Exclude torch.compile time from metrics computation (#31443)

* exclude compile time from metrics computation

* fix the quality issue

src/transformers/trainer.py

@@ -3670,6 +3670,8 @@ class Trainer:
         total_batch_size = self.args.eval_batch_size * self.args.world_size
         if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
             start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
+        if f"{metric_key_prefix}_model_preparation_time" in output.metrics:
+            start_time += output.metrics[f"{metric_key_prefix}_model_preparation_time"]
         output.metrics.update(
             speed_metrics(
                 metric_key_prefix,
@@ -3739,6 +3741,8 @@ class Trainer:
         total_batch_size = self.args.eval_batch_size * self.args.world_size
         if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
             start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
+        if f"{metric_key_prefix}_model_preparation_time" in output.metrics:
+            start_time += output.metrics[f"{metric_key_prefix}_model_preparation_time"]
         output.metrics.update(
             speed_metrics(
                 metric_key_prefix,
@@ -3777,11 +3781,13 @@ class Trainer:
         model = self._wrap_model(self.model, training=False, dataloader=dataloader)
 
         if len(self.accelerator._models) == 0 and model is self.model:
+            start_time = time.time()
             model = (
                 self.accelerator.prepare(model)
                 if self.is_deepspeed_enabled
                 else self.accelerator.prepare_model(model, evaluation_mode=True)
             )
+            self.model_preparation_time = round(time.time() - start_time, 4)
 
             if self.is_fsdp_enabled:
                 self.model = model
@@ -3954,6 +3960,8 @@ class Trainer:
             metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
         if hasattr(self, "jit_compilation_time"):
             metrics[f"{metric_key_prefix}_jit_compilation_time"] = self.jit_compilation_time
+        if hasattr(self, "model_preparation_time"):
+            metrics[f"{metric_key_prefix}_model_preparation_time"] = self.model_preparation_time
 
         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()):