import argparse from functools import partial from itertools import chain from typing import Dict, Tuple import pytorch_lightning as pl import torch from lit_module import LitModule from tokenization_qwen import QWenTokenizer from logger import TBLogger from meaning_dataset import MeaningDataset, BatchGroupMeaningDataloader from wit.configuration import ModelConfig pretrain_model_name = None # "qwen/Qwen-1_8B-Chat" learning_rate = 0.0001 use_tril_attention_mask = None precision = "32-true" # "precision:bf16-mixed,16-mixed,32-true" train_batch_size = 4 val_batch_size = 1 num_proc = 8 max_epochs = 1000 strategy = "auto" resume_from_ckpt_path = None seed = 42 vocab_size = 1024 level_ratio = 4 level = 4 dataset_level = 1 hidden_size = 256 # 128 1024 2048 32 num_attention_heads = 8 # 8 8 16 num_hidden_layers = 2 # 6 12 24 3 name = "vocab_ratio_level_data_hidden_head_layer" ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{dataset_level}" ver = ver + "_" + f"{hidden_size}" + "_" + f"{num_attention_heads}" + "_" + f"{num_hidden_layers}" if __name__ == "__main__": torch.manual_seed(seed) config = ModelConfig() config.vocab_size = vocab_size config.hidden_size = hidden_size config.num_hidden_layers = num_hidden_layers config.num_attention_heads = num_attention_heads lit_module = LitModule(pretrain_model_name, learning_rate, config, use_tril_attention_mask) tokenizer = QWenTokenizer("./wit_b64.tiktoken", "./wit_char.tiktoken") start = vocab_size * (level_ratio**level) end = start * level_ratio size = int(vocab_size * (level_ratio**dataset_level)) raw_dataset = MeaningDataset(start, end, size, vocab_size, level_ratio) train_dataset, val_dataset = raw_dataset.Split(0.9) train_dataloader = BatchGroupMeaningDataloader(train_dataset, train_batch_size) val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size) # it = iter(train_dataloader) # print("data samples:") # for i in range(10): # print(next(it)["input_ids"].numpy().tolist()) torch.set_float32_matmul_precision("medium") lit_trainer = pl.Trainer( accelerator="cuda", devices=[0, 1], precision=precision, logger=TBLogger("./log/", name=name, version=ver, default_hp_metric=False), strategy=strategy, max_epochs=max_epochs, ) lit_trainer.fit( lit_module, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader, ckpt_path=resume_from_ckpt_path, )