wehub-resource-sync
127 lines
4.5 KiB
C++
127 lines
4.5 KiB
C++
/* Copyright (c) 2022 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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#include "paddle/phi/common/int_array.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/tensor_utils.h"
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namespace paddle::experimental {
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template <typename T>
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IntArrayBase<T>::IntArrayBase(const DDim& dims) {
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AssignData(dims.Get(), dims.size());
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}
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template <>
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IntArrayBase<DenseTensor>::IntArrayBase(const DenseTensor& tensor) { // NOLINT
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is_from_tensor_ = true;
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if (tensor.place().GetType() == AllocationType::CPU) {
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AssignDataFromTensor(tensor);
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} else {
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DenseTensor tensor_tmp;
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(tensor.place());
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phi::Copy(*dev_ctx, tensor, CPUPlace(), true, &tensor_tmp);
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AssignDataFromTensor(tensor_tmp);
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}
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}
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template <>
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IntArrayBase<DenseTensor>::IntArrayBase(
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const std::vector<phi::TensorRef>& tensor_ref_list) {
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is_from_tensor_ = true;
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for (auto tensor_ref : tensor_ref_list) {
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DataType data_type = tensor_ref.Get()->dtype();
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switch (data_type) {
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case DataType::INT32:
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if (tensor_ref.Get()->place().GetType() == AllocationType::CPU) {
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array_.push_back(*tensor_ref.Get()->template data<int32_t>());
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} else {
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DenseTensor tensor_tmp;
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(tensor_ref.Get()->place());
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phi::Copy(
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*dev_ctx, *(tensor_ref.Get()), CPUPlace(), true, &tensor_tmp);
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array_.push_back(*tensor_tmp.template data<int32_t>());
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}
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break;
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case DataType::INT64:
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if (tensor_ref.Get()->place().GetType() == AllocationType::CPU) {
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array_.push_back(*tensor_ref.Get()->template data<int64_t>());
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} else {
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DenseTensor tensor_tmp;
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(tensor_ref.Get()->place());
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phi::Copy(
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*dev_ctx, *(tensor_ref.Get()), CPUPlace(), true, &tensor_tmp);
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array_.push_back(*tensor_tmp.template data<int64_t>());
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}
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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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data_type,
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"`.");
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}
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}
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}
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template <>
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IntArrayBase<DenseTensor>::IntArrayBase(
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const std::vector<DenseTensor>& tensor_list) {
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is_from_tensor_ = true;
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for (const auto& tensor_item : tensor_list) {
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DataType data_type = tensor_item.dtype();
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switch (data_type) {
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case DataType::INT32:
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if (tensor_item.place().GetType() == AllocationType::CPU) {
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array_.push_back(*tensor_item.template data<int32_t>());
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} else {
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DenseTensor tensor_tmp;
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(tensor_item.place());
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phi::Copy(*dev_ctx, tensor_item, CPUPlace(), true, &tensor_tmp);
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array_.push_back(*tensor_tmp.template data<int32_t>());
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}
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break;
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case DataType::INT64:
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if (tensor_item.place().GetType() == AllocationType::CPU) {
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array_.push_back(*tensor_item.template data<int64_t>());
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} else {
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DenseTensor tensor_tmp;
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phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
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auto dev_ctx = pool.Get(tensor_item.place());
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phi::Copy(*dev_ctx, tensor_item, CPUPlace(), true, &tensor_tmp);
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array_.push_back(*tensor_tmp.template data<int64_t>());
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}
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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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data_type,
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"`.");
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}
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}
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}
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} // namespace paddle::experimental
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