wehub-resource-sync
195 lines
6.7 KiB
Python
195 lines
6.7 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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import numpy as np
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from tensorflow.python.data.benchmarks import benchmark_base
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import map_op
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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 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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from tensorflow.python.ops import while_loop
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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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benchmark_id):
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dataset = dataset_ops.Dataset.range(10000)
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for _ in range(chain_length):
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dataset = map_op._MapDataset( # pylint: disable=protected-access
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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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extras={
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"model_name": "map.benchmark.%d" % benchmark_id,
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"parameters": "%d" % chain_length,
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},
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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(
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chain_length=chain_length,
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fn=lambda x: x + 1,
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use_inter_op_parallelism=True,
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label="",
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benchmark_id=1)
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benchmark_helper(
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chain_length=chain_length,
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fn=lambda x: x + 1,
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use_inter_op_parallelism=False,
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label="_single_threaded",
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benchmark_id=2)
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benchmark_helper(
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chain_length=chain_length,
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fn=lambda x: x,
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use_inter_op_parallelism=True,
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label="_short_circuit",
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benchmark_id=3)
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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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benchmark_id):
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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 = map_op._MapDataset( # pylint: disable=protected-access
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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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extras={
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"model_name": "map.benchmark.%d" % benchmark_id,
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"parameters": "%d" % fan_out,
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},
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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(
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fan_out=fan_out,
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fn=lambda *xs: [x + 1 for x in xs],
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use_inter_op_parallelism=True,
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label="",
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benchmark_id=4)
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benchmark_helper(
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fan_out=fan_out,
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fn=lambda *xs: [x + 1 for x in xs],
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use_inter_op_parallelism=False,
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label="_single_threaded",
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benchmark_id=5)
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benchmark_helper(
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fan_out=fan_out,
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fn=lambda *xs: xs,
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use_inter_op_parallelism=True,
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label="_short_circuit",
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benchmark_id=6)
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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 while_loop.while_loop(math_ops.less, body, [i, x])
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num_elements = 1
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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=num_elements,
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extras={
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"model_name": "map.benchmark.8",
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"parameters": "%d" % num_elements,
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},
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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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num_elements = 1
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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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extras={
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"model_name": "map.benchmark.9",
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"parameters": "%d" % num_elements,
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},
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name="parallel_control_flow",
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apply_default_optimizations=True)
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def _benchmark_nested_parallel_map(self, cycle_length, num_parallel_calls):
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k = 1024 * 1024
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num_map_elements = 10
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num_range_elements = 2000
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def g(_):
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return np.random.rand(50 * k).sum()
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def f(_):
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return dataset_ops.Dataset.range(num_map_elements).map(
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g, num_parallel_calls=num_parallel_calls)
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dataset = dataset_ops.Dataset.range(num_range_elements)
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dataset = dataset.interleave(
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f, cycle_length=cycle_length, num_parallel_calls=dataset_ops.AUTOTUNE)
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cycle_length_str = ("default"
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if cycle_length is None else str(cycle_length))
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num_parallel_calls_str = ("autotune"
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if num_parallel_calls == dataset_ops.AUTOTUNE else
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str(num_parallel_calls))
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map_dataset_str = ("map" if num_parallel_calls is None else
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"parallel_map_num_parallel_calls_%s" %
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num_parallel_calls_str)
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self.run_and_report_benchmark(
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dataset,
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num_elements=num_map_elements * num_range_elements,
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extras={
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"model_name": "map.benchmark.10",
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"parameters": "%s_%s" % (cycle_length_str, num_parallel_calls_str),
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},
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name=("%s_cycle_length_%s" % (map_dataset_str, cycle_length_str)))
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def benchmark_nested_parallel_map(self):
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cycle_lengths = [None, 100]
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nums_parallel_calls = [None, 1, 10, 100, dataset_ops.AUTOTUNE]
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for cycle_length in cycle_lengths:
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for num_parallel_calls in nums_parallel_calls:
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self._benchmark_nested_parallel_map(cycle_length, num_parallel_calls)
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if __name__ == "__main__":
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benchmark_base.test.main()
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