| 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) |
| ): |
| 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) |
|
|
