How to get cross attention weights of decoder when using 'encoderdecodermodel'
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I'm recently building a encoder-decoder model (Bert2Bert) using `encoderdecodermodel`.
But I found that it is really hard to get cross attention weights of the decoder.
The document of this API said the return of the `forward` function will be the following


- loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Languaged modeling loss.

- logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

- Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see past_key_values input) to speed up sequential decoding.

- decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

- Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

- **decoder_attentions** (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

- Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

- encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

- encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

- Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

- encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

- Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

The `decoder_attentions` in the above only can return the self-attention weight `(batch_size, num_heads, sequence_length, sequence_length)`. However I want the cross attention weight  `(batch_size, num_heads, input_sequence_length, target_sequence_length)`.  I checked the source code of `encoderdecodermodel` and found the decoder is instantiated from the `AutoModelForCausalLM ` class. I also checked the source code of `AutoModelForCausalLM` and found no way to get cross-attention weight.

Is there any method I can get this cross-attention weight? 

@patrickvonplaten Thanks very much! Would be really appreciated if you can help me. It bothers me for a while.
There isn't a method yet, you need to modify the source code.
Here is how I did it for for bart https://github.com/huggingface/transformers/pull/6967<|||||>Hey @kimmo1019, yeah we should probably add all those cross-attention weights to the output as well. Might be a good idea to do this for all Seq2Seq models at once in one PR.<|||||>> Hey @kimmo1019, yeah we should probably add all those cross-attention weights to the output as well. Might be a good idea to do this for all Seq2Seq models at once in one PR.

That would be great it this can be added!! Thanks in advance.<|||||>Looking forward to this feature, too. How is it going now?<|||||>It's on a ToDo-List!<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
