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238 lines
10 KiB
238 lines
10 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/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/activation_op.h"
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#include "paddle/fluid/operators/math/blas.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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enum GRUActivationType { identity = 0, sigmoid = 1, tanh = 2, relu = 3 };
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template <typename DeviceContext, typename T>
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class GRUUnitKernel : public framework::OpKernel<T> {
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public:
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template <typename Device, typename X, typename Y>
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void ActCompute(const int act_type, const Device& d, X x, Y y) const {
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if (act_type == identity)
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y.device(d) = x;
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else if (act_type == sigmoid)
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SigmoidFunctor<T>()(d, x, y);
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else if (act_type == tanh)
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TanhFunctor<T>()(d, x, y);
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else if (act_type == relu)
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ReluFunctor<T>()(d, x, y);
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else
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PADDLE_THROW("unsupported activation type");
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}
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input = context.Input<Tensor>("Input");
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auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
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auto* weight = context.Input<Tensor>("Weight");
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auto* bias = context.Input<Tensor>("Bias");
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auto* gate = context.Output<Tensor>("Gate");
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gate->mutable_data<T>(context.GetPlace());
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auto* reset_hidden_prev = context.Output<Tensor>("ResetHiddenPrev");
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reset_hidden_prev->mutable_data<T>(context.GetPlace());
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auto* hidden = context.Output<Tensor>("Hidden");
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hidden->mutable_data<T>(context.GetPlace());
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int batch_size = input->dims()[0];
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int frame_size = hidden_prev->dims()[1];
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auto x = EigenMatrix<T>::From(*input);
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auto h_p = EigenMatrix<T>::From(*hidden_prev);
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auto g = EigenMatrix<T>::From(*gate);
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auto r_h_p = EigenMatrix<T>::From(*reset_hidden_prev);
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auto h = EigenMatrix<T>::From(*hidden);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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// calculate unactivated gate outputs
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if (bias) {
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auto b = EigenMatrix<T>::From(*bias);
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g.device(place) = x +
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b.reshape(Eigen::array<int, 2>({{1, frame_size * 3}}))
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.broadcast(Eigen::array<int, 2>({{batch_size, 1}}));
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} else {
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g.device(place) = x;
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}
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const T* hidden_prev_data = hidden_prev->data<T>();
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const T* weight_data = weight->data<T>();
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T* gate_data = gate->data<T>();
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T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
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auto blas = math::GetBlas<DeviceContext, T>(context);
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blas.GEMM(false, false, batch_size, 2 * frame_size, frame_size, 1,
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hidden_prev_data, frame_size, weight_data, frame_size * 2, 1,
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gate_data, frame_size * 3);
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// calculate activited gate
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Eigen::array<int, 2> extents{{batch_size, frame_size}};
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Eigen::array<int, 2> u_offsets{{0, 0}};
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ActCompute(context.Attr<int>("gate_activation"), place,
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g.slice(u_offsets, extents), g.slice(u_offsets, extents));
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auto u = g.slice(u_offsets, extents); // update gate
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Eigen::array<int, 2> r_offsets{{0, frame_size}};
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ActCompute(context.Attr<int>("gate_activation"), place,
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g.slice(r_offsets, extents), g.slice(r_offsets, extents));
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auto r = g.slice(r_offsets, extents); // reset gate
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r_h_p.device(place) = r * h_p; // reset previous hidden state
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blas.GEMM(false, false, batch_size, frame_size, frame_size, 1,
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reset_hidden_prev_data, frame_size,
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weight_data + frame_size * frame_size * 2, frame_size, 1,
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gate_data + frame_size * 2, frame_size * 3);
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Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
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ActCompute(context.Attr<int>("activation"), place,
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g.slice(c_offsets, extents), g.slice(c_offsets, extents));
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auto c = g.slice(c_offsets, extents); // output candidate
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// calculate final output
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h.device(place) = u * (c - h_p) + h_p;
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}
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};
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template <typename DeviceContext, typename T>
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class GRUUnitGradKernel : public framework::OpKernel<T> {
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public:
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template <typename Device, typename X, typename Y, typename DX, typename DY>
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void ActGradCompute(const int act_type, const Device& d, X x, Y y, DX dx,
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DY dy) const {
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// x is dummy and won't be used even in Relu(use y instead)
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if (act_type == identity)
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dx.device(d) = dy;
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else if (act_type == sigmoid)
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SigmoidGradFunctor<T>()(d, x, y, dy, dx);
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else if (act_type == tanh)
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TanhGradFunctor<T>()(d, x, y, dy, dx);
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else if (act_type == relu)
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ReluGradFunctor<T>()(d, x, y, dy, dx);
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else
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PADDLE_THROW("unsupported activation type");
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}
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void Compute(const framework::ExecutionContext& context) const override {
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auto* input = context.Input<Tensor>("Input");
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auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
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auto* weight = context.Input<Tensor>("Weight");
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auto* gate = context.Input<Tensor>("Gate");
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auto* reset_hidden_prev = context.Input<Tensor>("ResetHiddenPrev");
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auto* hidden_grad = context.Input<Tensor>(framework::GradVarName("Hidden"));
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auto* input_grad = context.Output<Tensor>(framework::GradVarName("Input"));
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auto* hidden_prev_grad =
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context.Output<Tensor>(framework::GradVarName("HiddenPrev"));
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auto* weight_grad =
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context.Output<Tensor>(framework::GradVarName("Weight"));
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auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
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Tensor gate_grad;
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Tensor reset_hidden_prev_grad;
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const T* hidden_prev_data = hidden_prev->data<T>();
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const T* weight_data = weight->data<T>();
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T* gate_grad_data =
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gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
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const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
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T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.mutable_data<T>(
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reset_hidden_prev->dims(), context.GetPlace());
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auto h_p = EigenMatrix<T>::From(*hidden_prev);
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auto g = EigenMatrix<T>::From(*gate);
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auto d_h = EigenMatrix<T>::From(*hidden_grad);
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auto d_g = EigenMatrix<T>::From(gate_grad);
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auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
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auto& place =
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*context.template device_context<DeviceContext>().eigen_device();
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int batch_size = input->dims()[0];
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int frame_size = hidden_prev->dims()[1];
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Eigen::array<int, 2> extents{{batch_size, frame_size}};
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Eigen::array<int, 2> u_offsets{{0, 0}};
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auto u = g.slice(u_offsets, extents); // update gate
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Eigen::array<int, 2> r_offsets{{0, frame_size}};
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auto r = g.slice(r_offsets, extents); // reset gate
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Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
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auto c = g.slice(c_offsets, extents); // output candidate
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// backward for unactivated update gate
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ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
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d_g.slice(u_offsets, extents), d_h * (c - h_p));
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// backward for unactivated output candidate
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ActGradCompute(context.Attr<int>("activation"), place, c, c,
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d_g.slice(c_offsets, extents), d_h * u);
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// backward for reset_hidden_prev
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auto blas = math::GetBlas<DeviceContext, T>(context);
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blas.GEMM(false, true, batch_size, frame_size, frame_size, 1,
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gate_grad_data + frame_size * 2, frame_size * 3,
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weight_data + frame_size * frame_size * 2, frame_size, 0,
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reset_hidden_prev_grad_data, frame_size);
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// backward for unactivated reset gate
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ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
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d_g.slice(r_offsets, extents), d_r_h_p * h_p);
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// backward for weight
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if (weight_grad) {
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T* weight_grad_data = weight_grad->mutable_data<T>(context.GetPlace());
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// backward for state_weight
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blas.GEMM(true, false, frame_size, frame_size, batch_size, 1,
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reset_hidden_prev_data, frame_size,
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gate_grad_data + frame_size * 2, frame_size * 3, 0,
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weight_grad_data + frame_size * frame_size * 2, frame_size);
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// backward for update_gate_weight and reset_gate_weight
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blas.GEMM(true, false, frame_size, frame_size * 2, batch_size, 1,
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hidden_prev_data, frame_size, gate_grad_data, frame_size * 3, 0,
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weight_grad_data, frame_size * 2);
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}
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// backward for hidden_prev
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if (hidden_prev_grad) {
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T* hidden_prev_grad_data =
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hidden_prev_grad->mutable_data<T>(context.GetPlace());
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auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
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d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
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blas.GEMM(false, true, batch_size, frame_size, frame_size * 2, 1,
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gate_grad_data, frame_size * 3, weight_data, frame_size * 2, 1,
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hidden_prev_grad_data, frame_size);
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}
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// backward for input
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if (input_grad) {
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input_grad->mutable_data<T>(context.GetPlace());
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auto d_x = EigenMatrix<T>::From(*input_grad);
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d_x.device(place) = d_g;
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}
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// backward for bias
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if (bias_grad) {
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bias_grad->mutable_data<T>(context.GetPlace());
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auto d_b = EigenVector<T>::Flatten(*bias_grad);
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d_b.device(place) = d_g.sum(Eigen::array<int, 1>({{0}}));
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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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