mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-01 12:11:19 +00:00
5b178874a1
* fix tests * remove useless file * fix test build * reduce parallelism when compiling * fix test
326 lines
12 KiB
C++
326 lines
12 KiB
C++
#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "host_conv.hpp"
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#include "tensor_layout.hpp"
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#include "device_tensor.hpp"
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#include "device_conv_bwd_data.hpp"
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#include "element_wise_operation.hpp"
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#include "reference_conv_bwd_data.hpp"
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using F16 = ck::half_t;
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using F32 = float;
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using BF16 = ck::bhalf_t;
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using INT8 = int8_t;
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace device_conv2d_bwd_data_instance {
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using DeviceConvBwdDataNoOpPtr =
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DeviceConvBwdDataPtr<ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(
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std::vector<DeviceConvBwdDataNoOpPtr>&);
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void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(
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std::vector<DeviceConvBwdDataNoOpPtr>&);
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void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(
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std::vector<DeviceConvBwdDataNoOpPtr>&);
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void add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(
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std::vector<DeviceConvBwdDataNoOpPtr>&);
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} // namespace device_conv2d_bwd_data_instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
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template <typename T>
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static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
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{
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float max_diff = 1e-6;
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for(int i = 0; i < ref.mData.size(); ++i)
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{
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float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
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if(max_diff < diff)
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{
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return false;
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}
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}
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return true;
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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 = 0;
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int init_method = 0;
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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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if(argc == 1)
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{
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data_type = 1;
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init_method = 1;
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}
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else 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 == 18)
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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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}
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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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auto Run = [&](auto input_type, auto wei_type, auto out_type) {
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using InDataType = decltype(input_type);
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using WeiDataType = decltype(wei_type);
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using OutDataType = decltype(out_type);
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using ReferenceConvBwdInstance =
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ck::tensor_operation::host::ReferenceConvBwdData<InDataType,
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WeiDataType,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp>;
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const ck::index_t YEff = (Y - 1) * conv_dilation_h + 1;
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const ck::index_t XEff = (X - 1) * conv_dilation_w + 1;
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const ck::index_t Ho = (Hi + in_left_pad_h + in_right_pad_h - YEff) / conv_stride_h + 1;
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const ck::index_t Wo = (Wi + in_left_pad_w + in_right_pad_w - XEff) / conv_stride_w + 1;
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const std::vector<ck::index_t> input_spatial_lengths{{Hi, Wi}};
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const std::vector<ck::index_t> filter_spatial_lengths{{Y, X}};
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const std::vector<ck::index_t> output_spatial_lengths{{Ho, Wo}};
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const std::vector<ck::index_t> conv_filter_strides{{conv_stride_h, conv_stride_w}};
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const std::vector<ck::index_t> conv_filter_dilations{{conv_dilation_h, conv_dilation_w}};
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const std::vector<ck::index_t> input_left_pads{{in_left_pad_h, in_left_pad_w}};
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const std::vector<ck::index_t> input_right_pads{{in_right_pad_h, in_right_pad_w}};
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auto f_host_tensor_descriptor =
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[](std::size_t N_, std::size_t C_, std::size_t H, std::size_t W) {
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return HostTensorDescriptor(std::vector<std::size_t>({N_, C_, H, W}),
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std::vector<std::size_t>({C_ * H * W, 1, W * C_, C_}));
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};
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Tensor<OutDataType> out_n_k_ho_wo(f_host_tensor_descriptor(N, K, Ho, Wo));
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Tensor<WeiDataType> wei_k_c_y_x(f_host_tensor_descriptor(K, C, Y, X));
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Tensor<InDataType> in_n_c_hi_wi_host_result(f_host_tensor_descriptor(N, C, Hi, Wi));
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Tensor<InDataType> in_n_c_hi_wi_device_result(f_host_tensor_descriptor(N, C, Hi, Wi));
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std::cout << "in_n_c_hi_wi: " << in_n_c_hi_wi_host_result.mDesc << std::endl;
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std::cout << "wei_k_c_y_x: " << wei_k_c_y_x.mDesc << std::endl;
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std::cout << "out_n_k_ho_wo: " << out_n_k_ho_wo.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
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wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
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break;
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default:
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out_n_k_ho_wo.GenerateTensorValue(GeneratorTensor_1<OutDataType>{1});
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wei_k_c_y_x.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
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}
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DeviceMem in_device_buf(sizeof(InDataType) *
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in_n_c_hi_wi_device_result.mDesc.GetElementSpace());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_k_c_y_x.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * out_n_k_ho_wo.mDesc.GetElementSpace());
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out_device_buf.ToDevice(out_n_k_ho_wo.mData.data());
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wei_device_buf.ToDevice(wei_k_c_y_x.mData.data());
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in_n_c_hi_wi_device_result.GenerateTensorValue(GeneratorTensor_1<InDataType>{5});
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in_device_buf.ToDevice(in_n_c_hi_wi_device_result.mData.data());
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// get host result
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{
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auto ref_conv = ReferenceConvBwdInstance{};
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(in_n_c_hi_wi_host_result,
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wei_k_c_y_x,
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out_n_k_ho_wo,
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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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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_argument);
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}
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using DeviceConvBwdDataNoOpPtr = ck::tensor_operation::device::
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DeviceConvBwdDataPtr<PassThrough, PassThrough, PassThrough>;
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// add device Conv instances
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std::vector<DeviceConvBwdDataNoOpPtr> conv_ptrs;
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if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, float> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, float> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, float>)
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{
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ck::tensor_operation::device::device_conv2d_bwd_data_instance::
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add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f32_instances(conv_ptrs);
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}
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else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ck::half_t> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, ck::half_t> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, ck::half_t>)
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{
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ck::tensor_operation::device::device_conv2d_bwd_data_instance::
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add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_f16_instances(conv_ptrs);
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}
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else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, ushort> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, ushort> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, ushort>)
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{
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ck::tensor_operation::device::device_conv2d_bwd_data_instance::
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add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_bf16_instances(conv_ptrs);
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}
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else if constexpr(ck::is_same_v<ck::remove_cv_t<InDataType>, int8_t> &&
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ck::is_same_v<ck::remove_cv_t<WeiDataType>, int8_t> &&
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ck::is_same_v<ck::remove_cv_t<OutDataType>, int8_t>)
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{
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ck::tensor_operation::device::device_conv2d_bwd_data_instance::
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add_device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk_int8_instances(conv_ptrs);
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}
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if(conv_ptrs.size() <= 0)
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{
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throw std::runtime_error("wrong! no device Conv instance found");
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}
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// profile device Conv instances
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bool success = true;
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for(auto& conv_ptr : conv_ptrs)
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{
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auto argument_ptr = conv_ptr->MakeArgumentPointer(
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static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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N,
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K,
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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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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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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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if(conv_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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auto invoker_ptr = conv_ptr->MakeInvokerPointer();
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invoker_ptr->Run(argument_ptr.get(), 1);
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in_device_buf.FromDevice(in_n_c_hi_wi_device_result.mData.data());
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if(!check_out(in_n_c_hi_wi_host_result, in_n_c_hi_wi_device_result))
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{
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std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
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success = false;
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}
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else
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{
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std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
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}
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}
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else
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{
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std::cout << "Not support Info: " << conv_ptr->GetTypeString() << std::endl;
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}
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}
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if(success)
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{
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std::cout << "test conv2d bwd : Pass" << std::endl;
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}
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else
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{
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std::cout << "test conv2d bwd: Fail " << std::endl;
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}
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};
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if(data_type == 0)
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{
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Run(F32(), F32(), F32());
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}
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else if(data_type == 1)
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{
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Run(F16(), F16(), F16());
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}
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else if(data_type == 2)
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{
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Run(BF16(), BF16(), BF16());
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}
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else if(data_type == 3)
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{
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Run(INT8(), INT8(), INT8());
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}
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else
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{
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return 1;
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}
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return 0;
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}
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