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split.cc
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split.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/function/split.h"
#include "fastdeploy/utils/utils.h"
#include <cstring>
namespace fastdeploy {
namespace function {
/*
* All tensors' dimension should be the same and the values of
* each dimension must be the same, except the axis dimension.
*/
template <typename T> struct SplitFunctor {
public:
void operator()(const FDTensor& input,
const std::vector<const FDTensor*>& ref_inputs, int axis,
std::vector<FDTensor>* outputs) {
if (input.Numel() == 0) {
return;
}
size_t num = outputs->size();
int input_rows = 1;
auto dim_0 = ref_inputs[0]->Shape();
for (int i = 0; i < axis; ++i) {
input_rows *= dim_0[i];
}
int input_cols = 0;
std::vector<int64_t> output_cols(outputs->size());
for (size_t i = 0; i < num; ++i) {
int t_cols = ref_inputs[i]->Numel() / input_rows;
input_cols += t_cols;
output_cols[i] = t_cols;
}
// computation
for (int k = 0; k < input_rows; ++k) {
const T* src_ptr =
reinterpret_cast<const T*>(input.Data()) + k * input_cols;
int col_idx = 0;
for (size_t j = 0; j < num; ++j) {
int col_len = output_cols[j];
auto* out_tensor = &(outputs->at(j));
if (out_tensor != nullptr) {
T* dst_ptr = reinterpret_cast<T*>(out_tensor->Data()) + k * col_len;
std::memcpy(dst_ptr, src_ptr + col_idx, sizeof(T) * col_len);
}
col_idx += col_len;
}
}
}
};
inline int GetSplitAxisValue(const FDTensor& x, int axis) {
int rank = x.Shape().size();
FDASSERT(axis >= -rank && axis < rank,
"The axis is expected to be in range of [%d, %d), but got %d", -rank,
rank, axis);
if (axis < 0) {
axis = axis + rank;
}
return axis;
}
void CreateSplitOutputs(const FDTensor& x,
const std::vector<int>& sections_data,
std::vector<FDTensor>* outs, int axis) {
axis = GetSplitAxisValue(x, axis);
auto input_axis_dim = x.Shape().at(axis);
std::vector<int> sections_vec;
const int unknow_dim_val = -1;
int unknow_dim_idx = -1;
int num_of_unknow = 0;
int sum_of_section = 0;
for (size_t i = 0; i < sections_data.size(); ++i) {
sections_vec.push_back(sections_data[i]);
if (sections_data[i] == unknow_dim_val) {
num_of_unknow++;
unknow_dim_idx = i;
} else {
sum_of_section += sections_data[i];
}
}
FDASSERT(num_of_unknow <= 1,
"Only one dimension value of Attr(num_or_sections) "
"in SplitOp can be -1. "
"But received Attr(num_or_sections) = [%s].",
Str(sections_data).c_str());
if (unknow_dim_idx != -1) {
// for example, input shape = [4 ,5], axis = 1, sections = [2, 3, -1].
// input_axis_dim = 5, sum_of_sections = 5.
// the following check will fail.
FDASSERT(sum_of_section < input_axis_dim,
"Sum of Attr(num_or_sections) other than unknown section "
"must be less than the input's "
"size "
"along the split dimension. But received Attr(num_or_sections) "
"= [%s], input(X)'s shape = [%s], Attr(dim) = %d.",
Str(sections_data).c_str(), Str(x.Shape()).c_str(), axis);
sections_vec[unknow_dim_idx] = input_axis_dim - sum_of_section;
} else {
FDASSERT(sum_of_section == input_axis_dim,
"Sum of Attr(num_or_sections) must be equal to the input's "
"size "
"along the split dimension. But received Attr(num_or_sections)"
" = [%s], input(X)'s shape = [%s], Attr(dim) = %d.",
Str(sections_data).c_str(), Str(x.Shape()).c_str(), axis);
}
// fill out dims
std::vector<std::vector<int64_t>> out_dims(sections_vec.size(), x.Shape());
for (size_t i = 0; i < sections_vec.size(); ++i) {
out_dims[i][axis] = sections_vec[i];
}
for (size_t i = 0; i < sections_vec.size(); ++i) {
(*outs)[i].Allocate(out_dims[i], x.Dtype());
}
}
template <typename T>
void SplitKernel(const FDTensor& x, const std::vector<int>& section,
std::vector<FDTensor>* outs, int axis) {
size_t out_number = section.size();
outs->resize(out_number);
CreateSplitOutputs(x, section, outs, axis);
std::vector<const FDTensor*> shape_refer;
for (size_t j = 0; j < outs->size(); ++j) {
shape_refer.emplace_back(&((*outs)[j]));
}
SplitFunctor<T> functor;
functor(x, shape_refer, axis, outs);
}
void Split(const FDTensor& x, const std::vector<int>& num_or_sections,
std::vector<FDTensor>* out, int axis) {
FD_VISIT_ALL_TYPES(x.Dtype(), "Split", ([&] {
SplitKernel<data_t>(x, num_or_sections, out, axis);
}));
}
} // namespace function
} // namespace fastdeploy