1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_KERNELS_OPS_UTIL_H_ 17 #define TENSORFLOW_KERNELS_OPS_UTIL_H_ 18 19 // This file contains utilities for various operations. 20 21 #include <array> 22 23 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" 24 #include "tensorflow/core/framework/common_shape_fns.h" 25 #include "tensorflow/core/framework/tensor_shape.h" 26 #include "tensorflow/core/framework/tensor_types.h" 27 #include "tensorflow/core/lib/core/status.h" 28 #include "tensorflow/core/util/padding.h" 29 30 namespace tensorflow { 31 32 // Calculates broadcast starting index and size. For SAME padding, addition 33 // padding could be applied to right, left, top and bottom. Depending on the 34 // current index, input size, kernel size, stride, padding size, the starting 35 // index and size for broadcast for that dimension are different from the 36 // current index and kernel size. 37 // This is mainly used by gradient algorithms for pooling operations. 38 Status GetBroadcastSize(const int index, const int in_size, const int ksize, 39 const int stride, const int pad_size, int* bindex, 40 int* bsize); 41 42 // Converts Brain's Padding to Eigen's PaddingType. 43 Eigen::PaddingType BrainPadding2EigenPadding(Padding padding); 44 45 // Given a shape 's' of a tensor of type T. Returns true iff the 46 // number of bytes occupied by each dim 0 (i.e., &tensor(i + 1, ...) - 47 // &tensor(i, ...)) is multiple of EIGEN_MAX_ALIGN_BYTES. 48 template <typename T> 49 bool IsInnerDimsSizeAligned(const TensorShape& s) { 50 if (s.dims() == 0) return false; 51 const int64 dim0_size = s.dim_size(0); 52 if (dim0_size == 0) return false; 53 #if EIGEN_MAX_ALIGN_BYTES == 0 54 return true; 55 #else 56 const int64 bytes_per_dim0 = (s.num_elements() / dim0_size) * sizeof(T); 57 return bytes_per_dim0 % EIGEN_MAX_ALIGN_BYTES == 0; 58 #endif 59 } 60 61 // Given a shape 's' of a tensor of type T and the `start` and `end` index of a 62 // dim 0 slice, returns true iff slice is aligned with respect to original 63 // tensor. Here aligned implies the address is a multiple of 64 // EIGEN_MAX_ALIGN_BYTES. 65 template <typename T> 66 bool IsDim0SliceAligned(const TensorShape& s, int64 start, int64 end_or_size) { 67 if (s.dims() == 1) { 68 #if EIGEN_MAX_ALIGN_BYTES == 0 69 return true; 70 #else 71 bool start_aligned = (start * sizeof(T)) % EIGEN_MAX_ALIGN_BYTES == 0; 72 // End is aligned if either the explicit end index is passed and is a 73 // a multiple of EIGEN_MAX_ALIGN_BYTES, or the start index is aligned and 74 // the size is aligned. So for convenience we can either pass start and 75 // index, or start and size. 76 bool end_aligned = (end_or_size * sizeof(T)) % EIGEN_MAX_ALIGN_BYTES == 0; 77 return start_aligned && end_aligned; 78 #endif 79 } else { 80 return IsInnerDimsSizeAligned<T>(s); 81 } 82 } 83 84 // Returns <suffix> sanitized to have only [a-zA-Z0-9-_]. 85 string SanitizeThreadSuffix(string suffix); 86 87 // Helper to compute 'strides' given a tensor 'shape'. I.e., 88 // strides[i] = prod(shape.dim_size[(i+1):]) 89 template <typename T> 90 gtl::InlinedVector<T, 8> ComputeStride(const TensorShape& shape) { 91 const int ndims = shape.dims(); 92 gtl::InlinedVector<T, 8> strides(ndims); 93 T stride = 1; 94 for (int i = ndims - 1; i >= 0; --i) { 95 strides[i] = stride; 96 stride *= static_cast<T>(shape.dim_size(i)); 97 } 98 return strides; 99 } 100 101 // Helper to compute 'strides' given an Eigen TensorDimensions 102 template <typename T, typename EigenDimensions> 103 gtl::InlinedVector<T, 8> ComputeEigenStrides(const EigenDimensions& shape) { 104 const int ndims = shape.rank(); 105 gtl::InlinedVector<T, 8> strides(ndims); 106 T stride = 1; 107 for (int i = ndims - 1; i >= 0; --i) { 108 strides[i] = stride; 109 stride *= static_cast<T>(shape[i]); 110 } 111 return strides; 112 } 113 114 } // namespace tensorflow 115 116 #endif // TENSORFLOW_KERNELS_OPS_UTIL_H_ 117
