utils/models.py

from functools import lru_cache

import torch
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModel

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

list_models = [
    'sentence-transformers/paraphrase-multilingual-mpnet-base-v2',
    'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2',
    'sentence-transformers/all-mpnet-base-v2',
    'sentence-transformers/all-MiniLM-L12-v2',
    'cyclone/simcse-chinese-roberta-wwm-ext',
    'bert-base-chinese',
    'IDEA-CCNL/Erlangshen-SimCSE-110M-Chinese',
]


class SBert:
    def __init__(self, path):
        print(f'Loading model from {path} ...')
        self.model = SentenceTransformer(path, device=DEVICE)
        # from pprint import pprint
        # pprint(self.model.__dict__)

    @lru_cache(maxsize=10000)
    def __call__(self, x) -> torch.Tensor:
        y = self.model.encode(x, convert_to_tensor=True)
        return y


class ModelWithPooling:
    def __init__(self, path):
        self.tokenizer = AutoTokenizer.from_pretrained(path)
        self.model = AutoModel.from_pretrained(path)

    @lru_cache(maxsize=10000)
    @torch.no_grad()
    def __call__(self, text: str, pooling='mean'):
        inputs = self.tokenizer(text, padding=True, truncation=True, return_tensors="pt")
        outputs = self.model(**inputs, output_hidden_states=True)

        if pooling == 'cls':
            o = outputs.last_hidden_state[:, 0]  # [b, h]

        elif pooling == 'pooler':
            o = outputs.pooler_output  # [b, h]

        elif pooling in ['mean', 'last-avg']:
            last = outputs.last_hidden_state.transpose(1, 2)  # [b, h, s]
            o = torch.avg_pool1d(last, kernel_size=last.shape[-1]).squeeze(-1)  # [b, h]

        elif pooling == 'first-last-avg':
            first = outputs.hidden_states[1].transpose(1, 2)  # [b, h, s]
            last = outputs.hidden_states[-1].transpose(1, 2)  # [b, h, s]
            first_avg = torch.avg_pool1d(first, kernel_size=last.shape[-1]).squeeze(-1)  # [b, h]
            last_avg = torch.avg_pool1d(last, kernel_size=last.shape[-1]).squeeze(-1)  # [b, h]
            avg = torch.cat((first_avg.unsqueeze(1), last_avg.unsqueeze(1)), dim=1)  # [b, 2, h]
            o = torch.avg_pool1d(avg.transpose(1, 2), kernel_size=2).squeeze(-1)  # [b, h]

        else:
            raise Exception(f'Unknown pooling {pooling}')

        o = o.squeeze(0)
        return o


def test_sbert():
    m = SBert('bert-base-chinese')
    o = m('hello')
    print(o.size())
    assert o.size() == (768,)


def test_hf_model():
    m = ModelWithPooling('IDEA-CCNL/Erlangshen-SimCSE-110M-Chinese')
    o = m('hello', pooling='cls')
    print(o.size())
    assert o.size() == (768,)