267 lines
10 KiB
Python
267 lines
10 KiB
Python
# 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 pathlib import Path
|
|
from typing import List, Optional, Union
|
|
|
|
import torch
|
|
|
|
from pytorch_lightning.profiler.profilers import BaseProfiler
|
|
from pytorch_lightning.utilities.distributed import rank_zero_warn
|
|
from pytorch_lightning.utilities.exceptions import MisconfigurationException
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
|
|
class PyTorchProfiler(BaseProfiler):
|
|
|
|
PROFILED_FUNCTIONS = ("training_step_and_backward", "validation_step", "test_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,
|
|
enabled: bool = True,
|
|
use_cuda: bool = False,
|
|
record_shapes: bool = False,
|
|
profile_memory: bool = False,
|
|
group_by_input_shapes: bool = False,
|
|
with_stack: bool = False,
|
|
use_kineto: bool = False,
|
|
use_cpu: bool = True,
|
|
emit_nvtx: bool = False,
|
|
export_to_chrome: bool = False,
|
|
path_to_export_trace: str = None,
|
|
row_limit: int = 20,
|
|
sort_by_key: Optional[str] = None,
|
|
profiled_functions: Optional[List] = None,
|
|
output_filename: Optional[str] = None,
|
|
):
|
|
"""
|
|
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.
|
|
|
|
enabled: Setting this to False makes this context manager a no-op.
|
|
|
|
use_cuda: Enables timing of CUDA events as well using the cudaEvent API.
|
|
Adds approximately 4us of overhead to each tensor operation.
|
|
|
|
record_shapes: If shapes recording is set, information about input dimensions will be collected.
|
|
|
|
profile_memory: Whether to report memory usage, default: True (Introduced in PyTorch 1.6.0)
|
|
|
|
group_by_input_shapes: Include operator input shapes and group calls by shape.
|
|
|
|
with_stack: record source information (file and line number) for the ops (Introduced in PyTorch 1.7.0)
|
|
|
|
use_kineto: experimental support for Kineto profiler (Introduced in PyTorch 1.8.0)
|
|
|
|
use_cpu: use_kineto=True and can be used to lower the overhead
|
|
for GPU-only profiling (Introduced in PyTorch 1.8.0)
|
|
|
|
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: Wether to export the sequence of profiled operators for Chrome.
|
|
It will generate a ``.json`` file which can be read by Chrome.
|
|
|
|
path_to_export_trace: Directory path to export ``.json`` traces when using ``export_to_chrome=True``.
|
|
By default, it will be save where the file being is being run.
|
|
|
|
row_limit: Limit the number of rows in a table, `0` is a special value that
|
|
removes the limit completely.
|
|
|
|
sort_by_key: Keys to sort out profiled table
|
|
|
|
profiled_functions: list of profiled functions which will create a context manager on.
|
|
Any other will be pass through.
|
|
|
|
Raises:
|
|
MisconfigurationException:
|
|
If arg ``sort_by_key`` is not present in ``AVAILABLE_SORT_KEYS``.
|
|
ValueError:
|
|
If you attempt to stop recording an action which was never started.
|
|
"""
|
|
|
|
self.profiled_actions = {}
|
|
self.enabled = enabled
|
|
self.profiled_functions = profiled_functions or self.PROFILED_FUNCTIONS
|
|
self.use_cuda = use_cuda
|
|
self.record_shapes = record_shapes
|
|
self.profile_memory = profile_memory
|
|
self.sort_by_key = sort_by_key or ("cuda_time_total" if self.use_cuda else "cpu_time_total")
|
|
self.with_stack = with_stack
|
|
self.group_by_input_shapes = group_by_input_shapes and record_shapes
|
|
self.use_kineto = use_kineto
|
|
self.use_cpu = use_cpu
|
|
self.row_limit = row_limit
|
|
self.emit_nvtx = emit_nvtx
|
|
self.export_to_chrome = export_to_chrome
|
|
self.path_to_export_trace = path_to_export_trace
|
|
|
|
if export_to_chrome and path_to_export_trace is None:
|
|
rank_zero_warn(
|
|
"The exported trace would be save locally as `path_to_export_trace` is empty."
|
|
" Note: Each functions will generate its own traced file."
|
|
)
|
|
|
|
if self.sort_by_key not in self.AVAILABLE_SORT_KEYS:
|
|
raise MisconfigurationException(
|
|
f"Found sort_by_key: {sort_by_key}. Should be within {self.AVAILABLE_SORT_KEYS}. "
|
|
)
|
|
|
|
self.profiled_actions = {}
|
|
self.context_names = {}
|
|
self.running_stack = []
|
|
self.profiler = None
|
|
|
|
super().__init__(dirpath=dirpath, filename=filename, output_filename=output_filename)
|
|
|
|
def setup(
|
|
self,
|
|
stage: Optional[str] = None,
|
|
local_rank: Optional[int] = None,
|
|
log_dir: Optional[str] = None
|
|
) -> None:
|
|
super().setup(stage=stage, local_rank=local_rank, log_dir=log_dir)
|
|
|
|
# if the user didn't provide `path_to_export_trace`,
|
|
# set it as TensorBoardLogger log_dir if exists
|
|
if self.path_to_export_trace is None:
|
|
self.path_to_export_trace = log_dir
|
|
|
|
def start(self, action_name: str) -> None:
|
|
if action_name not in self.profiled_functions:
|
|
return
|
|
|
|
if len(self.running_stack) > 0:
|
|
self._stop(self.running_stack[-1])
|
|
self.running_stack.append(action_name)
|
|
|
|
self.context_names[action_name] = "/".join(self.running_stack)
|
|
|
|
self._start(action_name)
|
|
|
|
def _start(self, action_name: str) -> None:
|
|
if self.emit_nvtx:
|
|
self._parent_profiler = self._create_profiler(action_name, torch.cuda.profiler.profile, enter=True)
|
|
self._create_profiler(action_name, torch.autograd.profiler.emit_nvtx)
|
|
else:
|
|
self._create_profiler(action_name, torch.autograd.profiler.profile)
|
|
|
|
def _create_profiler(self, action_name, profiler, enter=True):
|
|
init_args = inspect.signature(profiler.__init__).parameters
|
|
profiler_args = {k: v for k, v in vars(self).items() if k in init_args}
|
|
pr = profiler(**profiler_args)
|
|
if enter:
|
|
out_pr = pr.__enter__()
|
|
if out_pr is not None:
|
|
pr = out_pr
|
|
self.profiler = pr
|
|
return self.profiler
|
|
|
|
def _stop(self, action_name: str) -> None:
|
|
if self.profiler is None:
|
|
return
|
|
|
|
self.profiler.__exit__(exc_type=None, exc_val=None, exc_tb=None)
|
|
|
|
if isinstance(self.profiler, torch.autograd.profiler.emit_nvtx):
|
|
# when running ``emit_nvtx``, PyTorch requires 2 context manager.
|
|
# The parent_profiler is being closed too.
|
|
self._parent_profiler.__exit__(None, None, None)
|
|
self._parent_profiler = None
|
|
return
|
|
|
|
function_events = self.profiler.function_events
|
|
self.profiler = None
|
|
for name in self.running_stack:
|
|
if name not in self.profiled_actions:
|
|
self.profiled_actions[name] = function_events
|
|
else:
|
|
self.profiled_actions[name] += function_events
|
|
|
|
def stop(self, action_name: str) -> None:
|
|
if action_name not in self.profiled_functions:
|
|
return
|
|
|
|
if len(self.running_stack) == 0 or self.running_stack[-1] != action_name:
|
|
raise ValueError( # pragma: no-cover
|
|
f"Attempting to stop recording an action ({action_name}) which was never started."
|
|
)
|
|
self._stop(action_name)
|
|
self.running_stack.pop()
|
|
# restore running profiler
|
|
if len(self.running_stack) > 0:
|
|
self._start(self.running_stack[-1])
|
|
|
|
def summary(self) -> str:
|
|
recorded_stats = {}
|
|
output_string = ''
|
|
|
|
if not self.enabled:
|
|
return output_string
|
|
|
|
for action_name, function_events in self.profiled_actions.items():
|
|
|
|
# next line is a workaround for a pytorch issue (fixed on master, still present
|
|
# on 1.7). Without it the code fails with `AssertionError: There is already a CPU
|
|
# parent event for detach`
|
|
function_events.populate_cpu_children = lambda: None
|
|
|
|
if self.export_to_chrome:
|
|
filename = f"{action_name}_{self.local_rank}_trace.json"
|
|
path_to_trace = filename if self.path_to_export_trace is None \
|
|
else os.path.join(self.path_to_export_trace, filename)
|
|
function_events.export_chrome_trace(path_to_trace)
|
|
|
|
if self.emit_nvtx:
|
|
return output_string
|
|
|
|
else:
|
|
data = 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[action_name] = table
|
|
return self._stats_to_str(recorded_stats)
|