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.
108 lines
3.3 KiB
108 lines
3.3 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/framework/op_registry.h"
|
|
#include "paddle/fluid/operators/math/softmax.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
using DDim = framework::DDim;
|
|
|
|
static inline int CanonicalAxis(const int axis, const int rank) {
|
|
if (axis < 0) {
|
|
return axis + rank;
|
|
}
|
|
return axis;
|
|
}
|
|
|
|
static inline int SizeToAxis(const int axis, DDim dims) {
|
|
int size = 1;
|
|
for (int i = 0; i < axis; i++) {
|
|
size *= dims[i];
|
|
}
|
|
return size;
|
|
}
|
|
|
|
static inline int SizeFromAxis(const int axis, DDim dims) {
|
|
int size = 1;
|
|
for (int i = axis; i < dims.size(); i++) {
|
|
size *= dims[i];
|
|
}
|
|
return size;
|
|
}
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class SoftmaxKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* X = context.Input<Tensor>("X");
|
|
auto* Out = context.Output<Tensor>("Out");
|
|
const int rank = X->dims().size();
|
|
const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
|
|
int axis_dim = X->dims()[axis];
|
|
|
|
// allocate memory on device.
|
|
Out->mutable_data<T>(context.GetPlace());
|
|
|
|
const int n = SizeToAxis(axis, X->dims());
|
|
const int d = SizeFromAxis(axis, X->dims());
|
|
Tensor X_2d, Out_2d;
|
|
X_2d.ShareDataWith(*X).Resize({n, d});
|
|
Out_2d.ShareDataWith(*Out).Resize({n, d});
|
|
|
|
#ifdef PADDLE_ON_INFERENCE
|
|
math::SoftmaxFunctor<DeviceContext, T, true>()(
|
|
context.template device_context<DeviceContext>(), axis_dim, &X_2d,
|
|
&Out_2d);
|
|
#else
|
|
math::SoftmaxFunctor<DeviceContext, T, false>()(
|
|
context.template device_context<DeviceContext>(), axis_dim, &X_2d,
|
|
&Out_2d);
|
|
#endif
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class SoftmaxGradKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& context) const override {
|
|
auto* Out = context.Input<Tensor>("Out");
|
|
auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
|
|
auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
|
|
const int rank = dX->dims().size();
|
|
const int axis = CanonicalAxis(context.Attr<int>("axis"), rank);
|
|
int axis_dim = dX->dims()[axis];
|
|
|
|
// allocate memory on device.
|
|
dX->mutable_data<T>(context.GetPlace());
|
|
|
|
const int n = SizeToAxis(axis, dX->dims());
|
|
const int d = SizeFromAxis(axis, dX->dims());
|
|
Tensor dX_2d, Out_2d, dOut_2d;
|
|
dX_2d.ShareDataWith(*dX).Resize({n, d});
|
|
Out_2d.ShareDataWith(*Out).Resize({n, d});
|
|
dOut_2d.ShareDataWith(*dOut).Resize({n, d});
|
|
|
|
math::SoftmaxGradFunctor<DeviceContext, T>()(
|
|
context.template device_context<DeviceContext>(), axis_dim, &Out_2d,
|
|
&dOut_2d, &dX_2d);
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|