Source code for torch.profiler.profiler
import gzip
import json
import os
import tempfile
from enum import Enum
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
from warnings import warn
import torch
import torch.autograd.profiler as prof
from torch._C._profiler import (
_add_execution_graph_observer,
_disable_execution_graph_observer,
_enable_execution_graph_observer,
_ExperimentalConfig,
_remove_execution_graph_observer,
)
from torch.autograd import kineto_available, ProfilerActivity
from torch.profiler._memory_profiler import MemoryProfile, MemoryProfileTimeline
__all__ = [
"supported_activities",
"ProfilerAction",
"schedule",
"tensorboard_trace_handler",
"profile",
"ExecutionGraphObserver",
]
PROFILER_STEP_NAME = "ProfilerStep"
def supported_activities():
"""
Returns a set of supported profiler tracing activities.
Note: profiler uses CUPTI library to trace on-device CUDA kernels.
In case when CUDA is enabled but CUPTI is not available, passing
``ProfilerActivity.CUDA`` to profiler results in using the legacy CUDA
profiling code (same as in the legacy ``torch.autograd.profiler``).
This, in turn, results in including CUDA time in the profiler table output,
but not in the JSON trace.
"""
return torch.autograd._supported_activities()
[docs]class _KinetoProfile:
"""Low-level profiler wrap the autograd profile
Args:
activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
record_shapes (bool): save information about operator's input shapes.
profile_memory (bool): track tensor memory allocation/deallocation.
with_stack (bool): record source information (file and line number) for the ops.
with_flops (bool): use formula to estimate the FLOPS of specific operators
(matrix multiplication and 2D convolution).
with_modules (bool): record module hierarchy (including function names)
corresponding to the callstack of the op. e.g. If module A's forward call's
module B's forward which contains an aten::add op,
then aten::add's module hierarchy is A.B
Note that this support exist, at the moment, only for TorchScript models
and not eager mode models.
experimental_config (_ExperimentalConfig) : A set of experimental options
used by profiler libraries like Kineto. Note, backward compatibility is not guaranteed.
.. note::
This API is experimental and subject to change in the future.
Enabling shape and stack tracing results in additional overhead.
When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
that may further prevent certain optimizations that depend on the reference count and introduce
extra tensor copies.
"""
def __init__(
self,
*,
activities: Optional[Iterable[ProfilerActivity]] = None,
record_shapes: bool = False,
profile_memory: bool = False,
with_stack: bool = False,
with_flops: bool = False,
with_modules: bool = False,
experimental_config: Optional[_ExperimentalConfig] = None):
self.activities = set(activities) if activities else supported_activities()
self.record_shapes = record_shapes
self.with_flops = with_flops
self.profile_memory = profile_memory
self.with_stack = with_stack
self.with_modules = with_modules
self.experimental_config = experimental_config
self.profiler: Optional[prof.profile] = None
self.mem_tl: Optional[MemoryProfileTimeline] = None
def start(self):
self.prepare_trace()
self.start_trace()
def stop(self):
self.stop_trace()
def prepare_trace(self):
self.profiler = prof.profile(
use_cuda=(ProfilerActivity.CUDA in self.activities),
use_cpu=(ProfilerActivity.CPU in self.activities),
record_shapes=self.record_shapes,
with_flops=self.with_flops,
profile_memory=self.profile_memory,
with_stack=self.with_stack,
with_modules=self.with_modules,
use_kineto=True,
experimental_config=self.experimental_config,
)
self.profiler._prepare_trace()
def start_trace(self):
assert self.profiler is not None
self.profiler._start_trace()
if self.profile_memory:
self.add_metadata_json("profile_memory", "1")
if self.with_stack:
self.add_metadata_json("with_stack", "1")
if self.record_shapes:
self.add_metadata_json("record_shapes", "1")
if self.with_modules:
self.add_metadata_json("with_modules", "1")
if self.with_flops:
self.add_metadata_json("with_flops", "1")
if kineto_available():
dist_info = self._get_distributed_info()
if dist_info:
self.add_metadata_json("distributedInfo", json.dumps(dist_info))
def stop_trace(self):
assert self.profiler is not None
self.profiler.__exit__(None, None, None)
[docs] def export_chrome_trace(self, path: str):
"""
Exports the collected trace in Chrome JSON format.
"""
assert self.profiler
if path.endswith('.gz'):
fp = tempfile.NamedTemporaryFile('w+t', suffix='.json', delete=False)
fp.close()
retvalue = self.profiler.export_chrome_trace(fp.name)
with open(fp.name) as fin:
with gzip.open(path, 'wt') as fout:
fout.writelines(fin)
os.remove(fp.name)
return retvalue
else:
return self.profiler.export_chrome_trace(path)
[docs] def export_stacks(self, path: str, metric: str = "self_cpu_time_total"):
"""Save stack traces in a file in a format suitable for visualization.
Args:
path (str): save stacks file to this location;
metric (str): metric to use: "self_cpu_time_total" or "self_cuda_time_total"
.. note::
Example of using FlameGraph tool:
- git clone https://github.com/brendangregg/FlameGraph
- cd FlameGraph
- ./flamegraph.pl --title "CPU time" --countname "us." profiler.stacks > perf_viz.svg
"""
assert self.profiler
return self.profiler.export_stacks(path, metric)
[docs] def key_averages(self, group_by_input_shape: bool = False, group_by_stack_n: int = 0):
"""Averages events, grouping them by operator name and (optionally) input shapes and
stack.
.. note::
To use shape/stack functionality make sure to set record_shapes/with_stack
when creating profiler context manager.
"""
assert self.profiler
return self.profiler.key_averages(group_by_input_shape, group_by_stack_n)
[docs] def events(self):
"""
Returns the list of unaggregated profiler events,
to be used in the trace callback or after the profiling is finished
"""
assert self.profiler
return self.profiler.function_events
[docs] def add_metadata(self, key: str, value: str):
"""
Adds a user defined metadata with a string key and a string value
into the trace file
"""
wrapped_value = "\"" + value.replace('"', '\\"') + "\""
torch.autograd._add_metadata_json(key, wrapped_value)
[docs] def add_metadata_json(self, key: str, value: str):
"""
Adds a user defined metadata with a string key and a valid json value
into the trace file
"""
torch.autograd._add_metadata_json(key, value)
def _get_distributed_info(self):
import torch.distributed as dist
if not dist.is_available() or not dist.is_initialized():
return None
return {
"backend": dist.get_backend(),
"rank": dist.get_rank(),
"world_size": dist.get_world_size()
}
def _memory_profile(self) -> MemoryProfile:
required = ("record_shapes", "profile_memory", "with_stack")
missing = [f"{i}=True" for i in required if not getattr(self, i)]
if missing:
raise ValueError(f"{', '.join(missing)} required for memory profiling.")
assert self.profiler is not None and self.profiler.kineto_results is not None
return MemoryProfile(self.profiler.kineto_results)
[docs] def export_memory_timeline(self, path: str, device: str = None) -> None:
"""Extract the memory information from the memory profile collected
tree for a given device, and export a timeline plot consisting of
[times, [sizes by category]], where times are timestamps and sizes
are memory usage for each category. The memory timeline plot will
be saved a JSON (by default) or gzipped JSON.
Input: (path of file, device)
Output: File written as JSON or gzipped JSON
"""
# Default to device 0, if unset. Fallback on cpu.
if device is None:
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# Construct the memory timeline plot data
self.mem_tl = MemoryProfileTimeline(self._memory_profile())
# Depending on the file suffix, save the data as json.gz or json.
if path.endswith('.gz'):
fp = tempfile.NamedTemporaryFile('w+t', suffix='.json', delete=False)
fp.close()
self.mem_tl.export_memory_timeline(fp.name, device)
with open(fp.name) as fin:
with gzip.open(path, 'wt') as fout:
fout.writelines(fin)
os.remove(fp.name)
else:
self.mem_tl.export_memory_timeline(path, device)
[docs]class ProfilerAction(Enum):
"""
Profiler actions that can be taken at the specified intervals
"""
NONE = 0
WARMUP = 1
RECORD = 2
RECORD_AND_SAVE = 3
[docs]def schedule(*, wait: int, warmup: int, active: int, repeat: int = 0, skip_first: int = 0) -> Callable:
"""
Returns a callable that can be used as profiler ``schedule`` argument. The profiler will skip
the first ``skip_first`` steps, then wait for ``wait`` steps, then do the warmup for the next ``warmup`` steps,
then do the active recording for the next ``active`` steps and then repeat the cycle starting with ``wait`` steps.
The optional number of cycles is specified with the ``repeat`` parameter, the zero value means that
the cycles will continue until the profiling is finished.
"""
def schedule_fn(step: int) -> ProfilerAction:
assert step >= 0
if step < skip_first:
return ProfilerAction.NONE
else:
step -= skip_first
num_steps = wait + warmup + active
if repeat > 0 and step / num_steps >= repeat:
return ProfilerAction.NONE
mod_step = step % num_steps
if mod_step < wait:
return ProfilerAction.NONE
elif mod_step < wait + warmup:
return ProfilerAction.WARMUP
else:
return ProfilerAction.RECORD if mod_step < num_steps - 1 \
else ProfilerAction.RECORD_AND_SAVE
assert wait >= 0 and warmup >= 0 and active > 0 and \
repeat >= 0 and skip_first >= 0, "Invalid profiler schedule arguments"
if warmup == 0:
warn("Profiler won't be using warmup, this can skew profiler results")
return schedule_fn
def _default_schedule_fn(_: int) -> ProfilerAction:
"""
Default profiler behavior - immediately starts recording the events,
keeps doing it on every profiler step.
"""
return ProfilerAction.RECORD
[docs]def tensorboard_trace_handler(dir_name: str, worker_name: Optional[str] = None, use_gzip: bool = False):
"""
Outputs tracing files to directory of ``dir_name``, then that directory can be
directly delivered to tensorboard as logdir.
``worker_name`` should be unique for each worker in distributed scenario,
it will be set to '[hostname]_[pid]' by default.
"""
import os
import socket
import time
def handler_fn(prof) -> None:
nonlocal worker_name
if not os.path.isdir(dir_name):
try:
os.makedirs(dir_name, exist_ok=True)
except Exception as e:
raise RuntimeError("Can't create directory: " + dir_name) from e
if not worker_name:
worker_name = f"{socket.gethostname()}_{os.getpid()}"
# Use nanosecond here to avoid naming clash when exporting the trace
file_name = f"{worker_name}.{time.time_ns()}.pt.trace.json"
if use_gzip:
file_name = file_name + '.gz'
prof.export_chrome_trace(os.path.join(dir_name, file_name))
return handler_fn
[docs]class profile(_KinetoProfile):
"""Profiler context manager.
Args:
activities (iterable): list of activity groups (CPU, CUDA) to use in profiling, supported values:
``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``.
Default value: ProfilerActivity.CPU and (when available) ProfilerActivity.CUDA.
schedule (Callable): callable that takes step (int) as a single parameter and returns
``ProfilerAction`` value that specifies the profiler action to perform at each step.
on_trace_ready (Callable): callable that is called at each step when ``schedule``
returns ``ProfilerAction.RECORD_AND_SAVE`` during the profiling.
record_shapes (bool): save information about operator's input shapes.
profile_memory (bool): track tensor memory allocation/deallocation.
with_stack (bool): record source information (file and line number) for the ops.
with_flops (bool): use formula to estimate the FLOPs (floating point operations) of specific operators
(matrix multiplication and 2D convolution).
with_modules (bool): record module hierarchy (including function names)
corresponding to the callstack of the op. e.g. If module A's forward call's
module B's forward which contains an aten::add op,
then aten::add's module hierarchy is A.B
Note that this support exist, at the moment, only for TorchScript models
and not eager mode models.
experimental_config (_ExperimentalConfig) : A set of experimental options
used for Kineto library features. Note, backward compatibility is not guaranteed.
use_cuda (bool):
.. deprecated:: 1.8.1
use ``activities`` instead.
.. note::
Use :func:`~torch.profiler.schedule` to generate the callable schedule.
Non-default schedules are useful when profiling long training jobs
and allow the user to obtain multiple traces at the different iterations
of the training process.
The default schedule simply records all the events continuously for the
duration of the context manager.
.. note::
Use :func:`~torch.profiler.tensorboard_trace_handler` to generate result files for TensorBoard:
``on_trace_ready=torch.profiler.tensorboard_trace_handler(dir_name)``
After profiling, result files can be found in the specified directory. Use the command:
``tensorboard --logdir dir_name``
to see the results in TensorBoard.
For more information, see
`PyTorch Profiler TensorBoard Plugin <https://github.com/pytorch/kineto/tree/master/tb_plugin>`__
.. note::
Enabling shape and stack tracing results in additional overhead.
When record_shapes=True is specified, profiler will temporarily hold references to the tensors;
that may further prevent certain optimizations that depend on the reference count and introduce
extra tensor copies.
Examples:
.. code-block:: python
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
]
) as p:
code_to_profile()
print(p.key_averages().table(
sort_by="self_cuda_time_total", row_limit=-1))
Using the profiler's ``schedule``, ``on_trace_ready`` and ``step`` functions:
.. code-block:: python
# Non-default profiler schedule allows user to turn profiler on and off
# on different iterations of the training loop;
# trace_handler is called every time a new trace becomes available
def trace_handler(prof):
print(prof.key_averages().table(
sort_by="self_cuda_time_total", row_limit=-1))
# prof.export_chrome_trace("/tmp/test_trace_" + str(prof.step_num) + ".json")
with torch.profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
# In this example with wait=1, warmup=1, active=2, repeat=1,
# profiler will skip the first step/iteration,
# start warming up on the second, record
# the third and the forth iterations,
# after which the trace will become available
# and on_trace_ready (when set) is called;
# the cycle repeats starting with the next step
schedule=torch.profiler.schedule(
wait=1,
warmup=1,
active=2,
repeat=1),
on_trace_ready=trace_handler
# on_trace_ready=torch.profiler.tensorboard_trace_handler('./log')
# used when outputting for tensorboard
) as p:
for iter in range(N):
code_iteration_to_profile(iter)
# send a signal to the profiler that the next iteration has started
p.step()
"""
def __init__(
self,
*,
activities: Optional[Iterable[ProfilerActivity]] = None,
schedule: Optional[Callable[[int], ProfilerAction]] = None,
on_trace_ready: Optional[Callable[..., Any]] = None,
record_shapes: bool = False,
profile_memory: bool = False,
with_stack: bool = False,
with_flops: bool = False,
with_modules: bool = False,
experimental_config: Optional[_ExperimentalConfig] = None,
# deprecated:
use_cuda: Optional[bool] = None):
activities_set = set(activities) if activities else supported_activities()
if use_cuda is not None:
warn("use_cuda is deprecated, use activities argument instead")
if use_cuda:
activities_set.add(ProfilerActivity.CUDA)
elif ProfilerActivity.CUDA in activities_set:
activities_set.remove(ProfilerActivity.CUDA)
assert len(activities_set) > 0, "No valid profiler activities found"
super().__init__(
activities=activities,
record_shapes=record_shapes,
profile_memory=profile_memory,
with_stack=with_stack,
with_flops=with_flops,
with_modules=with_modules,
experimental_config=experimental_config,
)
if schedule:
self.schedule = schedule
# add step markers into the trace and table view
self.record_steps = True
else:
self.schedule = _default_schedule_fn
self.record_steps = False
self.on_trace_ready = on_trace_ready
self.step_num = 0
self.current_action = self.schedule(self.step_num)
self.step_rec_fn: Optional[prof.record_function] = None
self.action_map: Dict[Tuple[ProfilerAction, Optional[ProfilerAction]], List[Any]] = {
# key is (prev_action, current_action), value is action list corresponding to the state pair.
(ProfilerAction.NONE, ProfilerAction.NONE): [],
(ProfilerAction.NONE, ProfilerAction.WARMUP): [self.prepare_trace],
(ProfilerAction.NONE, ProfilerAction.RECORD): [self.prepare_trace, self.start_trace],
(ProfilerAction.NONE, ProfilerAction.RECORD_AND_SAVE): [self.prepare_trace, self.start_trace],
(ProfilerAction.WARMUP, ProfilerAction.NONE): [
partial(warn, "Incorrect schedule: WARMUP followed by NONE"),
self.start_trace,
self.stop_trace],
(ProfilerAction.WARMUP, ProfilerAction.WARMUP): [],
(ProfilerAction.WARMUP, ProfilerAction.RECORD): [self.start_trace],
(ProfilerAction.WARMUP, ProfilerAction.RECORD_AND_SAVE): [self.start_trace],
(ProfilerAction.RECORD, ProfilerAction.NONE): [
partial(warn, "Incorrect schedule: RECORD followed by NONE"),
self.stop_trace],
(ProfilerAction.RECORD, ProfilerAction.WARMUP): [
partial(warn, "Incorrect schedule: RECORD followed by WARMUP"),
self.stop_trace],
(ProfilerAction.RECORD, ProfilerAction.RECORD): [],
(ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE): [],
(ProfilerAction.RECORD_AND_SAVE, ProfilerAction.NONE): [self.stop_trace, self._trace_ready],
(ProfilerAction.RECORD_AND_SAVE, ProfilerAction.WARMUP): [self.stop_trace, self._trace_ready, self.prepare_trace],
(ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD): [
self.stop_trace,
self._trace_ready,
self.prepare_trace,
self.start_trace],
(ProfilerAction.RECORD_AND_SAVE, ProfilerAction.RECORD_AND_SAVE): [
self.stop_trace,
self._trace_ready,
self.prepare_trace,
self.start_trace],
# used for exit action
(ProfilerAction.WARMUP, None): [self.start_trace, self.stop_trace],
(ProfilerAction.RECORD, None): [self.stop_trace, self._trace_ready],
(ProfilerAction.RECORD_AND_SAVE, None): [self.stop_trace, self._trace_ready]
}
# Start tracking increments to profiler step, this will be used
# by Kineto
prof.KinetoStepTracker.init_step_count(PROFILER_STEP_NAME)
def __enter__(self):
prof._enable_dynamo_cache_lookup_profiler(True)
self.start()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop()
prof._enable_dynamo_cache_lookup_profiler(False)
prof.KinetoStepTracker.erase_step_count(PROFILER_STEP_NAME)
def start(self):
self._transit_action(ProfilerAction.NONE, self.current_action)
if self.record_steps:
self.step_rec_fn = prof.record_function("ProfilerStep#" + str(self.step_num))
self.step_rec_fn.__enter__()
def stop(self):
if self.record_steps and self.step_rec_fn:
self.step_rec_fn.__exit__(None, None, None)
self._transit_action(self.current_action, None)
[docs] def step(self):
"""
Signals the profiler that the next profiling step has started.
"""
if self.record_steps and self.step_rec_fn:
self.step_rec_fn.__exit__(None, None, None)
prev_action = self.current_action
cur_step = self.step_num
self.step_num += 1
self.current_action = self.schedule(self.step_num)
self._transit_action(prev_action, self.current_action)
prof.KinetoStepTracker.increment_step(PROFILER_STEP_NAME)
if self.record_steps:
self.step_rec_fn = prof.record_function("ProfilerStep#" + str(cur_step))
self.step_rec_fn.__enter__()
def _trace_ready(self):
if self.on_trace_ready:
self.on_trace_ready(self)
def _transit_action(self, prev_action, current_action):
action_list = self.action_map.get((prev_action, current_action))
if action_list:
for action in action_list:
action()
class ExecutionGraphObserver:
"""Execution Graph Observer
Each process can have a single ExecutionGraphObserver instance. The observer
can be added to record function callbacks via calling register_callback()
explicitly. Without calling unregister_callback(), repeated calls to
register_callback() will not add additional observers to record function
callbacks. Once an ExecutionGraphObserver is created, the start() and stop()
methods control when the event data is recorded.
Deleting or calling unregister_callback() will remove the observer from the
record function callbacks, finalize the output file, and will stop
incurring any overheads.
"""
def __init__(self):
"""
Initializes the default states.
"""
self._registered = False
self._execution_graph_running = False
def __del__(self):
"""
Calls unregister_callback() to make sure to finalize outputs.
"""
self.unregister_callback()
def register_callback(self, output_file_path: str):
"""
Adds EG observer to record function callbacks. The the data will be
written to output_file_path.
"""
if not self._registered:
self._output_file_path = output_file_path
self._registered = _add_execution_graph_observer(output_file_path)
def unregister_callback(self):
"""
Removes EG observer from record function callbacks.
"""
if self._registered:
self.stop()
_remove_execution_graph_observer()
self._registered = False
@property
def is_registered(self):
"""
Return if the execution graph observer is registered.
"""
return self._registered
def start(self):
"""
Starts to capture.
"""
if self._registered and not self._execution_graph_running:
_enable_execution_graph_observer()
self._execution_graph_running = True
def stop(self):
"""
Stops to capture.
"""
if self._execution_graph_running:
_disable_execution_graph_observer()
self._execution_graph_running = False
def get_output_file_path(self) -> str:
"""
Returns the output file name.
"""
if self.is_registered:
return self._output_file_path
else:
raise RuntimeError(
"A callback to the EG profiler needs to be registered "
"first before getting the output file path"
)
