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#include <torch/csrc/utils/tensor_list.h>
#include <c10/util/irange.h>
#include <pybind11/pybind11.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/utils/pybind.h>
#include <torch/csrc/utils/python_scalars.h>
using namespace at;
namespace torch {
namespace utils {
static PyObject* recursive_to_list(
char* data,
IntArrayRef sizes,
IntArrayRef strides,
int64_t dim,
ScalarType scalarType,
int64_t elementSize) {
int64_t ndim = sizes.size();
if (dim == ndim) {
return torch::utils::load_scalar(data, scalarType);
}
auto n = sizes[dim];
auto list = THPObjectPtr(PyList_New(n));
if (!list)
throw python_error();
for (const auto i : c10::irange(n)) {
PyObject* obj = recursive_to_list(
data, sizes, strides, dim + 1, scalarType, elementSize);
if (!obj)
throw python_error();
PyList_SET_ITEM(list.get(), i, obj);
auto advance_data_ptr = strides[dim] * elementSize;
TORCH_INTERNAL_ASSERT(data || (advance_data_ptr == 0));
data += advance_data_ptr;
}
return list.release();
}
PyObject* tensor_to_list(const Tensor& tensor) {
TORCH_CHECK(
!tensor.unsafeGetTensorImpl()->is_python_dispatch(),
".tolist() is not supported for tensor subclasses.");
Tensor data = tensor.resolve_conj().resolve_neg();
if (!data.device().is_cpu()) {
pybind11::gil_scoped_release no_gil;
data = data.toBackend(Backend::CPU);
}
TORCH_CHECK(
tensor.numel() == 0 || data.data_ptr(),
"tolist() shouldn't be called on a tensor with unallocated storage");
return recursive_to_list(
(char*)data.data_ptr(),
data.sizes(),
data.strides(),
0,
data.scalar_type(),
tensor.numel() == 0 ? 0 : data.dtype().itemsize());
}
} // namespace utils
} // namespace torch
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