[debug] DebugUnderflowOverflow doesn't work with DP (#12816)

docs/source/debugging.rst

@@ -24,7 +24,11 @@ Underflow and Overflow Detection
 
 .. note::
 
-   This feature can be used with any ``nn.Module``-based model
+   For multi-GPU training it requires DDP (``torch.distributed.launch``).
+
+.. note::
+
+   This feature can be used with any ``nn.Module``-based model.
 
 If you start getting ``loss=NaN`` or the model inhibits some other abnormal behavior due to ``inf`` or ``nan`` in
 activations or weights one needs to discover where the first underflow or overflow happens and what led to it. Luckily

src/transformers/trainer.py

@@ -1114,6 +1114,13 @@ class Trainer:
             num_train_samples = args.max_steps * total_train_batch_size
 
         if DebugOption.UNDERFLOW_OVERFLOW in self.args.debug:
+            if self.args.n_gpu > 1:
+                # nn.DataParallel(model) replicates the model, creating new variables and module
+                # references registered here no longer work on other gpus, breaking the module
+                raise ValueError(
+                    "Currently --debug underflow_overflow is not supported under DP. Please use DDP (torch.distributed.launch)."
+                )
+            else:
                 debug_overflow = DebugUnderflowOverflow(self.model)  # noqa
 
         delay_optimizer_creation = self.sharded_ddp is not None and self.sharded_ddp != ShardedDDPOption.SIMPLE

src/transformers/trainer_utils.py

@@ -420,7 +420,7 @@ class TrainerMemoryTracker:
         self.cur_stage = None
 
     def update_metrics(self, stage, metrics):
-        """stop tracking for the passed stage"""
+        """updates the metrics"""
         if self.skip_memory_metrics:
             return
 
@@ -442,7 +442,7 @@ class TrainerMemoryTracker:
                     metrics[f"{stage}_mem_gpu_{t}_delta"] = self.gpu[stage][t]
 
     def stop_and_update_metrics(self, metrics=None):
-        """combine stop + update in one call for simpler code"""
+        """combine stop and metrics update in one call for simpler code"""
         if self.skip_memory_metrics:
             return