wehub-resource-sync
101 lines
3.3 KiB
C++
101 lines
3.3 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_grad_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/funcs/diagonal.h"
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namespace phi {
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template <typename T, typename Context>
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void DiagonalGradKernel(const Context& dev_ctx,
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const DenseTensor& x UNUSED,
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const DenseTensor& out_grad,
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int offset,
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int axis1,
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int axis2,
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DenseTensor* in_grad) {
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if (in_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(in_grad);
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return;
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}
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const auto* dout = &out_grad;
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const T* dout_data = dout->data<T>();
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auto dout_dim = vectorize(dout->dims());
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auto* dx = in_grad;
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T* dx_data = dev_ctx.template Alloc<T>(dx);
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auto dx_dim = vectorize(dx->dims());
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auto dx_dim_size = dx_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 ? dx_dim_size + axis1 : axis1);
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int64_t axis2_ =
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static_cast<int64_t>(axis2 < 0 ? dx_dim_size + axis2 : axis2);
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std::vector<int64_t> dout_stride = funcs::ComputeDimStride(dout_dim);
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std::vector<int64_t> dx_stride = funcs::ComputeDimStride(dx_dim);
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int64_t numel = dx->numel();
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for (int64_t idx = 0; idx < numel; idx++) {
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std::vector<int64_t> idx_dim(dx_dim_size);
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int64_t temp = 0;
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for (size_t i = 0; i < dx_dim_size; i++) {
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idx_dim[i] = (idx - temp) / dx_stride[i];
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temp = temp + idx_dim[i] * dx_stride[i];
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}
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int64_t axis1_dim = idx_dim[axis1_];
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int64_t axis2_dim = idx_dim[axis2_];
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idx_dim.erase(idx_dim.begin() + std::max(axis1_, axis2_));
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idx_dim.erase(idx_dim.begin() + std::min(axis1_, axis2_));
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bool flag = false;
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if ((offset_ == 0 && axis1_dim == axis2_dim) ||
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(offset_ > 0 && (axis1_dim + offset_) == axis2_dim)) {
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idx_dim.push_back(axis1_dim);
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flag = true;
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} else if (offset_ < 0 && (axis1_dim + offset_) == axis2_dim) {
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idx_dim.push_back(axis2_dim);
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flag = true;
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}
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if (flag) {
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int64_t idx_output = 0;
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for (size_t i = 0; i < idx_dim.size(); i++) {
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idx_output = idx_output + idx_dim[i] * dout_stride[i];
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}
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dx_data[idx] = dout_data[idx_output];
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} else {
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dx_data[idx] = static_cast<T>(0);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(diagonal_grad,
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CPU,
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ALL_LAYOUT,
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phi::DiagonalGradKernel,
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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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bool,
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phi::complex64,
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phi::complex128) {}
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