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