only fine tune the encoder part of BART
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## Details
<!-- Description of your issue -->
May i ask how can I fine-tune only the BART-encoder?

from transformers import BartTokenizer, BartModel

tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
model = BartModel.from_pretrained('facebook/bart-base', return_dict=True)

inputs = tokenizer(["i am a girl", "i am from germany"], return_tensors="pt", padding=True)
outputs = model(**inputs).encoder_last_hidden_state

i load the pretrained BART, and get the encoder_last_hiddene_state.

however, during fine-tuning, how about the decoder part?

how could i only load the part of encoder and fine-tune?

Thanks a lot
<!-- You should first ask your question on the forum or SO, and only if
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**A link to original question on the forum/Stack Overflow**:
What task are you working on? Or to rephrase, what do you do once you have ` encoder_output = model(**inputs).encoder_last_hidden_state`?<|||||>Thanks for your feedback. In my case, I only want the bart encoder and combine it to my own decoder.  Thanks for further feedback<|||||>How do you want to fine-tune the encoder? You'll either need to add a task specific head or pair it with your decoder and train both encoder and decoder. I think you should be able to Initialize only the encoder using following snippet 

```python
from torch import nn

from transformers.modeling_bart import BartEncoder, PretrainedBartModel, PretrainedBartModel
from transformers import BartConfig

class Encoder(PretrainedBartModel):
  def __init__(self, config: BartConfig):
    super().__init__(config)

    padding_idx, vocab_size = config.pad_token_id, config.vocab_size
    self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)

    self.encoder = BartEncoder(config, self.shared)
  
  def forward(
      self, input_ids, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=False
  ):

    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    encoder_outputs = self.encoder(
        input_ids=input_ids,
        attention_mask=attention_mask,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )

    return encoder_outputs

enc = Encoder.from_pretrained("facebook/bart-base")
```

@sshleifer  does this makes sense ?<|||||>The text makes sense. Does that code run!?<|||||>> How do you want to fine-tune the encoder? You'll either need to add a task specific head or pair it with your decoder and train both encoder and decoder. I think you should be able to Initialize only the encoder using following snippet
> 
> ```python
> from torch import nn
> 
> from transformers.modeling_bart import BartEncoder, PretrainedBartModel, PretrainedBartModel
> from transformers import BartConfig
> 
> class Encoder(PretrainedBartModel):
>   def __init__(self, config: BartConfig):
>     super().__init__(config)
> 
>     padding_idx, vocab_size = config.pad_token_id, config.vocab_size
>     self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx)
> 
>     self.encoder = BartEncoder(config, self.shared)
>   
>   def forward(
>       self, input_ids, attention_mask=None, output_attentions=False, output_hidden_states=False, return_dict=False
>   ):
> 
>     output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
>     output_hidden_states = (
>         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
>     )
>     return_dict = return_dict if return_dict is not None else self.config.use_return_dict
> 
>     encoder_outputs = self.encoder(
>         input_ids=input_ids,
>         attention_mask=attention_mask,
>         output_attentions=output_attentions,
>         output_hidden_states=output_hidden_states,
>         return_dict=return_dict,
>     )
> 
>     return encoder_outputs
> 
> enc = Encoder.from_pretrained("facebook/bart-base")
> ```
> 
> @sshleifer does this makes sense ?

 Thanks for your code @patil-suraj . Really appreciate!!<|||||>> The text makes sense. Does that code run!?

In my case, the code runs without problems, so I think it"s correct! Thanks a lot!