92 lines
2.5 KiB
Python
92 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 datasets
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import pytorch_lightning as pl
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import torch
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from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split, Subset
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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 special_dataset import SpecialDataset
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from meaning_dataset import MeaningDataset
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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 = 1
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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 = 256
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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 = 1024 # 128 1024 2048 32
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config.num_hidden_layers = 12 # 6 12 24 3
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config.num_attention_heads = 16 # 8 8 16
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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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# raw_dataset = SpecialDataset()
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level_scale = 4
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start = vocab_size * level_scale * level_scale
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raw_dataset = MeaningDataset(
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start=start,
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end=start * level_scale,
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size=start * level_scale * level_scale,
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max_subitem=level_scale,
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vocab_size=vocab_size,
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)
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train_dataset, val_dataset = random_split(raw_dataset, [0.95, 0.05])
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it = iter(train_dataset)
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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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train_dataloader = DataLoader(
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train_dataset,
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batch_size=train_batch_size,
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num_workers=num_proc,
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persistent_workers=True,
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shuffle=True,
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)
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val_dataloader = DataLoader(
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val_dataset,
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batch_size=val_batch_size,
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num_workers=num_proc,
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persistent_workers=True,
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)
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torch.set_float32_matmul_precision("medium")
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lit_trainer = pl.Trainer(
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accelerator="gpu",
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# devices=[0],
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precision=precision,
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logger=TBLogger("./", 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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