80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
import argparse
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from functools import partial
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from itertools import chain
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from typing import Dict, Tuple
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import pytorch_lightning as pl
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import torch
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from lit_module import LitModule
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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
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use_tril_attention_mask = None
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precision = "32-true" # "precision:bf16-mixed,16-mixed,32-true"
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train_batch_size = 4
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val_batch_size = 1
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num_proc = 8
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max_epochs = 1000
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strategy = "auto"
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resume_from_ckpt_path = None
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seed = 42
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vocab_size = 1024
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level_ratio = 4
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level = 4
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dataset_level = 1
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hidden_size = 256 # 128 1024 2048 32
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num_attention_heads = 8 # 8 8 16
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num_hidden_layers = 2 # 6 12 24 3
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name = "vocab_ratio_level_data_hidden_head_layer"
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ver = f"{vocab_size}" + "_" + f"{level_ratio}" + "_" + f"{level}" + "_" + f"{dataset_level}"
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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()
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config.vocab_size = vocab_size
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config.hidden_size = hidden_size
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config.num_hidden_layers = num_hidden_layers
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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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end = start * level_ratio
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size = int(vocab_size * (level_ratio**dataset_level))
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raw_dataset = MeaningDataset(start, end, 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)
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val_dataloader = BatchGroupMeaningDataloader(val_dataset, val_batch_size)
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# it = iter(train_dataloader)
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# print("data samples:")
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# for i in range(10):
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# print(next(it)["input_ids"].numpy().tolist())
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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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devices=[0, 1],
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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,
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max_epochs=max_epochs,
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)
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lit_trainer.fit(
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lit_module,
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train_dataloaders=train_dataloader,
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val_dataloaders=val_dataloader,
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ckpt_path=resume_from_ckpt_path,
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)
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