251 lines
9.4 KiB
Python
251 lines
9.4 KiB
Python
# Copyright 2019 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.
|
|
# ==============================================================================
|
|
"""Test utilities for tf.data benchmarking functionality."""
|
|
import time
|
|
|
|
import numpy as np
|
|
|
|
from tensorflow.python.client import session
|
|
from tensorflow.python.data.ops import dataset_ops
|
|
from tensorflow.python.data.ops import options as options_lib
|
|
from tensorflow.python.data.util import nest
|
|
from tensorflow.python.eager import context
|
|
from tensorflow.python.platform import test
|
|
|
|
|
|
class DatasetBenchmarkBase(test.Benchmark):
|
|
"""Base class for dataset benchmarks."""
|
|
|
|
def _run_eager_benchmark(self, iterable, iters, warmup):
|
|
"""Benchmark the iterable in eager mode.
|
|
|
|
Runs the iterable `iters` times. In each iteration, the benchmark measures
|
|
the time it takes to go execute the iterable.
|
|
|
|
Args:
|
|
iterable: The tf op or tf.data Dataset to benchmark.
|
|
iters: Number of times to repeat the timing.
|
|
warmup: If true, warms up the session caches by running an untimed run.
|
|
|
|
Returns:
|
|
A float, representing the median time (with respect to `iters`)
|
|
it takes for the iterable to be executed `iters` num of times.
|
|
|
|
Raises:
|
|
RuntimeError: When executed in graph mode.
|
|
"""
|
|
|
|
deltas = []
|
|
if not context.executing_eagerly():
|
|
raise RuntimeError(
|
|
"Eager mode benchmarking is not supported in graph mode.")
|
|
|
|
for _ in range(iters):
|
|
if warmup:
|
|
iterator = iter(iterable)
|
|
next(iterator)
|
|
|
|
iterator = iter(iterable)
|
|
start = time.time()
|
|
next(iterator)
|
|
end = time.time()
|
|
deltas.append(end - start)
|
|
return np.median(deltas)
|
|
|
|
def _run_graph_benchmark(self,
|
|
iterable,
|
|
iters,
|
|
warmup,
|
|
session_config,
|
|
initializer=None):
|
|
"""Benchmarks the iterable in graph mode.
|
|
|
|
Runs the iterable `iters` times. In each iteration, the benchmark measures
|
|
the time it takes to go execute the iterable.
|
|
|
|
Args:
|
|
iterable: The tf op or tf.data Dataset to benchmark.
|
|
iters: Number of times to repeat the timing.
|
|
warmup: If true, warms up the session caches by running an untimed run.
|
|
session_config: A ConfigProto protocol buffer with configuration options
|
|
for the session. Applicable only for benchmarking in graph mode.
|
|
initializer: The initializer op required to initialize the iterable.
|
|
|
|
Returns:
|
|
A float, representing the median time (with respect to `iters`)
|
|
it takes for the iterable to be executed `iters` num of times.
|
|
|
|
Raises:
|
|
RuntimeError: When executed in eager mode.
|
|
"""
|
|
|
|
deltas = []
|
|
if context.executing_eagerly():
|
|
raise RuntimeError(
|
|
"Graph mode benchmarking is not supported in eager mode.")
|
|
|
|
for _ in range(iters):
|
|
with session.Session(config=session_config) as sess:
|
|
if warmup:
|
|
# Run once to warm up the session caches.
|
|
if initializer:
|
|
sess.run(initializer)
|
|
sess.run(iterable)
|
|
|
|
if initializer:
|
|
sess.run(initializer)
|
|
start = time.time()
|
|
sess.run(iterable)
|
|
end = time.time()
|
|
deltas.append(end - start)
|
|
return np.median(deltas)
|
|
|
|
def run_op_benchmark(self, op, iters=1, warmup=True, session_config=None):
|
|
"""Benchmarks the op.
|
|
|
|
Runs the op `iters` times. In each iteration, the benchmark measures
|
|
the time it takes to go execute the op.
|
|
|
|
Args:
|
|
op: The tf op to benchmark.
|
|
iters: Number of times to repeat the timing.
|
|
warmup: If true, warms up the session caches by running an untimed run.
|
|
session_config: A ConfigProto protocol buffer with configuration options
|
|
for the session. Applicable only for benchmarking in graph mode.
|
|
|
|
Returns:
|
|
A float, representing the per-execution wall time of the op in seconds.
|
|
This is the median time (with respect to `iters`) it takes for the op
|
|
to be executed `iters` num of times.
|
|
"""
|
|
|
|
if context.executing_eagerly():
|
|
return self._run_eager_benchmark(iterable=op, iters=iters, warmup=warmup)
|
|
|
|
return self._run_graph_benchmark(
|
|
iterable=op, iters=iters, warmup=warmup, session_config=session_config)
|
|
|
|
def run_benchmark(self,
|
|
dataset,
|
|
num_elements,
|
|
iters=1,
|
|
warmup=True,
|
|
apply_default_optimizations=False,
|
|
session_config=None):
|
|
"""Benchmarks the dataset.
|
|
|
|
Runs the dataset `iters` times. In each iteration, the benchmark measures
|
|
the time it takes to go through `num_elements` elements of the dataset.
|
|
|
|
Args:
|
|
dataset: Dataset to benchmark.
|
|
num_elements: Number of dataset elements to iterate through each benchmark
|
|
iteration.
|
|
iters: Number of times to repeat the timing.
|
|
warmup: If true, warms up the session caches by running an untimed run.
|
|
apply_default_optimizations: Determines whether default optimizations
|
|
should be applied.
|
|
session_config: A ConfigProto protocol buffer with configuration options
|
|
for the session. Applicable only for benchmarking in graph mode.
|
|
|
|
Returns:
|
|
A float, representing the per-element wall time of the dataset in seconds.
|
|
This is the median time (with respect to `iters`) it takes for the dataset
|
|
to go through `num_elements` elements, divided by `num_elements.`
|
|
"""
|
|
|
|
# The options that have been applied to the dataset are preserved so that
|
|
# they are not overwritten while benchmarking.
|
|
options = options_lib.Options()
|
|
options.experimental_optimization.apply_default_optimizations = (
|
|
apply_default_optimizations)
|
|
dataset = dataset.with_options(options)
|
|
|
|
# NOTE: We use `dataset.skip()` to perform the iterations in C++, avoiding
|
|
# the overhead of having to execute a TensorFlow op for each step of the
|
|
# input pipeline. Note that this relies on the underlying implementation of
|
|
# `skip` to execute upstream computation. If it is optimized in the future,
|
|
# we will have to change this code.
|
|
dataset = dataset.skip(num_elements - 1)
|
|
|
|
if context.executing_eagerly():
|
|
median_duration = self._run_eager_benchmark(
|
|
iterable=dataset, iters=iters, warmup=warmup)
|
|
return median_duration / float(num_elements)
|
|
|
|
iterator = dataset_ops.make_initializable_iterator(dataset)
|
|
next_element = iterator.get_next()
|
|
op = nest.flatten(next_element)[0].op
|
|
median_duration = self._run_graph_benchmark(
|
|
iterable=op,
|
|
iters=iters,
|
|
warmup=warmup,
|
|
session_config=session_config,
|
|
initializer=iterator.initializer)
|
|
return median_duration / float(num_elements)
|
|
|
|
def run_and_report_benchmark(self,
|
|
dataset,
|
|
num_elements,
|
|
name,
|
|
iters=5,
|
|
extras=None,
|
|
warmup=True,
|
|
apply_default_optimizations=False,
|
|
session_config=None):
|
|
"""Benchmarks the dataset and reports the stats.
|
|
|
|
Runs the dataset `iters` times. In each iteration, the benchmark measures
|
|
the time it takes to go through `num_elements` elements of the dataset.
|
|
This is followed by logging/printing the benchmark stats.
|
|
|
|
Args:
|
|
dataset: Dataset to benchmark.
|
|
num_elements: Number of dataset elements to iterate through each benchmark
|
|
iteration.
|
|
name: Name of the benchmark.
|
|
iters: Number of times to repeat the timing.
|
|
extras: A dict which maps string keys to additional benchmark info.
|
|
warmup: If true, warms up the session caches by running an untimed run.
|
|
apply_default_optimizations: Determines whether default optimizations
|
|
should be applied.
|
|
session_config: A ConfigProto protocol buffer with configuration options
|
|
for the session. Applicable only for benchmarking in graph mode.
|
|
|
|
Returns:
|
|
A float, representing the per-element wall time of the dataset in seconds.
|
|
This is the median time (with respect to `iters`) it takes for the dataset
|
|
to go through `num_elements` elements, divided by `num_elements.`
|
|
"""
|
|
wall_time = self.run_benchmark(
|
|
dataset=dataset,
|
|
num_elements=num_elements,
|
|
iters=iters,
|
|
warmup=warmup,
|
|
apply_default_optimizations=apply_default_optimizations,
|
|
session_config=session_config)
|
|
if extras is None:
|
|
extras = {}
|
|
if context.executing_eagerly():
|
|
name = "{}.eager".format(name)
|
|
extras["implementation"] = "eager"
|
|
else:
|
|
name = "{}.graph".format(name)
|
|
extras["implementation"] = "graph"
|
|
extras["num_elements"] = num_elements
|
|
self.report_benchmark(
|
|
wall_time=wall_time, iters=iters, name=name, extras=extras)
|
|
return wall_time
|