Delete all mentions of Model2Model (#3019)

src/transformers/modeling_encoder_decoder.py

@@ -234,62 +234,3 @@ class PreTrainedEncoderDecoder(nn.Module):
         decoder_outputs = self.decoder(decoder_input_ids, **kwargs_decoder)
 
         return decoder_outputs + encoder_outputs
-
-
-class Model2Model(PreTrainedEncoderDecoder):
-    r"""
-        :class:`~transformers.Model2Model` instantiates a Seq2Seq2 model
-        where both of the encoder and decoder are of the same family. If the
-        name of or that path to a pretrained model is specified the encoder and
-        the decoder will be initialized with the pretrained weight (the
-        cross-attention will be intialized randomly if its weights are not
-        present).
-
-        It is possible to override this behavior and initialize, say, the decoder randomly
-        by creating it beforehand as follows
-
-            config = BertConfig.from_pretrained()
-            decoder = BertForMaskedLM(config)
-            model = Model2Model.from_pretrained('bert-base-uncased', decoder_model=decoder)
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.tie_weights()
-
-    def tie_weights(self):
-        """ Tying the encoder and decoders' embeddings together.
-
-       We need for each to get down to the embedding weights. However the
-        different model classes are inconsistent to that respect:
-        - BertModel: embeddings.word_embeddings
-        - RoBERTa: embeddings.word_embeddings
-        - XLMModel: embeddings
-        - GPT2: wte
-        - BertForMaskedLM: bert.embeddings.word_embeddings
-        - RobertaForMaskedLM: roberta.embeddings.word_embeddings
-
-        argument of the XEmbedding layer for each model, but it is "blocked"
-        by a model-specific keyword (bert, )...
-        """
-        # self._tie_or_clone_weights(self.encoder, self.decoder)
-        pass
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
-
-        if (
-            "bert" not in pretrained_model_name_or_path
-            or "roberta" in pretrained_model_name_or_path
-            or "distilbert" in pretrained_model_name_or_path
-        ):
-            raise ValueError("Only the Bert model is currently supported.")
-
-        model = super().from_pretrained(
-            encoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
-            decoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
-            *args,
-            **kwargs,
-        )
-
-        return model