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_DATA_FORMAT_OPS_H_
17#define TENSORFLOW_CORE_KERNELS_DATA_FORMAT_OPS_H_
18// Functor definition for data format dim mapping ops, must be compilable
19// by nvcc.
20#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
21#include "tensorflow/core/framework/tensor_types.h"
22
23namespace tensorflow {
24namespace functor {
25
26// Functor used by DataFormatDimMapOP to do the computations.
27template <typename Device, typename T>
28struct DataFormatDimMap {
29 void operator()(const Device& d, typename TTypes<T>::ConstFlat x,
30 typename TTypes<T>::Flat y, const TTypes<int>::Vec dst) {
31 if (dst.size() == 4) {
32 auto zero = x.constant(0);
33 auto one = x.constant(1);
34 auto two = x.constant(2);
35
36 auto f_zero = x.constant(dst(0));
37 auto f_one = x.constant(dst(1));
38 auto f_two = x.constant(dst(2));
39 auto f_three = x.constant(dst(3));
40
41 auto four = x.constant(4);
42 auto x_mod = (x + four) % 4;
43
44 auto is_zero = (x_mod == zero);
45 auto is_one = (x_mod == one);
46 auto is_two = (x_mod == two);
47
48 y.device(d) = is_zero.select(
49 f_zero, is_one.select(f_one, is_two.select(f_two, f_three)));
50 } else {
51 auto zero = x.constant(0);
52 auto one = x.constant(1);
53 auto two = x.constant(2);
54 auto three = x.constant(3);
55
56 auto f_zero = x.constant(dst(0));
57 auto f_one = x.constant(dst(1));
58 auto f_two = x.constant(dst(2));
59 auto f_three = x.constant(dst(3));
60 auto f_four = x.constant(dst(4));
61
62 auto five = x.constant(5);
63 auto x_mod = (x + five) % 5;
64
65 auto is_zero = (x_mod == zero);
66 auto is_one = (x_mod == one);
67 auto is_two = (x_mod == two);
68 auto is_three = (x_mod == three);
69
70 y.device(d) = is_zero.select(
71 f_zero,
72 is_one.select(
73 f_one, is_two.select(f_two, is_three.select(f_three, f_four))));
74 }
75 }
76};
77
78template <typename T>
79struct VecPermute {
80 explicit VecPermute(const Eigen::DSizes<Eigen::DenseIndex, 10>& dst)
81 : dst(dst) {}
82 Eigen::DSizes<Eigen::DenseIndex, 1> dimensions(
83 typename TTypes<T>::ConstFlat input) const {
84 Eigen::DSizes<Eigen::DenseIndex, 1> result;
85 result[0] = input.dimension(0);
86 return result;
87 }
88 template <typename Output, typename Device>
89 void eval(typename TTypes<T>::ConstFlat input, Output& output,
90 const Device& d) const {
91 for (int i = 0; i < input.size(); ++i) {
92 output.template chip<0>(dst[i]).device(d) = input.template chip<0>(i);
93 }
94 }
95
96 private:
97 Eigen::DSizes<Eigen::DenseIndex, 10> dst;
98};
99
100// Functor used by DataFormatVecPermuteOp to do the computations.
101template <typename Device, typename T>
102struct DataFormatVecPermute {
103 void operator()(const Device& d, typename TTypes<T>::ConstFlat x,
104 typename TTypes<T>::Flat y,
105 const Eigen::DSizes<Eigen::DenseIndex, 10>& dst) {
106 y.device(d) = x.customOp(VecPermute<T>(dst));
107 }
108};
109
110} // namespace functor
111} // namespace tensorflow
112
113#endif // TENSORFLOW_CORE_KERNELS_DATA_FORMAT_OPS_H_
114