wehub-resource-sync
138 lines
4.3 KiB
C++
138 lines
4.3 KiB
C++
/* Copyright (c) 2016 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/kernels/funcs/concat_and_split_functor.h"
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#include "paddle/common/enforce.h"
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namespace phi::funcs {
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/*
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* All tensors' dimension should be the same and the values of
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* each dimension must be the same, except the axis dimension.
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*/
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template <typename T>
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struct ConcatFunctor<CPUContext, T> {
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void operator()(const CPUContext& context,
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const std::vector<DenseTensor>& input,
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int axis,
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DenseTensor* output) {
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// TODO(zcd): Add input data validity checking
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size_t num = input.size();
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int64_t rows = 1;
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auto dim_0 = input[0].dims();
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for (int i = 0; i < axis; ++i) {
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rows *= dim_0[i];
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}
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int64_t out_rows = rows, out_cols = 0;
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PADDLE_ENFORCE_NE(
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rows,
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0,
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common::errors::InvalidArgument("The input size should not be 0."));
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std::vector<int64_t> input_cols(input.size());
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for (size_t i = 0; i < num; ++i) {
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int64_t t_cols = input[i].numel() / rows;
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out_cols += t_cols;
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input_cols[i] = t_cols;
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}
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auto cpu_place = context.GetPlace();
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// computation
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auto output_data = output->data<T>();
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int64_t col_idx = 0;
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for (size_t j = 0; j < num; ++j) {
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int64_t col_len = input_cols[j];
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auto input_data = input[j].data<T>();
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for (int64_t k = 0; k < out_rows; ++k) {
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memory_utils::Copy(cpu_place,
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output_data + k * out_cols + col_idx,
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cpu_place,
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input_data + k * col_len,
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sizeof(T) * col_len);
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}
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col_idx += col_len;
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}
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}
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};
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/*
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* All tensors' dimension should be the same and the values of
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* each dimension must be the same, except the axis dimension.
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*/
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template <typename T>
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struct SplitFunctor<CPUContext, T> {
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public:
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void operator()(const CPUContext& context,
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const DenseTensor& input,
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const std::vector<const DenseTensor*>& ref_inputs,
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int axis,
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std::vector<DenseTensor*>* outputs) {
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// NOTE(zhiqiu): split a tensor of shape [0,3,4] at axis=1, result in 3
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// tensors of shape [0,1,4]
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if (input.numel() == 0) {
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return;
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}
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// TODO(zcd): Add input data validity checking
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size_t num = outputs->size();
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int64_t input_rows = 1;
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auto dim_0 = ref_inputs[0]->dims();
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for (int i = 0; i < axis; ++i) {
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input_rows *= dim_0[i];
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}
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int64_t input_cols = 0;
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std::vector<int64_t> output_cols(outputs->size());
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for (size_t i = 0; i < num; ++i) {
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int64_t t_cols = ref_inputs[i]->numel() / input_rows;
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input_cols += t_cols;
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output_cols[i] = t_cols;
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}
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auto cpu_place = context.GetPlace();
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// computation
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for (int64_t k = 0; k < input_rows; ++k) {
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const int64_t src_offset = k * input_cols;
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const T* src_ptr = input.data<T>() + src_offset;
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int64_t col_idx = 0;
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for (size_t j = 0; j < num; ++j) {
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int64_t col_len = output_cols[j];
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auto* out_tensor = outputs->at(j);
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if (out_tensor != nullptr) {
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const int64_t dst_offset = k * col_len;
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T* dst_ptr = out_tensor->data<T>() + dst_offset;
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memory_utils::Copy(cpu_place,
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dst_ptr,
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cpu_place,
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src_ptr + col_idx,
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sizeof(T) * col_len);
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}
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col_idx += col_len;
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}
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}
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}
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};
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#define DEFINE_FUNCTOR(type) \
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template class PADDLE_API ConcatFunctor<CPUContext, type>; \
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template class PADDLE_API SplitFunctor<CPUContext, type>;
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FOR_ALL_TYPES(DEFINE_FUNCTOR);
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} // namespace phi::funcs
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