Potential incorrect application of layer norm in BlenderbotSmallDecoder
In BlenderbotSmallDecoder,  layer norm is applied only on the token embeddings, and not on the hidden_states, whereas in the BlenderbotSmallEncoder, layer norm is applied after adding the input_embeds and positional embeds

BlenderbotSmallEncoder:
`hidden_states = inputs_embeds + embed_pos`
`hidden_states = self.layernorm_embedding(hidden_states)`

BlenderbotSmallDecoder:
`inputs_embeds = self.layernorm_embedding(inputs_embeds)`
`hidden_states = inputs_embeds + positions`
cc @patrickvonplaten @patil-suraj <|||||>Great catch @sougata-ub! This indeed seems like a bug. Will check the original implementation and fix this.<|||||>Hey @sougata-ub - did you try to change the order in the `BlenderbotSmallDecoder` manually and run a couple of examples to see whether the model performs better? Think, we can check this in a first step -> If we get much worse results than the previous implementation was probably correct. If results seem to be better, it's a strong hint that there is a bug actually.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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