wehub-resource-sync
126 lines
4.1 KiB
C++
126 lines
4.1 KiB
C++
// Copyright (c) 2022 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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#pragma once
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/infermeta/unary.h"
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namespace phi {
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template <typename T, typename Context>
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void SplitKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& sections,
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const Scalar& axis,
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std::vector<DenseTensor*> out);
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template <typename T, typename Context>
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void SplitWithNumKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int num,
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const Scalar& axis,
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std::vector<DenseTensor*> out);
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template <typename Context>
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void SplitStridedKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& sections,
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const Scalar& axis,
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std::vector<DenseTensor*> out);
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template <typename Context>
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void SplitWithNumStridedKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int num,
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const Scalar& axis,
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std::vector<DenseTensor*> out);
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template <typename T, typename Context>
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void Split(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& sections,
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const Scalar& axis,
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std::vector<DenseTensor>* result) {
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size_t out_number = sections.GetData().size();
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std::vector<MetaTensor> out_meta;
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std::vector<MetaTensor*> out_meta_ptr;
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out_meta.reserve(out_number);
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out_meta_ptr.reserve(out_number);
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result->resize(out_number);
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for (size_t i = 0; i < out_number; ++i) {
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out_meta.emplace_back(&result->at(i));
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out_meta_ptr.push_back(&out_meta.back());
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}
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SplitInferMeta(x, sections, axis, out_meta_ptr);
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std::vector<DenseTensor*> outs;
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outs.reserve(out_meta.size());
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for (size_t i = 0; i < out_meta.size(); ++i) {
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outs.push_back(&result->at(i));
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}
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if (x.initialized()) {
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SplitKernel<T, Context>(dev_ctx, x, sections, axis, outs);
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}
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}
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template <typename T, typename Context>
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std::vector<DenseTensor> Split(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& sections,
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const Scalar& axis) {
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size_t out_number = sections.GetData().size();
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std::vector<DenseTensor> result(out_number);
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Split<T, Context>(dev_ctx, x, sections, axis, &result);
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return result;
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}
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template <typename T, typename Context>
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std::vector<DenseTensor> SplitWithNum(const Context& dev_ctx,
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const DenseTensor& x,
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int num,
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const Scalar& axis) {
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size_t out_number = num;
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std::vector<MetaTensor> out_meta;
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std::vector<MetaTensor*> out_meta_ptr;
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out_meta.reserve(out_number);
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out_meta_ptr.reserve(out_number);
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std::vector<DenseTensor> result(out_number);
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for (size_t i = 0; i < out_number; ++i) {
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out_meta.emplace_back(&result[i]);
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out_meta_ptr.push_back(&out_meta.back());
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}
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SplitWithNumInferMeta(x, num, axis, out_meta_ptr);
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std::vector<DenseTensor*> outs;
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outs.reserve(out_meta.size());
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for (size_t i = 0; i < out_meta.size(); ++i) {
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outs.push_back(&result[i]);
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}
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SplitWithNumKernel<T, Context>(dev_ctx, x, num, axis, outs);
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return result;
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}
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} // namespace phi
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