forked from p83651209/CPM-9G-8B
253 lines
11 KiB
Python
253 lines
11 KiB
Python
import argparse
|
|
|
|
|
|
def add_model_config_args(parser: argparse.ArgumentParser):
|
|
"""Model arguments"""
|
|
|
|
group = parser.add_argument_group("model", "model configuration")
|
|
group.add_argument("--model-config", type=str, help="model configuration file")
|
|
group.add_argument("--vocab", type=str, default=None, help="model vocabulary file")
|
|
return parser
|
|
|
|
|
|
def add_training_args(parser: argparse.ArgumentParser):
|
|
"""Training arguments."""
|
|
|
|
group = parser.add_argument_group("train", "training configurations")
|
|
group.add_argument("--platform-config", type=str, default="platform_config.json", help="Path to platform config")
|
|
group.add_argument("--dataset", type=str, default="dataset.json", help="Path to dataset")
|
|
group.add_argument("--val-dataset", type=str, default="dataset.json", help="Path to val dataset")
|
|
group.add_argument(
|
|
"--load",
|
|
type=str,
|
|
default=None,
|
|
help="Path to a directory containing a model checkpoint.",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--load-grad",
|
|
action="store_true",
|
|
default=False,
|
|
help="Load the gradient states",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--load-start-step",
|
|
action="store_true",
|
|
default=False,
|
|
help="Load the step state from checkpoints",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--save",
|
|
type=str,
|
|
default=None,
|
|
help="Output directory to save checkpoints to.",
|
|
)
|
|
group.add_argument(
|
|
"--save-name",
|
|
type=str,
|
|
default=None,
|
|
help="Output filename to save checkpoints to.",
|
|
)
|
|
group.add_argument(
|
|
"--save-model",
|
|
type=str,
|
|
default=None,
|
|
help="Output directory to save model to.",
|
|
)
|
|
|
|
group.add_argument(
|
|
"--tensorboard",
|
|
type=str,
|
|
default=None,
|
|
help="tensorboard directory",
|
|
)
|
|
|
|
group.add_argument("--inspect-iters", type=int, default=1000, help="number of inspecting")
|
|
group.add_argument("--batch-size", type=int, default=32, help="Data Loader batch size")
|
|
group.add_argument("--clip-grad", type=float, default=1.0, help="gradient clipping")
|
|
group.add_argument(
|
|
"--train-iters",
|
|
type=int,
|
|
default=1000000,
|
|
help="total number of iterations to train over all training runs",
|
|
)
|
|
group.add_argument("--max-length", type=int, default=512, help="max length of input")
|
|
|
|
group.add_argument("--seed", type=int, default=1234, help="random seed for reproducibility")
|
|
|
|
# Learning rate.
|
|
group.add_argument("--lr", type=float, default=1.0e-4, help="initial learning rate")
|
|
group.add_argument("--weight-decay", type=float, default=1.0e-2, help="weight decay rate")
|
|
group.add_argument("--loss-scale", type=float, default=65536, help="loss scale")
|
|
group.add_argument("--max-loss-scale", type=float, default=float("inf"), help="loss scale")
|
|
group.add_argument("--min-loss-scale", type=float, default=1, help="loss scale")
|
|
group.add_argument("--loss-scale-steps", type=float, default=1024, help="loss scale")
|
|
|
|
group.add_argument(
|
|
"--warmup-iters",
|
|
type=float,
|
|
default=0.01,
|
|
help="percentage of data to warmup on (.01 = 1% of all " "training iters). Default 0.01",
|
|
)
|
|
group.add_argument(
|
|
"--lr-decay-style",
|
|
type=str,
|
|
default="noam",
|
|
choices=["constant", "linear", "cosine", "exponential", "noam"],
|
|
help="learning rate decay function",
|
|
)
|
|
group.add_argument("--lr-decay-iters", type=int, default=None, help="lr decay steps")
|
|
group.add_argument("--start-step", type=int, default=0, help="step to start or continue training")
|
|
group.add_argument("--concat-data", action="store_true", help="whether we concatenate the dialogues")
|
|
group.add_argument("--offload", action="store_true", help="whether we use offload_adam")
|
|
group.add_argument("--new-bmt", action="store_true", help="new bmt without ckpt")
|
|
group.add_argument("--flash", default="none", choices=["none", "1d", "triton", "cuda"])
|
|
group.add_argument("--tp", default=1, type=int, help="whether we use tensor parallelism")
|
|
group.add_argument("--bf16", action="store_true", help="whether we use bf16")
|
|
group.add_argument("--gradient-accumulation-steps", type=int, default=1, help="gradient accumulation steps")
|
|
return parser
|
|
|
|
|
|
def add_pretrain_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("pretrain", "pretrain configurations")
|
|
group.add_argument(
|
|
"--save-iters",
|
|
type=int,
|
|
default=1000,
|
|
help="number of iterations between saves",
|
|
)
|
|
group.add_argument(
|
|
"--log-dir",
|
|
type=str,
|
|
default=None,
|
|
help="log directory",
|
|
)
|
|
group.add_argument(
|
|
"--worker-name",
|
|
type=str,
|
|
default=None,
|
|
help="worker name",
|
|
)
|
|
return parser
|
|
|
|
|
|
def add_finetune_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("finetune", "finetune configurations")
|
|
group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
|
|
group.add_argument("--task-name", type=str, default="task", help="name of training task")
|
|
group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
|
|
group.add_argument("--save-steps", type=int, default=0, help="number of training steps between saves")
|
|
group.add_argument(
|
|
"--drop-last",
|
|
action="store_true",
|
|
default=False,
|
|
help="drop data from each epoch that cannot be formed into a complete batch at the end",
|
|
)
|
|
group.add_argument("--delta-tuning", action="store_true", default=False)
|
|
group.add_argument("--each-epoch-save", default=False)
|
|
group.add_argument("--train-task-id", type=int, default=-1)
|
|
return parser
|
|
|
|
|
|
def add_rhlf_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("rhlf", "rhlf configurations")
|
|
|
|
group.add_argument(
|
|
"--load-reward",
|
|
type=str,
|
|
default=None,
|
|
help="Path to reward model checkpoint.",
|
|
)
|
|
group.add_argument("--actor-lr", type=float, default=1.0e-5, help="actor learning rate")
|
|
group.add_argument("--critic-lr", type=float, default=1.0e-6, help="critic learning rate")
|
|
group.add_argument("--actor-loss-scale", type=float, default=65536, help="actor loss scale")
|
|
group.add_argument("--critic-loss-scale", type=float, default=65536, help="critic loss scale")
|
|
group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
|
|
group.add_argument("--actor-delay-step", type=int, default=0, help="actor delay step")
|
|
group.add_argument("--entropy-coef", type=float, default=-1.0, help="coef of policy entropy")
|
|
##
|
|
return parser
|
|
|
|
|
|
def add_simple_rhlf_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("simple_rhlf", "simple rhlf configurations")
|
|
group.add_argument("--epoch", type=int, default=1, help="number of training epochs")
|
|
group.add_argument("--sample-batch-size", type=int, default=32, help="Data Loader sample batch size")
|
|
group.add_argument("--load-reward", type=str, default=None, help="Path to reward model checkpoint")
|
|
group.add_argument("--avg-reward-bias", type=float, default=0, help="reward bias")
|
|
group.add_argument("--sample-min-length", type=int, default=20, help="sample-min-length")
|
|
group.add_argument("--sample-max-inp-length", type=int, default=1024, help="sample-max-inp-length")
|
|
group.add_argument("--sample-max-length", type=int, default=64, help="sample-max-length")
|
|
group.add_argument("--sample-repetition-penalty", type=float, default=1.05, help="sample-repetition-penalty")
|
|
group.add_argument("--sample-temperature", type=float, default=1.0, help="sample-temperature")
|
|
group.add_argument("--encode-max-length", type=int, default=1024, help="encode-max-length")
|
|
group.add_argument("--generate-max-length", type=int, default=64, help="generate-max-length")
|
|
group.add_argument("--value-loss-weight", type=float, default=0.1, help="value-loss-weight")
|
|
group.add_argument("--ptx-loss-weight", type=float, default=0.001, help="ptx-loss-weight")
|
|
group.add_argument("--save-epochs", type=int, default=1, help="number of training epochs between saves")
|
|
##
|
|
return parser
|
|
|
|
|
|
def add_feedback_learning_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("rrhf", "rrhf configurations")
|
|
group.add_argument("--length-penalty", type=float, default=1.0, help="length_penalty")
|
|
group.add_argument("--feedback-weight", type=float, default=1.0, help="feedback_weight")
|
|
group.add_argument("--sample-num", type=int, default=6, help="sample_num")
|
|
group.add_argument("--dpo-beta", type=float, default=1.0, help="dpo_beta")
|
|
group.add_argument("--stable-alignment-margin", type=float, default=1.0, help="stable_alignment_margin")
|
|
group.add_argument("--feedback-learning-type", type=str, default="RRHF", help="feedback_learning_type")
|
|
group.add_argument("--save-iters", type=int, default=1000, help="number of iterations between saves")
|
|
##
|
|
return parser
|
|
|
|
|
|
def add_delta_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("LoRA","LoRA configurations")
|
|
group.add_argument("--delta-type", type=str, default=None, help="delta-tuning-type")
|
|
group.add_argument("--lora-r", type=int, default=8, help="lora-rank")
|
|
group.add_argument("--lora-alpha", type=int, default=8, help="lora-alpha")
|
|
group.add_argument("--lora-dropout", type=float, default=0.0, help="lora-dropout")
|
|
group.add_argument("--lora-layer", nargs='+', default=['project_q','project_k'], help="lora-layer")
|
|
group.add_argument("--save-origin-model", action="store_true", default=False)
|
|
return parser
|
|
|
|
|
|
def add_reward_args(parser: argparse.ArgumentParser):
|
|
group = parser.add_argument_group("reward", "reward configurations")
|
|
group.add_argument("--load-all", type=str, default=None, help="Path to a directory containing a model checkpoint.")
|
|
##
|
|
return parser
|
|
|
|
|
|
def get_args(
|
|
pretrain: bool = False,
|
|
finetune: bool = False,
|
|
rhlf: bool = False,
|
|
simple_rlhf: bool = False,
|
|
feedback_learning: bool = False,
|
|
reward: bool = False,
|
|
):
|
|
parser = argparse.ArgumentParser()
|
|
parser = add_model_config_args(parser) # config file need to be exported with model/ckpt
|
|
parser = add_training_args(parser)
|
|
if pretrain:
|
|
parser = add_pretrain_args(parser)
|
|
if finetune:
|
|
parser = add_finetune_args(parser)
|
|
parser = add_delta_args(parser)
|
|
if rhlf:
|
|
parser = add_rhlf_args(parser)
|
|
if simple_rlhf:
|
|
parser = add_simple_rhlf_args(parser)
|
|
if feedback_learning:
|
|
parser = add_feedback_learning_args(parser)
|
|
if reward:
|
|
parser = add_reward_args(parser)
|
|
|
|
args = parser.parse_args()
|
|
|
|
return args
|