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* Wrap ck host utitlies in CK namespace.
The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace.
Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace.
There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library.
* Add using declarations to test code.
After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace.
* Add using declarations to client examples.
[ROCm/composable_kernel commit: ad57f6ef0b]
173 lines
7.2 KiB
C++
173 lines
7.2 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#include "common.hpp"
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using ::ck::DeviceMem;
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using ::ck::HostTensorDescriptor;
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using ::ck::Tensor;
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using InDataType = FP32;
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using OutDataType = FP32;
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using ImLayout = ck::tensor_layout::convolution::GNHWC;
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using ImageToColumnOp = ck::conv_tensor_rearrange_op::ImageToColumn;
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// clang-format off
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using DeviceImgToColInstance = ck::tensor_operation::device::DeviceImageToColumnImpl
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//#####################| Num| ImLayout| InDataType| OutDataType| Block| MPer| KPer| Thread| Scalar|
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//#####################| Dim| | | | Size| Block| Block| Cluster| Per|
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//#####################| Spatial| | | | | | | Lengths| Vector|
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//#####################| | | | | | | | | |
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< NDimSpatial, ImLayout, InDataType, OutDataType, 256, 128, 128, S<16, 16>, 1>;
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// clang-format on
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bool RunImageToColumn(const ExecutionConfig& config, const ck::utils::conv::ConvParam& conv_params)
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{
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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::index_t NDoHoWo =
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N * ck::accumulate_n<ck::index_t>(
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conv_params.output_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
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const ck::index_t CZYX =
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C * ck::accumulate_n<ck::index_t>(
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conv_params.filter_spatial_lengths_.begin(), NDimSpatial, 1, std::multiplies<>());
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const auto in_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<ImLayout>(conv_params);
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const auto out_desc = HostTensorDescriptor({G, NDoHoWo, CZYX});
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std::array<ck::index_t, NDimSpatial> input_spatial_lengths{};
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std::array<ck::index_t, NDimSpatial> filter_spatial_lengths{};
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std::array<ck::index_t, NDimSpatial> output_spatial_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> image_g_n_c_wis_strides{};
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std::array<ck::index_t, 3> gemm_g_m_k_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input_left_pads{};
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std::array<ck::index_t, NDimSpatial> input_right_pads{};
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auto copy = [](const auto& x, auto& y) { std::copy(x.begin(), x.end(), y.begin()); };
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copy(conv_params.input_spatial_lengths_, input_spatial_lengths);
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copy(conv_params.filter_spatial_lengths_, filter_spatial_lengths);
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copy(conv_params.output_spatial_lengths_, output_spatial_lengths);
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copy(in_desc.GetStrides(), image_g_n_c_wis_strides);
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copy(out_desc.GetStrides(), gemm_g_m_k_strides);
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copy(conv_params.conv_filter_strides_, conv_filter_strides);
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copy(conv_params.conv_filter_dilations_, conv_filter_dilations);
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copy(conv_params.input_left_pads_, input_left_pads);
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copy(conv_params.input_right_pads_, input_right_pads);
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Tensor<InDataType> in(in_desc);
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Tensor<OutDataType> out_device(out_desc);
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Tensor<OutDataType> out_host(out_desc);
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std::cout << "in: " << in.mDesc << std::endl;
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std::cout << "out: " << out_device.mDesc << std::endl;
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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: in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5}); break;
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default: in.GenerateTensorValue(GeneratorTensor_3<InDataType>{-0.5, 0.5});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) * out_device.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(in.mData.data());
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// reset input to zero
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out_device_buf.SetZero();
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static_assert(std::is_default_constructible_v<DeviceImgToColInstance>);
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// do conv
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auto img2col = DeviceImgToColInstance{};
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auto invoker = img2col.MakeInvoker();
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auto argument = img2col.MakeArgument(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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input_spatial_lengths,
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filter_spatial_lengths,
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output_spatial_lengths,
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image_g_n_c_wis_strides,
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gemm_g_m_k_strides,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads);
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if(!img2col.IsSupportedArgument(argument))
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{
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std::cerr << "wrong! device_img2col with the specified compilation parameters does "
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"not support this img2col problem"
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<< std::endl;
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return false;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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std::size_t num_btype = G * NDoHoWo * CZYX * (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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if(config.do_verification)
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{
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auto ref_image_to_column = ck::tensor_operation::host::
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ReferenceImageToColumn<NDimSpatial, ImLayout, InDataType, OutDataType>();
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auto ref_invoker = ref_image_to_column.MakeInvoker();
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auto ref_argument = ref_image_to_column.MakeArgument(in,
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out_host,
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conv_params.filter_spatial_lengths_,
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conv_params.conv_filter_strides_,
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conv_params.conv_filter_dilations_,
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conv_params.input_left_pads_,
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conv_params.input_right_pads_);
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if(!ref_image_to_column.IsSupportedArgument(&ref_argument))
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{
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std::cerr << "wrong! ref_img2col with the specified compilation parameters does "
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"not support this img2col problem"
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<< std::endl;
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return false;
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}
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ref_invoker.Run(ref_argument);
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out_device_buf.FromDevice(out_device.mData.data());
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return ck::utils::check_err(out_device.mData, out_host.mData);
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}
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return true;
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}
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int RunImageToColumnExample(int argc, char* argv[])
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{
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ExecutionConfig config;
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ck::utils::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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return !RunImageToColumn(config, conv_params);
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}
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int main(int argc, char* argv[]) { return RunImageToColumnExample(argc, argv); }
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