⏰ Add start_time to _maybe_log_save_evaluate (#2373)

trl/trainer/online_dpo_trainer.py

@@ -25,6 +25,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.utils.data
+import transformers
 from datasets import Dataset
 from packaging import version
 from torch.utils.data import DataLoader, IterableDataset
@@ -587,8 +588,9 @@ def training_step(
 
         return loss.detach() / self.args.gradient_accumulation_steps
 
-    # Same as Trainer.evaluate but log our metrics
-    def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval):
+    # Same as Trainer._maybe_log_save_evaluate but log our metrics
+    # start_time defaults to None to allow compatibility with transformers<=4.46
+    def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval, start_time=None):
         if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
             logs: Dict[str, float] = {}
 
@@ -612,7 +614,10 @@ def _maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, igno
             self._globalstep_last_logged = self.state.global_step
             self.store_flos()
 
-            self.log(logs)
+            if version.parse(transformers.__version__) >= version.parse("4.47.0.dev0"):
+                self.log(logs, start_time)
+            else:  # transformers<=4.46
+                self.log(logs)
 
         metrics = None
         if self.control.should_evaluate: