fix compatibility for latest transformers release

Author: echarlaix

optimum/intel/neural_compressor/trainer.py

@@ -941,3 +941,42 @@ def get_model_sparsity(self):
         if self._compression_manager is not None:
             sparsity = self._compression_manager.model.report_sparsity()[-1]
         return sparsity
+
+    def _maybe_log_save_evaluate(self, tr_loss, model, trial, epoch, ignore_keys_for_eval):
+        # TODO : can be removed once transformers >= v4.38.0
+        if self.control.should_log and self.state.global_step > self._globalstep_last_logged:
+            if is_torch_tpu_available():
+                xm.mark_step()
+
+            logs: Dict[str, float] = {}
+
+            # all_gather + mean() to get average loss over all processes
+            tr_loss_scalar = self._nested_gather(tr_loss).mean().item()
+
+            # reset tr_loss to zero
+            tr_loss -= tr_loss
+
+            logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
+            logs["learning_rate"] = self._get_learning_rate()
+
+            self._total_loss_scalar += tr_loss_scalar
+            self._globalstep_last_logged = self.state.global_step
+            self.store_flos()
+
+            self.log(logs)
+
+        metrics = None
+        if self.control.should_evaluate:
+            metrics = self.evaluate(ignore_keys=ignore_keys_for_eval)
+            self._report_to_hp_search(trial, self.state.global_step, metrics)
+
+            # Run delayed LR scheduler now that metrics are populated
+            if isinstance(self.lr_scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+                metric_to_check = self.args.metric_for_best_model
+                if not metric_to_check.startswith("eval_"):
+                    metric_to_check = f"eval_{metric_to_check}"
+                self.lr_scheduler.step(metrics[metric_to_check])
+
+        if self.control.should_save:
+            self._save_checkpoint(model, trial, metrics=metrics)
+            self.control = self.callback_handler.on_save(self.args, self.state, self.control)

setup.py

@@ -14,7 +14,7 @@
 INSTALL_REQUIRE = [
     "torch>=1.11",
     "optimum>=1.17.0",
-    "transformers>=4.26.0",
+    "transformers>=4.29.0,<4.39.0",
     "datasets>=1.4.0",
     "sentencepiece",
     "scipy",