6d24ecfdcc
Also swap its arguments; to my eyes the call is more natural with the permutation second. It turns out that a significant number of users of xla::Permute() want the inverse behavior. The new semantics of xla::Permute() also match the permutation semantics of operations like xla::Transpose(); it seems more consistent to use a matching convention. PiperOrigin-RevId: 356796646 Change-Id: Ifd6c0d7d6eea69c50a342601a3f1cba725880696
86 lines
3.0 KiB
C++
86 lines
3.0 KiB
C++
/* Copyright 2017 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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// Utilities for working with permutations.
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#ifndef TENSORFLOW_COMPILER_XLA_PERMUTATION_UTIL_H_
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#define TENSORFLOW_COMPILER_XLA_PERMUTATION_UTIL_H_
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#include <vector>
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#include "absl/types/span.h"
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#include "tensorflow/core/platform/logging.h"
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#include "tensorflow/compiler/xla/types.h"
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namespace xla {
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// Returns true if permutation is a permutation of the integers
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// [0, permutation.size()).
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bool IsPermutation(absl::Span<const int64> permutation);
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// Applies `permutation` on `input` and returns the permuted array.
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// For each i, output[i] = input[permutation[i]].
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//
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// Precondition:
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// 1. `permutation` is a permutation of 0..permutation.size()-1.
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// 2. permutation.size() == input.size().
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template <typename Container>
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std::vector<typename Container::value_type> Permute(
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const Container& input, absl::Span<const int64> permutation) {
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using T = typename Container::value_type;
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absl::Span<const T> data(input);
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CHECK_EQ(permutation.size(), data.size());
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CHECK(IsPermutation(permutation));
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std::vector<T> output(data.size());
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for (size_t i = 0; i < permutation.size(); ++i) {
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output[i] = data[permutation[i]];
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}
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return output;
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}
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// Applies the inverse of `permutation` on `input` and returns the permuted
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// array. For each i, output[permutation[i]] = input[i].
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//
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// Precondition:
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// 1. `permutation` is a permutation of 0..permutation.size()-1.
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// 2. permutation.size() == input.size().
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template <typename Container>
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std::vector<typename Container::value_type> PermuteInverse(
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const Container& input, absl::Span<const int64> permutation) {
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using T = typename Container::value_type;
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absl::Span<const T> data(input);
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CHECK_EQ(permutation.size(), data.size());
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CHECK(IsPermutation(permutation));
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std::vector<T> output(data.size());
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for (size_t i = 0; i < permutation.size(); ++i) {
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output[permutation[i]] = data[i];
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}
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return output;
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}
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// Inverts a permutation, i.e., output_permutation[input_permutation[i]] = i.
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std::vector<int64> InversePermutation(
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absl::Span<const int64> input_permutation);
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// Composes two permutations: output[i] = p1[p2[i]].
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std::vector<int64> ComposePermutations(absl::Span<const int64> p1,
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absl::Span<const int64> p2);
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// Returns true iff permutation == {0, 1, 2, ...}.
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bool IsIdentityPermutation(absl::Span<const int64> permutation);
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_PERMUTATION_UTIL_H_
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