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ColossalAI/examples/language/opt/args.py

69 lines
3.0 KiB

from colossalai import get_default_parser
def parse_demo_args():
parser = get_default_parser()
parser.add_argument("--model_name_or_path",
type=str,
default="facebook/opt-350m",
help="Path to pretrained model or model identifier from huggingface.co/models.")
parser.add_argument("--output_path",
type=str,
default="./output_model.bin",
help="The path of your saved model after finetuning.")
parser.add_argument(
"--plugin",
type=str,
default="gemini",
help=
"Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero', 'hybrid_parallel'."
)
parser.add_argument("--num_epoch", type=int, default=10, help="Number of epochs.")
parser.add_argument("--batch_size",
type=int,
default=32,
help="Batch size (per dp group) for the training dataloader.")
parser.add_argument("--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.")
parser.add_argument("--warmup_ratio",
type=float,
default=0.1,
help="Ratio of warmup steps against total training steps.")
parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay to use.")
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
args = parser.parse_args()
return args
def parse_benchmark_args():
parser = get_default_parser()
parser.add_argument("--model_name_or_path",
type=str,
default="facebook/opt-125m",
help="Path to pretrained model or model identifier from huggingface.co/models.")
parser.add_argument(
"--plugin",
type=str,
default="gemini",
help="Plugin to use. Valid plugins include 'torch_ddp','torch_ddp_fp16','gemini','low_level_zero'.")
parser.add_argument("--batch_size",
type=int,
default=32,
help="Batch size (per dp group) for the training dataloader.")
parser.add_argument("--learning_rate",
type=float,
default=5e-5,
help="Initial learning rate (after the potential warmup period) to use.")
parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
parser.add_argument("--max_train_steps", type=int, default=20, help="Total number of training steps to perform.")
parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
parser.add_argument("--mem_cap", type=int, default=0, help="Limit on the usage of space for each GPU (in GB).")
args = parser.parse_args()
return args