mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 19:52:50 +08:00
109 lines
3.8 KiB
Python
109 lines
3.8 KiB
Python
import os
|
|
import json
|
|
import gradio as gr
|
|
from typing import Any, Dict, Optional
|
|
from peft.utils import WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME
|
|
|
|
from llmtuner.extras.constants import (
|
|
DEFAULT_MODULE,
|
|
DEFAULT_TEMPLATE,
|
|
SUPPORTED_MODELS,
|
|
TRAINING_STAGES,
|
|
DownloadSource
|
|
)
|
|
from llmtuner.extras.misc import use_modelscope
|
|
from llmtuner.hparams.data_args import DATA_CONFIG
|
|
|
|
|
|
ADAPTER_NAMES = {WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME}
|
|
DEFAULT_CACHE_DIR = "cache"
|
|
DEFAULT_DATA_DIR = "data"
|
|
DEFAULT_SAVE_DIR = "saves"
|
|
USER_CONFIG = "user.config"
|
|
|
|
|
|
def get_save_dir(*args) -> os.PathLike:
|
|
return os.path.join(DEFAULT_SAVE_DIR, *args)
|
|
|
|
|
|
def get_config_path() -> os.PathLike:
|
|
return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
|
|
|
|
|
|
def load_config() -> Dict[str, Any]:
|
|
try:
|
|
with open(get_config_path(), "r", encoding="utf-8") as f:
|
|
return json.load(f)
|
|
except:
|
|
return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
|
|
|
|
|
|
def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None:
|
|
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
|
|
user_config = load_config()
|
|
user_config["lang"] = lang or user_config["lang"]
|
|
if model_name:
|
|
user_config["last_model"] = model_name
|
|
user_config["path_dict"][model_name] = model_path
|
|
with open(get_config_path(), "w", encoding="utf-8") as f:
|
|
json.dump(user_config, f, indent=2, ensure_ascii=False)
|
|
|
|
|
|
def get_model_path(model_name: str) -> str:
|
|
user_config = load_config()
|
|
path_dict: Dict[DownloadSource, str] = SUPPORTED_MODELS.get(model_name, [])
|
|
model_path = user_config["path_dict"].get(model_name, None) or path_dict.get(DownloadSource.DEFAULT, "")
|
|
if (
|
|
use_modelscope()
|
|
and path_dict.get(DownloadSource.MODELSCOPE)
|
|
and model_path == path_dict.get(DownloadSource.DEFAULT)
|
|
): # replace path
|
|
model_path = path_dict.get(DownloadSource.MODELSCOPE)
|
|
return model_path
|
|
|
|
|
|
def get_prefix(model_name: str) -> str:
|
|
return model_name.split("-")[0]
|
|
|
|
|
|
def get_module(model_name: str) -> str:
|
|
return DEFAULT_MODULE.get(get_prefix(model_name), "q_proj,v_proj")
|
|
|
|
|
|
def get_template(model_name: str) -> str:
|
|
if model_name and model_name.endswith("Chat") and get_prefix(model_name) in DEFAULT_TEMPLATE:
|
|
return DEFAULT_TEMPLATE[get_prefix(model_name)]
|
|
return "default"
|
|
|
|
|
|
def list_adapters(model_name: str, finetuning_type: str) -> Dict[str, Any]:
|
|
adapters = []
|
|
if model_name and finetuning_type == "lora": # full and freeze have no adapter
|
|
save_dir = get_save_dir(model_name, finetuning_type)
|
|
if save_dir and os.path.isdir(save_dir):
|
|
for adapter in os.listdir(save_dir):
|
|
if (
|
|
os.path.isdir(os.path.join(save_dir, adapter))
|
|
and any([os.path.isfile(os.path.join(save_dir, adapter, name)) for name in ADAPTER_NAMES])
|
|
):
|
|
adapters.append(adapter)
|
|
return gr.update(value=[], choices=adapters)
|
|
|
|
|
|
def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]:
|
|
try:
|
|
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
|
|
return json.load(f)
|
|
except Exception as err:
|
|
print("Cannot open {} due to {}.".format(os.path.join(dataset_dir, DATA_CONFIG), str(err)))
|
|
return {}
|
|
|
|
|
|
def list_dataset(
|
|
dataset_dir: Optional[str] = None, training_stage: Optional[str] = list(TRAINING_STAGES.keys())[0]
|
|
) -> Dict[str, Any]:
|
|
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
|
|
ranking = TRAINING_STAGES[training_stage] in ["rm", "dpo"]
|
|
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
|
|
return gr.update(value=[], choices=datasets)
|