Convolution with cuDNN

conv.cu

#include <cudnn.h>
#include <cassert>
#include <cstdlib>
#include <iostream>
#include <opencv2/opencv.hpp>

#define checkCUDNN(expression)                               \
  {                                                          \
    cudnnStatus_t status = (expression);                     \
    if (status != CUDNN_STATUS_SUCCESS) {                    \
      std::cerr << "Error on line " << __LINE__ << ": "      \
                << cudnnGetErrorString(status) << std::endl; \
      std::exit(EXIT_FAILURE);                               \
    }                                                        \
  }

cv::Mat load_image(const char* image_path) {
  cv::Mat image = cv::imread(image_path, CV_LOAD_IMAGE_COLOR);
  image.convertTo(image, CV_32FC3);
  cv::normalize(image, image, 0, 1, cv::NORM_MINMAX);
  std::cerr << "Input Image: " << image.rows << " x " << image.cols << " x "
            << image.channels() << std::endl;
  return image;
}

void save_image(const char* output_filename,
                float* buffer,
                int height,
                int width) {
  cv::Mat output_image(height, width, CV_32FC3, buffer);
  // Make negative values zero.
  cv::threshold(output_image,
                output_image,
                /*threshold=*/0,
                /*maxval=*/0,
                cv::THRESH_TOZERO);
  cv::normalize(output_image, output_image, 0.0, 255.0, cv::NORM_MINMAX);
  output_image.convertTo(output_image, CV_8UC3);
  cv::imwrite(output_filename, output_image);
  std::cerr << "Wrote output to " << output_filename << std::endl;
}

int main(int argc, const char* argv[]) {
  if (argc < 2) {
    std::cerr << "usage: conv <image> [gpu=0] [sigmoid=0]" << std::endl;
    std::exit(EXIT_FAILURE);
  }

  int gpu_id = (argc > 2) ? std::atoi(argv[2]) : 0;
  std::cerr << "GPU: " << gpu_id << std::endl;

  bool with_sigmoid = (argc > 3) ? std::atoi(argv[3]) : 0;
  std::cerr << "With sigmoid: " << std::boolalpha << with_sigmoid << std::endl;

  cv::Mat image = load_image(argv[1]);

  cudaSetDevice(gpu_id);

  cudnnHandle_t cudnn;
  cudnnCreate(&cudnn);

  cudnnTensorDescriptor_t input_descriptor;
  checkCUDNN(cudnnCreateTensorDescriptor(&input_descriptor));
  checkCUDNN(cudnnSetTensor4dDescriptor(input_descriptor,
                                        /*format=*/CUDNN_TENSOR_NHWC,
                                        /*dataType=*/CUDNN_DATA_FLOAT,
                                        /*batch_size=*/1,
                                        /*channels=*/3,
                                        /*image_height=*/image.rows,
                                        /*image_width=*/image.cols));

  cudnnFilterDescriptor_t kernel_descriptor;
  checkCUDNN(cudnnCreateFilterDescriptor(&kernel_descriptor));
  checkCUDNN(cudnnSetFilter4dDescriptor(kernel_descriptor,
                                        /*dataType=*/CUDNN_DATA_FLOAT,
                                        /*format=*/CUDNN_TENSOR_NCHW,
                                        /*out_channels=*/3,
                                        /*in_channels=*/3,
                                        /*kernel_height=*/3,
                                        /*kernel_width=*/3));

  cudnnConvolutionDescriptor_t convolution_descriptor;
  checkCUDNN(cudnnCreateConvolutionDescriptor(&convolution_descriptor));
  checkCUDNN(cudnnSetConvolution2dDescriptor(convolution_descriptor,
                                             /*pad_height=*/1,
                                             /*pad_width=*/1,
                                             /*vertical_stride=*/1,
                                             /*horizontal_stride=*/1,
                                             /*dilation_height=*/1,
                                             /*dilation_width=*/1,
                                             /*mode=*/CUDNN_CROSS_CORRELATION,
                                             /*computeType=*/CUDNN_DATA_FLOAT));

  int batch_size{0}, channels{0}, height{0}, width{0};
  checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convolution_descriptor,
                                                   input_descriptor,
                                                   kernel_descriptor,
                                                   &batch_size,
                                                   &channels,
                                                   &height,
                                                   &width));

  std::cerr << "Output Image: " << height << " x " << width << " x " << channels
            << std::endl;

  cudnnTensorDescriptor_t output_descriptor;
  checkCUDNN(cudnnCreateTensorDescriptor(&output_descriptor));
  checkCUDNN(cudnnSetTensor4dDescriptor(output_descriptor,
                                        /*format=*/CUDNN_TENSOR_NHWC,
                                        /*dataType=*/CUDNN_DATA_FLOAT,
                                        /*batch_size=*/1,
                                        /*channels=*/3,
                                        /*image_height=*/image.rows,
                                        /*image_width=*/image.cols));

  cudnnConvolutionFwdAlgo_t convolution_algorithm;
  checkCUDNN(
      cudnnGetConvolutionForwardAlgorithm(cudnn,
                                          input_descriptor,
                                          kernel_descriptor,
                                          convolution_descriptor,
                                          output_descriptor,
                                          CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
                                          /*memoryLimitInBytes=*/0,
                                          &convolution_algorithm));

  size_t workspace_bytes{0};
  checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnn,
                                                     input_descriptor,
                                                     kernel_descriptor,
                                                     convolution_descriptor,
                                                     output_descriptor,
                                                     convolution_algorithm,
                                                     &workspace_bytes));
  std::cerr << "Workspace size: " << (workspace_bytes / 1048576.0) << "MB"
            << std::endl;
  assert(workspace_bytes > 0);

  void* d_workspace{nullptr};
  cudaMalloc(&d_workspace, workspace_bytes);

  int image_bytes = batch_size * channels * height * width * sizeof(float);

  float* d_input{nullptr};
  cudaMalloc(&d_input, image_bytes);
  cudaMemcpy(d_input, image.ptr<float>(0), image_bytes, cudaMemcpyHostToDevice);

  float* d_output{nullptr};
  cudaMalloc(&d_output, image_bytes);
  cudaMemset(d_output, 0, image_bytes);

  // clang-format off
  const float kernel_template[3][3] = {
    {1, 1, 1},
    {1, -8, 1},
    {1, 1, 1}
  };
  // clang-format on

  float h_kernel[3][3][3][3];
  for (int kernel = 0; kernel < 3; ++kernel) {
    for (int channel = 0; channel < 3; ++channel) {
      for (int row = 0; row < 3; ++row) {
        for (int column = 0; column < 3; ++column) {
          h_kernel[kernel][channel][row][column] = kernel_template[row][column];
        }
      }
    }
  }

  float* d_kernel{nullptr};
  cudaMalloc(&d_kernel, sizeof(h_kernel));
  cudaMemcpy(d_kernel, h_kernel, sizeof(h_kernel), cudaMemcpyHostToDevice);

  const float alpha = 1.0f, beta = 0.0f;

  checkCUDNN(cudnnConvolutionForward(cudnn,
                                     &alpha,
                                     input_descriptor,
                                     d_input,
                                     kernel_descriptor,
                                     d_kernel,
                                     convolution_descriptor,
                                     convolution_algorithm,
                                     d_workspace,
                                     workspace_bytes,
                                     &beta,
                                     output_descriptor,
                                     d_output));

  if (with_sigmoid) {
    cudnnActivationDescriptor_t activation_descriptor;
    checkCUDNN(cudnnCreateActivationDescriptor(&activation_descriptor));
    checkCUDNN(cudnnSetActivationDescriptor(activation_descriptor,
                                            CUDNN_ACTIVATION_SIGMOID,
                                            CUDNN_PROPAGATE_NAN,
                                            /*relu_coef=*/0));
    checkCUDNN(cudnnActivationForward(cudnn,
                                      activation_descriptor,
                                      &alpha,
                                      output_descriptor,
                                      d_output,
                                      &beta,
                                      output_descriptor,
                                      d_output));
    cudnnDestroyActivationDescriptor(activation_descriptor);
  }

  float* h_output = new float[image_bytes];
  cudaMemcpy(h_output, d_output, image_bytes, cudaMemcpyDeviceToHost);

  save_image("cudnn-out.png", h_output, height, width);

  delete[] h_output;
  cudaFree(d_kernel);
  cudaFree(d_input);
  cudaFree(d_output);
  cudaFree(d_workspace);

  cudnnDestroyTensorDescriptor(input_descriptor);
  cudnnDestroyTensorDescriptor(output_descriptor);
  cudnnDestroyFilterDescriptor(kernel_descriptor);
  cudnnDestroyConvolutionDescriptor(convolution_descriptor);

  cudnnDestroy(cudnn);
}

It would be great if this example could come with a full prerequisites for Cuda toolkit and cuDNN as well as a Makefile that parallels the examples in cudnn. It is missing the instructions for opencv2 that is required in the headerfile.

I install cudnn6.5 and cuda6.5 on jetson Tk1. When i run your code, i will get an error,
cudnnSetFilter4dDescriptor: too many arguments
Thanks for your answer

@aerodame

The Makefile can be found here: Peter's blog post

For your convenience:

CXX := nvcc
TARGET := conv
CUDNN_PATH := cudnn
HEADERS := -I $(CUDNN_PATH)/include
LIBS := -L $(CUDNN_PATH)/lib64 -L/usr/local/lib
CXXFLAGS := -arch=sm_35 -std=c++11 -O2

all: conv

conv: $(TARGET).cu
	$(CXX) $(CXXFLAGS) $(HEADERS) $(LIBS) $(TARGET).cu -o $(TARGET) \
	-lcudnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core

.phony: clean

clean:
	rm $(TARGET) || echo -n ""

I've read your blog, it helps a lot!

What is the execution time of this