HiCache storage, style change and bug fix (#8719)

python/sglang/srt/mem_cache/hicache_storage.py

@@ -33,8 +33,7 @@ class HiCacheStorage(ABC):
     It abstracts the underlying storage mechanism, allowing different implementations to be used.
     """
 
-    # todo, translate tensor object access for different TP ranks
-    # potentially pass model and TP configs into storage backend
+    # todo, potentially pass model and TP configs into storage backend
     # todo, the page size of storage backend does not have to be the same as the same as host memory pool
 
     @abstractmethod
@@ -117,35 +116,28 @@ class HiCacheFile(HiCacheStorage):
     def get(
         self,
         key: str,
-        target_location: Optional[Any] = None,
+        target_location: torch.Tensor,
         target_sizes: Optional[Any] = None,
     ) -> torch.Tensor | None:
         key = self._get_suffixed_key(key)
         tensor_path = os.path.join(self.file_path, f"{key}.bin")
         try:
-            if target_location is not None:
             # Load directly into target_location's memory buffer
             with open(tensor_path, "rb") as f:
                 target_location.set_(
                     torch.frombuffer(f.read(), dtype=target_location.dtype)
                     .reshape(target_location.shape)
-                        .storage()
+                    .untyped_storage()
                 )
             return target_location
-            else:
-                loaded_tensor = torch.load(tensor_path)
-                if isinstance(loaded_tensor, torch.Tensor):
-                    return loaded_tensor
-                else:
-                    logger.error(f"Loaded data for key {key} is not a tensor.")
-                    return None
         except FileNotFoundError:
+            logger.warning(f"Failed to fetch {key} from HiCacheFile storage.")
             return None
 
     def batch_get(
         self,
         keys: List[str],
-        target_locations: Optional[Any] = None,
+        target_locations: List[torch.Tensor],
         target_sizes: Optional[Any] = None,
     ) -> List[torch.Tensor | None]:
         return [
@@ -168,7 +160,7 @@ class HiCacheFile(HiCacheStorage):
             logger.debug(f"Key {key} already exists. Skipped.")
             return True
         try:
-            torch.save(value, tensor_path)
+            value.contiguous().view(dtype=torch.uint8).numpy().tofile(tensor_path)
             return True
         except Exception as e:
             logger.error(f"Failed to save tensor {key}: {e}")