mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-07-19 02:01:01 +00:00
Add grouped conv fwd direction profiling into CK Tile profiler.
This commit is contained in:
Ville Pietilä
@@ -12,10 +12,7 @@ message(STATUS "CK_PROFILER_INSTANCE_FILTER: ${CK_PROFILER_INSTANCE_FILTER}")
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if(SUPPORTED_GPU_TARGETS MATCHES "gfx9" OR SUPPORTED_GPU_TARGETS MATCHES "gfx1[12]")
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list(APPEND PROFILER_OPS tile_profile_grouped_conv_bwd_weight.cpp)
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endif()
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if(DL_KERNELS)
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list(APPEND PROFILER_OPS tile_profile_grouped_conv_bwd_weight.cpp)
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list(APPEND PROFILER_OPS tile_profile_grouped_conv_fwd.cpp)
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endif()
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set(PROFILER_SOURCES tile_profiler.cpp)
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@@ -33,7 +30,6 @@ message(VERBOSE "ckTileProfiler sources: ${PROFILER_SOURCES}")
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set(PROFILER_EXECUTABLE ckTileProfiler)
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add_executable(${PROFILER_EXECUTABLE} ${PROFILER_SOURCES})
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#target_include_directories(${PROFILER_EXECUTABLE} PRIVATE ${CMAKE_PROJECT_DIR}/include)
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target_compile_options(${PROFILER_EXECUTABLE} PRIVATE -Wno-global-constructors)
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# flags to compress the library
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if(NOT WIN32 AND ${hip_VERSION_FLAT} GREATER 600241132)
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237
profiler/ck_tile/src/tile_profile_grouped_conv_fwd.cpp
Normal file
237
profiler/ck_tile/src/tile_profile_grouped_conv_fwd.cpp
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@@ -0,0 +1,237 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <initializer_list>
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#include <iostream>
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#include <numeric>
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#include "tile_profile_grouped_conv_fwd_impl.hpp"
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#include "tile_profiler_operation_registry.hpp"
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// CK Tile library dependencies
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#include "ck_tile/core/numeric/integral_constant.hpp"
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#include "ck_tile/ops/common/tensor_layout.hpp"
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namespace {
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enum struct ConvLayout
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{
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GNCHW_GKCYX_GNKHW, // 0
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GNHWC_GKYXC_GNHWK, // 1
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NHWGC_GKYXC_NHWGK, // 2
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NGCHW_GKYXC_NGKHW, // 3
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NGCHW_GKCYX_NGKHW, // 4
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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_F32_BF16, // 2
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F16_F16_F16_BF8_F8, // 3
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I8_I8_I8, // 4
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BF16_BF16_BF16, // 5
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F32_F32_F32_TF32, // 6
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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::string conv_param_parser_helper_msg;
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conv_param_parser_helper_msg += "Following arguments (depending on number of spatial dims):\n"
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" Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)\n"
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" G, N, K, C, \n"
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" <filter spatial dimensions>, (ie Y, X for 2D)\n"
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" <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
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" <strides>, (ie Sy, Sx for 2D)\n"
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" <dilations>, (ie Dy, Dx for 2D)\n"
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" <left padding>, (ie LeftPy, LeftPx for 2D)\n"
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" <right padding>, (ie RightPy, RightPx for 2D)\n";
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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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<< " 8: Input fp32, Weight fp32, Output fp32, Compute tf32)\n"
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<< "arg3: 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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<< " 2: Input[N, G, C, Hi, Wi], Weight[G, K, Y, X, C], Output[N, "
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"G, K, Ho, Wo]\n"
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<< " 3: Input[N, G, C, Hi, Wi], Weight[G, K, C, Y, X], Output[N, "
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"G, K, Ho, Wo])\n"
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<< "arg4: indexing data type (0: 32-bit, 1: 64-bit)\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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<< 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 tile_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 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_tile::conv::parse_conv_param(num_dim_spatial, 10, argv);
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constexpr ck_tile::index_t k_batch = 1;
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using F32 = float;
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using F16 = ck_tile::half_t;
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using BF16 = ck_tile::bfloat16_t;
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using F8 = ck_tile::fp8_t;
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using BF8 = ck_tile::bf8_t;
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#if defined(__gfx942__)
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using TF32 = ck::tf32_t;
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#endif
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using NHWGC = ck_tile::tensor_layout::convolution::NHWGC;
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using NDHWGC = ck_tile::tensor_layout::convolution::NDHWGC;
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using GKYXC = ck_tile::tensor_layout::convolution::GKYXC;
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using GKZYXC = ck_tile::tensor_layout::convolution::GKZYXC;
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using NHWGK = ck_tile::tensor_layout::convolution::NHWGK;
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using NDHWGK = ck_tile::tensor_layout::convolution::NDHWGK;
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constexpr auto I2 = ck_tile::number<2>{};
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constexpr auto I3 = ck_tile::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 compute_type_a,
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auto compute_type_b) {
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constexpr ck_tile::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 ComputeTypeA = decltype(compute_type_a);
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using ComputeTypeB = decltype(compute_type_b);
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bool pass = ck_tile::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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ComputeTypeA,
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ComputeTypeB>(
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do_verification, init_method, do_log, time_kernel, params, k_batch);
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return pass ? 0 : 1;
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};
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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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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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if(data_type == ConvDataType::BF16_F32_BF16)
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{
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// fp32 atomic add is used for weight tensor in bf16 kernel
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return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
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}
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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::F32_F32_F32_TF32)
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{
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#if defined(__gfx942__)
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return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, TF32{}, TF32{});
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#endif
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}
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}
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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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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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if(data_type == ConvDataType::BF16_F32_BF16)
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{
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// fp32 atomic add is used for weight tensor in bf16 kernel
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
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}
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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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if(data_type == ConvDataType::F16_F16_F16_BF8_F8)
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{
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, BF8{}, F8{});
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}
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else if(data_type == ConvDataType::I8_I8_I8)
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{
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return profile(
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I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, int8_t{}, int8_t{}, int8_t{}, int8_t{}, int8_t{});
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}
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else if(data_type == ConvDataType::F32_F32_F32_TF32)
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{
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#if defined(__gfx942__)
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return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, TF32{}, TF32{});
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#endif
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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, tile_profile_grouped_conv_fwd);
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