86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
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 = 1
|
|
val_batch_size = 1
|
|
num_proc = 8
|
|
max_epochs = 1000
|
|
strategy = "auto"
|
|
resume_from_ckpt_path = None
|
|
seed = 42
|
|
|
|
vocab_size = 256
|
|
level_ratio = 6
|
|
level = 4
|
|
dataset_level = 1.5
|
|
min_subitem = 2
|
|
|
|
hidden_size = 1024 # 128 1024 2048 32
|
|
num_attention_heads = 16 # 8 8 16
|
|
num_hidden_layers = 6 # 6 12 24 3
|
|
|
|
mask_level = [0]
|
|
mask_idx = [-1]
|
|
|
|
# name = "vocab_ratio_level_data_hidden_head_layer"
|
|
# name = "mask_level_idx"
|
|
name = "small"
|
|
|
|
ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{min_subitem}" + "_" + f"{dataset_level}"
|
|
ver = ver + "_" + f"{hidden_size}" + "_" + f"{num_attention_heads}" + "_" + f"{num_hidden_layers}"
|
|
ver = ver + "_" + f"{mask_level}" + "_" + f"{mask_idx}"
|
|
|
|
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)
|
|
size = vocab_size * int((level_ratio**dataset_level))
|
|
raw_dataset = MeaningDataset(start, start + size, size, vocab_size, level_ratio, min_subitem)
|
|
raw_dataset.set_mask(mask_level, mask_idx)
|
|
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)
|
|
|
|
# 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=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,
|
|
)
|