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105 lines
4.0 KiB
105 lines
4.0 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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You 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/memory/memcpy.h"
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#include "unsupported/Eigen/CXX11/Tensor"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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template <typename DeviceContext, typename T>
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class SequenceExpandKernel : 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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auto* x = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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const T* x_data = x->data<T>();
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auto x_dims = x->dims();
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auto* y = context.Input<LoDTensor>("Y");
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PADDLE_ENFORCE(!y->lod().empty(), "y should have lod");
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PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims[0]),
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y->lod().back().size() - 1,
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"The size of last lod level in Input(Y)"
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"must be equal to dims[0] of Input(X).");
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out->set_lod(y->lod());
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auto* place =
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context.template device_context<DeviceContext>().eigen_device();
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size_t element_len = framework::product(x_dims) / x_dims[0];
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T* out_data = out->mutable_data<T>(context.GetPlace());
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auto out_starts = out->lod().back();
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for (size_t i = 0; i < out_starts.size() - 1; i++) {
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int scale = out_starts[i + 1] - out_starts[i];
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Eigen::TensorMap<
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Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
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x_t(x_data, 1, element_len);
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Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
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out_t(out_data, scale, element_len);
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Eigen::array<int, 2> cast({{scale, 1}});
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out_t.device(*place) = x_t.broadcast(cast);
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x_data += element_len;
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out_data += element_len * scale;
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}
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}
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};
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/*
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*Given Grad(Out)
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*
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* Grad(Out).lod = [[0, 2],
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* [0, 3, 6]]
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* Grad(Out).data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6]
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* Then
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* Grad(X).data = [(0.1 + 0.2 + 0.3), (0.4 + 0.5 + 0.6)]
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* = [0.6, 1.5]
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* Grad(X).lod = Input(X).lod
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*
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* */
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template <typename DeviceContext, typename T>
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class SequenceExpandGradKernel : 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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auto* d_out = context.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* x = context.Input<LoDTensor>("X");
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auto* out = context.Input<LoDTensor>("Out");
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auto* d_x = context.Output<LoDTensor>(framework::GradVarName("X"));
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auto out_last_level = out->lod().back();
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d_x->set_lod(x->lod());
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const T* d_out_data = d_out->data<T>();
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T* d_x_data = d_x->mutable_data<T>(context.GetPlace());
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size_t element_len = d_out->numel() / d_out->dims()[0];
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for (size_t i = 0; i < out_last_level.size() - 1; ++i) {
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size_t repeat = out_last_level[i + 1] - out_last_level[i];
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Eigen::TensorMap<
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Eigen::Tensor<const T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
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d_out_t(d_out_data, static_cast<int>(repeat), element_len);
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Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor, Eigen::DenseIndex>>
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d_x_t(d_x_data, static_cast<int>(element_len));
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auto place =
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context.template device_context<DeviceContext>().eigen_device();
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d_x_t.device(*place) = d_out_t.sum(Eigen::array<int, 1>({{0}}));
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d_out_data += (repeat * element_len);
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d_x_data += element_len;
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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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