wehub-resource-sync
132 lines
3.6 KiB
C++
132 lines
3.6 KiB
C++
/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include <vector>
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#include "paddle/common/exception.h"
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#include "paddle/common/macros.h"
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#include "paddle/phi/common/data_type.h"
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#include "paddle/phi/common/tensor_ref.h"
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namespace common {
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class DDim;
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} // namespace common
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namespace paddle {
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class Tensor;
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namespace experimental {
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template <typename T>
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class IntArrayBase {
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public:
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// Constructor support implicit
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PADDLE_API IntArrayBase() = default;
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IntArrayBase(const std::vector<int64_t>& vec) : array_(vec) {} // NOLINT
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IntArrayBase(const std::vector<int32_t>& vec) { // NOLINT
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array_.insert(array_.begin(), vec.begin(), vec.end());
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}
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IntArrayBase(std::initializer_list<int64_t> array_list)
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: array_(array_list) {}
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IntArrayBase(const int64_t* data_value, int64_t n) {
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AssignData(data_value, n);
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}
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IntArrayBase(const int32_t* data_value, int64_t n) {
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AssignData(data_value, n);
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}
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bool FromTensor() const { return is_from_tensor_; }
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void SetFromTensor(bool val) { is_from_tensor_ = val; }
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explicit IntArrayBase(const common::DDim& dims);
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// The Tensor must have one dim
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PADDLE_API IntArrayBase(const T& tensor); // NOLINT
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// The Tensor in vec must have only one element
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PADDLE_API IntArrayBase(const std::vector<T>& tensor_list); // NOLINT
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PADDLE_API explicit IntArrayBase(
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const std::vector<phi::TensorRef>& tensor_ref_list);
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template <typename OtherT>
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IntArrayBase(const IntArrayBase<OtherT>& other) : array_(other.GetData()) {}
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size_t size() const { return array_.size(); }
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int64_t operator[](int64_t i) const { return array_[i]; }
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const std::vector<int64_t>& GetData() const { return array_; }
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private:
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/// \brief Assign the data_ from const data pointer value of type T.
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template <typename TYPE>
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void AssignData(const TYPE* value_data, int64_t n) {
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if (value_data || n == 0) {
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array_.reserve(n);
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for (auto i = 0; i < n; ++i) {
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array_.push_back(static_cast<int64_t>(value_data[i]));
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}
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} else {
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PD_THROW("The input data pointer is null.");
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}
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}
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void AssignDataFromTensor(const T& tensor) {
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size_t n = tensor.numel();
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array_.reserve(n);
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switch (tensor.dtype()) {
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case DataType::INT32:
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AssignData(tensor.template data<int32_t>(), n);
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break;
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case DataType::INT64:
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AssignData(tensor.template data<int64_t>(), n);
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break;
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default:
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PD_THROW(
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"Data type error. Currently, The data type of IntArrayBase "
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"only supports Tensor with int32 and int64, "
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"but now received `",
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tensor.dtype(),
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"`.");
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}
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}
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private:
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// TODO(zhangyunfei) Replace std::vector with a more efficient container
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// structure.
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std::vector<int64_t> array_;
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bool is_from_tensor_{false};
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};
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using IntArray = paddle::experimental::IntArrayBase<paddle::Tensor>;
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} // namespace experimental
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} // namespace paddle
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namespace phi {
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class DenseTensor;
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using IntArray = paddle::experimental::IntArrayBase<DenseTensor>;
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} // namespace phi
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