285 lines
10 KiB
Python
285 lines
10 KiB
Python
# Copyright The PyTorch Lightning team.
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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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"""Profiler to check if there are any bottlenecks in your code."""
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import cProfile
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import io
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import os
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import pstats
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import time
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from abc import ABC, abstractmethod
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from collections import defaultdict
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from contextlib import contextmanager
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from typing import Optional, Union
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import numpy as np
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from pytorch_lightning import _logger as log
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from pytorch_lightning.utilities.cloud_io import get_filesystem
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class BaseProfiler(ABC):
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"""
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If you wish to write a custom profiler, you should inhereit from this class.
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"""
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def __init__(self, output_streams: Optional[Union[list, tuple]] = None):
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"""
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Args:
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output_streams: callable
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"""
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if output_streams:
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if not isinstance(output_streams, (list, tuple)):
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output_streams = [output_streams]
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else:
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output_streams = []
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self.write_streams = output_streams
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@abstractmethod
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def start(self, action_name: str) -> None:
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"""Defines how to start recording an action."""
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@abstractmethod
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def stop(self, action_name: str) -> None:
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"""Defines how to record the duration once an action is complete."""
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@contextmanager
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def profile(self, action_name: str) -> None:
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"""
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Yields a context manager to encapsulate the scope of a profiled action.
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Example::
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with self.profile('load training data'):
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# load training data code
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The profiler will start once you've entered the context and will automatically
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stop once you exit the code block.
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"""
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try:
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self.start(action_name)
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yield action_name
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finally:
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self.stop(action_name)
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def profile_iterable(self, iterable, action_name: str) -> None:
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iterator = iter(iterable)
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while True:
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try:
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self.start(action_name)
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value = next(iterator)
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self.stop(action_name)
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yield value
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except StopIteration:
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self.stop(action_name)
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break
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def describe(self) -> None:
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"""Logs a profile report after the conclusion of the training run."""
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for write in self.write_streams:
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write(self.summary())
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@abstractmethod
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def summary(self) -> str:
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"""Create profiler summary in text format."""
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class PassThroughProfiler(BaseProfiler):
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"""
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This class should be used when you don't want the (small) overhead of profiling.
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The Trainer uses this class by default.
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"""
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def __init__(self):
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super().__init__(output_streams=None)
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def start(self, action_name: str) -> None:
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pass
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def stop(self, action_name: str) -> None:
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pass
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def summary(self) -> str:
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return ""
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class SimpleProfiler(BaseProfiler):
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"""
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This profiler simply records the duration of actions (in seconds) and reports
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the mean duration of each action and the total time spent over the entire training run.
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"""
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def __init__(self, output_filename: Optional[str] = None, extended=True):
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"""
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Args:
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output_filename: optionally save profile results to file instead of printing
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to std out when training is finished.
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"""
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self.current_actions = {}
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self.recorded_durations = defaultdict(list)
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self.extended = extended
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self.output_fname = output_filename
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self.output_file = None
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if self.output_fname:
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fs = get_filesystem(self.output_fname)
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self.output_file = fs.open(self.output_fname, "w")
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streaming_out = [self.output_file.write] if self.output_file else [log.info]
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self.start_time = time.monotonic()
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super().__init__(output_streams=streaming_out)
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def start(self, action_name: str) -> None:
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if action_name in self.current_actions:
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raise ValueError(
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f"Attempted to start {action_name} which has already started."
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)
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self.current_actions[action_name] = time.monotonic()
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def stop(self, action_name: str) -> None:
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end_time = time.monotonic()
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if action_name not in self.current_actions:
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raise ValueError(
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f"Attempting to stop recording an action ({action_name}) which was never started."
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)
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start_time = self.current_actions.pop(action_name)
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duration = end_time - start_time
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self.recorded_durations[action_name].append(duration)
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def make_report(self):
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total_duration = time.monotonic() - self.start_time
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report = [[a, d, 100. * np.sum(d) / total_duration] for a, d in self.recorded_durations.items()]
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report.sort(key=lambda x: x[2], reverse=True)
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return report, total_duration
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def summary(self) -> str:
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output_string = "\n\nProfiler Report\n"
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if self.extended:
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if len(self.recorded_durations) > 0:
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max_key = np.max([len(k) for k in self.recorded_durations.keys()])
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def log_row(action, mean, num_calls, total, per):
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row = f"{os.linesep}{action:<{max_key}s}\t| {mean:<15}\t|"
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row += f"{num_calls:<15}\t| {total:<15}\t| {per:<15}\t|"
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return row
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output_string += log_row("Action", "Mean duration (s)", "Num calls", "Total time (s)", "Percentage %")
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output_string_len = len(output_string)
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output_string += f"{os.linesep}{'-' * output_string_len}"
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report, total_duration = self.make_report()
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output_string += log_row("Total", "-", "_", f"{total_duration:.5}", "100 %")
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output_string += f"{os.linesep}{'-' * output_string_len}"
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for action, durations, duration_per in report:
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output_string += log_row(
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action, f"{np.mean(durations):.5}", f"{len(durations):}",
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f"{np.sum(durations):.5}", f"{duration_per:.5}"
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)
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else:
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def log_row(action, mean, total):
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return f"{os.linesep}{action:<20s}\t| {mean:<15}\t| {total:<15}"
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output_string += log_row("Action", "Mean duration (s)", "Total time (s)")
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output_string += f"{os.linesep}{'-' * 65}"
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for action, durations in self.recorded_durations.items():
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output_string += log_row(
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action, f"{np.mean(durations):.5}", f"{np.sum(durations):.5}"
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)
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output_string += os.linesep
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return output_string
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def describe(self):
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"""Logs a profile report after the conclusion of the training run."""
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super().describe()
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if self.output_file:
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self.output_file.flush()
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def __del__(self):
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"""Close profiler's stream."""
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if self.output_file:
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self.output_file.close()
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class AdvancedProfiler(BaseProfiler):
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"""
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This profiler uses Python's cProfiler to record more detailed information about
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time spent in each function call recorded during a given action. The output is quite
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verbose and you should only use this if you want very detailed reports.
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"""
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def __init__(self, output_filename: Optional[str] = None, line_count_restriction: float = 1.0):
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"""
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Args:
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output_filename: optionally save profile results to file instead of printing
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to std out when training is finished.
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line_count_restriction: this can be used to limit the number of functions
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reported for each action. either an integer (to select a count of lines),
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or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines)
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"""
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self.profiled_actions = {}
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self.line_count_restriction = line_count_restriction
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self.output_fname = output_filename
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self.output_file = None
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if self.output_fname:
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fs = get_filesystem(self.output_fname)
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self.output_file = fs.open(self.output_fname, "w")
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streaming_out = [self.output_file.write] if self.output_file else [log.info]
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super().__init__(output_streams=streaming_out)
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def start(self, action_name: str) -> None:
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if action_name not in self.profiled_actions:
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self.profiled_actions[action_name] = cProfile.Profile()
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self.profiled_actions[action_name].enable()
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def stop(self, action_name: str) -> None:
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pr = self.profiled_actions.get(action_name)
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if pr is None:
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raise ValueError( # pragma: no-cover
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f"Attempting to stop recording an action ({action_name}) which was never started."
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)
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pr.disable()
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def summary(self) -> str:
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recorded_stats = {}
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for action_name, pr in self.profiled_actions.items():
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s = io.StringIO()
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ps = pstats.Stats(pr, stream=s).strip_dirs().sort_stats('cumulative')
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ps.print_stats(self.line_count_restriction)
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recorded_stats[action_name] = s.getvalue()
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# log to standard out
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output_string = f"{os.linesep}Profiler Report{os.linesep}"
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for action, stats in recorded_stats.items():
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output_string += (
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f"{os.linesep}Profile stats for: {action}{os.linesep}{stats}"
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)
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return output_string
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def describe(self):
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"""Logs a profile report after the conclusion of the training run."""
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super().describe()
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if self.output_file:
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self.output_file.flush()
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def __del__(self):
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"""Close profiler's stream."""
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if self.output_file:
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self.output_file.close()
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