Disable checkpointing, earlystopping and logging with fast_dev_run

CHANGELOG.md

@@ -20,6 +20,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 
 ### Fixed
+- Disabled checkpointing, earlystopping and logger with `fast_dev_run` ([#5277](https://github.com/PyTorchLightning/pytorch-lightning/pull/5277))
 
 
 

docs/source/debugging.rst

@@ -28,13 +28,18 @@ The point is to detect any bugs in the training/validation loop without having t
 argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
 
 .. testcode::
-    
+
     # runs 1 train, val, test batch and program ends
     trainer = Trainer(fast_dev_run=True)
 
     # runs 7 train, val, test batches and program ends
     trainer = Trainer(fast_dev_run=7)
 
+.. note::
+
+    This argument will disable tuner, checkpoint callbacks, early stopping callbacks,
+    loggers and logger callbacks like ``LearningRateLogger`` and runs for only 1 epoch.
+
 ----------------
 
 Inspect gradient norms

docs/source/trainer.rst

@@ -666,9 +666,9 @@ Under the hood the pseudocode looks like this when running *fast_dev_run* with a
 .. note::
 
     This argument is a bit different from ``limit_train/val/test_batches``. Setting this argument will
-    disable tuner, logger callbacks like ``LearningRateLogger`` and runs for only 1 epoch. This must be
-    used only for debugging purposes. ``limit_train/val/test_batches`` only limits the number of batches and won't
-    disable anything.
+    disable tuner, checkpoint callbacks, early stopping callbacks, loggers and logger callbacks like
+    ``LearningRateLogger`` and runs for only 1 epoch. This must be used only for debugging purposes.
+    ``limit_train/val/test_batches`` only limits the number of batches and won't disable anything.
 
 gpus
 ^^^^

pytorch_lightning/callbacks/early_stopping.py

@@ -28,7 +28,7 @@
 from pytorch_lightning import _logger as log
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.metrics.metric import Metric
-from pytorch_lightning.utilities import TPU_AVAILABLE, rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn, TPU_AVAILABLE
 
 
 class EarlyStopping(Callback):
@@ -166,10 +166,10 @@ def on_validation_end(self, trainer, pl_module):
         self._run_early_stopping_check(trainer, pl_module)
 
     def on_validation_epoch_end(self, trainer, pl_module):
-        if trainer.running_sanity_check:
+        if trainer.fast_dev_run or trainer.running_sanity_check:
             return
 
-        if self._validate_condition_metric(trainer.logger_connector.callback_metrics):
+        if self._validate_condition_metric(trainer.callback_metrics):
             # turn off early stopping in on_train_epoch_end
             self.based_on_eval_results = True
 
@@ -178,24 +178,19 @@ def on_train_epoch_end(self, trainer, pl_module, outputs):
         if self.based_on_eval_results:
             return
 
-        # early stopping can also work in the train loop when there is no val loop
-        should_check_early_stop = False
-
-        # fallback to monitor key in result dict
-        if trainer.logger_connector.callback_metrics.get(self.monitor, None) is not None:
-            should_check_early_stop = True
-
-        if should_check_early_stop:
-            self._run_early_stopping_check(trainer, pl_module)
+        self._run_early_stopping_check(trainer, pl_module)
 
     def _run_early_stopping_check(self, trainer, pl_module):
         """
         Checks whether the early stopping condition is met
         and if so tells the trainer to stop the training.
         """
-        logs = trainer.logger_connector.callback_metrics
+        logs = trainer.callback_metrics
 
-        if not self._validate_condition_metric(logs):
+        if (
+            trainer.fast_dev_run  # disable early_stopping with fast_dev_run
+            or not self._validate_condition_metric(logs)  # short circuit if metric not present
+        ):
             return  # short circuit if metric not present
 
         current = logs.get(self.monitor)

pytorch_lightning/callbacks/gpu_stats_monitor.py

@@ -24,7 +24,7 @@
 import shutil
 import subprocess
 import time
-from typing import List, Tuple, Dict
+from typing import Dict, List, Tuple
 
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities import rank_zero_only
@@ -213,5 +213,4 @@ def _should_log(trainer) -> bool:
             or trainer.should_stop
         )
 
-        should_log = should_log and not trainer.fast_dev_run
         return should_log

pytorch_lightning/callbacks/lr_monitor.py

@@ -105,15 +105,15 @@ def on_train_batch_start(self, trainer, *args, **kwargs):
             interval = 'step' if self.logging_interval is None else 'any'
             latest_stat = self._extract_stats(trainer, interval)
 
-            if trainer.logger is not None and latest_stat:
+            if latest_stat:
                 trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
 
     def on_train_epoch_start(self, trainer, *args, **kwargs):
         if self.logging_interval != 'step':
             interval = 'epoch' if self.logging_interval is None else 'any'
             latest_stat = self._extract_stats(trainer, interval)
 
-            if trainer.logger is not None and latest_stat:
+            if latest_stat:
                 trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
 
     def _extract_stats(self, trainer, interval: str) -> Dict[str, float]:
@@ -190,5 +190,4 @@ def _should_log(trainer) -> bool:
             or trainer.should_stop
         )
 
-        should_log = should_log and not trainer.fast_dev_run
         return should_log

pytorch_lightning/callbacks/model_checkpoint.py

@@ -224,7 +224,8 @@ def save_checkpoint(self, trainer, pl_module):
         global_step = trainer.global_step
 
         if (
-            self.save_top_k == 0  # no models are saved
+            trainer.fast_dev_run  # disable checkpointing with fast_dev_run
+            or self.save_top_k == 0  # no models are saved
             or self.period < 1  # no models are saved
             or (epoch + 1) % self.period  # skip epoch
             or trainer.running_sanity_check  # don't save anything during sanity check
@@ -478,14 +479,14 @@ def __resolve_ckpt_dir(self, trainer, pl_module):
             version, name = trainer.accelerator_backend.broadcast((version, trainer.logger.name))
 
             ckpt_path = os.path.join(
-                save_dir, name, version, "checkpoints"
+                save_dir, str(name), version, "checkpoints"
             )
         else:
             ckpt_path = os.path.join(trainer.weights_save_path, "checkpoints")
 
         self.dirpath = ckpt_path
 
-        if trainer.is_global_zero:
+        if not trainer.fast_dev_run and trainer.is_global_zero:
             self._fs.makedirs(self.dirpath, exist_ok=True)
 
     def _add_backward_monitor_support(self, trainer):

pytorch_lightning/callbacks/progress.py

@@ -22,7 +22,6 @@
 import importlib
 import sys
 
-
 # check if ipywidgets is installed before importing tqdm.auto
 # to ensure it won't fail and a progress bar is displayed
 if importlib.util.find_spec('ipywidgets') is not None:
@@ -323,7 +322,7 @@ def on_epoch_start(self, trainer, pl_module):
         super().on_epoch_start(trainer, pl_module)
         total_train_batches = self.total_train_batches
         total_val_batches = self.total_val_batches
-        if total_train_batches != float('inf') and not trainer.fast_dev_run:
+        if total_train_batches != float('inf'):
             # val can be checked multiple times per epoch
             val_checks_per_epoch = total_train_batches // trainer.val_check_batch
             total_val_batches = total_val_batches * val_checks_per_epoch

pytorch_lightning/trainer/connectors/debugging_connector.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from typing import Union
-from pytorch_lightning.utilities import rank_zero_warn, rank_zero_info
+
+from pytorch_lightning.loggers.base import DummyLogger
+from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
 class DebuggingConnector:
@@ -54,11 +56,16 @@ def on_init_start(
             limit_train_batches = fast_dev_run
             limit_val_batches = fast_dev_run
             limit_test_batches = fast_dev_run
+            self.trainer.max_steps = fast_dev_run
             self.trainer.num_sanity_val_steps = 0
             self.trainer.max_epochs = 1
+            self.trainer.val_check_interval = 1.0
+            self.trainer.check_val_every_n_epoch = 1
+            self.trainer.logger = DummyLogger()
+
             rank_zero_info(
                 'Running in fast_dev_run mode: will run a full train,'
-                f' val and test loop using {fast_dev_run} batch(es)'
+                f' val and test loop using {fast_dev_run} batch(es).'
             )
 
         self.trainer.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')

pytorch_lightning/trainer/properties.py

@@ -11,14 +11,14 @@
 # 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.
-import inspect
-import os
 from abc import ABC
 from argparse import ArgumentParser, Namespace
-from typing import List, Optional, Type, TypeVar, Union, cast
+import inspect
+import os
+from typing import cast, List, Optional, Type, TypeVar, Union
 
 from pytorch_lightning.accelerators.accelerator import Accelerator
-from pytorch_lightning.callbacks import Callback, ModelCheckpoint, ProgressBarBase
+from pytorch_lightning.callbacks import Callback, EarlyStopping, ModelCheckpoint, ProgressBarBase
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.core.optimizer import is_lightning_optimizer
 from pytorch_lightning.loggers.base import LightningLoggerBase
@@ -27,7 +27,7 @@
 from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
 from pytorch_lightning.trainer.connectors.model_connector import ModelConnector
 from pytorch_lightning.trainer.states import TrainerState
-from pytorch_lightning.utilities import HOROVOD_AVAILABLE, TPU_AVAILABLE, argparse_utils, rank_zero_warn
+from pytorch_lightning.utilities import argparse_utils, HOROVOD_AVAILABLE, rank_zero_warn, TPU_AVAILABLE
 from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.utilities.model_utils import is_overridden
 
@@ -196,7 +196,7 @@ def enable_validation(self) -> bool:
         """ Check if we should run validation during training. """
         model_ref = self.model_connector.get_model()
         val_loop_enabled = is_overridden('validation_step', model_ref) and self.limit_val_batches > 0
-        return val_loop_enabled or self.fast_dev_run
+        return val_loop_enabled
 
     @property
     def default_root_dir(self) -> str:
@@ -218,18 +218,38 @@ def weights_save_path(self) -> str:
             return os.path.normpath(self._weights_save_path)
         return self._weights_save_path
 
+    @property
+    def early_stopping_callback(self) -> Optional[EarlyStopping]:
+        """
+        The first :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`
+        callback in the Trainer.callbacks list, or ``None`` if it doesn't exist.
+        """
+        callbacks = self.early_stopping_callbacks
+        return callbacks[0] if len(callbacks) > 0 else None
+
+    @property
+    def early_stopping_callbacks(self) -> List[EarlyStopping]:
+        """
+        A list of all instances of :class:`~pytorch_lightning.callbacks.early_stopping.EarlyStopping`
+        found in the Trainer.callbacks list.
+        """
+        return [c for c in self.callbacks if isinstance(c, EarlyStopping)]
+
     @property
     def checkpoint_callback(self) -> Optional[ModelCheckpoint]:
         """
-        The first checkpoint callback in the Trainer.callbacks list, or ``None`` if
-        no checkpoint callbacks exist.
+        The first :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
+        callback in the Trainer.callbacks list, or ``None`` if it doesn't exist.
         """
         callbacks = self.checkpoint_callbacks
         return callbacks[0] if len(callbacks) > 0 else None
 
     @property
     def checkpoint_callbacks(self) -> List[ModelCheckpoint]:
-        """ A list of all instances of ModelCheckpoint found in the Trainer.callbacks list. """
+        """
+        A list of all instances of :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint`
+        found in the Trainer.callbacks list.
+        """
         return [c for c in self.callbacks if isinstance(c, ModelCheckpoint)]
 
     def save_checkpoint(self, filepath, weights_only: bool = False):

pytorch_lightning/trainer/training_loop.py

@@ -915,9 +915,8 @@ def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
     def save_loggers_on_train_batch_end(self):
         # when loggers should save to disk
         should_flush_logs = self.trainer.logger_connector.should_flush_logs
-        if should_flush_logs or self.trainer.fast_dev_run is True:
-            if self.trainer.is_global_zero and self.trainer.logger is not None:
-                self.trainer.logger.save()
+        if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
+            self.trainer.logger.save()
 
     def process_train_step_outputs(self, all_train_step_outputs, early_stopping_accumulator, checkpoint_accumulator):
         """

tests/callbacks/test_early_stopping.py

@@ -13,18 +13,17 @@
 # limitations under the License.
 import os
 import pickle
+from unittest import mock
 
 import cloudpickle
 import numpy as np
 import pytest
 import torch
-from unittest import mock
 
-from pytorch_lightning import _logger
-from pytorch_lightning import Trainer, seed_everything
+from pytorch_lightning import _logger, seed_everything, Trainer
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-from tests.base import EvalModelTemplate, BoringModel
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from tests.base import BoringModel, EvalModelTemplate
 
 
 class EarlyStoppingTestRestore(EarlyStopping):
@@ -87,15 +86,18 @@ def test_resume_early_stopping_from_checkpoint(tmpdir):
 def test_early_stopping_no_extraneous_invocations(tmpdir):
     """Test to ensure that callback methods aren't being invoked outside of the callback handler."""
     model = EvalModelTemplate()
+    early_stop_callback = EarlyStopping()
     expected_count = 4
     trainer = Trainer(
         default_root_dir=tmpdir,
-        callbacks=[EarlyStopping()],
+        callbacks=[early_stop_callback],
         val_check_interval=1.0,
         max_epochs=expected_count,
     )
     trainer.fit(model)
 
+    assert trainer.early_stopping_callback == early_stop_callback
+    assert trainer.early_stopping_callbacks == [early_stop_callback]
     assert len(trainer.dev_debugger.early_stopping_history) == expected_count
 
 

tests/checkpointing/test_checkpoint_callback_frequency.py

@@ -17,55 +17,10 @@
 import pytest
 import torch
 
-from pytorch_lightning import Trainer, callbacks, seed_everything
+from pytorch_lightning import callbacks, seed_everything, Trainer
 from tests.base import BoringModel
 
 
-@mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
-def test_mc_called_on_fastdevrun(tmpdir):
-    seed_everything(1234)
-
-    train_val_step_model = BoringModel()
-
-    # fast dev run = called once
-    # train loop only, dict, eval result
-    trainer = Trainer(fast_dev_run=True)
-    trainer.fit(train_val_step_model)
-
-    # checkpoint should have been called once with fast dev run
-    assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
-
-    # -----------------------
-    # also called once with no val step
-    # -----------------------
-    class TrainingStepCalled(BoringModel):
-        def __init__(self):
-            super().__init__()
-            self.training_step_called = False
-            self.validation_step_called = False
-            self.test_step_called = False
-
-        def training_step(self, batch, batch_idx):
-            self.training_step_called = True
-            return super().training_step(batch, batch_idx)
-
-    train_step_only_model = TrainingStepCalled()
-    train_step_only_model.validation_step = None
-
-    # fast dev run = called once
-    # train loop only, dict, eval result
-    trainer = Trainer(fast_dev_run=True)
-    trainer.fit(train_step_only_model)
-
-    # make sure only training step was called
-    assert train_step_only_model.training_step_called
-    assert not train_step_only_model.validation_step_called
-    assert not train_step_only_model.test_step_called
-
-    # checkpoint should have been called once with fast dev run
-    assert len(trainer.dev_debugger.checkpoint_callback_history) == 1
-
-
 @mock.patch.dict(os.environ, {"PL_DEV_DEBUG": "1"})
 def test_mc_called(tmpdir):
     seed_everything(1234)