ConvBC01CuDNN<T>::ConvBC01CuDNN(int pad_y, int pad_x, int stride_y,
int stride_x) : pad_y(pad_y), pad_x(pad_x), stride_y(stride_y),
stride_x(stride_x), n_imgs(0), n_channels(0), n_filters(0), img_h(0),
img_w(0), filter_h(0), filter_w(0), workspace_size(0) {
CUDNN_CHECK(cudnnCreateTensorDescriptor(&imgs_desc));
CUDNN_CHECK(cudnnCreateTensorDescriptor(&convout_desc));
CUDNN_CHECK(cudnnCreateFilterDescriptor(&filters_desc));
CUDNN_CHECK(cudnnCreateConvolutionDescriptor(&conv_desc));
}
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
int n, int c, int h, int w) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
#if CUDNN_VERSION_MIN(5, 0, 0)
CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
CUDNN_TENSOR_NCHW, n, c, h, w));
#else
CUDNN_CHECK(cudnnSetFilter4dDescriptor_v4(*desc, dataType<Dtype>::type,
CUDNN_TENSOR_NCHW, n, c, h, w));
#endif
}
convolution_layer_updater_cuda::convolution_layer_updater_cuda()
: input_data_desc(0)
, output_data_desc(0)
, weights_desc(0)
, convolution_desc(0)
, bias_desc(0)
{
cudnn_safe_call(cudnnCreateTensorDescriptor(&input_data_desc));
cudnn_safe_call(cudnnCreateTensorDescriptor(&output_data_desc));
cudnn_safe_call(cudnnCreateFilterDescriptor(&weights_desc));
cudnn_safe_call(cudnnCreateConvolutionDescriptor(&convolution_desc));
cudnn_safe_call(cudnnCreateTensorDescriptor(&bias_desc));
}
static int c_make_filter(PyGpuArrayObject *var, cudnnFilterDescriptor_t *desc) {
cudnnStatus_t err;
err = cudnnCreateFilterDescriptor(desc);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"Could not create tensor descriptor: %s",
cudnnGetErrorString(err));
return -1;
}
if (c_set_filter(var, *desc, 1) != 0) {
cudnnDestroyFilterDescriptor(*desc);
return -1;
}
return 0;
}
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
const int_tp num_spatial_dims,
const int_tp n, const int_tp c, const int_tp* shape) {
std::vector<int> shape_int(num_spatial_dims + 2);
shape_int[0] = n;
shape_int[1] = c;
for (int_tp i = 0; i < num_spatial_dims; ++i) {
shape_int[2+i] = shape[i];
}
const int* shape_ptr = &shape_int[0];
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
CUDNN_CHECK(cudnnSetFilterNdDescriptor(*desc, dataType<Dtype>::type,
num_spatial_dims + 2,
shape_ptr));
}
inline void createFilterDesc(cudnnFilterDescriptor_t* desc, Size size) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
size.num(), size.channels(), size.height(), size.width()));
}
inline void createFilterDesc(cudnnFilterDescriptor_t* desc,
int n, int c, int h, int w) {
CUDNN_CHECK(cudnnCreateFilterDescriptor(desc));
CUDNN_CHECK(cudnnSetFilter4dDescriptor(*desc, dataType<Dtype>::type,
n, c, h, w));
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary, int xnor)
{
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
l.h = h;
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = pad;
l.batch_normalize = batch_normalize;
l.filters = calloc(c*n*size*size, sizeof(float));
l.filter_updates = calloc(c*n*size*size, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c));
for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1);
int out_h = convolutional_out_height(l);
int out_w = convolutional_out_width(l);
l.out_h = out_h;
l.out_w = out_w;
l.out_c = n;
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
if(binary){
l.binary_filters = calloc(c*n*size*size, sizeof(float));
l.cfilters = calloc(c*n*size*size, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_filters = calloc(c*n*size*size, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
l.scales[i] = 1;
}
l.mean = calloc(n, sizeof(float));
l.variance = calloc(n, sizeof(float));
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
}
#ifdef GPU
l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);
l.biases_gpu = cuda_make_array(l.biases, n);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
if(binary){
l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
}
if(xnor){
l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
}
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
l.variance_gpu = cuda_make_array(l.variance, n);
l.rolling_mean_gpu = cuda_make_array(l.mean, n);
l.rolling_variance_gpu = cuda_make_array(l.variance, n);
l.mean_delta_gpu = cuda_make_array(l.mean, n);
l.variance_delta_gpu = cuda_make_array(l.variance, n);
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
}
#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.srcTensorDesc);
cudnnCreateTensorDescriptor(&l.dstTensorDesc);
cudnnCreateFilterDescriptor(&l.filterDesc);
cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
cudnnCreateFilterDescriptor(&l.dfilterDesc);
cudnnCreateConvolutionDescriptor(&l.convDesc);
cudnnSetTensor4dDescriptor(l.dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w);
cudnnSetTensor4dDescriptor(l.ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
cudnnSetFilter4dDescriptor(l.dfilterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size);
cudnnSetTensor4dDescriptor(l.srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.c, l.h, l.w);
cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w);
cudnnSetFilter4dDescriptor(l.filterDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l.n, l.c, l.size, l.size);
int padding = l.pad ? l.size/2 : 0;
cudnnSetConvolution2dDescriptor(l.convDesc, padding, padding, l.stride, l.stride, 1, 1, CUDNN_CROSS_CORRELATION);
cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
l.srcTensorDesc,
l.filterDesc,
l.convDesc,
l.dstTensorDesc,
CUDNN_CONVOLUTION_FWD_PREFER_FASTEST,
0,
&l.fw_algo);
cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
l.filterDesc,
l.ddstTensorDesc,
l.convDesc,
l.dsrcTensorDesc,
CUDNN_CONVOLUTION_BWD_DATA_PREFER_FASTEST,
0,
&l.bd_algo);
cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
l.srcTensorDesc,
l.ddstTensorDesc,
l.convDesc,
l.dfilterDesc,
CUDNN_CONVOLUTION_BWD_FILTER_PREFER_FASTEST,
0,
&l.bf_algo);
#endif
#endif
l.workspace_size = get_workspace_size(l);
l.activation = activation;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
return l;
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam)
{
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
l.groups = groups;
l.h = h;
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = padding;
l.batch_normalize = batch_normalize;
l.weights = calloc(c/groups*n*size*size, sizeof(float));
l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
l.nweights = c/groups*n*size*size;
l.nbiases = n;
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c/l.groups));
//scale = .02;
//for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
int out_w = convolutional_out_width(l);
int out_h = convolutional_out_height(l);
l.out_h = out_h;
l.out_w = out_w;
l.out_c = n;
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
l.output = calloc(l.batch*l.outputs, sizeof(float));
l.delta = calloc(l.batch*l.outputs, sizeof(float));
l.forward = forward_convolutional_layer;
l.backward = backward_convolutional_layer;
l.update = update_convolutional_layer;
if(binary){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.cweights = calloc(l.nweights, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
l.scales[i] = 1;
}
l.mean = calloc(n, sizeof(float));
l.variance = calloc(n, sizeof(float));
l.mean_delta = calloc(n, sizeof(float));
l.variance_delta = calloc(n, sizeof(float));
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
l.x = calloc(l.batch*l.outputs, sizeof(float));
l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
}
if(adam){
l.m = calloc(l.nweights, sizeof(float));
l.v = calloc(l.nweights, sizeof(float));
l.bias_m = calloc(n, sizeof(float));
l.scale_m = calloc(n, sizeof(float));
l.bias_v = calloc(n, sizeof(float));
l.scale_v = calloc(n, sizeof(float));
}
#ifdef GPU
l.forward_gpu = forward_convolutional_layer_gpu;
l.backward_gpu = backward_convolutional_layer_gpu;
l.update_gpu = update_convolutional_layer_gpu;
if(gpu_index >= 0){
if (adam) {
l.m_gpu = cuda_make_array(l.m, l.nweights);
l.v_gpu = cuda_make_array(l.v, l.nweights);
l.bias_m_gpu = cuda_make_array(l.bias_m, n);
l.bias_v_gpu = cuda_make_array(l.bias_v, n);
l.scale_m_gpu = cuda_make_array(l.scale_m, n);
l.scale_v_gpu = cuda_make_array(l.scale_v, n);
}
l.weights_gpu = cuda_make_array(l.weights, l.nweights);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights);
l.biases_gpu = cuda_make_array(l.biases, n);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
if(binary){
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
}
if(xnor){
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
}
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
l.variance_gpu = cuda_make_array(l.variance, n);
l.rolling_mean_gpu = cuda_make_array(l.mean, n);
l.rolling_variance_gpu = cuda_make_array(l.variance, n);
l.mean_delta_gpu = cuda_make_array(l.mean, n);
l.variance_delta_gpu = cuda_make_array(l.variance, n);
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
}
#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.normTensorDesc);
cudnnCreateTensorDescriptor(&l.srcTensorDesc);
cudnnCreateTensorDescriptor(&l.dstTensorDesc);
cudnnCreateFilterDescriptor(&l.weightDesc);
cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
cudnnCreateFilterDescriptor(&l.dweightDesc);
cudnnCreateConvolutionDescriptor(&l.convDesc);
cudnn_convolutional_setup(&l);
#endif
}
#endif
l.workspace_size = get_workspace_size(l);
l.activation = activation;
//fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c);
return l;
}
static PyObject *conv_dfilter_buffers(PyObject *self, PyObject *args) {
cudaError_t err;
cudnnStatus_t status;
int PAD, gpu_ind, filters_ind, imgs_ind, conv_out_ind, out_ind, stream_ind;
if (!PyArg_ParseTuple(args, "iiiiiii", &filters_ind, &imgs_ind, &conv_out_ind, &out_ind, &PAD, &stream_ind, &gpu_ind))
return NULL;
if(filters_ind >= N_BUFFERS || filters_ind < 0 || imgs_ind >= N_BUFFERS || imgs_ind < 0 ||
conv_out_ind >= N_BUFFERS || conv_out_ind < 0 || out_ind >= N_BUFFERS || out_ind < 0){
printf("invalid buffer index\n");
return NULL;
}
if(gpu_ind < 0 || gpu_ind > N_GPUS){
printf("invalid gpu index %i\n", gpu_ind);
return NULL;
}
if(stream_ind < 0 || stream_ind > N_ALT_STREAMS){
printf("invalid stream index %i\n", stream_ind);
return NULL;
}
if(data_buffers[gpu_ind][filters_ind] == NULL || data_buffers[gpu_ind][imgs_ind] == NULL ||
data_buffers[gpu_ind][conv_out_ind] == NULL){
printf("one or more buffers not initialized on this gpu\n");
return NULL;
}
if(filter_flags[gpu_ind][filters_ind] == 0 || filter_flags[gpu_ind][imgs_ind] == 1 ||
filter_flags[gpu_ind][conv_out_ind] == 1){
printf("one or more buffers was not initialized correctly, filters when should be tensor or vice versa\n");
return NULL;
}
cudaSetDevice(gpu_ind); CHECK_CUDA_ERR
cudaStreamSynchronize(streams[gpu_ind]); // make sure the inputs are in the buffers first
cudnnSetStream(handle, alt_streams[gpu_ind][stream_ind]);
int n_filters = data_dims[0][gpu_ind][filters_ind];
int n_channels = data_dims[1][gpu_ind][filters_ind];
int filter_sz = data_dims[2][gpu_ind][filters_ind];
if(data_buffers[gpu_ind][out_ind] == NULL){ // allocate output
status = cudnnCreateFilterDescriptor(&desc_filters[gpu_ind][out_ind]); ERR_CHECK
status = cudnnSetFilterDescriptor(desc_filters[gpu_ind][out_ind], dataType, n_filters, n_channels, filter_sz, filter_sz); ERR_CHECK
err = cudaMalloc((void**) &data_buffers[gpu_ind][out_ind], n_filters*n_channels*filter_sz*filter_sz * DATA_TYPE_SZ); MALLOC_ERR_CHECK
data_dims[0][gpu_ind][out_ind] = n_filters;
data_dims[1][gpu_ind][out_ind] = n_channels;
data_dims[2][gpu_ind][out_ind] = filter_sz;
data_dims[3][gpu_ind][out_ind] = filter_sz;
filter_flags[gpu_ind][out_ind] = 1;
}else if(filter_flags[gpu_ind][out_ind] == 0 || data_dims[0][gpu_ind][out_ind] != n_filters ||
data_dims[1][gpu_ind][out_ind] != n_channels || data_dims[2][gpu_ind][out_ind] != filter_sz ||
data_dims[3][gpu_ind][out_ind] != filter_sz){ // make sure output buffer is of correct size
printf("output buffer size is not matching output of this function and/or initialized as a tensor, %s %i\n", __FILE__, __LINE__);
return NULL;
}
//---------------------------------------
// Set decriptors
//---------------------------------------
status = cudnnSetConvolutionDescriptor(convDesc, desc_buffers[gpu_ind][imgs_ind], desc_filters[gpu_ind][out_ind], PAD, PAD, 1, 1, 1, 1, CUDNN_CROSS_CORRELATION); ERR_CHECK
//---------------------------------------
// Query output layout
//---------------------------------------
int n_imgs_out, n_filters_out, conv_out_sz_x, conv_out_sz_y;
status = cudnnGetOutputTensor4dDim(convDesc, CUDNN_CONVOLUTION_FWD, &n_imgs_out, &n_filters_out, &conv_out_sz_x, &conv_out_sz_y); ERR_CHECK
//--------------------------------------
// set filter and image values
//--------------------------------------
if(n_imgs_out*n_filters_out*conv_out_sz_x*conv_out_sz_x != data_dims[0][gpu_ind][conv_out_ind]*data_dims[1][gpu_ind][conv_out_ind]*
data_dims[2][gpu_ind][conv_out_ind]*data_dims[3][gpu_ind][conv_out_ind]){
printf("predicted conv output not matching given input %s %i\n", __FILE__, __LINE__);
printf("%i %i\n", n_imgs_out*n_filters_out*conv_out_sz_x*conv_out_sz_x, data_dims[0][gpu_ind][conv_out_ind]*data_dims[1][gpu_ind][conv_out_ind]*
data_dims[2][gpu_ind][conv_out_ind]*data_dims[3][gpu_ind][conv_out_ind]);
printf("%i %i\n", n_imgs_out, data_dims[0][gpu_ind][conv_out_ind]);
printf("%i %i\n", n_filters_out, data_dims[1][gpu_ind][conv_out_ind]);
printf("%i %i\n", conv_out_sz_x, data_dims[2][gpu_ind][conv_out_ind]);
printf("%i %i\n", conv_out_sz_y, data_dims[3][gpu_ind][conv_out_ind]);
//return NULL;
}
//--------------------------------------
// Convolution
//--------------------------------------
status = cudnnConvolutionBackwardFilter(handle, desc_buffers[gpu_ind][imgs_ind], data_buffers[gpu_ind][imgs_ind],
desc_buffers[gpu_ind][conv_out_ind], data_buffers[gpu_ind][conv_out_ind], convDesc,
desc_filters[gpu_ind][out_ind], data_buffers[gpu_ind][out_ind], CUDNN_RESULT_NO_ACCUMULATE); ERR_CHECK
cudnnSetStream(handle, NULL);
cudaSetDevice(0); CHECK_CUDA_ERR
Py_INCREF(Py_None);
return Py_None;
}
THFloatTensor *cudnn_SpatialConvolution_updateOutput(struct module *module, THFloatTensor *input)
{
int kW = module->SpatialConvolution.kW;
int kH = module->SpatialConvolution.kH;
int dW = module->SpatialConvolution.dW;
int dH = module->SpatialConvolution.dH;
int padW = module->SpatialConvolution.padW;
int padH = module->SpatialConvolution.padH;
int nInputPlane = module->SpatialConvolution.nInputPlane;
int nOutputPlane = module->SpatialConvolution.nOutputPlane;
THFloatTensor *weight = module->SpatialConvolution.weight;
THFloatTensor *bias = module->SpatialConvolution.bias;
THFloatTensor *output = module->output;
int sizes[4];
int pad[2], filterStride[2], upscale[2];
cudnnTensorDescriptor_t dinput, dbias, doutput;
cudnnConvolutionDescriptor_t dconv;
cudnnFilterDescriptor_t dweight;
float one = 1, zero = 0;
size_t reqwssize;
static void *ws;
static size_t wssize;
static const int alg = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
pad[0] = padH;
pad[1] = padW;
filterStride[0] = dH;
filterStride[1] = dW;
upscale[0] = 1;
upscale[1] = 1;
if(input->nDimension <= 2)
{
// Here we use the SpatialConvolution module to perform a linear transformation
errcheck(cudnnCreateTensorDescriptor(&dinput));
if(input->nDimension == 1)
errcheck(cudnnSetTensor4dDescriptor(dinput, CUDNN_TENSOR_NCHW, floattype, 1, input->size[0], 1, 1));
else errcheck(cudnnSetTensor4dDescriptor(dinput, CUDNN_TENSOR_NCHW, floattype, input->size[0], input->size[1], 1, 1));
} else errcheck(THcudnn_TensorDescriptor(&dinput, input));
errcheck(cudnnCreateFilterDescriptor(&dweight));
errcheck(cudnnSetFilter4dDescriptor(dweight, floattype, nOutputPlane, nInputPlane, kH, kW));
errcheck(cudnnCreateTensorDescriptor(&dbias));
errcheck(cudnnSetTensor4dDescriptor(dbias, CUDNN_TENSOR_NCHW, floattype, 1, bias->size[0], 1, 1));
errcheck(cudnnCreateConvolutionDescriptor(&dconv));
errcheck(cudnnSetConvolutionNdDescriptor(dconv, 2, pad, filterStride, upscale, CUDNN_CROSS_CORRELATION, floattype));
errcheck(cudnnGetConvolutionNdForwardOutputDim(dconv, dinput, dweight, 4, sizes));
THCudaTensor_resize4d(output, sizes[0], sizes[1], sizes[2], sizes[3]);
errcheck(THcudnn_TensorDescriptor(&doutput, output));
if(alg == CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM || alg == CUDNN_CONVOLUTION_FWD_ALGO_GEMM ||
alg == CUDNN_CONVOLUTION_FWD_ALGO_FFT || alg == CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING)
{
errcheck(cudnnGetConvolutionForwardWorkspaceSize(THcudnn_getHandle(), dinput, dweight, dconv, doutput, alg, &reqwssize));
if(reqwssize > wssize)
{
wssize = reqwssize;
errcheck(cudaMalloc(&ws, reqwssize));
}
}
errcheck(cudnnConvolutionForward(THcudnn_getHandle(), &one, dinput, THFloatTensor_data(input),
dweight, THFloatTensor_data(weight), dconv, alg, ws, wssize, &zero,
doutput, THFloatTensor_data(output)));
errcheck(cudnnAddTensor_v3(THcudnn_getHandle(), &one, dbias, THFloatTensor_data(bias),
&one, doutput, THFloatTensor_data(output)));
cudnnDestroyTensorDescriptor(dinput);
cudnnDestroyFilterDescriptor(dweight);
cudnnDestroyTensorDescriptor(dbias);
cudnnDestroyTensorDescriptor(doutput);
cudnnDestroyConvolutionDescriptor(dconv);
return output;
}
