refactor(trainer): adapt to new compute_loss signature

huggingface · Jan 7, 2025 · 6639b1d · 6639b1d
1 parent f3a82ee
commit 6639b1d
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/optimum/neuron/trainers.py b/optimum/neuron/trainers.py
@@ -401,14 +401,14 @@ def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[s
         self._update_input_specs_in_model_cache_entry(input_specs_for_cache_entry)
         return inputs
 
-    def compute_loss(self, model, inputs, return_outputs: bool = False):
+    def compute_loss(self, model, inputs, num_items_in_batch):
         from neuronx_distributed.pipeline import NxDPPModel
 
         if isinstance(model, NxDPPModel):
             inputs = self._prepare_inputs(inputs)
             loss = model.run_train(**inputs)
         else:
-            loss = super().compute_loss(model, inputs, return_outputs=return_outputs)
+            loss = super().compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
         return loss
 
     def autocast_smart_context_manager(self, cache_enabled: Optional[bool] = True):