Witllm/wit/train.py

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