples fixes * Remove not need ds arrays * Fix tests * Add 2GB check in gridwise dl * Fixes
331 lines
13 KiB
C++
331 lines
13 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_conv_fwd_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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GNHWC_GKYXC_GNHWK, // 0
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NHWGC_GKYXC_NHWGK, // 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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F8_F8_F8, // 4
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BF8_BF8_F8, // 5
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F8_BF8_F8, // 6
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BF8_F8_F8, // 7
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};
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enum struct IndexType
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{
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INDEX_T, // 0
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LONG_INDEX_T, // 1
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};
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#define OP_NAME "grouped_conv_fwd"
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#define OP_DESC "Grouped Convolution Forward"
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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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<< " 4: Input fp8, Weight fp8, Output fp8\n"
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<< " 5: Input bf8, Weight bf8, Output fp8\n"
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<< " 6: Input fp8, Weight bf8, Output fp8\n"
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<< " 7: Input bf8, Weight fp8, Output fp8)\n"
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<< "arg3: indexing data type (0: 32-bit, 1: 64-bit)\n"
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<< "arg4: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
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<< " 1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K])\n"
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<< "arg5: verification (0: no, 1: yes)\n"
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<< "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
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<< "arg7: print tensor value (0: no; 1: yes)\n"
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<< "arg8: 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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// clang-format on
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}
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} // namespace
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int profile_grouped_conv_fwd(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 < 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<ConvDataType>(std::stoi(argv[2]));
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const auto layout = static_cast<ConvLayout>(std::stoi(argv[3]));
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const auto index_type = static_cast<IndexType>(std::stoi(argv[4]));
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const bool do_verification = std::stoi(argv[5]);
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const int init_method = std::stoi(argv[6]);
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const bool do_log = std::stoi(argv[7]);
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const bool time_kernel = 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 F8 = ck::f8_t;
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using BF8 = ck::bf8_t;
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//
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using GNWC = ck::tensor_layout::convolution::GNWC;
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using GNHWC = ck::tensor_layout::convolution::GNHWC;
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using GNDHWC = ck::tensor_layout::convolution::GNDHWC;
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using GKXC = ck::tensor_layout::convolution::GKXC;
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using GKYXC = ck::tensor_layout::convolution::GKYXC;
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using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
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using GNWK = ck::tensor_layout::convolution::GNWK;
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using GNHWK = ck::tensor_layout::convolution::GNHWK;
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using GNDHWK = ck::tensor_layout::convolution::GNDHWK;
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//
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using NWGC = ck::tensor_layout::convolution::NWGC;
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using NHWGC = ck::tensor_layout::convolution::NHWGC;
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using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
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using NWGK = ck::tensor_layout::convolution::NWGK;
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using NHWGK = ck::tensor_layout::convolution::NHWGK;
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using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
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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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auto a_compute_type,
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auto b_compute_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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using AComputeType = decltype(a_compute_type);
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using BComputeType = decltype(b_compute_type);
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if(index_type == IndexType::INDEX_T)
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{
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bool pass = ck::profiler::profile_grouped_conv_fwd_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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AComputeType,
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BComputeType,
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ck::index_t>(
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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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else if(index_type == IndexType::LONG_INDEX_T)
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{
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bool pass = ck::profiler::profile_grouped_conv_fwd_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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AComputeType,
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BComputeType,
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ck::long_index_t>(
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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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else
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{
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std::cout << "this indexing data type is not implemented" << std::endl;
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return 1;
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}
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};
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// GNHWC_GKYXC_GNHWK
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if(num_dim_spatial == 1 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
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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, GNWC{}, GKXC{}, GNWK{}, F32{}, F32{}, 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, GNWC{}, GKXC{}, GNWK{}, F16{}, F16{}, 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, GNWC{}, GKXC{}, GNWK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I1, GNWC{}, GKXC{}, GNWK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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}
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else if(num_dim_spatial == 2 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
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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, GNHWC{}, GKYXC{}, GNHWK{}, F32{}, F32{}, 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, GNHWC{}, GKYXC{}, GNHWK{}, F16{}, F16{}, 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, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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}
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else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
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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, GNDHWC{}, GKZYXC{}, GNDHWK{}, F32{}, F32{}, 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, GNDHWC{}, GKZYXC{}, GNDHWK{}, F16{}, F16{}, 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(
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I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(
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I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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}
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// NHWGC_GKYXC_NHWGK
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else if(num_dim_spatial == 1 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
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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, NWGC{}, GKXC{}, NWGK{}, F32{}, F32{}, 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, NWGC{}, GKXC{}, NWGK{}, F16{}, F16{}, 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, NWGC{}, GKXC{}, NWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I1, NWGC{}, GKXC{}, NWGK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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}
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else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
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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, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, 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, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, 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, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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}
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else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
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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, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, 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, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, 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(
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I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
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}
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else if(data_type == ConvDataType::INT8_INT8_INT8)
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{
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return profile(
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I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, INT8{}, INT8{}, INT8{}, INT8{}, INT8{});
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}
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else if(data_type == ConvDataType::F8_F8_F8)
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{
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F8{}, F8{}, F8{}, F8{}, F8{});
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}
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else if(data_type == ConvDataType::BF8_BF8_F8)
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{
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF8{}, BF8{}, F8{}, BF8{}, BF8{});
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}
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else if(data_type == ConvDataType::F8_BF8_F8)
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{
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F8{}, BF8{}, F8{}, F8{}, BF8{});
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}
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else if(data_type == ConvDataType::BF8_F8_F8)
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{
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF8{}, F8{}, F8{}, BF8{}, F8{});
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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_grouped_conv_fwd);
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