File size: 1,888 Bytes
0b06187 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import torch
from einops import rearrange
def exists(val):
return val is not None
# singleton globals
MODEL = None
TOKENIZER = None
BERT_MODEL_DIM = 768
def get_tokenizer():
global TOKENIZER
if not exists(TOKENIZER):
TOKENIZER = torch.hub.load(
'huggingface/pytorch-transformers', 'tokenizer', 'bert-base-cased')
return TOKENIZER
def get_bert():
global MODEL
if not exists(MODEL):
MODEL = torch.hub.load(
'huggingface/pytorch-transformers', 'model', 'bert-base-cased')
if torch.cuda.is_available():
MODEL = MODEL.cuda()
return MODEL
# tokenize
def tokenize(texts, add_special_tokens=True):
if not isinstance(texts, (list, tuple)):
texts = [texts]
tokenizer = get_tokenizer()
encoding = tokenizer.batch_encode_plus(
texts,
add_special_tokens=add_special_tokens,
padding=True,
return_tensors='pt'
)
token_ids = encoding.input_ids
return token_ids
# embedding function
@torch.no_grad()
def bert_embed(
token_ids,
return_cls_repr=False,
eps=1e-8,
pad_id=0.
):
model = get_bert()
mask = token_ids != pad_id
if torch.cuda.is_available():
token_ids = token_ids.cuda()
mask = mask.cuda()
outputs = model(
input_ids=token_ids,
attention_mask=mask,
output_hidden_states=True
)
hidden_state = outputs.hidden_states[-1]
if return_cls_repr:
# return [cls] as representation
return hidden_state[:, 0]
if not exists(mask):
return hidden_state.mean(dim=1)
# mean all tokens excluding [cls], accounting for length
mask = mask[:, 1:]
mask = rearrange(mask, 'b n -> b n 1')
numer = (hidden_state[:, 1:] * mask).sum(dim=1)
denom = mask.sum(dim=1)
masked_mean = numer / (denom + eps)
return
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