wehub-resource-sync
100 lines
3.4 KiB
C++
100 lines
3.4 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 <limits>
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/cum_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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namespace phi {
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template <typename T>
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struct LogGradPositiveFunctor {
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HOSTDEVICE T operator()(const T& x) const {
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const T kMin = std::numeric_limits<T>::lowest();
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return x > 0 ? std::log(x) : kMin;
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}
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};
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template <typename T>
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struct LogGradNegativeFunctor {
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HOSTDEVICE T operator()(const T& x) const {
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const T kMin = std::numeric_limits<T>::lowest();
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return x < 0 ? std::log(-x) : kMin;
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}
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};
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template <typename T, typename Context>
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void LogcumsumexpGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out,
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const DenseTensor& d_out,
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int axis,
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bool flatten,
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bool exclusive,
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bool reverse,
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DenseTensor* d_x) {
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if (d_x && d_x->numel() == 0) {
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dev_ctx.template Alloc<T>(d_x);
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return;
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}
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reverse = !reverse;
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dev_ctx.template Alloc<T>(d_x);
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auto eigen_x = EigenVector<T>::Flatten(x);
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auto eigen_out = EigenVector<T>::Flatten(out);
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auto eigen_d_out = EigenVector<T>::Flatten(d_out);
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auto& place = *dev_ctx.eigen_device();
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using MT = typename MPTypeTrait<T>::Type;
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DenseTensor output_pos;
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output_pos.Resize(d_out.dims());
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dev_ctx.template Alloc<MT>(&output_pos);
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auto eigen_output_pos = EigenVector<MT>::Flatten(output_pos);
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DenseTensor output_neg;
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output_neg.Resize(d_out.dims());
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dev_ctx.template Alloc<MT>(&output_neg);
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auto eigen_output_neg = EigenVector<MT>::Flatten(output_neg);
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DenseTensor tmp;
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tmp.Resize(d_out.dims());
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dev_ctx.template Alloc<MT>(&tmp);
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auto eigen_tmp = EigenVector<MT>::Flatten(tmp);
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eigen_tmp.device(place) =
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eigen_d_out.template cast<MT>().unaryExpr(LogGradPositiveFunctor<MT>()) -
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eigen_out.template cast<MT>();
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LogcumsumexpKernel<MT, Context>(
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dev_ctx, tmp, axis, flatten, exclusive, reverse, &output_pos);
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auto out_pos = eigen_output_pos + eigen_x.template cast<MT>();
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eigen_output_pos.device(place) = out_pos.exp();
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eigen_tmp.device(place) =
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eigen_d_out.template cast<MT>().unaryExpr(LogGradNegativeFunctor<MT>()) -
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eigen_out.template cast<MT>();
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LogcumsumexpKernel<MT, Context>(
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dev_ctx, tmp, axis, flatten, exclusive, reverse, &output_neg);
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auto out_neg = eigen_output_neg + eigen_x.template cast<MT>();
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eigen_output_neg.device(place) = out_neg.exp();
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auto eigen_d_x = EigenVector<T>::Flatten(*d_x);
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eigen_d_x.device(place) =
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(eigen_output_pos - eigen_output_neg).template cast<T>();
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}
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} // namespace phi
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