Config to Model mapping

examples/summarization/configuration_bertabs.py

@@ -62,6 +62,7 @@ class BertAbsConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
+    model_type = "bertabs"
 
     def __init__(
         self,

src/transformers/configuration_albert.py

@@ -37,6 +37,7 @@ class AlbertConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "albert"
 
     def __init__(
         self,

src/transformers/configuration_auto.py

@@ -188,7 +188,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
             assert unused_kwargs == {'foo': False}
 
         """
-        config_dict, _ = PretrainedConfig.resolved_config_dict(
+        config_dict, _ = PretrainedConfig.get_config_dict(
             pretrained_model_name_or_path, pretrained_config_archive_map=ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, **kwargs
         )
 

src/transformers/configuration_bert.py

@@ -78,6 +78,7 @@ class BertConfig(PretrainedConfig):
             layer_norm_eps: The epsilon used by LayerNorm.
     """
     pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "bert"
 
     def __init__(
         self,

src/transformers/configuration_camembert.py

@@ -30,3 +30,4 @@
 
 class CamembertConfig(RobertaConfig):
     pretrained_config_archive_map = CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "camembert"

src/transformers/configuration_ctrl.py

@@ -48,6 +48,7 @@ class CTRLConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "ctrl"
 
     def __init__(
         self,

src/transformers/configuration_distilbert.py

@@ -32,6 +32,7 @@
 
 class DistilBertConfig(PretrainedConfig):
     pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "distilbert"
 
     def __init__(
         self,

src/transformers/configuration_gpt2.py

@@ -54,6 +54,7 @@ class GPT2Config(PretrainedConfig):
     """
 
     pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "gpt2"
 
     def __init__(
         self,

src/transformers/configuration_openai.py

@@ -54,6 +54,7 @@ class OpenAIGPTConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "openai-gpt"
 
     def __init__(
         self,

src/transformers/configuration_t5.py

@@ -60,6 +60,7 @@ class T5Config(PretrainedConfig):
             layer_norm_eps: The epsilon used by LayerNorm.
     """
     pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "t5"
 
     def __init__(
         self,

src/transformers/configuration_transfo_xl.py

@@ -65,6 +65,7 @@ class TransfoXLConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "transfo-xl"
 
     def __init__(
         self,

src/transformers/configuration_utils.py

@@ -46,7 +46,8 @@ class PretrainedConfig(object):
             ``output_hidden_states``: string, default `False`. Should the model returns all hidden-states.
             ``torchscript``: string, default `False`. Is the model used with Torchscript.
     """
-    pretrained_config_archive_map = {}
+    pretrained_config_archive_map: Dict[str, str] = {}
+    model_type: str
 
     def __init__(self, **kwargs):
         # Attributes with defaults
@@ -155,11 +156,11 @@ def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
             assert unused_kwargs == {'foo': False}
 
         """
-        config_dict, kwargs = cls.resolved_config_dict(pretrained_model_name_or_path, **kwargs)
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
         return cls.from_dict(config_dict, **kwargs)
 
     @classmethod
-    def resolved_config_dict(
+    def get_config_dict(
         cls, pretrained_model_name_or_path: str, pretrained_config_archive_map: Optional[Dict] = None, **kwargs
     ) -> Tuple[Dict, Dict]:
         """
@@ -257,7 +258,7 @@ def from_dict(cls, config_dict: Dict, **kwargs):
 
     @classmethod
     def from_json_file(cls, json_file: str):
-        """Constructs a `Config` from a json file of parameters."""
+        """Constructs a `Config` from the path to a json file of parameters."""
         config_dict = cls._dict_from_json_file(json_file)
         return cls(**config_dict)
 

src/transformers/configuration_xlm.py

@@ -78,6 +78,7 @@ class XLMConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = XLM_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "xlm"
 
     def __init__(
         self,

src/transformers/configuration_xlm_roberta.py

@@ -35,3 +35,4 @@
 
 class XLMRobertaConfig(RobertaConfig):
     pretrained_config_archive_map = XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "xlm-roberta"

src/transformers/configuration_xlnet.py

@@ -69,6 +69,7 @@ class XLNetConfig(PretrainedConfig):
     """
 
     pretrained_config_archive_map = XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "xlnet"
 
     def __init__(
         self,

src/transformers/modeling_auto.py

@@ -16,6 +16,8 @@
 
 
 import logging
+from collections import OrderedDict
+from typing import Type
 
 from .configuration_auto import (
     AlbertConfig,
@@ -76,6 +78,7 @@
 )
 from .modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5Model, T5WithLMHeadModel
 from .modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TransfoXLLMHeadModel, TransfoXLModel
+from .modeling_utils import PreTrainedModel
 from .modeling_xlm import (
     XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
     XLMForQuestionAnswering,
@@ -123,6 +126,35 @@
     for key, value, in pretrained_map.items()
 )
 
+MODEL_MAPPING: OrderedDict[Type[PretrainedConfig], Type[PreTrainedModel]] = OrderedDict(
+    [
+        (T5Config, T5Model),
+        (DistilBertConfig, DistilBertModel),
+        (AlbertConfig, AlbertModel),
+        (CamembertConfig, CamembertModel),
+        (RobertaConfig, XLMRobertaModel),
+        (XLMRobertaConfig, RobertaModel),
+        (BertConfig, BertModel),
+        (OpenAIGPTConfig, OpenAIGPTModel),
+        (GPT2Config, GPT2Model),
+        (TransfoXLConfig, TransfoXLModel),
+        (XLNetConfig, XLNetModel),
+        (XLMConfig, XLMModel),
+        (CTRLConfig, CTRLModel),
+    ]
+)
+
+MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING: OrderedDict[Type[PretrainedConfig], Type[PreTrainedModel]] = OrderedDict(
+    [
+        (DistilBertConfig, DistilBertForTokenClassification),
+        (CamembertConfig, CamembertForTokenClassification),
+        (RobertaConfig, XLMRobertaForTokenClassification),
+        (XLMRobertaConfig, RobertaForTokenClassification),
+        (BertConfig, BertForTokenClassification),
+        (XLNetConfig, XLNetForTokenClassification),
+    ]
+)
+
 
 class AutoModel(object):
     r"""
@@ -183,30 +215,9 @@ def from_config(cls, config):
             config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
             model = AutoModel.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
         """
-        if isinstance(config, DistilBertConfig):
-            return DistilBertModel(config)
-        elif isinstance(config, RobertaConfig):
-            return RobertaModel(config)
-        elif isinstance(config, BertConfig):
-            return BertModel(config)
-        elif isinstance(config, OpenAIGPTConfig):
-            return OpenAIGPTModel(config)
-        elif isinstance(config, GPT2Config):
-            return GPT2Model(config)
-        elif isinstance(config, TransfoXLConfig):
-            return TransfoXLModel(config)
-        elif isinstance(config, XLNetConfig):
-            return XLNetModel(config)
-        elif isinstance(config, XLMConfig):
-            return XLMModel(config)
-        elif isinstance(config, CTRLConfig):
-            return CTRLModel(config)
-        elif isinstance(config, AlbertConfig):
-            return AlbertModel(config)
-        elif isinstance(config, CamembertConfig):
-            return CamembertModel(config)
-        elif isinstance(config, XLMRobertaConfig):
-            return XLMRobertaModel(config)
+        for config_class, model_class in MODEL_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class(config)
         raise ValueError("Unrecognized configuration class {}".format(config))
 
     @classmethod
@@ -294,32 +305,9 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         if not isinstance(config, PretrainedConfig):
             config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
-        if isinstance(config, T5Config):
-            return T5Model.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, DistilBertConfig):
-            return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, AlbertConfig):
-            return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, CamembertConfig):
-            return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, XLMRobertaConfig):
-            return XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, RobertaConfig):
-            return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, BertConfig):
-            return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, OpenAIGPTConfig):
-            return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, GPT2Config):
-            return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, TransfoXLConfig):
-            return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, XLNetConfig):
-            return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, XLMConfig):
-            return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
-        elif isinstance(config, CTRLConfig):
-            return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
+        for config_class, model_class in MODEL_MAPPING.items():
+            if isinstance(config, config_class):
+                return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
         raise ValueError(
             "Unrecognized model identifier in {}. Should contains one of "
             "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "

templates/adding_a_new_model/configuration_xxx.py

@@ -58,6 +58,7 @@ class XxxConfig(PretrainedConfig):
             layer_norm_eps: The epsilon used by LayerNorm.
     """
     pretrained_config_archive_map = XXX_PRETRAINED_CONFIG_ARCHIVE_MAP
+    model_type = "xxx"
 
     def __init__(
         self,