106 lines
3.5 KiB
C++
106 lines
3.5 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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#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
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#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
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#include <vector>
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#include "tensorflow/compiler/xla/shape_util.h"
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namespace xla {
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namespace cpu {
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// ShapePartitionAssigner partitions the most-major dimensions of 'shape' such
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// that the total partition count <= 'target_partition_count'.
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//
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// Example 1:
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//
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// Let 'shape' = [8, 16, 32] and 'target_partition_count' = 6.
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//
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// Because the most-major dimension size is <= 'target_partition_count', we
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// can generate our target number of partitions by partition the most-major
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// dimensions.
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//
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// This will result in the following partitions of the most-major dimension:
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//
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// [0, 1), [1, 2), [2, 3), [3, 4), [4, 5) [5, 8)
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//
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// Note that the last partition has residual because the dimension size is
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// not a multiple of the partition count.
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//
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//
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// Example 2:
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//
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// Let 'shape' = [8, 16, 32] and 'target_partition_count' = 16.
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//
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// Because the most-major dimension only has size 8, we must also partition
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// the next most-major dimension to generate the target of 16 partitions.
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// We factor 'target_partition_count' by the number of most-major dimensions
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// we need to partition, to get a per-dimension target partition count:
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//
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// target_dimension_partition_count = 16 ^ (1 / 2) == 4
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//
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// This will result in the following partitions of the most-major dimension:
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//
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// [0, 2), [2, 4), [4, 6), [6, 8)
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//
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// This will result in the following partitions of the second most-major
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// dimension:
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//
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// [0, 4), [4, 8), [8, 12), [12, 16)
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//
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class ShapePartitionAssigner {
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public:
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ShapePartitionAssigner(const Shape& shape) : shape_(shape) {}
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// Returns dimension partition counts (starting at outer-most dimension).
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std::vector<int64> Run(int64 target_partition_count);
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// Returns the total partition count based on 'dimension_partition_counts'.
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static int64 GetTotalPartitionCount(
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const std::vector<int64>& dimension_partition_counts);
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private:
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const Shape& shape_;
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};
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// ShapePartitionIterator iterates through outer-dimension partitions of
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// 'shape' as specified by 'dimension_partition_counts'.
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class ShapePartitionIterator {
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public:
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ShapePartitionIterator(const Shape& shape,
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const std::vector<int64>& dimension_partition_counts);
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// Returns a partition [start, size] for each dimension.
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// Partitions are listed starting from outer-most dimension first.
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std::vector<std::pair<int64, int64>> GetPartition(int64 index) const;
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int64 GetTotalPartitionCount() const;
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private:
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const Shape& shape_;
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const std::vector<int64> dimension_partition_counts_;
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std::vector<int64> dimensions_;
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std::vector<int64> dimension_partition_sizes_;
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std::vector<int64> dimension_partition_strides_;
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};
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} // namespace cpu
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} // namespace xla
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#endif // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_SHAPE_PARTITION_H_
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