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from torch import Tensor, nn
from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
T5Tokenizer)
class HFEmbedder(nn.Module):
def __init__(self, version: str, max_length: int, is_clip, **hf_kwargs):
super().__init__()
self.is_clip = is_clip
self.max_length = max_length
self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
if self.is_clip:
self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
else:
self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)
self.hf_module = self.hf_module.eval().requires_grad_(False)
def forward(self, text: list[str]) -> Tensor:
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
if not self.is_clip:
pass
outputs = self.hf_module(
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
attention_mask=None,
output_hidden_states=False,
)
return outputs[self.output_key]
def forward_length(self, text: list[str]) -> Tensor:
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
if not self.is_clip:
pass
outputs = self.hf_module(
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
attention_mask=None,
output_hidden_states=False,
)
# -1 to delete the end token
return outputs[self.output_key],batch_encoding['length']-1
def get_word_embed(self, text: list[str]) -> Tensor:
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=16,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
input_ids = batch_encoding["input_ids"].to(self.hf_module.device)
attention_mask = batch_encoding["attention_mask"].to(self.hf_module.device)
outputs = self.hf_module(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=False,
)
token_embeddings = outputs[self.output_key] # [B, T, D]
mask = attention_mask.unsqueeze(-1).float() # [B, T, 1]
summed = (token_embeddings * mask).sum(dim=1) # [B, D]
counts = mask.sum(dim=1).clamp(min=1e-6)
mean_pooled = summed / counts # [B, D]
return mean_pooled
def get_text_embeddings_with_diff(self, src_text: str, tgt_text: str, replacements: list[tuple[str, str, int, int]], show_tokens=False, return_embeds=False):
batch_encoding = self.tokenizer(
[src_text, tgt_text],
truncation=True,
max_length=self.max_length,
return_tensors="pt",
padding="max_length",
)
src_ids, tgt_ids = batch_encoding["input_ids"]
src_tokens = self.tokenizer.tokenize(src_text)
tgt_tokens = self.tokenizer.tokenize(tgt_text)
if show_tokens:
print("src tokens", src_tokens)
print("tgt tokens", tgt_tokens)
src_dif_ids = []
tgt_dif_ids = []
def find_mappings(tokens,words,start_idx):
if (words is None) or start_idx<0: # some samples do not need this
return [-1]
res = []
l_words = len(words.replace(" ", ""))
l_find = 0
for i in range(start_idx,len(tokens)):
this_token = tokens[i].strip('▁')
if this_token == "":
continue
if words.startswith(this_token):
res.append(i)
l_find += len(this_token)
if l_find >= l_words:
break
else:
continue
if l_find:
l_find += len(this_token)
res.append(i)
if l_find >= l_words:
break
return res
for src_words, tgt_words, src_index, tgt_index in replacements:
if src_words:
src_dif_ids.append(find_mappings(src_tokens,src_words,src_index))
else:
src_dif_ids.append([-1])
if tgt_words:
tgt_dif_ids.append(find_mappings(tgt_tokens,tgt_words,tgt_index))
else:
tgt_dif_ids.append([-1])
if return_embeds:
outputs = self.hf_module(
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
attention_mask=None,
output_hidden_states=False,
)
embeddings = outputs[self.output_key]
else:
embeddings = (None,None)
return embeddings[0], embeddings[1], src_dif_ids, tgt_dif_ids
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