Catch PyTorch warning when saving/loading scheduler (#7401)

src/transformers/trainer.py

@@ -59,6 +59,8 @@ from .utils import logging
 _use_native_amp = False
 _use_apex = False
 
+PT_LR_SCHEDULER_WARNING = "Please also save or load the state of the optimzer when saving or loading the scheduler."
+
 # Check if Pytorch version >= 1.6 to switch between Native AMP and Apex
 if version.parse(torch.__version__) < version.parse("1.6"):
     from .file_utils import is_apex_available
@@ -99,6 +101,14 @@ if is_ray_available():
 logger = logging.get_logger(__name__)
 
 
+def reissue_pt_warnings(caught_warnings):
+    # Reissue warnings that are not the PT_LR_SCHEDULER_WARNING
+    if len(caught_warnings) > 1:
+        for w in caught_warnings:
+            if w.category != UserWarning or w.message != PT_LR_SCHEDULER_WARNING:
+                warnings.warn(w.message, w.category)
+
+
 @contextmanager
 def torch_distributed_zero_first(local_rank: int):
     """
@@ -643,7 +653,9 @@ class Trainer:
             self.optimizer.load_state_dict(
                 torch.load(os.path.join(model_path, "optimizer.pt"), map_location=self.args.device)
             )
-            self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
+            with warnings.catch_warnings(record=True) as caught_warnings:
+                self.lr_scheduler.load_state_dict(torch.load(os.path.join(model_path, "scheduler.pt")))
+            reissue_pt_warnings(caught_warnings)
 
         model = self.model
         if self.args.fp16 and _use_apex:
@@ -821,10 +833,14 @@ class Trainer:
                         if is_torch_tpu_available():
                             xm.rendezvous("saving_optimizer_states")
                             xm.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                            xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
+                            with warnings.catch_warnings(record=True) as caught_warnings:
+                                xm.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
+                            reissue_pt_warnings(caught_warnings)
                         elif self.is_world_process_zero():
                             torch.save(self.optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                            torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
+                            with warnings.catch_warnings(record=True) as caught_warnings:
+                                torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
+                            reissue_pt_warnings(caught_warnings)
 
                 epoch_pbar.update(1)
                 if self.args.max_steps > 0 and self.global_step >= self.args.max_steps: