50 lines
2.0 KiB
Python
50 lines
2.0 KiB
Python
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from transformers import AutoConfig, AutoModelForCausalLM
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from peft import LoraConfig, TaskType, get_peft_model
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from modeling import PreLlamaModel
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def get_model(model_args, use_gradient_checkpointing: bool = False):
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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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)
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else:
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raise ValueError(
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"You are instantiating a new config instance from scratch. This is not supported by this script."
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)
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if use_gradient_checkpointing:
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config.use_cache = False
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if model_args.model_name_or_path:
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model = PreLlamaModel.from_pretrained(
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model_args.model_name_or_path,
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use_flash_attention_2=True if model_args.use_flash_attn else False,
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attn_implementation='sdpa',
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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)
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else:
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print("Training new model from scratch")
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model = model_args.from_config(config)
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if model_args.use_lora:
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peft_config = LoraConfig(
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task_type="CAUSAL_LM",
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inference_mode=False,
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r=model_args.lora_rank,
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target_modules=model_args.target_modules,
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lora_alpha=model_args.lora_alpha,
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lora_dropout=model_args.lora_dropout
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)
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model = get_peft_model(model, peft_config)
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model.print_trainable_parameters()
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return model |