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kronos-venv/lib/python3.12/site-packages/torch/include/ATen/ScalarOps.h

@@ -0,0 +1,58 @@
+#if !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)
+#pragma once
+
+#include <ATen/Tensor.h>
+#include <c10/core/Scalar.h>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/scalar_tensor.h>
+#endif
+
+namespace at::detail {
+// When filling a number to 1-element CPU tensor, we want to skip
+// everything but manipulate data ptr directly.
+// Ideally this fast pass should be implemented in TensorIterator,
+// but we also want to skip compute_types which in not avoidable
+// in TensorIterator for now.
+Tensor& scalar_fill(Tensor& self, const Scalar& value);
+TORCH_API Tensor scalar_tensor_static(
+    const Scalar& s,
+    std::optional<ScalarType> dtype_opt,
+    std::optional<Device> device_opt);
+} // namespace at::detail
+
+// This is in the c10 namespace because we use ADL to find the functions in it.
+namespace c10 {
+
+// FIXME: this should be (and was) Scalar::toTensor, but there is currently no
+// way to implement this without going through Derived Types (which are not part
+// of core).
+inline at::Tensor scalar_to_tensor(
+    const Scalar& s,
+    const Device device = at::kCPU) {
+  // This is the fast track we have for CPU scalar tensors.
+  if (device == at::kCPU) {
+    return at::detail::scalar_tensor_static(s, s.type(), at::kCPU);
+  }
+  return at::scalar_tensor(s, at::device(device).dtype(s.type()));
+}
+
+} // namespace c10
+
+namespace at::native {
+
+inline Tensor wrapped_scalar_tensor(
+    const Scalar& scalar,
+    const Device device = at::kCPU) {
+  auto tensor = scalar_to_tensor(scalar, device);
+  tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
+  return tensor;
+}
+
+} // namespace at::native
+
+#else
+#error "This file should not be included when either TORCH_STABLE_ONLY or TORCH_TARGET_VERSION is defined."
+#endif  // !defined(TORCH_STABLE_ONLY) && !defined(TORCH_TARGET_VERSION)