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1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15r"""Benchmark base to run and report benchmark results."""
16
17from __future__ import absolute_import as _absolute_import
18from __future__ import division as _division
19from __future__ import print_function as _print_function
20
21import os
22import uuid
23
24from tensorflow.python.eager import test
25from tensorflow.python.platform import flags
26from tensorflow.python.profiler import profiler_v2 as profiler
27
28flags.DEFINE_bool("xprof", False, "Run and report benchmarks with xprof on")
29flags.DEFINE_string("logdir", "/tmp/xprof/", "Directory to store xprof data")
30
31
32class MicroBenchmarksBase(test.Benchmark):
33  """Run and report benchmark results.
34
35  The first run is without any profilng.
36  Second run is with xprof and python trace. Third run is with xprof without
37  python trace. Note: xprof runs are with fewer iterations.
38  """
39
40  def run_with_xprof(self, enable_python_trace, run_benchmark, func,
41                     num_iters_xprof, execution_mode, suid):
42    if enable_python_trace:
43      options = profiler.ProfilerOptions(python_tracer_level=1)
44      logdir = os.path.join(flags.FLAGS.logdir, suid + "_with_python")
45    else:
46      options = profiler.ProfilerOptions(python_tracer_level=0)
47      logdir = os.path.join(flags.FLAGS.logdir, suid)
48    with profiler.Profile(logdir, options):
49      total_time = run_benchmark(func, num_iters_xprof, execution_mode)
50    us_per_example = float("{0:.3f}".format(total_time * 1e6 / num_iters_xprof))
51    return logdir, us_per_example
52
53  def run_report(self, run_benchmark, func, num_iters, execution_mode=None):
54    """Run and report benchmark results."""
55    total_time = run_benchmark(func, num_iters, execution_mode)
56    mean_us = total_time * 1e6 / num_iters
57    extras = {
58        "examples_per_sec": float("{0:.3f}".format(num_iters / total_time)),
59        "us_per_example": float("{0:.3f}".format(total_time * 1e6 / num_iters))
60    }
61
62    if flags.FLAGS.xprof:
63      suid = str(uuid.uuid4())
64      # Re-run with xprof and python trace.
65      num_iters_xprof = min(100, num_iters)
66      xprof_link, us_per_example = self.run_with_xprof(True, run_benchmark,
67                                                       func, num_iters_xprof,
68                                                       execution_mode, suid)
69      extras["xprof link with python trace"] = xprof_link
70      extras["us_per_example with xprof and python"] = us_per_example
71
72      # Re-run with xprof but no python trace.
73      xprof_link, us_per_example = self.run_with_xprof(False, run_benchmark,
74                                                       func, num_iters_xprof,
75                                                       execution_mode, suid)
76      extras["xprof link"] = xprof_link
77      extras["us_per_example with xprof"] = us_per_example
78
79    benchmark_name = self._get_benchmark_name()
80    self.report_benchmark(
81        iters=num_iters, wall_time=mean_us, extras=extras, name=benchmark_name)
82