57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import pytorch_lightning as pl
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import torch
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from model.qwen_module import QwenModule
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from model.modeling_wit import QwenRunner
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from model.tokenization_qwen import QWenTokenizer
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import numpy as np
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import configuration
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import dataset.dataset as ds
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if __name__ == "__main__":
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conf = configuration.TrainConfig()
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config = conf.model_config
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conf.name = "bigger" # current train process name
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conf.pretrain_model_name = None # "qwen/Qwen-1_8B-Chat"
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conf.learning_rate = 0.0001
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conf.use_tril_attention_mask = None
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conf.precision = "bf16-mixed" # "precision:bf16-mixed,16-mixed,32-true"
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conf.train_batch_size = 16
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conf.val_batch_size = 4
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conf.num_proc = 8
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conf.max_epochs = 1000
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conf.strategy = "auto"
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conf.resume_from_ckpt_path = None
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conf.seed = 42
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conf.dataloader_works = 2
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conf.dataset.meaning.val_mask_level = [0, 1, 2]
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conf.dataset.meaning.val_mask_idx = [0, 0, -1]
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config.vocab_size = 256
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config.hidden_size = 128 # 128 1024 2048 32
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config.num_hidden_layers = 3 # 6 12 24 3
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config.num_attention_heads = 16 # 8 8 16
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torch.manual_seed(conf.seed)
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checkpoint_path = "log/bigger/version_1/checkpoints/epoch=23-step=24792.ckpt"
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qwen = QwenModule.load_from_checkpoint(checkpoint_path=checkpoint_path)
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qwen.eval()
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runner = QwenRunner(qwen.llm)
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val = ds.InitValDataset(conf).dataset
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data = val.dataset
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item = data.get_token(0)
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print(data.print_tree(0))
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batch = torch.tensor([item[:-1]], dtype=torch.int64)
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batch = batch.cuda()
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print(item)
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next_token = runner.ChatToken(batch)
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print(next_token.detach().cpu().numpy())
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