wehub-resource-sync
65 lines
2.2 KiB
C++
65 lines
2.2 KiB
C++
// Copyright (c) 2024 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 "paddle/phi/kernels/sequence_expand_kernel.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/impl/sequence_expand_kernel_impl.h"
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namespace phi {
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template <typename T>
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struct SequenceExpandFunctor<CPUContext, T> {
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void operator()(const CPUContext& context UNUSED,
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const DenseTensor& x,
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const Vector<size_t>& x_lod, /*expand source lod*/
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const Vector<size_t>& ref_lod, /*expand referenced lod*/
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DenseTensor* out) {
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int out_offset = 0;
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int x_item_length = x.numel() / x.dims()[0];
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auto out_data = out->data<T>();
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auto x_data = x.data<T>();
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for (size_t i = 1; i < ref_lod.size(); ++i) {
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int repeat_num = ref_lod[i] - ref_lod[i - 1];
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int x_start = x_lod[i - 1];
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int x_end = x_lod[i];
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int x_seq_len = x_end - x_start;
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if (repeat_num > 0) {
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int out_start = out_offset;
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if (out->lod().size() == 1) {
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out_start = out->lod()[0][out_offset];
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}
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for (int j = 0; j < repeat_num; j++) {
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for (int k = 0; k < x_seq_len; k++) {
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for (int l = 0; l < x_item_length; l++) {
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out_data[(out_start + j * x_seq_len + k) * x_item_length + l] =
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x_data[(x_start + k) * x_item_length + l];
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}
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}
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}
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}
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out_offset += repeat_num;
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}
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}
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};
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} // namespace phi
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PD_REGISTER_KERNEL(sequence_expand,
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CPU,
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ALL_LAYOUT,
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phi::SequenceExpandKernel,
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float,
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double,
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int,
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int64_t) {}
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