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https://github.com/PaddlePaddle/FastDeploy.git
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de98163efa
* Add +-*/ functions * Add same dims test case for operations * add broadcast 0 * Add broadcast dim2 testcase * Add broadcast dim3 and dim4 testcase * Add +-*/ operators * Add mixed operation * refresh code style * Add reshape op * update code style
117 lines
3.8 KiB
C++
117 lines
3.8 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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#include "fastdeploy/function/concat.h"
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#include "fastdeploy/utils/utils.h"
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#include <cstring>
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#include <limits>
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#include <set>
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#include <sstream>
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namespace fastdeploy {
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namespace function {
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std::vector<int64_t>
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ComputeAndCheckConcatOutputShape(const std::vector<FDTensor>& input, int axis) {
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const size_t n = input.size();
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auto out_dims = input[0].shape;
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size_t in_zero_dims_size = out_dims.size();
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for (size_t i = 1; i < n; ++i) {
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FDASSERT(input[i].shape.size() == out_dims.size(),
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"The shape of input[0] and input[%d] is expected to be equal. But "
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"received input[0]'s shape = %s, input[%d]'s shape = %s.",
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i, Str(out_dims).c_str(), i, Str(input[i].shape).c_str());
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for (size_t j = 0; j < in_zero_dims_size; j++) {
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if (j == axis) {
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out_dims[axis] += input[i].shape[axis];
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} else {
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FDASSERT(
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input[0].shape[j] == input[i].shape[j],
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"The %d-th dimension of input[0] and input[%d] is expected to be "
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"equal."
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"But received input[0]'s shape = %s, input[%d]'s shape = %s.",
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j, i, Str(input[0].shape).c_str(), i, Str(input[i].shape).c_str());
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}
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}
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}
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return out_dims;
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}
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template <typename T> struct ConcatFunctor {
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void operator()(const std::vector<FDTensor>& input, int axis,
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FDTensor* output) {
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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].shape;
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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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std::vector<int64_t> input_cols(num);
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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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// computation
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T* output_data = reinterpret_cast<T*>(output->Data());
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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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const T* input_data = reinterpret_cast<const T*>(input[j].Data());
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for (int64_t k = 0; k < out_rows; ++k) {
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FDTensor::CopyBuffer(output_data + k * out_cols + col_idx,
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input_data + k * col_len, sizeof(T) * col_len,
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input[j].device, input[j].is_pinned_memory);
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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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template <typename T>
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void ConcatKernel(const std::vector<FDTensor>& input, FDTensor* output,
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int axis) {
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auto output_shape = ComputeAndCheckConcatOutputShape(input, axis);
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output->Resize(output_shape, TypeToDataType<T>::dtype, output->name,
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input[0].device);
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ConcatFunctor<T> functor;
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functor(input, axis, output);
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}
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void Concat(const std::vector<FDTensor>& x, FDTensor* out, int axis) {
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FDASSERT(x.size() > 0,
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"The number of FDTensor array should be larger than 0, but the size "
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"of input is %d",
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x.size());
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int64_t rank = x[0].shape.size();
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FDASSERT(axis >= -rank && axis < rank,
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"The axis is expected to be in range of [%d, %d), but got %d", -rank,
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rank, axis);
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if (axis < 0) {
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axis += rank;
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}
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FD_VISIT_ALL_TYPES(x[0].dtype, "Concat",
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([&] { ConcatKernel<data_t>(x, out, axis); }));
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}
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} // namespace function
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} // namespace fastdeploy
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