consistent ignore keys + make private (#8737)

* consistent ignore keys + make private

* style

* - authorized_missing_keys    => _keys_to_ignore_on_load_missing
  - authorized_unexpected_keys => _keys_to_ignore_on_load_unexpected

* move public doc of private attributes to private comment

src/transformers/modeling_tf_pytorch_utils.py

@@ -164,9 +164,9 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
             if allow_missing_keys:
                 missing_keys.append(name)
                 continue
-            elif tf_model.authorized_missing_keys is not None:
+            elif tf_model._keys_to_ignore_on_load_missing is not None:
                 # authorized missing keys don't have to be loaded
-                if any(re.search(pat, name) is not None for pat in tf_model.authorized_missing_keys):
+                if any(re.search(pat, name) is not None for pat in tf_model._keys_to_ignore_on_load_missing):
                     continue
 
             raise AttributeError("{} not found in PyTorch model".format(name))
@@ -209,11 +209,11 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, a
 
     unexpected_keys = list(all_pytorch_weights)
 
-    if tf_model.authorized_missing_keys is not None:
+    if tf_model._keys_to_ignore_on_load_missing is not None:
-        for pat in tf_model.authorized_missing_keys:
+        for pat in tf_model._keys_to_ignore_on_load_missing:
             missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
-    if tf_model.authorized_unexpected_keys is not None:
+    if tf_model._keys_to_ignore_on_load_unexpected is not None:
-        for pat in tf_model.authorized_unexpected_keys:
+        for pat in tf_model._keys_to_ignore_on_load_unexpected:
             unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
     if len(unexpected_keys) > 0:

src/transformers/modeling_tf_utils.py

@@ -343,15 +343,15 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
           :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
         - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
           derived classes of the same architecture adding modules on top of the base model.
-        - **authorized_missing_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to ignore
-          from the model when loading the model weights (and avoid unnecessary warnings).
-        - **authorized_unexpected_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to
-          ignore from the weights when loading the model weights (and avoid unnecessary warnings).
     """
     config_class = None
     base_model_prefix = ""
-    authorized_missing_keys = None
+    # a list of re pattern of tensor names to ignore from the model when loading the model weights
-    authorized_unexpected_keys = None
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_missing = None
+    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_unexpected = None
 
     @property
     def dummy_inputs(self) -> Dict[str, tf.Tensor]:
@@ -742,12 +742,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
 
         model(model.dummy_inputs, training=False)  # Make sure restore ops are run
 
-        if cls.authorized_missing_keys is not None:
+        if cls._keys_to_ignore_on_load_missing is not None:
-            for pat in cls.authorized_missing_keys:
+            for pat in cls._keys_to_ignore_on_load_missing:
                 missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
 
-        if cls.authorized_unexpected_keys is not None:
+        if cls._keys_to_ignore_on_load_unexpected is not None:
-            for pat in cls.authorized_unexpected_keys:
+            for pat in cls._keys_to_ignore_on_load_unexpected:
                 unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
         if len(unexpected_keys) > 0:

src/transformers/modeling_utils.py

@@ -404,17 +404,18 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
         - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
           derived classes of the same architecture adding modules on top of the base model.
-        - **authorized_missing_keys** (:obj:`Optional[List[str]]`) -- A list of re pattern of tensor names to ignore
-          when loading the model (and avoid unnecessary warnings).
-        - **keys_to_never_save** (:obj:`Optional[List[str]]`) -- A list of of tensor names to ignore when saving the
-          model (useful for keys that aren't trained, but which are deterministic)
-
     """
     config_class = None
     base_model_prefix = ""
-    authorized_missing_keys = None
+    # a list of re pattern of tensor names to ignore from the model when loading the model weights
-    authorized_unexpected_keys = None
+    # (and avoid unnecessary warnings).
-    keys_to_never_save = None
+    _keys_to_ignore_on_load_missing = None
+    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
+    # (and avoid unnecessary warnings).
+    _keys_to_ignore_on_load_unexpected = None
+    # a list of of tensor names to ignore when saving the model (useful for keys that aren't
+    # trained, but which are deterministic)
+    _keys_to_ignore_on_save = None
 
     @property
     def dummy_inputs(self) -> Dict[str, torch.Tensor]:
@@ -719,8 +720,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         state_dict = model_to_save.state_dict()
 
         # Handle the case where some state_dict keys shouldn't be saved
-        if self.keys_to_never_save is not None:
+        if self._keys_to_ignore_on_save is not None:
-            state_dict = {k: v for k, v in state_dict.items() if k not in self.keys_to_never_save}
+            state_dict = {k: v for k, v in state_dict.items() if k not in self._keys_to_ignore_on_save}
 
         # If we save using the predefined names, we can load using `from_pretrained`
         output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
@@ -1034,12 +1035,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
             # Some models may have keys that are not in the state by design, removing them before needlessly warning
             # the user.
-            if cls.authorized_missing_keys is not None:
+            if cls._keys_to_ignore_on_load_missing is not None:
-                for pat in cls.authorized_missing_keys:
+                for pat in cls._keys_to_ignore_on_load_missing:
                     missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
 
-            if cls.authorized_unexpected_keys is not None:
+            if cls._keys_to_ignore_on_load_unexpected is not None:
-                for pat in cls.authorized_unexpected_keys:
+                for pat in cls._keys_to_ignore_on_load_unexpected:
                     unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]
 
             if len(unexpected_keys) > 0:

src/transformers/models/albert/modeling_albert.py

@@ -459,7 +459,7 @@ class AlbertPreTrainedModel(PreTrainedModel):
 
     config_class = AlbertConfig
     base_model_prefix = "albert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """Initialize the weights."""
@@ -851,7 +851,7 @@ class AlbertSOPHead(nn.Module):
 )
 class AlbertForMaskedLM(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1021,7 +1021,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
 )
 class AlbertForTokenClassification(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1110,7 +1110,7 @@ class AlbertForTokenClassification(AlbertPreTrainedModel):
 )
 class AlbertForQuestionAnswering(AlbertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/albert/modeling_tf_albert.py

@@ -843,7 +843,7 @@ class TFAlbertSOPHead(tf.keras.layers.Layer):
 @add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING)
 class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1013,7 +1013,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
 )
 class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1100,7 +1100,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
 )
 class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/bart/modeling_bart.py

@@ -946,7 +946,7 @@ class BartModel(PretrainedBartModel):
 )
 class BartForConditionalGeneration(PretrainedBartModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]
+    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]
 
     def __init__(self, config: BartConfig):
         super().__init__(config)

src/transformers/models/bart/modeling_tf_bart.py

@@ -1020,10 +1020,10 @@ class TFBartModel(TFPretrainedBartModel):
 )
 class TFBartForConditionalGeneration(TFPretrainedBartModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"final_logits_bias",
     ]
-    authorized_unexpected_keys = [
+    _keys_to_ignore_on_load_unexpected = [
         r"model.encoder.embed_tokens.weight",
         r"model.decoder.embed_tokens.weight",
     ]

src/transformers/models/bert/modeling_bert.py

@@ -598,7 +598,7 @@ class BertPreTrainedModel(PreTrainedModel):
     config_class = BertConfig
     load_tf_weights = load_tf_weights_in_bert
     base_model_prefix = "bert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -969,8 +969,8 @@ class BertForPreTraining(BertPreTrainedModel):
 )
 class BertLMHeadModel(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1087,8 +1087,8 @@ class BertLMHeadModel(BertPreTrainedModel):
 @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class BertForMaskedLM(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1469,7 +1469,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
 )
 class BertForTokenClassification(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1560,7 +1560,7 @@ class BertForTokenClassification(BertPreTrainedModel):
 )
 class BertForQuestionAnswering(BertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/bert/modeling_tf_bert.py

@@ -938,8 +938,8 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
 @add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1023,8 +1023,8 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
 class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1416,8 +1416,8 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
 )
 class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1502,8 +1502,8 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
 )
 class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -173,7 +173,7 @@ class BertGenerationPreTrainedModel(PreTrainedModel):
 
     config_class = BertGenerationConfig
     base_model_prefix = "bert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/deberta/modeling_deberta.py

@@ -756,7 +756,7 @@ class DebertaPreTrainedModel(PreTrainedModel):
 
     config_class = DebertaConfig
     base_model_prefix = "deberta"
-    authorized_missing_keys = ["position_ids"]
+    _keys_to_ignore_on_load_missing = ["position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/dpr/modeling_dpr.py

@@ -279,7 +279,7 @@ class DPRPretrainedContextEncoder(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "ctx_encoder"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.ctx_encoder.init_weights()
@@ -294,7 +294,7 @@ class DPRPretrainedQuestionEncoder(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "question_encoder"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.question_encoder.init_weights()
@@ -309,7 +309,7 @@ class DPRPretrainedReader(PreTrainedModel):
     config_class = DPRConfig
     load_tf_weights = None
     base_model_prefix = "span_predictor"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def init_weights(self):
         self.span_predictor.encoder.init_weights()

src/transformers/models/electra/modeling_electra.py

@@ -544,8 +544,8 @@ class ElectraPreTrainedModel(PreTrainedModel):
     config_class = ElectraConfig
     load_tf_weights = load_tf_weights_in_electra
     base_model_prefix = "electra"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
-    authorized_unexpected_keys = [r"electra\.embeddings_project\.weight", r"electra\.embeddings_project\.bias"]
+    _keys_to_ignore_on_load_unexpected = [r"electra\.embeddings_project\.weight", r"electra\.embeddings_project\.bias"]
 
     # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
     def _init_weights(self, module):

src/transformers/models/fsmt/modeling_fsmt.py

@@ -1005,11 +1005,11 @@ class FSMTModel(PretrainedFSMTModel):
 )
 class FSMTForConditionalGeneration(PretrainedFSMTModel):
     base_model_prefix = "model"
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/gpt2/modeling_gpt2.py

@@ -780,7 +780,7 @@ class GPT2Model(GPT2PreTrainedModel):
     GPT2_START_DOCSTRING,
 )
 class GPT2LMHeadModel(GPT2PreTrainedModel):
-    authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1097,7 +1097,7 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
     GPT2_START_DOCSTRING,
 )
 class GPT2ForSequenceClassification(GPT2PreTrainedModel):
-    authorized_missing_keys = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
+    _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head\.weight"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/layoutlm/modeling_layoutlm.py

@@ -509,7 +509,7 @@ class LayoutLMPreTrainedModel(PreTrainedModel):
 
     config_class = LayoutLMConfig
     base_model_prefix = "layoutlm"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

src/transformers/models/longformer/modeling_longformer.py

@@ -1303,7 +1303,7 @@ class LongformerPreTrainedModel(PreTrainedModel):
 
     config_class = LongformerConfig
     base_model_prefix = "longformer"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -1621,7 +1621,7 @@ class LongformerModel(LongformerPreTrainedModel):
 @add_start_docstrings("""Longformer Model with a `language modeling` head on top. """, LONGFORMER_START_DOCSTRING)
 class LongformerForMaskedLM(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1718,7 +1718,7 @@ class LongformerForMaskedLM(LongformerPreTrainedModel):
 )
 class LongformerForSequenceClassification(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1827,7 +1827,7 @@ class LongformerClassificationHead(nn.Module):
 )
 class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1961,7 +1961,7 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 )
 class LongformerForTokenClassification(LongformerPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/longformer/modeling_tf_longformer.py

@@ -1961,7 +1961,7 @@ class TFLongformerModel(TFLongformerPreTrainedModel):
 )
 class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2048,7 +2048,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
 )
 class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2199,7 +2199,7 @@ class TFLongformerClassificationHead(tf.keras.layers.Layer):
 )
 class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSequenceClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -2443,7 +2443,7 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic
 )
 class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/marian/modeling_marian.py

@@ -47,11 +47,11 @@ class MarianMTModel(BartForConditionalGeneration):
 
     """
     config_class = MarianConfig
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/marian/modeling_tf_marian.py

@@ -37,7 +37,7 @@ logger = logging.get_logger(__name__)
 
 @add_start_docstrings("Marian model for machine translation", START_DOCSTRING)
 class TFMarianMTModel(TFBartForConditionalGeneration):
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"model.encoder.embed_positions.weight",
         r"model.decoder.embed_positions.weight",
     ]

src/transformers/models/mbart/modeling_mbart.py

@@ -29,11 +29,11 @@ class MBartForConditionalGeneration(BartForConditionalGeneration):
     """
     model_type = "mbart"
     config_class = MBartConfig
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/mobilebert/modeling_mobilebert.py

@@ -677,7 +677,7 @@ class MobileBertPreTrainedModel(PreTrainedModel):
     pretrained_model_archive_map = MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST
     load_tf_weights = load_tf_weights_in_mobilebert
     base_model_prefix = "mobilebert"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -1054,7 +1054,7 @@ class MobileBertForPreTraining(MobileBertPreTrainedModel):
 @add_start_docstrings("""MobileBert Model with a `language modeling` head on top. """, MOBILEBERT_START_DOCSTRING)
 class MobileBertForMaskedLM(MobileBertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1350,7 +1350,7 @@ class MobileBertForSequenceClassification(MobileBertPreTrainedModel):
 )
 class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1545,7 +1545,7 @@ class MobileBertForMultipleChoice(MobileBertPreTrainedModel):
 )
 class MobileBertForTokenClassification(MobileBertPreTrainedModel):
 
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/mobilebert/modeling_tf_mobilebert.py

@@ -1030,7 +1030,7 @@ class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel):
 @add_start_docstrings("""MobileBert Model with a `language modeling` head on top. """, MOBILEBERT_START_DOCSTRING)
 class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1297,7 +1297,7 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
 )
 class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1529,7 +1529,7 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
 )
 class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/mt5/modeling_mt5.py

@@ -42,12 +42,12 @@ class MT5Model(T5Model):
     """
     model_type = "mt5"
     config_class = MT5Config
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
         r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
     ]
@@ -71,13 +71,13 @@ class MT5ForConditionalGeneration(T5ForConditionalGeneration):
 
     model_type = "mt5"
     config_class = MT5Config
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
         r"lm_head\.weight",
         r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
     ]

src/transformers/models/openai/modeling_openai.py

@@ -279,7 +279,7 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
     config_class = OpenAIGPTConfig
     load_tf_weights = load_tf_weights_in_openai_gpt
     base_model_prefix = "transformer"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """Initialize the weights."""

src/transformers/models/pegasus/modeling_pegasus.py

@@ -46,14 +46,14 @@ class PegasusForConditionalGeneration(BartForConditionalGeneration):
     """
     # All the code is in src/transformers/models/bart/modeling_bart.py
     config_class = PegasusConfig
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"final_logits_bias",
         r"encoder\.version",
         r"decoder\.version",
         "model.encoder.embed_positions",
         "model.decoder.embed_positions",
     ]
-    keys_to_never_save = [
+    _keys_to_ignore_on_save = [
         "model.encoder.embed_positions.weight",
         "model.decoder.embed_positions.weight",
     ]

src/transformers/models/pegasus/modeling_tf_pegasus.py

@@ -32,7 +32,7 @@ logger = logging.get_logger(__name__)
 
 @add_start_docstrings("Pegasus model for summarization", START_DOCSTRING)
 class TFPegasusForConditionalGeneration(TFBartForConditionalGeneration):
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"final_logits_bias",
         r"model.encoder.embed_positions.weight",
         r"model.decoder.embed_positions.weight",

src/transformers/models/rag/modeling_rag.py

@@ -216,7 +216,7 @@ class RagPreTrainedModel(PreTrainedModel):
     """
     config_class = RagConfig
     base_model_prefix = "rag"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     @classmethod
     def from_pretrained_question_encoder_generator(

src/transformers/models/roberta/modeling_roberta.py

@@ -576,7 +576,7 @@ class RobertaModel(RobertaPreTrainedModel):
 
     """
 
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     # Copied from transformers.models.bert.modeling_bert.BertModel.__init__ with Bert->Roberta
     def __init__(self, config, add_pooling_layer=True):
@@ -711,8 +711,8 @@ class RobertaModel(RobertaPreTrainedModel):
     """RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. """, ROBERTA_START_DOCSTRING
 )
 class RobertaForCausalLM(RobertaPreTrainedModel):
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -829,8 +829,8 @@ class RobertaForCausalLM(RobertaPreTrainedModel):
 
 @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
 class RobertaForMaskedLM(RobertaPreTrainedModel):
-    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -948,7 +948,7 @@ class RobertaLMHead(nn.Module):
     ROBERTA_START_DOCSTRING,
 )
 class RobertaForSequenceClassification(RobertaPreTrainedModel):
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1031,7 +1031,7 @@ class RobertaForSequenceClassification(RobertaPreTrainedModel):
     ROBERTA_START_DOCSTRING,
 )
 class RobertaForMultipleChoice(RobertaPreTrainedModel):
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1123,8 +1123,8 @@ class RobertaForMultipleChoice(RobertaPreTrainedModel):
     ROBERTA_START_DOCSTRING,
 )
 class RobertaForTokenClassification(RobertaPreTrainedModel):
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def __init__(self, config):
         super().__init__(config)
@@ -1233,8 +1233,8 @@ class RobertaClassificationHead(nn.Module):
     ROBERTA_START_DOCSTRING,
 )
 class RobertaForQuestionAnswering(RobertaPreTrainedModel):
-    authorized_unexpected_keys = [r"pooler"]
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/roberta/modeling_tf_roberta.py

@@ -765,7 +765,7 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
 @add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
 class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -877,7 +877,7 @@ class TFRobertaClassificationHead(tf.keras.layers.Layer):
 )
 class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1084,7 +1084,7 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
 )
 class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassificationLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
@@ -1171,7 +1171,7 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
 )
 class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnsweringLoss):
 
-    authorized_missing_keys = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"pooler"]
 
     def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)

src/transformers/models/squeezebert/modeling_squeezebert.py

@@ -428,7 +428,7 @@ class SqueezeBertPreTrainedModel(PreTrainedModel):
 
     config_class = SqueezeBertConfig
     base_model_prefix = "transformer"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """
@@ -642,7 +642,7 @@ class SqueezeBertModel(SqueezeBertPreTrainedModel):
 @add_start_docstrings("""SqueezeBERT Model with a `language modeling` head on top. """, SQUEEZEBERT_START_DOCSTRING)
 class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
 
-    authorized_missing_keys = [r"predictions.decoder.bias"]
+    _keys_to_ignore_on_load_missing = [r"predictions.decoder.bias"]
 
     def __init__(self, config):
         super().__init__(config)

src/transformers/models/t5/modeling_t5.py

@@ -1086,7 +1086,7 @@ T5_INPUTS_DOCSTRING = r"""
     T5_START_DOCSTRING,
 )
 class T5Model(T5PreTrainedModel):
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
         r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
@@ -1258,7 +1258,7 @@ class T5Model(T5PreTrainedModel):
 
 @add_start_docstrings("""T5 Model with a `language modeling` head on top. """, T5_START_DOCSTRING)
 class T5ForConditionalGeneration(T5PreTrainedModel):
-    authorized_missing_keys = [
+    _keys_to_ignore_on_load_missing = [
         r"encoder\.embed_tokens\.weight",
         r"decoder\.embed_tokens\.weight",
         r"lm_head\.weight",

src/transformers/models/xlm/modeling_xlm.py

@@ -399,7 +399,7 @@ XLM_INPUTS_DOCSTRING = r"""
     XLM_START_DOCSTRING,
 )
 class XLMModel(XLMPreTrainedModel):
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def __init__(self, config):
         super().__init__(config)

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -540,7 +540,7 @@ class {{cookiecutter.camelcase_modelname}}PreTrainedModel(PreTrainedModel):
     config_class = {{cookiecutter.camelcase_modelname}}Config
     load_tf_weights = load_tf_weights_in_{{cookiecutter.lowercase_modelname}}
     base_model_prefix = "{{cookiecutter.lowercase_modelname}}"
-    authorized_missing_keys = [r"position_ids"]
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
         """ Initialize the weights """

tests/test_modeling_common.py

@@ -135,17 +135,17 @@ class ModelTesterMixin:
                 max_diff = np.amax(np.abs(out_1 - out_2))
                 self.assertLessEqual(max_diff, 1e-5)
 
-    def test_save_load_keys_to_never_save(self):
+    def test_save_load__keys_to_ignore_on_save(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_model_classes:
             model = model_class(config)
-            keys_to_never_save = getattr(model, "keys_to_never_save", None)
+            _keys_to_ignore_on_save = getattr(model, "_keys_to_ignore_on_save", None)
-            if keys_to_never_save is None:
+            if _keys_to_ignore_on_save is None:
                 continue
 
             # check the keys are in the original state_dict
-            for k in keys_to_never_save:
+            for k in _keys_to_ignore_on_save:
                 self.assertIn(k, model.state_dict())
 
             # check that certain keys didn't get saved with the model
@@ -153,7 +153,7 @@ class ModelTesterMixin:
                 model.save_pretrained(tmpdirname)
                 output_model_file = os.path.join(tmpdirname, WEIGHTS_NAME)
                 state_dict_saved = torch.load(output_model_file)
-                for k in keys_to_never_save:
+                for k in _keys_to_ignore_on_save:
                     self.assertNotIn(k, state_dict_saved)
 
     def test_initialization(self):

tests/test_modeling_marian.py

@@ -60,7 +60,7 @@ class ModelTester:
 class SelectiveCommonTest(unittest.TestCase):
     all_model_classes = (MarianMTModel,) if is_torch_available() else ()
 
-    test_save_load_keys_to_never_save = ModelTesterMixin.test_save_load_keys_to_never_save
+    test_save_load__keys_to_ignore_on_save = ModelTesterMixin.test_save_load__keys_to_ignore_on_save
 
     def setUp(self):
         self.model_tester = ModelTester(self)

tests/test_modeling_mbart.py

@@ -47,7 +47,7 @@ class ModelTester:
 class SelectiveCommonTest(unittest.TestCase):
     all_model_classes = (MBartForConditionalGeneration,) if is_torch_available() else ()
 
-    test_save_load_keys_to_never_save = ModelTesterMixin.test_save_load_keys_to_never_save
+    test_save_load__keys_to_ignore_on_save = ModelTesterMixin.test_save_load__keys_to_ignore_on_save
 
     def setUp(self):
         self.model_tester = ModelTester(self)

tests/test_modeling_pegasus.py

@@ -43,7 +43,7 @@ class ModelTester:
 class SelectiveCommonTest(unittest.TestCase):
     all_model_classes = (PegasusForConditionalGeneration,) if is_torch_available() else ()
 
-    test_save_load_keys_to_never_save = ModelTesterMixin.test_save_load_keys_to_never_save
+    test_save_load__keys_to_ignore_on_save = ModelTesterMixin.test_save_load__keys_to_ignore_on_save
 
     def setUp(self):
         self.model_tester = ModelTester(self)