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https://github.com/ROCm/composable_kernel.git
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* Convolution ND * Code unification across dimensions for generating tensor descriptors. * Example * Instances * Move convnd f32 instance file to comply with repo structure. * Conv 1D tensor layouts. * Formatting and use ReferenceConv * Reference ConvFwd supporting 1D and 2D convolution. * Debug printing TensorLayout name. * Conv fwd 1D instance f32 * Refactor conv ND example. Needed to support various conv dimensio. Needed to support various conv dimensions * Rename conv nd example director to prevent conflicts. * Refactor some common utility to single file. Plus some tests. * Refactor GetHostTensorDescriptor + UT. * Add 1D test case. * Test reference convolution 1d/2d * Remove some leftovers. * Fix convolution example error for 1D * Refactor test check errors utility function. * Test Conv2D Fwd XDL * More UT for 1D case. * Parameterize input & weight initializers. * Rename example to prevent conflicts. * Split convnd instance into separate files for 1d/2d * Address review comments. * Fix data type for flops/gbytes calculations. * Assign example number 11. * 3D cases for convolution utility functions. * 3D reference convolution. * Add support for 3D convolution. * Check for inputs bigger than 2GB. * Formatting * Support for bf16/f16/f32/i8 - conv instances + UT. * Use check_err from test_util.hpp. * Split convnd test into separate files for each dim. * Fix data generation and use proper instances. * Formatting * Skip tensor initialization if not necessary. * Fix CMakefiles. * Remove redundant conv2d_fwd test. * Lower problem size for conv3D UT. * 3D case for convnd example. * Remove leftovers after merge. * Add Conv Specialization string to GetTypeString * Skip instance causing numerical errors. * Small fixes. * Remove redundant includes. * Fix namespace name error. * Script for automatic testing and logging convolution fwd UTs * Comment out numactl cmd. * Refine weights initalization and relax rtol for fp16 * Move test_util.hpp to check_err.hpp * Refine weights initalization and relax rtol for fp16 * Refactor common part of test conv utils. * Move utility function to single common place. * Add additional common functions to utility. * Refactor convnd_fwd_xdl examples. * Remove redundant files. * Unify structure. * Add constructor to ConvParams. * And add input parameters validation. * Modify conv examples to use single utility file. * Remove check_error from host_tensor.hpp * Get rid of check_indices function. * Remove bf16_to_f32 function overload for scalars. * Fix namespace. * Add half_float::half for check_err. * Fix conv params size in UT. * Fix weights initialization for int8. * Fix weights initialization for int8. * Add type_convert when store output in ref conv 1D. * Get back old conv2d_fwd_xdl operation. * Silence conv debug print. * format * clean * clean * Fix merge. * Fix namespace for check_err * Formatting. * Fix merge artifacts. * Remove deleted header. * Fix some includes and use ck::utils::check_err. * Remove unused check_indices restored by previous merge. * Fix namespaces after merge. * Fix compilation error. * Small fixes. * Use common functions. * Fix filename * Fix namespaces. * Fix merge artifact - retrieve removed by accident fun. * Fix ConvForwardSpecialization. * Working example of OpInstanceRunEngine for conv2dfwd UT. * Adhere to coding style rules. * Formatting and adhere to coding style rules. * Fix merge artifacts. * Utility for collecting conv fwd instances. + Plus commmon part for parsing cmdline params. * Refactor FillUniform because of segfault for int8_t. * Naming convention. * Elegant version of device mem allocation. * Use OpInstanceRunEngine in conv fwd nd tests. * Multiple refinements. * conditional init * don't run reference op if not provided. * Use OpInstanceRunEngine for ckProfiler conv_fwd * Refactor common tensor fill function to separate file. * Clean up unused functions. * Support different init methods. * Create CMake target for conv_fwd_util. * Add header for profile_convnd_fwd.cpp * Fix CMakefiles to link with conv_fwd_util where needed. * Fix some clutter. Co-authored-by: Adam Osewski <aosewski@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
239 lines
8.3 KiB
C++
239 lines
8.3 KiB
C++
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#include "conv_fwd_util.hpp"
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namespace ck {
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namespace utils {
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namespace conv {
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/**
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* @brief Calculate number of FLOPs for Convolution
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*
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* @param[in] N Batch size.
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* @param[in] C Number of input channels.
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* @param[in] K Number of output channels.
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* @param[in] filter_spatial_lengths Filter spatial dimensions lengths.
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* @param[in] output_spatial_lengths Convolution output spatial dimensions
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* lengths.
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*
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* @return The number of flops.
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*/
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std::size_t get_flops(ck::index_t N,
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ck::index_t C,
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ck::index_t K,
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const std::vector<ck::index_t>& filter_spatial_lengths,
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const std::vector<ck::index_t>& output_spatial_lengths)
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{
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// 2 * N * K * <output spatial lengths product> * C * <filter spatial lengths product>
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return static_cast<std::size_t>(2) * N * K *
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std::accumulate(std::begin(output_spatial_lengths),
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std::end(output_spatial_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>()) *
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C *
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std::accumulate(std::begin(filter_spatial_lengths),
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std::end(filter_spatial_lengths),
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>());
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}
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ConvParams::ConvParams()
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: num_dim_spatial(2),
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N(128),
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K(256),
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C(192),
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filter_spatial_lengths(2, 3),
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input_spatial_lengths(2, 71),
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conv_filter_strides(2, 2),
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conv_filter_dilations(2, 1),
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input_left_pads(2, 1),
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input_right_pads(2, 1)
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{
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}
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ConvParams::ConvParams(ck::index_t n_dim,
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ck::index_t n_batch,
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ck::index_t n_out_channels,
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ck::index_t n_in_channels,
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const std::vector<ck::index_t>& filters_len,
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const std::vector<ck::index_t>& input_len,
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const std::vector<ck::index_t>& strides,
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const std::vector<ck::index_t>& dilations,
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const std::vector<ck::index_t>& left_pads,
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const std::vector<ck::index_t>& right_pads)
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: num_dim_spatial(n_dim),
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N(n_batch),
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K(n_out_channels),
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C(n_in_channels),
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filter_spatial_lengths(filters_len),
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input_spatial_lengths(input_len),
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conv_filter_strides(strides),
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conv_filter_dilations(dilations),
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input_left_pads(left_pads),
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input_right_pads(right_pads)
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{
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if(filter_spatial_lengths.size() != num_dim_spatial ||
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input_spatial_lengths.size() != num_dim_spatial ||
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conv_filter_strides.size() != num_dim_spatial ||
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conv_filter_dilations.size() != num_dim_spatial ||
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input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
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{
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throw(std::runtime_error(
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"ConvParams::GetOutputSpatialLengths: "
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"parameter size is different from number of declared dimensions!"));
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}
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}
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std::vector<ck::index_t> ConvParams::GetOutputSpatialLengths() const
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{
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if(filter_spatial_lengths.size() != num_dim_spatial ||
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input_spatial_lengths.size() != num_dim_spatial ||
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conv_filter_strides.size() != num_dim_spatial ||
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conv_filter_dilations.size() != num_dim_spatial ||
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input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
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{
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throw(std::runtime_error(
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"ConvParams::GetOutputSpatialLengths: "
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"parameter size is different from number of declared dimensions!"));
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}
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std::vector<ck::index_t> out_spatial_len(num_dim_spatial, 0);
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for(ck::index_t i = 0; i < num_dim_spatial; ++i)
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{
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// XEff = (X - 1) * conv_dilation_w + 1;
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// Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
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const ck::index_t idx_eff =
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(filter_spatial_lengths[i] - 1) * conv_filter_dilations[i] + 1;
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out_spatial_len[i] =
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(input_spatial_lengths[i] + input_left_pads[i] + input_right_pads[i] - idx_eff) /
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conv_filter_strides[i] +
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1;
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}
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return out_spatial_len;
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}
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ConvParams parse_conv_params(int num_dim_spatial, int arg_idx, char* const argv[])
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{
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ck::utils::conv::ConvParams params;
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params.num_dim_spatial = num_dim_spatial;
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params.N = std::stoi(argv[arg_idx++]);
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params.K = std::stoi(argv[arg_idx++]);
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params.C = std::stoi(argv[arg_idx++]);
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params.filter_spatial_lengths.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_spatial_lengths.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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params.conv_filter_strides.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
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}
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params.conv_filter_dilations.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_left_pads.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_left_pads[i] = std::stoi(argv[arg_idx++]);
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}
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params.input_right_pads.resize(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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params.input_right_pads[i] = std::stoi(argv[arg_idx++]);
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}
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return params;
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}
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HostTensorDescriptor get_output_host_tensor_descriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial)
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{
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namespace tl = ck::tensor_layout::convolution;
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switch(num_dim_spatial)
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{
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case 3: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWK{});
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}
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case 2: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWK{});
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}
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case 1: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWK{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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}
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}
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HostTensorDescriptor get_filters_host_tensor_descriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial)
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{
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namespace tl = ck::tensor_layout::convolution;
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switch(num_dim_spatial)
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{
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case 3: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KZYXC{});
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}
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case 2: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KYXC{});
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}
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case 1: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::KXC{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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}
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}
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HostTensorDescriptor get_input_host_tensor_descriptor(const std::vector<std::size_t>& dims,
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int num_dim_spatial)
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{
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namespace tl = ck::tensor_layout::convolution;
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switch(num_dim_spatial)
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{
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case 3: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NDHWC{});
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}
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case 2: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NHWC{});
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}
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case 1: {
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return ck::utils::conv::get_host_tensor_descriptor(dims, tl::NWC{});
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}
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default: {
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throw std::runtime_error("Unsupported number of spatial dimensions provided!");
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}
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}
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}
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} // namespace conv
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} // namespace utils
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} // namespace ck
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std::ostream& operator<<(std::ostream& os, const ck::utils::conv::ConvParams& p)
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{
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os << "ConvParams {"
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<< "\nnum_dim_spatial: " << p.num_dim_spatial << "\nN: " << p.N << "\nK: " << p.K
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<< "\nC: " << p.C << "\nfilter_spatial_lengths: " << p.filter_spatial_lengths
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<< "\ninput_spatial_lengths: " << p.input_spatial_lengths
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<< "\nconv_filter_strides: " << p.conv_filter_strides
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<< "\nconv_filter_dilations: " << p.conv_filter_dilations
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<< "\ninput_left_pads: " << p.input_left_pads
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<< "\ninput_right_pads: " << p.input_right_pads;
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return os;
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}
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