Profiler summary

CHANGELOG.md

@@ -18,6 +18,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added a check that stops the training when loss or weights contain `NaN` or `inf` values. ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
 - Updated references to self.forward() to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
 - Added support for `IterableDataset` when `val_check_interval=1.0` (default), this will trigger validation at the end of each epoch. ([#1283](https://github.com/PyTorchLightning/pytorch-lightning/pull/1283))
+- Added `summary` method to Profilers. ([#1259](https://github.com/PyTorchLightning/pytorch-lightning/pull/1259))
 - Added informative errors if user defined dataloader has zero length ([#1280](https://github.com/PyTorchLightning/pytorch-lightning/pull/1280))
 
 ### Changed
@@ -72,7 +73,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Support for user defined callbacks ([#889](https://github.com/PyTorchLightning/pytorch-lightning/pull/889) and [#950](https://github.com/PyTorchLightning/pytorch-lightning/pull/950))
 - Added support for multiple loggers to be passed to `Trainer` as an iterable (e.g. list, tuple, etc.) ([#903](https://github.com/PyTorchLightning/pytorch-lightning/pull/903))
 - Added support for step-based learning rate scheduling ([#941](https://github.com/PyTorchLightning/pytorch-lightning/pull/941))
-- Added support for logging hparams as dict ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
+- Added support for logging `hparams` as dict ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
 - Checkpoint and early stopping now work without val. step ([#1041](https://github.com/PyTorchLightning/pytorch-lightning/pull/1041))
 - Support graceful training cleanup after Keyboard Interrupt ([#856](https://github.com/PyTorchLightning/pytorch-lightning/pull/856), [#1019](https://github.com/PyTorchLightning/pytorch-lightning/pull/1019))
 - Added type hints for function arguments ([#912](https://github.com/PyTorchLightning/pytorch-lightning/pull/912), )

pytorch_lightning/core/lightning.py

@@ -20,7 +20,7 @@
 from pytorch_lightning.core.memory import ModelSummary
 from pytorch_lightning.core.saving import ModelIO, load_hparams_from_tags_csv
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     import torch_xla.core.xla_model as xm

pytorch_lightning/loggers/comet.py

@@ -28,7 +28,7 @@
 
 from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
 class CometLogger(LightningLoggerBase):

pytorch_lightning/profiler/__init__.py

@@ -3,7 +3,7 @@
 
 
 Built-in checks
-----------------
+---------------
 
 PyTorch Lightning supports profiling standard actions in the training loop out of the box, including:
 
@@ -20,7 +20,7 @@
 - on_training_end
 
 Enable simple profiling
--------------------------
+-----------------------
 
 If you only wish to profile the standard actions, you can set `profiler=True` when constructing
 your `Trainer` object.
@@ -113,10 +113,11 @@ def custom_processing_step(self, data):
 
 """
 
-from pytorch_lightning.profiler.profiler import Profiler, AdvancedProfiler, PassThroughProfiler
+from pytorch_lightning.profiler.profilers import SimpleProfiler, AdvancedProfiler, PassThroughProfiler, BaseProfiler
 
 __all__ = [
-    'Profiler',
+    'BaseProfiler',
+    'SimpleProfiler',
     'AdvancedProfiler',
     'PassThroughProfiler',
 ]

pytorch_lightning/profiler/profiler.py → pytorch_lightning/profiler/profilers.py

@@ -1,5 +1,6 @@
 import cProfile
 import io
+import os
 import pstats
 import time
 from abc import ABC, abstractmethod
@@ -16,6 +17,18 @@ class BaseProfiler(ABC):
     If you wish to write a custom profiler, you should inhereit from this class.
     """
 
+    def __init__(self, output_streams: list = None):
+        """
+        Params:
+            stream_out: callable
+        """
+        if output_streams:
+            if not isinstance(output_streams, (list, tuple)):
+                output_streams = [output_streams]
+        else:
+            output_streams = []
+        self.write_streams = output_streams
+
     @abstractmethod
     def start(self, action_name: str) -> None:
         """Defines how to start recording an action."""
@@ -57,7 +70,12 @@ def profile_iterable(self, iterable, action_name: str) -> None:
 
     def describe(self) -> None:
         """Logs a profile report after the conclusion of the training run."""
-        pass
+        for write in self.write_streams:
+            write(self.summary())
+
+    @abstractmethod
+    def summary(self) -> str:
+        """Create profiler summary in text format."""
 
 
 class PassThroughProfiler(BaseProfiler):
@@ -67,25 +85,39 @@ class PassThroughProfiler(BaseProfiler):
     """
 
     def __init__(self):
-        pass
+        super().__init__(output_streams=None)
 
     def start(self, action_name: str) -> None:
         pass
 
     def stop(self, action_name: str) -> None:
         pass
 
+    def summary(self) -> str:
+        return ""
+
 
-class Profiler(BaseProfiler):
+class SimpleProfiler(BaseProfiler):
     """
     This profiler simply records the duration of actions (in seconds) and reports
     the mean duration of each action and the total time spent over the entire training run.
     """
 
-    def __init__(self):
+    def __init__(self, output_filename: str = None):
+        """
+        Params:
+            output_filename (str): optionally save profile results to file instead of printing
+                to std out when training is finished.
+        """
         self.current_actions = {}
         self.recorded_durations = defaultdict(list)
 
+        self.output_fname = output_filename
+        self.output_file = open(self.output_fname, 'w') if self.output_fname else None
+
+        streaming_out = [self.output_file.write] if self.output_file else [log.info]
+        super().__init__(output_streams=streaming_out)
+
     def start(self, action_name: str) -> None:
         if action_name in self.current_actions:
             raise ValueError(
@@ -103,20 +135,31 @@ def stop(self, action_name: str) -> None:
         duration = end_time - start_time
         self.recorded_durations[action_name].append(duration)
 
-    def describe(self) -> None:
+    def summary(self) -> str:
         output_string = "\n\nProfiler Report\n"
 
         def log_row(action, mean, total):
-            return f"\n{action:<20s}\t|  {mean:<15}\t|  {total:<15}"
+            return f"{os.linesep}{action:<20s}\t|  {mean:<15}\t|  {total:<15}"
 
         output_string += log_row("Action", "Mean duration (s)", "Total time (s)")
-        output_string += f"\n{'-' * 65}"
+        output_string += f"{os.linesep}{'-' * 65}"
         for action, durations in self.recorded_durations.items():
             output_string += log_row(
                 action, f"{np.mean(durations):.5}", f"{np.sum(durations):.5}",
             )
-        output_string += "\n"
-        log.info(output_string)
+        output_string += os.linesep
+        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()
 
 
 class AdvancedProfiler(BaseProfiler):
@@ -136,9 +179,14 @@ def __init__(self, output_filename: str = None, line_count_restriction: float =
                 or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines)
         """
         self.profiled_actions = {}
-        self.output_filename = output_filename
         self.line_count_restriction = line_count_restriction
 
+        self.output_fname = output_filename
+        self.output_file = open(self.output_fname, 'w') if self.output_fname else None
+
+        streaming_out = [self.output_file.write] if self.output_file else [log.info]
+        super().__init__(output_streams=streaming_out)
+
     def start(self, action_name: str) -> None:
         if action_name not in self.profiled_actions:
             self.profiled_actions[action_name] = cProfile.Profile()
@@ -152,22 +200,28 @@ def stop(self, action_name: str) -> None:
             )
         pr.disable()
 
-    def describe(self) -> None:
-        self.recorded_stats = {}
+    def summary(self) -> str:
+        recorded_stats = {}
         for action_name, pr in self.profiled_actions.items():
             s = io.StringIO()
             ps = pstats.Stats(pr, stream=s).strip_dirs().sort_stats('cumulative')
             ps.print_stats(self.line_count_restriction)
-            self.recorded_stats[action_name] = s.getvalue()
-        if self.output_filename is not None:
-            # save to file
-            with open(self.output_filename, "w") as f:
-                for action, stats in self.recorded_stats.items():
-                    f.write(f"Profile stats for: {action}")
-                    f.write(stats)
-        else:
-            # log to standard out
-            output_string = "\nProfiler Report\n"
-            for action, stats in self.recorded_stats.items():
-                output_string += f"\nProfile stats for: {action}\n{stats}"
-            log.info(output_string)
+            recorded_stats[action_name] = s.getvalue()
+
+        # 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}{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()

pytorch_lightning/trainer/data_loading.py

@@ -6,7 +6,7 @@
 from torch.utils.data.distributed import DistributedSampler
 
 from pytorch_lightning.core import LightningModule
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     from apex import amp

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -122,7 +122,7 @@ def train_fx(trial_hparams, cluster_manager, _):
 import torch
 from pytorch_lightning import _logger as log
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     from apex import amp

pytorch_lightning/trainer/distrib_parts.py

@@ -344,7 +344,7 @@
     LightningDistributedDataParallel,
     LightningDataParallel,
 )
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     from apex import amp

pytorch_lightning/trainer/evaluation_loop.py

@@ -135,7 +135,7 @@
 
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel, LightningDataParallel
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 try:
     import torch_xla.distributed.parallel_loader as xla_pl

pytorch_lightning/trainer/trainer.py

@@ -18,8 +18,7 @@
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.profiler import Profiler, PassThroughProfiler
-from pytorch_lightning.profiler.profiler import BaseProfiler
+from pytorch_lightning.profiler import SimpleProfiler, PassThroughProfiler, BaseProfiler
 from pytorch_lightning.trainer.auto_mix_precision import TrainerAMPMixin
 from pytorch_lightning.trainer.callback_config import TrainerCallbackConfigMixin
 from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
@@ -33,7 +32,7 @@
 from pytorch_lightning.trainer.training_io import TrainerIOMixin
 from pytorch_lightning.trainer.training_loop import TrainerTrainLoopMixin
 from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.trainer.supporters import TensorRunningMean
 
 try:
@@ -364,7 +363,7 @@ def __init__(
 
         # configure profiler
         if profiler is True:
-            profiler = Profiler()
+            profiler = SimpleProfiler()
         self.profiler = profiler or PassThroughProfiler()
 
         # configure early stop callback
@@ -490,10 +489,10 @@ def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
              ('print_nan_grads', (<class 'bool'>,), False),
              ('process_position', (<class 'int'>,), 0),
              ('profiler',
-              (<class 'pytorch_lightning.profiler.profiler.BaseProfiler'>,
+              (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,
                <class 'NoneType'>),
               None),
-            ...
+             ...
         """
         trainer_default_params = inspect.signature(cls).parameters
         name_type_default = []

pytorch_lightning/trainer/training_loop.py

@@ -145,7 +145,7 @@ def training_step(self, batch, batch_idx):
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.trainer.supporters import TensorRunningMean
 
 try:

tests/loggers/test_comet.py

@@ -8,7 +8,7 @@
 import tests.base.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import CometLogger
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import LightningTestModel
 
 

tests/models/test_amp.py

@@ -5,7 +5,7 @@
 
 import tests.base.utils as tutils
 from pytorch_lightning import Trainer
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import (
     LightningTestModel,
 )

tests/models/test_gpu.py

@@ -11,7 +11,7 @@
     parse_gpu_ids,
     determine_root_gpu_device,
 )
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import LightningTestModel
 
 PRETEND_N_OF_GPUS = 16

tests/models/test_restore.py

@@ -8,7 +8,7 @@
 import tests.base.utils as tutils
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.utilities.debugging import MisconfigurationException
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import (
     LightningTestModel,
     LightningTestModelWithoutHyperparametersArg,

tests/test_deprecated.py

@@ -57,6 +57,8 @@ def test_tbd_remove_in_v0_9_0_module_imports():
     from pytorch_lightning.logging.test_tube import TestTubeLogger  # noqa: F402
     from pytorch_lightning.logging.wandb import WandbLogger  # noqa: F402
 
+    from pytorch_lightning.profiler import SimpleProfiler, AdvancedProfiler  # noqa: F402
+
 
 class ModelVer0_6(LightTrainDataloader, LightEmptyTestStep, TestModelBase):