54 lines
1.6 KiB
C++
54 lines
1.6 KiB
C++
#pragma once
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#include <ATen/Tensor.h>
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#include <c10/core/Scalar.h>
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#ifndef AT_PER_OPERATOR_HEADERS
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#include <ATen/Functions.h>
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#else
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#include <ATen/ops/scalar_tensor.h>
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#endif
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namespace at::detail {
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// When filling a number to 1-element CPU tensor, we want to skip
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// everything but manipulate data ptr directly.
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// Ideally this fast pass should be implemented in TensorIterator,
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// but we also want to skip compute_types which in not avoidable
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// in TensorIterator for now.
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Tensor& scalar_fill(Tensor& self, const Scalar& value);
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TORCH_API Tensor scalar_tensor_static(
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const Scalar& s,
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c10::optional<ScalarType> dtype_opt,
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c10::optional<Device> device_opt);
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} // namespace at::detail
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// This is in the c10 namespace because we use ADL to find the functions in it.
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namespace c10 {
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// FIXME: this should be (and was) Scalar::toTensor, but there is currently no
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// way to implement this without going through Derived Types (which are not part
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// of core).
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inline at::Tensor scalar_to_tensor(
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const Scalar& s,
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const Device device = at::kCPU) {
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// This is the fast track we have for CPU scalar tensors.
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if (device == at::kCPU) {
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return at::detail::scalar_tensor_static(s, s.type(), at::kCPU);
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}
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return at::scalar_tensor(s, at::device(device).dtype(s.type()));
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}
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} // namespace c10
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namespace at::native {
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inline Tensor wrapped_scalar_tensor(
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const Scalar& scalar,
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const Device device = at::kCPU) {
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auto tensor = scalar_to_tensor(scalar, device);
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tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
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return tensor;
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}
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} // namespace at::native
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