Remove debugging_connector.py (#10113)

Author: daniellepintz

pytorch_lightning/trainer/connectors/debugging_connector.py

@@ -33,7 +33,7 @@
 from pytorch_lightning.core.datamodule import LightningDataModule
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.loggers.base import LoggerCollection
+from pytorch_lightning.loggers.base import DummyLogger, LoggerCollection
 from pytorch_lightning.loggers.tensorboard import TensorBoardLogger
 from pytorch_lightning.loops import PredictionLoop, TrainingBatchLoop, TrainingEpochLoop
 from pytorch_lightning.loops.dataloader.evaluation_loop import EvaluationLoop
@@ -53,7 +53,6 @@
 from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
 from pytorch_lightning.trainer.connectors.checkpoint_connector import CheckpointConnector
 from pytorch_lightning.trainer.connectors.data_connector import DataConnector
-from pytorch_lightning.trainer.connectors.debugging_connector import DebuggingConnector
 from pytorch_lightning.trainer.connectors.env_vars_connector import _defaults_from_env_vars
 from pytorch_lightning.trainer.connectors.logger_connector import LoggerConnector
 from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
@@ -450,7 +449,6 @@ def __init__(
         )
         self.logger_connector = LoggerConnector(self, log_gpu_memory)
         self._callback_connector = CallbackConnector(self)
-        self.debugging_connector = DebuggingConnector(self)
         self.checkpoint_connector = CheckpointConnector(self, resume_from_checkpoint)
         self.signal_connector = SignalConnector(self)
         self.tuner = Tuner(self)
@@ -574,7 +572,7 @@ def __init__(
         self.logger_connector.on_trainer_init(logger, flush_logs_every_n_steps, log_every_n_steps, move_metrics_to_cpu)
 
         # init debugging flags
-        self.debugging_connector.on_init_start(
+        self._init_debugging_flags(
             limit_train_batches,
             limit_val_batches,
             limit_test_batches,
@@ -587,6 +585,65 @@ def __init__(
         # Callback system
         self.on_init_end()
 
+    def _init_debugging_flags(
+        self,
+        limit_train_batches,
+        limit_val_batches,
+        limit_test_batches,
+        limit_predict_batches,
+        val_check_interval,
+        overfit_batches,
+        fast_dev_run,
+    ):
+        if not isinstance(fast_dev_run, (bool, int)):
+            raise MisconfigurationException(
+                f"fast_dev_run={fast_dev_run} is not a valid configuration. It should be either a bool or an int >= 0"
+            )
+
+        if isinstance(fast_dev_run, int) and (fast_dev_run < 0):
+            raise MisconfigurationException(
+                f"fast_dev_run={fast_dev_run} is not a valid configuration. It should be >= 0."
+            )
+
+        self.fast_dev_run = fast_dev_run
+        fast_dev_run = int(fast_dev_run)
+
+        # set fast_dev_run=True when it is 1, used while logging
+        if fast_dev_run == 1:
+            self.fast_dev_run = True
+
+        if fast_dev_run:
+            limit_train_batches = fast_dev_run
+            limit_val_batches = fast_dev_run
+            limit_test_batches = fast_dev_run
+            limit_predict_batches = fast_dev_run
+            self.fit_loop.max_steps = fast_dev_run
+            self.num_sanity_val_steps = 0
+            self.fit_loop.max_epochs = 1
+            val_check_interval = 1.0
+            self.check_val_every_n_epoch = 1
+            self.logger = DummyLogger() if self.logger is not None else None
+
+            rank_zero_info(
+                "Running in fast_dev_run mode: will run a full train,"
+                f" val, test and prediction loop using {fast_dev_run} batch(es)."
+            )
+
+        self.limit_train_batches = _determine_batch_limits(limit_train_batches, "limit_train_batches")
+        self.limit_val_batches = _determine_batch_limits(limit_val_batches, "limit_val_batches")
+        self.limit_test_batches = _determine_batch_limits(limit_test_batches, "limit_test_batches")
+        self.limit_predict_batches = _determine_batch_limits(limit_predict_batches, "limit_predict_batches")
+        self.val_check_interval = _determine_batch_limits(val_check_interval, "val_check_interval")
+        self.overfit_batches = _determine_batch_limits(overfit_batches, "overfit_batches")
+        self.determine_data_use_amount(self.overfit_batches)
+
+    def determine_data_use_amount(self, overfit_batches: float) -> None:
+        """Use less data for debugging purposes."""
+        if overfit_batches > 0:
+            self.limit_train_batches = overfit_batches
+            self.limit_val_batches = overfit_batches
+            self.limit_test_batches = overfit_batches
+
     def _setup_on_init(self, num_sanity_val_steps: int) -> None:
         self._log_device_info()
 
@@ -2133,3 +2190,13 @@ def terminate_on_nan(self, val: bool) -> None:
             f" Please set `Trainer(detect_anomaly={val})` instead."
         )
         self._terminate_on_nan = val  # : 212
+
+
+def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
+    if 0 <= batches <= 1:
+        return batches
+    if batches > 1 and batches % 1.0 == 0:
+        return int(batches)
+    raise MisconfigurationException(
+        f"You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int."
+    )