def training_function()

in sagemaker/28_train_llms_with_qlora/scripts/run_clm.py [0:0]

• 60 lines of code
• 12 McCabe index (conditional complexity)

def training_function(args):
    # set seed
    set_seed(args.seed)

    dataset = load_from_disk(args.dataset_path)
    # load model from the hub with a bnb config
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.bfloat16,
    )

    model = AutoModelForCausalLM.from_pretrained(
        args.model_id,
        use_cache=False if args.gradient_checkpointing else True,  # this is needed for gradient checkpointing
        trust_remote_code=True,  # ATTENTION: This allows remote code execution
        device_map="auto",
        quantization_config=bnb_config,
    )

    # create peft config
    model = create_peft_config(model, args.gradient_checkpointing)

    # Define training args
    output_dir = "/tmp"
    training_args = TrainingArguments(
        output_dir=output_dir,
        overwrite_output_dir=True,
        per_device_train_batch_size=args.per_device_train_batch_size,
        bf16=args.bf16,  # Use BF16 if available
        learning_rate=args.lr,
        num_train_epochs=args.epochs,
        gradient_checkpointing=args.gradient_checkpointing,
        # logging strategies
        logging_dir=f"{output_dir}/logs",
        logging_strategy="steps",
        logging_steps=10,
        save_strategy="no",
    )

    # Create Trainer instance
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
        data_collator=default_data_collator,
    )

    # pre-process the model by upcasting the layer norms in float 32 for
    for name, module in trainer.model.named_modules():
        if "norm" in name:
            module = module.to(torch.float32)

    # Start training
    trainer.train()

    if args.merge_weights:
        # merge adapter weights with base model and save
        # save int 4 model
        trainer.model.save_pretrained(output_dir, safe_serialization=False)
        # clear memory
        del model
        del trainer
        torch.cuda.empty_cache()

        from peft import AutoPeftModelForCausalLM

        # load PEFT model in fp16
        offload_folder = "/tmp/offload"
        model = AutoPeftModelForCausalLM.from_pretrained(
            output_dir,
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True,
            trust_remote_code=True,  # ATTENTION: This allows remote code execution
        )  
        # Merge LoRA and base model and save
        merged_model = model.merge_and_unload()
        merged_model.save_pretrained("/opt/ml/model/",safe_serialization=True)
    else:
        trainer.model.save_pretrained("/opt/ml/model/", safe_serialization=True)

    # save tokenizer for easy inference
    tokenizer = AutoTokenizer.from_pretrained(args.model_id, trust_remote_code=True)
    tokenizer.save_pretrained("/opt/ml/model/")