| # Copyright 2017 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Benchmarks for `tf.data.Dataset.map()`.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| from tensorflow.python.data.benchmarks import benchmark_base |
| from tensorflow.python.data.experimental.ops import stats_aggregator |
| from tensorflow.python.data.ops import dataset_ops |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import control_flow_ops |
| from tensorflow.python.ops import map_fn as map_fn |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import random_ops |
| |
| |
| # TODO(b/119837791): Add eager benchmarks. |
| class MapBenchmark(benchmark_base.DatasetBenchmarkBase): |
| """Benchmarks for `tf.data.Dataset.map()`.""" |
| |
| def benchmark_chain_of_maps(self): |
| |
| def benchmark_helper(chain_length, fn, use_inter_op_parallelism, label): |
| dataset = dataset_ops.Dataset.range(10000) |
| for _ in range(chain_length): |
| dataset = dataset_ops.MapDataset( |
| dataset, fn, use_inter_op_parallelism=use_inter_op_parallelism) |
| self.run_and_report_benchmark( |
| dataset, |
| num_elements=10000, |
| name="chain_length_%d%s" % (chain_length, label)) |
| |
| chain_lengths = [0, 1, 2, 5, 10, 20, 50] |
| for chain_length in chain_lengths: |
| benchmark_helper(chain_length, lambda x: x + 1, True, "") |
| benchmark_helper(chain_length, lambda x: x + 1, False, "_single_threaded") |
| benchmark_helper(chain_length, lambda x: x, True, "_short_circuit") |
| |
| def benchmark_map_fan_out(self): |
| fan_outs = [1, 2, 5, 10, 20, 50, 100] |
| |
| def benchmark_helper(fan_out, fn, use_inter_op_parallelism, label): |
| dataset = dataset_ops.Dataset.from_tensors( |
| tuple(0 for _ in range(fan_out))).repeat(None) |
| dataset = dataset_ops.MapDataset( |
| dataset, fn, use_inter_op_parallelism=use_inter_op_parallelism) |
| self.run_and_report_benchmark( |
| dataset, |
| num_elements=10000, |
| name="fan_out_%d%s" % (fan_out, label)) |
| |
| for fan_out in fan_outs: |
| benchmark_helper(fan_out, lambda *xs: [x + 1 for x in xs], True, "") |
| benchmark_helper(fan_out, lambda *xs: [x + 1 for x in xs], False, |
| "_single_threaded") |
| benchmark_helper(fan_out, lambda *xs: xs, True, "_short_circuit") |
| |
| def benchmark_stats(self): |
| for stats in [True, False]: |
| dataset = dataset_ops.Dataset.range(1000).repeat() |
| dataset = dataset.map(lambda x: x + 1, num_parallel_calls=32) |
| options = dataset_ops.Options() |
| options.experimental_deterministic = False |
| if stats: |
| aggregator = stats_aggregator.StatsAggregator() |
| options.experimental_stats.aggregator = aggregator |
| dataset = dataset.with_options(options) |
| self.run_and_report_benchmark( |
| dataset, num_elements=10000, name="stats_%s" % stats) |
| |
| def benchmark_sequential_control_flow(self): |
| dataset = dataset_ops.Dataset.from_tensors(100000) |
| |
| def fn(x): |
| i = constant_op.constant(0) |
| |
| def body(i, x): |
| return math_ops.add(i, 1), x |
| |
| return control_flow_ops.while_loop(math_ops.less, body, [i, x]) |
| |
| dataset = dataset.map(fn) |
| self.run_and_report_benchmark( |
| dataset, |
| num_elements=1, |
| name="sequential_control_flow", |
| apply_default_optimizations=True) |
| |
| def benchmark_parallel_control_flow(self): |
| dataset = dataset_ops.Dataset.from_tensors( |
| random_ops.random_uniform([100, 10000000])) |
| |
| def fn(x): |
| return map_fn.map_fn( |
| lambda y: y * array_ops.transpose(y), x, parallel_iterations=10) |
| |
| dataset = dataset.map(fn) |
| self.run_and_report_benchmark( |
| dataset, |
| num_elements=1, |
| name="parallel_control_flow", |
| apply_default_optimizations=True) |
| |
| |
| if __name__ == "__main__": |
| benchmark_base.test.main() |