wehub-resource-sync
112 lines
3.1 KiB
C++
112 lines
3.1 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/kernels/funcs/math_function.h"
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namespace phi {
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namespace funcs {
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template <typename T, typename Context>
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void PostprocessMedianGradKernel(const Context& dev_ctx,
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DenseTensor* input,
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const IntArray& raw_axes,
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DenseTensor* x) {
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auto input_dim = input->dims();
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auto rank = input_dim.size();
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std::vector<int64_t> axes = raw_axes.GetData();
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int64_t axes_size = static_cast<int>(axes.size());
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for (int64_t i = 0; i < axes_size; i++) {
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if (axes[i] < 0) {
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axes[i] += rank;
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}
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}
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std::vector<int> trans_back;
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std::vector<int> reshape_back;
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trans_back.resize(rank);
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int offset = 0;
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for (int64_t i = 0; i < rank; i++) {
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if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
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reshape_back.push_back(input_dim[i]);
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trans_back[i] = offset;
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offset += 1;
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}
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}
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for (int64_t i = 0; i < rank; i++) {
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if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
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trans_back[i] = offset;
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reshape_back.push_back(input_dim[i]);
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offset += 1;
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}
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}
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input->Resize(reshape_back);
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funcs::TransCompute<Context, T>(
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static_cast<int>(trans_back.size()), dev_ctx, *input, x, trans_back);
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}
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template <typename T, typename Context>
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void PreprocessMedianKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const IntArray& raw_axes,
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DenseTensor* x) {
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auto input_dim = input.dims();
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auto rank = input_dim.size();
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std::vector<int> perm;
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std::vector<int64_t> reshape;
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std::vector<int64_t> axes = raw_axes.GetData();
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int axes_size = static_cast<int>(axes.size());
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for (int i = 0; i < axes_size; i++) {
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if (axes[i] < 0) {
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axes[i] += rank;
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}
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}
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for (int i = 0; i < rank; i++) {
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if (std::find(axes.begin(), axes.end(), i) == axes.end()) {
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perm.push_back(i);
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reshape.push_back(input_dim[i]);
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}
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}
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int64_t post_numel = 1;
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for (int i = 0; i < rank; i++) {
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if (std::find(axes.begin(), axes.end(), i) != axes.end()) {
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perm.push_back(i);
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post_numel *= input_dim[i];
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}
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}
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reshape.push_back(post_numel);
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DDim trans_dim(input_dim);
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int ndims = perm.size();
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for (int i = 0; i < ndims; i++) {
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trans_dim[i] = input_dim[perm[i]];
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}
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x->Resize(trans_dim);
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dev_ctx.template Alloc<T>(x);
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funcs::TransCompute<Context, T>(ndims, dev_ctx, input, x, perm);
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x->Resize(reshape);
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}
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} // namespace funcs
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} // namespace phi
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