wehub-resource-sync
99 lines
3.5 KiB
C++
99 lines
3.5 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/core/dense_tensor.h"
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#include "paddle/phi/core/tensor_array.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 SliceKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends,
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const std::vector<int64_t>& infer_flags,
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const std::vector<int64_t>& decrease_axis,
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DenseTensor* out);
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template <typename T, typename Context>
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void SliceArrayKernel(const Context& dev_ctx,
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const TensorArray& input,
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const IntArray& starts,
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const IntArray& ends,
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TensorArray* out);
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template <typename T, typename Context>
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void SliceArrayDenseKernel(const Context& dev_ctx,
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const TensorArray& input,
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const IntArray& starts,
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DenseTensor* out);
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template <typename Context>
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void SliceStridedKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends,
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const std::vector<int64_t>& infer_flags,
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const std::vector<int64_t>& decrease_axis,
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DenseTensor* out);
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template <typename T, typename Context>
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DenseTensor Slice(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends) {
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DenseTensor dense_out;
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MetaTensor meta_out(&dense_out);
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std::vector<int64_t> infer_flags = {1};
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std::vector<int64_t> decrease_axis = {};
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SliceRawInferMeta(
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input, axes, starts, ends, infer_flags, decrease_axis, &meta_out);
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SliceKernel<T, Context>(dev_ctx,
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input,
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axes,
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starts,
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ends,
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infer_flags,
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decrease_axis,
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&dense_out);
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return dense_out;
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}
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template <typename T, typename Context>
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void Slice(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& axes,
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const IntArray& starts,
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const IntArray& ends,
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DenseTensor* out) {
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MetaTensor meta_out(out);
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std::vector<int64_t> infer_flags = {1};
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std::vector<int64_t> decrease_axis = {};
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SliceRawInferMeta(
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input, axes, starts, ends, infer_flags, decrease_axis, &meta_out);
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if (input.initialized()) {
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SliceKernel<T, Context>(
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dev_ctx, input, axes, starts, ends, infer_flags, decrease_axis, out);
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}
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}
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} // namespace phi
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