Tensor tensor_new(const Type& type, PyObject* args, PyObject* kwargs) {
static PythonArgParser parser({
"new(*, int64_t device=-1)",
"new(IntList size, *, int64_t device=-1)",
"new(Storage storage)",
"new(*, int64_t cdata)|hidden",
"new(Tensor other)",
"new(PyObject* data, *, int64_t device=-1)",
});
PyObject* parsed_args[2];
auto r = parser.parse(args, kwargs, parsed_args);
if (r.idx == 0) {
AutoGPU auto_gpu(r.toInt64(0));
return type.tensor();
} else if (r.idx == 1) {
PyObject* arg = parsed_args[0];
if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
// new(sequence) binds to this signature but should be treated differently
// unless the sequences is a torch.Size
return new_from_sequence(type, r.toInt64(1), r.pyobject(0));
}
return new_with_sizes(type, r.toInt64(1), r.intlist(0));
} else if (r.idx == 2) {
return new_with_storage(type, *r.storage(0));
} else if (r.idx == 3) {
auto cdata = reinterpret_cast<void*>(r.toInt64(0));
return type.unsafeTensorFromTH(cdata, true);
} else if (r.idx == 4) {
return new_with_tensor(type, r.tensor(0));
} else if (r.idx == 5) {
return new_from_sequence(type, r.toInt64(1), r.pyobject(0));
}
throw std::runtime_error("new(): invalid arguments");
}
static Tensor new_from_sequence(const Type & type, int device, PyObject* data) {
auto tensor = new_from_sequence(type.scalarType(), data);
if (tensor.type() != type) {
AutoNoGIL no_gil;
AutoGPU auto_gpu(device);
tensor = tensor.toType(type);
}
return tensor;
}
void InputBuffer::add(size_t pos, Variable var) {
TORCH_ASSERT(pos < buffer.size());
if (!var.defined()) {
return;
}
auto& old_var = buffer[pos];
if (!old_var.defined()) {
buffer[pos] = std::move(var);
} else {
AutoGPU auto_gpu(var);
// ATen doesn't route sparse additions correctly...
if (old_var.type().is_sparse()) {
buffer[pos] = var + old_var;
} else {
buffer[pos] = old_var + var;
}
}
}
static Tensor new_with_sizes(const Type& type, int device, IntList sizes) {
AutoNoGIL no_gil;
AutoGPU auto_gpu(device);
return type.tensor(sizes);
}
