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pytensor
提交 35535b19 authored 作者: Boris Fomitchev's avatar Boris Fomitchev 提交者： notoraptor
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removing extra files

上级 5a180d18
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theano/gpuarray/c_code/dnn_conv_find.c deleted 100644 → 0
浏览文件 @ 5a180d18
#section support_code
#include <cuda.h>
#include <sstream>
#include <vector>
#include <string>
#if __cplusplus < 201103L
#include <tr1/unordered_map>
typedef std::tr1::unordered_map<std::string, AlgoRec> AlgoCache;
#else
#include <unordered_map>
typedef std::unordered_map<std::string, AlgoRec> AlgoCache;
#endif
#include "pthread.h"
#line 10 "dnn_conv_find.c"
using std::vector;
using std::string;
pthread_mutex_t algoMutex;
AlgoCache algoCache;
static std::string shape(int* res, int size)
{
std::stringstream s;
if (size>0) {
s<<res[0];
for (int i=1; i< size; ++i)
s <<',' << res[i];
}
return std::string(s.str().c_str());
}
static std::string shape(cudnnTensorDescriptor_t t)
{
std::vector<int> res;
std::vector<int> stride;
int nbDims;
cudnnDataType_t type;
checkCudnnStatus(cudnnGetTensorNdDescriptor(t, 0, &type, &nbDims,0,0));
res.resize(nbDims);
stride.resize(nbDims);
checkCudnnStatus(cudnnGetTensorNdDescriptor(t, nbDims, &type, &nbDims, res.data(), stride.data()));
return shape(&res[0], nbDims) + shape(&stride[0], nbDims);
};
static std::string shape(cudnnFilterDescriptor_t t, cudnnDataType_t* type)
{
cudnnTensorFormat_t format;
int sizes = 8;
std::vector<int> res(sizes);
int outDims;
checkCudnnStatus(cudnnGetFilterNdDescriptor(t, sizes, type, &format, &outDims, res.data()));
return shape(&res[0], outDims);
};
static std::string shape(cudnnConvolutionDescriptor_t convDesc)
{
const int maxDim = 5;
int nDim=0;
cudnnConvolutionMode_t mode;
cudnnDataType_t computeType;
int padA[maxDim];
int strideA[maxDim];
int dilationA[maxDim];
checkCudnnStatus(
cudnnGetConvolutionNdDescriptor( convDesc, maxDim,
&nDim,
&padA[0],
&strideA[0],
&dilationA[0],
&mode,
&computeType ));
return std::string("-mode ") + (((int)mode==0) ? "conv" : "corr") + " -padA" + shape(padA,nDim) + " -convStrideA " + shape(strideA, nDim) + " -dilationA " + shape(dilationA, nDim);
}
static bool all_aligned(cudnnDataType_t type, void* in, void* out, void* filter)
{
size_t alignMask = (type == CUDNN_DATA_HALF) ? 0x7F : 0xFF ;
// there have to be entries for both aligned and not
if (((size_t)in | (size_t)out | (size_t)filter) & alignMask)
{
return false;
}
return true;
}
static std::string dnn_conv_shape(cudnnTensorDescriptor_t inputDesc, PyGpuArrayObject* input,
cudnnFilterDescriptor_t filterDesc, PyGpuArrayObject* filter,
cudnnConvolutionDescriptor_t convDesc,
PyGpuArrayObject* output, int groups)
{
cudnnDataType_t dType;
std::stringstream s;
int expected_output_dims[5] = {0};
cudnnStatus_t err = cudnnGetConvolutionNdForwardOutputDim(convDesc, inputDesc, filterDesc,
PyGpuArray_NDIM(filter), expected_output_dims);
if (err != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error computing convolution output dim: %s",
cudnnGetErrorString(err));
return "";
}
if (PyGpuArray_NDIM(filter) == 4) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] / groups != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%ldx%ld"
" but received gradient with shape %dx%dx% dx%d",
expected_output_dims[0], expected_output_dims[1] / groups,
expected_output_dims[2], expected_output_dims[3],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3]);
return "";
}
} else if (PyGpuArray_NDIM(filter) == 5) {
if ((PyGpuArray_DIMS(output)[0] != expected_output_dims[0]) ||
(PyGpuArray_DIMS(output)[1] != expected_output_dims[1]) ||
(PyGpuArray_DIMS(output)[2] != expected_output_dims[2]) ||
(PyGpuArray_DIMS(output)[3] != expected_output_dims[3]) ||
(PyGpuArray_DIMS(output)[4] != expected_output_dims[4])) {
PyErr_Format(PyExc_ValueError, "impossible convolution output dim: expected %ldx%ldx%ldx%ldx%ld"
" but received gradient with shape %ldx%ldx%ldx%ldx%ld",
expected_output_dims[0], expected_output_dims[1],
expected_output_dims[2], expected_output_dims[3],
expected_output_dims[4],
PyGpuArray_DIMS(output)[0], PyGpuArray_DIMS(output)[1],
PyGpuArray_DIMS(output)[2], PyGpuArray_DIMS(output)[3],
PyGpuArray_DIMS(output)[4]);
return "";
}
}
s << "-g" << groups << " -dimA" << shape(inputDesc) << " -filtA" <<
shape(filterDesc, &dType) << shape(convDesc);
// there have to be entries for both aligned and not
if (!all_aligned(dType, PyGpuArray_DEV_DATA(input), PyGpuArray_DEV_DATA(output), PyGpuArray_DEV_DATA(filter)))
{
s << " [unaligned] ";
}
return std::string(s.str().c_str());
}
static void dnn_conv_update_cache(const std::string& hash, const AlgoRec& rec)
{
pthread_mutex_lock(&algoMutex);
algoCache[hash] = rec;
pthread_mutex_unlock(&algoMutex);
}
static const AlgoRec* dnn_conv_check_cache(const std::string& hash)
{
pthread_mutex_lock(&algoMutex);
bool cacheHit = false;
const AlgoRec* ret = 0;
// cout << "dnn_conv_check_cache: "<< hash << endl;
AlgoCache::iterator hit = algoCache.find(hash);
if (hit != algoCache.end())
ret = &hit->second;
pthread_mutex_unlock(&algoMutex);
return ret;
}
theano/gpuarray/c_code/dnn_conv_find.h deleted 100644 → 0
浏览文件 @ 5a180d18
#pragma once
#include <cuda.h>
#include <cudnn.h>
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