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lenet.cu
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lenet.cu
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/*
* This code is released into the public domain.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
* IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
* OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
* ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
* OTHER DEALINGS IN THE SOFTWARE.
*/
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <ctime>
#include <cfloat>
#include <algorithm>
#include <chrono>
#include <iomanip>
#include <iostream>
#include <map>
#include <memory>
#include <random>
#include <sstream>
#include <string>
#include <vector>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <cublas_v2.h>
#include <cudnn.h>
#include "readubyte.h"
///////////////////////////////////////////////////////////////////////////////////////////
// Definitions and helper utilities
// Block width for CUDA kernels
#define BW 128
#ifdef USE_GFLAGS
#include <gflags/gflags.h>
#ifndef _WIN32
#define gflags google
#endif
#else
// Constant versions of gflags
#define DEFINE_int32(flag, default_value, description) const int FLAGS_##flag = (default_value)
#define DEFINE_uint64(flag, default_value, description) const unsigned long long FLAGS_##flag = (default_value)
#define DEFINE_bool(flag, default_value, description) const bool FLAGS_##flag = (default_value)
#define DEFINE_double(flag, default_value, description) const double FLAGS_##flag = (default_value)
#define DEFINE_string(flag, default_value, description) const std::string FLAGS_##flag ((default_value))
#endif
/**
* Computes ceil(x / y) for integral nonnegative values.
*/
static inline unsigned int RoundUp(unsigned int nominator, unsigned int denominator)
{
return (nominator + denominator - 1) / denominator;
}
/**
* Saves a PGM grayscale image out of unsigned 8-bit data
*/
void SavePGMFile(const unsigned char *data, size_t width, size_t height, const char *filename)
{
FILE *fp = fopen(filename, "wb");
if (fp)
{
fprintf(fp, "P5\n%lu %lu\n255\n", width, height);
fwrite(data, sizeof(unsigned char), width * height, fp);
fclose(fp);
}
}
//////////////////////////////////////////////////////////////////////////////
// Error handling
// Adapted from the CUDNN classification code
// sample: https://developer.nvidia.com/cuDNN
#define FatalError(s) do { \
std::stringstream _where, _message; \
_where << __FILE__ << ':' << __LINE__; \
_message << std::string(s) + "\n" << __FILE__ << ':' << __LINE__; \
std::cerr << _message.str() << "\nAborting...\n"; \
cudaDeviceReset(); \
exit(1); \
} while(0)
#define checkCUDNN(status) do { \
std::stringstream _error; \
if (status != CUDNN_STATUS_SUCCESS) { \
_error << "CUDNN failure: " << cudnnGetErrorString(status); \
FatalError(_error.str()); \
} \
} while(0)
#define checkCudaErrors(status) do { \
std::stringstream _error; \
if (status != 0) { \
_error << "Cuda failure: " << status; \
FatalError(_error.str()); \
} \
} while(0)
///////////////////////////////////////////////////////////////////////////////////////////
// Command-line flags
// Application parameters
DEFINE_int32(gpu, 0, "The GPU ID to use");
DEFINE_int32(iterations, 1000, "Number of iterations for training");
DEFINE_int32(random_seed, -1, "Override random seed (default uses std::random_device)");
DEFINE_int32(classify, -1, "Number of images to classify to compute error rate (default uses entire test set)");
// Batch parameters
DEFINE_uint64(batch_size, 64, "Batch size for training");
// Filenames
DEFINE_bool(pretrained, false, "Use the pretrained CUDNN model as input");
DEFINE_bool(save_data, false, "Save pretrained weights to file");
DEFINE_string(train_images, "train-images-idx3-ubyte", "Training images filename");
DEFINE_string(train_labels, "train-labels-idx1-ubyte", "Training labels filename");
DEFINE_string(test_images, "t10k-images-idx3-ubyte", "Test images filename");
DEFINE_string(test_labels, "t10k-labels-idx1-ubyte", "Test labels filename");
// Solver parameters
DEFINE_double(learning_rate, 0.01, "Base learning rate");
DEFINE_double(lr_gamma, 0.0001, "Learning rate policy gamma");
DEFINE_double(lr_power, 0.75, "Learning rate policy power");
///////////////////////////////////////////////////////////////////////////////////////////
// Layer representations
/**
* Represents a convolutional layer with bias.
*/
struct ConvBiasLayer
{
int in_channels, out_channels, kernel_size;
int in_width, in_height, out_width, out_height;
std::vector<float> pconv, pbias;
ConvBiasLayer(int in_channels_, int out_channels_, int kernel_size_,
int in_w_, int in_h_) : pconv(in_channels_ * kernel_size_ * kernel_size_ * out_channels_),
pbias(out_channels_)
{
in_channels = in_channels_;
out_channels = out_channels_;
kernel_size = kernel_size_;
in_width = in_w_;
in_height = in_h_;
out_width = in_w_ - kernel_size_ + 1;
out_height = in_h_ - kernel_size_ + 1;
}
bool FromFile(const char *fileprefix)
{
std::stringstream ssf, ssbf;
ssf << fileprefix << ".bin";
ssbf << fileprefix << ".bias.bin";
// Read weights file
FILE *fp = fopen(ssf.str().c_str(), "rb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssf.str().c_str());
return false;
}
fread(&pconv[0], sizeof(float), in_channels * out_channels * kernel_size * kernel_size, fp);
fclose(fp);
// Read bias file
fp = fopen(ssbf.str().c_str(), "rb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssbf.str().c_str());
return false;
}
fread(&pbias[0], sizeof(float), out_channels, fp);
fclose(fp);
return true;
}
void ToFile(const char *fileprefix)
{
std::stringstream ssf, ssbf;
ssf << fileprefix << ".bin";
ssbf << fileprefix << ".bias.bin";
// Write weights file
FILE *fp = fopen(ssf.str().c_str(), "wb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssf.str().c_str());
exit(2);
}
fwrite(&pconv[0], sizeof(float), in_channels * out_channels * kernel_size * kernel_size, fp);
fclose(fp);
// Write bias file
fp = fopen(ssbf.str().c_str(), "wb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssbf.str().c_str());
exit(2);
}
fwrite(&pbias[0], sizeof(float), out_channels, fp);
fclose(fp);
}
};
/**
* Represents a max-pooling layer.
*/
struct MaxPoolLayer
{
int size, stride;
MaxPoolLayer(int size_, int stride_) : size(size_), stride(stride_) {}
};
/**
* Represents a fully-connected neural network layer with bias.
*/
struct FullyConnectedLayer
{
int inputs, outputs;
std::vector<float> pneurons, pbias;
FullyConnectedLayer(int inputs_, int outputs_) : outputs(outputs_), inputs(inputs_),
pneurons(inputs_ * outputs_), pbias(outputs_) {}
bool FromFile(const char *fileprefix)
{
std::stringstream ssf, ssbf;
ssf << fileprefix << ".bin";
ssbf << fileprefix << ".bias.bin";
// Read weights file
FILE *fp = fopen(ssf.str().c_str(), "rb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssf.str().c_str());
return false;
}
fread(&pneurons[0], sizeof(float), inputs * outputs, fp);
fclose(fp);
// Read bias file
fp = fopen(ssbf.str().c_str(), "rb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssbf.str().c_str());
return false;
}
fread(&pbias[0], sizeof(float), outputs, fp);
fclose(fp);
return true;
}
void ToFile(const char *fileprefix)
{
std::stringstream ssf, ssbf;
ssf << fileprefix << ".bin";
ssbf << fileprefix << ".bias.bin";
// Write weights file
FILE *fp = fopen(ssf.str().c_str(), "wb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssf.str().c_str());
exit(2);
}
fwrite(&pneurons[0], sizeof(float), inputs * outputs, fp);
fclose(fp);
// Write bias file
fp = fopen(ssbf.str().c_str(), "wb");
if (!fp)
{
printf("ERROR: Cannot open file %s\n", ssbf.str().c_str());
exit(2);
}
fwrite(&pbias[0], sizeof(float), outputs, fp);
fclose(fp);
}
};
///////////////////////////////////////////////////////////////////////////////////////////
// GPU Kernels
/**
* Fills a floating-point array with ones.
*
* @param vec The array to fill.
* @param size The number of elements in the array.
*/
__global__ void FillOnes(float *vec, int size)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= size)
return;
vec[idx] = 1.0f;
}
/**
* Computes the backpropagation results of the Softmax loss for each result in a batch.
* Uses the softmax values obtained from forward propagation to compute the difference.
*
* @param label The training batch label values.
* @param num_labels The number of possible labels.
* @param batch_size The size of the trained batch.
* @param diff The resulting gradient.
*/
__global__ void SoftmaxLossBackprop(const float *label, int num_labels, int batch_size, float *diff)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= batch_size)
return;
const int label_value = static_cast<int>(label[idx]);
// For each item in the batch, decrease the result of the label's value by 1
diff[idx * num_labels + label_value] -= 1.0f;
}
///////////////////////////////////////////////////////////////////////////////////////////
// CUDNN/CUBLAS training context
struct TrainingContext
{
cudnnHandle_t cudnnHandle;
cublasHandle_t cublasHandle;
cudnnTensorDescriptor_t dataTensor, conv1Tensor, conv1BiasTensor, pool1Tensor,
conv2Tensor, conv2BiasTensor, pool2Tensor, fc1Tensor, fc2Tensor;
cudnnFilterDescriptor_t conv1filterDesc, conv2filterDesc;
cudnnConvolutionDescriptor_t conv1Desc, conv2Desc;
cudnnConvolutionFwdAlgo_t conv1algo, conv2algo;
cudnnConvolutionBwdFilterAlgo_t conv1bwfalgo, conv2bwfalgo;
cudnnConvolutionBwdDataAlgo_t conv2bwdalgo;
cudnnPoolingDescriptor_t poolDesc;
cudnnActivationDescriptor_t fc1Activation;
int m_gpuid;
int m_batchSize;
size_t m_workspaceSize;
FullyConnectedLayer& ref_fc1, &ref_fc2;
// Disable copying
TrainingContext& operator=(const TrainingContext&) = delete;
TrainingContext(const TrainingContext&) = delete;
TrainingContext(int gpuid, int batch_size,
ConvBiasLayer& conv1, MaxPoolLayer& pool1, ConvBiasLayer& conv2, MaxPoolLayer& pool2,
FullyConnectedLayer& fc1, FullyConnectedLayer& fc2) : ref_fc1(fc1), ref_fc2(fc2), m_gpuid(gpuid)
{
m_batchSize = batch_size;
// Create CUBLAS and CUDNN handles
checkCudaErrors(cudaSetDevice(gpuid));
checkCudaErrors(cublasCreate(&cublasHandle));
checkCUDNN(cudnnCreate(&cudnnHandle));
// Create tensor descriptors
checkCUDNN(cudnnCreateTensorDescriptor(&dataTensor));
checkCUDNN(cudnnCreateTensorDescriptor(&conv1Tensor));
checkCUDNN(cudnnCreateTensorDescriptor(&conv1BiasTensor));
checkCUDNN(cudnnCreateTensorDescriptor(&pool1Tensor));
checkCUDNN(cudnnCreateTensorDescriptor(&conv2Tensor));
checkCUDNN(cudnnCreateTensorDescriptor(&conv2BiasTensor));
checkCUDNN(cudnnCreateTensorDescriptor(&pool2Tensor));
checkCUDNN(cudnnCreateTensorDescriptor(&fc1Tensor));
checkCUDNN(cudnnCreateTensorDescriptor(&fc2Tensor));
checkCUDNN(cudnnCreateActivationDescriptor(&fc1Activation));
checkCUDNN(cudnnCreateFilterDescriptor(&conv1filterDesc));
checkCUDNN(cudnnCreateFilterDescriptor(&conv2filterDesc));
checkCUDNN(cudnnCreateConvolutionDescriptor(&conv1Desc));
checkCUDNN(cudnnCreateConvolutionDescriptor(&conv2Desc));
checkCUDNN(cudnnCreatePoolingDescriptor(&poolDesc));
// Set tensor descriptor sizes
checkCUDNN(cudnnSetTensor4dDescriptor(conv1BiasTensor,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
1, conv1.out_channels,
1, 1));
checkCUDNN(cudnnSetTensor4dDescriptor(conv2BiasTensor,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
1, conv2.out_channels,
1, 1));
checkCUDNN(cudnnSetPooling2dDescriptor(poolDesc,
CUDNN_POOLING_MAX,
CUDNN_PROPAGATE_NAN,
pool1.size, pool1.size,
0, 0,
pool1.stride, pool1.stride));
checkCUDNN(cudnnSetTensor4dDescriptor(pool2Tensor,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
batch_size, conv2.out_channels,
conv2.out_height / pool2.stride,
conv2.out_width / pool2.stride));
checkCUDNN(cudnnSetTensor4dDescriptor(fc1Tensor,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
batch_size, fc1.outputs, 1, 1));
checkCUDNN(cudnnSetTensor4dDescriptor(fc2Tensor,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
batch_size, fc2.outputs, 1, 1));
checkCUDNN(cudnnSetActivationDescriptor(fc1Activation, CUDNN_ACTIVATION_RELU,
CUDNN_PROPAGATE_NAN, 0.0));
// Set convolution tensor sizes and compute workspace size
size_t workspace = 0;
workspace = std::max(workspace, SetFwdConvolutionTensors(conv1, dataTensor, conv1Tensor, conv1filterDesc, conv1Desc, conv1algo));
workspace = std::max(workspace, SetBwdConvolutionTensors(dataTensor, conv1Tensor, conv1filterDesc, conv1Desc, &conv1bwfalgo, nullptr));
workspace = std::max(workspace, SetFwdConvolutionTensors(conv2, pool1Tensor, conv2Tensor, conv2filterDesc, conv2Desc, conv2algo));
workspace = std::max(workspace, SetBwdConvolutionTensors(pool1Tensor, conv2Tensor, conv2filterDesc, conv2Desc, &conv2bwfalgo, &conv2bwdalgo));
// The workspace is allocated later (if necessary)
m_workspaceSize = workspace;
}
~TrainingContext()
{
checkCudaErrors(cudaSetDevice(m_gpuid));
checkCudaErrors(cublasDestroy(cublasHandle));
checkCUDNN(cudnnDestroy(cudnnHandle));
checkCUDNN(cudnnDestroyTensorDescriptor(dataTensor));
checkCUDNN(cudnnDestroyTensorDescriptor(conv1Tensor));
checkCUDNN(cudnnDestroyTensorDescriptor(conv1BiasTensor));
checkCUDNN(cudnnDestroyTensorDescriptor(pool1Tensor));
checkCUDNN(cudnnDestroyTensorDescriptor(conv2Tensor));
checkCUDNN(cudnnDestroyTensorDescriptor(conv2BiasTensor));
checkCUDNN(cudnnDestroyTensorDescriptor(pool2Tensor));
checkCUDNN(cudnnDestroyTensorDescriptor(fc1Tensor));
checkCUDNN(cudnnDestroyTensorDescriptor(fc2Tensor));
checkCUDNN(cudnnDestroyActivationDescriptor(fc1Activation));
checkCUDNN(cudnnDestroyFilterDescriptor(conv1filterDesc));
checkCUDNN(cudnnDestroyFilterDescriptor(conv2filterDesc));
checkCUDNN(cudnnDestroyConvolutionDescriptor(conv1Desc));
checkCUDNN(cudnnDestroyConvolutionDescriptor(conv2Desc));
checkCUDNN(cudnnDestroyPoolingDescriptor(poolDesc));
}
size_t SetFwdConvolutionTensors(ConvBiasLayer& conv, cudnnTensorDescriptor_t& srcTensorDesc, cudnnTensorDescriptor_t& dstTensorDesc,
cudnnFilterDescriptor_t& filterDesc, cudnnConvolutionDescriptor_t& convDesc,
cudnnConvolutionFwdAlgo_t& algo)
{
size_t sizeInBytes = 0;
int n = m_batchSize;
int c = conv.in_channels;
int h = conv.in_height;
int w = conv.in_width;
checkCUDNN(cudnnSetTensor4dDescriptor(srcTensorDesc,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
n, c,
h, w));
checkCUDNN(cudnnSetFilter4dDescriptor(filterDesc,
CUDNN_DATA_FLOAT,
CUDNN_TENSOR_NCHW,
conv.out_channels,
conv.in_channels,
conv.kernel_size,
conv.kernel_size));
#if CUDNN_MAJOR > 5
checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
0, 0,
1, 1,
1, 1,
CUDNN_CROSS_CORRELATION,
CUDNN_DATA_FLOAT));
#else
checkCUDNN(cudnnSetConvolution2dDescriptor(convDesc,
0, 0,
1, 1,
1, 1,
CUDNN_CROSS_CORRELATION));
#endif
// Find dimension of convolution output
checkCUDNN(cudnnGetConvolution2dForwardOutputDim(convDesc,
srcTensorDesc,
filterDesc,
&n, &c, &h, &w));
checkCUDNN(cudnnSetTensor4dDescriptor(dstTensorDesc,
CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT,
n, c,
h, w));
checkCUDNN(cudnnGetConvolutionForwardAlgorithm(cudnnHandle,
srcTensorDesc,
filterDesc,
convDesc,
dstTensorDesc,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0,
&algo));
checkCUDNN(cudnnGetConvolutionForwardWorkspaceSize(cudnnHandle,
srcTensorDesc,
filterDesc,
convDesc,
dstTensorDesc,
algo,
&sizeInBytes));
return sizeInBytes;
}
void ForwardPropagation(float *data, float *conv1, float *pool1, float *conv2, float *pool2, float *fc1, float *fc1relu,
float *fc2, float *result,
float *pconv1, float *pconv1bias,
float *pconv2, float *pconv2bias,
float *pfc1, float *pfc1bias,
float *pfc2, float *pfc2bias, void *workspace, float *onevec)
{
float alpha = 1.0f, beta = 0.0f;
checkCudaErrors(cudaSetDevice(m_gpuid));
// Conv1 layer
checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, dataTensor,
data, conv1filterDesc, pconv1, conv1Desc,
conv1algo, workspace, m_workspaceSize, &beta,
conv1Tensor, conv1));
checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, conv1BiasTensor,
pconv1bias, &alpha, conv1Tensor, conv1));
// Pool1 layer
checkCUDNN(cudnnPoolingForward(cudnnHandle, poolDesc, &alpha, conv1Tensor,
conv1, &beta, pool1Tensor, pool1));
// Conv2 layer
checkCUDNN(cudnnConvolutionForward(cudnnHandle, &alpha, pool1Tensor,
pool1, conv2filterDesc, pconv2, conv2Desc,
conv2algo, workspace, m_workspaceSize, &beta,
conv2Tensor, conv2));
checkCUDNN(cudnnAddTensor(cudnnHandle, &alpha, conv2BiasTensor,
pconv2bias, &alpha, conv2Tensor, conv2));
// Pool2 layer
checkCUDNN(cudnnPoolingForward(cudnnHandle, poolDesc, &alpha, conv2Tensor,
conv2, &beta, pool2Tensor, pool2));
// FC1 layer
// Forward propagate neurons using weights (fc1 = pfc1'*pool2)
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_T, CUBLAS_OP_N,
ref_fc1.outputs, m_batchSize, ref_fc1.inputs,
&alpha,
pfc1, ref_fc1.inputs,
pool2, ref_fc1.inputs,
&beta,
fc1, ref_fc1.outputs));
// Add bias using GEMM's "beta" (fc1 += pfc1bias*1_vec')
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
ref_fc1.outputs, m_batchSize, 1,
&alpha,
pfc1bias, ref_fc1.outputs,
onevec, 1,
&alpha,
fc1, ref_fc1.outputs));
// ReLU activation
checkCUDNN(cudnnActivationForward(cudnnHandle, fc1Activation, &alpha,
fc1Tensor, fc1, &beta, fc1Tensor, fc1relu));
// FC2 layer
// Forward propagate neurons using weights (fc2 = pfc2'*fc1relu)
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_T, CUBLAS_OP_N,
ref_fc2.outputs, m_batchSize, ref_fc2.inputs,
&alpha,
pfc2, ref_fc2.inputs,
fc1relu, ref_fc2.inputs,
&beta,
fc2, ref_fc2.outputs));
// Add bias using GEMM's "beta" (fc2 += pfc2bias*1_vec')
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N,
ref_fc2.outputs, m_batchSize, 1,
&alpha,
pfc2bias, ref_fc2.outputs,
onevec, 1,
&alpha,
fc2, ref_fc2.outputs));
// Softmax loss
checkCUDNN(cudnnSoftmaxForward(cudnnHandle, CUDNN_SOFTMAX_ACCURATE, CUDNN_SOFTMAX_MODE_CHANNEL,
&alpha, fc2Tensor, fc2, &beta, fc2Tensor, result));
}
size_t SetBwdConvolutionTensors(cudnnTensorDescriptor_t& srcTensorDesc, cudnnTensorDescriptor_t& dstTensorDesc,
cudnnFilterDescriptor_t& filterDesc, cudnnConvolutionDescriptor_t& convDesc,
cudnnConvolutionBwdFilterAlgo_t *falgo, cudnnConvolutionBwdDataAlgo_t *dalgo)
{
size_t sizeInBytes = 0, tmpsize = 0;
// If backprop filter algorithm was requested
if (falgo)
{
checkCUDNN(cudnnGetConvolutionBackwardFilterAlgorithm(
cudnnHandle, srcTensorDesc, dstTensorDesc, convDesc, filterDesc,
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST, 0, falgo));
checkCUDNN(cudnnGetConvolutionBackwardFilterWorkspaceSize(
cudnnHandle, srcTensorDesc, dstTensorDesc, convDesc, filterDesc,
*falgo, &tmpsize));
sizeInBytes = std::max(sizeInBytes, tmpsize);
}
// If backprop data algorithm was requested
if (dalgo)
{
checkCUDNN(cudnnGetConvolutionBackwardDataAlgorithm(
cudnnHandle, filterDesc, dstTensorDesc, convDesc, srcTensorDesc,
CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST, 0, dalgo));
checkCUDNN(cudnnGetConvolutionBackwardDataWorkspaceSize(
cudnnHandle, filterDesc, dstTensorDesc, convDesc, srcTensorDesc,
*dalgo, &tmpsize));
sizeInBytes = std::max(sizeInBytes, tmpsize);
}
return sizeInBytes;
}
void Backpropagation(ConvBiasLayer& layer_conv1, MaxPoolLayer& layer_pool1, ConvBiasLayer& layer_conv2, MaxPoolLayer& layer_pool2,
float *data, float *labels, float *conv1, float *pool1, float *conv2, float *pool2, float *fc1, float *fc1relu,
float *fc2, float *fc2smax, float *dloss_data,
float *pconv1, float *pconv1bias,
float *pconv2, float *pconv2bias,
float *pfc1, float *pfc1bias,
float *pfc2, float *pfc2bias,
float *gconv1, float *gconv1bias, float *dpool1,
float *gconv2, float *gconv2bias, float *dconv2, float *dpool2,
float *gfc1, float *gfc1bias, float *dfc1, float *dfc1relu,
float *gfc2, float *gfc2bias, float *dfc2,
void *workspace, float *onevec)
{
float alpha = 1.0f, beta = 0.0f;
float scalVal = 1.0f / static_cast<float>(m_batchSize);
checkCudaErrors(cudaSetDevice(m_gpuid));
// Initialization (using the training error function)
checkCudaErrors(cudaMemcpyAsync(dloss_data, fc2smax, sizeof(float) * m_batchSize * ref_fc2.outputs, cudaMemcpyDeviceToDevice));
// Softmax layer
SoftmaxLossBackprop<<<RoundUp(m_batchSize, BW), BW>>>(labels, ref_fc2.outputs, m_batchSize, dloss_data);
// Accounting for batch size in SGD
checkCudaErrors(cublasSscal(cublasHandle, ref_fc2.outputs * m_batchSize, &scalVal, dloss_data, 1));
// FC2 layer
// Compute derivative with respect to weights: gfc2 = (fc1relu * dfc2smax')
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_T, ref_fc2.inputs, ref_fc2.outputs, m_batchSize,
&alpha, fc1relu, ref_fc2.inputs, dloss_data, ref_fc2.outputs, &beta, gfc2, ref_fc2.inputs));
// Compute derivative with respect to bias: gfc2bias = dfc2smax * 1_vec
checkCudaErrors(cublasSgemv(cublasHandle, CUBLAS_OP_N, ref_fc2.outputs, m_batchSize,
&alpha, dloss_data, ref_fc2.outputs, onevec, 1, &beta, gfc2bias, 1));
// Compute derivative with respect to data (for previous layer): pfc2*dfc2smax (500x10*10xN)
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, ref_fc2.inputs, m_batchSize, ref_fc2.outputs,
&alpha, pfc2, ref_fc2.inputs, dloss_data, ref_fc2.outputs, &beta, dfc2, ref_fc2.inputs));
// ReLU activation
checkCUDNN(cudnnActivationBackward(cudnnHandle, fc1Activation, &alpha,
fc1Tensor, fc1relu, fc1Tensor, dfc2,
fc1Tensor, fc1, &beta, fc1Tensor, dfc1relu));
// FC1 layer
// Compute derivative with respect to weights: gfc1 = (pool2 * dfc1relu')
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_T, ref_fc1.inputs, ref_fc1.outputs, m_batchSize,
&alpha, pool2, ref_fc1.inputs, dfc1relu, ref_fc1.outputs, &beta, gfc1, ref_fc1.inputs));
// Compute derivative with respect to bias: gfc1bias = dfc1relu * 1_vec
checkCudaErrors(cublasSgemv(cublasHandle, CUBLAS_OP_N, ref_fc1.outputs, m_batchSize,
&alpha, dfc1relu, ref_fc1.outputs, onevec, 1, &beta, gfc1bias, 1));
// Compute derivative with respect to data (for previous layer): pfc1*dfc1relu (800x500*500xN)
checkCudaErrors(cublasSgemm(cublasHandle, CUBLAS_OP_N, CUBLAS_OP_N, ref_fc1.inputs, m_batchSize, ref_fc1.outputs,
&alpha, pfc1, ref_fc1.inputs, dfc1relu, ref_fc1.outputs, &beta, dfc1, ref_fc1.inputs));
// Pool2 layer
checkCUDNN(cudnnPoolingBackward(cudnnHandle, poolDesc, &alpha,
pool2Tensor, pool2, pool2Tensor, dfc1,
conv2Tensor, conv2, &beta, conv2Tensor, dpool2));
// Conv2 layer
checkCUDNN(cudnnConvolutionBackwardBias(cudnnHandle, &alpha, conv2Tensor,
dpool2, &beta, conv2BiasTensor, gconv2bias));
checkCUDNN(cudnnConvolutionBackwardFilter(cudnnHandle, &alpha, pool1Tensor,
pool1, conv2Tensor, dpool2, conv2Desc,
conv2bwfalgo, workspace, m_workspaceSize,
&beta, conv2filterDesc, gconv2));
checkCUDNN(cudnnConvolutionBackwardData(cudnnHandle, &alpha, conv2filterDesc,
pconv2, conv2Tensor, dpool2, conv2Desc,
conv2bwdalgo, workspace, m_workspaceSize,
&beta, pool1Tensor, dconv2));
// Pool1 layer
checkCUDNN(cudnnPoolingBackward(cudnnHandle, poolDesc, &alpha,
pool1Tensor, pool1, pool1Tensor, dconv2,
conv1Tensor, conv1, &beta, conv1Tensor, dpool1));
// Conv1 layer
checkCUDNN(cudnnConvolutionBackwardBias(cudnnHandle, &alpha, conv1Tensor,
dpool1, &beta, conv1BiasTensor, gconv1bias));
checkCUDNN(cudnnConvolutionBackwardFilter(cudnnHandle, &alpha, dataTensor,
data, conv1Tensor, dpool1, conv1Desc,
conv1bwfalgo, workspace, m_workspaceSize,
&beta, conv1filterDesc, gconv1));
// No need for convBackwardData because there are no more layers below
}
void UpdateWeights(float learning_rate,
ConvBiasLayer& conv1, ConvBiasLayer& conv2,
float *pconv1, float *pconv1bias,
float *pconv2, float *pconv2bias,
float *pfc1, float *pfc1bias,
float *pfc2, float *pfc2bias,
float *gconv1, float *gconv1bias,
float *gconv2, float *gconv2bias,
float *gfc1, float *gfc1bias,
float *gfc2, float *gfc2bias)
{
float alpha = -learning_rate;
checkCudaErrors(cudaSetDevice(m_gpuid));
// Conv1
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(conv1.pconv.size()),
&alpha, gconv1, 1, pconv1, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(conv1.pbias.size()),
&alpha, gconv1bias, 1, pconv1bias, 1));
// Conv2
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(conv2.pconv.size()),
&alpha, gconv2, 1, pconv2, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(conv2.pbias.size()),
&alpha, gconv2bias, 1, pconv2bias, 1));
// Fully connected 1
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(ref_fc1.pneurons.size()),
&alpha, gfc1, 1, pfc1, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(ref_fc1.pbias.size()),
&alpha, gfc1bias, 1, pfc1bias, 1));
// Fully connected 2
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(ref_fc2.pneurons.size()),
&alpha, gfc2, 1, pfc2, 1));
checkCudaErrors(cublasSaxpy(cublasHandle, static_cast<int>(ref_fc2.pbias.size()),
&alpha, gfc2bias, 1, pfc2bias, 1));
}
};
///////////////////////////////////////////////////////////////////////////////////////////
// Main function
int main(int argc, char **argv)
{
#ifdef USE_GFLAGS
gflags::ParseCommandLineFlags(&argc, &argv, true);
#endif
size_t width, height, channels = 1;
// Open input data
printf("Reading input data\n");
// Read dataset sizes
size_t train_size = ReadUByteDataset(FLAGS_train_images.c_str(), FLAGS_train_labels.c_str(), nullptr, nullptr, width, height);
size_t test_size = ReadUByteDataset(FLAGS_test_images.c_str(), FLAGS_test_labels.c_str(), nullptr, nullptr, width, height);
if (train_size == 0)
return 1;
std::vector<uint8_t> train_images(train_size * width * height * channels), train_labels(train_size);
std::vector<uint8_t> test_images(test_size * width * height * channels), test_labels(test_size);
// Read data from datasets
if (ReadUByteDataset(FLAGS_train_images.c_str(), FLAGS_train_labels.c_str(), &train_images[0], &train_labels[0], width, height) != train_size)
return 2;
if (ReadUByteDataset(FLAGS_test_images.c_str(), FLAGS_test_labels.c_str(), &test_images[0], &test_labels[0], width, height) != test_size)
return 3;
printf("Done. Training dataset size: %d, Test dataset size: %d\n", (int)train_size, (int)test_size);
printf("Batch size: %lld, iterations: %d\n", FLAGS_batch_size, FLAGS_iterations);
// This code snippet saves a random image and its label
/*
std::random_device rd_image;
int random_image = rd_image() % train_size;
std::stringstream ss; ss << "image-" << (int)train_labels[random_image] << ".pgm";
SavePGMFile(&train_images[0] + random_image * width*height*channels, width, height, ss.str().c_str());
*/
// Choose GPU
int num_gpus;
checkCudaErrors(cudaGetDeviceCount(&num_gpus));
if (FLAGS_gpu < 0 || FLAGS_gpu >= num_gpus)
{
printf("ERROR: Invalid GPU ID %d (There are %d GPUs on this machine)\n",
FLAGS_gpu, num_gpus);
return 4;
}
// Create the LeNet network architecture
ConvBiasLayer conv1((int)channels, 20, 5, (int)width, (int)height);
MaxPoolLayer pool1(2, 2);
ConvBiasLayer conv2(conv1.out_channels, 50, 5, conv1.out_width / pool1.stride, conv1.out_height / pool1.stride);
MaxPoolLayer pool2(2, 2);
FullyConnectedLayer fc1((conv2.out_channels*conv2.out_width*conv2.out_height) / (pool2.stride * pool2.stride),
500);
FullyConnectedLayer fc2(fc1.outputs, 10);
// Initialize CUDNN/CUBLAS training context
TrainingContext context(FLAGS_gpu, FLAGS_batch_size, conv1, pool1, conv2, pool2, fc1, fc2);
// Determine initial network structure
bool bRet = true;
if (FLAGS_pretrained)
{
bRet = conv1.FromFile("conv1");
bRet &= conv2.FromFile("conv2");
bRet &= fc1.FromFile("ip1");
bRet &= fc2.FromFile("ip2");
}
if (!bRet || !FLAGS_pretrained)
{
// Create random network
std::random_device rd;
std::mt19937 gen(FLAGS_random_seed < 0 ? rd() : static_cast<unsigned int>(FLAGS_random_seed));
// Xavier weight filling
float wconv1 = sqrt(3.0f / (conv1.kernel_size * conv1.kernel_size * conv1.in_channels));
std::uniform_real_distribution<> dconv1(-wconv1, wconv1);
float wconv2 = sqrt(3.0f / (conv2.kernel_size * conv2.kernel_size * conv2.in_channels));
std::uniform_real_distribution<> dconv2(-wconv2, wconv2);
float wfc1 = sqrt(3.0f / (fc1.inputs * fc1.outputs));
std::uniform_real_distribution<> dfc1(-wfc1, wfc1);
float wfc2 = sqrt(3.0f / (fc2.inputs * fc2.outputs));
std::uniform_real_distribution<> dfc2(-wfc2, wfc2);
// Randomize network
for (auto&& iter : conv1.pconv)
iter = static_cast<float>(dconv1(gen));
for (auto&& iter : conv1.pbias)
iter = static_cast<float>(dconv1(gen));
for (auto&& iter : conv2.pconv)
iter = static_cast<float>(dconv2(gen));
for (auto&& iter : conv2.pbias)
iter = static_cast<float>(dconv2(gen));
for (auto&& iter : fc1.pneurons)
iter = static_cast<float>(dfc1(gen));
for (auto&& iter : fc1.pbias)
iter = static_cast<float>(dfc1(gen));
for (auto&& iter : fc2.pneurons)
iter = static_cast<float>(dfc2(gen));
for (auto&& iter : fc2.pbias)
iter = static_cast<float>(dfc2(gen));
}
/////////////////////////////////////////////////////////////////////////////
// Create GPU data structures
// Forward propagation data
float *d_data, *d_labels, *d_conv1, *d_pool1, *d_conv2, *d_pool2, *d_fc1, *d_fc1relu, *d_fc2, *d_fc2smax;
// Buffer | Element | N | C | H | W
//-----------------------------------------------------------------------------------------------------------------------------------------
checkCudaErrors(cudaMalloc(&d_data, sizeof(float) * context.m_batchSize * channels * height * width));
checkCudaErrors(cudaMalloc(&d_labels, sizeof(float) * context.m_batchSize * 1 * 1 * 1));
checkCudaErrors(cudaMalloc(&d_conv1, sizeof(float) * context.m_batchSize * conv1.out_channels * conv1.out_height * conv1.out_width));
checkCudaErrors(cudaMalloc(&d_pool1, sizeof(float) * context.m_batchSize * conv1.out_channels * (conv1.out_height / pool1.stride) * (conv1.out_width / pool1.stride)));
checkCudaErrors(cudaMalloc(&d_conv2, sizeof(float) * context.m_batchSize * conv2.out_channels * conv2.out_height * conv2.out_width));
checkCudaErrors(cudaMalloc(&d_pool2, sizeof(float) * context.m_batchSize * conv2.out_channels * (conv2.out_height / pool2.stride) * (conv2.out_width / pool2.stride)));
checkCudaErrors(cudaMalloc(&d_fc1, sizeof(float) * context.m_batchSize * fc1.outputs));
checkCudaErrors(cudaMalloc(&d_fc1relu, sizeof(float) * context.m_batchSize * fc1.outputs));
checkCudaErrors(cudaMalloc(&d_fc2, sizeof(float) * context.m_batchSize * fc2.outputs));
checkCudaErrors(cudaMalloc(&d_fc2smax, sizeof(float) * context.m_batchSize * fc2.outputs));
// Network parameters
float *d_pconv1, *d_pconv1bias, *d_pconv2, *d_pconv2bias;
float *d_pfc1, *d_pfc1bias, *d_pfc2, *d_pfc2bias;
checkCudaErrors(cudaMalloc(&d_pconv1, sizeof(float) * conv1.pconv.size()));
checkCudaErrors(cudaMalloc(&d_pconv1bias, sizeof(float) * conv1.pbias.size()));
checkCudaErrors(cudaMalloc(&d_pconv2, sizeof(float) * conv2.pconv.size()));
checkCudaErrors(cudaMalloc(&d_pconv2bias, sizeof(float) * conv2.pbias.size()));
checkCudaErrors(cudaMalloc(&d_pfc1, sizeof(float) * fc1.pneurons.size()));
checkCudaErrors(cudaMalloc(&d_pfc1bias, sizeof(float) * fc1.pbias.size()));
checkCudaErrors(cudaMalloc(&d_pfc2, sizeof(float) * fc2.pneurons.size()));
checkCudaErrors(cudaMalloc(&d_pfc2bias, sizeof(float) * fc2.pbias.size()));
// Network parameter gradients
float *d_gconv1, *d_gconv1bias, *d_gconv2, *d_gconv2bias;
float *d_gfc1, *d_gfc1bias, *d_gfc2, *d_gfc2bias;
checkCudaErrors(cudaMalloc(&d_gconv1, sizeof(float) * conv1.pconv.size()));
checkCudaErrors(cudaMalloc(&d_gconv1bias, sizeof(float) * conv1.pbias.size()));
checkCudaErrors(cudaMalloc(&d_gconv2, sizeof(float) * conv2.pconv.size()));
checkCudaErrors(cudaMalloc(&d_gconv2bias, sizeof(float) * conv2.pbias.size()));
checkCudaErrors(cudaMalloc(&d_gfc1, sizeof(float) * fc1.pneurons.size()));
checkCudaErrors(cudaMalloc(&d_gfc1bias, sizeof(float) * fc1.pbias.size()));
checkCudaErrors(cudaMalloc(&d_gfc2, sizeof(float) * fc2.pneurons.size()));
checkCudaErrors(cudaMalloc(&d_gfc2bias, sizeof(float) * fc2.pbias.size()));
// Differentials w.r.t. data
float *d_dpool1, *d_dpool2, *d_dconv2, *d_dfc1, *d_dfc1relu, *d_dfc2, *d_dfc2smax, *d_dlossdata;
// Buffer | Element | N | C | H | W
//-----------------------------------------------------------------------------------------------------------------------------------------
checkCudaErrors(cudaMalloc(&d_dpool1, sizeof(float) * context.m_batchSize * conv1.out_channels * conv1.out_height * conv1.out_width));
checkCudaErrors(cudaMalloc(&d_dpool2, sizeof(float) * context.m_batchSize * conv2.out_channels * conv2.out_height * conv2.out_width));
checkCudaErrors(cudaMalloc(&d_dconv2, sizeof(float) * context.m_batchSize * conv1.out_channels * (conv1.out_height / pool1.stride) * (conv1.out_width / pool1.stride)));
checkCudaErrors(cudaMalloc(&d_dfc1, sizeof(float) * context.m_batchSize * fc1.inputs));
checkCudaErrors(cudaMalloc(&d_dfc1relu, sizeof(float) * context.m_batchSize * fc1.outputs));
checkCudaErrors(cudaMalloc(&d_dfc2, sizeof(float) * context.m_batchSize * fc2.inputs));
checkCudaErrors(cudaMalloc(&d_dfc2smax, sizeof(float) * context.m_batchSize * fc2.outputs));
checkCudaErrors(cudaMalloc(&d_dlossdata,sizeof(float) * context.m_batchSize * fc2.outputs));
// Temporary buffers and workspaces
float *d_onevec;
void *d_cudnn_workspace = nullptr;
checkCudaErrors(cudaMalloc(&d_onevec, sizeof(float)* context.m_batchSize));
if (context.m_workspaceSize > 0)
checkCudaErrors(cudaMalloc(&d_cudnn_workspace, context.m_workspaceSize));
/////////////////////////////////////////////////////////////////////////////
// Copy initial network to device
checkCudaErrors(cudaMemcpyAsync(d_pconv1, &conv1.pconv[0], sizeof(float) * conv1.pconv.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pconv1bias, &conv1.pbias[0], sizeof(float) * conv1.pbias.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pconv2, &conv2.pconv[0], sizeof(float) * conv2.pconv.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pconv2bias, &conv2.pbias[0], sizeof(float) * conv2.pbias.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pfc1, &fc1.pneurons[0], sizeof(float) * fc1.pneurons.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pfc1bias, &fc1.pbias[0], sizeof(float) * fc1.pbias.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pfc2, &fc2.pneurons[0], sizeof(float) * fc2.pneurons.size(), cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_pfc2bias, &fc2.pbias[0], sizeof(float) * fc2.pbias.size(), cudaMemcpyHostToDevice));
// Fill one-vector with ones
FillOnes<<<RoundUp(context.m_batchSize, BW), BW>>>(d_onevec, context.m_batchSize);
printf("Preparing dataset\n");
// Normalize training set to be in [0,1]
std::vector<float> train_images_float(train_images.size()), train_labels_float(train_size);
for (size_t i = 0; i < train_size * channels * width * height; ++i)
train_images_float[i] = (float)train_images[i] / 255.0f;
for (size_t i = 0; i < train_size; ++i)
train_labels_float[i] = (float)train_labels[i];
printf("Training...\n");
// Use SGD to train the network
checkCudaErrors(cudaDeviceSynchronize());
auto t1 = std::chrono::high_resolution_clock::now();
for (int iter = 0; iter < FLAGS_iterations; ++iter)
{
// Train
int imageid = iter % (train_size / context.m_batchSize);
// Prepare current batch on device
checkCudaErrors(cudaMemcpyAsync(d_data, &train_images_float[imageid * context.m_batchSize * width*height*channels],
sizeof(float) * context.m_batchSize * channels * width * height, cudaMemcpyHostToDevice));
checkCudaErrors(cudaMemcpyAsync(d_labels, &train_labels_float[imageid * context.m_batchSize],
sizeof(float) * context.m_batchSize, cudaMemcpyHostToDevice));