static int cutorch_CudaTensorOperator___sub__(lua_State *L)
{
THCudaTensor *tensor1 = luaT_toudata(L, 1, "torch.CudaTensor");
THCudaTensor *tensor2 = luaT_toudata(L, 2, "torch.CudaTensor");
THCudaTensor *r;
if(!tensor1 && !tensor2)
luaL_error(L, "expecting two Tensors or one Tensor and one number");
else
{
r = THCudaTensor_new();
luaT_pushudata(L, r, "torch.CudaTensor");
if(!tensor1 && tensor2)
{
THCudaTensor_resizeAs(r, tensor2);
THCudaTensor_fill(r, luaL_checknumber(L, 1));
THCudaTensor_cadd(r, r, -1, tensor2);
}
else if(tensor1 && !tensor2)
{
THCudaTensor_resizeAs(r, tensor1);
THCudaTensor_copy(r, tensor1);
THCudaTensor_add(r, r, -luaL_checknumber(L, 2));
}
else
{
THCudaTensor_resizeAs(r, tensor1);
THCudaTensor_copy(r, tensor1);
THCudaTensor_cadd(r, r, -1, tensor2);
}
}
return 1;
}
static int cutorch_CudaTensorOperator___mul__(lua_State *L)
{
THCudaTensor *tensor1 = luaT_toudata(L, 1, "torch.CudaTensor");
THCudaTensor *tensor2 = luaT_toudata(L, 2, "torch.CudaTensor");
THCudaTensor *r;
if(!tensor1 && !tensor2)
luaL_error(L, "expecting two Tensors or one Tensor and one number");
else
{
r = THCudaTensor_new();
luaT_pushudata(L, r, "torch.CudaTensor");
if(!tensor1 && tensor2)
{
THCudaTensor_resizeAs(r, tensor2);
THCudaTensor_copy(r, tensor2);
THCudaTensor_mul(r, r, luaL_checknumber(L, 1));
}
else if(tensor1 && !tensor2)
{
THCudaTensor_resizeAs(r, tensor1);
THCudaTensor_copy(r, tensor1);
THCudaTensor_mul(r, r, luaL_checknumber(L, 2));
}
else
{
int dimt = tensor1->nDimension;
int dims = tensor2->nDimension;
if(dimt == 1 && dims == 1)
lua_pushnumber(L, THCudaTensor_dot(tensor1, tensor2)); /* ok, we wasted r, but who cares */
else if(dimt == 2 && dims == 1)
{
THCudaTensor_resize1d(r, tensor1->size[0]);
THCudaTensor_zero(r);
THCudaTensor_addmv(r, 1, r, 1, tensor1, tensor2);
}
else if(dimt == 2 && dims == 2)
{
THCudaTensor_resize2d(r, tensor1->size[0], tensor2->size[1]);
THCudaTensor_zero(r);
THCudaTensor_addmm(r, 1, r, 1, tensor1, tensor2);
}
else
luaL_error(L, "multiplication between %dD and %dD tensors not yet supported", tensor1->nDimension, tensor2->nDimension);
}
}
return 1;
}
void THCudaTensor_freeCopyTo(THCState *state, THCudaTensor *self, THCudaTensor *dst)
{
if(self != dst)
THCudaTensor_copy(state, dst, self);
THCudaTensor_free(state, self);
}
THCudaTensor *THCudaTensor_newClone(THCState *state, THCudaTensor *self)
{
THCudaTensor *tensor = THCudaTensor_new(state);
THCudaTensor_resizeAs(state, tensor, self);
THCudaTensor_copy(state, tensor, self);
return tensor;
}
/*
* Based on the implementation of the THTensor_(indexCopy) in torch7
*/
static void THCudaTensor_indexCopy(THCudaTensor *tensor, int dim, THLongTensor *index, THCudaTensor *src)
{
long i, numel;
THCudaTensor *tSlice, *sSlice;
long *index_data;
numel = THLongTensor_nElement(index);
THArgCheck(index->nDimension == 1, 3, "Index is supposed to be a vector");
THArgCheck(dim < src->nDimension,4,"Indexing dim is out of bounds");
index = THLongTensor_newContiguous(index);
index_data = THLongTensor_data(index);
for (i=0; i<numel; i++)
{
if (tensor->nDimension > 1 )
{
tSlice = THCudaTensor_new();
sSlice = THCudaTensor_new();
THCudaTensor_select(tSlice, tensor, dim, index_data[i]-1);
THCudaTensor_select(sSlice, src, dim, i);
THCudaTensor_copy(tSlice, sSlice);
THCudaTensor_free(tSlice);
THCudaTensor_free(sSlice);
}
else
{
// It's faster to copy a float from an address in the device to another address in the device than
// retrieving it to the host memory and recopy it to the device memory
THCudaCheck(cudaMemcpy(tensor->storage->data + tensor->storageOffset + index_data[i]-1,\
src->storage->data + src->storageOffset + i, sizeof(float), cudaMemcpyDeviceToDevice));
}
}
THLongTensor_free(index);
}
/* now we overwrite some methods specific to CudaTensor */
static int cutorch_CudaTensor_copy(lua_State *L)
{
THCState *state = cutorch_getstate(L);
THCudaTensor *storage = luaT_checkudata(L, 1, "torch.CudaTensor");
void *src;
if( (src = luaT_toudata(L, 2, "torch.CudaTensor")) )
THCudaTensor_copy(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.ByteTensor")) )
THCudaTensor_copyByte(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.CharTensor")) )
THCudaTensor_copyChar(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.ShortTensor")) )
THCudaTensor_copyShort(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.IntTensor")) )
THCudaTensor_copyInt(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.LongTensor")) )
THCudaTensor_copyLong(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.FloatTensor")) )
THCudaTensor_copyFloat(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.DoubleTensor")) )
THCudaTensor_copyDouble(state, storage, src);
else if( (src = luaT_toudata(L, 2, "torch.CudaTensor")) )
THCudaTensor_copyCuda(state, storage, src);
else
luaL_typerror(L, 2, "torch.*Tensor");
lua_settop(L, 1);
return 1;
}
static int cutorch_CudaTensorOperator___unm__(lua_State *L)
{
THCudaTensor *tensor = luaT_checkudata(L, 1, "torch.CudaTensor");
THCudaTensor *r;
r = THCudaTensor_new();
luaT_pushudata(L, r, "torch.CudaTensor");
THCudaTensor_resizeAs(r, tensor);
THCudaTensor_copy(r, tensor);
THCudaTensor_mul(r, r, -1);
return 1;
}
static int cutorch_CudaTensorOperator___div__(lua_State *L)
{
THCudaTensor *tensor = luaT_checkudata(L, 1, "torch.CudaTensor");
THCudaTensor *r;
luaL_argcheck(L, lua_isnumber(L,2), 2, "number expected");
r = THCudaTensor_new();
luaT_pushudata(L, r, "torch.CudaTensor");
THCudaTensor_resizeAs(r, tensor);
THCudaTensor_copy(r, tensor);
THCudaTensor_mul(r, r, 1/lua_tonumber(L, 2));
return 1;
}
void THCudaTensor_copyCuda(THCudaTensor *self, THCudaTensor *src)
{
THCudaTensor_copy(self, src);
}