/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_CORE_FRAMEWORK_OPS_UTIL_H_
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#define TENSORFLOW_CORE_FRAMEWORK_OPS_UTIL_H_
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// This file contains utilities for various operations.
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#include <array>
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#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
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#include "tensorflow/core/framework/common_shape_fns.h"
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#include "tensorflow/core/framework/tensor_shape.h"
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#include "tensorflow/core/framework/tensor_types.h"
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#include "tensorflow/core/lib/core/status.h"
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#include "tensorflow/core/util/padding.h"
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namespace tensorflow {
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// Calculates broadcast starting index and size. For SAME padding, addition
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// padding could be applied to right, left, top and bottom. Depending on the
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// current index, input size, kernel size, stride, padding size, the starting
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// index and size for broadcast for that dimension are different from the
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// current index and kernel size.
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// This is mainly used by gradient algorithms for pooling operations.
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Status GetBroadcastSize(const int index, const int in_size, const int ksize,
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const int stride, const int pad_size, int* bindex,
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int* bsize);
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// Converts Brain's Padding to Eigen's PaddingType.
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Eigen::PaddingType BrainPadding2EigenPadding(Padding padding);
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// Given a shape 's' of a tensor of type T. Returns true iff the
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// number of bytes occupied by each dim 0 (i.e., &tensor(i + 1, ...) -
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// &tensor(i, ...)) is multiple of EIGEN_MAX_ALIGN_BYTES.
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template <typename T>
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bool IsInnerDimsSizeAligned(const TensorShape& s) {
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if (s.dims() == 0) return false;
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const int64 dim0_size = s.dim_size(0);
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if (dim0_size == 0) return false;
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#if EIGEN_MAX_ALIGN_BYTES == 0
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return true;
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#else
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const int64 bytes_per_dim0 = (s.num_elements() / dim0_size) * sizeof(T);
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return bytes_per_dim0 % EIGEN_MAX_ALIGN_BYTES == 0;
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#endif
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}
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// Given a shape 's' of a tensor of type T and the `start` and `end` index of a
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// dim 0 slice, returns true iff slice is aligned with respect to original
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// tensor. Here aligned implies the address is a multiple of
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// EIGEN_MAX_ALIGN_BYTES.
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template <typename T>
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bool IsDim0SliceAligned(const TensorShape& s, int64 start, int64 end_or_size) {
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if (s.dims() == 1) {
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#if EIGEN_MAX_ALIGN_BYTES == 0
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return true;
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#else
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bool start_aligned = (start * sizeof(T)) % EIGEN_MAX_ALIGN_BYTES == 0;
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// End is aligned if either the explicit end index is passed and is a
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// a multiple of EIGEN_MAX_ALIGN_BYTES, or the start index is aligned and
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// the size is aligned. So for convenience we can either pass start and
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// index, or start and size.
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bool end_aligned = (end_or_size * sizeof(T)) % EIGEN_MAX_ALIGN_BYTES == 0;
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return start_aligned && end_aligned;
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#endif
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} else {
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return IsInnerDimsSizeAligned<T>(s);
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}
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}
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// Returns <suffix> sanitized to have only [a-zA-Z0-9-_].
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string SanitizeThreadSuffix(string suffix);
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// Helper to compute 'strides' given a tensor 'shape'. I.e.,
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// strides[i] = prod(shape.dim_size[(i+1):])
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template <typename T>
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gtl::InlinedVector<T, 8> ComputeStride(const TensorShape& shape) {
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const int ndims = shape.dims();
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gtl::InlinedVector<T, 8> strides(ndims);
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T stride = 1;
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for (int i = ndims - 1; i >= 0; --i) {
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strides[i] = stride;
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stride *= static_cast<T>(shape.dim_size(i));
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}
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return strides;
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}
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// Helper to compute 'strides' given an Eigen TensorDimensions
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template <typename T, typename EigenDimensions>
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gtl::InlinedVector<T, 8> ComputeEigenStrides(const EigenDimensions& shape) {
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const int ndims = shape.rank();
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gtl::InlinedVector<T, 8> strides(ndims);
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T stride = 1;
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for (int i = ndims - 1; i >= 0; --i) {
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strides[i] = stride;
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stride *= static_cast<T>(shape[i]);
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}
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return strides;
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}
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} // namespace tensorflow
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#endif // TENSORFLOW_CORE_FRAMEWORK_OPS_UTIL_H_
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