I was following a paper on BERT-based lexical substitution (specifically trying to implement equation (2) - if someone has already implemented the whole paper that would also be great). Thus, I wanted to obtain both the last hidden layers (only thing I am unsure is the ordering of the layers in the output: last first or first first?) and the attention from a basic BERT model (bert-base-uncased).
However, I am a bit unsure whether the huggingface/transformers library actually outputs the attention (I was using torch, but am open to using TF instead) for bert-base-uncased?
From what I had read, I was expected to get a tuple of (logits, hidden_states, attentions), but with the example below (runs e.g. in Google Colab), I get of length 2 instead.
Am I misinterpreting what I am getting or going about this the wrong way? I did the obvious test and used output_attention=False instead of output_attention=True (while output_hidden_states=True does indeed seem to add the hidden states, as expected) and nothing change in the output I got. That's clearly a bad sign about my understanding of the library or indicates an issue.
import numpy as np
import torch
!pip install transformers
from transformers import (AutoModelWithLMHead,
AutoTokenizer,
BertConfig)
bert_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
config = BertConfig.from_pretrained('bert-base-uncased', output_hidden_states=True, output_attention=True) # Nothign changes, when I switch to output_attention=False
bert_model = AutoModelWithLMHead.from_config(config)
sequence = "We went to an ice cream cafe and had a chocolate ice cream."
bert_tokenized_sequence = bert_tokenizer.tokenize(sequence)
indexed_tokens = bert_tokenizer.encode(bert_tokenized_sequence, return_tensors='pt')
predictions = bert_model(indexed_tokens)
########## Now let's have a look at what the predictions look like #############
print(len(predictions)) # Length is 2, I expected 3: logits, hidden_layers, attention
print(predictions[0].shape) # torch.Size([1, 16, 30522]) - seems to be logits (shape is 1 x sequence length x vocabulary
print(len(predictions[1])) # Length is 13 - the hidden layers?! There are meant to be 12, right? Is one somehow the attention?
for k in range(len(predictions[1])):
print(predictions[1][k].shape) # These all seem to be torch.Size([1, 16, 768]), so presumably the hidden layers?
import numpy as np
import torch
!pip install transformers
from transformers import BertModel, BertConfig, BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
config = BertConfig.from_pretrained('bert-base-uncased', output_hidden_states=True, output_attentions=True)
model = BertModel.from_pretrained('bert-base-uncased', config=config)
sequence = "We went to an ice cream cafe and had a chocolate ice cream."
tokenized_sequence = tokenizer.tokenize(sequence)
indexed_tokens = tokenizer.encode(tokenized_sequence, return_tensors='pt'
enter code here`outputs = model(indexed_tokens)
print( len(outputs) ) # 4
print( outputs[0].shape ) #1, 16, 768
print( outputs[1].shape ) # 1, 768
print( len(outputs[2]) ) # 13 = input embedding (index 0) + 12 hidden layers (indices 1 to 12)
print( outputs[2][0].shape ) # for each of these 13: 1,16,768 = input sequence, index of each input id in sequence, size of hidden layer
print( len(outputs[3]) ) # 12 (=attenion for each layer)
print( outputs[3][0].shape ) # 0 index = first layer, 1,12,16,16 = , layer, index of each input id in sequence, index of each input id in sequence
The reason is that you are using AutoModelWithLMHead which is a wrapper for the actual model. It calls the BERT model (i.e., an instance of BERTModel) and then it uses the embedding matrix as a weight matrix for the word prediction. In between the underlying model indeed returns attentions, but the wrapper does not care and only returns the logits.
You can either get the BERT model directly by calling AutoModel. Note that this model does not return the logits, but the hidden states.
bert_model = AutoModel.from_config(config)
Or you can get it from the BertWithLMHead object by calling:
wrapped_model = bert_model.base_model