arg_parser.py

import os
import argparse
from Finetune4bConfig import Finetune4bConfig

def parse_commandline():
    parser = argparse.ArgumentParser(
        prog=__file__.split(os.path.sep)[-1],
        description="Produce LoRA in 4bit training",
        usage="%(prog)s [config] [training]\n\nAll arguments are optional"
    )
    
    parser.add_argument("dataset", nargs="?",
        default="./dataset.json", 
        help="Path to dataset file. Default: %(default)s"
    )
    
    parser_config = parser.add_argument_group("config")
    parser_training = parser.add_argument_group("training")

    # Config args group
    parser_config.add_argument("--ds_type", choices=["txt", "alpaca", "gpt4all", "plaintext"], default="alpaca", required=False,
        help="Dataset structure format. Default: %(default)s"
    )
    parser_config.add_argument("--lora_out_dir", default="alpaca_lora", required=False,
        help="Directory to place new LoRA. Default: %(default)s"
    )
    parser_config.add_argument("--lora_apply_dir", default=None, required=False,
        help="Path to directory from which LoRA has to be applied before training. Default: %(default)s"
    )
    parser_training.add_argument("--resume_checkpoint", default=None, required=False,
        help="Resume training from specified checkpoint. Default: %(default)s"
    )
    parser_config.add_argument("--llama_q4_config_dir", default="./llama-13b-4bit/", required=False,
        help="Path to the config.json, tokenizer_config.json, etc. Default: %(default)s"
    )
    parser_config.add_argument("--llama_q4_model", default="./llama-13b-4bit.pt", required=False,
        help="Path to the quantized model in huggingface format. Default: %(default)s"
    )

    # Training args group
    parser_training.add_argument("--mbatch_size", default=1, type=int, help="Micro-batch size. Default: %(default)s")
    parser_training.add_argument("--batch_size", default=2, type=int, help="Batch size. Default: %(default)s")
    parser_training.add_argument("--epochs", default=3, type=int, help="Epochs. Default: %(default)s")
    parser_training.add_argument("--lr", default=2e-4, type=float, help="Learning rate. Default: %(default)s")
    parser_training.add_argument("--cutoff_len", default=256, type=int, help="Default: %(default)s")
    parser_training.add_argument("--max_position_embeddings", default=2048, type=int, help="Extend LLaMA position embedding table to embed sequences longer than the default. Default: %(default)s")
    parser_training.add_argument("--lora_r", default=8, type=int, help="Default: %(default)s")
    parser_training.add_argument("--lora_alpha", default=16, type=int, help="Default: %(default)s")
    parser_training.add_argument("--lora_dropout", default=0.05, type=float, help="Default: %(default)s")
    parser_training.add_argument("--grad_chckpt", action="store_true", required=False, help="Use gradient checkpoint. For 30B model. Default: %(default)s")
    parser_training.add_argument("--grad_chckpt_ratio", default=1, type=float, help="Gradient checkpoint ratio. Default: %(default)s")
    parser_training.add_argument("--val_set_size", default=0.2, type=float, help="Validation set size. Default: %(default)s")
    parser_training.add_argument("--warmup_steps", default=50, type=int, help="Default: %(default)s")
    parser_training.add_argument("--save_steps", default=50, type=int, help="Default: %(default)s")
    parser_training.add_argument("--save_total_limit", default=3, type=int, help="Default: %(default)s")
    parser_training.add_argument("--logging_steps", default=10, type=int, help="Default: %(default)s")
    parser_training.add_argument("-c", "--checkpoint", action="store_true", help="Produce checkpoint instead of LoRA. Default: %(default)s")
    parser_training.add_argument("--skip", action="store_true", help="Don't train model. Can be useful to produce checkpoint from existing LoRA. Default: %(default)s")
    parser_training.add_argument("--verbose", action="store_true", help="If output log of training. Default: %(default)s")

    # Data args
    parser_training.add_argument("--txt_row_thd", default=-1, type=int, help="Custom thd for txt rows.")
    parser_training.add_argument("--use_eos_token", default=1, type=int, help="Use eos token instead if padding with 0. enable with 1, disable with 0.")
    
    # V2 model support
    parser_training.add_argument("--groupsize", type=int, default=-1, help="Groupsize of v2 model")
    parser_training.add_argument("--v1", action="store_true", help="Use V1 model")

    # Multi GPU Support
    parser_training.add_argument("--local_rank", type=int, default=0, help="local rank if using torch.distributed.launch")
    
    # Flash Attention
    parser_training.add_argument("--flash_attention", action="store_true", help="enables flash attention, can improve performance and reduce VRAM use")
    parser_training.add_argument("--xformers", action="store_true", help="enables xformers memory efficient attention, can improve performance and reduce VRAM use")

    # Train Backend
    parser_training.add_argument("--backend", type=str, default='cuda', help="Backend to use. Triton or Cuda.")

    return vars(parser.parse_args())


def get_config() -> Finetune4bConfig:
    args = parse_commandline()
    return Finetune4bConfig(
        dataset=args["dataset"], 
        ds_type=args["ds_type"], 
        lora_out_dir=args["lora_out_dir"], 
        lora_apply_dir=args["lora_apply_dir"],
        resume_checkpoint=args["resume_checkpoint"],
        llama_q4_config_dir=args["llama_q4_config_dir"],
        llama_q4_model=args["llama_q4_model"],
        mbatch_size=args["mbatch_size"],
        batch_size=args["batch_size"],
        epochs=args["epochs"], 
        lr=args["lr"],
        cutoff_len=args["cutoff_len"],
        max_position_embeddings=args["max_position_embeddings"],
        lora_r=args["lora_r"],
        lora_alpha=args["lora_alpha"], 
        lora_dropout=args["lora_dropout"],
        val_set_size=args["val_set_size"],
        gradient_checkpointing=args["grad_chckpt"],
        gradient_checkpointing_ratio=args["grad_chckpt_ratio"],
        warmup_steps=args["warmup_steps"],
        save_steps=args["save_steps"],
        save_total_limit=args["save_total_limit"],
        logging_steps=args["logging_steps"],
        checkpoint=args["checkpoint"],
        skip=args["skip"],
        verbose=args["verbose"],
        txt_row_thd=args["txt_row_thd"],
        use_eos_token=args["use_eos_token"]!=0,
        groupsize=args["groupsize"],
        v1=args["v1"],
        local_rank=args["local_rank"],
        flash_attention=args["flash_attention"],
        xformers=args["xformers"],
        backend=args["backend"],
    )