Test checkpointing (#11682)

* Add test and see where CI is unhappy

* Load with strict=False

Author: sgugger

src/transformers/trainer.py

@@ -1059,7 +1059,18 @@ def train(
                 # We load the model state dict on the CPU to avoid an OOM error.
                 state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME), map_location="cpu")
                 # If the model is on the GPU, it still works!
-                self.model.load_state_dict(state_dict)
+                load_result = self.model.load_state_dict(state_dict, strict=False)
+                if len(load_result.missing_keys) != 0:
+                    if load_result.missing_keys == self.model._keys_to_ignore_on_save:
+                        self.model.tie_weights()
+                    else:
+                        logger.warn(
+                            f"There were missing keys in the checkpoint model loaded: {load_result.missing_keys}."
+                        )
+                if len(load_result.unexpected_keys) != 0:
+                    logger.warn(
+                        f"There were unexpected keys in the checkpoint model loaded: {load_result.unexpected_keys}."
+                    )
 
         # If model was re-initialized, put it on the right device and update self.model_wrapped
         if model_reloaded:

tests/test_modeling_common.py

@@ -177,6 +177,13 @@ def test_save_load__keys_to_ignore_on_save(self):
                 for k in _keys_to_ignore_on_save:
                     self.assertNotIn(k, state_dict_saved)
 
+                # Test we can load the state dict in the model, necessary for the checkpointing API in Trainer.
+                load_result = model.load_state_dict(state_dict_saved, strict=False)
+                self.assertTrue(
+                    len(load_result.missing_keys) == 0 or load_result.missing_keys == model._keys_to_ignore_on_save
+                )
+                self.assertTrue(len(load_result.unexpected_keys) == 0)
+
     def _mock_init_weights(self, module):
         if hasattr(module, "weight") and module.weight is not None:
             module.weight.data.fill_(3)