1/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#ifndef TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_
17#define TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_
18// Functor definition for SoftplusOp and SoftplusGradOp, must be compilable by
19// nvcc.
20
21// clang-format off
22#include "tensorflow/core/platform/bfloat16.h"
23#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
24// clang-format on
25#include "tensorflow/core/framework/tensor_types.h"
26
27namespace tensorflow {
28namespace functor {
29
30// Functor used by SoftplusOp to do the computations.
31template <typename Device, typename T>
32struct Softplus {
33 // Computes Softplus activation.
34 //
35 // features: any shape.
36 // activations: same shape as "features".
37 void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
38 typename TTypes<T>::Tensor activations) {
39 // Choose a threshold on x below which exp(x) may underflow
40 // when added to 1, but for which exp(x) is always within epsilon of the
41 // true softplus(x). Offset of 2 from machine epsilon checked
42 // experimentally for float16, float32, float64. Checked against
43 // softplus implemented with numpy's log1p and numpy's logaddexp.
44 static const T threshold =
45 Eigen::numext::log(Eigen::NumTraits<T>::epsilon()) + T(2);
46 // Value above which exp(x) may overflow, but softplus(x) == x
47 // is within machine epsilon.
48 auto too_large = features > features.constant(-threshold);
49 // Value below which exp(x) may underflow, but softplus(x) == exp(x)
50 // is within machine epsilon.
51 auto too_small = features < features.constant(threshold);
52 auto features_exp = features.exp();
53 activations.device(d) = too_large.select(
54 features, // softplus(x) ~= x for x large
55 too_small.select(features_exp, // softplus(x) ~= exp(x) for x small
56 features_exp.log1p()));
57 }
58};
59
60// Functor used by SoftplusGradOp to do the computations.
61template <typename Device, typename T>
62struct SoftplusGrad {
63 // Computes SoftplusGrad backprops.
64 //
65 // gradients: gradients backpropagated to the Softplus op.
66 // features: inputs that where passed to the Softplus op.
67 // backprops: gradients to backpropagate to the Softplus inputs.
68 void operator()(const Device& d, typename TTypes<T>::ConstTensor gradients,
69 typename TTypes<T>::ConstTensor features,
70 typename TTypes<T>::Tensor backprops) {
71 backprops.device(d) =
72 gradients / ((-features).exp() + features.constant(T(1)));
73 }
74};
75
76} // namespace functor
77} // namespace tensorflow
78
79#endif // TENSORFLOW_CORE_KERNELS_SOFTPLUS_OP_H_
80