mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 16:48:03 +08:00
124 lines
4.7 KiB
C++
124 lines
4.7 KiB
C++
// Copyright (c) 2022 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.
|
|
|
|
#include "fastdeploy/function/softmax.h"
|
|
|
|
#include <cstdlib>
|
|
|
|
#include "fastdeploy/function/eigen.h"
|
|
#include "fastdeploy/utils/axis_utils.h"
|
|
#include "fastdeploy/utils/utils.h"
|
|
|
|
namespace fastdeploy {
|
|
template <typename T>
|
|
struct ValueClip {
|
|
T operator()(const T& x) const {
|
|
const T kThreshold = static_cast<T>(-64.);
|
|
return x < kThreshold ? kThreshold : x;
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
struct SoftmaxEigen {
|
|
void operator()(const FDTensor& x, FDTensor* out, int axis_dim) {
|
|
constexpr int kBatchDim = 0;
|
|
constexpr int kClassDim = 1;
|
|
constexpr int kAxisDim = 1;
|
|
|
|
auto logits = EigenMatrix<T>::From(x);
|
|
auto softmax = EigenMatrix<T>::From(*out);
|
|
|
|
const int batch_size = logits.dimension(kBatchDim);
|
|
const int num_classes = logits.dimension(kClassDim);
|
|
const int num_remain = num_classes / axis_dim;
|
|
Eigen::DSizes<int, 1> along_axis(kAxisDim);
|
|
Eigen::DSizes<int, 2> batch_classes(batch_size, num_classes);
|
|
Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
|
|
Eigen::DSizes<int, 2> one_by_class(1, num_classes);
|
|
Eigen::DSizes<int, 3> batch_one_remain(batch_size, 1, num_remain);
|
|
Eigen::DSizes<int, 3> one_axis_one(1, axis_dim, 1);
|
|
Eigen::DSizes<int, 2> one_axis(1, axis_dim);
|
|
Eigen::DSizes<int, 3> batch_axis_remain(batch_size, axis_dim, num_remain);
|
|
|
|
const auto& dev = *EigenDeviceWrapper::GetInstance()->GetDevice();
|
|
// For numerical stability, logits should be shifted by maximum number along
|
|
// axis, calculate shifted_logits into softmax tensor for memory reuse.
|
|
if (num_remain == 1) {
|
|
// axis == -1, axis and class in same dimension, calculate along
|
|
// class dimension directly for higher performance
|
|
softmax.device(dev) = (logits -
|
|
logits.maximum(along_axis)
|
|
.eval()
|
|
.reshape(batch_by_one)
|
|
.broadcast(one_by_class))
|
|
.unaryExpr(ValueClip<T>());
|
|
} else {
|
|
// axis != -1, class dimension split into (axis, remain), max and sum
|
|
// should be calculated along axis dimension
|
|
softmax.device(dev) = (logits.reshape(batch_axis_remain) -
|
|
logits.reshape(batch_axis_remain)
|
|
.maximum(along_axis)
|
|
.eval()
|
|
.reshape(batch_one_remain)
|
|
.broadcast(one_axis_one)
|
|
.reshape(batch_axis_remain))
|
|
.reshape(batch_classes)
|
|
.unaryExpr(ValueClip<T>());
|
|
}
|
|
softmax.device(dev) = softmax.exp();
|
|
softmax.device(dev) = (softmax *
|
|
softmax.reshape(batch_axis_remain)
|
|
.sum(along_axis)
|
|
.inverse()
|
|
.eval()
|
|
.broadcast(one_axis));
|
|
}
|
|
};
|
|
|
|
template <typename T>
|
|
void SoftmaxFunctor(const FDTensor& x, FDTensor* out, int axis) {
|
|
SoftmaxEigen<T>()(x, out, axis);
|
|
}
|
|
|
|
template <typename T>
|
|
void SoftmaxKernel(const FDTensor& x, FDTensor* out, int axis) {
|
|
const int rank = x.shape.size();
|
|
const int calc_axis = CanonicalAxis(axis, rank);
|
|
int axis_dim = x.shape[calc_axis];
|
|
out->Allocate(x.shape, x.dtype);
|
|
if (out->Numel() == 0) {
|
|
return;
|
|
}
|
|
const int n = SizeToAxis(calc_axis, x.shape);
|
|
const int d = SizeFromAxis(calc_axis, x.shape);
|
|
// Reshape to 2d tensor
|
|
|
|
FDTensor x_2d, out_2d;
|
|
x_2d.SetExternalData({n, d}, x.dtype, const_cast<void*>(x.Data()));
|
|
out_2d.SetExternalData({n, d}, out->dtype, out->Data());
|
|
|
|
SoftmaxFunctor<T>(x_2d, &out_2d, axis_dim);
|
|
}
|
|
|
|
void Softmax(const FDTensor& x, FDTensor* out, int axis) {
|
|
FDASSERT(
|
|
std::abs(axis) < x.shape.size(),
|
|
"The absolute given axis should be smaller than the input's "
|
|
"dimension. Expected absolute axis is smaller than %lu, but receive %d.",
|
|
x.shape.size(), std::abs(axis));
|
|
FD_VISIT_FLOAT_TYPES(x.dtype, "SoftmaxKernel",
|
|
([&] { SoftmaxKernel<data_t>(x, out, axis); }));
|
|
}
|
|
} // namespace fastdeploy
|