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https://github.com/ROCm/composable_kernel.git
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Add column to image kernel (#930)
* Add column to image kernel
* Minor fixes for dtypes and client examples
* Disable tests for disabled dtypes
* Disable add instances functions for disabled data types
* Minor stylistic fixes
* Revert "Disable add instances functions for disabled data types"
This reverts commit 728b869563.
* Instances reduction
* Add comments in device_column_to_image_impl
* Update changelog and Copyrights
* Improve changelog
This commit is contained in:
Bartłomiej Kocot
@@ -1,2 +0,0 @@
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add_executable(client_image_to_column image_to_column.cpp)
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target_link_libraries(client_image_to_column PRIVATE composable_kernel::device_operations)
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5
client_example/22_im2col_col2im/CMakeLists.txt
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5
client_example/22_im2col_col2im/CMakeLists.txt
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@@ -0,0 +1,5 @@
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add_executable(client_image_to_column image_to_column.cpp)
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target_link_libraries(client_image_to_column PRIVATE composable_kernel::device_operations)
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add_executable(client_column_to_image column_to_image.cpp)
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target_link_libraries(client_column_to_image PRIVATE composable_kernel::device_operations)
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173
client_example/22_im2col_col2im/column_to_image.cpp
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173
client_example/22_im2col_col2im/column_to_image.cpp
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@@ -0,0 +1,173 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
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#include <cstdlib>
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#include <iomanip>
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#include <iostream>
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#include <iterator>
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#include <numeric>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp"
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#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using ImageLayout = ck::tensor_layout::convolution::GNHWC;
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static constexpr ck::index_t NumDimSpatial = 2;
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static constexpr ck::index_t G = 1;
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static constexpr ck::index_t N = 32; // batch size
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static constexpr ck::index_t C = 32; // input channel (per group)
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static constexpr ck::index_t Y = 3; // filter H
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static constexpr ck::index_t X = 3; // filter W
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static constexpr ck::index_t Hi = 28; // input H
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static constexpr ck::index_t Wi = 28; // input W
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static constexpr ck::index_t Ho = 28; // output H
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static constexpr ck::index_t Wo = 28; // output W
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main()
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{
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std::array<ck::index_t, 2> in_spatial_lengths{Hi, Wi};
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std::array<ck::index_t, 2> wei_spatial_lengths{Y, X};
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std::array<ck::index_t, 2> out_spatial_lengths{Ho, Wo};
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// We have NHWGC in memory space (G is dummy)
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// However, CK's API only accept length and stride with order of GNCHW
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// Hence, we need to adjust the order of stride
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std::array<ck::index_t, 5> image_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
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std::array<ck::index_t, 2> gemm_strides{Y * X * C, 1};
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std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
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std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
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std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1};
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std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1};
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SimpleDeviceMem in(sizeof(InDataType) * N * Ho * Wo * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * N * Hi * Wi * G * C);
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using namespace ck::conv_tensor_rearrange_op;
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using DeviceOp = ck::tensor_operation::device::DeviceConvTensorRearrange<NumDimSpatial,
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ImageLayout,
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InDataType,
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OutDataType,
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ColumnToImage>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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out.GetDeviceBuffer(),
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N,
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C,
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in_spatial_lengths,
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out_spatial_lengths,
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wei_spatial_lengths,
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image_strides,
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gemm_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t num_bytes = sizeof(InDataType) * N * Hi * Wi * G * C +
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sizeof(OutDataType) * N * Ho * Wo * Y * X * C;
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float gb_per_sec = num_bytes / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << gb_per_sec << " GB/s, "
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<< op_name << std::endl;
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if(avg_time < best_avg_time)
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{
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best_op_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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}
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}
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else
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{
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std::cerr << op_name << " does not support this problem" << std::endl;
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}
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}
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if(best_op_id < 0)
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{
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std::cerr << "no suitable instance" << std::endl;
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return EXIT_FAILURE;
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_gb_per_sec
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<< " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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out.GetDeviceBuffer(),
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N,
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C,
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in_spatial_lengths,
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out_spatial_lengths,
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wei_spatial_lengths,
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image_strides,
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gemm_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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input_right_pads);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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}
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@@ -9,13 +9,14 @@
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/image_to_column.hpp"
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#include "ck/library/tensor_operation_instance/gpu/conv_tensor_rearrange.hpp"
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#include "ck/tensor_operation/gpu/device/conv_tensor_rearrange_op.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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using InDataType = ck::half_t;
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using OutDataType = ck::half_t;
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using InLayout = ck::tensor_layout::convolution::GNHWC;
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using ImageLayout = ck::tensor_layout::convolution::GNHWC;
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static constexpr ck::index_t NumDimSpatial = 2;
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static constexpr ck::index_t G = 1;
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@@ -54,8 +55,8 @@ int main()
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// We have NHWGC in memory space (G is dummy)
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// However, CK's API only accept length and stride with order of GNCHW
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// Hence, we need to adjust the order of stride
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std::array<ck::index_t, 5> in_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
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std::array<ck::index_t, 2> out_strides{Y * X * C, 1};
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std::array<ck::index_t, 5> image_strides{C, Hi * Wi * G * C, 1, Wi * G * C, G * C};
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std::array<ck::index_t, 2> gemm_strides{Y * X * C, 1};
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std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1};
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std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1};
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@@ -65,8 +66,13 @@ int main()
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SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * G * C);
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SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * Y * X * C);
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using DeviceOp = ck::tensor_operation::device::
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DeviceImageToColumn<NumDimSpatial, InLayout, InDataType, OutDataType>;
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using namespace ck::conv_tensor_rearrange_op;
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using DeviceOp = ck::tensor_operation::device::DeviceConvTensorRearrange<NumDimSpatial,
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ImageLayout,
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InDataType,
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OutDataType,
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ImageToColumn>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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@@ -92,8 +98,8 @@ int main()
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in_spatial_lengths,
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out_spatial_lengths,
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wei_spatial_lengths,
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in_strides,
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out_strides,
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image_strides,
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gemm_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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@@ -148,8 +154,8 @@ int main()
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in_spatial_lengths,
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out_spatial_lengths,
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wei_spatial_lengths,
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in_strides,
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out_strides,
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image_strides,
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gemm_strides,
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filter_strides,
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filter_dilations,
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input_left_pads,
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