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from transformers import RobertaTokenizer, RobertaForMaskedLM
import torch
import torch.nn.functional as F
from models import TransformerVisualizer
from transformers import (
RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForQuestionAnswering,
)
class RoBERTaVisualizer(TransformerVisualizer):
def __init__(self, task):
super().__init__()
self.task = task
self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
if self.task == 'mlm':
self.model = RobertaForMaskedLM.from_pretrained("roberta-base")
elif self.task == 'sst':
self.model = RobertaForSequenceClassification.from_pretrained('textattack/roberta-base-SST-2')
elif self.task == 'mnli':
self.model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli")
self.model.to(self.device)
self.model.eval()
self.num_attention_layers = self.model.config.num_hidden_layers
def tokenize(self, text, hypothesis = ''):
if len(hypothesis) == 0:
encoded = self.tokenizer(text, return_tensors='pt', return_attention_mask=True,padding=False, truncation=True)
else:
encoded = self.tokenizer(text, hypothesis, return_tensors='pt', return_attention_mask=True,padding=False, truncation=True)
input_ids = encoded['input_ids'].to(self.device)
attention_mask = encoded['attention_mask'].to(self.device)
tokens = self.tokenizer.convert_ids_to_tokens(input_ids[0])
print('First time tokenizing:', tokens, len(tokens))
response = {
'input_ids': input_ids,
'attention_mask': attention_mask,
'tokens': tokens
}
print(response)
return response
def predict(self, task, text, hypothesis='', maskID = None):
if task == 'mlm':
inputs = self.tokenizer(text, return_tensors='pt', padding=False, truncation=True)
if maskID is not None and 0 <= maskID < inputs['input_ids'].size(1):
inputs['input_ids'][0][maskID] = self.tokenizer.mask_token_id
mask_index = maskID
else:
raise ValueError(f"Invalid maskID {maskID} for input of length {inputs['input_ids'].size(1)}")
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs.logits
mask_logits = logits[0, mask_index]
top_probs, top_indices = torch.topk(F.softmax(mask_logits, dim=-1), 10)
decoded = self.tokenizer.convert_ids_to_tokens(top_indices.tolist())
return decoded, top_probs
elif task == 'sst':
inputs = self.tokenizer(text, return_tensors='pt', padding=False, truncation=True).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=1).squeeze()
labels = ["negative", "positive"]
return labels, probs
elif task == 'mnli':
inputs = self.tokenizer(text, hypothesis, return_tensors='pt', padding=True, truncation=True).to(self.device)
with torch.no_grad():
outputs = self.model(**inputs)
logits = outputs.logits
probs = F.softmax(logits, dim=1).squeeze()
labels = ["entailment", "neutral", "contradiction"]
return labels, probs
else:
raise NotImplementedError(f"Task '{task}' not supported for RoBERTa")
def get_all_grad_attn_matrix(self, task, sentence, hypothesis='', maskID = None):
print(task, sentence, hypothesis)
print('Tokenize')
if task == 'mnli':
inputs = self.tokenizer(sentence, hypothesis, return_tensors='pt', padding=False, truncation=True)
elif task == 'mlm':
inputs = self.tokenizer(sentence, return_tensors='pt', padding=False, truncation=True)
if maskID is not None and 0 <= maskID < inputs['input_ids'].size(1):
inputs['input_ids'][0][maskID] = self.tokenizer.mask_token_id
else:
inputs = self.tokenizer(sentence, return_tensors='pt', padding=False, truncation=True)
tokens = self.tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
print(tokens)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
print('Input embeddings with grad')
embedding_layer = self.model.roberta.embeddings.word_embeddings
inputs_embeds = embedding_layer(inputs["input_ids"])
inputs_embeds.requires_grad_()
print('Forward pass')
outputs = self.model.roberta(
inputs_embeds=inputs_embeds,
attention_mask=inputs["attention_mask"],
output_attentions=True
)
attentions = outputs.attentions # list of [1, heads, seq, seq]
print('Average attentions per layer')
mean_attns = [a.squeeze(0).mean(dim=0).detach().cpu() for a in attentions]
attn_matrices_all = []
grad_matrices_all = []
for target_layer in range(len(attentions)):
grad_matrix, attn_matrix = self.get_grad_attn_matrix(inputs_embeds, attentions, mean_attns, target_layer)
grad_matrices_all.append(grad_matrix.tolist())
attn_matrices_all.append(attn_matrix.tolist())
return grad_matrices_all, attn_matrices_all
def get_grad_attn_matrix(self,inputs_embeds, attentions, mean_attns, target_layer):
attn_matrix = mean_attns[target_layer]
seq_len = attn_matrix.shape[0]
attn_layer = attentions[target_layer].squeeze(0).mean(dim=0) # [seq, seq]
print('Computing grad norms')
grad_norms_list = []
for k in range(seq_len):
scalar = attn_layer[:, k].sum()
grad = torch.autograd.grad(scalar, inputs_embeds, retain_graph=True)[0].squeeze(0)
grad_norms = grad.norm(dim=1)
grad_norms_list.append(grad_norms.unsqueeze(1))
grad_matrix = torch.cat(grad_norms_list, dim=1)
grad_matrix = grad_matrix[:seq_len, :seq_len]
attn_matrix = attn_matrix[:seq_len, :seq_len]
return grad_matrix, attn_matrix
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