Load checkpoint without re-creating the model (#11318)

src/transformers/configuration_utils.py

@@ -271,7 +271,7 @@ class PretrainedConfig(object):
         self._name_or_path = str(kwargs.pop("name_or_path", ""))
 
         # Drop the transformers version info
-        kwargs.pop("transformers_version", None)
+        self.transformers_version = kwargs.pop("transformers_version", None)
 
         # Additional attributes without default values
         for key, value in kwargs.items():

src/transformers/trainer.py

@@ -55,9 +55,12 @@ from torch.utils.data.dataset import Dataset, IterableDataset
 from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import RandomSampler, SequentialSampler
 
+from . import __version__
+from .configuration_utils import PretrainedConfig
 from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
 from .dependency_versions_check import dep_version_check
 from .file_utils import (
+    CONFIG_NAME,
     WEIGHTS_NAME,
     is_apex_available,
     is_datasets_available,
@@ -999,14 +1002,23 @@ class Trainer:
 
             logger.info(f"Loading model from {resume_from_checkpoint}).")
 
+            if os.path.isfile(os.path.join(resume_from_checkpoint, CONFIG_NAME)):
+                config = PretrainedConfig.from_json_file(os.path.join(resume_from_checkpoint, CONFIG_NAME))
+                checkpoint_version = config.transformers_version
+                if checkpoint_version is not None and checkpoint_version != __version__:
+                    logger.warn(
+                        f"You are resuming training from a checkpoint trained with {checkpoint_version} of "
+                        f"Transformers but your current version is {__version__}. This is not recommended and could "
+                        "yield to errors or unwanted behaviors."
+                    )
+
             if self.deepspeed:
                 # will be resumed in deepspeed_init
                 pass
-            elif isinstance(self.model, PreTrainedModel):
-                self.model = self.model.from_pretrained(resume_from_checkpoint)
-                model_reloaded = True
             else:
-                state_dict = torch.load(os.path.join(resume_from_checkpoint, WEIGHTS_NAME))
+                # 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)
 
         # If model was re-initialized, put it on the right device and update self.model_wrapped
@@ -1293,13 +1305,10 @@ class Trainer:
             logger.info(
                 f"Loading best model from {self.state.best_model_checkpoint} (score: {self.state.best_metric})."
             )
-            if isinstance(self.model, PreTrainedModel):
-                self.model = self.model.from_pretrained(self.state.best_model_checkpoint)
-                if self.place_model_on_device:
-                    self.model = self.model.to(args.device)
-            else:
-                state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME))
-                self.model.load_state_dict(state_dict)
+            # We load the model state dict on the CPU to avoid an OOM error.
+            state_dict = torch.load(os.path.join(self.state.best_model_checkpoint, WEIGHTS_NAME), map_location="cpu")
+            # If the model is on the GPU, it still works!
+            self.model.load_state_dict(state_dict)
 
             if self.deepspeed:
                 self.deepspeed.load_checkpoint(

tests/test_trainer.py

@@ -725,6 +725,46 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             self.assertEqual(b, b1)
             self.check_trainer_state_are_the_same(state, state1)
 
+    def test_resume_training_with_frozen_params(self):
+        if torch.cuda.device_count() > 2:
+            # This test will fail for more than 2 GPUs since the batch size will get bigger and with the number of
+            # save_steps, the checkpoint will resume training at epoch 2 or more (so the data seen by the model
+            # won't be the same since the training dataloader is shuffled).
+            return
+        with tempfile.TemporaryDirectory() as tmpdir:
+            trainer = get_regression_trainer(
+                output_dir=tmpdir,
+                train_len=128,
+                per_device_train_batch_size=4,
+                save_steps=5,
+                learning_rate=0.1,
+            )
+            trainer.model.a.requires_grad_(False)
+            trainer.train()
+            (a, b) = trainer.model.a.item(), trainer.model.b.item()
+            state = dataclasses.asdict(trainer.state)
+
+            checkpoint = os.path.join(tmpdir, "checkpoint-5")
+
+            # Reinitialize trainer
+            trainer = get_regression_trainer(
+                output_dir=tmpdir,
+                train_len=128,
+                per_device_train_batch_size=4,
+                save_steps=5,
+                learning_rate=0.1,
+            )
+            trainer.model.a.requires_grad_(False)
+
+            trainer.train(resume_from_checkpoint=checkpoint)
+
+            self.assertFalse(trainer.model.a.requires_grad)
+            (a1, b1) = trainer.model.a.item(), trainer.model.b.item()
+            state1 = dataclasses.asdict(trainer.state)
+            self.assertEqual(a, a1)
+            self.assertEqual(b, b1)
+            self.check_trainer_state_are_the_same(state, state1)
+
     def test_load_best_model_at_end(self):
         total = int(self.n_epochs * 64 / self.batch_size)
         with tempfile.TemporaryDirectory() as tmpdir: