diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8bf2ee6664270..71f9cb424884e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -13,6 +13,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Avoid the deprecated `onnx.export(example_outputs=...)` in torch 1.10 ([#11116](https://github.com/PyTorchLightning/pytorch-lightning/pull/11116))
 - 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 an `AttributeError` occuring when using a `CombinedLoader` (multiple dataloaders) for prediction ([#11111](https://github.com/PyTorchLightning/pytorch-lightning/pull/11111))
+- Fixed bug where `Trainer(track_grad_norm=..., logger=False)' would fail ([#11114](https://github.com/PyTorchLightning/pytorch-lightning/pull/11114))
 
 ### Changed
 
diff --git a/pytorch_lightning/callbacks/device_stats_monitor.py b/pytorch_lightning/callbacks/device_stats_monitor.py
index b743ed3e1bbeb..016d2015a81e1 100644
--- a/pytorch_lightning/callbacks/device_stats_monitor.py
+++ b/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)
 
 
diff --git a/pytorch_lightning/callbacks/gpu_stats_monitor.py b/pytorch_lightning/callbacks/gpu_stats_monitor.py
index 7ee6771056666..7bb0289050a1e 100644
--- a/pytorch_lightning/callbacks/gpu_stats_monitor.py
+++ b/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
diff --git a/pytorch_lightning/plugins/precision/precision_plugin.py b/pytorch_lightning/plugins/precision/precision_plugin.py
index f1ebbf58d8326..012922ea2b60a 100644
--- a/pytorch_lightning/plugins/precision/precision_plugin.py
+++ b/pytorch_lightning/plugins/precision/precision_plugin.py
@@ -165,7 +165,8 @@ 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"
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index 18be005715aca..a21763259ece2 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -569,6 +569,7 @@ 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