wehub-resource-sync
110 lines
3.6 KiB
C++
110 lines
3.6 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 "paddle/phi/kernels/diagonal_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/diagonal.h"
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namespace phi {
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template <typename T, typename Context>
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void DiagonalKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int offset,
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int axis1,
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int axis2,
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DenseTensor* out) {
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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auto* input = &x;
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const T* input_data = input->data<T>();
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auto input_dim = vectorize(input->dims());
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auto input_dim_size = input_dim.size();
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auto* output = out;
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T* output_data = dev_ctx.template Alloc<T>(output);
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auto output_dim = vectorize(output->dims());
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auto output_dim_size = output_dim.size();
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const int64_t offset_ = offset;
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int64_t axis1_ =
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static_cast<int64_t>(axis1 < 0 ? input_dim_size + axis1 : axis1);
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int64_t axis2_ =
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static_cast<int64_t>(axis2 < 0 ? input_dim_size + axis2 : axis2);
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std::vector<int64_t> input_stride = funcs::ComputeDimStride(input_dim);
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std::vector<int64_t> output_stride = funcs::ComputeDimStride(output_dim);
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int64_t out_numel = out->numel();
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for (int64_t idx = 0; idx < out_numel; idx++) {
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std::vector<int64_t> idx_dim(output_dim_size);
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int64_t temp = 0;
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for (size_t i = 0; i < output_dim_size; i++) {
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idx_dim[i] = (idx - temp) / output_stride[i];
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temp = temp + idx_dim[i] * output_stride[i];
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}
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int64_t tmp = idx_dim[output_dim_size - 1];
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std::vector<int64_t> list;
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list.clear();
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int64_t l = std::min(axis1_, axis2_);
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int64_t r = std::max(axis1_, axis2_);
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for (size_t j = 0; j < output_dim_size - 1; j++) {
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list.push_back(idx_dim[j]);
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}
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if (offset_ == 0) {
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list.insert(list.begin() + l, tmp);
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list.insert(list.begin() + r, tmp);
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} else if (offset_ > 0) {
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if (axis1_ < axis2_) {
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list.insert(list.begin() + l, tmp);
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list.insert(list.begin() + r, tmp + offset_);
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} else {
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list.insert(list.begin() + l, tmp + offset_);
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list.insert(list.begin() + r, tmp);
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}
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} else if (offset_ < 0) {
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if (axis1_ < axis2_) {
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list.insert(list.begin() + l, tmp - offset_);
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list.insert(list.begin() + r, tmp);
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} else {
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list.insert(list.begin() + l, tmp);
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list.insert(list.begin() + r, tmp - offset_);
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}
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}
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int64_t input_offset = 0;
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for (size_t i = 0; i < input_dim_size; i++) {
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input_offset = input_offset + list[i] * input_stride[i];
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}
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output_data[idx] = input_data[input_offset];
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(diagonal,
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CPU,
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ALL_LAYOUT,
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phi::DiagonalKernel,
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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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phi::complex64,
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phi::complex128,
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bool) {}
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