Environment info
Python version: 3.7.10
PyTorch version (GPU?): '1.7.1+cu110' (True)
Transformer version: '4.5.0.dev0'


Details
I am trying to use the cross-attention from the T5 model for paraphrasing. The idea is to map the input sentence and output generated sequence based on the attention. But the first results I got are very strange.
I generated an example with the following code:
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch

pretrained_model = "ramsrigouthamg/t5_paraphraser"

model = T5ForConditionalGeneration.from_pretrained(pretrained_model,
                                                   output_attentions=True,
                                                   output_scores=True)

translated_sentence = "I like drinking Fanta and Cola."

text = "paraphrase: " + translated_sentence + " </s>"

encoding = tokenizer.encode_plus(text,
                                 pad_to_max_length=True,
                                 return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"].

Then, I gave a look to the cross attention for each generated token by selecting the last layer of the encoder and the first head.

beam_outputs = model.generate(
    input_ids=input_ids,
    attention_mask=attention_masks,
    do_sample=True,
    max_length=256,
    top_k=100,
    top_p=0.95,
    num_return_sequences=1,
    output_attentions = True,
    output_scores=True,
    return_dict_in_generate=True
)

sentence_id = 0
print("Input phrase: ", tokenizer.decode(encoding.input_ids[0],
                                      skip_special_tokens=False,
                                      clean_up_tokenization_spaces=False))
print("Predicted phrase: ", tokenizer.decode(beam_outputs.sequences[sentence_id],
                                      skip_special_tokens=True,
                                      clean_up_tokenization_spaces=True))
for out in range(len(beam_outputs.sequences[sentence_id])-1):
    print(
        "\nPredicted word: ",
        tokenizer.decode(beam_outputs.sequences[sentence_id][out],
                         skip_special_tokens=True,
                         clean_up_tokenization_spaces=True))
    att = torch.stack(beam_outputs.cross_attentions[out])
    # Last layer of the encoder
    att = att[-1]

   # First batch and first head
    att = att[0, 0, :, :]
    att = torch.squeeze(att)

    idx = torch.argsort(att)
    idx = idx.cpu().numpy()

    print("Input words ordered by attention: ")
    for i in range(min(5, len(idx))):
        token_smallest_attention =tokenizer.decode(encoding.input_ids[0][idx[i]],
                                      skip_special_tokens=True,
                                      clean_up_tokenization_spaces=True)
        token_largest_attention =tokenizer.decode(encoding.input_ids[0][idx[-(1+i)]],
                                      skip_special_tokens=True,
                                      clean_up_tokenization_spaces=True)
        print(f"{i+1}: Largest attention: {token_largest_attention} | smallest attention:{token_smallest_attention}")
    

The attention scores are sorted and each generated token is associated with the input with the highest attention (5 values) and with the lowest attentions (also 5 values).
Input phrase:  paraphrase: I like drinking Fanta and Cola.</s>
Predicted phrase:  I like to drink Fanta and Cola.

Predicted word:  <pad>
Input words ordered by attention: 
1: Largest attention: I | smallest attention:Col
2: Largest attention: like | smallest attention:a
3: Largest attention: : | smallest attention:t
4: Largest attention: para | smallest attention:a
5: Largest attention: . | smallest attention:Fan

Predicted word:  I
Input words ordered by attention: 
1: Largest attention: phrase | smallest attention:t
2: Largest attention: </s> | smallest attention:a
3: Largest attention: para | smallest attention:a
4: Largest attention: : | smallest attention:Col
5: Largest attention: like | smallest attention:and

Predicted word:  like
Input words ordered by attention: 
1: Largest attention: Fan | smallest attention:I
2: Largest attention: Col | smallest attention:.
3: Largest attention: phrase | smallest attention:like
4: Largest attention: a | smallest attention:para
5: Largest attention: </s> | smallest attention:a


Expecting results
I was expecting an almost one-to-one mapping as the paraphrase is very close to the input but it is not the case. The model gives good paraphrases.  Do you think that I made some errors in the interpretation of the cross-attention object?

Thank you for your help!
Hopefully, it is something simple that I am missing.