diff --git a/optimum/onnxruntime/trainer.py b/optimum/onnxruntime/trainer.py
index 66273cbcf96..4933ed73f4e 100644
--- a/optimum/onnxruntime/trainer.py
+++ b/optimum/onnxruntime/trainer.py
@@ -14,6 +14,7 @@
 """
 The ORTTrainer class, to easily train a 🤗 Transformers from scratch or finetune it on a new task with ONNX Runtime.
 """
+
 import functools
 import math
 import os
@@ -131,11 +132,11 @@ def __init__(self, model, args, label_smoother):
         # Label smoothing
         self.label_smoother = label_smoother
 
-    def forward(self, inputs: Dict[str, Union[torch.Tensor, Any]], return_outputs):
+    def forward(self, inputs: Dict[str, Union[torch.Tensor, Any]], return_outputs, num_items_in_batch):
         # The compute_model_plus_loss_internal is assigned once the class is instantiated.
         # It should have same signature as Trainer.compute_loss().
         # We do this to avoid potential un-synced states if we duplicated compute loss codes .
-        return self.compute_model_plus_loss_internal(self._original_model, inputs, return_outputs)
+        return self.compute_model_plus_loss_internal(self._original_model, inputs, return_outputs, num_items_in_batch)
 
     @property
     def module(self):
@@ -291,14 +292,14 @@ def _set_signature_columns_if_needed(self):
             # Labels may be named label or label_ids, the default data collator handles that.
             self._signature_columns += list(set(["label", "label_ids"] + self.label_names))
 
-    def compute_loss(self, model_with_loss, inputs, return_outputs=False):
+    def compute_loss(self, model_with_loss, inputs, return_outputs=False, num_items_in_batch=None):
         # Run model forward + loss compute.
         if isinstance(self.model, ModuleWithLoss):
             # ORTModule Does not support the BatchEncoding Type so we have to convert to a dict.
             dict_inputs = dict(inputs.items())
-            return model_with_loss(dict_inputs, return_outputs)
+            return model_with_loss(dict_inputs, return_outputs, num_items_in_batch)
         else:
-            return super().compute_loss(model_with_loss, inputs, return_outputs)
+            return super().compute_loss(model_with_loss, inputs, return_outputs, num_items_in_batch)
 
     def train(
         self,
@@ -803,7 +804,9 @@ def get_dataloader_sampler(dataloader):
                     self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch
                     self.control = self.callback_handler.on_step_end(args, self.state, self.control)
 
-                    self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
+                    self._maybe_log_save_evaluate(
+                        tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time
+                    )
                 else:
                     self.control = self.callback_handler.on_substep_end(args, self.state, self.control)
 
@@ -818,7 +821,7 @@ def get_dataloader_sampler(dataloader):
                 self.control.should_training_stop = True
 
             self.control = self.callback_handler.on_epoch_end(args, self.state, self.control)
-            self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
+            self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time)
 
             if DebugOption.TPU_METRICS_DEBUG in self.args.debug:
                 logger.warning(