enable cache by default (#9296)

src/transformers/models/bert_generation/configuration_bert_generation.py

@@ -61,6 +61,9 @@ class BertGenerationConfig(PretrainedConfig):
             <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
             `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
             <https://arxiv.org/abs/2009.13658>`__.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if ``config.is_decoder=True``.
 
     Examples::
 
@@ -95,6 +98,7 @@ class BertGenerationConfig(PretrainedConfig):
         eos_token_id=1,
         gradient_checkpointing=False,
         position_embedding_type="absolute",
+        use_cache=True,
         **kwargs
     ):
         super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@@ -112,3 +116,4 @@ class BertGenerationConfig(PretrainedConfig):
         self.layer_norm_eps = layer_norm_eps
         self.gradient_checkpointing = gradient_checkpointing
         self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -339,6 +339,11 @@ class BertGenerationEncoder(BertGenerationPreTrainedModel):
         )
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None: