//------------------------------------------------------------
int handle_cuda_notvalid(RESULT& result_1, RESULT& result_2) {
//------------------------------------------------------------
int retval=0;
bool result_1_is_cuda, result_2_is_cuda;
char result_1_cuda_str[16];
char result_2_cuda_str[16];
result_1_is_cuda = is_cuda(result_1);
result_2_is_cuda = is_cuda(result_2);
if (result_1_is_cuda ^ result_2_is_cuda) {
if (result_1_is_cuda) {
strcpy(result_1_cuda_str, "cuda");
} else {
strcpy(result_1_cuda_str, "noncuda");
}
if (result_2_is_cuda) {
strcpy(result_2_cuda_str, "cuda");
} else {
strcpy(result_2_cuda_str, "noncuda");
}
log_messages.printf(
SCHED_MSG_LOG::MSG_DEBUG,
"[%s RESULT#%d and %s RESULT#%d] do not covalidate\n",
result_1_cuda_str, result_1.id, result_2_cuda_str, result_2.id
);
// insert additonal "cuda vs noncuda not valid" code here
retval = 1;
}
return(retval);
}
Tensor Type::copy(const Tensor & src, bool non_blocking) const {
AT_CHECK(src.defined(), "attempt to copy an undefined tensor");
if (is_sparse()) {
auto indices = src._indices();
auto values = src._values();
auto & this_dense = toBackend(is_cuda() ? Backend::CUDA : Backend::CPU);
auto & this_dense_idx = this_dense.toScalarType(ScalarType::Long);
auto indices_copy = this_dense_idx.copy(indices, non_blocking);
auto values_copy = this_dense.copy(values, non_blocking);
return _sparse_coo_tensor_unsafe(indices_copy, values_copy, src.sizes());
} else {
Tensor r = this->tensor(src.sizes());
r.copy_(src, non_blocking);
return r;
}
}
