Add support for multiple models for one config in auto classes

src/transformers/modeling_flax_utils.py

@@ -387,6 +387,7 @@ def save_pretrained(self, save_directory: Union[str, os.PathLike]):
         # 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 @@ def save_pretrained(self, save_directory, saved_model=False, version=1):
             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 @@
 
 
 _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 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 @@
 """
 
 
+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 @@ def __init__(self):
 
     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 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
             )
 
         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 @@
 )
 
 
+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 ..fsmt.modeling_fsmt import FSMTForConditionalGeneration, FSMTModel
 from ..funnel.modeling_funnel import (
+    FunnelBaseModel,
     FunnelForMaskedLM,
     FunnelForMultipleChoice,
     FunnelForPreTraining,
@@ -377,7 +378,7 @@
         (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 @@
     TFFlaubertWithLMHeadModel,
 )
 from ..funnel.modeling_tf_funnel import (
+    TFFunnelBaseModel,
     TFFunnelForMaskedLM,
     TFFunnelForMultipleChoice,
     TFFunnelForPreTraining,
@@ -242,7 +243,7 @@
         (XLMConfig, TFXLMModel),
         (CTRLConfig, TFCTRLModel),
         (ElectraConfig, TFElectraModel),
-        (FunnelConfig, TFFunnelModel),
+        (FunnelConfig, (TFFunnelModel, TFFunnelBaseModel)),
         (DPRConfig, TFDPRQuestionEncoder),
         (MPNetConfig, TFMPNetModel),
         (BartConfig, TFBartModel),

tests/test_modeling_albert.py

@@ -17,6 +17,7 @@
 import unittest
 
 from transformers import is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -234,7 +235,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
 
         if return_labels:
-            if model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
+            if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )

tests/test_modeling_auto.py

@@ -13,7 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import copy
+import tempfile
 import unittest
 
 from transformers import is_torch_available
@@ -46,6 +47,8 @@
         BertForSequenceClassification,
         BertForTokenClassification,
         BertModel,
+        FunnelBaseModel,
+        FunnelModel,
         GPT2Config,
         GPT2LMHeadModel,
         RobertaForMaskedLM,
@@ -218,6 +221,21 @@ def test_from_identifier_from_model_type(self):
         self.assertEqual(model.num_parameters(), 14410)
         self.assertEqual(model.num_parameters(only_trainable=True), 14410)
 
+    def test_from_pretrained_with_tuple_values(self):
+        # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
+        model = AutoModel.from_pretrained("sgugger/funnel-random-tiny")
+        self.assertIsInstance(model, FunnelModel)
+
+        config = copy.deepcopy(model.config)
+        config.architectures = ["FunnelBaseModel"]
+        model = AutoModel.from_config(config)
+        self.assertIsInstance(model, FunnelBaseModel)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir)
+            model = AutoModel.from_pretrained(tmp_dir)
+            self.assertIsInstance(model, FunnelBaseModel)
+
     def test_parents_and_children_in_mappings(self):
         # Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
         # by the parents and will return the wrong configuration type when using auto models
@@ -242,6 +260,12 @@ def test_parents_and_children_in_mappings(self):
                     assert not issubclass(
                         child_config, parent_config
                     ), f"{child_config.__name__} is child of {parent_config.__name__}"
-                    assert not issubclass(
-                        child_model, parent_model
-                    ), f"{child_config.__name__} is child of {parent_config.__name__}"
+
+                    # Tuplify child_model and parent_model since some of them could be tuples.
+                    if not isinstance(child_model, (list, tuple)):
+                        child_model = (child_model,)
+                    if not isinstance(parent_model, (list, tuple)):
+                        parent_model = (parent_model,)
+
+                    for child, parent in [(a, b) for a in child_model for b in parent_model]:
+                        assert not issubclass(child, parent), f"{child.__name__} is child of {parent.__name__}"

tests/test_modeling_bert.py

@@ -17,6 +17,7 @@
 import unittest
 
 from transformers import is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -444,7 +445,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
 
         if return_labels:
-            if model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
+            if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )

tests/test_modeling_big_bird.py

@@ -19,6 +19,7 @@
 
 from tests.test_modeling_common import floats_tensor
 from transformers import is_torch_available
+from transformers.models.auto import get_values
 from transformers.models.big_bird.tokenization_big_bird import BigBirdTokenizer
 from transformers.testing_utils import require_torch, slow, torch_device
 
@@ -458,7 +459,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
 
         if return_labels:
-            if model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
+            if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )

tests/test_modeling_common.py

@@ -24,6 +24,7 @@
 
 from transformers import is_torch_available
 from transformers.file_utils import WEIGHTS_NAME
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
 
 
@@ -79,7 +80,7 @@ class ModelTesterMixin:
 
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = copy.deepcopy(inputs_dict)
-        if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
+        if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
             inputs_dict = {
                 k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
                 if isinstance(v, torch.Tensor) and v.ndim > 1
@@ -88,28 +89,28 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
             }
 
         if return_labels:
-            if model_class in MODEL_FOR_MULTIPLE_CHOICE_MAPPING.values():
+            if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                 inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
-            elif model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
+            elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                 inputs_dict["start_positions"] = torch.zeros(
                     self.model_tester.batch_size, dtype=torch.long, device=torch_device
                 )
                 inputs_dict["end_positions"] = torch.zeros(
                     self.model_tester.batch_size, dtype=torch.long, device=torch_device
                 )
             elif model_class in [
-                *MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.values(),
-                *MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING.values(),
-                *MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.values(),
+                *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
+                *get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING),
+                *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
             ]:
                 inputs_dict["labels"] = torch.zeros(
                     self.model_tester.batch_size, dtype=torch.long, device=torch_device
                 )
             elif model_class in [
-                *MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.values(),
-                *MODEL_FOR_CAUSAL_LM_MAPPING.values(),
-                *MODEL_FOR_MASKED_LM_MAPPING.values(),
-                *MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.values(),
+                *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
+                *get_values(MODEL_FOR_CAUSAL_LM_MAPPING),
+                *get_values(MODEL_FOR_MASKED_LM_MAPPING),
+                *get_values(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
             ]:
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
@@ -229,7 +230,7 @@ def test_training(self):
         config.return_dict = True
 
         for model_class in self.all_model_classes:
-            if model_class in MODEL_MAPPING.values():
+            if model_class in get_values(MODEL_MAPPING):
                 continue
             model = model_class(config)
             model.to(torch_device)
@@ -248,7 +249,7 @@ def test_training_gradient_checkpointing(self):
         config.return_dict = True
 
         for model_class in self.all_model_classes:
-            if model_class in MODEL_MAPPING.values():
+            if model_class in get_values(MODEL_MAPPING):
                 continue
             model = model_class(config)
             model.to(torch_device)
@@ -312,7 +313,7 @@ def test_attention_outputs(self):
                 if "labels" in inputs_dict:
                     correct_outlen += 1  # loss is added to beginning
                 # Question Answering model returns start_logits and end_logits
-                if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
+                if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                     correct_outlen += 1  # start_logits and end_logits instead of only 1 output
                 if "past_key_values" in outputs:
                     correct_outlen += 1  # past_key_values have been returned

tests/test_modeling_convbert.py

@@ -19,6 +19,7 @@
 
 from tests.test_modeling_common import floats_tensor
 from transformers import is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -352,7 +353,7 @@ def test_attention_outputs(self):
                 if "labels" in inputs_dict:
                     correct_outlen += 1  # loss is added to beginning
                 # Question Answering model returns start_logits and end_logits
-                if model_class in MODEL_FOR_QUESTION_ANSWERING_MAPPING.values():
+                if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                     correct_outlen += 1  # start_logits and end_logits instead of only 1 output
                 if "past_key_values" in outputs:
                     correct_outlen += 1  # past_key_values have been returned

tests/test_modeling_electra.py

@@ -17,6 +17,7 @@
 import unittest
 
 from transformers import is_torch_available
+from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -292,7 +293,7 @@ def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
         inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
 
         if return_labels:
-            if model_class in MODEL_FOR_PRETRAINING_MAPPING.values():
+            if model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
                 inputs_dict["labels"] = torch.zeros(
                     (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
                 )