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| PyObject* THPVariable_pynew(
PyTypeObject* type,
PyObject* args,
PyObject* kwargs) {
HANDLE_TH_ERRORS
TORCH_CHECK(
type != &THPVariableType,
"Cannot directly construct _TensorBase; subclass it and then construct that");
jit::tracer::warn("torch.Tensor", jit::tracer::WARN_CONSTRUCTOR);
auto tensor = torch::utils::base_tensor_ctor(args, kwargs);
return THPVariable_NewWithVar(
type,
std::move(tensor),
c10::impl::PyInterpreterStatus::MAYBE_UNINITIALIZED,
true);
END_HANDLE_TH_ERRORS
}
Tensor legacy_tensor_generic_ctor_new(
c10::DispatchKey dispatch_key,
at::ScalarType scalar_type,
PyObject* args,
PyObject* kwargs,
CtorOrNew ctor_or_new) {
auto options = dispatchKeyToTensorOptions(dispatch_key);
static PythonArgParser parser({
"new(*, Device? device=None)",
"new(Storage storage)",
"new(*, int64_t cdata)|hidden",
"new(Tensor other)",
"new(Tensor other, *, Device? device=None)|hidden",
"new(SymIntArrayRef size, *, Device? device=None)",
"new(PyObject* data, *, Device? device=None)",
});
...
ParsedArgs<2> parsed_args;
auto r = parser.parse(args, kwargs, parsed_args);
if (r.idx == 0) {
...
} else if (r.idx == 1) {
...
} else if (r.idx == 2) {
...
} else if (r.idx == 3) {
...
} else if (r.idx == 4) {
...
} else if (r.idx == 5) {
...
} else if (r.idx == 6) {
auto deviceOptional = r.deviceOptional(1);
...
return legacy_new_from_sequence(
options, scalar_type, deviceOptional, r.pyobject(0));
}
throw std::runtime_error("new(): invalid arguments");
}
Tensor legacy_new_from_sequence(
c10::TensorOptions options,
at::ScalarType scalar_type,
c10::optional<Device> device,
PyObject* data) {
if (!PySequence_Check(data)) {
throw TypeError(
"new(): data must be a sequence (got %s)", Py_TYPE(data)->tp_name);
}
return internal_new_from_data(
options,
scalar_type,
device,
data,
false,
false,
false);
}
Tensor internal_new_from_data(
c10::TensorOptions options,
at::ScalarType scalar_type,
c10::optional<Device> device_opt,
PyObject* data,
bool copy_variables,
bool copy_numpy,
bool type_inference,
bool pin_memory = false) {
...
auto device = device_opt.has_value() ? *device_opt : options.device();
auto sizes = compute_sizes(data, scalar_type);
ScalarType inferred_scalar_type =
type_inference ? infer_scalar_type(data) : scalar_type;
Tensor tensor;
{
...
TensorOptions opts =
at::initialTensorOptions().dtype(inferred_scalar_type);
if (device == at::kMeta) {
return at::empty(sizes, opts.device(device));
}
tensor = at::empty(sizes, opts.pinned_memory(pin_memory));
if (c10::multiply_integers(tensor.sizes()) != 0) {
recursive_store(
(char*)tensor.data_ptr(),
tensor.sizes(),
tensor.strides(),
0,
inferred_scalar_type,
tensor.dtype().itemsize(),
data);
}
}
pybind11::gil_scoped_release no_gil;
maybe_initialize_cuda(device);
tensor = tensor.to(
device, inferred_scalar_type, false, false);
...
return at::lift_fresh(tensor);
}
inline at::Tensor empty(at::IntArrayRef size, at::TensorOptions options={}, c10::optional<at::MemoryFormat> memory_format=c10::nullopt) {
return at::_ops::empty_memory_format::call(c10::fromIntArrayRefSlow(size), optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), c10::impl::check_tensor_options_and_extract_memory_format(options, memory_format));
}
at::Tensor empty_memory_format::call(c10::SymIntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory, c10::optional<at::MemoryFormat> memory_format) {
static auto op = create_empty_memory_format_typed_handle();
return op.call(size, dtype, layout, device, pin_memory, memory_format);
}
static PyObject* THPVariable_NewWithVar(
PyTypeObject* type,
Variable _var,
c10::impl::PyInterpreterStatus status,
bool allow_preexisting_pyobj) {
...
PyObject* obj = type->tp_alloc(type, 0);
if (obj) {
auto v = (THPVariable*)obj;
new (&v->cdata) MaybeOwned<Variable>();
...
v->cdata = MaybeOwned<Variable>::owned(std::move(_var));
...
}
return obj;
}
|
