Merge branch 'master' of github.com:huggingface/transformers

.circleci/config.yml

@@ -348,7 +348,7 @@ jobs:
                       - v0.4-{{ checksum "setup.py" }}
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
             - run: pip install --upgrade pip
-            - run: pip install ."[all, docs]"
+            - run: pip install ."[docs]"
             - save_cache:
                   key: v0.4-build_doc-{{ checksum "setup.py" }}
                   paths:
@@ -370,7 +370,7 @@ jobs:
                   keys:
                       - v0.4-deploy_doc-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
-            - run: pip install ."[all,docs]"
+            - run: pip install ."[docs]"
             - save_cache:
                   key: v0.4-deploy_doc-{{ checksum "setup.py" }}
                   paths:

.github/workflows/self-scheduled.yml

@@ -33,7 +33,7 @@ jobs:
         run: |
           apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech,deepspeed]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -155,7 +155,7 @@ jobs:
         run: |
           apt -y update && apt install -y libsndfile1-dev
           pip install --upgrade pip
-          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech]
+          pip install .[sklearn,testing,onnxruntime,sentencepiece,speech,deepspeed,fairscale]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |

docs/source/main_classes/trainer.rst

@@ -274,6 +274,14 @@ Install the library via pypi:
 
     pip install fairscale
 
+or via ``transformers``' ``extras``:
+
+.. code-block:: bash
+
+    pip install transformers[fairscale]
+
+(will become available starting from ``transformers==4.6.0``)
+
 or find more details on `the FairScale's GitHub page <https://github.com/facebookresearch/fairscale/#installation>`__.
 
 If you're still struggling with the build, first make sure to read :ref:`zero-install-notes`.
@@ -419,6 +427,14 @@ Install the library via pypi:
 
     pip install deepspeed
 
+or via ``transformers``' ``extras``:
+
+.. code-block:: bash
+
+    pip install transformers[deepspeed]
+
+(will become available starting from ``transformers==4.6.0``)
+
 or find more details on `the DeepSpeed's GitHub page <https://github.com/microsoft/deepspeed#installation>`__ and
 `advanced install <https://www.deepspeed.ai/tutorials/advanced-install/>`__.
 
@@ -525,7 +541,7 @@ Here is an example of running ``run_translation.py`` under DeepSpeed deploying a
 .. code-block:: bash
 
     deepspeed examples/seq2seq/run_translation.py \
-    --deepspeed examples/tests/deepspeed/ds_config.json \
+    --deepspeed tests/deepspeed/ds_config.json \
     --model_name_or_path t5-small --per_device_train_batch_size 1   \
     --output_dir output_dir --overwrite_output_dir --fp16 \
     --do_train --max_train_samples 500 --num_train_epochs 1 \
@@ -550,7 +566,7 @@ To deploy DeepSpeed with one GPU adjust the :class:`~transformers.Trainer` comma
 .. code-block:: bash
 
     deepspeed --num_gpus=1 examples/seq2seq/run_translation.py \
-    --deepspeed examples/tests/deepspeed/ds_config.json \
+    --deepspeed tests/deepspeed/ds_config.json \
     --model_name_or_path t5-small --per_device_train_batch_size 1   \
     --output_dir output_dir --overwrite_output_dir --fp16 \
     --do_train --max_train_samples 500 --num_train_epochs 1 \

docs/source/testing.rst

-.. 
+..
     Copyright 2020 The HuggingFace Team. All rights reserved.
 
     Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
@@ -388,7 +388,7 @@ For a single or a group of tests via ``pytest`` (after ``pip install pytest-pspe
 
 .. code-block:: bash
 
-   pytest --pspec tests/test_optimization.py 
+   pytest --pspec tests/test_optimization.py
 
 
 
@@ -672,7 +672,7 @@ and it will list:
 
     test_this2.py::test_floor[integer-1-1.0]
     test_this2.py::test_floor[negative--1.5--2.0]
-    test_this2.py::test_floor[large fraction-1.6-1]       
+    test_this2.py::test_floor[large fraction-1.6-1]
 
 So now you can run just the specific test:
 
@@ -795,6 +795,23 @@ leave any data in there.
    otherwise.
 
 
+Temporary sys.path override
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you need to temporary override ``sys.path`` to import from another test for example, you can use the
+``ExtendSysPath`` context manager. Example:
+
+
+.. code-block:: python
+
+    import os
+    from transformers.testing_utils import ExtendSysPath
+    bindir = os.path.abspath(os.path.dirname(__file__))
+    with ExtendSysPath(f"{bindir}/.."):
+        from test_trainer import TrainerIntegrationCommon  # noqa
+
+
+
 Skipping tests
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

examples/language-modeling/run_mlm.py

@@ -422,7 +422,12 @@ def main():
 
     # Data collator
     # This one will take care of randomly masking the tokens.
-    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
+    pad_to_multiple_of_8 = data_args.line_by_line and training_args.fp16 and not data_args.pad_to_max_length
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm_probability=data_args.mlm_probability,
+        pad_to_multiple_of=8 if pad_to_multiple_of_8 else None,
+    )
 
     # Initialize our Trainer
     trainer = Trainer(

setup.py

@@ -19,7 +19,7 @@ To create the package for pypi.
 
 1. Run `make pre-release` (or `make pre-patch` for a patch release) then run `make fix-copies` to fix the index of the
    documentation.
-   
+
 2. Run Tests for Amazon Sagemaker. The documentation is located in `./tests/sagemaker/README.md`, otherwise @philschmid.
 
 3. Unpin specific versions from setup.py that use a git install.
@@ -85,11 +85,14 @@ if stale_egg_info.exists():
 # 1. all dependencies should be listed here with their version requirements if any
 # 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
 _deps = [
+    "Pillow",
     "black>=20.8b1",
     "cookiecutter==1.7.2",
     "dataclasses",
     "datasets",
+    "deepspeed>0.3.13",
     "docutils==0.16.0",
+    "fairscale>0.3",
     "faiss-cpu",
     "fastapi",
     "filelock",
@@ -102,13 +105,13 @@ _deps = [
     "jax>=0.2.8",
     "jaxlib>=0.1.59",
     "keras2onnx",
+    "nltk",
     "numpy>=1.17",
     "onnxconverter-common",
     "onnxruntime-tools>=1.4.2",
     "onnxruntime>=1.4.0",
     "packaging",
     "parameterized",
-    "Pillow",
     "protobuf",
     "psutil",
     "pydantic",
@@ -119,15 +122,18 @@ _deps = [
     "recommonmark",
     "regex!=2019.12.17",
     "requests",
+    "rouge-score",
+    "sacrebleu>=1.4.12",
     "sacremoses",
+    "sagemaker>=2.31.0",
     "scikit-learn",
     "sentencepiece==0.1.91",
     "soundfile",
     "sphinx-copybutton",
     "sphinx-markdown-tables",
     "sphinx-rtd-theme==0.4.3",  # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
-    "sphinxext-opengraph==0.4.1",
     "sphinx==3.2.1",
+    "sphinxext-opengraph==0.4.1",
     "starlette",
     "tensorflow-cpu>=2.3",
     "tensorflow>=2.3",
@@ -139,7 +145,6 @@ _deps = [
     "unidic>=1.0.2",
     "unidic_lite>=1.0.7",
     "uvicorn",
-    "sagemaker>=2.31.0",
 ]
 
 
@@ -230,6 +235,8 @@ extras["onnx"] = deps_list("onnxconverter-common", "keras2onnx") + extras["onnxr
 extras["modelcreation"] = deps_list("cookiecutter")
 
 extras["sagemaker"] = deps_list("sagemaker")
+extras["deepspeed"] = deps_list("deepspeed")
+extras["fairscale"] = deps_list("fairscale")
 
 extras["serving"] = deps_list("pydantic", "uvicorn", "fastapi", "starlette")
 extras["speech"] = deps_list("soundfile", "torchaudio")
@@ -238,20 +245,12 @@ extras["vision"] = deps_list("Pillow")
 extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
 extras["testing"] = (
     deps_list(
-        "pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "pytest-sugar", "black"
+        "pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil", "datasets", "pytest-sugar", "black", "sacrebleu", "rouge-score", "nltk"
     )
     + extras["retrieval"]
     + extras["modelcreation"]
 )
-extras["docs"] = deps_list(
-    "docutils",
-    "recommonmark",
-    "sphinx",
-    "sphinx-markdown-tables",
-    "sphinx-rtd-theme",
-    "sphinx-copybutton",
-    "sphinxext-opengraph",
-)
+
 extras["quality"] = deps_list("black", "isort", "flake8")
 
 extras["all"] = (
@@ -264,12 +263,24 @@ extras["all"] = (
     + extras["vision"]
 )
 
+extras["docs_specific"] = deps_list(
+    "docutils",
+    "recommonmark",
+    "sphinx",
+    "sphinx-markdown-tables",
+    "sphinx-rtd-theme",
+    "sphinx-copybutton",
+    "sphinxext-opengraph",
+)
+# "docs" needs "all" to resolve all the references
+extras["docs"] = extras["all"] + extras["docs_specific"]
+
 extras["dev"] = (
     extras["all"]
     + extras["testing"]
     + extras["quality"]
     + extras["ja"]
-    + extras["docs"]
+    + extras["docs_specific"]
     + extras["sklearn"]
     + extras["modelcreation"]
 )

src/transformers/data/data_collator.py

@@ -192,7 +192,7 @@ class DataCollatorForTokenClassification:
         return batch
 
 
-def _collate_batch(examples, tokenizer):
+def _collate_batch(examples, tokenizer, pad_to_multiple_of: Optional[int] = None):
     """Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
     # Tensorize if necessary.
     if isinstance(examples[0], (list, tuple)):
@@ -201,7 +201,7 @@ def _collate_batch(examples, tokenizer):
     # Check if padding is necessary.
     length_of_first = examples[0].size(0)
     are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
-    if are_tensors_same_length:
+    if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
         return torch.stack(examples, dim=0)
 
     # If yes, check if we have a `pad_token`.
@@ -213,6 +213,8 @@ def _collate_batch(examples, tokenizer):
 
     # Creating the full tensor and filling it with our data.
     max_length = max(x.size(0) for x in examples)
+    if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+        max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
     result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
     for i, example in enumerate(examples):
         if tokenizer.padding_side == "right":
@@ -311,6 +313,8 @@ class DataCollatorForLanguageModeling:
             non-masked tokens and the value to predict for the masked token.
         mlm_probability (:obj:`float`, `optional`, defaults to 0.15):
             The probability with which to (randomly) mask tokens in the input, when :obj:`mlm` is set to :obj:`True`.
+        pad_to_multiple_of (:obj:`int`, `optional`):
+            If set will pad the sequence to a multiple of the provided value.
 
     .. note::
 
@@ -323,6 +327,7 @@ class DataCollatorForLanguageModeling:
     tokenizer: PreTrainedTokenizerBase
     mlm: bool = True
     mlm_probability: float = 0.15
+    pad_to_multiple_of: Optional[int] = None
 
     def __post_init__(self):
         if self.mlm and self.tokenizer.mask_token is None:
@@ -336,9 +341,9 @@ class DataCollatorForLanguageModeling:
     ) -> Dict[str, torch.Tensor]:
         # Handle dict or lists with proper padding and conversion to tensor.
         if isinstance(examples[0], (dict, BatchEncoding)):
-            batch = self.tokenizer.pad(examples, return_tensors="pt")
+            batch = self.tokenizer.pad(examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of)
         else:
-            batch = {"input_ids": _collate_batch(examples, self.tokenizer)}
+            batch = {"input_ids": _collate_batch(examples, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)}
 
         # If special token mask has been preprocessed, pop it from the dict.
         special_tokens_mask = batch.pop("special_tokens_mask", None)

src/transformers/dependency_versions_check.py

@@ -14,7 +14,7 @@
 import sys
 
 from .dependency_versions_table import deps
-from .utils.versions import require_version_core
+from .utils.versions import require_version, require_version_core
 
 
 # define which module versions we always want to check at run time
@@ -41,3 +41,7 @@ for pkg in pkgs_to_check_at_runtime:
         require_version_core(deps[pkg])
     else:
         raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
+
+
+def dep_version_check(pkg, hint=None):
+    require_version(deps[pkg], hint)

src/transformers/dependency_versions_table.py

@@ -2,11 +2,14 @@
 # 1. modify the `_deps` dict in setup.py
 # 2. run `make deps_table_update``
 deps = {
+    "Pillow": "Pillow",
     "black": "black>=20.8b1",
     "cookiecutter": "cookiecutter==1.7.2",
     "dataclasses": "dataclasses",
     "datasets": "datasets",
+    "deepspeed": "deepspeed>0.3.13",
     "docutils": "docutils==0.16.0",
+    "fairscale": "fairscale>0.3",
     "faiss-cpu": "faiss-cpu",
     "fastapi": "fastapi",
     "filelock": "filelock",
@@ -19,13 +22,13 @@ deps = {
     "jax": "jax>=0.2.8",
     "jaxlib": "jaxlib>=0.1.59",
     "keras2onnx": "keras2onnx",
+    "nltk": "nltk",
     "numpy": "numpy>=1.17",
     "onnxconverter-common": "onnxconverter-common",
     "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
     "onnxruntime": "onnxruntime>=1.4.0",
     "packaging": "packaging",
     "parameterized": "parameterized",
-    "Pillow": "Pillow",
     "protobuf": "protobuf",
     "psutil": "psutil",
     "pydantic": "pydantic",
@@ -36,15 +39,18 @@ deps = {
     "recommonmark": "recommonmark",
     "regex": "regex!=2019.12.17",
     "requests": "requests",
+    "rouge-score": "rouge-score",
+    "sacrebleu": "sacrebleu>=1.4.12",
     "sacremoses": "sacremoses",
+    "sagemaker": "sagemaker>=2.31.0",
     "scikit-learn": "scikit-learn",
     "sentencepiece": "sentencepiece==0.1.91",
     "soundfile": "soundfile",
     "sphinx-copybutton": "sphinx-copybutton",
     "sphinx-markdown-tables": "sphinx-markdown-tables",
     "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
-    "sphinxext-opengraph": "sphinxext-opengraph==0.4.1",
     "sphinx": "sphinx==3.2.1",
+    "sphinxext-opengraph": "sphinxext-opengraph==0.4.1",
     "starlette": "starlette",
     "tensorflow-cpu": "tensorflow-cpu>=2.3",
     "tensorflow": "tensorflow>=2.3",
@@ -56,5 +62,4 @@ deps = {
     "unidic": "unidic>=1.0.2",
     "unidic_lite": "unidic_lite>=1.0.7",
     "uvicorn": "uvicorn",
-    "sagemaker": "sagemaker>=2.31.0",
 }

src/transformers/integrations.py

@@ -24,8 +24,8 @@ import tempfile
 from copy import deepcopy
 from pathlib import Path
 
+from .dependency_versions_check import dep_version_check
 from .utils import logging
-from .utils.versions import require_version
 
 
 logger = logging.get_logger(__name__)
@@ -324,7 +324,7 @@ def deepspeed_parse_config(ds_config):
 
     If it's already a dict, return a copy of it, so that we can freely modify it.
     """
-    require_version("deepspeed>0.3.13")
+    dep_version_check("deepspeed")
 
     if isinstance(ds_config, dict):
         # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
@@ -604,9 +604,11 @@ class TensorBoardCallback(TrainerCallback):
                 self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={})
 
     def on_log(self, args, state, control, logs=None, **kwargs):
-        if state.is_world_process_zero:
-            if self.tb_writer is None:
-                self._init_summary_writer(args)
+        if not state.is_world_process_zero:
+            return
+
+        if self.tb_writer is None:
+            self._init_summary_writer(args)
 
         if self.tb_writer is not None:
             logs = rewrite_logs(logs)

src/transformers/modeling_flax_utils.py

@@ -387,6 +387,7 @@ class FlaxPreTrainedModel(ABC):
         # get abs dir
         save_directory = os.path.abspath(save_directory)
         # save config as well
+        self.config.architectures = [self.__class__.__name__[4:]]
         self.config.save_pretrained(save_directory)
 
         # save model

src/transformers/modeling_tf_utils.py

@@ -1037,6 +1037,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
             logger.info(f"Saved model created in {saved_model_dir}")
 
         # Save configuration file
+        self.config.architectures = [self.__class__.__name__[2:]]
         self.config.save_pretrained(save_directory)
 
         # If we save using the predefined names, we can load using `from_pretrained`

src/transformers/models/auto/__init__.py

@@ -22,6 +22,7 @@ from ...file_utils import _BaseLazyModule, is_flax_available, is_tf_available, i
 
 
 _import_structure = {
+    "auto_factory": ["get_values"],
     "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
     "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
     "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
@@ -104,6 +105,7 @@ if is_flax_available():
 
 
 if TYPE_CHECKING:
+    from .auto_factory import get_values
     from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
     from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
     from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

src/transformers/models/auto/auto_factory.py

@@ -328,6 +328,26 @@ FROM_PRETRAINED_FLAX_DOCSTRING = """
 """
 
 
+def _get_model_class(config, model_mapping):
+    supported_models = model_mapping[type(config)]
+    if not isinstance(supported_models, (list, tuple)):
+        return supported_models
+
+    name_to_model = {model.__name__: model for model in supported_models}
+    architectures = getattr(config, "architectures", [])
+    for arch in architectures:
+        if arch in name_to_model:
+            return name_to_model[arch]
+        elif f"TF{arch}" in name_to_model:
+            return name_to_model[f"TF{arch}"]
+        elif f"Flax{arch}" in name_to_model:
+            return name_to_model[f"Flax{arch}"]
+
+    # If not architecture is set in the config or match the supported models, the first element of the tuple is the
+    # defaults.
+    return supported_models[0]
+
+
 class _BaseAutoModelClass:
     # Base class for auto models.
     _model_mapping = None
@@ -341,7 +361,8 @@ class _BaseAutoModelClass:
 
     def from_config(cls, config, **kwargs):
         if type(config) in cls._model_mapping.keys():
-            return cls._model_mapping[type(config)](config, **kwargs)
+            model_class = _get_model_class(config, cls._model_mapping)
+            return model_class(config, **kwargs)
         raise ValueError(
             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
             f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
@@ -356,9 +377,8 @@ class _BaseAutoModelClass:
             )
 
         if type(config) in cls._model_mapping.keys():
-            return cls._model_mapping[type(config)].from_pretrained(
-                pretrained_model_name_or_path, *model_args, config=config, **kwargs
-            )
+            model_class = _get_model_class(config, cls._model_mapping)
+            return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
         raise ValueError(
             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
             f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."
@@ -418,3 +438,14 @@ def auto_class_factory(name, model_mapping, checkpoint_for_example="bert-base-ca
     from_pretrained = replace_list_option_in_docstrings(model_mapping)(from_pretrained)
     new_class.from_pretrained = classmethod(from_pretrained)
     return new_class
+
+
+def get_values(model_mapping):
+    result = []
+    for model in model_mapping.values():
+        if isinstance(model, (list, tuple)):
+            result += list(model)
+        else:
+            result.append(model)
+
+    return result

src/transformers/models/auto/configuration_auto.py

@@ -247,29 +247,38 @@ MODEL_NAMES_MAPPING = OrderedDict(
 )
 
 
+def _get_class_name(model_class):
+    if isinstance(model_class, (list, tuple)):
+        return " or ".join([f":class:`~transformers.{c.__name__}`" for c in model_class])
+    return f":class:`~transformers.{model_class.__name__}`"
+
+
 def _list_model_options(indent, config_to_class=None, use_model_types=True):
     if config_to_class is None and not use_model_types:
         raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
     if use_model_types:
         if config_to_class is None:
-            model_type_to_name = {model_type: config.__name__ for model_type, config in CONFIG_MAPPING.items()}
+            model_type_to_name = {
+                model_type: f":class:`~transformers.{config.__name__}`"
+                for model_type, config in CONFIG_MAPPING.items()
+            }
         else:
             model_type_to_name = {
-                model_type: config_to_class[config].__name__
+                model_type: _get_class_name(config_to_class[config])
                 for model_type, config in CONFIG_MAPPING.items()
                 if config in config_to_class
             }
         lines = [
-            f"{indent}- **{model_type}** -- :class:`~transformers.{model_type_to_name[model_type]}` ({MODEL_NAMES_MAPPING[model_type]} model)"
+            f"{indent}- **{model_type}** -- {model_type_to_name[model_type]} ({MODEL_NAMES_MAPPING[model_type]} model)"
             for model_type in sorted(model_type_to_name.keys())
         ]
     else:
-        config_to_name = {config.__name__: clas.__name__ for config, clas in config_to_class.items()}
+        config_to_name = {config.__name__: _get_class_name(clas) for config, clas in config_to_class.items()}
         config_to_model_name = {
             config.__name__: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING.items()
         }
         lines = [
-            f"{indent}- :class:`~transformers.{config_name}` configuration class: :class:`~transformers.{config_to_name[config_name]}` ({config_to_model_name[config_name]} model)"
+            f"{indent}- :class:`~transformers.{config_name}` configuration class: {config_to_name[config_name]} ({config_to_model_name[config_name]} model)"
             for config_name in sorted(config_to_name.keys())
         ]
     return "\n".join(lines)

src/transformers/models/auto/modeling_auto.py

@@ -124,6 +124,7 @@ from ..flaubert.modeling_flaubert import (
 )
 from ..fsmt.modeling_fsmt import FSMTForConditionalGeneration, FSMTModel
 from ..funnel.modeling_funnel import (
+    FunnelBaseModel,
     FunnelForMaskedLM,
     FunnelForMultipleChoice,
     FunnelForPreTraining,
@@ -377,7 +378,7 @@ MODEL_MAPPING = OrderedDict(
         (CTRLConfig, CTRLModel),
         (ElectraConfig, ElectraModel),
         (ReformerConfig, ReformerModel),
-        (FunnelConfig, FunnelModel),
+        (FunnelConfig, (FunnelModel, FunnelBaseModel)),
         (LxmertConfig, LxmertModel),
         (BertGenerationConfig, BertGenerationEncoder),
         (DebertaConfig, DebertaModel),

src/transformers/models/auto/modeling_tf_auto.py

@@ -91,6 +91,7 @@ from ..flaubert.modeling_tf_flaubert import (
     TFFlaubertWithLMHeadModel,
 )
 from ..funnel.modeling_tf_funnel import (
+    TFFunnelBaseModel,
     TFFunnelForMaskedLM,
     TFFunnelForMultipleChoice,
     TFFunnelForPreTraining,
@@ -242,7 +243,7 @@ TF_MODEL_MAPPING = OrderedDict(
         (XLMConfig, TFXLMModel),
         (CTRLConfig, TFCTRLModel),
         (ElectraConfig, TFElectraModel),
-        (FunnelConfig, TFFunnelModel),
+        (FunnelConfig, (TFFunnelModel, TFFunnelBaseModel)),
         (DPRConfig, TFDPRQuestionEncoder),
         (MPNetConfig, TFMPNetModel),
         (BartConfig, TFBartModel),

src/transformers/testing_utils.py

@@ -24,6 +24,7 @@ import unittest
 from distutils.util import strtobool
 from io import StringIO
 from pathlib import Path
+from typing import Iterator, Union
 
 from .file_utils import (
     is_datasets_available,
@@ -621,6 +622,27 @@ class CaptureLogger:
         return f"captured: {self.out}\n"
 
 
+@contextlib.contextmanager
+# adapted from https://stackoverflow.com/a/64789046/9201239
+def ExtendSysPath(path: Union[str, os.PathLike]) -> Iterator[None]:
+    """
+    Temporary add given path to `sys.path`.
+
+    Usage ::
+
+       with ExtendSysPath('/path/to/dir'):
+           mymodule = importlib.import_module('mymodule')
+
+    """
+
+    path = os.fspath(path)
+    try:
+        sys.path.insert(0, path)
+        yield
+    finally:
+        sys.path.remove(path)
+
+
 class TestCasePlus(unittest.TestCase):
     """
     This class extends `unittest.TestCase` with additional features.

src/transformers/trainer.py

@@ -54,6 +54,7 @@ from torch.utils.data.distributed import DistributedSampler
 from torch.utils.data.sampler import RandomSampler, SequentialSampler
 
 from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
+from .dependency_versions_check import dep_version_check
 from .file_utils import (
     WEIGHTS_NAME,
     is_apex_available,
@@ -139,17 +140,14 @@ if is_torch_tpu_available():
     import torch_xla.distributed.parallel_loader as pl
 
 if is_fairscale_available():
+    dep_version_check("fairscale")
     import fairscale
+    from fairscale.nn.data_parallel import FullyShardedDataParallel as FullyShardedDDP
     from fairscale.nn.data_parallel import ShardedDataParallel as ShardedDDP
+    from fairscale.nn.wrap import auto_wrap
     from fairscale.optim import OSS
     from fairscale.optim.grad_scaler import ShardedGradScaler
 
-    if version.parse(fairscale.__version__) >= version.parse("0.3"):
-        from fairscale.nn.data_parallel import FullyShardedDataParallel as FullyShardedDDP
-        from fairscale.nn.wrap import auto_wrap
-    else:
-        FullyShardedDDP = None
-
 if is_sagemaker_dp_enabled():
     import smdistributed.dataparallel.torch.distributed as dist
     from smdistributed.dataparallel.torch.parallel.distributed import DistributedDataParallel as DDP

src/transformers/training_args.py

@@ -531,6 +531,12 @@ class TrainingArguments:
     )
 
     def __post_init__(self):
+        # Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then).
+        # This needs to happen before any call to self.device or self.n_gpu.
+        env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+        if env_local_rank != -1 and env_local_rank != self.local_rank:
+            self.local_rank = env_local_rank
+
         # expand paths, if not os.makedirs("~/bar") will make directory
         # in the current directory instead of the actual home
         #  see https://github.com/huggingface/transformers/issues/10628