128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
"""
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llama-4b trainer with support of Stanford Alpaca-like JSON datasets (short for SAD)
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Intended to use with https://github.com/johnsmith0031/alpaca_lora_4bit
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SAD structure:
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[
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{
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"instruction": "Give null hypothesis",
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"input": "6 subjects were given a drug (treatment group) and an additional 6 subjects a placebo (control group).",
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"output": "Drug is equivalent of placebo"
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},
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{
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"instruction": "What does RNA stand for?",
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"input": "",
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"output": "RNA stands for ribonucleic acid."
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}
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]
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"""
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import sys
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sys.path.insert(0, './repository/transformers/src')
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sys.path.insert(0, './repository/GPTQ-for-LLaMa')
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sys.path.insert(0, './repository/peft/src')
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import peft
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import peft.tuners.lora
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assert peft.tuners.lora.is_gptq_available()
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import torch
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import transformers
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from autograd_4bit import load_llama_model_4bit_low_ram
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from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, PeftModel
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# ! Config
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from arg_parser import get_config
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import train_data
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ft_config = get_config()
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# * Show loaded parameters
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print(f"{ft_config}\n")
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# Load Basic Model
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model, tokenizer = load_llama_model_4bit_low_ram(ft_config.llama_q4_config_dir, ft_config.llama_q4_model)
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# Config Lora
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lora_config = LoraConfig(
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r=ft_config.lora_r,
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lora_alpha=ft_config.lora_alpha,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=ft_config.lora_dropout,
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bias="none",
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task_type="CAUSAL_LM",
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)
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if ft_config.lora_apply_dir is None:
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model = get_peft_model(model, lora_config)
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else:
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model = PeftModel.from_pretrained(model, ft_config.lora_apply_dir, device_map={'': 0}, torch_dtype=torch.float32) # ! Direct copy from inference.py
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print(ft_config.lora_apply_dir, 'loaded')
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# Scales to half
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print('Fitting 4bit scales and zeros to half')
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for n, m in model.named_modules():
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if '4bit' in str(type(m)):
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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# Set tokenizer
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tokenizer.pad_token_id = 0
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if not ft_config.skip:
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# ! TODO: Refactor to load both SAD and LLAMA datasets
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# Load Data
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data = None
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match ft_config.ds_type:
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case "txt" if not ft_config.skip:
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#### LLaMA
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data = train_data.TrainTxt(ft_config.dataset, ft_config.val_set_size, tokenizer, ft_config.cutoff_len)
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case "alpaca" if not ft_config.skip:
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#### Stanford Alpaca-like Data
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data = train_data.TrainSAD(ft_config.dataset, ft_config.val_set_size, tokenizer, ft_config.cutoff_len)
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case _:
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raise NotImplementedError("ERROR: Unknown dataset format")
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data.prepare_data()
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####
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trainer = transformers.Trainer(
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model=model,
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train_dataset=data.train_data,
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eval_dataset=data.val_data,
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args=transformers.TrainingArguments(
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per_device_train_batch_size=ft_config.mbatch_size,
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gradient_accumulation_steps=ft_config.gradient_accumulation_steps,
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warmup_steps=ft_config.warmup_steps,
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num_train_epochs=ft_config.epochs,
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learning_rate=ft_config.lr,
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fp16=True,
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logging_steps=ft_config.logging_steps,
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evaluation_strategy="no",
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save_strategy="steps",
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eval_steps=None,
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save_steps=ft_config.save_steps,
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output_dir=ft_config.lora_out_dir,
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save_total_limit=ft_config.save_total_limit,
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load_best_model_at_end=False
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),
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data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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# Set Model dict
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old_state_dict = model.state_dict
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model.state_dict = (
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lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
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).__get__(model, type(model))
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# Run Trainer
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trainer.train()
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print('Train completed.')
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# Save Model
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model.save_pretrained(ft_config.lora_out_dir)
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print('Model Saved.')
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