chore: import upstream snapshot with attribution

paddle/phi/api/include/compat/ATen/Utils.cpp

@@ -0,0 +1,97 @@
+// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <ATen/Utils.h>
+
+#include <ATen/ops/empty.h>
+#include <ATen/ops/to.h>
+#include <c10/core/Layout.h>
+#include <c10/core/ScalarType.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/accumulate.h>
+
+#include <algorithm>
+
+#include "paddle/common/macros.h"
+#include "paddle/phi/api/include/sparse_api.h"
+#include "paddle/phi/api/include/tensor.h"
+
+namespace at {
+namespace detail {
+
+template <typename T>
+Tensor tensor_cpu(ArrayRef<T> values, const TensorOptions& options) {
+  constexpr auto native_scalar_type = c10::CppTypeToScalarType<T>::value;
+  auto result = at::empty(values.size(), options.dtype(native_scalar_type));
+  PD_CHECK(result.is_contiguous());
+  std::copy(values.begin(), values.end(), result.template data_ptr<T>());
+  if (options.dtype() != native_scalar_type) {
+    return result.to(at::TensorOptions().dtype(options.dtype()));
+  }
+  return result;
+}
+
+template <typename T>
+Tensor tensor_backend(ArrayRef<T> values, const TensorOptions& options) {
+  auto cpu_tensor =
+      tensor_cpu(values, options.device(c10::Device(c10::DeviceType::CPU)));
+  return cpu_tensor.to(options.device());
+}
+
+template <typename T>
+Tensor tensor_complex_cpu(ArrayRef<T> values, const TensorOptions& options) {
+  constexpr auto native_scalar_type = c10::CppTypeToScalarType<T>::value;
+  auto result = at::empty(values.size(), options.dtype(native_scalar_type));
+  PD_CHECK(result.is_contiguous());
+  std::copy(values.begin(), values.end(), result.template data_ptr<T>());
+  if (options.dtype() != native_scalar_type) {
+    return result.to(at::TensorOptions().dtype(options.dtype()));
+  }
+  return result;
+}
+
+template <typename T>
+Tensor tensor_complex_backend(ArrayRef<T> values,
+                              const TensorOptions& options) {
+  auto cpu_tensor = tensor_complex_cpu(
+      values, options.device(c10::Device(c10::DeviceType::CPU)));
+  return cpu_tensor.to(options.device());
+}
+
+}  // namespace detail
+
+#define TENSOR(T, _1)                                                          \
+  PADDLE_API Tensor tensor(ArrayRef<T> values, const TensorOptions& options) { \
+    if (options.device().type() != c10::DeviceType::CPU) {                     \
+      return at::detail::tensor_backend(values, options);                      \
+    } else {                                                                   \
+      return at::detail::tensor_cpu(values, options);                          \
+    }                                                                          \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TENSOR)
+#undef TENSOR
+
+#define TENSOR(T, _1)                                                          \
+  PADDLE_API Tensor tensor(ArrayRef<T> values, const TensorOptions& options) { \
+    if (options.device().type() != c10::DeviceType::CPU) {                     \
+      return at::detail::tensor_complex_backend(values, options);              \
+    } else {                                                                   \
+      return at::detail::tensor_complex_cpu(values, options);                  \
+    }                                                                          \
+  }
+AT_FORALL_COMPLEX_TYPES(TENSOR)
+#undef TENSOR
+
+}  // namespace at