You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
245 lines
10 KiB
245 lines
10 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License. */
|
|
|
|
#pragma once
|
|
|
|
#include "paddle/fluid/operators/activation_op.h"
|
|
#include "paddle/fluid/operators/math/math_function.h"
|
|
|
|
#include "paddle/fluid/framework/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
|
|
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
|
|
|
|
enum GRUActivationType { identity = 0, sigmoid = 1, tanh = 2, relu = 3 };
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class GRUUnitKernel : public framework::OpKernel<T> {
|
|
public:
|
|
template <typename Device, typename X, typename Y>
|
|
void ActCompute(const int act_type, const Device& d, X x, Y y) const {
|
|
if (act_type == identity)
|
|
y.device(d) = x;
|
|
else if (act_type == sigmoid)
|
|
SigmoidFunctor<T>()(d, x, y);
|
|
else if (act_type == tanh)
|
|
TanhFunctor<T>()(d, x, y);
|
|
else if (act_type == relu)
|
|
ReluFunctor<T>()(d, x, y);
|
|
else
|
|
PADDLE_THROW("unsupported activation type");
|
|
}
|
|
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* input = context.Input<Tensor>("Input");
|
|
auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
|
|
auto* weight = context.Input<Tensor>("Weight");
|
|
auto* bias = context.Input<Tensor>("Bias");
|
|
auto* gate = context.Output<Tensor>("Gate");
|
|
gate->mutable_data<T>(context.GetPlace());
|
|
auto* reset_hidden_prev = context.Output<Tensor>("ResetHiddenPrev");
|
|
reset_hidden_prev->mutable_data<T>(context.GetPlace());
|
|
auto* hidden = context.Output<Tensor>("Hidden");
|
|
hidden->mutable_data<T>(context.GetPlace());
|
|
|
|
int batch_size = input->dims()[0];
|
|
int frame_size = hidden_prev->dims()[1];
|
|
|
|
auto x = EigenMatrix<T>::From(*input);
|
|
auto h_p = EigenMatrix<T>::From(*hidden_prev);
|
|
auto g = EigenMatrix<T>::From(*gate);
|
|
auto r_h_p = EigenMatrix<T>::From(*reset_hidden_prev);
|
|
auto h = EigenMatrix<T>::From(*hidden);
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
|
|
// calculate unactivated gate outputs
|
|
if (bias) {
|
|
auto b = EigenMatrix<T>::From(*bias);
|
|
g.device(place) = x +
|
|
b.reshape(Eigen::array<int, 2>({{1, frame_size * 3}}))
|
|
.broadcast(Eigen::array<int, 2>({{batch_size, 1}}));
|
|
} else {
|
|
g.device(place) = x;
|
|
}
|
|
const T* hidden_prev_data = hidden_prev->data<T>();
|
|
const T* weight_data = weight->data<T>();
|
|
T* gate_data = gate->data<T>();
|
|
T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), false, false,
|
|
batch_size, 2 * frame_size, frame_size, 1, hidden_prev_data, frame_size,
|
|
weight_data, frame_size * 2, 1, gate_data, frame_size * 3);
|
|
|
|
// calculate activited gate
|
|
Eigen::array<int, 2> extents({{batch_size, frame_size}});
|
|
Eigen::array<int, 2> u_offsets({{0, 0}});
|
|
ActCompute(context.Attr<int>("gate_activation"), place,
|
|
g.slice(u_offsets, extents), g.slice(u_offsets, extents));
|
|
auto u = g.slice(u_offsets, extents); // update gate
|
|
Eigen::array<int, 2> r_offsets({{0, frame_size}});
|
|
ActCompute(context.Attr<int>("gate_activation"), place,
|
|
g.slice(r_offsets, extents), g.slice(r_offsets, extents));
|
|
auto r = g.slice(r_offsets, extents); // reset gate
|
|
r_h_p.device(place) = r * h_p; // reset previous hidden state
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), false, false,
|
|
batch_size, frame_size, frame_size, 1, reset_hidden_prev_data,
|
|
frame_size, weight_data + frame_size * frame_size * 2, frame_size, 1,
|
|
gate_data + frame_size * 2, frame_size * 3);
|
|
|
|
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
|
|
ActCompute(context.Attr<int>("activation"), place,
|
|
g.slice(c_offsets, extents), g.slice(c_offsets, extents));
|
|
auto c = g.slice(c_offsets, extents); // output candidate
|
|
|
|
// calculate final output
|
|
h.device(place) = u * (c - h_p) + h_p;
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class GRUUnitGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
template <typename Device, typename X, typename Y, typename DX, typename DY>
|
|
void ActGradCompute(const int act_type, const Device& d, X x, Y y, DX dx,
|
|
DY dy) const {
|
|
// x is dummy and won't be used even in Relu(use y instead)
|
|
if (act_type == identity)
|
|
dx.device(d) = dy;
|
|
else if (act_type == sigmoid)
|
|
SigmoidGradFunctor<T>()(d, x, y, dy, dx);
|
|
else if (act_type == tanh)
|
|
TanhGradFunctor<T>()(d, x, y, dy, dx);
|
|
else if (act_type == relu)
|
|
ReluGradFunctor<T>()(d, x, y, dy, dx);
|
|
else
|
|
PADDLE_THROW("unsupported activation type");
|
|
}
|
|
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* input = context.Input<Tensor>("Input");
|
|
auto* hidden_prev = context.Input<Tensor>("HiddenPrev");
|
|
auto* weight = context.Input<Tensor>("Weight");
|
|
auto* gate = context.Input<Tensor>("Gate");
|
|
auto* reset_hidden_prev = context.Input<Tensor>("ResetHiddenPrev");
|
|
auto* hidden_grad = context.Input<Tensor>(framework::GradVarName("Hidden"));
|
|
auto* input_grad = context.Output<Tensor>(framework::GradVarName("Input"));
|
|
auto* hidden_prev_grad =
|
|
context.Output<Tensor>(framework::GradVarName("HiddenPrev"));
|
|
auto* weight_grad =
|
|
context.Output<Tensor>(framework::GradVarName("Weight"));
|
|
auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
|
|
Tensor gate_grad;
|
|
Tensor reset_hidden_prev_grad;
|
|
|
|
const T* hidden_prev_data = hidden_prev->data<T>();
|
|
const T* weight_data = weight->data<T>();
|
|
T* gate_grad_data =
|
|
gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
|
|
const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
|
|
T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.mutable_data<T>(
|
|
reset_hidden_prev->dims(), context.GetPlace());
|
|
|
|
auto h_p = EigenMatrix<T>::From(*hidden_prev);
|
|
auto g = EigenMatrix<T>::From(*gate);
|
|
auto d_h = EigenMatrix<T>::From(*hidden_grad);
|
|
auto d_g = EigenMatrix<T>::From(gate_grad);
|
|
auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
|
|
int batch_size = input->dims()[0];
|
|
int frame_size = hidden_prev->dims()[1];
|
|
|
|
Eigen::array<int, 2> extents({{batch_size, frame_size}});
|
|
Eigen::array<int, 2> u_offsets({{0, 0}});
|
|
auto u = g.slice(u_offsets, extents); // update gate
|
|
Eigen::array<int, 2> r_offsets({{0, frame_size}});
|
|
auto r = g.slice(r_offsets, extents); // reset gate
|
|
Eigen::array<int, 2> c_offsets({{0, frame_size * 2}});
|
|
auto c = g.slice(c_offsets, extents); // output candidate
|
|
|
|
// backward for unactivated update gate
|
|
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
|
|
d_g.slice(u_offsets, extents), d_h * (c - h_p));
|
|
// backward for unactivated output candidate
|
|
ActGradCompute(context.Attr<int>("activation"), place, c, c,
|
|
d_g.slice(c_offsets, extents), d_h * u);
|
|
// backward for reset_hidden_prev
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), false, true,
|
|
batch_size, frame_size, frame_size, 1, gate_grad_data + frame_size * 2,
|
|
frame_size * 3, weight_data + frame_size * frame_size * 2, frame_size,
|
|
0, reset_hidden_prev_grad_data, frame_size);
|
|
// backward for unactivated reset gate
|
|
ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
|
|
d_g.slice(r_offsets, extents), d_r_h_p * h_p);
|
|
// backward for weight
|
|
if (weight_grad) {
|
|
T* weight_grad_data = weight_grad->mutable_data<T>(context.GetPlace());
|
|
// backward for state_weight
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), true, false,
|
|
frame_size, frame_size, batch_size, 1, reset_hidden_prev_data,
|
|
frame_size, gate_grad_data + frame_size * 2, frame_size * 3, 0,
|
|
weight_grad_data + frame_size * frame_size * 2, frame_size);
|
|
|
|
// backward for update_gate_weight and reset_gate_weight
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), true, false,
|
|
frame_size, frame_size * 2, batch_size, 1, hidden_prev_data,
|
|
frame_size, gate_grad_data, frame_size * 3, 0, weight_grad_data,
|
|
frame_size * 2);
|
|
}
|
|
// backward for hidden_prev
|
|
if (hidden_prev_grad) {
|
|
T* hidden_prev_grad_data =
|
|
hidden_prev_grad->mutable_data<T>(context.GetPlace());
|
|
auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
|
|
d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
|
|
math::gemm<DeviceContext, T>(
|
|
context.template device_context<DeviceContext>(), false, true,
|
|
batch_size, frame_size, frame_size * 2, 1, gate_grad_data,
|
|
frame_size * 3, weight_data, frame_size * 2, 1, hidden_prev_grad_data,
|
|
frame_size);
|
|
}
|
|
// backward for input
|
|
if (input_grad) {
|
|
input_grad->mutable_data<T>(context.GetPlace());
|
|
auto d_x = EigenMatrix<T>::From(*input_grad);
|
|
d_x.device(place) = d_g;
|
|
}
|
|
// backward for bias
|
|
if (bias_grad) {
|
|
bias_grad->mutable_data<T>(context.GetPlace());
|
|
auto d_b = EigenVector<T>::Flatten(*bias_grad);
|
|
d_b.device(place) = d_g.sum(Eigen::array<int, 1>({{0}}));
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|