# T5 cross-attention - inconsistent results **URL:** **Author:** ![romaindupuis](https://avatars.discourse-cdn.com/v4/letter/r/7ea924/32.png) [@romaindupuis](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fromaindupuis)%5C **Post date:** [April 15, 2021, 11:39am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ft5-cross-attention-inconsistent-results%2F5520%2F1 "2021-04-15T11:39:43Z") ## Environment info ```auto 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: ```python 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 + " " 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. ```python 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). ```auto Input phrase: paraphrase: I like drinking Fanta and Cola. Predicted phrase: I like to drink Fanta and Cola. Predicted word: 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: | 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: | 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. ---
**Author:** ![romaindupuis](https://avatars.discourse-cdn.com/v4/letter/r/7ea924/32.png) [@romaindupuis](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fromaindupuis)%5C **Post date:** [May 10, 2021, 10:11am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ft5-cross-attention-inconsistent-results%2F5520%2F2 "2021-05-10T10:11:32Z")
We found the problem: the shape of the attention matrix was not correct. Here are the modifications ```python # From T5 documentation # Initial shape: Tuple (one element for each generated token) of tuples (one element for # each layer of the decoder) of torch.FloatTensor of shape # (batch_size, num_heads, generated_length, sequence_length). # combine all cross attention into one tensor x = [torch.stack(beam_outputs['cross_attentions'][i]) for i in range(len(beam_outputs['cross_attentions']))] x = torch.stack(x) # Shape: (nb_generated, nb_layer, (batch_size, num_heads, generated_length, sequence_length)) print(x.shape) x = x.transpose(1,0) # (nb_layer, nb_generated, batch_size, num_heads, generated_length, sequence_length) print(x.shape) x = x.transpose(1,3) # (nb_layer, num_heads, batch_size, nb_generated, generated_length, sequence_length) print(x.shape) x = torch.squeeze(x, 4) # (nb_layer, num_heads, batch_size, nb_generated, sequence_length) print(x.shape) x = x.transpose(2, 1) # (nb_layer, batch_size, num_heads, nb_generated, sequence_length) print(x.shape) cross_attentions = x ``` We can compute the encoder/decoder tokens: ```python encoder_text = tokenizer.convert_ids_to_tokens(input_ids[0]) decoder_text = tokenizer.convert_ids_to_tokens(beam_outputs.sequences[0]) encoder_tokens=np.array(encoder_text) decoder_tokens=np.array(decoder_text[:-1]) ``` The initial sentence is: `In Belgium, summers can be very dry and the heat is burning.` and the paraphrase: `'In Belgium the summers can be very dry and the heat is burning.` The associated tokens are: ```auto ['▁para', 'phrase', ':', '▁In', '▁Belgium', ',', '▁summer', 's', '▁can', '▁be', '▁very', '▁dry', '▁and', '▁the', '▁heat', '▁is', '▁burning', '.', ''] ['', '▁In', '▁Belgium', '▁the', '▁summer', 's', '▁can', '▁be', '▁very', '▁dry', '▁and', '▁the', '▁heat', '▁is', '▁burning', '.', ''] ``` Here are the attention score sorted by importance: ```python layer = 0 head = 0 # choose head to analyze att = cross_attentions[layer, 0, head] att.shape for i in range(att.shape[0]): idx = np.argsort(att[i].cpu().numpy())[::-1][:6] print(f"Predicted token: {decoder_tokens[i]}, input related tokens: {encoder_tokens[idx]}") ``` Here are some results: ```auto Predicted token: ▁In, input related tokens: ['' '▁In' 'phrase' '.' ':' '▁Belgium'] Predicted token: ▁Belgium, input related tokens: ['▁Belgium' '' 'phrase' ':' '▁para' '▁summer'] Predicted token: ▁the, input related tokens: ['' 'phrase' ',' '.' '▁the' ':'] Predicted token: ▁summer, input related tokens: ['▁Belgium' '' 'phrase' '▁In' 's' '▁summer'] ``` We solved this problem with the help of the [decoder encoder version of bertviz](https://github.com/jessevig/bertviz/issues/72)