task_sentence_embedding_unsup_SimCSE.py

#! -*- coding: utf-8 -*-
# SimCSE 中文测试
# bert4keras链接：https://kexue.fm/archives/8348
# |     solution    |   ATEC  |  BQ  |  LCQMC  |  PAWSX  |  STS-B  |
# |      SimCSE     |  33.90  | 50.29|  71.81  |  13.14  |  71.09  |

from bert4torch.snippets import sequence_padding
from tqdm import tqdm
import numpy as np
import scipy.stats
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.tokenizers import Tokenizer
from bert4torch.snippets import sequence_padding, Callback, get_pool_emb
from torch.utils.data import DataLoader
from torch import optim, nn
import torch
from bert4torch.snippets import ListDataset
import sys
import jieba
jieba.initialize()


# =============================基本参数=============================
model_type, pooling, task_name, dropout_rate = sys.argv[1:]  # 传入参数
# model_type, pooling, task_name, dropout_rate = 'BERT', 'cls', 'ATEC', 0.3  # debug使用
print(model_type, pooling, task_name, dropout_rate)

assert model_type in {'BERT', 'RoBERTa', 'NEZHA', 'RoFormer', 'SimBERT'}
assert pooling in {'first-last-avg', 'last-avg', 'cls', 'pooler'}
assert task_name in {'ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STS-B'}
if model_type in {'BERT', 'RoBERTa', 'SimBERT'}:
    model_name = 'bert'
elif model_type in {'RoFormer'}:
    model_name = 'roformer'
elif model_type in {'NEZHA'}:
    model_name = 'nezha'

dropout_rate = float(dropout_rate)
batch_size = 32

if task_name == 'PAWSX':
    maxlen = 128
else:
    maxlen = 64

# bert配置
model_dir = {
    'BERT': 'F:/Projects/pretrain_ckpt/bert/[google_tf_base]--chinese_L-12_H-768_A-12',
    'RoBERTa': 'F:/Projects/pretrain_ckpt/robert/[hit_torch_base]--chinese-roberta-wwm-ext-base',
    'NEZHA': 'F:/Projects/pretrain_ckpt/nezha/[huawei_noah_torch_base]--nezha-cn-base',
    'RoFormer': 'F:/Projects/pretrain_ckpt/roformer/[sushen_torch_base]--roformer_v1_base',
    'SimBERT': 'F:/Projects/pretrain_ckpt/simbert/[sushen_torch_base]--simbert_chinese_base',
}[model_type]

config_path = f'{model_dir}/bert_config.json' if model_type == 'BERT' else f'{model_dir}/config.json'
checkpoint_path = f'{model_dir}/pytorch_model.bin'
dict_path = f'{model_dir}/vocab.txt'
data_path = 'F:/Projects/data/corpus/sentence_embedding/'
device = 'cuda' if torch.cuda.is_available() else 'cpu'

# =============================加载数据集=============================
# 建立分词器
if model_type in ['RoFormer']:
    tokenizer = Tokenizer(dict_path, do_lower_case=True, pre_tokenize=lambda s: jieba.lcut(s, HMM=False))
else:
    tokenizer = Tokenizer(dict_path, do_lower_case=True)

# 读数据
all_names = [f'{data_path}{task_name}/{task_name}.{f}.data' for f in ['train', 'valid', 'test']]
print(all_names)

def load_data(filenames):
    """加载数据（带标签）
    单条格式：(文本1, 文本2, 标签)
    """
    D = []
    for filename in filenames:
        with open(filename, encoding='utf-8') as f:
            for l in f:
                l = l.strip().split('\t')
                if len(l) == 3:
                    D.append((l[0], l[1], float(l[2])))
    return D

all_texts = load_data(all_names)
train_texts = [j for i in all_texts for j in i[:2]]

if task_name != 'PAWSX':
    np.random.shuffle(train_texts)
    train_texts = train_texts[:10000]

# 加载训练数据集
def collate_fn(batch):
    texts_list = [[] for _ in range(2)]
    for text in batch:
        token_ids = tokenizer.encode(text, maxlen=maxlen)[0]
        texts_list[0].append(token_ids)
        texts_list[1].append(token_ids)
    for i, texts in enumerate(texts_list):
        texts_list[i] = torch.tensor(sequence_padding(texts), dtype=torch.long, device=device)
    labels = torch.arange(texts_list[0].size(0), device=texts_list[0].device)
    return texts_list, labels
train_dataloader = DataLoader(ListDataset(data=train_texts), shuffle=True, batch_size=batch_size, collate_fn=collate_fn)

# 加载测试数据集
def collate_fn_eval(batch):
    texts_list = [[] for _ in range(2)]
    labels = []
    for text1, text2, label in batch:
        texts_list[0].append(tokenizer.encode(text1, maxlen=maxlen)[0])
        texts_list[1].append(tokenizer.encode(text2, maxlen=maxlen)[0])
        labels.append(label)
    for i, texts in enumerate(texts_list):
        texts_list[i] = torch.tensor(sequence_padding(texts), dtype=torch.long, device=device)
    labels = torch.tensor(labels, dtype=torch.float, device=device)
    return texts_list, labels
valid_dataloader = DataLoader(ListDataset(data=all_texts), batch_size=batch_size, collate_fn=collate_fn_eval)

# 建立模型
class Model(BaseModel):
    def __init__(self, pool_method='cls', scale=20.0):
        super().__init__()
        self.pool_method = pool_method
        with_pool = 'linear' if pool_method == 'pooler' else True
        output_all_encoded_layers = True if pool_method == 'first-last-avg' else False
        self.bert = build_transformer_model(config_path, checkpoint_path, model=model_name, segment_vocab_size=0, dropout_rate=dropout_rate,
                                            with_pool=with_pool, output_all_encoded_layers=output_all_encoded_layers)
        self.scale = scale
    
    def forward(self, token_ids_list):
        reps = []
        for token_ids in token_ids_list:
            hidden_state1, pooler = self.bert([token_ids])
            rep = get_pool_emb(hidden_state1, pooler, token_ids.gt(0).long(), self.pool_method)
            reps.append(rep)
        embeddings_a = reps[0]
        embeddings_b = torch.cat(reps[1:])
        scores = self.cos_sim(embeddings_a, embeddings_b) * self.scale  # [btz, btz]
        return scores
    
    def encode(self, token_ids):
        self.eval()
        with torch.no_grad():
            hidden_state, pooler = self.bert([token_ids])
            output = get_pool_emb(hidden_state, pooler, token_ids.gt(0).long(), self.pool_method)
        return output

    @staticmethod
    def cos_sim(a, b):
        a_norm = torch.nn.functional.normalize(a, p=2, dim=1)
        b_norm = torch.nn.functional.normalize(b, p=2, dim=1)
        return torch.mm(a_norm, b_norm.transpose(0, 1))

model = Model(pool_method=pooling).to(device)
model.compile(loss=nn.CrossEntropyLoss(), optimizer=optim.Adam(model.parameters(), 1e-5))

class Evaluator(Callback):
    """评估与保存
    """
    def __init__(self):
        self.best_val_consine = 0.

    def on_epoch_end(self, global_step, epoch, logs=None):
        val_consine = evaluate(valid_dataloader)
        if val_consine > self.best_val_consine:
            self.best_val_consine = val_consine
            # model.save_weights('best_model.pt')
        print(f'val_consine: {val_consine:.5f}, best_val_consine: {self.best_val_consine:.5f}\n')

def evaluate(dataloader):
    # 模型预测
    # 标准化，相似度，相关系数
    sims_list, labels = [], []
    for (a_token_ids, b_token_ids), label in tqdm(dataloader):
        a_vecs = model.encode(a_token_ids)
        b_vecs = model.encode(b_token_ids)
        a_vecs = torch.nn.functional.normalize(a_vecs, p=2, dim=1).cpu().numpy()
        b_vecs = torch.nn.functional.normalize(b_vecs, p=2, dim=1).cpu().numpy()
        sims = (a_vecs * b_vecs).sum(axis=1)
        sims_list.append(sims)
        labels.append(label.cpu().numpy())

    corrcoef = scipy.stats.spearmanr(np.concatenate(labels), np.concatenate(sims_list)).correlation
    return corrcoef

if  __name__ == '__main__':
    evaluator = Evaluator()
    model.fit(train_dataloader, steps_per_epoch=None, epochs=5, callbacks=[evaluator])