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ProntoMAV.py
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import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
import os
device = "cuda" if torch.cuda.is_available() else "cpu"
device = "mps" if torch.backends.mps.is_available() else device
class ProntoMAV(torch.nn.Module):
def __init__(self,
template="[1] [MASK] [2].",
special_tokens=None, # ("[R1]", "[R2]")
pretrained_model="FacebookAI/roberta-large",
inner_dim=256,
*args, **kwargs):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model)
self.model = AutoModelForMaskedLM.from_pretrained(pretrained_model).to(device)
self.template = template.replace("[MASK]", self.tokenizer.mask_token)
self.pretrained_model = pretrained_model
if "roberta" in self.pretrained_model.lower():
self.plm_num_embeddings = self.model.roberta.embeddings.word_embeddings.num_embeddings
self.plm_embedding_dim = self.model.roberta.embeddings.word_embeddings.embedding_dim
else:
self.plm_num_embeddings = self.model.bert.embeddings.word_embeddings.num_embeddings
self.plm_embedding_dim = self.model.bert.embeddings.word_embeddings.embedding_dim
for param in self.model.parameters():
param.requires_grad = False
self.inner_dim = inner_dim
self.vocab_extractor = torch.nn.Linear(self.plm_num_embeddings, self.inner_dim)
self.dropout = torch.nn.Dropout(0.1)
self.layernorm = torch.nn.LayerNorm(self.plm_num_embeddings)
self.out_proj = torch.nn.Linear(self.inner_dim, 1)
self.special_tokens = None
# SPECIAL TOKENS
if special_tokens:
self.special_tokens = special_tokens
self.tokenizer.add_tokens(self.special_tokens, special_tokens=True)
self.special_tokens_ids = self.tokenizer.convert_tokens_to_ids(self.special_tokens) # converts tokens to tokenizer ids
self.soft_prompts = torch.nn.Parameter(torch.nn.init.xavier_uniform_(
torch.empty(len(self.special_tokens), self.plm_embedding_dim)), requires_grad=True)
def get_worst_tokens(self):
pass
def get_top_tokens(self):
pass
def generate_word_cloud(self, filename, top_tokens=50):
pass
def forward(self, x, prefixes=None, training=False):
# x: np.array, pairs of (child, parent) (str)
sentences = list(map(lambda pair: self.template.replace("[1]", pair[0]).replace("[2]", pair[1]), x))
# print(sentences)
if prefixes is not None:
assert len(prefixes) == len(sentences)
sentences = list(map(lambda x: x[1] + sentences[x[0]], enumerate(prefixes)))
#print(sentences[0])
tok = self.tokenizer(
sentences,
add_special_tokens=True,
padding=True,
return_tensors="pt",
)["input_ids"].to(device)
special_token_ids = []
attention_mask = 1 - tok.eq(self.tokenizer.pad_token_id).int()
if self.special_tokens:
for i, t in enumerate(self.special_tokens):
special_token_ids.append(torch.nonzero(
tok == self.special_tokens_ids[i], as_tuple=True
))
tok[special_token_ids[i]] = self.tokenizer.pad_token_id
# tok[special_token_index_2] = self.tokenizer.pad_token_id
if "roberta" in self.pretrained_model.lower():
inputs_embeds = self.model.roberta.embeddings.word_embeddings(tok.int())
else:
inputs_embeds = self.model.bert.embeddings.word_embeddings(tok.int())
# reparametrization trick on special tokens (soft prompts)
if self.special_tokens:
for i, t in enumerate(self.special_tokens):
inputs_embeds[special_token_ids[i]] = inputs_embeds[special_token_ids[i]] * 0 + self.soft_prompts[i]
# inputs_embeds[special_token_index_2] = inputs_embeds[special_token_index_2] * 0 + self.soft_prompts[1]
logits = self.model(
input_ids=None,
attention_mask=attention_mask,
token_type_ids=None,
inputs_embeds=inputs_embeds,
).logits
mask_token_index = torch.nonzero(
tok == self.tokenizer.mask_token_id, as_tuple=True
)
mask_logits = logits[mask_token_index]
x = self.layernorm(mask_logits)
x = self.dropout(x)
x = self.vocab_extractor(x)
x = torch.tanh(x)
x = self.dropout(x)
x = self.out_proj(x)
return torch.sigmoid(x.squeeze())
def reg(self):
return 0
def save(self, filepath):
torch.save(self.out_proj, os.path.join(filepath, "W_c.pt"))
torch.save(self.vocab_extractor, os.path.join(filepath, "W_ve.pt"))
if self.special_tokens:
torch.save(self.soft_prompts, os.path.join(filepath, "soft_prompts.pt"))
#torch.save(self.filter, os.path.join(filepath, "filter.pt"))
@classmethod
def load(cls, filepath, *args, **kwargs):
model = cls(*args, **kwargs)
model.vocab_extractor = torch.load(os.path.join(filepath, "W_ve.pt"), map_location=device)
model.out_proj = torch.load(os.path.join(filepath, "W_c.pt"), map_location=device)
if os.path.exists(os.path.join(filepath, "soft_prompts.pt")):
model.soft_prompts = torch.load(os.path.join(filepath, "soft_prompts.pt"), map_location=device)
return model