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133 lines
4.7 KiB
133 lines
4.7 KiB
/* Copyright (c) 2016 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 NormKernel : 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_y = ctx.Output<framework::Tensor>("Out");
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auto* out_norm = ctx.Output<framework::Tensor>("Norm");
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out_y->mutable_data<T>(ctx.GetPlace());
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out_norm->mutable_data<T>(ctx.GetPlace());
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auto xdim = in_x->dims();
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auto ndim = out_norm->dims();
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T eps = static_cast<T>(ctx.Attr<float>("epsilon"));
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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 y_e = framework::EigenVector<T>::Flatten(*out_y);
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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 y = y_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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// y = x / sqrt((sum(x * x) + epsilon))
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// norm = sqrt(sum(x * x) + epsilon)
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auto x2 = x * x;
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auto sum = x2.sum(rdim) + eps;
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norm.device(*place) = sum.sqrt();
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// y = x / norm
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Eigen::DSizes<int, 3> rshape(pre, 1, post);
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Eigen::DSizes<int, 3> bcast(1, n, 1);
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y.device(*place) = x / norm.reshape(rshape).broadcast(bcast);
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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 NormGradKernel : 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>("Norm");
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auto* in_dy = 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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auto xdim = in_x->dims();
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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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auto x_e = framework::EigenVector<T>::Flatten(*in_x);
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auto dy_e = framework::EigenVector<T>::Flatten(*in_dy);
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auto norm_e = framework::EigenVector<T>::Flatten(*in_norm);
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auto dx_e = framework::EigenVector<T>::Flatten(*out_dx);
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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 = x_e.reshape(shape);
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auto dy = dy_e.reshape(shape);
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auto norm = norm_e.reshape(norm_shape);
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auto dx = dx_e.reshape(shape);
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framework::Tensor rsum;
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rsum.mutable_data<T>({pre, post}, ctx.GetPlace());
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auto sum = framework::EigenTensor<T, 2>::From(rsum);
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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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Eigen::DSizes<int, 3> rshape(pre, 1, post);
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// dx = ( dy/sqrt(sum(x*x)) ) * [1 - x*sum(x) / (sum(x*x) + e)]
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// = [dy - dy * x * sum(x) / (sum(x*x) + e)] / sqrt(sum(x*x))
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// = [dy - x * sum(x*dy) / (sum(x*x) + e)] / sqrt(sum(x*x))
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// 1. sum = sum(x*dy)
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sum.device(*place) = (x * dy).sum(rdim);
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// 2. dx = x * sum
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dx.device(*place) = sum.reshape(rshape).broadcast(bcast) * x;
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// 3. dx / (sum(x*x) + e)
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// where, norm.pow(2) = sum(x*x) + e, which is calculated in forward.
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dx.device(*place) = dx / norm.pow(2).broadcast(bcast);
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// 4. [dy - dx] / sqrt(sum(x*x))
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dx.device(*place) = (dy - dx) / norm.broadcast(bcast);
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}
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};
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} // namespace operators
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} // namespace paddle
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