release v0.1.0

Former-commit-id: 63c8d3a17cb18f0d8a8e37bfa147daf5bdd28ea9

src/llmtuner/webui/common.py

@@ -0,0 +1,75 @@
+import json
+import os
+from typing import Any, Dict, Optional
+
+import gradio as gr
+from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
+from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
+
+from llmtuner.extras.constants import SUPPORTED_MODELS
+
+
+DEFAULT_CACHE_DIR = "cache"
+DEFAULT_DATA_DIR = "data"
+DEFAULT_SAVE_DIR = "saves"
+USER_CONFIG = "user.config"
+DATA_CONFIG = "dataset_info.json"
+
+
+def get_save_dir(model_name: str) -> str:
+    return os.path.join(DEFAULT_SAVE_DIR, os.path.split(model_name)[-1])
+
+
+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 {"last_model": "", "path_dict": {}}
+
+
+def save_config(model_name: str, model_path: str) -> None:
+    os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
+    user_config = load_config()
+    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()
+    return user_config["path_dict"].get(model_name, SUPPORTED_MODELS.get(model_name, ""))
+
+
+def list_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]:
+    checkpoints = []
+    save_dir = os.path.join(get_save_dir(model_name), finetuning_type)
+    if save_dir and os.path.isdir(save_dir):
+        for checkpoint in os.listdir(save_dir):
+            if (
+                os.path.isdir(os.path.join(save_dir, checkpoint))
+                and any([
+                    os.path.isfile(os.path.join(save_dir, checkpoint, name))
+                    for name in (WEIGHTS_NAME, WEIGHTS_INDEX_NAME, PEFT_WEIGHTS_NAME)
+                ])
+            ):
+                checkpoints.append(checkpoint)
+    return gr.update(value=[], choices=checkpoints)
+
+
+def load_dataset_info(dataset_dir: 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:
+        return {}
+
+
+def list_dataset(dataset_dir: Optional[str] = None) -> Dict[str, Any]:
+    dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
+    return gr.update(value=[], choices=list(dataset_info.keys()))