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#include <torch/csrc/utils/tensor_apply.h>
#include <ATen/ExpandUtils.h>
#include <ATen/TensorUtils.h>
#include <c10/util/irange.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/python_scalars.h>
using namespace at;
namespace torch::utils {
struct StridedData {
StridedData(const Tensor& tensor)
: data(tensor.data_ptr()),
strides(tensor.strides()),
elementSize(tensor.element_size()) {}
void* data;
IntArrayRef strides;
int64_t elementSize;
void step(int dim) {
data = (char*)data + (strides[dim] * elementSize);
}
};
template <size_t N>
static void recursive_apply(
IntArrayRef sizes,
ScalarType scalarType,
int64_t dim,
PyObject* fn,
std::array<StridedData, N> strided_data) {
int64_t ndim = static_cast<int64_t>(sizes.size());
if (dim == ndim) {
auto args = THPObjectPtr(PyTuple_New(N));
if (!args)
throw python_error();
for (const auto i : c10::irange(N)) {
PyObject* arg = load_scalar(strided_data[i].data, scalarType);
if (!arg)
throw python_error();
PyTuple_SET_ITEM(args.get(), i, arg);
}
auto ret = THPObjectPtr(PyObject_CallObject(fn, args.get()));
if (!ret)
throw python_error();
store_scalar(strided_data[0].data, scalarType, ret.get());
return;
}
auto n = sizes[dim];
for ([[maybe_unused]] const auto i : c10::irange(n)) {
recursive_apply(sizes, scalarType, dim + 1, fn, strided_data);
for (auto& td : strided_data) {
td.step(dim);
}
}
}
const Tensor& apply_(const Tensor& self, PyObject* fn) {
if (self.is_meta()) {
return self; // Just skip
}
TORCH_CHECK_TYPE(
self.device().is_cpu(), "apply_ is only implemented on CPU tensors");
auto scalarType = self.scalar_type();
recursive_apply<1>(self.sizes(), scalarType, 0, fn, {{self}});
return self;
}
const Tensor& map_(const Tensor& self, const Tensor& other_, PyObject* fn) {
TORCH_CHECK_TYPE(
other_.options().type_equal(self.options()),
"map_: expected ",
self.toString(),
" for 'other' (got ",
other_.toString(),
")");
if (self.is_meta()) {
return self; // Just skip
}
TORCH_CHECK_TYPE(
self.device().is_cpu(), "map_ is only implemented on CPU tensors");
c10::MaybeOwned<Tensor> other = expand_inplace(self, other_, "map_");
auto scalarType = self.scalar_type();
recursive_apply<2>(self.sizes(), scalarType, 0, fn, {{self, *other}});
return self;
}
const Tensor& map2_(
const Tensor& self,
const Tensor& x_,
const Tensor& y_,
PyObject* fn) {
TORCH_CHECK_TYPE(
x_.options().type_equal(self.options()),
"map2_: expected ",
self.toString(),
" for argument 'x' (got ",
x_.toString(),
")");
TORCH_CHECK_TYPE(
y_.options().type_equal(self.options()),
"map2_: expected ",
self.toString(),
" for argument 'y' (got ",
y_.toString(),
")");
if (self.is_meta()) {
return self; // Just skip
}
TORCH_CHECK_TYPE(
(self.device().is_cpu() && x_.device().is_cpu() && y_.device().is_cpu()),
"map2_ is only implemented on CPU tensors");
auto others = expand_inplace(self, x_, y_, "map2_");
auto scalarType = self.scalar_type();
recursive_apply<3>(
self.sizes(),
scalarType,
0,
fn,
{{self, *std::get<0>(others), *std::get<1>(others)}});
return self;
}
} // namespace torch::utils
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