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[bug] Fix Pytorch profiler with emit_nvtx (#6260)
* resolve bug * update changelog * Update tests/trainer/test_trainer.py * Update pytorch_lightning/profiler/profilers.py Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * resolve comments * resolve flake8 Co-authored-by: Carlos Mocholí <carlossmocholi@gmail.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,303 @@ | ||
| # 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 typing import List, Optional | ||
|
|
||
| import torch | ||
|
|
||
| from pytorch_lightning.profiler.profilers import BaseProfiler | ||
| from pytorch_lightning.utilities import rank_zero_only | ||
| from pytorch_lightning.utilities.cloud_io import get_filesystem | ||
| from pytorch_lightning.utilities.distributed import rank_zero_warn | ||
| from pytorch_lightning.utilities.exceptions import MisconfigurationException | ||
|
|
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| log = logging.getLogger(__name__) | ||
|
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|
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| class PyTorchProfiler(BaseProfiler): | ||
|
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| 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, | ||
| output_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, | ||
| local_rank: Optional[int] = 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: | ||
| output_filename: optionally save profile results to file instead of printing | ||
| to std out when training is finished. When using ``ddp``, | ||
| each rank will stream the profiled operation to their own file | ||
| with the extension ``_{rank}.txt`` | ||
| 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. | ||
| local_rank: When running in distributed setting, local_rank is used for each process | ||
| to write to their own file if `output_fname` is provided. | ||
| Raises: | ||
| MisconfigurationException: | ||
| If arg ``sort_by_key`` is not present in ``AVAILABLE_SORT_KEYS``, or | ||
| if log file is not a ``.txt`` file. | ||
| 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 | ||
|
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| 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." | ||
| ) | ||
|
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| 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}. " | ||
| ) | ||
|
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| self.profiled_actions = {} | ||
| self.context_names = {} | ||
| self.running_stack = [] | ||
| self.profiler = None | ||
|
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| self.output_fname = output_filename | ||
| self.output_file = None | ||
| if local_rank is not None: | ||
| self.on_train_start(local_rank=local_rank) | ||
| self.on_train_start = super().on_train_start | ||
|
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| def on_train_start(self, local_rank: Optional[str] = None): | ||
| self.local_rank = local_rank | ||
|
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| # when logging to `log.info`, only perform profiling on rank 0 | ||
| if local_rank != 0 and self.output_fname is None: | ||
| self.wrap_functions_into_rank_zero_only() | ||
|
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| if self.output_fname: | ||
| if local_rank is not None: | ||
| if '.txt' not in self.output_fname: | ||
| raise MisconfigurationException("Log file should be .txt file.") | ||
|
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| self.output_fname = self.output_fname.replace(".txt", f"_{self.local_rank}.txt") | ||
|
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| fs = get_filesystem(self.output_fname) | ||
| 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] | ||
| super().__init__(output_streams=streaming_out) | ||
|
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| def wrap_functions_into_rank_zero_only(self): | ||
| self.start = rank_zero_only(self.start) | ||
| self.stop = rank_zero_only(self.stop) | ||
| self.summary = rank_zero_only(self.summary) | ||
| self.describe = rank_zero_only(self.describe) | ||
|
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| def start(self, action_name: str) -> None: | ||
| if action_name not in self.profiled_functions: | ||
| return | ||
|
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| if len(self.running_stack) > 0: | ||
| self._stop(self.running_stack[-1]) | ||
| self.running_stack.append(action_name) | ||
|
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| self.context_names[action_name] = "/".join(self.running_stack) | ||
|
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| self._start(action_name) | ||
|
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| 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) | ||
|
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| 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 | ||
|
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| def _stop(self, action_name: str) -> None: | ||
| if self.profiler is None: | ||
| return | ||
|
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| self.profiler.__exit__(exc_type=None, exc_val=None, exc_tb=None) | ||
|
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| 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) | ||
| return | ||
|
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||
| 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 | ||
|
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| def stop(self, action_name: str) -> None: | ||
| if action_name not in self.profiled_functions: | ||
| return | ||
|
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| 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]) | ||
|
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||
| def summary(self) -> str: | ||
| recorded_stats = {} | ||
| output_string = '' | ||
| local_rank = '0' if self.local_rank is None else self.local_rank | ||
|
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| if not self.enabled: | ||
| return output_string | ||
|
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| for action_name, function_events in self.profiled_actions.items(): | ||
|
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| # 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 | ||
|
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| if self.export_to_chrome: | ||
| filename = f"{action_name}_{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) | ||
|
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| if self.emit_nvtx: | ||
| return output_string | ||
|
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| 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 | ||
|
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| # log to standard out | ||
| output_string = f"{os.linesep}Profiler Report{os.linesep}" | ||
| for action, stats in recorded_stats.items(): | ||
| output_string += (f"{os.linesep}Profile stats for: {action} rank: {local_rank} {os.linesep}{stats}") | ||
|
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| return output_string | ||
|
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| def describe(self): | ||
| """Logs a profile report after the conclusion of the training run.""" | ||
| super().describe() | ||
| if self.output_file: | ||
| self.output_file.flush() | ||
|
|
||
| def __del__(self): | ||
| """Close profiler's stream.""" | ||
| if self.output_file: | ||
| self.output_file.close() |
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