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176 lines
8.1 KiB
176 lines
8.1 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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template <typename DeviceContext, typename T, typename AttrType = 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& context) const override {
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const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
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const framework::Tensor* scale = context.Input<framework::Tensor>("Scale");
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auto* out = context.Output<framework::Tensor>("Out");
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auto epsilon = static_cast<T>(context.Attr<AttrType>("epsilon"));
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out->mutable_data<T>(context.GetPlace());
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int batch_size = in_x->dims()[0];
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int channels = in_x->dims()[1];
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int height = in_x->dims()[2];
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int width = in_x->dims()[3];
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int fea_len = height * width;
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auto* place =
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context.template device_context<DeviceContext>().eigen_device();
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auto x =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*in_x, framework::make_ddim({batch_size, fea_len * channels}));
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// get square
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framework::Tensor x_square;
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x_square.mutable_data<T>(in_x->dims(), context.GetPlace());
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auto x_square_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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x_square, framework::make_ddim({batch_size, fea_len * channels}));
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x_square_eigen.device(*place) = x.square();
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auto scale_eigen =
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framework::EigenVector<T, Eigen::RowMajor, Eigen::DenseIndex>::Flatten(
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*scale);
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for (int n = 0; n < batch_size; ++n) {
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framework::Tensor in_x_batch = in_x->Slice(n, n + 1);
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auto in_x_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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in_x_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor x_square_batch = x_square.Slice(n, n + 1);
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auto x_square_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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x_square_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor out_batch = out->Slice(n, n + 1);
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auto out_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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out_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor tmp_tensor;
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tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
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context.GetPlace());
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auto tmp = framework::EigenVector<T, Eigen::RowMajor,
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Eigen::DenseIndex>::Flatten(tmp_tensor);
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// get colsum and sqrt , inverse
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auto dim = Eigen::array<int, 1>({{0}});
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tmp.device(*place) = x_square_batch_eigen.sum(dim);
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tmp.device(*place) = (tmp + epsilon).sqrt().inverse();
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Eigen::array<int, 2> broadcast_dim_col;
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broadcast_dim_col[1] = 1;
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broadcast_dim_col[0] = channels;
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out_batch_eigen.device(*place) =
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in_x_batch_eigen * (tmp.broadcast(broadcast_dim_col));
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Eigen::array<int, 2> broadcast_dim_row;
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broadcast_dim_row[1] = fea_len;
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broadcast_dim_row[0] = 1;
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out_batch_eigen.device(*place) =
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out_batch_eigen * (scale_eigen.broadcast(broadcast_dim_row));
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}
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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& context) const override {
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const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
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const framework::Tensor* scale = context.Input<framework::Tensor>("Scale");
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const framework::Tensor* out_grad =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto epsilon = static_cast<T>(context.Attr<AttrType>("epsilon"));
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framework::Tensor* in_x_grad =
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context.Output<framework::Tensor>(framework::GradVarName("X"));
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in_x_grad->mutable_data<T>(context.GetPlace());
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int batch_size = in_x->dims()[0];
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int channels = in_x->dims()[1];
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int height = in_x->dims()[2];
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int width = in_x->dims()[3];
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int fea_len = height * width;
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auto* place =
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context.template device_context<DeviceContext>().eigen_device();
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auto scale_eigen =
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framework::EigenVector<T, Eigen::RowMajor, Eigen::DenseIndex>::Flatten(
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*scale);
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auto x =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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*in_x, framework::make_ddim({batch_size, fea_len * channels}));
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// get square
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framework::Tensor x_square;
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x_square.mutable_data<T>(in_x->dims(), context.GetPlace());
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auto x_square_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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x_square, framework::make_ddim({batch_size, fea_len * channels}));
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x_square_eigen.device(*place) = x.square();
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for (int n = 0; n < batch_size; ++n) {
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framework::Tensor in_x_batch = in_x->Slice(n, n + 1);
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auto in_x_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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in_x_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor in_g_batch = in_x_grad->Slice(n, n + 1);
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auto in_g_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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in_g_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor x_square_batch = x_square.Slice(n, n + 1);
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auto x_square_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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x_square_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor outg_batch = out_grad->Slice(n, n + 1);
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auto outg_batch_eigen =
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framework::EigenMatrix<T, Eigen::RowMajor, Eigen::DenseIndex>::From(
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outg_batch, framework::make_ddim({channels, fea_len}));
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framework::Tensor tmp_tensor;
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tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
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context.GetPlace());
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auto tmp_eigen =
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framework::EigenVector<T, Eigen::RowMajor,
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Eigen::DenseIndex>::Flatten(tmp_tensor);
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auto dim = Eigen::array<int, 1>({{0}});
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tmp_eigen.device(*place) = (in_x_batch_eigen * outg_batch_eigen).sum(dim);
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framework::Tensor norm_tmp_tensor;
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norm_tmp_tensor.mutable_data<T>(framework::make_ddim({1, fea_len}),
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context.GetPlace());
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auto norm_tmp_eigen =
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framework::EigenVector<T, Eigen::RowMajor,
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Eigen::DenseIndex>::Flatten(norm_tmp_tensor);
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norm_tmp_eigen.device(*place) =
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(x_square_batch_eigen.sum(dim) + epsilon).sqrt();
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Eigen::array<int, 2> broadcast_dim_col;
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broadcast_dim_col[1] = 1;
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broadcast_dim_col[0] = channels;
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in_g_batch_eigen.device(*place) =
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in_x_batch_eigen * tmp_eigen.broadcast(broadcast_dim_col);
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in_g_batch_eigen.device(*place) =
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in_g_batch_eigen /
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(norm_tmp_eigen * norm_tmp_eigen).broadcast(broadcast_dim_col);
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in_g_batch_eigen.device(*place) = outg_batch_eigen - in_g_batch_eigen;
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// outg_batch_eigen + (in_g_batch_eigen * -1);
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in_g_batch_eigen.device(*place) =
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in_g_batch_eigen / norm_tmp_eigen.broadcast(broadcast_dim_col);
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Eigen::array<int, 2> broadcast_dim_row;
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broadcast_dim_row[1] = fea_len;
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broadcast_dim_row[0] = 1;
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in_g_batch_eigen.device(*place) =
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in_g_batch_eigen * (scale_eigen.broadcast(broadcast_dim_row));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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