Source code for pytorch_lightning.profilers.pytorch
# Copyright The PyTorch Lightning team.
#
# 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.
"""Profiler to check if there are any bottlenecks in your code."""
import inspect
import logging
import os
from functools import lru_cache, partial
from pathlib import Path
from typing import Any, Callable, ContextManager, Dict, List, Optional, Type, TYPE_CHECKING, Union
import torch
from lightning_utilities.core.rank_zero import WarningCache
from torch import nn, Tensor
from torch.autograd.profiler import record_function
from lightning_lite.accelerators.cuda import is_cuda_available
from pytorch_lightning.profilers.profiler import Profiler
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.imports import _KINETO_AVAILABLE
from pytorch_lightning.utilities.rank_zero import rank_zero_warn
if TYPE_CHECKING:
from torch.autograd.profiler import EventList
from torch.utils.hooks import RemovableHandle
from pytorch_lightning.core.module import LightningModule
if _KINETO_AVAILABLE:
from torch.profiler import ProfilerAction, ProfilerActivity, tensorboard_trace_handler
log = logging.getLogger(__name__)
warning_cache = WarningCache()
_PROFILER = Union[torch.profiler.profile, torch.autograd.profiler.profile, torch.autograd.profiler.emit_nvtx]
class RegisterRecordFunction:
"""While profiling autograd operations, this class will add labels for module names around the forward
function.
The Lightning PyTorch Profiler will activate this feature automatically. It can be deactivated as follows:
Example::
from pytorch_lightning.profilers import PyTorchProfiler
profiler = PyTorchProfiler(record_module_names=False)
Trainer(profiler=profiler)
It can be used outside of Lightning as follows:
Example::
from pytorch_lightning import Trainer, seed_everything
with RegisterRecordFunction(model):
out = model(batch)
"""
def __init__(self, model: nn.Module) -> None:
self._model = model
self._records: Dict[str, record_function] = {}
self._handles: Dict[str, List["RemovableHandle"]] = {}
def _start_recording_forward(self, _: nn.Module, input: Tensor, record_name: str) -> Tensor:
# Add [pl][module] in name for pytorch profiler to recognize
record = record_function("[pl][module]" + record_name)
record.__enter__()
self._records[record_name] = record
return input
def _stop_recording_forward(self, _: nn.Module, __: Tensor, output: Tensor, record_name: str) -> Tensor:
self._records[record_name].__exit__(None, None, None)
return output
def __enter__(self) -> None:
for module_name, module in self._model.named_modules():
if module_name:
full_name = f"{type(module).__module__}.{type(module).__name__}"
record_name = f"{full_name}: {module_name}"
pre_forward_handle = module.register_forward_pre_hook(
partial(self._start_recording_forward, record_name=record_name)
)
post_forward_handle = module.register_forward_hook(
partial(self._stop_recording_forward, record_name=record_name)
)
self._handles[module_name] = [pre_forward_handle, post_forward_handle]
def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
for handles in self._handles.values():
for h in handles:
h.remove()
self._handles = {}
class ScheduleWrapper:
"""This class is used to override the schedule logic from the profiler and perform recording for both
`training_step`, `validation_step`."""
def __init__(self, schedule: Callable) -> None:
if not _KINETO_AVAILABLE:
raise ModuleNotFoundError("You are trying to use `ScheduleWrapper` which require kineto install.")
self._schedule = schedule
self.reset()
def reset(self) -> None:
# handle properly `fast_dev_run`. PyTorch Profiler will fail otherwise.
self._num_training_step = 0
self._num_validation_step = 0
self._num_test_step = 0
self._num_predict_step = 0
self._training_step_reached_end = False
self._validation_step_reached_end = False
self._test_step_reached_end = False
self._predict_step_reached_end = False
# used to stop profiler when `ProfilerAction.RECORD_AND_SAVE` is reached.
self._current_action: Optional[str] = None
self._prev_schedule_action: Optional[ProfilerAction] = None
self._start_action_name: Optional[str] = None
def setup(self, start_action_name: str) -> None:
self._start_action_name = start_action_name
def pre_step(self, current_action: str) -> None:
self._current_action = current_action
@property
def is_training(self) -> bool:
assert self._current_action is not None
return self._current_action.endswith("training_step")
@property
def is_validating(self) -> bool:
assert self._current_action is not None
return self._current_action.endswith("validation_step")
@property
def is_testing(self) -> bool:
assert self._current_action is not None
return self._current_action.endswith("test_step")
@property
def is_predicting(self) -> bool:
assert self._current_action is not None
return self._current_action.endswith("predict_step")
@property
def num_step(self) -> int:
if self.is_training:
return self._num_training_step
if self.is_validating:
return self._num_validation_step
if self.is_testing:
return self._num_test_step
if self.is_predicting:
return self._num_predict_step
return 0
def _step(self) -> None:
if self.is_training:
self._num_training_step += 1
elif self.is_validating:
assert self._start_action_name is not None
if self._start_action_name.endswith("on_fit_start"):
if self._num_training_step > 0:
self._num_validation_step += 1
else:
self._num_validation_step += 1
elif self.is_testing:
self._num_test_step += 1
elif self.is_predicting:
self._num_predict_step += 1
@property
def has_finished(self) -> bool:
if self.is_training:
return self._training_step_reached_end
if self.is_validating:
return self._validation_step_reached_end
if self.is_testing:
return self._test_step_reached_end
if self.is_predicting:
return self._predict_step_reached_end
return False
def __call__(self, num_step: int) -> "ProfilerAction":
# ignore the provided input. Keep internal state instead.
if self._current_action is None or self.has_finished:
return ProfilerAction.NONE
self._step()
action = self._schedule(max(self.num_step, 0))
if self._prev_schedule_action == ProfilerAction.RECORD and action == ProfilerAction.WARMUP:
# Work around the corner case when validation starts before train.
# In this case, the action is RECORD in validation loop, and then call into the train
# and the action is still WARMUP in train and pytorch will recognize this as error.
action = ProfilerAction.RECORD
if action == ProfilerAction.RECORD_AND_SAVE:
if self.is_training:
self._training_step_reached_end = True
elif self.is_validating:
self._validation_step_reached_end = True
elif self.is_testing:
self._test_step_reached_end = True
elif self.is_predicting:
self._predict_step_reached_end = True
self._prev_schedule_action = action
return action
[docs]class PyTorchProfiler(Profiler):
STEP_FUNCTIONS = {"training_step", "validation_step", "test_step", "predict_step"}
AVAILABLE_SORT_KEYS = {
"cpu_time",
"cuda_time",
"cpu_time_total",
"cuda_time_total",
"cpu_memory_usage",
"cuda_memory_usage",
"self_cpu_memory_usage",
"self_cuda_memory_usage",
"count",
}
def __init__(
self,
dirpath: Optional[Union[str, Path]] = None,
filename: Optional[str] = None,
group_by_input_shapes: bool = False,
emit_nvtx: bool = False,
export_to_chrome: bool = True,
row_limit: int = 20,
sort_by_key: Optional[str] = None,
record_module_names: bool = True,
**profiler_kwargs: Any,
) -> None:
r"""This profiler uses PyTorch's Autograd Profiler and lets you inspect the cost of.
different operators inside your model - both on the CPU and GPU
Args:
dirpath: Directory path for the ``filename``. If ``dirpath`` is ``None`` but ``filename`` is present, the
``trainer.log_dir`` (from :class:`~pytorch_lightning.loggers.tensorboard.TensorBoardLogger`)
will be used.
filename: If present, filename where the profiler results will be saved instead of printing to stdout.
The ``.txt`` extension will be used automatically.
group_by_input_shapes: Include operator input shapes and group calls by shape.
emit_nvtx: Context manager that makes every autograd operation emit an NVTX range
Run::
nvprof --profile-from-start off -o trace_name.prof -- <regular command here>
To visualize, you can either use::
nvvp trace_name.prof
torch.autograd.profiler.load_nvprof(path)
export_to_chrome: Whether to export the sequence of profiled operators for Chrome.
It will generate a ``.json`` file which can be read by Chrome.
row_limit: Limit the number of rows in a table, ``-1`` is a special value that
removes the limit completely.
sort_by_key: Attribute used to sort entries. By default
they are printed in the same order as they were registered.
Valid keys include: ``cpu_time``, ``cuda_time``, ``cpu_time_total``,
``cuda_time_total``, ``cpu_memory_usage``, ``cuda_memory_usage``,
``self_cpu_memory_usage``, ``self_cuda_memory_usage``, ``count``.
record_module_names: Whether to add module names while recording autograd operation.
\**profiler_kwargs: Keyword arguments for the PyTorch profiler. This depends on your PyTorch version
Raises:
MisconfigurationException:
If arg ``sort_by_key`` is not present in ``AVAILABLE_SORT_KEYS``.
If arg ``schedule`` is not a ``Callable``.
If arg ``schedule`` does not return a ``torch.profiler.ProfilerAction``.
"""
super().__init__(dirpath=dirpath, filename=filename)
self._group_by_input_shapes = group_by_input_shapes and profiler_kwargs.get("record_shapes", False)
self._emit_nvtx = emit_nvtx
self._export_to_chrome = export_to_chrome
self._row_limit = row_limit
self._sort_by_key = sort_by_key or f"{'cuda' if profiler_kwargs.get('use_cuda', False) else 'cpu'}_time_total"
self._record_module_names = record_module_names
self._profiler_kwargs = profiler_kwargs
self.profiler: Optional[_PROFILER] = None
self.function_events: Optional["EventList"] = None
self._lightning_module: Optional["LightningModule"] = None # set by ProfilerConnector
self._register: Optional[RegisterRecordFunction] = None
self._parent_profiler: Optional[ContextManager] = None
self._recording_map: Dict[str, record_function] = {}
self._start_action_name: Optional[str] = None
self._schedule: Optional[ScheduleWrapper] = None
if _KINETO_AVAILABLE:
self._init_kineto(profiler_kwargs)
if self._sort_by_key not in self.AVAILABLE_SORT_KEYS:
raise MisconfigurationException(
f"Found sort_by_key: {self._sort_by_key}. Should be within {self.AVAILABLE_SORT_KEYS}. "
)
def _init_kineto(self, profiler_kwargs: Any) -> None:
has_schedule = "schedule" in profiler_kwargs
self._has_on_trace_ready = "on_trace_ready" in profiler_kwargs
schedule = profiler_kwargs.get("schedule", None)
if schedule is not None:
if not callable(schedule):
raise MisconfigurationException(f"Schedule should be a callable. Found: {schedule}")
action = schedule(0)
if not isinstance(action, ProfilerAction):
raise MisconfigurationException(
f"Schedule should return a `torch.profiler.ProfilerAction`. Found: {action}"
)
self._default_schedule()
schedule = schedule if has_schedule else self._default_schedule()
self._schedule = ScheduleWrapper(schedule) if schedule is not None else schedule
self._profiler_kwargs["schedule"] = self._schedule
activities = profiler_kwargs.get("activities", None)
self._profiler_kwargs["activities"] = activities or self._default_activities()
self._export_to_flame_graph = profiler_kwargs.get("export_to_flame_graph", False)
self._metric = profiler_kwargs.get("metric", "self_cpu_time_total")
with_stack = profiler_kwargs.get("with_stack", False) or self._export_to_flame_graph
self._profiler_kwargs["with_stack"] = with_stack
@property
def _total_steps(self) -> Union[int, float]:
assert self._schedule is not None
assert self._lightning_module is not None
trainer = self._lightning_module.trainer
if self._schedule.is_training:
return trainer.num_training_batches
if self._schedule.is_validating:
return sum(trainer.num_val_batches) + sum(trainer.num_sanity_val_batches)
if self._schedule.is_testing:
return sum(trainer.num_test_batches)
if self._schedule.is_predicting:
return sum(trainer.num_predict_batches)
raise NotImplementedError("Unsupported schedule")
def _should_override_schedule(self) -> bool:
return (
self._lightning_module is not None
and self._schedule is not None
and self._total_steps < 5
and self._schedule._schedule == self._default_schedule()
)
@staticmethod
@lru_cache(1)
def _default_schedule() -> Optional[Callable]:
if _KINETO_AVAILABLE:
# Those schedule defaults allow the profiling overhead to be negligible over training time.
return torch.profiler.schedule(wait=1, warmup=1, active=3)
def _default_activities(self) -> List["ProfilerActivity"]:
activities: List["ProfilerActivity"] = []
if not _KINETO_AVAILABLE:
return activities
if self._profiler_kwargs.get("use_cpu", True):
activities.append(ProfilerActivity.CPU)
if self._profiler_kwargs.get("use_cuda", is_cuda_available()):
activities.append(ProfilerActivity.CUDA)
return activities
[docs] def start(self, action_name: str) -> None:
if self.profiler is None:
# close profiler if it is already opened. might happen if 2 profilers
# are created and the first one did not call `describe`
if torch.autograd._profiler_enabled():
torch.autograd._disable_profiler()
if self._schedule is not None:
self._schedule.setup(action_name)
self._create_profilers()
profiler = self.profiler.__enter__()
if profiler is not None:
self.profiler = profiler
if self._parent_profiler is not None:
self._parent_profiler.__enter__()
if self._lightning_module is not None and self._register is None and self._record_module_names:
self._register = RegisterRecordFunction(self._lightning_module)
self._register.__enter__()
if self.profiler is not None and action_name not in self._recording_map:
# Add [pl][profile] in name for pytorch profiler to recognize
recording = record_function("[pl][profile]" + action_name)
recording.__enter__()
self._recording_map[action_name] = recording
[docs] def stop(self, action_name: str) -> None:
if action_name in self._recording_map:
self._recording_map[action_name].__exit__(None, None, None)
del self._recording_map[action_name]
if not _KINETO_AVAILABLE or self._emit_nvtx:
return
if self.profiler is not None and any(action_name.endswith(func) for func in self.STEP_FUNCTIONS):
assert isinstance(self.profiler, torch.profiler.profile)
if self._schedule is not None:
self._schedule.pre_step(action_name)
# the default schedule requires a minimum of 5 steps to properly work: `wait=1, warmup=1, active=3`.
# otherwise, this will raise a `segmentation fault`.
if self._should_override_schedule():
warning_cache.warn(
"The PyTorch Profiler default schedule will be overridden as there is not enough "
"steps to properly record traces."
)
self._schedule = None
self.profiler.schedule = torch.profiler.profiler._default_schedule_fn
def on_trace_ready(profiler: _PROFILER) -> None:
if self.dirpath is not None:
if self._export_to_chrome:
handler = tensorboard_trace_handler(
str(self.dirpath), self._prepare_filename(action_name=action_name, extension="")
)
handler(profiler)
if self._export_to_flame_graph:
path = os.path.join(
self.dirpath, self._prepare_filename(action_name=action_name, extension=".stack")
)
assert isinstance(profiler, torch.autograd.profiler.profile)
profiler.export_stacks(path, metric=self._metric)
else:
rank_zero_warn("The PyTorchProfiler failed to export trace as `dirpath` is None")
if not self._has_on_trace_ready:
self.profiler.on_trace_ready = on_trace_ready
if self._schedule is not None:
self.profiler.step_num = self._schedule.num_step
self.profiler.step()
self.profiler.add_metadata("Framework", "pytorch-lightning")
def summary(self) -> str:
if not self._profiler_kwargs.get("enabled", True) or self._emit_nvtx:
return ""
self._delete_profilers()
if not self.function_events:
return ""
if self._export_to_chrome and not _KINETO_AVAILABLE:
filename = f"{self.local_rank}_trace.json"
path_to_trace = filename if self.dirpath is None else os.path.join(self.dirpath, filename)
self.function_events.export_chrome_trace(path_to_trace)
data = self.function_events.key_averages(group_by_input_shapes=self._group_by_input_shapes)
table = data.table(sort_by=self._sort_by_key, row_limit=self._row_limit)
recorded_stats = {"records": table}
return self._stats_to_str(recorded_stats)
def _create_profilers(self) -> None:
if self.profiler is not None:
return
if self._emit_nvtx:
if self._parent_profiler is None:
self._parent_profiler = torch.cuda.profiler.profile()
self.profiler = self._create_profiler(torch.autograd.profiler.emit_nvtx)
else:
self._parent_profiler = None
self.profiler = self._create_profiler(
torch.profiler.profile if _KINETO_AVAILABLE else torch.autograd.profiler.profile
)
def _create_profiler(self, profiler: Type[_PROFILER]) -> _PROFILER:
init_parameters = inspect.signature(profiler.__init__).parameters
kwargs = {k: v for k, v in self._profiler_kwargs.items() if k in init_parameters}
return profiler(**kwargs)
def _cache_functions_events(self) -> None:
if self._emit_nvtx:
return
if _KINETO_AVAILABLE:
assert isinstance(self.profiler, torch.profiler.profile)
self.function_events = self.profiler.events()
else:
assert isinstance(self.profiler, torch.autograd.profiler.profile)
self.function_events = self.profiler.function_events
def _delete_profilers(self) -> None:
if self.profiler is not None:
self.profiler.__exit__(None, None, None)
self._cache_functions_events()
self.profiler = None
if self._schedule is not None:
self._schedule.reset()
if self._parent_profiler is not None:
self._parent_profiler.__exit__(None, None, None)
self._parent_profiler = None
if self._register is not None:
self._register.__exit__(None, None, None)
self._register = None
[docs] def teardown(self, stage: Optional[str]) -> None:
self._delete_profilers()
for k in list(self._recording_map):
self.stop(k)
self._recording_map = {}
super().teardown(stage=stage)
