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.
258 lines
8.9 KiB
258 lines
8.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
|
|
|
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 "glog/logging.h"
|
|
#include "paddle/framework/eigen.h"
|
|
#include "paddle/framework/op_registry.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
using DDim = framework::DDim;
|
|
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
|
|
template <typename T, int MajorType = Eigen::RowMajor,
|
|
typename IndexType = Eigen::DenseIndex>
|
|
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
|
|
|
|
struct SumFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
|
|
y.device(place) = x.sum(dim);
|
|
}
|
|
};
|
|
|
|
struct SumGradFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename DX,
|
|
typename DY, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
|
|
const Dim& dim, int size) {
|
|
dx.device(place) = dy.broadcast(dim);
|
|
}
|
|
};
|
|
|
|
struct MeanFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
|
|
y.device(place) = x.mean(dim);
|
|
}
|
|
};
|
|
|
|
struct MeanGradFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename DX,
|
|
typename DY, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
|
|
const Dim& dim, int size) {
|
|
dx.device(place) = dy.broadcast(dim) / dx.constant(size);
|
|
}
|
|
};
|
|
|
|
struct MaxFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
|
|
y.device(place) = x.maximum(dim);
|
|
}
|
|
};
|
|
|
|
struct MinFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, const Dim& dim) {
|
|
y.device(place) = x.minimum(dim);
|
|
}
|
|
};
|
|
|
|
struct MaxOrMinGradFunctor {
|
|
template <typename DeviceContext, typename X, typename Y, typename DX,
|
|
typename DY, typename Dim>
|
|
void operator()(const DeviceContext& place, X& x, Y& y, DX& dx, DY& dy,
|
|
const Dim& dim, int size) {
|
|
auto equals = x == y.broadcast(dim);
|
|
auto ones = dx.constant(1);
|
|
auto zeros = dx.constant(0);
|
|
// If there are multiple minimum or maximum elements, the subgradient of
|
|
// each is the set [0, 1], and we pass gradient to all of them here.
|
|
dx.device(place) = dy.broadcast(dim) * equals.select(ones, zeros);
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T, typename Functor>
|
|
class ReduceKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
bool reduce_all = context.Attr<bool>("reduce_all");
|
|
if (reduce_all) {
|
|
// Flatten and reduce 1-D tensor
|
|
auto* input = context.Input<Tensor>("X");
|
|
auto* output = context.Output<Tensor>("Out");
|
|
output->mutable_data<T>(context.GetPlace());
|
|
auto x = EigenVector<T>::Flatten(*input);
|
|
auto out = EigenScalar<T>::From(*output);
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
auto reduce_dim = Eigen::array<int, 1>({{0}});
|
|
Functor functor;
|
|
functor(place, x, out, reduce_dim);
|
|
} else {
|
|
int rank = context.Input<Tensor>("X")->dims().size();
|
|
switch (rank) {
|
|
case 1:
|
|
ReduceCompute<1>(context);
|
|
break;
|
|
case 2:
|
|
ReduceCompute<2>(context);
|
|
break;
|
|
case 3:
|
|
ReduceCompute<3>(context);
|
|
break;
|
|
case 4:
|
|
ReduceCompute<4>(context);
|
|
break;
|
|
case 5:
|
|
ReduceCompute<5>(context);
|
|
break;
|
|
case 6:
|
|
ReduceCompute<6>(context);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
template <size_t D>
|
|
void ReduceCompute(const framework::ExecutionContext& context) const {
|
|
auto* input = context.Input<Tensor>("X");
|
|
auto* output = context.Output<Tensor>("Out");
|
|
output->mutable_data<T>(context.GetPlace());
|
|
|
|
auto x = EigenTensor<T, D>::From(*input);
|
|
auto x_rank = static_cast<int>(x.dimensions().size());
|
|
int dim = static_cast<int>(context.Attr<int>("dim"));
|
|
if (dim < 0) dim = x_rank + dim;
|
|
auto reduce_dim = Eigen::array<int, 1>({{dim}});
|
|
// construct the squeezed output tensor
|
|
bool keep_dim = context.Attr<bool>("keep_dim");
|
|
DDim dims = output->dims();
|
|
auto dims_vector = vectorize(dims);
|
|
if (keep_dim && x_rank > 1) {
|
|
dims_vector.erase(dims_vector.begin() + dim);
|
|
dims = framework::make_ddim(dims_vector);
|
|
}
|
|
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
Functor functor;
|
|
|
|
if (D == 1) {
|
|
auto out = EigenScalar<T>::From(*output);
|
|
functor(place, x, out, reduce_dim);
|
|
} else {
|
|
auto out = EigenTensor<T, (D - 1)>::From(*output, dims);
|
|
functor(place, x, out, reduce_dim);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T, typename Functor>
|
|
class ReduceGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
bool reduce_all = context.Attr<bool>("reduce_all");
|
|
if (reduce_all) {
|
|
auto* input0 = context.Input<Tensor>("X");
|
|
auto* input1 = context.Input<Tensor>("Out");
|
|
auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
|
|
auto* output = context.Output<Tensor>(framework::GradVarName("X"));
|
|
output->mutable_data<T>(context.GetPlace());
|
|
auto x = EigenVector<T>::Flatten(*input0);
|
|
auto x_reduce = EigenVector<T>::From(*input1);
|
|
auto x_reduce_grad = EigenVector<T>::From(*input2);
|
|
auto x_grad = EigenVector<T>::Flatten(*output);
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
auto broadcast_dim =
|
|
Eigen::array<int, 1>({{static_cast<int>(input0->numel())}});
|
|
Functor functor;
|
|
functor(place, x, x_reduce, x_grad, x_reduce_grad, broadcast_dim,
|
|
broadcast_dim[0]);
|
|
} else {
|
|
int rank = context.Input<Tensor>("X")->dims().size();
|
|
switch (rank) {
|
|
case 1:
|
|
ReduceGradCompute<1>(context);
|
|
break;
|
|
case 2:
|
|
ReduceGradCompute<2>(context);
|
|
break;
|
|
case 3:
|
|
ReduceGradCompute<3>(context);
|
|
break;
|
|
case 4:
|
|
ReduceGradCompute<4>(context);
|
|
break;
|
|
case 5:
|
|
ReduceGradCompute<5>(context);
|
|
break;
|
|
case 6:
|
|
ReduceGradCompute<6>(context);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
template <size_t D>
|
|
void ReduceGradCompute(const framework::ExecutionContext& context) const {
|
|
auto* input0 = context.Input<Tensor>("X");
|
|
auto* input1 = context.Input<Tensor>("Out");
|
|
auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
|
|
auto* output = context.Output<Tensor>(framework::GradVarName("X"));
|
|
|
|
output->mutable_data<T>(context.GetPlace());
|
|
auto x = EigenTensor<T, D>::From(*input0);
|
|
auto x_grad = EigenTensor<T, D>::From(*output);
|
|
auto x_rank = static_cast<int>(x.dimensions().size());
|
|
int dim = static_cast<int>(context.Attr<int>("dim"));
|
|
if (dim < 0) dim = x_rank + dim;
|
|
DDim dims = input0->dims();
|
|
dims[dim] = 1;
|
|
auto x_reduce = EigenTensor<T, D>::From(*input1, dims);
|
|
auto x_reduce_grad = EigenTensor<T, D>::From(*input2, dims);
|
|
|
|
Eigen::array<int, D> broadcast_dim;
|
|
for (size_t i = 0; i < D; ++i) broadcast_dim[i] = 1;
|
|
broadcast_dim[dim] = input0->dims()[dim];
|
|
auto& place =
|
|
*context.template device_context<DeviceContext>().eigen_device();
|
|
Functor functor;
|
|
functor(place, x, x_reduce, x_grad, x_reduce_grad, broadcast_dim,
|
|
broadcast_dim[dim]);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|
|
|
|
#define FOR_EACH_KERNEL_FUNCTOR(__macro) \
|
|
__macro(reduce_sum, SumFunctor, SumGradFunctor); \
|
|
__macro(reduce_mean, MeanFunctor, MeanGradFunctor); \
|
|
__macro(reduce_max, MaxFunctor, MaxOrMinGradFunctor); \
|
|
__macro(reduce_min, MinFunctor, MaxOrMinGradFunctor);
|