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import json |
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import os |
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from collections import defaultdict |
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from typing import Any, Dict, Optional |
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import gradio as gr |
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from peft.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME |
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from ..extras.constants import ( |
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DATA_CONFIG, |
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DEFAULT_MODULE, |
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DEFAULT_TEMPLATE, |
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PEFT_METHODS, |
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SUPPORTED_MODELS, |
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TRAINING_STAGES, |
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DownloadSource, |
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) |
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from ..extras.misc import use_modelscope |
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ADAPTER_NAMES = {WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME} |
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DEFAULT_CACHE_DIR = "cache" |
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DEFAULT_DATA_DIR = "data" |
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DEFAULT_SAVE_DIR = "saves" |
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USER_CONFIG = "user.config" |
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def get_save_dir(*args) -> os.PathLike: |
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return os.path.join(DEFAULT_SAVE_DIR, *args) |
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def get_config_path() -> os.PathLike: |
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return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG) |
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def load_config() -> Dict[str, Any]: |
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try: |
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with open(get_config_path(), "r", encoding="utf-8") as f: |
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return json.load(f) |
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except Exception: |
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return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None} |
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def save_config(lang: str, model_name: Optional[str] = None, model_path: Optional[str] = None) -> None: |
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os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True) |
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user_config = load_config() |
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user_config["lang"] = lang or user_config["lang"] |
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if model_name: |
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user_config["last_model"] = model_name |
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user_config["path_dict"][model_name] = model_path |
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with open(get_config_path(), "w", encoding="utf-8") as f: |
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json.dump(user_config, f, indent=2, ensure_ascii=False) |
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def get_model_path(model_name: str) -> str: |
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user_config = load_config() |
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path_dict: Dict[DownloadSource, str] = SUPPORTED_MODELS.get(model_name, defaultdict(str)) |
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model_path = user_config["path_dict"].get(model_name, None) or path_dict.get(DownloadSource.DEFAULT, None) |
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if ( |
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use_modelscope() |
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and path_dict.get(DownloadSource.MODELSCOPE) |
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and model_path == path_dict.get(DownloadSource.DEFAULT) |
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): |
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model_path = path_dict.get(DownloadSource.MODELSCOPE) |
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return model_path |
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def get_prefix(model_name: str) -> str: |
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return model_name.split("-")[0] |
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def get_module(model_name: str) -> str: |
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return DEFAULT_MODULE.get(get_prefix(model_name), "q_proj,v_proj") |
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def get_template(model_name: str) -> str: |
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if model_name and model_name.endswith("Chat") and get_prefix(model_name) in DEFAULT_TEMPLATE: |
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return DEFAULT_TEMPLATE[get_prefix(model_name)] |
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return "default" |
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def list_adapters(model_name: str, finetuning_type: str) -> Dict[str, Any]: |
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if finetuning_type not in PEFT_METHODS: |
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return gr.update(value=[], choices=[], interactive=False) |
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adapters = [] |
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if model_name and finetuning_type == "lora": |
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save_dir = get_save_dir(model_name, finetuning_type) |
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if save_dir and os.path.isdir(save_dir): |
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for adapter in os.listdir(save_dir): |
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if os.path.isdir(os.path.join(save_dir, adapter)) and any( |
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os.path.isfile(os.path.join(save_dir, adapter, name)) for name in ADAPTER_NAMES |
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): |
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adapters.append(adapter) |
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return gr.update(value=[], choices=adapters, interactive=True) |
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def load_dataset_info(dataset_dir: str) -> Dict[str, Dict[str, Any]]: |
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try: |
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with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f: |
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return json.load(f) |
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except Exception as err: |
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print("Cannot open {} due to {}.".format(os.path.join(dataset_dir, DATA_CONFIG), str(err))) |
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return {} |
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def list_dataset( |
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dataset_dir: Optional[str] = None, training_stage: Optional[str] = list(TRAINING_STAGES.keys())[0] |
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) -> Dict[str, Any]: |
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dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR) |
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ranking = TRAINING_STAGES[training_stage] in ["rm", "dpo"] |
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datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking] |
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return gr.update(value=[], choices=datasets) |
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