118 lines
6.4 KiB
Python
118 lines
6.4 KiB
Python
import os
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import argparse
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from Finetune4bConfig import Finetune4bConfig
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def parse_commandline():
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parser = argparse.ArgumentParser(
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prog=__file__.split(os.path.sep)[-1],
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description="Produce LoRA in 4bit training",
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usage="%(prog)s [config] [training]\n\nAll arguments are optional"
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)
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parser.add_argument("dataset", nargs="?",
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default="./dataset.json",
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help="Path to dataset file. Default: %(default)s"
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)
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parser_config = parser.add_argument_group("config")
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parser_training = parser.add_argument_group("training")
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# Config args group
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parser_config.add_argument("--ds_type", choices=["txt", "alpaca", "gpt4all"], default="alpaca", required=False,
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help="Dataset structure format. Default: %(default)s"
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)
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parser_config.add_argument("--lora_out_dir", default="alpaca_lora", required=False,
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help="Directory to place new LoRA. Default: %(default)s"
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)
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parser_config.add_argument("--lora_apply_dir", default=None, required=False,
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help="Path to directory from which LoRA has to be applied before training. Default: %(default)s"
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)
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parser_training.add_argument("--resume_checkpoint", default=None, required=False,
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help="Resume training from specified checkpoint. Default: %(default)s"
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)
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parser_config.add_argument("--llama_q4_config_dir", default="./llama-13b-4bit/", required=False,
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help="Path to the config.json, tokenizer_config.json, etc. Default: %(default)s"
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)
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parser_config.add_argument("--llama_q4_model", default="./llama-13b-4bit.pt", required=False,
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help="Path to the quantized model in huggingface format. Default: %(default)s"
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)
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# Training args group
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parser_training.add_argument("--mbatch_size", default=1, type=int, help="Micro-batch size. Default: %(default)s")
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parser_training.add_argument("--batch_size", default=2, type=int, help="Batch size. Default: %(default)s")
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parser_training.add_argument("--epochs", default=3, type=int, help="Epochs. Default: %(default)s")
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parser_training.add_argument("--lr", default=2e-4, type=float, help="Learning rate. Default: %(default)s")
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parser_training.add_argument("--cutoff_len", default=256, type=int, help="Default: %(default)s")
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parser_training.add_argument("--lora_r", default=8, type=int, help="Default: %(default)s")
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parser_training.add_argument("--lora_alpha", default=16, type=int, help="Default: %(default)s")
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parser_training.add_argument("--lora_dropout", default=0.05, type=float, help="Default: %(default)s")
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parser_training.add_argument("--grad_chckpt", action="store_true", required=False, help="Use gradient checkpoint. For 30B model. Default: %(default)s")
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parser_training.add_argument("--grad_chckpt_ratio", default=1, type=float, help="Gradient checkpoint ratio. Default: %(default)s")
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parser_training.add_argument("--val_set_size", default=0.2, type=float, help="Validation set size. Default: %(default)s")
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parser_training.add_argument("--warmup_steps", default=50, type=int, help="Default: %(default)s")
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parser_training.add_argument("--save_steps", default=50, type=int, help="Default: %(default)s")
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parser_training.add_argument("--save_total_limit", default=3, type=int, help="Default: %(default)s")
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parser_training.add_argument("--logging_steps", default=10, type=int, help="Default: %(default)s")
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parser_training.add_argument("-c", "--checkpoint", action="store_true", help="Produce checkpoint instead of LoRA. Default: %(default)s")
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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")
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parser_training.add_argument("--verbose", action="store_true", help="If output log of training. Default: %(default)s")
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# Data args
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parser_training.add_argument("--txt_row_thd", default=-1, type=int, help="Custom thd for txt rows.")
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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.")
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# V2 model support
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parser_training.add_argument("--groupsize", type=int, default=-1, help="Groupsize of v2 model")
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parser_training.add_argument("--v1", action="store_true", help="Use V1 model")
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# Multi GPU Support
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parser_training.add_argument("--local_rank", type=int, default=0, help="local rank if using torch.distributed.launch")
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# Flash Attention
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parser_training.add_argument("--flash_attention", action="store_true", help="enables flash attention, can improve performance and reduce VRAM use")
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parser_training.add_argument("--xformers", action="store_true", help="enables xformers memory efficient attention, can improve performance and reduce VRAM use")
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# Train Backend
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parser_training.add_argument("--backend", type=str, default='cuda', help="Backend to use. Triton or Cuda.")
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return vars(parser.parse_args())
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def get_config() -> Finetune4bConfig:
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args = parse_commandline()
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return Finetune4bConfig(
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dataset=args["dataset"],
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ds_type=args["ds_type"],
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lora_out_dir=args["lora_out_dir"],
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lora_apply_dir=args["lora_apply_dir"],
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resume_checkpoint=args["resume_checkpoint"],
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llama_q4_config_dir=args["llama_q4_config_dir"],
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llama_q4_model=args["llama_q4_model"],
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mbatch_size=args["mbatch_size"],
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batch_size=args["batch_size"],
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epochs=args["epochs"],
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lr=args["lr"],
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cutoff_len=args["cutoff_len"],
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lora_r=args["lora_r"],
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lora_alpha=args["lora_alpha"],
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lora_dropout=args["lora_dropout"],
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val_set_size=args["val_set_size"],
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gradient_checkpointing=args["grad_chckpt"],
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gradient_checkpointing_ratio=args["grad_chckpt_ratio"],
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warmup_steps=args["warmup_steps"],
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save_steps=args["save_steps"],
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save_total_limit=args["save_total_limit"],
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logging_steps=args["logging_steps"],
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checkpoint=args["checkpoint"],
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skip=args["skip"],
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verbose=args["verbose"],
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txt_row_thd=args["txt_row_thd"],
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use_eos_token=args["use_eos_token"]!=0,
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groupsize=args["groupsize"],
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v1=args["v1"],
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local_rank=args["local_rank"],
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flash_attention=args["flash_attention"],
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xformers=args["xformers"],
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backend=args["backend"],
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)
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