update

Author: ananthsub

docs/source/common/debugging.rst

@@ -98,9 +98,7 @@ By default it only prints the top-level modules. If you want to show all submodu
 `'max_depth'` option:
 
 .. testcode::
-    from pytorch_lightning.callbacks import ModelSummary
-
-    trainer = Trainer(callbacks=[ModelSummary(max_depth=-1)])
+    trainer = Trainer(weights_summary="full")
 
 You can also display the intermediate input- and output sizes of all your layers by setting the
 ``example_input_array`` attribute in your LightningModule. It will print a table like this

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -118,9 +118,9 @@ def _configure_accumulated_gradients(
         if grad_accum_callback:
             if accumulate_grad_batches is not None:
                 raise MisconfigurationException(
-                    "You have set both `accumulate_grad_batches` and passed an instance of "
-                    "`GradientAccumulationScheduler` inside callbacks. Either remove `accumulate_grad_batches` "
-                    "from trainer or remove `GradientAccumulationScheduler` from callbacks list."
+                    "You have set both `accumulate_grad_batches` and passed an instance of"
+                    " `GradientAccumulationScheduler` inside callbacks. Either remove `accumulate_grad_batches`"
+                    " from trainer or remove `GradientAccumulationScheduler` from callbacks list."
                 )
             grad_accum_callback = grad_accum_callback[0]
         else:
@@ -162,19 +162,27 @@ def _configure_checkpoint_callbacks(self, checkpoint_callback: bool) -> None:
     def _configure_model_summary_callback(
         self, enable_model_summary: bool, weights_summary: Optional[str] = None
     ) -> None:
-        if not enable_model_summary:
-            return
-        if any(isinstance(cb, ModelSummary) for cb in self.trainer.callbacks):
-            return
         if weights_summary is None:
             rank_zero_deprecation(
                 "Setting `Trainer(weights_summary=None)` is deprecated in v1.5 and will be removed"
                 " in v1.7. Please set `Trainer(enable_model_summary=False) instead."
             )
             return
-        # Prior default in the Trainer for `weights_summary` which we explicitly check here
-        # to preserve backwards compatibility
-        if weights_summary != "top":
+        if not enable_model_summary:
+            return
+
+        model_summary_cbs = [type(cb) for cb in self.trainer.callbacks if isinstance(cb, ModelSummary)]
+        if model_summary_cbs:
+            rank_zero_info(
+                f"Trainer already configured with model summary callbacks: {model_summary_cbs}."
+                " Skipping setting a default `ModelSummary` callback."
+            )
+            return
+
+        if weights_summary == "top":
+            # special case the default value for weights_summary to preserve backward compatibility
+            max_depth = 1
+        else:
             rank_zero_deprecation(
                 f"Setting `Trainer(weights_summary={weights_summary})` is deprecated in v1.5 and will be removed"
                 " in v1.7. Please pass `pytorch_lightning.callbacks.model_summary.ModelSummary` with"
@@ -186,8 +194,7 @@ def _configure_model_summary_callback(
                     f" but got {weights_summary}",
                 )
             max_depth = ModelSummaryMode.get_max_depth(weights_summary)
-        else:
-            max_depth = 1
+
         if self.trainer._progress_bar_callback is not None and isinstance(
             self.trainer._progress_bar_callback, RichProgressBar
         ):