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benchmark.py
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benchmark.py
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# coding=utf-8
# Copyright 2023 The TensorFlow Datasets Authors.
#
# 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.
"""Benchmark utils."""
from __future__ import annotations
import dataclasses
import statistics
import textwrap
import time
from typing import Any, Dict, Iterable, List, Optional, Union
from absl import logging
from tensorflow_datasets.core.utils import tqdm_utils
from tensorflow_datasets.core.utils.lazy_imports_utils import pandas as pd
# pylint: disable=logging-format-interpolation
StatDict = Dict[str, Union[int, float]]
def _ns_to_s(time_in_ns: int) -> float:
return time_in_ns / 1e9
@dataclasses.dataclass(frozen=True)
class RawBenchmarkResult:
"""Raw results of running the benchmark.
Attributes:
num_iter: the number of iterations over examples that were done.
num_examples: the number of examples that were processed in these
iterations. Note that when examples are batched, then one iteration
processes multiple examples.
start_time: the time (in ns) at which when the benchmark started.
first_batch_time: the time (in ns) at which the first iteration was
processed.
end_time: the time (in ns) at which the benchmark ended.
batch_size: the number of examples in each iteration.
durations_ns: the duration in ns of each iteration that was processed.
"""
num_iter: int
num_examples: int
start_time: int
first_batch_time: int
end_time: int
batch_size: int
durations_ns: Optional[List[int]] = None
def examples(self, include_first: bool = True) -> int:
if include_first:
return self.num_examples
return self.num_examples - 1
def total_time_s(self, include_first: bool = True) -> float:
if include_first:
return _ns_to_s(self.end_time - self.start_time)
return _ns_to_s(self.end_time - self.first_batch_time)
def examples_per_second(self, include_first: bool = True) -> float:
return self.examples(include_first) / self.total_time_s(include_first)
def time_until_first(self, include_first: bool = True) -> Optional[float]:
"""Time in seconds that it took to load the first example."""
if include_first:
return _ns_to_s(self.first_batch_time - self.start_time)
if self.durations_ns is not None and len(self.durations_ns) > 1:
return _ns_to_s(self.durations_ns[1])
return None
def durations_s(self, include_first: bool = True) -> List[float]:
if not include_first:
return [_ns_to_s(d) for d in self.durations_ns[1:]]
return [_ns_to_s(d) for d in self.durations_ns]
def summary_statistics(
self, include_first: bool
) -> Dict[str, Union[float, List[float]]]:
if self.durations_ns is None:
return {}
durations = self.durations_s(include_first)
return {
'mean': statistics.mean(durations),
'variance': statistics.pvariance(durations),
'stdev': statistics.stdev(durations),
'quantiles': statistics.quantiles(durations),
}
def raw_stats_pd(self) -> pd.DataFrame: # pytype: disable=invalid-annotation # typed-pandas
raw_stats = {
'start_time': _ns_to_s(self.start_time),
'first_batch_time': _ns_to_s(self.first_batch_time),
'end_time': _ns_to_s(self.end_time),
'num_iter': self.num_iter,
}
return pd.DataFrame.from_dict(
raw_stats, orient='index', columns=['duration']
)
def stats(self) -> Dict[str, StatDict]:
return {
'first+lasts': _log_stats(
'First included',
self.start_time,
self.end_time,
self.num_examples + self.batch_size,
),
'first': _log_stats(
'First only',
self.start_time,
self.first_batch_time,
self.batch_size,
),
'lasts': _log_stats(
'First excluded',
self.first_batch_time,
self.end_time,
self.num_examples,
),
}
def stats_pd(self) -> pd.DataFrame: # pytype: disable=invalid-annotation # typed-pandas
return pd.DataFrame.from_dict(self.stats(), orient='index')
def __repr__(self) -> str:
return textwrap.dedent(
f"""
BenchmarkResult(
num_iter = {self.num_iter}
num_examples = {self.num_examples}
batch_size = {self.batch_size}
examples / sec = {self.examples_per_second()}
duration: {self.summary_statistics(include_first=True)}
duration without first: {self.summary_statistics(include_first=False)}
)"""
)
@dataclasses.dataclass(frozen=True)
class BenchmarkResult:
stats: pd.DataFrame # pytype: disable=invalid-annotation # typed-pandas
raw_stats: pd.DataFrame # pytype: disable=invalid-annotation # typed-pandas
def _repr_html_(self) -> str:
"""Colab/notebook representation."""
return '<strong>BenchmarkResult:</strong><br/>' + self.stats._repr_html_() # pylint: disable=protected-access
def raw_benchmark(
ds: Iterable[Any],
*,
num_iter: Optional[int] = None,
batch_size: int = 1,
detailed_stats: bool = False,
) -> RawBenchmarkResult:
"""Benchmarks any iterable (e.g `tf.data.Dataset`).
Usage:
```py
ds = tfds.load('mnist', split='train')
ds = ds.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)
tfds.benchmark(ds, batch_size=32)
```
Args:
ds: Dataset to benchmark. Can be any iterable. Note: The iterable will be
fully consumed.
num_iter: Number of iteration to perform (iteration might be batched)
batch_size: Batch size of the dataset, used to normalize iterations
detailed_stats: Whether to collect detailed statistics such as the time that
each iteration took.
Returns:
raw results.
"""
try:
total = len(ds) # pytype: disable=wrong-arg-types
except TypeError:
total = num_iter
if num_iter is not None:
total = min(total, num_iter)
results = []
actual_num_iter = 0
first_batch_time = None
start_time = time.perf_counter_ns()
end_time = start_time
for _ in tqdm_utils.tqdm(iter(ds), total=total):
actual_num_iter += 1
end_time = time.perf_counter_ns()
if first_batch_time is None:
first_batch_time = end_time
if detailed_stats:
results.append(end_time)
if num_iter and actual_num_iter >= num_iter:
break
if not actual_num_iter:
raise ValueError('Cannot benchmark dataset with 0 elements.')
if num_iter and actual_num_iter < num_iter:
logging.warning(
'Number of iterations is shorter than expected ({} vs {})'.format(
actual_num_iter, num_iter
)
)
durations_ns = []
for i in range(len(results)):
if i == 0:
durations_ns.append(results[i] - start_time)
else:
durations_ns.append(results[i] - results[i - 1])
return RawBenchmarkResult(
num_iter=actual_num_iter,
num_examples=actual_num_iter * batch_size,
start_time=start_time,
first_batch_time=first_batch_time,
end_time=end_time,
batch_size=batch_size,
durations_ns=durations_ns,
)
def benchmark(
ds: Iterable[Any],
*,
num_iter: Optional[int] = None,
batch_size: int = 1,
) -> BenchmarkResult:
"""Benchmarks any iterable (e.g `tf.data.Dataset`).
Usage:
```py
ds = tfds.load('mnist', split='train')
ds = ds.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)
tfds.benchmark(ds, batch_size=32)
```
Reports:
- Total execution time
- Setup time (first warmup batch)
- Number of examples/sec
Args:
ds: Dataset to benchmark. Can be any iterable. Note: The iterable will be
fully consumed.
num_iter: Number of iteration to perform (iteration might be batched)
batch_size: Batch size of the dataset, used to normalize iterations
Returns:
statistics: The recorded statistics, for eventual post-processing
"""
print('\n************ Summary ************\n')
raw_results = raw_benchmark(ds=ds, num_iter=num_iter, batch_size=batch_size)
return BenchmarkResult(
stats=raw_results.stats_pd(),
raw_stats=raw_results.raw_stats_pd(),
)
def _log_stats(msg: str, start: int, end: int, num_examples: int) -> StatDict:
"""Log and returns stats."""
if not num_examples:
stats = {
'duration': 0.0,
'num_examples': 0,
'avg': 0.0,
}
else:
# Make sure the total time is not 0.
total_time_ns = (end - start) or 1
total_time_s = _ns_to_s(total_time_ns)
stats = {
'duration': total_time_s,
'num_examples': num_examples,
'avg': num_examples / total_time_s,
}
print(
'Examples/sec ({}) {avg:.2f} ex/sec (total: {num_examples} ex, '
'{duration:.2f} sec)'.format(msg, **stats)
)
return stats