def train()

in fastchat/train/train_lora_t5.py [0:0]

• 91 lines of code
• 21 McCabe index (conditional complexity)

def train():
    parser = transformers.HfArgumentParser(
        (ModelArguments, DataArguments, TrainingArguments, LoraArguments)
    )
    (
        model_args,
        data_args,
        training_args,
        lora_args,
    ) = parser.parse_args_into_dataclasses()

    device_map = None
    world_size = int(os.environ.get("WORLD_SIZE", 1))
    ddp = world_size != 1
    if lora_args.q_lora:
        device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
        if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
            logging.warning(
                "FSDP and ZeRO3 are both currently incompatible with QLoRA."
            )

    compute_dtype = (
        torch.float16
        if training_args.fp16
        else (torch.bfloat16 if training_args.bf16 else torch.float32)
    )

    model = transformers.AutoModelForSeq2SeqLM.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        device_map=device_map,
        quantization_config=BitsAndBytesConfig(
            load_in_4bit=True,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
            bnb_4bit_compute_dtype=compute_dtype,
        )
        if lora_args.q_lora
        else None,
    )

    lora_config = LoraConfig(
        r=lora_args.lora_r,
        lora_alpha=lora_args.lora_alpha,
        target_modules=lora_args.lora_target_modules,
        lora_dropout=lora_args.lora_dropout,
        bias=lora_args.lora_bias,
        task_type=TaskType.SEQ_2_SEQ_LM,
    )

    if lora_args.q_lora:
        model = prepare_model_for_kbit_training(
            model, use_gradient_checkpointing=training_args.gradient_checkpointing
        )
        if not ddp and torch.cuda.device_count() > 1:
            # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available
            model.is_parallelizable = True
            model.model_parallel = True

    model = get_peft_model(model, lora_config)
    if training_args.deepspeed is not None and training_args.local_rank == 0:
        model.print_trainable_parameters()

    if training_args.gradient_checkpointing:
        model.enable_input_require_grads()

    # Dacheng: Note we can only use T5Tokenizer, otherwise it will prepend
    # a space before special tokens.
    tokenizer = transformers.T5Tokenizer.from_pretrained(
        model_args.model_name_or_path,
        cache_dir=training_args.cache_dir,
        model_max_length=training_args.model_max_length,
        padding_side="right",
        use_fast=False,
    )

    smart_tokenizer_and_embedding_resize(
        special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
        other_tokens=["<", "{", "\n", "}", "`", " ", "\\", "^", "\t"],
        tokenizer=tokenizer,
        model=model,
    )

    data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)

    trainer = Trainer(
        model=model, tokenizer=tokenizer, args=training_args, **data_module
    )

    if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
        trainer.train(resume_from_checkpoint=True)
    else:
        trainer.train()
    trainer.save_state()
    # check if zero3 mode enabled
    if deepspeed.is_deepspeed_zero3_enabled():
        # use deepspeed engine internal function to gather state dict
        # state_dict_zero3 contains whole parameters of base and lora adapters
        # we will not extract lora parameters since peft save_pretrained will do that
        # https://github.com/huggingface/peft/blob/3714aa2fff158fdfa637b2b65952580801d890b2/src/peft/peft_model.py#L125
        # https://github.com/huggingface/peft/blob/3714aa2fff158fdfa637b2b65952580801d890b2/src/peft/utils/save_and_load.py#L19
        state_dict_zero3 = trainer.model_wrapped._zero3_consolidated_16bit_state_dict()
        if training_args.local_rank == 0:
            state_dict = state_dict_zero3
    else:
        # in other mode we use original code from fastchat team, to make sure our change is minimum
        state_dict = get_peft_state_maybe_zero_3(
            model.named_parameters(), lora_args.lora_bias
        )

    if training_args.local_rank == 0:
        safe_save_model_for_hf_trainer(
            trainer=trainer, output_dir=training_args.output_dir, state_dict=state_dict
        )