Release: v4.8.1

* Fix torchscript tests

* Better test

* Remove bogus print

examples/pytorch/language-modeling/run_clm.py

@@ -50,7 +50,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/language-modeling/run_mlm.py

@@ -49,7 +49,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/language-modeling/run_plm.py

@@ -45,7 +45,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/multiple-choice/run_swag.py

@@ -46,7 +46,7 @@ from transformers.utils import check_min_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
 
 logger = logging.getLogger(__name__)
 

examples/pytorch/question-answering/run_qa.py

@@ -47,7 +47,8 @@ from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -46,7 +46,8 @@ from utils_qa import postprocess_qa_predictions_with_beam_search
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -51,7 +51,8 @@ from utils_qa import postprocess_qa_predictions_with_beam_search
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -53,7 +53,8 @@ from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/summarization/run_summarization.py

@@ -47,7 +47,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/text-classification/run_glue.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_xnli.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner.py

@@ -46,7 +46,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/pytorch/translation/run_translation.py

@@ -51,7 +51,8 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
+
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
 
 logger = logging.getLogger(__name__)

examples/tensorflow/question-answering/run_qa.py

@@ -45,7 +45,7 @@ from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.7.0.dev0")
+check_min_version("4.8.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/text-classification/run_glue.py

@@ -100,7 +100,7 @@ class SavePretrainedCallback(tf.keras.callbacks.Callback):
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.8.0.dev0")
+check_min_version("4.8.0")
 
 task_to_keys = {
     "cola": ("sentence", None),

setup.py

@@ -125,7 +125,7 @@ _deps = [
     "pytest-sugar",
     "pytest-xdist",
     "python>=3.6.0",
-    "ray",
+    "ray[tune]",
     "recommonmark",
     "regex!=2019.12.17",
     "requests",
@@ -246,7 +246,7 @@ extras["sagemaker"] = deps_list("sagemaker")
 extras["deepspeed"] = deps_list("deepspeed")
 extras["fairscale"] = deps_list("fairscale")
 extras["optuna"] = deps_list("optuna")
-extras["ray"] = deps_list("ray")
+extras["ray"] = deps_list("ray[tune]")
 
 extras["integrations"] = extras["optuna"] + extras["ray"]
 
@@ -336,7 +336,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.8.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.8.1",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Sam Shleifer, Patrick von Platen, Sylvain Gugger, Suraj Patil, Stas Bekman, Google AI Language Team Authors, Open AI team Authors, Facebook AI Authors, Carnegie Mellon University Authors",
     author_email="thomas@huggingface.co",
     description="State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch",

src/transformers/__init__.py

@@ -22,7 +22,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.8.0.dev0"
+__version__ = "4.8.1"
 
 # Work around to update TensorFlow's absl.logging threshold which alters the
 # default Python logging output behavior when present.

src/transformers/training_args.py

@@ -434,7 +434,7 @@ class TrainingArguments:
             "help": "When doing a multinode distributed training, whether to log once per node or just once on the main node."
         },
     )
-    logging_dir: Optional[str] = field(default_factory=default_logdir, metadata={"help": "Tensorboard log dir."})
+    logging_dir: Optional[str] = field(default=None, metadata={"help": "Tensorboard log dir."})
     logging_strategy: IntervalStrategy = field(
         default="steps",
         metadata={"help": "The logging strategy to use."},

tests/test_modeling_common.py

@@ -564,13 +564,34 @@ class ModelTesterMixin:
             model_state_dict = model.state_dict()
             loaded_model_state_dict = loaded_model.state_dict()
 
+            non_persistent_buffers = {}
+            for key in loaded_model_state_dict.keys():
+                if key not in model_state_dict.keys():
+                    non_persistent_buffers[key] = loaded_model_state_dict[key]
+
+            loaded_model_state_dict = {
+                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
+            }
+
             self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
 
+            model_buffers = list(model.buffers())
+            for non_persistent_buffer in non_persistent_buffers.values():
+                found_buffer = False
+                for i, model_buffer in enumerate(model_buffers):
+                    if torch.equal(non_persistent_buffer, model_buffer):
+                        found_buffer = True
+                        break
+
+                self.assertTrue(found_buffer)
+                model_buffers.pop(i)
+
             models_equal = True
             for layer_name, p1 in model_state_dict.items():
-                p2 = loaded_model_state_dict[layer_name]
-                if p1.data.ne(p2.data).sum() > 0:
-                    models_equal = False
+                if layer_name in loaded_model_state_dict:
+                    p2 = loaded_model_state_dict[layer_name]
+                    if p1.data.ne(p2.data).sum() > 0:
+                        models_equal = False
 
             self.assertTrue(models_equal)