mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 01:10:17 +00:00
Add v3 support for Groupd fwd conv+bias+clamp & ckProfiler (#2463)
* Add logging to IsSupported. * Less casting in AddClamp * Conv+bias+clamp instances & profiler BF16 * Fix 3D instances & run just 1x for verification. * :Run just once for verification conv fwd. * ckProfiler conv fwd clampwq * Remove exec bit & formatting * Add support for MultiD for grouped conv fwd v3. * Enable 2Lds. * clean * align instances * align instances * profiler fixes * Fixes * fix * fix --------- Co-authored-by: Adam Osewski <root@quanta-ccs-aus-f01-19.cs-aus.dcgpu> Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
This commit is contained in:
Adam Osewski
@@ -94,6 +94,8 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx11" OR SUPPORTED_GPU_TARGETS MATCHES "gfx12
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list(APPEND PROFILER_OPS profile_batched_gemm.cpp)
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list(APPEND PROFILER_OPS profile_gemm_b_scale.cpp)
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list(APPEND PROFILER_OPS profile_grouped_conv_fwd.cpp)
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list(APPEND PROFILER_OPS profile_grouped_conv_fwd_bias_clamp.cpp)
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list(APPEND PROFILER_OPS profile_grouped_conv_fwd_clamp.cpp)
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list(APPEND PROFILER_OPS profile_grouped_conv_bwd_data.cpp)
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list(APPEND PROFILER_OPS profile_grouped_conv_bwd_weight.cpp)
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endif()
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@@ -197,6 +199,10 @@ if(SUPPORTED_GPU_TARGETS MATCHES "gfx9")
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list(APPEND DEVICE_INSTANCES device_grouped_convnd_bwd_weight_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_convscale_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_convinvscale_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv2d_fwd_clamp_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_clamp_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv2d_fwd_bias_clamp_instance)
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list(APPEND DEVICE_INSTANCES device_grouped_conv3d_fwd_bias_clamp_instance)
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endif()
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if((SUPPORTED_GPU_TARGETS MATCHES "gfx9" AND (DTYPES MATCHES "fp16" OR NOT DEFINED DTYPES)) OR
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191
profiler/src/profile_grouped_conv_fwd_bias_clamp.cpp
Normal file
191
profiler/src/profile_grouped_conv_fwd_bias_clamp.cpp
Normal file
@@ -0,0 +1,191 @@
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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 "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "profiler/profile_grouped_conv_fwd_bias_clamp_impl.hpp"
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/ignore.hpp"
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#include "profiler_operation_registry.hpp"
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#include <iostream>
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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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NGCHW_GKYXC_NGKHW, // 2
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NGCHW_GKCYX_NGKHW, // 3
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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_bias_clamp"
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#define OP_DESC "Grouped Convolution Forward+Bias+Clamp"
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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: 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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<< 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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int grouped_conv_fwd_bias_clamp(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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if(index_type != IndexType::INDEX_T)
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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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using F32 = float;
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using BF16 = ck::bhalf_t;
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using F16 = ck::half_t;
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using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
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using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
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using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
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using GKYXC = ck::tensor_layout::convolution::GKYXC;
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using NHWGC = ck::tensor_layout::convolution::NHWGC;
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using NHWGK = ck::tensor_layout::convolution::NHWGK;
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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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bool pass = ck::profiler::profile_grouped_conv_fwd_bias_clamp_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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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 == 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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}
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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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}
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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, grouped_conv_fwd_bias_clamp);
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194
profiler/src/profile_grouped_conv_fwd_clamp.cpp
Normal file
194
profiler/src/profile_grouped_conv_fwd_clamp.cpp
Normal file
@@ -0,0 +1,194 @@
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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 "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "profiler/profile_grouped_conv_fwd_impl.hpp"
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/ignore.hpp"
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#include "profiler_operation_registry.hpp"
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#include <iostream>
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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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NGCHW_GKYXC_NGKHW, // 2
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NGCHW_GKCYX_NGKHW, // 3
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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_clamp"
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#define OP_DESC "Grouped Convolution Forward+Clamp"
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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: 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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<< 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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int grouped_conv_fwd_clamp(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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if(index_type != IndexType::INDEX_T)
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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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using F32 = float;
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using BF16 = ck::bhalf_t;
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using F16 = ck::half_t;
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using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
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using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
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using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
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using GKYXC = ck::tensor_layout::convolution::GKYXC;
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using NHWGC = ck::tensor_layout::convolution::NHWGC;
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using NHWGK = ck::tensor_layout::convolution::NHWGK;
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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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bool pass =
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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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ck::tensor_operation::element_wise::Clamp>(
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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 == 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(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
|
||||
{
|
||||
if(data_type == ConvDataType::F32_F32_F32)
|
||||
{
|
||||
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
|
||||
}
|
||||
else if(data_type == ConvDataType::F16_F16_F16)
|
||||
{
|
||||
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
|
||||
}
|
||||
else if(data_type == ConvDataType::BF16_BF16_BF16)
|
||||
{
|
||||
return profile(
|
||||
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, BF16{}, BF16{}, BF16{}, BF16{});
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "this data_type & layout is not implemented" << std::endl;
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
||||
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, grouped_conv_fwd_clamp);
|
||||
Reference in New Issue
Block a user