Add typing for trainer.logger

CHANGELOG.md

@@ -281,7 +281,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed an issue when torch-scripting a `LightningModule` after training with `Trainer(sync_batchnorm=True)` ([#11078](https://github.com/PyTorchLightning/pytorch-lightning/pull/11078))
 
 
--
+- Fixed bug where `Trainer(track_grad_norm=..., logger=False)' would fail ([#11114](https://github.com/PyTorchLightning/pytorch-lightning/pull/11114))
 
 
 ## [1.5.6] - 2021-12-15

pyproject.toml

@@ -42,7 +42,6 @@ warn_no_return = "False"
 # the list can be generated with:
 # mypy | tr ':' ' ' |  awk '{print $1}' | sort | uniq | sed 's/\.py//g' | sed 's|\/|\.|g' | xargs -I {} echo '"{}",'
 module = [
-    "pytorch_lightning.accelerators.accelerator",
     "pytorch_lightning.accelerators.gpu",
     "pytorch_lightning.callbacks.finetuning",
     "pytorch_lightning.callbacks.lr_monitor",
@@ -106,7 +105,6 @@ module = [
     "pytorch_lightning.utilities.distributed",
     "pytorch_lightning.utilities.enums",
     "pytorch_lightning.utilities.fetching",
-    "pytorch_lightning.utilities.imports",
     "pytorch_lightning.utilities.memory",
     "pytorch_lightning.utilities.meta",
     "pytorch_lightning.utilities.metrics",

pytorch_lightning/callbacks/device_stats_monitor.py

@@ -59,6 +59,7 @@ def on_train_batch_start(
 
         device_stats = trainer.accelerator.get_device_stats(pl_module.device)
         prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_start")
+        assert trainer.logger is not None
         trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
 
     def on_train_batch_end(
@@ -75,6 +76,7 @@ def on_train_batch_end(
 
         device_stats = trainer.accelerator.get_device_stats(pl_module.device)
         prefixed_device_stats = prefix_metrics_keys(device_stats, "on_train_batch_end")
+        assert trainer.logger is not None
         trainer.logger.log_metrics(prefixed_device_stats, step=trainer.global_step)
 
 

pytorch_lightning/callbacks/gpu_stats_monitor.py

@@ -161,6 +161,7 @@ def on_train_batch_start(
             # First log at beginning of second step
             logs["batch_time/inter_step (ms)"] = (time.time() - self._snap_inter_step_time) * 1000
 
+        assert trainer.logger is not None
         trainer.logger.log_metrics(logs, step=trainer.global_step)
 
     @rank_zero_only
@@ -185,6 +186,7 @@ def on_train_batch_end(
         if self._log_stats.intra_step_time and self._snap_intra_step_time:
             logs["batch_time/intra_step (ms)"] = (time.time() - self._snap_intra_step_time) * 1000
 
+        assert trainer.logger is not None
         trainer.logger.log_metrics(logs, step=trainer.global_step)
 
     @staticmethod

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -155,7 +155,8 @@ def optimizer_step(
     def _track_grad_norm(self, trainer: "pl.Trainer") -> None:
         if trainer.track_grad_norm == -1:
             return
-        grad_norm_dict = grad_norm(trainer.lightning_module, trainer.track_grad_norm, trainer.logger.group_separator)
+        kwargs = {"group_separator": trainer.logger.group_separator} if trainer.logger is not None else {}
+        grad_norm_dict = grad_norm(trainer.lightning_module, trainer.track_grad_norm, **kwargs)
         if grad_norm_dict:
             prev_fx = trainer.lightning_module._current_fx_name
             trainer.lightning_module._current_fx_name = "on_before_optimizer_step"

pytorch_lightning/trainer/trainer.py

@@ -570,6 +570,7 @@ def __init__(
         self.__init_profiler(profiler)
 
         # init logger flags
+        self.logger: Optional[LightningLoggerBase]
         self.logger_connector.on_trainer_init(logger, flush_logs_every_n_steps, log_every_n_steps, move_metrics_to_cpu)
 
         # init debugging flags