Non blocking support to torch DL's (#30465)

* Non blocking support

* Check for optimization

* Doc

src/transformers/trainer.py

@@ -4361,6 +4361,18 @@ class Trainer:
                 even_batches=accelerator_config.pop("even_batches"),
                 use_seedable_sampler=accelerator_config.pop("use_seedable_sampler"),
             )
+        non_blocking = accelerator_config.pop("non_blocking")
+        if not is_accelerate_available("0.30.0"):
+            if non_blocking:
+                raise ImportError(
+                    "`non_blocking` is only supported in accelerate v0.30.0 and above. Please upgrade accelerate to use this feature."
+                )
+        else:
+            if non_blocking and not self.args.dataloader_pin_memory:
+                logger.warning(
+                    "`non_blocking` is enabled but `dataloader_pin_memory` is not. For the best performance, it's recommended to enable both."
+                )
+            dataloader_config.non_blocking = non_blocking
         # this would have been updated above, no need for it anymore
         accelerator_config.pop("gradient_accumulation_kwargs")
 

src/transformers/trainer_pt_utils.py

@@ -1246,6 +1246,10 @@ class AcceleratorConfig:
                 The [`accelerate.utils.GradientAccumulationPlugin`] default is `True`.
               sync_each_batch (`bool`): Whether to synchronize the gradients at each data batch.
                 The [`accelerate.utils.GradientAccumulationPlugin`] default is `False`.
+        non_blocking (`bool`, *optional*, defaults to `False`):
+            Whether to use non-blocking CUDA calls to help minimize synchronization during
+            distributed training with prepared `DataLoader` inputs being moved to device.
+            Best if used with `pin_memory=True` in the `TrainingArguments`.
 
     """
 
@@ -1284,6 +1288,17 @@ class AcceleratorConfig:
             "multiple different seeds to compare. Should also be ran with [`~utils.set_seed`] for the best results."
         },
     )
+
+    non_blocking: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to use non-blocking CUDA calls to help minimize synchronization during "
+            "distributed training with prepared `DataLoader` inputs being moved to device. "
+            "Best if used with `pin_memory=True` in the `TrainingArguments`. Requires accelerate "
+            "v0.30.0."
+        },
+    )
+
     gradient_accumulation_kwargs: Optional[Dict] = field(
         default=None,
         metadata={