update

Author: tchaton

pytorch_lightning/profiler/__init__.py

@@ -194,13 +194,8 @@ def custom_processing_step(self, data):
 
 """
 
-from pytorch_lightning.profiler.profilers import (
-    AdvancedProfiler,
-    BaseProfiler,
-    PassThroughProfiler,
-    PyTorchProfiler,
-    SimpleProfiler,
-)
+from pytorch_lightning.profiler.profilers import AdvancedProfiler, BaseProfiler, PassThroughProfiler, SimpleProfiler
+from pytorch_lightning.profiler.pytorch import PyTorchProfiler
 
 __all__ = [
     'BaseProfiler',

pytorch_lightning/profiler/profilers.py

@@ -284,261 +284,3 @@ def __del__(self):
         """Close profiler's stream."""
         if self.output_file:
             self.output_file.close()
-
-
-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,
-        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.
-        """
-
-        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
-
-        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
-
-    def on_train_start(self, local_rank: Optional[str] = None):
-        self.local_rank = local_rank
-
-        # 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()
-
-        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.")
-
-                self.output_fname = self.output_fname.replace(".txt", f"_{self.local_rank}.txt")
-
-            fs = get_filesystem(self.output_fname)
-            self.output_file = fs.open(self.output_fname, "w")
-
-        streaming_out = [self.output_file.write] if self.output_file else [log.info]
-        super().__init__(output_streams=streaming_out)
-
-    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)
-
-    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._create_profiler(action_name, torch.cuda.profiler.profile, enter=False)
-            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:
-            pr = pr.__enter__()
-        self.profiler = pr
-
-    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)
-
-        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 = ''
-        local_rank = '0' if self.local_rank is None else self.local_rank
-
-        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}_{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
-
-        # 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}")
-
-        return output_string
-
-    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()

tests/trainer/test_trainer.py

@@ -34,7 +34,7 @@
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.core.saving import load_hparams_from_tags_csv, load_hparams_from_yaml, save_hparams_to_tags_csv
 from pytorch_lightning.loggers import TensorBoardLogger
-from pytorch_lightning.profiler.profilers import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
+from pytorch_lightning.profiler import AdvancedProfiler, PassThroughProfiler, PyTorchProfiler, SimpleProfiler
 from pytorch_lightning.trainer.logging import TrainerLoggingMixin
 from pytorch_lightning.trainer.states import TrainerState
 from pytorch_lightning.utilities import _NATIVE_AMP_AVAILABLE