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import json
import os
from collections import defaultdict
from typing import Any, Dict, Optional
import gradio as gr
from peft.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME
from ..extras.constants import (
DATA_CONFIG,
DEFAULT_MODULE,
DEFAULT_TEMPLATE,
PEFT_METHODS,
SUPPORTED_MODELS,
TRAINING_STAGES,
DownloadSource,
)
from ..extras.misc import use_modelscope
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 Exception:
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, defaultdict(str))
model_path = user_config["path_dict"].get(model_name, None) or path_dict.get(DownloadSource.DEFAULT, None)
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]:
if finetuning_type not in PEFT_METHODS:
return gr.update(value=[], choices=[], interactive=False)
adapters = []
if model_name and finetuning_type == "lora":
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, interactive=True)
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)
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