mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-19 20:40:07 +00:00
569dd9f47b
* Add host API
* manually rebase on develop
* clean
* manually rebase on develop
* exclude tests from all target
* address review comments
* update client app name
* fix missing lib name
* clang-format update
* refactor
* refactor
* refactor
* refactor
* refactor
* fix test issue
* refactor
* refactor
* refactor
* upate cmake and readme
Co-authored-by: Chao Liu <chao.liu2@amd.com>
[ROCm/composable_kernel commit: cec69bc3bc]
217 lines
8.1 KiB
C++
217 lines
8.1 KiB
C++
#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include <stdlib.h>
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#include <half.hpp>
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#include <vector>
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#include "conv_util.hpp"
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#include "profile_conv_bwd_weight_impl.hpp"
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int test_self()
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{
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bool pass = true;
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std::vector<ck::utils::conv::ConvParams> params;
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params.push_back({2, 128, 256, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
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params.push_back({2, 128, 256, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
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params.push_back({2, 128, 256, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
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for(auto& param : params)
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{
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// f32
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pass &= ck::profiler::profile_conv_bwd_weight_impl<2,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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true, // do_verification
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1, // init_method
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false, // do_log
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false, // time_kernel
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param.N_,
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param.K_,
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param.C_,
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param.input_spatial_lengths_,
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param.filter_spatial_lengths_,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides_,
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param.conv_filter_dilations_,
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param.input_left_pads_,
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param.input_right_pads_,
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2);
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// fp16
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pass &= ck::profiler::profile_conv_bwd_weight_impl<2,
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ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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true, // do_verification
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1, // init_method
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false, // do_log
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false, // time_kernel
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param.N_,
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param.K_,
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param.C_,
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param.input_spatial_lengths_,
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param.filter_spatial_lengths_,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides_,
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param.conv_filter_dilations_,
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param.input_left_pads_,
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param.input_right_pads_,
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2);
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}
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return pass;
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}
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int main(int argc, char* argv[])
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{
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int data_type = 1;
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int init_method = 1;
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// Conv shape
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ck::index_t N = 128;
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ck::index_t K = 256;
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ck::index_t C = 192;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 71;
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ck::index_t Wi = 71;
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ck::index_t conv_stride_h = 2;
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ck::index_t conv_stride_w = 2;
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ck::index_t conv_dilation_h = 1;
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ck::index_t conv_dilation_w = 1;
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ck::index_t in_left_pad_h = 1;
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ck::index_t in_left_pad_w = 1;
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ck::index_t in_right_pad_h = 1;
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ck::index_t in_right_pad_w = 1;
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ck::index_t split_k = 1;
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bool pass = true;
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if(argc == 1)
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{
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pass = test_self();
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}
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else
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{
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if(argc == 3)
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{
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data_type = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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}
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else if(argc == 19)
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{
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data_type = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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N = std::stoi(argv[3]);
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K = std::stoi(argv[4]);
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C = std::stoi(argv[5]);
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Y = std::stoi(argv[6]);
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X = std::stoi(argv[7]);
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Hi = std::stoi(argv[8]);
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Wi = std::stoi(argv[9]);
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conv_stride_h = std::stoi(argv[10]);
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conv_stride_w = std::stoi(argv[11]);
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conv_dilation_h = std::stoi(argv[12]);
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conv_dilation_w = std::stoi(argv[13]);
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in_left_pad_h = std::stoi(argv[14]);
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in_left_pad_w = std::stoi(argv[15]);
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in_right_pad_h = std::stoi(argv[16]);
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in_right_pad_w = std::stoi(argv[17]);
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split_k = std::stoi(argv[18]);
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}
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else
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{
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printf("arg1: data type (0=fp32, 1=fp16, 2= bfp16, 3= int8_t )\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3 to 17: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
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"RightPx\n");
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exit(1);
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}
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ck::utils::conv::ConvParams param{2,
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N,
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K,
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C,
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{Y, X},
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{Hi, Wi},
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{conv_stride_h, conv_stride_w},
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{conv_dilation_h, conv_dilation_w},
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{in_left_pad_h, in_left_pad_w},
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{in_right_pad_h, in_right_pad_w}};
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if(data_type == 0)
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{
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pass = ck::profiler::profile_conv_bwd_weight_impl<2,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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true, // do_verification
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init_method,
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false, // do_log
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false, // time_kernel
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param.N_,
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param.K_,
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param.C_,
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param.input_spatial_lengths_,
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param.filter_spatial_lengths_,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides_,
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param.conv_filter_dilations_,
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param.input_left_pads_,
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param.input_right_pads_,
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split_k);
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}
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else if(data_type == 1)
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{
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pass = ck::profiler::profile_conv_bwd_weight_impl<2,
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ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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true, // do_verification
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init_method,
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false, // do_log
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false, // time_kernel
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param.N_,
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param.K_,
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param.C_,
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param.input_spatial_lengths_,
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param.filter_spatial_lengths_,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides_,
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param.conv_filter_dilations_,
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param.input_left_pads_,
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param.input_right_pads_,
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split_k);
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}
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else
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{
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std::cout << "Not support data type" << std::endl;
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return 1;
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}
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}
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if(pass)
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{
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std::cout << "test conv2d bwd weight : Pass" << std::endl;
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return 0;
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}
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else
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{
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std::cout << "test conv2d bwd weight: Fail " << std::endl;
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return -1;
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}
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}
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