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https://github.com/ROCm/composable_kernel.git
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Clean up conv example, Instances, profiler and test (#324)
* convnd_fwd fp16 example * update example * update example * update instance * updating refernce conv * update reference conv * update conv fwd profiler * update conv 1d and 3d instance * update include path * clean * update profiler for conv bwd data and weight * update conv bwd weight * clean * update conv example * update profiler for conv bwd weight * update ckprofiler for conv bwd data * fix reference conv bwd data bug; update conv bwd data test * update examples * fix initialization issue * update test for conv fwd * clean * clean * remove test case too sensitive to error threshhold * fix test * clean * fix build * adding conv multiple d * adding conv multiple D * add matrix padder * add gemm padding to convnd * adding group conv * update gemm multi-d * refactor * refactor * refactor * clean * clean * refactor * refactor * reorg * add ds * add bias * clean * add G * adding group * adding group * adding group * update Tensor * clean * update example * update DeviceGemmMultipleD_Xdl_CShuffle * update conv bwd-data and bwd-weight * upate contraction example * update gemm and batch gemm with e permute * fix example build * instance for grouped conv1d * update example * adding group conv instance * update gemm bilinear instance * update gemm+add+add+fastgelu instance * update profiler * update profiler * update test * update test and client example * clean * add grouped conv into profiler * update profiler * clean * add test grouped conv, update all conv test to gtest * update test
This commit is contained in:
Chao Liu
175
library/src/utility/convolution_parameter.cpp
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175
library/src/utility/convolution_parameter.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include "ck/host_utility/io.hpp"
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#include "ck/library/utility/convolution_parameter.hpp"
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namespace ck {
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namespace utils {
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namespace conv {
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ConvParam::ConvParam(ck::index_t n_dim,
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ck::index_t group_count,
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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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G_(group_count),
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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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output_spatial_lengths_(num_dim_spatial_),
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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(static_cast<ck::index_t>(filter_spatial_lengths_.size()) != num_dim_spatial_ ||
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static_cast<ck::index_t>(input_spatial_lengths_.size()) != num_dim_spatial_ ||
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static_cast<ck::index_t>(conv_filter_strides_.size()) != num_dim_spatial_ ||
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static_cast<ck::index_t>(conv_filter_dilations_.size()) != num_dim_spatial_ ||
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static_cast<ck::index_t>(input_left_pads_.size()) != num_dim_spatial_ ||
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static_cast<ck::index_t>(input_right_pads_.size()) != num_dim_spatial_)
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{
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throw(
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std::runtime_error("ConvParam::ConvParam: "
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"parameter size is different from number of declared dimensions!"));
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}
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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 x_eff = (filter_spatial_lengths_[i] - 1) * conv_filter_dilations_[i] + 1;
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output_spatial_lengths_[i] =
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(input_spatial_lengths_[i] + input_left_pads_[i] + input_right_pads_[i] - x_eff) /
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conv_filter_strides_[i] +
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1;
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}
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}
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ConvParam::ConvParam()
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: ConvParam::ConvParam(2, 1, 128, 256, 192, {3, 3}, {71, 71}, {2, 2}, {1, 1}, {1, 1}, {1, 1})
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{
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}
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std::vector<ck::index_t> ConvParam::GetOutputSpatialLengths() const
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{
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return output_spatial_lengths_;
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}
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std::size_t ConvParam::GetFlops() const
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{
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// 2 * G * N * K * C * <output spatial lengths product> * <filter spatial lengths product>
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return static_cast<std::size_t>(2) * G_ * N_ * K_ * C_ *
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std::accumulate(std::begin(output_spatial_lengths_),
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std::begin(output_spatial_lengths_) + num_dim_spatial_,
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static_cast<std::size_t>(1),
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std::multiplies<std::size_t>()) *
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std::accumulate(std::begin(filter_spatial_lengths_),
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std::begin(filter_spatial_lengths_) + num_dim_spatial_,
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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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std::string get_conv_param_parser_helper_msg()
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{
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std::string msg;
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msg += "Following arguments (depending on number of spatial dims):\n"
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" Number of spatial dimensions (1=Conv1d, 2=Conv2d, 3=Conv3d)\n"
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" G, N, K, C, \n"
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" <filter spatial dimensions>, (ie Y, X for 2D)\n"
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" <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
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" <strides>, (ie Sy, Sx for 2D)\n"
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" <dilations>, (ie Dy, Dx for 2D)\n"
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" <left padding>, (ie LeftPy, LeftPx for 2D)\n"
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" <right padding>, (ie RightPy, RightPx for 2D)\n";
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return msg;
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}
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ck::utils::conv::ConvParam parse_conv_param(int num_dim_spatial, int arg_idx, char* const argv[])
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{
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const ck::index_t G = std::stoi(argv[arg_idx++]);
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const ck::index_t N = std::stoi(argv[arg_idx++]);
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const ck::index_t K = std::stoi(argv[arg_idx++]);
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const ck::index_t C = std::stoi(argv[arg_idx++]);
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std::vector<ck::index_t> filter_spatial_lengths(num_dim_spatial);
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std::vector<ck::index_t> input_spatial_lengths(num_dim_spatial);
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std::vector<ck::index_t> conv_filter_strides(num_dim_spatial);
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std::vector<ck::index_t> conv_filter_dilations(num_dim_spatial);
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std::vector<ck::index_t> input_left_pads(num_dim_spatial);
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std::vector<ck::index_t> input_right_pads(num_dim_spatial);
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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filter_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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input_spatial_lengths[i] = std::stoi(argv[arg_idx++]);
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}
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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conv_filter_strides[i] = std::stoi(argv[arg_idx++]);
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}
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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conv_filter_dilations[i] = std::stoi(argv[arg_idx++]);
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}
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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input_left_pads[i] = std::stoi(argv[arg_idx++]);
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}
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for(int i = 0; i < num_dim_spatial; ++i)
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{
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input_right_pads[i] = std::stoi(argv[arg_idx++]);
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}
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return ck::utils::conv::ConvParam{num_dim_spatial,
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G,
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N,
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K,
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C,
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filter_spatial_lengths,
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input_spatial_lengths,
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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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}
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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::ConvParam& p)
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{
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os << "ConvParam {"
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<< "\nnum_dim_spatial: " << p.num_dim_spatial_ << "\nG: " << p.G_ << "\nN: " << p.N_
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<< "\nK: " << p.K_ << "\nC: " << p.C_
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<< "\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_ << "}\n";
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return os;
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}
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