Source code for torch.profiler.profiler
import torch.autograd.profiler as prof
from torch.autograd import ProfilerActivity
from enum import Enum
from typing import Any, Callable, Iterable, Optional
from warnings import warn
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) -> Callable:
"""
Returns a callable that can be used as profiler ``schedule`` argument. The profiler will 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 staring with the next step.
"""
def schedule_fn(step: int) -> ProfilerAction:
assert step >= 0
num_steps = wait + warmup + active
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, \
"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]class profile(object):
"""
Profiler context manager.
Args:
- ``activities`` - list of activity groups (CPU, CUDA) to use in profiling, supported values:
``torch.profiler.ProfilerActivity.CPU``, ``torch.profiler.ProfilerActivity.CUDA``
- ``schedule`` - 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 that is called at each step when ``schedule`` returns ``ProfilerAction.RECORD_AND_SAVE``
during the profiling;
- ``record_shapes`` - save information about operator's input shapes;
- ``profile_memory`` - track tensor memory allocation/deallocation;
- ``with_stack`` - record source information (file and line number) for the ops.
- ``use_gpu`` - (deprecated, use ``activities``).
.. note::
Use ``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::
Enabling shape and stack tracing results in additional overhead.
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,
# 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),
on_trace_ready=trace_handler
) 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,
# deprecated:
use_gpu: Optional[bool] = None):
if activities:
self.activities = activities
else:
if use_gpu is not None:
warn("use_gpu is deprecated, use activities argument instead")
self.activities = set([ProfilerActivity.CPU])
if use_gpu:
self.activities.add(ProfilerActivity.CUDA)
else:
raise RuntimeError("Profiler activities are not specified")
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.record_shapes = record_shapes
self.profile_memory = profile_memory
self.with_stack = with_stack
self.step_num = 0
self.current_action = self.schedule(self.step_num)
self.profiler: Optional[prof.profile] = None
self.step_rec_fn: Optional[prof.record_function] = None
def __enter__(self):
self._enter_actions()
if self.record_steps:
self.step_rec_fn = prof.record_function("ProfilerStep#" + str(self.step_num))
self.step_rec_fn.__enter__()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.record_steps and self.step_rec_fn:
self.step_rec_fn.__exit__(None, None, None)
self._exit_actions()
[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
self.step_num += 1
self.current_action = self.schedule(self.step_num)
if self.current_action == ProfilerAction.NONE:
if prev_action == ProfilerAction.NONE:
pass
elif prev_action == ProfilerAction.WARMUP:
warn("Incorrect schedule: WARMUP followed by NONE")
self._start_trace()
self._stop_trace()
elif prev_action == ProfilerAction.RECORD:
warn("Incorrect schedule: RECORD followed by NONE")
self._stop_trace()
else:
assert prev_action == ProfilerAction.RECORD_AND_SAVE
self._stop_trace()
if self.on_trace_ready:
self.on_trace_ready(self)
elif self.current_action == ProfilerAction.WARMUP:
if prev_action == ProfilerAction.NONE:
self._start_warmup()
elif prev_action == ProfilerAction.WARMUP:
pass
elif prev_action == ProfilerAction.RECORD:
warn("Incorrect schedule: RECORD followed by WARMUP")
self._stop_trace()
else:
assert prev_action == ProfilerAction.RECORD_AND_SAVE
self._stop_trace()
if self.on_trace_ready:
self.on_trace_ready(self)
self._start_warmup()
elif self.current_action in \
[ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
if prev_action == ProfilerAction.NONE:
self._start_warmup()
self._start_trace()
elif prev_action == ProfilerAction.WARMUP:
self._start_trace()
elif prev_action == ProfilerAction.RECORD:
pass
else:
assert prev_action == ProfilerAction.RECORD_AND_SAVE
self._stop_trace()
if self.on_trace_ready:
self.on_trace_ready(self)
self._start_warmup()
self._start_trace()
if self.record_steps:
self.step_rec_fn = prof.record_function("ProfilerStep#" + str(self.step_num))
self.step_rec_fn.__enter__()
[docs] def export_chrome_trace(self, path: str):
"""
Exports the collected trace in Chrome JSON format.
"""
assert self.profiler
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`` - save stacks file to this location;
- ``metric`` - 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
def _enter_actions(self):
if self.current_action == ProfilerAction.WARMUP:
self._start_warmup()
elif self.current_action in \
[ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
self._start_warmup()
self._start_trace()
def _exit_actions(self):
if self.current_action == ProfilerAction.WARMUP:
self._start_trace()
self._stop_trace()
elif self.current_action in \
[ProfilerAction.RECORD, ProfilerAction.RECORD_AND_SAVE]:
self._stop_trace()
if self.on_trace_ready:
self.on_trace_ready(self)
def _start_warmup(self):
self.profiler = prof.profile(
use_cuda=(ProfilerActivity.CUDA in self.activities),
use_cpu=(ProfilerActivity.CPU in self.activities),
record_shapes=self.record_shapes,
profile_memory=self.profile_memory,
with_stack=self.with_stack,
use_kineto=True,
)
self.profiler._prepare_kineto_trace()
def _start_trace(self):
assert self.profiler is not None
self.profiler._start_kineto_trace()
def _stop_trace(self):
assert self.profiler is not None
self.profiler.__exit__(None, None, None)
