For the simple use cases where no Enum or unexpected config objects a…

…re in the save files, use weights_only=True. Significantly cuts down on the number of torch warnings. #1429

stanza/models/charlm.py

@@ -206,7 +206,7 @@ def get_current_lr(trainer, args):
     return trainer.scheduler.state_dict().get('_last_lr', [args['lr0']])[0]
 
 def load_char_vocab(vocab_file):
-    return {'char': CharVocab.load_state_dict(torch.load(vocab_file, lambda storage, loc: storage))}
+    return {'char': CharVocab.load_state_dict(torch.load(vocab_file, lambda storage, loc: storage, weights_only=True))}
 
 def train(args):
     utils.log_training_args(args, logger)

stanza/models/classifiers/trainer.py

@@ -69,6 +69,8 @@ def load(filename, args, foundation_cache=None, load_optimizer=False):
             else:
                 raise FileNotFoundError("Cannot find model in {} or in {}".format(filename, os.path.join(args.save_dir, filename)))
         try:
+            # TODO: switch to weights_only=True
+            # need to convert enums to int first
             checkpoint = torch.load(filename, lambda storage, loc: storage)
         except BaseException:
             logger.exception("Cannot load model from {}".format(filename))

stanza/models/common/char_model.py

@@ -268,7 +268,7 @@ def from_full_state(cls, state, finetune=False):
 
     @classmethod
     def load(cls, filename, finetune=False):
-        state = torch.load(filename, lambda storage, loc: storage)
+        state = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         # allow saving just the Model object,
         # and allow for old charlms to still work
         if 'state_dict' in state:
@@ -342,7 +342,7 @@ def load(cls, args, filename, finetune=False):
         Note that you MUST set finetune=True if planning to continue training
         Otherwise the only benefit you will get will be a warm GPU
         """
-        state = torch.load(filename, lambda storage, loc: storage)
+        state = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         model = CharacterLanguageModel.from_full_state(state['model'], finetune)
         model = model.to(args['device'])
 

stanza/models/common/pretrain.py

@@ -53,6 +53,7 @@ def emb(self):
     def load(self):
         if self.filename is not None and os.path.exists(self.filename):
             try:
+                # TODO: update all pretrains to satisfy weights_only=True
                 data = torch.load(self.filename, lambda storage, loc: storage)
                 logger.debug("Loaded pretrain from {}".format(self.filename))
                 if not isinstance(data, dict):

stanza/models/common/trainer.py

@@ -13,7 +13,7 @@ def save(self, filename):
         torch.save(savedict, filename)
 
     def load(self, filename):
-        savedict = torch.load(filename, lambda storage, loc: storage)
+        savedict = torch.load(filename, lambda storage, loc: storage, weights_only=True)
 
         self.model.load_state_dict(savedict['model'])
         if self.args['mode'] == 'train':

stanza/models/constituency/base_trainer.py

@@ -84,6 +84,9 @@ def load(filename, args=None, load_optimizer=False, foundation_cache=None, peft_
             else:
                 raise FileNotFoundError("Cannot find model in {} or in {}".format(filename, os.path.join(args['save_dir'], filename)))
         try:
+            # TODO: currently cannot switch this to weights_only=True
+            # without in some way changing the model to save enums in
+            # a safe manner, probably by converting to int
             checkpoint = torch.load(filename, lambda storage, loc: storage)
         except BaseException:
             logger.exception("Cannot load model from %s", filename)

stanza/models/coref/model.py

@@ -224,6 +224,7 @@ def load_weights(self,
         if map_location is None:
             map_location = self.config.device
         logger.debug(f"Loading from {path}...")
+        # TODO: the config is preventing us from using weights_only=True
         state_dicts = torch.load(path, map_location=map_location)
         self.epochs_trained = state_dicts.pop("epochs_trained", 0)
         # just ignore a config in the model, since we should already have one

stanza/models/depparse/trainer.py

@@ -191,7 +191,7 @@ def load(self, filename, pretrain, args=None, foundation_cache=None, device=None
         and the actual use of pretrain embeddings will depend on the boolean config "pretrain" in the loaded args.
         """
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/models/langid/model.py

@@ -114,7 +114,7 @@ def load(cls, path, device=None, batch_size=64, lang_subset=None):
             raise FileNotFoundError("Trying to load langid model, but path not specified!  Try --load_name")
         if not os.path.exists(path):
             raise FileNotFoundError("Trying to load langid model from path which does not exist: %s" % path)
-        checkpoint = torch.load(path, map_location=torch.device("cpu"))
+        checkpoint = torch.load(path, map_location=torch.device("cpu"), weights_only=True)
         weights = checkpoint["model_state_dict"]["loss_train.weight"]
         model = cls(checkpoint["char_to_idx"], checkpoint["tag_to_idx"], checkpoint["num_layers"],
                     checkpoint["embedding_dim"], checkpoint["hidden_dim"], batch_size=batch_size, weights=weights,

stanza/models/lemma/trainer.py

@@ -236,7 +236,7 @@ def save(self, filename, skip_modules=True):
 
     def load(self, filename, args, foundation_cache):
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/models/mwt/trainer.py

@@ -198,7 +198,7 @@ def save(self, filename):
 
     def load(self, filename):
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/models/ner/data.py

@@ -55,7 +55,7 @@ def __init__(self, doc, batch_size, args, pretrain=None, vocab=None, evaluation=
     def init_vocab(self, data):
         def from_model(model_filename):
             """ Try loading vocab from charLM model file. """
-            state_dict = torch.load(model_filename, lambda storage, loc: storage)
+            state_dict = torch.load(model_filename, lambda storage, loc: storage, weights_only=True)
             if 'vocab' in state_dict:
                 return state_dict['vocab']
             if 'model' in state_dict and 'vocab' in state_dict['model']:

stanza/models/ner/trainer.py

@@ -194,7 +194,7 @@ def save(self, filename, skip_modules=True):
 
     def load(self, filename, pretrain=None, args=None, foundation_cache=None):
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/models/pos/trainer.py

@@ -136,7 +136,7 @@ def load(self, filename, pretrain, args=None, foundation_cache=None):
         and the actual use of pretrain embeddings will depend on the boolean config "pretrain" in the loaded args.
         """
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/models/tokenization/trainer.py

@@ -79,7 +79,7 @@ def save(self, filename):
 
     def load(self, filename):
         try:
-            checkpoint = torch.load(filename, lambda storage, loc: storage)
+            checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         except BaseException:
             logger.error("Cannot load model from {}".format(filename))
             raise

stanza/tests/common/test_pretrain.py

@@ -71,7 +71,7 @@ def test_resave_pretrain():
                                vec_filename=f'unban_mox_opal')
         check_pretrain(pt2)
 
-        pt3 = torch.load(test_pt_file.name)
+        pt3 = torch.load(test_pt_file.name, weights_only=True)
         check_embedding(pt3['emb'])
     finally:
         os.unlink(test_pt_file.name)

stanza/tests/depparse/test_parser.py

@@ -145,7 +145,7 @@ def test_with_bert_finetuning_resaved(self, tmp_path, wordvec_pretrain_file):
         save_name = trainer.args['save_name']
         filename = tmp_path / save_name
         assert os.path.exists(filename)
-        checkpoint = torch.load(filename, lambda storage, loc: storage)
+        checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         assert any(x.startswith("bert_model") for x in checkpoint['model'].keys())
 
         # Test loading the saved model, saving it, and still having bert in it
@@ -157,7 +157,7 @@ def test_with_bert_finetuning_resaved(self, tmp_path, wordvec_pretrain_file):
         saved_model.save(filename)
 
         # This is the part that would fail if the force_bert_saved option did not exist
-        checkpoint = torch.load(filename, lambda storage, loc: storage)
+        checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
         assert any(x.startswith("bert_model") for x in checkpoint['model'].keys())
 
     def test_with_peft(self, tmp_path, wordvec_pretrain_file):

stanza/tests/lemma/test_lemma_trainer.py

@@ -150,5 +150,5 @@ def test_charlm_train(self, tmp_path, charlm_args):
         # check that the charlm wasn't saved in here
         args = saved_model.args
         save_name = os.path.join(args['save_dir'], args['save_name'])
-        checkpoint = torch.load(save_name, lambda storage, loc: storage)
+        checkpoint = torch.load(save_name, lambda storage, loc: storage, weights_only=True)
         assert not any(x.startswith("contextual_embedding") for x in checkpoint['model'].keys())

stanza/tests/ner/test_ner_training.py

@@ -226,7 +226,7 @@ def test_train_model_cpu(pretrain_file, tmp_path):
     assert str(device).startswith("cpu")
 
 def model_file_has_bert(filename):
-    checkpoint = torch.load(filename, lambda storage, loc: storage)
+    checkpoint = torch.load(filename, lambda storage, loc: storage, weights_only=True)
     return any(x.startswith("bert_model.") for x in checkpoint['model'].keys())
 
 def test_with_bert(pretrain_file, tmp_path):
@@ -253,7 +253,7 @@ def test_with_peft_finetune(pretrain_file, tmp_path):
     # TODO: check that the peft tensors are moving when training?
     trainer = run_training(pretrain_file, tmp_path, '--bert_model', 'hf-internal-testing/tiny-bert', '--use_peft')
     model_file = os.path.join(trainer.args['save_dir'], trainer.args['save_name'])
-    checkpoint = torch.load(model_file, lambda storage, loc: storage)
+    checkpoint = torch.load(model_file, lambda storage, loc: storage, weights_only=True)
     assert 'bert_lora' in checkpoint
     assert not any(x.startswith("bert_model.") for x in checkpoint['model'].keys())