fix model2model

transformers/modeling_seq2seq.py

@@ -28,7 +28,7 @@ from .modeling_utils import PreTrainedModel, SequenceSummary
 logger = logging.getLogger(__name__)
 
 
-class PreTrainedSeq2seq(PreTrainedModel):
+class PreTrainedSeq2seq(nn.Module):
     r"""
         :class:`~transformers.Seq2seq` is a generic model class that will be
         instantiated as a Seq2seq model with one of the base model classes of
@@ -43,7 +43,7 @@ class PreTrainedSeq2seq(PreTrainedModel):
         self.decoder = decoder
 
     @classmethod
-    def from_pretrained(cls, encoder_pretrained_model_name_or_path, decoder_pretrained_model_name_or_path, *model_args, **kwargs):
+    def from_pretrained(cls, encoder_pretrained_model_name_or_path=None, decoder_pretrained_model_name_or_path=None, *model_args, **kwargs):
         r""" Instantiates an encoder and a decoder from one or two base classes
         of the library from pre-trained model checkpoints.
 
@@ -177,8 +177,8 @@ class PreTrainedSeq2seq(PreTrainedModel):
 
 
 class Model2Model(PreTrainedSeq2seq):
-    def __init__(self):
-        super(Model2Model, self).__init__()
+    def __init__(self, *args, **kwargs):
+        super(Model2Model, self).__init__(*args, **kwargs)
         self.tie_weights()
 
     def tie_weights(self):
@@ -197,7 +197,14 @@ class Model2Model(PreTrainedSeq2seq):
         by a model-specific keyword (bert, )...
         """
         # self._tie_or_clone_weights(self.encoder, self.decoder)
-        raise NotImplementedError
+        pass
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        model = super(Model2Model, cls).from_pretrained(encoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
+                                                        decoder_pretrained_model_name_or_path=pretrained_model_name_or_path,
+                                                        **kwargs)
+        return model
 
 
 class Model2LSTM(PreTrainedSeq2seq):