int
APPLY_SPECIFIC(conv_gi)(CudaNdarray *kerns, CudaNdarray *output,
CudaNdarray *im, cudnnConvolutionDescriptor_t desc,
float alpha, float beta, CudaNdarray **input) {
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
if (CudaNdarray_HOST_DIMS(im)[1] != CudaNdarray_HOST_DIMS(kerns)[1]) {
PyErr_SetString(PyExc_ValueError,
"GpuDnnConv images and kernel must have the same stack size\n");
return 1;
}
if (c_set_tensorNd(output, APPLY_SPECIFIC(output)) == -1)
return 1;
if (c_set_filterNd(kerns, APPLY_SPECIFIC(kerns)) == -1)
return 1;
int nb_dim = CudaNdarray_NDIM(output);
#ifdef CONV_INPLACE
Py_XDECREF(*input);
*input = im;
Py_INCREF(*input);
#else
if (CudaNdarray_prep_output(input, nb_dim, CudaNdarray_HOST_DIMS(im)) != 0)
return 1;
if (beta != 0.0 && CudaNdarray_CopyFromCudaNdarray(*input, im))
return 1;
#endif
if (c_set_tensorNd(*input, APPLY_SPECIFIC(input)) == -1)
return 1;
#if defined(CUDNN_VERSION) && CUDNN_VERSION >= 3000
{
size_t worksize;
void *workspace;
cudnnConvolutionBwdDataAlgo_t chosen_algo;
if (CHOOSE_ALGO)
{
// A new convolution implementation should be selected, based either on
// timing or heuristics, if in one of the two following cases :
// - The implementation should only be chosen during the first execution
// of an apply node and this is the first execution of the apply node.
// - The implementation should be chosen as often as necessary and the
// shapes of the inputs differ from the last time an implementation
// was chosen.
bool reuse_previous_algo;
if (CHOOSE_ALGO_ONCE)
{
// Only choose a new implementation of none has been chosen before.
reuse_previous_algo = APPLY_SPECIFIC(previous_algo_set);
}
else
{
// Reuse the previous implementation if the the kernels and the outputs
// have the same shapes as they had when the previous implementation
// was selected
bool same_shapes = true;
for (int i = 0; (i < nb_dim) && same_shapes; i++)
{
same_shapes &= (CudaNdarray_HOST_DIMS(kerns)[i] ==
APPLY_SPECIFIC(previous_kerns_shape)[i]);
same_shapes &= (CudaNdarray_HOST_DIMS(output)[i] ==
APPLY_SPECIFIC(previous_output_shape)[i]);
}
reuse_previous_algo = same_shapes;
}
// If the previously choosen implementation can't be reused, select a
// new one based on the shapes of the current inputs
if (!reuse_previous_algo)
{
// Obtain a convolution algorithm appropriate for the kernel and output
// shapes. Either by choosing one according to heuristics or by making
// CuDNN time every implementation and choose the best one.
if (CHOOSE_ALGO_TIME)
{
// Time the different implementations to choose the best one
int requestedCount = 1;
int count;
cudnnConvolutionBwdDataAlgoPerf_t choosen_algo_perf;
err = cudnnFindConvolutionBackwardDataAlgorithm(_handle,
APPLY_SPECIFIC(kerns),
APPLY_SPECIFIC(output),
desc,
APPLY_SPECIFIC(input),
requestedCount,
&count,
&choosen_algo_perf);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"GpuDnnConvGradI: error selecting convolution algo: "
"%s", cudnnGetErrorString(err));
return 1;
}
chosen_algo = choosen_algo_perf.algo;
}
else
{
// Choose the convolution implementation using heuristics based on the
// shapes of the inputs and the amount of memory available.
// Get the amount of available memory
size_t free = 0, total = 0;
cudaError_t err2 = cudaMemGetInfo(&free, &total);
if (err2 != cudaSuccess){
cudaGetLastError();
fprintf(stderr,
"Error when trying to find the memory information"
" on the GPU: %s\n", cudaGetErrorString(err2));
return 1;
}
// Use heuristics to choose the implementation
err = cudnnGetConvolutionBackwardDataAlgorithm(_handle,
APPLY_SPECIFIC(kerns),
APPLY_SPECIFIC(output),
desc,
APPLY_SPECIFIC(input),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
free,
&chosen_algo);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"GpuDnnConvGradI: error selecting convolution algo: %s",
cudnnGetErrorString(err));
return 1;
}
}
// Store the shapes of the kernels and output as well as the chosen
// algorithm for future use.
APPLY_SPECIFIC(previous_bwd_d_algo) = chosen_algo;
for (int i = 0; i < nb_dim; i++)
{
APPLY_SPECIFIC(previous_kerns_shape)[i] =
CudaNdarray_HOST_DIMS(kerns)[i];
APPLY_SPECIFIC(previous_output_shape)[i] =
CudaNdarray_HOST_DIMS(output)[i];
}
}
else
{
// Reuse the previously chosen convlution implementation
chosen_algo = APPLY_SPECIFIC(previous_bwd_d_algo);
}
}
else
{
chosen_algo = CONV_ALGO;
}
// The FFT implementation (only in v3 and onward) does not support strides,
// 1x1 filters or inputs with a spatial dimension larger than 1024.
// If the chosen implementation is FFT, validate that it can be used
// on the current data and default on a safe implementation if it
// can't.
if (chosen_algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT && nb_dim == 4)
{
// Extract the properties of the convolution descriptor
int pad_h, pad_w, stride_v, stride_h, upscale_x, upscale_y;
cudnnConvolutionMode_t mode;
err = cudnnGetConvolution2dDescriptor(desc, &pad_h, &pad_w,
&stride_v, &stride_h,
&upscale_x, &upscale_y,
&mode);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"GpuDnnConvGradI: error getting convolution properties: %s",
cudnnGetErrorString(err));
return 1;
}
// Extract the spatial size of the filters
int filter_h = CudaNdarray_HOST_DIMS(kerns)[3];
int filter_w = CudaNdarray_HOST_DIMS(kerns)[4];
// Extract the spatial size of the input
int input_h = CudaNdarray_HOST_DIMS(*input)[3];
int input_w = CudaNdarray_HOST_DIMS(*input)[4];
// Ensure that the selected implementation supports the requested
// convolution. Fall back to a safe implementation otherwise.
if (stride_v != 1 || stride_h != 1 || input_h > 1024 ||
input_w > 1024 || (filter_h == 1 && filter_w == 1))
{
chosen_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
}
}
// Infer required workspace size from the chosen implementation
err = cudnnGetConvolutionBackwardDataWorkspaceSize(_handle,
APPLY_SPECIFIC(kerns),
APPLY_SPECIFIC(output),
desc,
APPLY_SPECIFIC(input),
chosen_algo,
&worksize);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"GpuDnnConvGradI: error getting worksize: %s",
cudnnGetErrorString(err));
return 1;
}
// Allocate workspace for the convolution
workspace = get_work_mem(worksize);
if (workspace == NULL && worksize != 0)
return 1;
// Perform the convolution
err = cudnnConvolutionBackwardData_v3(
_handle,
(void *)&alpha,
APPLY_SPECIFIC(kerns), CudaNdarray_DEV_DATA(kerns),
APPLY_SPECIFIC(output), CudaNdarray_DEV_DATA(output),
desc,
chosen_algo,
workspace, worksize,
(void *)&beta,
APPLY_SPECIFIC(input), CudaNdarray_DEV_DATA(*input));
}
#else
err = cudnnConvolutionBackwardData(
_handle,
(void *)&alpha,
APPLY_SPECIFIC(kerns), CudaNdarray_DEV_DATA(kerns),
APPLY_SPECIFIC(output), CudaNdarray_DEV_DATA(output),
desc,
(void *)&beta,
APPLY_SPECIFIC(input), CudaNdarray_DEV_DATA(*input));
#endif
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "GpuDnnConvGradI: error doing operation: %s",
cudnnGetErrorString(err));
return 1;
}
return 0;
}
int
APPLY_SPECIFIC(conv_gi)(PyGpuArrayObject *kerns, PyGpuArrayObject *output,
PyGpuArrayObject *im,
cudnnConvolutionDescriptor_t desc,
double alpha, double beta, PyGpuArrayObject **input,
PyGpuContextObject *c) {
cudnnStatus_t err = CUDNN_STATUS_SUCCESS;
float af = alpha, bf = beta;
void *alpha_p;
void *beta_p;
if (PyGpuArray_DIMS(im)[1] != PyGpuArray_DIMS(kerns)[1]) {
PyErr_SetString(PyExc_ValueError, "images and kernel must have the same "
"stack size");
return 1;
}
if (c_set_tensorNd(output, APPLY_SPECIFIC(output)) == -1)
return 1;
if (c_set_filter(kerns, APPLY_SPECIFIC(kerns)) == -1)
return 1;
switch (im->ga.typecode) {
case GA_DOUBLE:
alpha_p = (void *)α
beta_p = (void *)β
break;
case GA_FLOAT:
case GA_HALF:
alpha_p = (void *)⁡
beta_p = (void *)&bf;
break;
default:
PyErr_SetString(PyExc_TypeError, "Unsupported type in convolution");
return 1;
}
#ifdef CONV_INPLACE
Py_XDECREF(*input);
*input = im;
Py_INCREF(*input);
#else
if (theano_prep_output(input, PyGpuArray_NDIM(im), PyGpuArray_DIMS(im),
im->ga.typecode, GA_C_ORDER, c) != 0)
return 1;
if (beta != 0.0 && pygpu_move(*input, im))
return 1;
#endif
if (c_set_tensorNd(*input, APPLY_SPECIFIC(input)) == -1)
return 1;
cudnnConvolutionBwdDataAlgo_t algo = CONV_ALGO;
cuda_enter(c->ctx);
#ifdef CHOOSE_ALGO
static int reuse_algo = 0;
static cudnnConvolutionBwdDataAlgo_t prev_algo = CONV_ALGO;
#ifndef CHOOSE_ONCE
static size_t prev_kern_dims[5] = {0};
static size_t prev_top_dims[5] = {0};
reuse_algo = 1;
for (unsigned int i = 0; i < PyGpuArray_NDIM(kerns); i++) {
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(kerns, i) == prev_kern_dims[i]);
reuse_algo = (reuse_algo &&
PyGpuArray_DIM(output, i) == prev_top_dims[i]);
}
#endif
if (!reuse_algo) {
#ifdef CHOOSE_TIME
int count;
cudnnConvolutionBwdDataAlgoPerf_t choice;
err = cudnnFindConvolutionBackwardDataAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(output), desc,
APPLY_SPECIFIC(kerns), 1, &count, &choice);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
algo = choice.algo;
#else
size_t free = 0, total = 0;
cudaError_t err2 = cudaMemGetInfo(&free, &total);
if (err2 != cudaSuccess) {
cudaGetLastError();
PyErr_Format(PyExc_RuntimeError, "Error when trying to find the memory "
"information on the GPU: %s\n", cudaGetErrorString(err2));
cuda_exit(c->ctx);
return 1;
}
err = cudnnGetConvolutionBackwardDataAlgorithm(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(input), APPLY_SPECIFIC(output),
desc, APPLY_SPECIFIC(kerns),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, free, &algo);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error selecting convolution algo: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
#endif
prev_algo = algo;
} else {
algo = prev_algo;
}
#ifdef CHOOSE_ONCE
reuse_algo = 1;
#else
for (unsigned int i = 0; i < PyGpuArray_NDIM(kerns); i++) {
prev_kern_dims[i] = PyGpuArray_DIM(kerns, i);
prev_top_dims[i] = PyGpuArray_DIM(output, i);
}
#endif
#endif
#if CUDNN_VERSION > 3000
if (algo == CUDNN_CONVOLUTION_BWD_DATA_ALGO_FFT) {
int nd;
int pad[2];
int stride[2];
int upscale[2];
cudnnConvolutionMode_t mode;
err = cudnnGetConvolutionNdDescriptor(desc, 2, &nd, pad, stride,
upscale, &mode);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError,
"error getting convolution properties: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (stride[0] != 1 || stride[1] != 1 ||
PyGpuArray_DIM(*input, 2) > 1024 || PyGpuArray_DIM(*input, 3) > 1024 ||
(PyGpuArray_DIM(kerns, 2) == 1 && PyGpuArray_DIM(kerns, 3) == 1)) {
algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
}
}
#endif
size_t worksize;
gpudata *workspace;
err = cudnnGetConvolutionBackwardDataWorkspaceSize(
APPLY_SPECIFIC(_handle), APPLY_SPECIFIC(kerns), APPLY_SPECIFIC(output), desc,
APPLY_SPECIFIC(input), algo, &worksize);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error getting worksize: %s",
cudnnGetErrorString(err));
cuda_exit(c->ctx);
return 1;
}
if (worksize != 0) {
workspace = c->ops->buffer_alloc(c->ctx, worksize, NULL, 0, NULL);
if (workspace == NULL) {
PyErr_SetString(PyExc_RuntimeError,
"Could not allocate working memory");
cuda_exit(c->ctx);
return 1;
}
}
cuda_wait(kerns->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_wait(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_wait((*input)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);
err = cudnnConvolutionBackwardData_v3(
APPLY_SPECIFIC(_handle),
alpha_p,
APPLY_SPECIFIC(kerns), PyGpuArray_DEV_DATA(kerns),
APPLY_SPECIFIC(output), PyGpuArray_DEV_DATA(output),
desc, algo, worksize == 0 ? NULL : *(void **)workspace, worksize,
beta_p,
APPLY_SPECIFIC(input), PyGpuArray_DEV_DATA(*input));
if (worksize != 0)
c->ops->buffer_release(workspace);
cuda_record(kerns->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_record(output->ga.data, GPUARRAY_CUDA_WAIT_READ);
cuda_record((*input)->ga.data, GPUARRAY_CUDA_WAIT_WRITE);
cuda_exit(c->ctx);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error doing operation: %s",
cudnnGetErrorString(err));
return 1;
}
return 0;
}
