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.
119 lines
4.3 KiB
119 lines
4.3 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/eigen.h"
|
|
#include "paddle/fluid/framework/op_registry.h"
|
|
#include "paddle/fluid/operators/math/math_function.h"
|
|
#include "paddle/fluid/platform/for_range.h"
|
|
|
|
namespace paddle {
|
|
namespace operators {
|
|
|
|
using Tensor = framework::Tensor;
|
|
/*Todo:
|
|
*Find a way to adapt TolerableValue, using blas or eigen.
|
|
*/
|
|
template <typename T>
|
|
struct TolerableValue {
|
|
HOSTDEVICE T operator()(const T& x) const {
|
|
PADDLE_ASSERT(std::is_floating_point<T>::value);
|
|
const T kApproInf = 1e20;
|
|
if (x == INFINITY) return kApproInf;
|
|
if (x == -INFINITY) return -kApproInf;
|
|
return x;
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class BprLossOpKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* x = ctx.Input<Tensor>("X");
|
|
auto* label = ctx.Input<Tensor>("Label");
|
|
auto* y = ctx.Output<Tensor>("Y");
|
|
y->mutable_data<T>(ctx.GetPlace());
|
|
int rank = x->dims().size();
|
|
|
|
Tensor x_2d = framework::ReshapeToMatrix(*x, rank - 1);
|
|
Tensor labels_2d = framework::ReshapeToMatrix(*label, rank - 1);
|
|
Tensor y_2d = framework::ReshapeToMatrix(*y, rank - 1);
|
|
|
|
const framework::Tensor* logits = &x_2d;
|
|
const framework::Tensor* labels = &labels_2d;
|
|
framework::Tensor* out = &y_2d;
|
|
|
|
const int step_size = logits->dims()[0];
|
|
const int class_num = logits->dims()[1];
|
|
const T* logits_data = logits->data<T>();
|
|
T* loss_data = out->data<T>();
|
|
|
|
const int64_t* label_data = labels->data<int64_t>();
|
|
for (int i = 0; i < step_size; ++i) {
|
|
int lbl_pos = label_data[i];
|
|
PADDLE_ENFORCE_GE(lbl_pos, 0);
|
|
PADDLE_ENFORCE_LT(lbl_pos, class_num);
|
|
int index_pos = i * class_num + lbl_pos;
|
|
T sum = static_cast<T>(0);
|
|
for (int j = 0; j < class_num; j++) {
|
|
if (j == lbl_pos) continue;
|
|
int index_neg = i * class_num + j;
|
|
sum += TolerableValue<T>()(-std::log(
|
|
1.0f + TolerableValue<T>()(std::exp(logits_data[index_neg] -
|
|
logits_data[index_pos]))));
|
|
}
|
|
loss_data[i] = -sum / (class_num - 1);
|
|
}
|
|
}
|
|
};
|
|
|
|
template <typename DeviceContext, typename T>
|
|
class BprLossGradientOpKernel : public framework::OpKernel<T> {
|
|
public:
|
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
|
auto* x = ctx.Input<Tensor>("X");
|
|
auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
|
|
auto* label = ctx.Input<Tensor>("Label");
|
|
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
|
|
|
|
const size_t step_size = static_cast<size_t>(x->dims()[0]);
|
|
const size_t num_classes = static_cast<size_t>(x->dims()[1]);
|
|
T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
|
|
const T* dy_data = dy->data<T>();
|
|
const T* x_data = x->data<T>();
|
|
const int64_t* label_data = label->data<int64_t>();
|
|
|
|
for (size_t sample_id = 0; sample_id < step_size; sample_id++) {
|
|
for (size_t x_offset = sample_id * num_classes;
|
|
x_offset < (sample_id + 1) * num_classes; x_offset++) {
|
|
dx_data[x_offset] = static_cast<T>(0);
|
|
}
|
|
auto p_index = sample_id * num_classes + label_data[sample_id];
|
|
for (size_t ni = 0; ni < num_classes; ni++) {
|
|
if (label_data[sample_id] == ni) continue;
|
|
auto n_index = sample_id * num_classes + ni;
|
|
auto grad_ = -dy_data[sample_id] /
|
|
((num_classes - 1) *
|
|
(1.0f + TolerableValue<T>()(std::exp(x_data[p_index] -
|
|
x_data[n_index]))));
|
|
dx_data[p_index] += grad_;
|
|
dx_data[n_index] -= grad_;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
} // namespace operators
|
|
} // namespace paddle
|