Witllm/wit/train.py

import pytorch_lightning as pl
import torch

from model.lit_module import LitModule
from wit.model.tokenization_qwen import QWenTokenizer
from logger import MLFLogger

import configuration
import dataset.dataset as ds

if __name__ == "__main__":

    train_config = configuration.TrainConfig()
    config = train_config.model_config

    torch.manual_seed(train_config.seed)

    config.vocab_size = 256
    config.hidden_size = 128  # 128 1024 2048  32
    config.num_hidden_layers = 6  # 6 12 24  3
    config.num_attention_heads = 16  # 8 8 16

    lit_module = LitModule(
        train_config.pretrain_model_name, train_config.learning_rate, config, train_config.use_tril_attention_mask
    )
    tokenizer = QWenTokenizer("./model/wit_b64.tiktoken", "./model/wit_char.tiktoken")

    train_dataloader, val_dataloader = ds.InitDataset(train_config)
    # for i in range(len(train_dataloader)):
    #     print(train_dataloader.print_mapping(i))

    torch.set_float32_matmul_precision("medium")
    lit_trainer = pl.Trainer(
        accelerator="cuda",
        precision=train_config.precision,
        logger=MLFLogger("./log/", run_name=train_config.name),
        strategy=train_config.strategy,
        max_epochs=train_config.max_epochs,
    )
    lit_trainer.fit(
        lit_module,
        train_dataloaders=train_dataloader,
        val_dataloaders=val_dataloader,
        ckpt_path=train_config.resume_from_ckpt_path,
    )