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113 lines
3.8 KiB
113 lines
3.8 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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Indicesou may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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inline void GetDims(const framework::DDim& dim, int axis, int* pre, int* n,
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int* post) {
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*pre = 1;
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*post = 1;
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*n = dim[axis];
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for (int i = 0; i < axis; ++i) {
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(*pre) *= dim[i];
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}
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for (int i = axis + 1; i < dim.size(); ++i) {
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(*post) *= dim[i];
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}
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}
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template <typename DeviceContext, typename T>
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class PnormKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* in_x = ctx.Input<framework::Tensor>("X");
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auto* out_norm = ctx.Output<framework::Tensor>("Out");
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out_norm->mutable_data<T>(ctx.GetPlace());
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auto xdim = in_x->dims();
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float porder = ctx.Attr<float>("porder");
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int axis = ctx.Attr<int>("axis");
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if (axis < 0) axis = xdim.size() + axis;
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int pre, n, post;
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GetDims(xdim, axis, &pre, &n, &post);
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auto* place = ctx.template device_context<DeviceContext>().eigen_device();
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Eigen::DSizes<int, 3> shape(pre, n, post);
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Eigen::DSizes<int, 2> norm_shape(pre, post);
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auto x_e = framework::EigenVector<T>::Flatten(*in_x);
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auto norm_e = framework::EigenVector<T>::Flatten(*out_norm);
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auto x = x_e.reshape(shape);
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auto norm = norm_e.reshape(norm_shape);
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Eigen::DSizes<int, 1> rdim(1);
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auto xp = (x.abs()).pow(porder);
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auto sum = xp.sum(rdim);
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norm.device(*place) = sum.pow(1.0f / porder);
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}
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};
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template <typename DeviceContext, typename T, typename AttrType = T>
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class PnormGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* in_x = ctx.Input<framework::Tensor>("X");
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auto* in_norm = ctx.Input<framework::Tensor>("Out");
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auto* in_norm_dy =
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ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* out_dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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out_dx->mutable_data<T>(ctx.GetPlace());
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T eps = static_cast<T>(ctx.Attr<float>("epsilon"));
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auto xdim = in_x->dims();
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float porder = ctx.Attr<float>("porder");
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int axis = ctx.Attr<int>("axis");
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if (axis < 0) axis = xdim.size() + axis;
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int pre, n, post;
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GetDims(xdim, axis, &pre, &n, &post);
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Eigen::DSizes<int, 3> shape(pre, n, post);
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Eigen::DSizes<int, 3> rshape(pre, 1, post);
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auto* place = ctx.template device_context<DeviceContext>().eigen_device();
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auto x_e = framework::EigenVector<T>::Flatten(*in_x);
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auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
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auto norm_e = framework::EigenVector<T>::Flatten(*in_norm);
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auto norm_dy_e = framework::EigenVector<T>::Flatten(*in_norm_dy);
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auto x = x_e.reshape(shape);
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auto dx = dx_e.reshape(shape);
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auto norm = norm_e.reshape(rshape);
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auto norm_dy = norm_dy_e.reshape(rshape);
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Eigen::DSizes<int, 1> rdim(1);
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Eigen::DSizes<int, 3> bcast(1, n, 1);
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dx.device(*place) = (x.abs()).pow(porder - 1.0f);
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dx.device(*place) =
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dx / ((norm.broadcast(bcast)).pow(porder - 1.0f) + x.constant(eps));
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dx.device(*place) = dx * norm_dy.broadcast(bcast) * x.sign();
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}
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};
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} // namespace operators
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} // namespace paddle
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