DiffAtlas/ddpm/text.py

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