TH_API void THLab_(syev)(THTensor *a_, THTensor *w_, const char *jobz, const char *uplo)
{
int n, lda, lwork, info;
THTensor *A;
THTensor *work;
real wkopt;
THArgCheck(a_->nDimension == 2, 2, "A should be 2 dimensional");
A = THTensor_(newContiguous)(a_);
n = A->size[1];
lda = n;
THTensor_(resize1d)(w_,n);
// get optimal workspace size
THLapack_(syev)(jobz[0], uplo[0], n, THTensor_(data)(A), lda,
THTensor_(data)(w_), &wkopt, -1, &info);
lwork = (int)wkopt;
work = THTensor_(newWithSize1d)(lwork);
THLapack_(syev)(jobz[0], uplo[0], n, THTensor_(data)(A), lda,
THTensor_(data)(w_), THTensor_(data)(work), lwork, &info);
if (info > 0)
{
THError(" Lapack syev : Failed to converge. %d off-diagonal elements of an didn't converge to zero",info);
}
else if (info < 0)
{
THError("Lapack syev : Argument %d : illegal value", -info);
}
THTensor_(free)(A);
THTensor_(free)(work);
}
void THFloatTensor_addr(THFloatTensor *r_, float beta, THFloatTensor *t, float alpha, THFloatTensor *vec1, THFloatTensor *vec2)
{
if( (vec1->nDimension != 1) || (vec2->nDimension != 1) )
THError("vector and vector expected, got %dD, %dD tensors", vec1->nDimension, vec2->nDimension);
if(t->nDimension != 2)
THError("expected matrix, got %dD tensor for t", t->nDimension);
if( (t->size[0] != vec1->size[0]) || (t->size[1] != vec2->size[0]) )
THError("size mismatch, t: %ld, vec1: %ld, t: %ld, vec2: %ld", t->size[0], vec1->size[0], t->size[1], vec2->size[0]);
if(r_ != t)
THError("r_ != t not implemented");
if(beta != 1)
THFloatTensor_mul(r_, r_, beta);
if(r_->stride[0] == 1)
{
THBlas_ger(vec1->size[0], vec2->size[0],
alpha, THFloatTensor_data(vec1), vec1->stride[0],
THFloatTensor_data(vec2), vec2->stride[0],
THFloatTensor_data(r_), r_->stride[1]);
}
else if(r_->stride[1] == 1)
{
THBlas_ger(vec2->size[0], vec1->size[0],
alpha, THFloatTensor_data(vec2), vec2->stride[0],
THFloatTensor_data(vec1), vec1->stride[0],
THFloatTensor_data(r_), r_->stride[0]);
}
else THError("addr for non-contiguous not implemented");
}
void* THRealloc(void *ptr, ptrdiff_t size)
{
if(!ptr)
return(THAlloc(size));
if(size == 0)
{
THFree(ptr);
return NULL;
}
if(size < 0)
THError("$ Torch: invalid memory size -- maybe an overflow?");
ptrdiff_t oldSize = -getAllocSize(ptr);
void *newptr = realloc(ptr, size);
if(!newptr && torchGCFunction) {
torchGCFunction(torchGCData);
newptr = realloc(ptr, size);
}
if(!newptr)
THError("$ Torch: not enough memory: you tried to reallocate %dGB. Buy new RAM!", size/1073741824);
// update heapSize only after successfully reallocated
THHeapUpdate(oldSize + getAllocSize(newptr));
return newptr;
}
int checkAndCountListOfStreams(lua_State *L, THCState *state, int arg,
int device)
{
if (!lua_istable(L, arg)) {
THError("expecting table of device streams");
}
/* Push table to top */
lua_pushvalue(L, arg);
/* Check that all values in the table are numeric and in bounds */
int streams = 0;
lua_pushnil(L);
while (lua_next(L, -2)) {
if (!lua_isnumber(L, -1)) {
THError("streamWaitFor: list of streams must be numeric");
}
int streamId = (int) lua_tonumber(L, -1);
/* This will error out if the stream is not in bounds */
THCState_getDeviceStream(state, device, streamId);
++streams;
lua_pop(L, 1);
}
/* Pop table from top */
lua_pop(L, 1);
return streams;
}
void THNN_(ClassNLLCriterion_updateOutput)(THNNState *state, THTensor *input,
THIndexTensor *target,
THTensor *output, bool sizeAverage,
THTensor *weights,
THTensor *total_weight)
{
int n_dims = THTensor_(nDimension)(input);
int n_classes = THTensor_(size)(input, n_dims - 1);
if (THIndexTensor_(nDimension)(target) > 1) {
THError("multi-target not supported");
}
if (THTensor_(nDimension)(input) > 2) {
THError("input tensor should be 1D or 2D");
}
input = THTensor_(newContiguous)(input);
target = THIndexTensor_(newContiguous)(target);
weights = weights ? THTensor_(newContiguous)(weights) : NULL;
real *input_data = THTensor_(data)(input);
THIndex_t *target_data = THIndexTensor_(data)(target);
real *weights_data = weights ? THTensor_(data)(weights) : NULL;
real *output_data = THTensor_(data)(output);
real *total_weight_data = THTensor_(data)(total_weight);
output_data[0] = total_weight_data[0] = 0.0;
if (THTensor_(nDimension)(input) == 1) {
int cur_target = target_data[0] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
total_weight_data[0] = weights ? weights_data[cur_target] : 1.0f;
output_data[0] = -input_data[cur_target] * total_weight_data[0];
} else if (THTensor_(nDimension)(input) == 2) {
int batch_size = THTensor_(size)(input, 0);
THAssert(THIndexTensor_(size)(target, 0) == batch_size);
int n_target = THTensor_(size)(input, 1);
int i;
for (i = 0; i < batch_size; i++) {
int cur_target = target_data[i] - 1;
THAssert(cur_target >= 0 && cur_target < n_classes);
real cur_weight = weights ? weights_data[cur_target] : 1.0f;
total_weight_data[0] += cur_weight;
output_data[0] -= input_data[i * n_target + cur_target] * cur_weight;
}
}
if (sizeAverage && total_weight_data[0]) {
output_data[0] /= total_weight_data[0];
}
if (weights) {
THTensor_(free)(weights);
}
THTensor_(free)(input);
THIndexTensor_(free)(target);
}
static void THMapAllocator_free(void* ctx_, void* data) {
THMapAllocatorContext *ctx = ctx_;
#ifdef _WIN32
if(UnmapViewOfFile(data) == 0)
THError("could not unmap the shared memory file");
#else /* _WIN32 */
if (ctx->flags & TH_ALLOCATOR_MAPPED_KEEPFD) {
if (close(ctx->fd) == -1)
THError("could not close file descriptor %d", ctx->fd);
}
if (munmap(data, ctx->size))
THError("could not unmap the shared memory file");
if (!(ctx->flags & (TH_ALLOCATOR_MAPPED_FROMFD | TH_ALLOCATOR_MAPPED_UNLINK)))
{
if (ctx->flags & TH_ALLOCATOR_MAPPED_SHAREDMEM)
{
#ifdef HAVE_SHM_UNLINK
if (shm_unlink(ctx->filename) == -1)
THError("could not unlink the shared memory file %s", ctx->filename);
#else
THError("could not unlink the shared memory file %s, shm_unlink not available on platform", ctx->filename);
#endif
}
}
#endif /* _WIN32 */
THMapAllocatorContext_free(ctx);
}
void* THRealloc(void *ptr, long size)
{
if(!ptr)
return(THAlloc(size));
if(size == 0)
{
THFree(ptr);
return NULL;
}
if(size < 0)
THError("$ Torch: invalid memory size -- maybe an overflow?");
THHeapUpdate(-getAllocSize(ptr));
void *newptr = realloc(ptr, size);
if(!newptr && torchGCFunction) {
torchGCFunction(torchGCData);
newptr = realloc(ptr, size);
}
THHeapUpdate(getAllocSize(newptr ? newptr : ptr));
if(!newptr)
THError("$ Torch: not enough memory: you tried to reallocate %dGB. Buy new RAM!", size/1073741824);
return newptr;
}
void THNN_(LookupTable_renorm)(
THNNState *state,
THIndexTensor *idx,
THTensor *weight,
real maxNorm,
real normType)
{
if (!THTensor_(isContiguous)(weight))
THError("weight must be contiguous");
if (!THIndexTensor_(isContiguous)(idx))
THError("input must be contiguous");
if (THIndexTensor_(nDimension)(idx) != 1)
THError("idx must be a vector");
if (normType <= 0)
THError("non-positive-norm not supported");
long i;
THIndex_t *row_idx = THIndexTensor_(data)(idx);
long numel = THIndexTensor_(nElement)(idx);
long numw = THTensor_(size)(weight, 0);
long stride = THTensor_(stride)(weight, 0);
real *gw = THTensor_(data)(weight);
for (i=0; i<numel; i++)
if (row_idx[i] < 1 || row_idx[i] > numw)
THError("input out of range");
// get unique indices
qsort(row_idx, numel, sizeof(THIndex_t), THNN_(compare_THIndex));
long ptr = 0;
for (i=0; i<numel; i++)
if (i == 0 || row_idx[i] != row_idx[i-1])
row_idx[ptr++] = row_idx[i];
numel = ptr;
#ifdef _OPENMP
if (numel > 1000)
{
// The strategy is to parallelize over the rows that appear in
// row_idx, so that thread 1 handles the rows in row_idx[0..numel/nThreads].
// This distributes the work evenly to each thread.
#pragma omp parallel for private(i)
for (i=0; i<numel; i++)
{
long k = row_idx[i] - 1;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
return;
}
#endif
for (i=0; i<numel; i++)
{
long k = row_idx[i] - 1;
THNN_(LookupTable_renormRow)(gw + k*stride, stride, maxNorm, normType);
}
}
TH_API void THLab_(gesvd)(THTensor *a_, THTensor *s_, THTensor *u_, THTensor *vt_, char jobu)
{
int k,m, n, lda, ldu, ldvt, lwork, info;
THTensor *A, *work;
real wkopt;
char jobvt = jobu;
THArgCheck(a_->nDimension == 2, 2, "A should be 2 dimensional");
THArgCheck(jobu == 'A' || jobu == 'S',4, "jobu can be 'A' or 'S'");
A = THTensor_(newContiguous)(a_);
m = A->size[1];
n = A->size[0];
k = (m < n ? m : n);
lda = m;
ldu = m;
ldvt = n;
THTensor_(resize1d)(s_,k);
THTensor_(resize2d)(vt_,n,ldvt);
if (jobu == 'A')
{
THTensor_(resize2d)(u_,m,ldu);
}
else
{
THTensor_(resize2d)(u_,k,ldu);
}
THLapack_(gesvd)(jobu,jobvt,
m,n,THTensor_(data)(A),lda,
THTensor_(data)(s_),
THTensor_(data)(u_),
ldu,
THTensor_(data)(vt_), ldvt,
&wkopt, -1, &info);
lwork = (int)wkopt;
work = THTensor_(newWithSize1d)(lwork);
THLapack_(gesvd)(jobu,jobvt,
m,n,THTensor_(data)(A),lda,
THTensor_(data)(s_),
THTensor_(data)(u_),
ldu,
THTensor_(data)(vt_), ldvt,
THTensor_(data)(work),lwork, &info);
if (info > 0)
{
THError(" Lapack gesvd : %d superdiagonals failed to converge.",info);
}
else if (info < 0)
{
THError("Lapack gesvd : Argument %d : illegal value", -info);
}
THTensor_(free)(A);
THTensor_(free)(work);
}
static ptrdiff_t applyHeapDelta() {
ptrdiff_t oldHeapSize = THAtomicAddPtrdiff(&heapSize, heapDelta);
#ifdef DEBUG
if (heapDelta > 0 && oldHeapSize > PTRDIFF_MAX - heapDelta)
THError("applyHeapDelta: heapSize(%td) + increased(%td) > PTRDIFF_MAX, heapSize overflow!", oldHeapSize, heapDelta);
if (heapDelta < 0 && oldHeapSize < PTRDIFF_MIN - heapDelta)
THError("applyHeapDelta: heapSize(%td) + decreased(%td) < PTRDIFF_MIN, heapSize underflow!", oldHeapSize, heapDelta);
#endif
ptrdiff_t newHeapSize = oldHeapSize + heapDelta;
heapDelta = 0;
return newHeapSize;
}
int THCState_getPeerToPeerAccess(THCState* state, int dev, int devToAccess)
{
if (dev < 0 || dev >= state->numDevices) {
THError("%d is not a device", dev);
}
if (devToAccess < 0 || dev >= state->numDevices) {
THError("%d is not a device", devToAccess);
}
return state->p2pAccessEnabled[dev][devToAccess];
}
int THCState_getPeerToPeerAccess(THCState* state, int dev, int devToAccess)
{
int numDevices = 0;
THCudaCheck(cudaGetDeviceCount(&numDevices));
if (dev < 0 || dev >= numDevices) {
THError("%d is not a device", dev);
}
if (devToAccess < 0 || dev >= numDevices) {
THError("%d is not a device", devToAccess);
}
return state->p2pAccessEnabled[dev][devToAccess];
}
cublasHandle_t THCState_getCurrentBlasHandle(THCState *state)
{
/* This is called at the point of kernel execution.
For some debugging code or improperly instrumented kernels,
`state` is null */
if (state) {
if (state->currentBlasHandle <= 0) {
THError("%d is not a valid handle, valid range is: (1, %d)",
state->currentBlasHandle, state->numUserBlasHandles);
}
return state->currentBlasHandle;
}
THError("THCState and blasHandles must be set as there is no default blasHandle");
return NULL;
}
TH_API void THLab_(gesv)(THTensor *a_, THTensor *b_)
{
int n, nrhs, lda, ldb, info;
THIntTensor *ipiv;
THTensor *A, *B;
THArgCheck(a_->nDimension == 2, 2, "A should be 2 dimensional");
THArgCheck(a_->size[0] == a_->size[1], 2, "A should be symmetric");
n = (int)a_->size[1];
lda = n;
ldb = n;
if (b_->nDimension == 1)
{
nrhs = 1;
THArgCheck(n == b_->size[0], 1, "size incompatible A,b");
}
else
{
nrhs = b_->size[0];
THArgCheck(n == b_->size[1], 1, "size incompatible A,b");
}
A = THTensor_(newContiguous)(a_);
B = THTensor_(newContiguous)(b_);
ipiv = THIntTensor_newWithSize1d((long)n);
THLapack_(gesv)(n, nrhs,
THTensor_(data)(A), lda, THIntTensor_data(ipiv),
THTensor_(data)(B), ldb, &info);
if(!THTensor_(isContiguous)(b_))
{
THTensor_(copy)(b_,B);
}
if (info < 0)
{
THError("Lapack gesv : Argument %d : illegal value", -info);
}
else if (info > 0)
{
THError("Lapack gesv : U(%d,%d) is zero, singular U.", info,info);
}
THIntTensor_free(ipiv);
THTensor_(free)(A);
THTensor_(free)(B);
}
static void THCudaTensor_rawSet(THCState *state, THCudaTensor *self, THCudaStorage *storage, long storageOffset, int nDimension, long *size, long *stride)
{
THAssert(self->storage != NULL);
/* storage */
if(self->storage != storage)
{
if(self->storage)
THCudaStorage_free(state, self->storage);
if(storage)
{
self->storage = storage;
THCudaStorage_retain(state, self->storage);
}
else
self->storage = THCudaStorage_new(state);
}
/* storageOffset */
if(storageOffset < 0)
THError("Tensor: invalid storage offset");
self->storageOffset = storageOffset;
/* size and stride */
THCudaTensor_rawResize(state, self, nDimension, size, stride);
}
static void THNN_(SpatialMaxUnpooling_updateGradInput_frame)(scalar_t *gradInput_p, scalar_t *gradOutput_p,
THIndex_t *ind_p,
int nslices,
int iwidth, int iheight,
int owidth, int oheight)
{
at::parallel_for(0, nslices, 0, [&](int64_t start, int64_t end) {
for (auto k = start; k < end; k++)
{
scalar_t *gradInput_p_k = gradInput_p + k*iwidth*iheight;
scalar_t *gradOutput_p_k = gradOutput_p + k*owidth*oheight;
THIndex_t *ind_p_k = ind_p + k*iwidth*iheight;
int i, j;
THIndex_t maxp;
for(i = 0; i < iheight; i++)
{
for(j = 0; j < iwidth; j++)
{
maxp = ind_p_k[i*iwidth + j]; /* retrieve position of max */
if(maxp < 0 || maxp >= owidth * oheight) {
THError("invalid max index %ld, owidth= %d, oheight= %d", maxp, owidth, oheight);
}
gradInput_p_k[i*iwidth + j] = gradOutput_p_k[maxp]; /* update gradient */
}
}
}
});
}
THFloatTensor *nn_SpatialConvolutionMM_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;
THFloatTensor *finput = module->SpatialConvolution.finput;
THFloatTensor *weight = module->SpatialConvolution.weight;
THFloatTensor *bias = module->SpatialConvolution.bias;
THFloatTensor *output = module->output;
int batch = 1;
if (input->nDimension == 3) {
batch = 0;
THFloatTensor_resize4d(input, 1, input->size[0], input->size[1], input->size[2]);
}
long batchSize = input->size[0];
long nInputPlane = module->SpatialConvolution.nInputPlane;
long nOutputPlane = module->SpatialConvolution.nOutputPlane;
long inputWidth = input->size[3];
long inputHeight = input->size[2];
long outputWidth = (inputWidth + 2*padW - kW) / dW + 1;
long outputHeight = (inputHeight + 2*padH - kH) / dH + 1;
if (outputWidth < 1 || outputHeight < 1)
THError("Given input size: (%dx%dx%d). Calculated output size: (%dx%dx%d). Output size is too small",
nInputPlane,inputHeight,inputWidth,nOutputPlane,outputHeight,outputWidth);
THFloatTensor_resize3d(finput, batchSize, kW*kH*nInputPlane, outputHeight*outputWidth);
THFloatTensor_resize4d(output, batchSize, nOutputPlane, outputHeight, outputWidth);
long t;
#pragma omp parallel for if(batchSize >= 4) private(t)
for (t = 0; t < batchSize; t++) {
THFloatTensor *input_t = THFloatTensor_newSelect(input, 0, t);
THFloatTensor *output_t = THFloatTensor_newSelect(output, 0, t);
THFloatTensor *finput_t = THFloatTensor_newSelect(finput, 0, t);
nn_SpatialConvolutionMM_updateOutput_frame(input_t, output_t, weight, bias, finput_t,
kW, kH, dW, dH, padW, padH,
nInputPlane, inputWidth, inputHeight,
nOutputPlane, outputWidth, outputHeight);
THFloatTensor_free(input_t);
THFloatTensor_free(output_t);
THFloatTensor_free(finput_t);
}
if (batch == 0) {
THFloatTensor_resize3d(output, nOutputPlane, outputHeight, outputWidth);
THFloatTensor_resize3d(input, nInputPlane, inputHeight, inputWidth);
}
return output;
}
static inline void THNN_(VolumetricFullConvolution_shapeCheck)(
THTensor *input, THTensor *gradOutput,
THTensor *weight, THTensor *bias,
int dT, int dW, int dH, int pT, int pW, int pH,
int aT, int aW, int aH) {
THNN_ARGCHECK(input->nDimension == 4 || input->nDimension == 5, 2, input,
"4D or 5D (batch mode) tensor expected for input, but got: %s");
// number of input & output planes and kernel size is indirectly defined by the weight tensor
THNN_ARGCHECK(weight->nDimension == 5, 4, weight,
"5D (nOutputPlane x nInputPlane x kT x kH x kW) tensor "
"expected for weight, but got: %s");
THArgCheck(dT > 0 && dW > 0 && dH > 0, 11,
"stride should be greater than zero, but got dT: %d dH: %d dW: %d", dT, dH, dW);
THArgCheck(aT < dT && aW < dW && aH < dH, 15,
"output adjustment must be smaller than stride, but got "
"adjT: %d adjH: %d adjW: %d dT: %d dH: %d dW: %d",
aT, aH, aW, dT, dH, dW);
int ndim = input->nDimension;
const int nInputPlane = (int)weight->size[0];
const int nOutputPlane = (int)weight->size[1];
const int kT = (int)weight->size[2];
const int kH = (int)weight->size[3];
const int kW = (int)weight->size[4];
if (bias != NULL) {
THNN_CHECK_DIM_SIZE(bias, 1, 0, weight->size[1]);
}
int dimf = 0;
int dimd = 1;
int dimh = 2;
int dimw = 3;
if (ndim == 5) {
dimf++;
dimd++;
dimh++;
dimw++;
}
const long inputWidth = input->size[dimw];
const long inputHeight = input->size[dimh];
const long inputDepth = input->size[dimd];
const long outputWidth = (inputWidth - 1) * dW - 2*pW + kW + aW;
const long outputHeight = (inputHeight - 1) * dH - 2*pH + kH + aH;
const long outputDepth = (inputDepth - 1) * dT - 2*pT + kT + aT;
if (outputDepth < 1 || outputWidth < 1 || outputHeight < 1)
THError("Given input size: (%dx%dx%dx%d). Calculated output size: (%dx%dx%dx%d). Output size is too small",
nInputPlane,inputDepth,inputHeight,inputWidth,nOutputPlane,outputDepth,outputHeight,outputWidth);
THNN_CHECK_DIM_SIZE(input, ndim, dimf, nInputPlane);
if (gradOutput != NULL) {
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimf, nOutputPlane);
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimd, outputDepth);
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimh, outputHeight);
THNN_CHECK_DIM_SIZE(gradOutput, ndim, dimw, outputWidth);
}
}
static void THNN_(SpatialMaxUnpooling_updateGradInput_frame)(real *gradInput_p, real *gradOutput_p,
THIndex_t *ind_p,
long nslices,
long iwidth, long iheight,
long owidth, long oheight)
{
long k;
#pragma omp parallel for private(k)
for (k = 0; k < nslices; k++)
{
real *gradInput_p_k = gradInput_p + k*iwidth*iheight;
real *gradOutput_p_k = gradOutput_p + k*owidth*oheight;
THIndex_t *ind_p_k = ind_p + k*iwidth*iheight;
long i, j, maxp;
for(i = 0; i < iheight; i++)
{
for(j = 0; j < iwidth; j++)
{
maxp = ind_p_k[i*iwidth + j] - TH_INDEX_BASE; /* retrieve position of max */
if(maxp<0 || maxp>=owidth*oheight){
THError("invalid max index %d, owidth= %d, oheight= %d",maxp,owidth,oheight);
}
gradInput_p_k[i*iwidth + j] = gradOutput_p_k[maxp]; /* update gradient */
}
}
}
}
void THCudaGetGridSize(int *nBlockPerColumn_, int *nBlockPerRow_, int *nThreadPerBlock_, long size)
{
const int nThreadPerBlock = 256;
long nBlockPerGrid = size / nThreadPerBlock;
long nBlockPerColumn = 0L;
long nBlockPerRow = 0L;
if(size % nThreadPerBlock)
nBlockPerGrid++;
if(nBlockPerGrid <= 65535)
{
nBlockPerRow = nBlockPerGrid;
nBlockPerColumn = 1;
}
else if(nBlockPerGrid <= (65355L * 65355L))
{
unsigned int uiSqrt = (unsigned int)(sqrt((float)nBlockPerGrid));
nBlockPerRow = uiSqrt;
nBlockPerColumn = uiSqrt;
while((nBlockPerRow * nBlockPerColumn) < nBlockPerGrid)
nBlockPerRow++;
}
else
THError("too large vector for Cuda, sorry");
*nBlockPerColumn_ = (int)nBlockPerColumn;
*nBlockPerRow_ = (int)nBlockPerRow;
*nThreadPerBlock_ = (int)nThreadPerBlock;
}
cudaStream_t THCState_getDeviceStream(THCState *state, int device, int stream)
{
/* `device` is a CUDA index */
if (device >= state->numDevices || device < 0)
{
THError("%d is not a device", device + 1 /* back to Torch index */);
}
/* Stream 0 is the default stream, 1 ... `numUserStreams` are Torch streams */
if (stream > state->numUserStreams || stream < 0)
{
THError("%d is not a stream", stream);
}
return state->streamsPerDevice[device][stream];
}
THMapAllocatorContext *THMapAllocatorContext_new(const char *filename, int flags)
{
THMapAllocatorContext *ctx = THAlloc(sizeof(THMapAllocatorContext));
if (!(flags & TH_ALLOCATOR_MAPPED_SHARED) && !(flags & TH_ALLOCATOR_MAPPED_SHAREDMEM))
flags &= ~TH_ALLOCATOR_MAPPED_NOCREATE;
if ((flags ^ TH_ALLOCATOR_MAPPED_EXCLUSIVE) == 0)
THError("TH_ALLOCATOR_MAPPED_EXCLUSIVE flag requires opening the file "
"in shared mode");
if (filename) {
ctx->filename = THAlloc(strlen(filename)+1);
strcpy(ctx->filename, filename);
} else {
ctx->filename = unknown_filename;
}
ctx->flags = flags;
ctx->size = 0;
#ifdef _WIN32
ctx->handle = INVALID_HANDLE_VALUE;
#else
ctx->fd = -1;
#endif
return ctx;
}
static int loadnpz_l(lua_State *L){
try{
const char *filename = lua_tostring(L, 1);
std::string fpath = std::string(filename);
cnpy::npz_t npzData = cnpy::npz_load(filename);
// create a new table
lua_newtable(L);
int tbl = lua_gettop(L);
for (cnpy::npz_t::iterator i=npzData.begin(); i!=npzData.end(); ++i){
std::string name = i->first;
cnpy::NpyArray arr = i->second;
lua_pushstring(L, name.c_str());
load_array_to_lua(L, arr);
lua_rawset(L, tbl);
}
}catch (std::exception& e){
THError(e.what());
}
return 1;
}
static void nn_(SpatialMaxUnpooling_updateOutput_frame)(real *input_p, real *output_p,
real *ind_p,
long nslices,
long iwidth, long iheight,
long owidth, long oheight)
{
long k;
#pragma omp parallel for private(k)
for (k = 0; k < nslices; k++)
{
real *output_p_k = output_p + k*owidth*oheight;
real *input_p_k = input_p + k*iwidth*iheight;
real *ind_p_k = ind_p + k*iwidth*iheight;
long i, j, maxp;
for(i = 0; i < iheight; i++)
{
for(j = 0; j < iwidth; j++)
{
maxp = ind_p_k[i*iwidth + j] - 1; /* retrieve position of max */
if(maxp<0 || maxp>=owidth*oheight){
THError("invalid max index %d, owidth= %d, oheight= %d",maxp,owidth,oheight);
}
output_p_k[maxp] = input_p_k[i*iwidth + j]; /* update output */
}
}
}
}
void THCState_setPeerToPeerAccess(THCState* state, int dev, int devToAccess,
int enable)
{
/* This will perform device bounds checking for us */
int prevEnabled = THCState_getPeerToPeerAccess(state, dev, devToAccess);
if (enable != prevEnabled) {
/* If we're attempting to enable p2p access but p2p access isn't */
/* supported, throw an error */
if (enable) {
int access = 0;
THCudaCheck(cudaDeviceCanAccessPeer(&access, dev, devToAccess));
if (!access) {
THError("p2p access not supported for %d accessing %d",
dev, devToAccess);
}
}
state->p2pAccessEnabled[dev][devToAccess] = enable;
int prevDev = 0;
THCudaCheck(cudaGetDevice(&prevDev));
THCudaCheck(cudaSetDevice(dev));
/* This should be in sync with the current access state */
if (enable) {
THCudaCheck(cudaDeviceEnablePeerAccess(devToAccess, 0));
} else {
THCudaCheck(cudaDeviceDisablePeerAccess(devToAccess));
}
THCudaCheck(cudaSetDevice(prevDev));
}
}
int THProcessYUYV(THNETWORK *network, unsigned char *image, int width, int height, float **results, int *outwidth, int *outheight)
{
THFloatTensor *out;
THFloatStorage *st;
#ifdef CUDNN
if(network->net->cuda)
THError("This function is not supported with CUDNN");
#endif
st = THFloatStorage_new(width * height * 3);
yuyv2fRGB(image, st->data, width*height, width, width, height, network->mean, network->std);
THFloatTensor *t = THFloatTensor_new();
t->storage = st;
t->nDimension = 3;
t->size[0] = 3;
t->size[1] = height;
t->size[2] = width;
t->stride[0] = width * height;
t->stride[1] = width;
t->stride[2] = 1;
out = forward(network->net, t);
THFloatTensor_free(t);
*results = out->storage->data;
if(out->nDimension >= 3)
{
*outwidth = out->size[out->nDimension - 1];
*outheight = out->size[out->nDimension - 2];
} else *outwidth = *outheight = 1;
return THFloatTensor_nElement(out);
}
void* THAlloc(long size)
{
void *ptr;
if(size < 0)
THError("$ Torch: invalid memory size -- maybe an overflow?");
if(size == 0)
return NULL;
ptr = malloc(size);
if(!ptr)
THError("$ Torch: not enough memory: you tried to allocate %dGB. Buy new RAM!", size/1073741824);
return ptr;
}
static void THGPUTensor_rawSet(THGPUTensor *self, THGPUStorage *storage, long storageOffset, int nDimension, long *size, long *stride)
{
/* storage */
if (self->storage != storage)
{
if (self->storage)
THGPUStorage_free(self->storage);
if (storage)
{
self->storage = storage;
THGPUStorage_retain(self->storage);
}
else
self->storage = NULL;
}
/* storageOffset */
if (storageOffset < 0)
THError("Tensor: invalid storage offset");
self->storageOffset = storageOffset;
/* size and stride */
THGPUTensor_rawResize(self, nDimension, size, stride);
}
static inline void THNN_(Col2Im_shapeCheck)(
THNNState *state,
THTensor *input,
THTensor *gradOutput,
int64_t outputHeight, int64_t outputWidth,
int64_t kH, int64_t kW, int64_t dilationH, int64_t dilationW,
int64_t padH, int64_t padW, int64_t dH, int64_t dW) {
THArgCheck(kW > 0 && kH > 0, 6,
"kernel size should be greater than zero, but got kH: %d kW: %d", kH, kW);
THArgCheck(dW > 0 && dH > 0, 12,
"stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
THArgCheck(dilationW > 0 && dilationH > 0, 8,
"dilation should be greater than zero, but got dilationH: %d dilationW: %d", dilationH, dilationW);
int64_t ndim = THTensor_(nDimensionLegacyNoScalars)(input);
THNN_ARGCHECK(!input->is_empty() && (ndim == 2 || ndim == 3), 2, input,
"Expected non-empty 2D or 3D input tensor, but got input of shape %s");
int64_t batch_dim = (ndim == 3) ? 0 : -1;
int64_t nInputPlane = input->size(batch_dim + 1);
if (nInputPlane % (kW * kH) != 0) {
THError("Expected size of input's dimension 1 to be divisible by the "
"product of kernel_size, but got input.size(1)=%lld and "
"kernel_size=(%d, %d).", (long long) nInputPlane, kH, kW);
}
int64_t inputLength = input->size(batch_dim + 2);
int64_t nBlocksH = div_rtn<int64_t>(outputHeight + 2 * padH - dilationH * (kH - 1) - 1, dH) + 1;
int64_t nBlocksW = div_rtn<int64_t>(outputWidth + 2 * padW - dilationW * (kW - 1) - 1, dW) + 1;
if (inputLength != (nBlocksH * nBlocksW)) {
THError("Given output_size=(%d, %d), kernel_size=(%d, %d), "
"dilation=(%d, %d), padding=(%d, %d), stride=(%d, %d), expected "
"size of input's dimension 2 to match the calculated number of "
"sliding blocks %lld * %lld = %lld, but got input.size(2)=%lld.",
outputHeight, outputWidth, kH, kW, dilationH, dilationW, padH, padW, dH, dW,
(long long) nBlocksH, (long long) nBlocksW,
(long long) (nBlocksH * nBlocksW), (long long) inputLength);
}
if (outputWidth < 1 || outputHeight < 1) {
THError("Expected output spatial size to be positive, but got: output_size=(%d, %d).",
outputHeight, outputWidth);
}
}
static void jhu_THLogSum1d_init(lua_State *L) {
int ret = luaT_pushmetatable(L, "torch.DoubleTensor");
if(ret == 0) {
THError("problem pushing metatable");
}
luaT_registeratname(L, jhu_THLogSum1d__, "jhu");
lua_pop(L, 1);
}