mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
import os
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import json
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import gradio as gr
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from typing import Any, Dict, Optional
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from transformers.utils import (
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WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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SAFE_WEIGHTS_INDEX_NAME,
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ADAPTER_WEIGHTS_NAME,
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ADAPTER_SAFE_WEIGHTS_NAME
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)
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from llmtuner.extras.constants import DEFAULT_MODULE, DEFAULT_TEMPLATE, SUPPORTED_MODELS, TRAINING_STAGES
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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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DATA_CONFIG = "dataset_info.json"
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CKPT_NAMES = [
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WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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SAFE_WEIGHTS_INDEX_NAME,
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ADAPTER_WEIGHTS_NAME,
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ADAPTER_SAFE_WEIGHTS_NAME
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]
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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:
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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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return user_config["path_dict"].get(model_name, None) or SUPPORTED_MODELS.get(model_name, "")
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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_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]:
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checkpoints = []
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if model_name:
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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 checkpoint in os.listdir(save_dir):
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if (
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os.path.isdir(os.path.join(save_dir, checkpoint))
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and any([os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CKPT_NAMES])
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):
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checkpoints.append(checkpoint)
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return gr.update(value=[], choices=checkpoints)
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def load_dataset_info(dataset_dir: 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:
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print("Cannot find {} in {}.".format(DATA_CONFIG, dataset_dir))
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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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