121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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"""Benchmarks for `tf.data.Dataset.map()`."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.data.benchmarks import benchmark_base
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from tensorflow.python.data.experimental.ops import stats_aggregator
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.framework import constant_op
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import map_fn as map_fn
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import random_ops
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# TODO(b/119837791): Add eager benchmarks.
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class MapBenchmark(benchmark_base.DatasetBenchmarkBase):
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"""Benchmarks for `tf.data.Dataset.map()`."""
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def benchmark_chain_of_maps(self):
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def benchmark_helper(chain_length, fn, use_inter_op_parallelism, label):
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dataset = dataset_ops.Dataset.range(10000)
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for _ in range(chain_length):
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dataset = dataset_ops.MapDataset(
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dataset, fn, use_inter_op_parallelism=use_inter_op_parallelism)
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self.run_and_report_benchmark(
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dataset,
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num_elements=10000,
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name="chain_length_%d%s" % (chain_length, label))
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chain_lengths = [0, 1, 2, 5, 10, 20, 50]
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for chain_length in chain_lengths:
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benchmark_helper(chain_length, lambda x: x + 1, True, "")
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benchmark_helper(chain_length, lambda x: x + 1, False, "_single_threaded")
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benchmark_helper(chain_length, lambda x: x, True, "_short_circuit")
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def benchmark_map_fan_out(self):
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fan_outs = [1, 2, 5, 10, 20, 50, 100]
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def benchmark_helper(fan_out, fn, use_inter_op_parallelism, label):
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dataset = dataset_ops.Dataset.from_tensors(
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tuple(0 for _ in range(fan_out))).repeat(None)
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dataset = dataset_ops.MapDataset(
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dataset, fn, use_inter_op_parallelism=use_inter_op_parallelism)
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self.run_and_report_benchmark(
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dataset,
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num_elements=10000,
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name="fan_out_%d%s" % (fan_out, label))
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for fan_out in fan_outs:
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benchmark_helper(fan_out, lambda *xs: [x + 1 for x in xs], True, "")
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benchmark_helper(fan_out, lambda *xs: [x + 1 for x in xs], False,
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"_single_threaded")
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benchmark_helper(fan_out, lambda *xs: xs, True, "_short_circuit")
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def benchmark_stats(self):
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for stats in [True, False]:
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dataset = dataset_ops.Dataset.range(1000).repeat()
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dataset = dataset.map(lambda x: x + 1, num_parallel_calls=32)
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options = dataset_ops.Options()
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options.experimental_deterministic = False
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if stats:
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aggregator = stats_aggregator.StatsAggregator()
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options.experimental_stats.aggregator = aggregator
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dataset = dataset.with_options(options)
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self.run_and_report_benchmark(
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dataset, num_elements=10000, name="stats_%s" % stats)
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def benchmark_sequential_control_flow(self):
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dataset = dataset_ops.Dataset.from_tensors(100000)
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def fn(x):
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i = constant_op.constant(0)
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def body(i, x):
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return math_ops.add(i, 1), x
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return control_flow_ops.while_loop(math_ops.less, body, [i, x])
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dataset = dataset.map(fn)
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self.run_and_report_benchmark(
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dataset,
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num_elements=1,
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name="sequential_control_flow",
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apply_default_optimizations=True)
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def benchmark_parallel_control_flow(self):
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dataset = dataset_ops.Dataset.from_tensors(
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random_ops.random_uniform([100, 10000000]))
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def fn(x):
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return map_fn.map_fn(
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lambda y: y * array_ops.transpose(y), x, parallel_iterations=10)
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dataset = dataset.map(fn)
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self.run_and_report_benchmark(
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dataset,
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num_elements=1,
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name="parallel_control_flow",
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apply_default_optimizations=True)
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if __name__ == "__main__":
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benchmark_base.test.main()
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