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Ding, Yi
6
example/ck_tile/04_img2col/CMakeLists.txt
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6
example/ck_tile/04_img2col/CMakeLists.txt
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# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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# SPDX-License-Identifier: MIT
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# not using add_example_executable() to add this target, since we don't want this to have
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# to be included in "make all/install/check"
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add_executable(tile_example_img2col image_to_column.cpp)
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51
example/ck_tile/04_img2col/README.md
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51
example/ck_tile/04_img2col/README.md
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# Image to Column (im2col) with CK Tile
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This example demonstrates the im2col transformation using the CK Tile programming model, a key step for converting convolution into GEMM for efficient GPU execution.
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---
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## Algorithm and Math
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Given an input image tensor $X$ and convolution kernel size, im2col rearranges sliding windows of $X$ into columns:
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- For each patch, flatten and stack as a column in the output matrix.
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- Enables convolution as matrix multiplication: $\text{im2col}(X) \times W$.
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---
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## Tile Programming Model
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- **Tiles**: Each thread block processes a tile (block of patches).
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- **Pipeline**: Modular, can be extended for fused operations (e.g., quantization, activation).
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---
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## Build & Run
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```bash
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mkdir build && cd build
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# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
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../script/cmake-ck-dev.sh ../ <arch>
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make tile_example_img2col -j
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./bin/tile_example_img2col -?
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```
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---
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## Source Structure
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- **Kernel**: `image_to_column.hpp` (tile-programming kernel template)
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- **Executable**: `image_to_column.cpp` (argument parsing, kernel launch)
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- **Build**: `CMakeLists.txt`
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---
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## Related CK Tile Examples
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- [03_gemm](../03_gemm/README.md): GEMM with tiles (im2col output as input)
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- [05_reduce](../05_reduce/README.md): Reductions with tiles
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- [06_permute](../06_permute/README.md): Permutation with tiles
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For distribution, see `include/ck_tile/tile_program/tile_distribution/`.
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---
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[Back to CK Tile Examples](../README.md)
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177
example/ck_tile/04_img2col/image_to_column.cpp
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177
example/ck_tile/04_img2col/image_to_column.cpp
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <algorithm>
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#include <cstring>
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#include "ck_tile/host.hpp"
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#include "image_to_column.hpp"
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// Host API implementation
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template <>
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float image_to_column(const image_to_column_traits& traits,
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const image_to_column_args<2>& args,
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const ck_tile::stream_config& stream_conf)
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{
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if(traits.data_type.compare("fp16") == 0)
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{
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constexpr ck_tile::index_t NDimSpatial = 2;
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constexpr ck_tile::index_t VectorSize = 8;
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using thread_tile = ck_tile::sequence<8, 8>;
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using warp_tile = ck_tile::sequence<64, 64>;
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using block_tile = ck_tile::sequence<128, 128>;
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using Shape = ck_tile::TileImageToColumnShape<thread_tile, warp_tile, block_tile>;
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using InDataType = ck_tile::half_t;
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using OutDataType = ck_tile::half_t;
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using PipelineProblem = ck_tile::BlockImageToColumnProblem<InDataType,
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OutDataType,
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Shape,
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NDimSpatial,
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VectorSize,
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VectorSize>;
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using Kernel = ck_tile::ImageToColumn<PipelineProblem>;
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auto kargs = Kernel::MakeKargs(args.p_in,
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args.p_out,
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args.G,
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args.N,
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args.C,
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args.input_spatial_lengths,
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args.filter_spatial_lengths,
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args.output_spatial_lengths,
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args.image_g_n_c_wis_strides,
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args.gemm_g_m_k_strides,
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args.conv_filter_strides,
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args.conv_filter_dilations,
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args.input_left_pads,
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args.input_right_pads);
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const dim3 grids = Kernel::GridSize(
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args.N * args.output_spatial_lengths[0] * args.output_spatial_lengths[1],
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args.filter_spatial_lengths[0] * args.filter_spatial_lengths[1] * args.C,
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args.G);
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const dim3 blocks = Kernel::BlockSize();
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constexpr ck_tile::index_t kBlockPerCu = 2;
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float ave_time = ck_tile::launch_kernel(
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stream_conf, ck_tile::make_kernel<kBlockPerCu>(Kernel{}, grids, blocks, 0, kargs));
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return ave_time;
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}
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return 0;
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}
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int main(int argc, char* argv[])
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{
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constexpr ck_tile::index_t NDimSpatial = 2;
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ExecutionConfig config;
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ck_tile::conv::ConvParam conv_params = DefaultConvParams;
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if(!parse_cmd_args(argc, argv, config, conv_params))
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{
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return EXIT_FAILURE;
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}
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if(conv_params.num_dim_spatial_ != NDimSpatial)
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{
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std::cerr << "unsupported # of spatial dimensions" << std::endl;
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return EXIT_FAILURE;
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}
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using InDataType = ck_tile::half_t;
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using OutDataType = ck_tile::half_t;
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using ImLayout = ck_tile::tensor_layout::convolution::NHWGC;
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const auto G = conv_params.G_;
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const auto N = conv_params.N_;
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const auto C = conv_params.C_;
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const ck_tile::long_index_t NHoWo =
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N * std::accumulate(conv_params.output_spatial_lengths_.begin(),
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std::next(conv_params.output_spatial_lengths_.begin(), NDimSpatial),
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1,
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std::multiplies<>());
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const ck_tile::long_index_t CYX =
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C * std::accumulate(conv_params.filter_spatial_lengths_.begin(),
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std::next(conv_params.filter_spatial_lengths_.begin(), NDimSpatial),
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1,
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std::multiplies<>());
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const auto in_desc =
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ck_tile::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
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const auto out_desc = ck_tile::HostTensorDescriptor({G, NHoWo, CYX});
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// host verify
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ck_tile::HostTensor<InDataType> in(in_desc);
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ck_tile::HostTensor<OutDataType> out_device(out_desc);
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ck_tile::HostTensor<OutDataType> out_host(out_desc);
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switch(config.init_method)
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{
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case 0: break;
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case 1: ck_tile::FillUniformDistributionIntegerValue<InDataType>{-5.f, 5.f}(in); break;
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default: ck_tile::FillUniformDistribution<InDataType>{-0.5, 0.5}(in); break;
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}
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ck_tile::DeviceMem in_device_buf(in.get_element_space_size_in_bytes());
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ck_tile::DeviceMem out_device_buf(out_device.get_element_space_size_in_bytes());
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in_device_buf.ToDevice(in.data());
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image_to_column_traits traits{"fp16"};
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image_to_column_args<NDimSpatial> args{
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in_device_buf.GetDeviceBuffer(),
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out_device_buf.GetDeviceBuffer(),
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G,
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N,
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C,
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_spatial_lengths_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.filter_spatial_lengths_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.output_spatial_lengths_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial + 3>(in_desc.get_strides()),
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ck_tile::to_array<ck_tile::long_index_t, 3>(out_desc.get_strides()),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_strides_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.conv_filter_dilations_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_left_pads_),
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ck_tile::to_array<ck_tile::long_index_t, NDimSpatial>(conv_params.input_right_pads_)};
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float ave_time =
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image_to_column(traits, args, ck_tile::stream_config{nullptr, config.time_kernel});
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if(config.time_kernel)
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{
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std::size_t num_btype = G * NHoWo * CYX * (sizeof(OutDataType) + sizeof(InDataType));
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << gb_per_sec << " GB/s" << std::endl;
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}
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else
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{
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std::cout << "image_to_column: pass, No Perf generated due to config.time_kernel=0"
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<< std::endl;
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}
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bool pass = true;
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if(config.do_verification)
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{
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// reference
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ck_tile::reference_im2col<InDataType, OutDataType, NDimSpatial>(in, out_host, conv_params);
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out_device_buf.FromDevice(out_device.data());
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pass = ck_tile::check_err(out_device, out_host);
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std::cout << "valid:" << (pass ? "y" : "n") << std::endl;
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}
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return !pass;
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}
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105
example/ck_tile/04_img2col/image_to_column.hpp
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105
example/ck_tile/04_img2col/image_to_column.hpp
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@@ -0,0 +1,105 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include "ck_tile/core.hpp"
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#include "ck_tile/host/kernel_launch.hpp"
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#include "ck_tile/ops/image_to_column.hpp"
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#include <string>
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#define DefaultConvParams \
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ck_tile::conv::ConvParam \
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{ \
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2, 2, 32, 32, 32, {4, 4}, {64, 64}, {1, 1}, {1, 1}, {0, 0}, { 0, 0 } \
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}
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struct ExecutionConfig final
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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};
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inline void print_help_msg()
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{
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std::cerr << "arg1: verification (0=no, 1=yes)\n"
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
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<< "arg3: time kernel (0=no, 1=yes)\n"
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<< ck_tile::conv::get_conv_param_parser_helper_msg() << std::endl;
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}
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inline bool parse_cmd_args(int argc,
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char* argv[],
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ExecutionConfig& config,
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ck_tile::conv::ConvParam& conv_params)
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{
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constexpr int num_execution_config_args =
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3; // arguments for do_verification, init_method, time_kernel
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constexpr int num_conv_param_leading_args = 5; // arguments for num_dim_spatial_, G_, N_, K_, C_
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constexpr int threshold_to_catch_partial_args = 1 + num_execution_config_args;
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constexpr int threshold_to_catch_all_args =
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threshold_to_catch_partial_args + num_conv_param_leading_args;
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if(argc == 1)
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{
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// use default
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config = ExecutionConfig{};
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}
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// catch only ExecutionConfig arguments
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else if(argc == threshold_to_catch_partial_args)
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{
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config.do_verification = std::stoi(argv[1]);
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config.init_method = std::stoi(argv[2]);
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config.time_kernel = std::stoi(argv[3]);
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}
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// catch both ExecutionConfig & ConvParam arguments
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else if(threshold_to_catch_all_args < argc && ((argc - threshold_to_catch_all_args) % 3 == 0))
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{
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config.do_verification = std::stoi(argv[1]);
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config.init_method = std::stoi(argv[2]);
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config.time_kernel = std::stoi(argv[3]);
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const ck_tile::index_t num_dim_spatial = std::stoi(argv[4]);
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conv_params =
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ck_tile::conv::parse_conv_param(num_dim_spatial, threshold_to_catch_partial_args, argv);
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}
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else
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{
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print_help_msg();
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return false;
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}
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return true;
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}
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struct image_to_column_traits
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{
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std::string data_type;
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};
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template <ck_tile::index_t NDimSpatial>
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struct image_to_column_args
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{
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const void* p_in;
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void* p_out;
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const ck_tile::long_index_t G;
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const ck_tile::long_index_t N;
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const ck_tile::long_index_t C;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_spatial_lengths;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> filter_spatial_lengths;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> output_spatial_lengths;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial + 3> image_g_n_c_wis_strides;
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const ck_tile::array<ck_tile::long_index_t, 3> gemm_g_m_k_strides;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_strides;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> conv_filter_dilations;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_left_pads;
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const ck_tile::array<ck_tile::long_index_t, NDimSpatial> input_right_pads;
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};
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// host API
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template <ck_tile::index_t NDimSpatial>
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float image_to_column(const image_to_column_traits&,
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const image_to_column_args<NDimSpatial>&,
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const ck_tile::stream_config&);
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