feat : load_json/load_safetensors 支持 broadcast，跨节点分布式加载

- load_json/load_safetensors/load_state_dict 新增 broadcast 参数
- broadcast=True 时 rank-0 读取后 broadcast_object_list 分发到所有 rank
- load_state_dict 改为逐张量 broadcast，避免大模型 pickle 内存瓶颈
- 删除 _get_meta/_get_config wrapper，Checkpoint.load 直接调用 load_json
- 参数注解 str | Path 统一为 Union[str, Path]

astrai/serialization.py

@@ -3,7 +3,7 @@ import json
 import time
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Dict, List, Tuple
+from typing import Any, Dict, Union
 
 import safetensors.torch as st
 import torch
@@ -16,29 +16,50 @@ _CONFIG_FILE = "config.json"
 _WEIGHTS_FILE = "model.safetensors"
 
 
-def save_safetensors(state_dict: dict, path: str | Path):
+def save_safetensors(state_dict: dict, path: Union[str, Path]):
     st.save_file(state_dict, str(path))
 
 
-def load_safetensors(path: str | Path) -> dict:
+def load_safetensors(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
         return st.load_file(str(path))
 
+    rank = get_rank()
+    if rank == 0:
+        state_dict = st.load_file(str(path))
+    else:
+        state_dict = {}
+    tmp = [state_dict]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
 
-def save_json(data: dict, path: str | Path):
+
+def save_json(data: dict, path: Union[str, Path]):
     with open(str(path), "w") as f:
         json.dump(data, f, indent=2)
 
 
-def load_json(path: str | Path) -> dict:
+def load_json(path: Union[str, Path], broadcast: bool = False) -> dict:
+    if not broadcast or not dist.is_initialized():
         with open(str(path), "r") as f:
             return json.load(f)
 
+    rank = get_rank()
+    if rank == 0:
+        with open(str(path), "r") as f:
+            data = json.load(f)
+    else:
+        data = {}
+    tmp = [data]
+    dist.broadcast_object_list(tmp, src=0)
+    return tmp[0]
 
-def save_torch(obj: Any, path: str | Path):
+
+def save_torch(obj: Any, path: Union[str, Path]):
     torch.save(obj, str(path))
 
 
-def load_torch(path: str | Path, broadcast: bool = False) -> Any:
+def load_torch(path: Union[str, Path], broadcast: bool = False) -> Any:
     if not broadcast or not dist.is_initialized():
         return torch.load(str(path), map_location="cpu", weights_only=False)
 
@@ -76,17 +97,18 @@ def load_model_config(save_directory: str) -> dict:
 
 
 def load_model_weights(save_directory: str) -> dict:
-    return load_safetensors(Path(save_directory) / _WEIGHTS_FILE)
+    return load_state_dict(Path(save_directory) / _WEIGHTS_FILE)
 
 
-def _load_state_dict(save_path: Path, broadcast: bool = False) -> dict:
+def load_state_dict(path: Union[str, Path], broadcast: bool = False) -> dict:
+    path = Path(path)
     if not broadcast or not dist.is_initialized():
-        return load_safetensors(save_path / _WEIGHTS_FILE)
+        return load_safetensors(path)
 
     rank = get_rank()
     if rank == 0:
-        state_dict = load_safetensors(save_path / _WEIGHTS_FILE)
+        state_dict = load_safetensors(path)
-        specs: List[Tuple[str, List[int], str]] = [
+        specs = [
             (k, list(state_dict[k].shape), str(state_dict[k].dtype).split(".")[-1])
             for k in sorted(state_dict)
         ]
@@ -142,10 +164,9 @@ class Checkpoint:
     def load(cls, save_dir: str, broadcast: bool = False) -> "Checkpoint":
         save_path = Path(save_dir)
 
-        meta = load_json(save_path / _META_FILE)
+        meta = load_json(save_path / _META_FILE, broadcast)
-        state_dict = _load_state_dict(save_path, broadcast=broadcast)
+        config = load_json(save_path / _CONFIG_FILE, broadcast)
-        config_path = save_path / _CONFIG_FILE
+        state_dict = load_state_dict(save_path / _WEIGHTS_FILE, broadcast=broadcast)
-        config = load_json(config_path)
 
         extra = {}
         for f in sorted(save_path.iterdir()):