bert训练代码

from torch.optim import Adam
from torch.utils.data import DataLoader

from dataset.wiki_dataset import BERTDataset
from models.bert_model import *
import tqdm
import pandas as pd
import numpy as np
import os


config = {}
#训练集
config["train_corpus_path"] = "./pretraining_data/wiki_dataset/train_wiki.txt"
#测试集
config["test_corpus_path"] = "./pretraining_data/wiki_dataset/test_wiki.txt"
#字转换为idx
config["word2idx_path"] = "./pretraining_data/wiki_dataset/bert_word2idx_extend.json"
#模型存储位置
config["output_path"] = "./output_wiki_bert"

#batchsize大小
config["batch_size"] = 1
#最大句子长度
config["max_seq_len"] = 200
#总共的字数
config["vocab_size"] = 32162
#学习率
config["lr"] = 2e-6
config["num_workers"] = 0

class Pretrainer:
    def __init__(self, bert_model,
                 vocab_size,
                 max_seq_len,
                 batch_size,
                 lr,
                 with_cuda=True,
                 ):
        # 词量, 注意在这里实际字(词)汇量 = vocab_size - 20,
        # 因为前20个token用来做一些特殊功能, 如padding等等
        self.vocab_size = vocab_size
        self.batch_size = batch_size
        # 学习率
        self.lr = lr
        # 是否使用GPU
        cuda_condition = torch.cuda.is_available() and with_cuda
        self.device = torch.device("cuda:0" if cuda_condition else "cpu")
        # 限定的单句最大长度
        self.max_seq_len = max_seq_len
        # 初始化超参数的配置
        bertconfig = BertConfig(vocab_size=config["vocab_size"])
        # 初始化bert模型
        self.bert_model = bert_model(config=bertconfig)
        self.bert_model.to(self.device)
        # 初始化训练数据集
        train_dataset = BERTDataset(corpus_path=config["train_corpus_path"],
                                    word2idx_path=config["word2idx_path"],
                                    seq_len=self.max_seq_len,
                                    hidden_dim=bertconfig.hidden_size,
                                    on_memory=False,
                                    )
        # 初始化训练dataloader
        self.train_dataloader = DataLoader(train_dataset,
                                           batch_size=self.batch_size,
                                           num_workers=config["num_workers"],
                                           collate_fn=lambda x: x)
        # 初始化测试数据集
        test_dataset = BERTDataset(corpus_path=config["test_corpus_path"],
                                   word2idx_path=config["word2idx_path"],
                                   seq_len=self.max_seq_len,
                                   hidden_dim=bertconfig.hidden_size,
                                   on_memory=True,
                                   )
        # 初始化测试dataloader
        self.test_dataloader = DataLoader(test_dataset, batch_size=self.batch_size,
                                          num_workers=config["num_workers"],
                                          collate_fn=lambda x: x)
        # 初始化positional encoding
        self.positional_enc = self.init_positional_encoding(hidden_dim=bertconfig.hidden_size,
                                                            max_seq_len=self.max_seq_len)
        # 拓展positional encoding的维度为[1, max_seq_len, hidden_size]
        self.positional_enc = torch.unsqueeze(self.positional_enc, dim=0)

        # 列举需要优化的参数并传入优化器
        optim_parameters = list(self.bert_model.parameters())
        self.optimizer = torch.optim.Adam(optim_parameters, lr=self.lr)

        print("Total Parameters:", sum([p.nelement() for p in self.bert_model.parameters()]))

    def init_positional_encoding(self, hidden_dim, max_seq_len):
        position_enc = np.array([
            [pos / np.power(10000, 2 * i / hidden_dim) for i in range(hidden_dim)]
            if pos != 0 else np.zeros(hidden_dim) for pos in range(max_seq_len)])

        position_enc[1:, 0::2] = np.sin(position_enc[1:, 0::2])  # dim 2i
        position_enc[1:, 1::2] = np.cos(position_enc[1:, 1::2])  # dim 2i+1
        denominator = np.sqrt(np.sum(position_enc**2, axis=1, keepdims=True))
        position_enc = position_enc / (denominator + 1e-8)
        position_enc = torch.from_numpy(position_enc).type(torch.FloatTensor)
        return position_enc

    def test(self, epoch, df_path="./output_wiki_bert/df_log.pickle"):
        self.bert_model.eval()
        with torch.no_grad():
            return self.iteration(epoch, self.test_dataloader, train=False, df_path=df_path)

    def load_model(self, model, dir_path="./output"):
        # 加载模型
        checkpoint_dir = self.find_most_recent_state_dict(dir_path)
        checkpoint = torch.load(checkpoint_dir)
        model.load_state_dict(checkpoint["model_state_dict"], strict=False)
        torch.cuda.empty_cache()
        model.to(self.device)
        print("{} loaded for training!".format(checkpoint_dir))

    def train(self, epoch, df_path="./output_wiki_bert/df_log.pickle"):
        self.bert_model.train()
        self.iteration(epoch, self.train_dataloader, train=True, df_path=df_path)

    def compute_loss(self, predictions, labels, num_class=2, ignore_index=None):
        if ignore_index is None:
            loss_func = CrossEntropyLoss()
        else:
            loss_func = CrossEntropyLoss(ignore_index=ignore_index)
        return loss_func(predictions.view(-1, num_class), labels.view(-1))

    def get_mlm_accuracy(self, predictions, labels):
        predictions = torch.argmax(predictions, dim=-1, keepdim=False)
        mask = (labels > 0).to(self.device)
        mlm_accuracy = torch.sum((predictions == labels) * mask).float()
        mlm_accuracy /= (torch.sum(mask).float() + 1e-8)
        return mlm_accuracy.item()

    def padding(self, output_dic_lis):
        bert_input = [i["bert_input"] for i in output_dic_lis]
        bert_label = [i["bert_label"] for i in output_dic_lis]
        segment_label = [i["segment_label"] for i in output_dic_lis]
        bert_input = torch.nn.utils.rnn.pad_sequence(bert_input, batch_first=True)
        bert_label = torch.nn.utils.rnn.pad_sequence(bert_label, batch_first=True)
        segment_label = torch.nn.utils.rnn.pad_sequence(segment_label, batch_first=True)
        is_next = torch.cat([i["is_next"] for i in output_dic_lis])
        return {"bert_input": bert_input,
                "bert_label": bert_label,
                "segment_label": segment_label,
                "is_next": is_next}



    def iteration(self, epoch, data_loader, train=True, df_path="./output_wiki_bert/df_log.pickle"):
        if not os.path.isfile(df_path) and epoch != 0:
            raise RuntimeError("log DataFrame path not found and can't create a new one because we're not training from scratch!")
        if not os.path.isfile(df_path) and epoch == 0:
            df = pd.DataFrame(columns=["epoch", "train_next_sen_loss", "train_mlm_loss",
                                       "train_next_sen_acc", "train_mlm_acc",
                                       "test_next_sen_loss", "test_mlm_loss",
                                       "test_next_sen_acc", "test_mlm_acc"
                                       ])
            df.to_pickle(df_path)
            print("log DataFrame created!")

        str_code = "train" if train else "test"

        # Setting the tqdm progress bar
        data_iter = tqdm.tqdm(enumerate(data_loader),
                              desc="EP_%s:%d" % (str_code, epoch),
                              total=len(data_loader),
                              bar_format="{l_bar}{r_bar}")

        total_next_sen_loss = 0
        total_mlm_loss = 0
        total_next_sen_acc = 0
        total_mlm_acc = 0
        total_element = 0

        for i, data in data_iter:
            # print('IDX of data_iter:', i)
            data = self.padding(data)
            # 0. batch_data will be sent into the device(GPU or cpu)
            data = {key: value.to(self.device) for key, value in data.items()}
            positional_enc = self.positional_enc[:, :data["bert_input"].size()[-1], :].to(self.device)

            # 1. forward the next_sentence_prediction and masked_lm model
            mlm_preds, next_sen_preds = self.bert_model.forward(input_ids=data["bert_input"],
                                                                positional_enc=positional_enc,
                                                                token_type_ids=data["segment_label"])

            mlm_acc = self.get_mlm_accuracy(mlm_preds, data["bert_label"])
            next_sen_acc = next_sen_preds.argmax(dim=-1, keepdim=False).eq(data["is_next"]).sum().item()
            mlm_loss = self.compute_loss(mlm_preds, data["bert_label"], self.vocab_size, ignore_index=0)
            next_sen_loss = self.compute_loss(next_sen_preds, data["is_next"])
            loss = mlm_loss + next_sen_loss


            # 3. backward and optimization only in train
            if train:
                self.optimizer.zero_grad()
                loss.backward()
                # for param in self.model.parameters():
                #     print(param.grad.data.sum())
                self.optimizer.step()


            total_next_sen_loss += next_sen_loss.item()
            total_mlm_loss += mlm_loss.item()
            total_next_sen_acc += next_sen_acc
            total_element += data["is_next"].nelement()
            total_mlm_acc += mlm_acc

            if train:
                log_dic = {
                    "epoch": epoch,
                   "train_next_sen_loss": total_next_sen_loss / (i + 1),
                   "train_mlm_loss": total_mlm_loss / (i + 1),
                   "train_next_sen_acc": total_next_sen_acc / total_element,
                   "train_mlm_acc": total_mlm_acc / (i + 1),
                   "test_next_sen_loss": 0, "test_mlm_loss": 0,
                   "test_next_sen_acc": 0, "test_mlm_acc": 0
                }

            else:
                log_dic = {
                    "epoch": epoch,
                   "test_next_sen_loss": total_next_sen_loss / (i + 1),
                   "test_mlm_loss": total_mlm_loss / (i + 1),
                   "test_next_sen_acc": total_next_sen_acc / total_element,
                   "test_mlm_acc": total_mlm_acc / (i + 1),
                   "train_next_sen_loss": 0, "train_mlm_loss": 0,
                   "train_next_sen_acc": 0, "train_mlm_acc": 0
                }


            if i % 10 == 0:
                data_iter.write(str({k: v for k, v in log_dic.items() if v != 0 and k != "epoch"}))

        if train:
            df = pd.read_pickle(df_path)
            df = df.append([log_dic])
            df.reset_index(inplace=True, drop=True)
            df.to_pickle(df_path)
        else:
            log_dic = {k: v for k, v in log_dic.items() if v != 0 and k != "epoch"}
            df = pd.read_pickle(df_path)
            df.reset_index(inplace=True, drop=True)
            for k, v in log_dic.items():
                df.at[epoch, k] = v
            df.to_pickle(df_path)
            return float(log_dic["test_next_sen_loss"])+float(log_dic["test_mlm_loss"])

    def find_most_recent_state_dict(self, dir_path):
        dic_lis = [i for i in os.listdir(dir_path)]
        if len(dic_lis) == 0:
            raise FileNotFoundError("can not find any state dict in {}!".format(dir_path))
        dic_lis = [i for i in dic_lis if "model" in i]
        dic_lis = sorted(dic_lis, key=lambda k: int(k.split(".")[-1]))
        return dir_path + "/" + dic_lis[-1]

    def save_state_dict(self, model, epoch, dir_path="./output", file_path="bert.model"):
        if not os.path.exists(dir_path):
            os.mkdir(dir_path)
        save_path = dir_path+ "/" + file_path + ".epoch.{}".format(str(epoch))
        model.to("cpu")
        torch.save({"model_state_dict": model.state_dict()}, save_path)
        print("{} saved!".format(save_path))
        model.to(self.device)


if __name__ == '__main__':
    def init_trainer(dynamic_lr, load_model=False):
        trainer = Pretrainer(BertForPreTraining,
                             vocab_size=config["vocab_size"],
                             max_seq_len=config["max_seq_len"],
                             batch_size=config["batch_size"],
                             lr=dynamic_lr,
                             with_cuda=True)
        if load_model:
            trainer.load_model(trainer.bert_model, dir_path=config["output_path"])
        return trainer


    start_epoch = 3
    train_epoches = 1
    trainer = init_trainer(config["lr"], load_model=True)
    # if train from scratch
    all_loss = []
    threshold = 0
    patient = 10
    best_f1 = 0
    dynamic_lr = config["lr"]
    for epoch in range(start_epoch, start_epoch + train_epoches):
        print("train with learning rate {}".format(str(dynamic_lr)))
        trainer.train(epoch)

        trainer.save_state_dict(trainer.bert_model, epoch, dir_path=config["output_path"],
                                file_path="bert.model")
        trainer.test(epoch)