[HiCacheStorage]: Improve 3fs kvstore‘s performance and resolve mla issues (#9876)

python/sglang/srt/mem_cache/storage/hf3fs/mini_3fs_metadata_server.py

@@ -4,10 +4,12 @@ import json
 import logging
 import threading
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, OrderedDict, Tuple
 
+import orjson
 import requests
-from fastapi import FastAPI, HTTPException, Request, status
+from fastapi import FastAPI, HTTPException, Request, Response
+from fastapi.responses import ORJSONResponse
 from requests.adapters import HTTPAdapter
 from urllib3.util.retry import Retry
 
@@ -24,10 +26,10 @@ class RankMetadata:
     """Holds all metadata for a single rank."""
 
     def __init__(self, num_pages: int):
-        self.lock = threading.RLock()
+        self.lock = threading.Lock()
         self.num_pages = num_pages
         self.free_pages: List[int] = list(range(num_pages))
-        self.key_to_index: Dict[str, int] = {}
+        self.key_to_index: OrderedDict[str, int] = OrderedDict()
         # Todo: Support multi files for HF3FS
 
     def exists_keys(self, keys: List[str]) -> List[bool]:
@@ -46,16 +48,18 @@ class RankMetadata:
             for i, (key, prefix_key) in enumerate(keys):
                 if key in self.key_to_index:
                     results[i] = (True, self.key_to_index[key])
+                    self.key_to_index.move_to_end(key)
                 else:
                     new_keys_to_process.append((i, key, prefix_key))
 
             # Todo: Implementing data eviction logic after HiCache supports prefix information pass-through
             for i, key, prefix_key in new_keys_to_process:
                 if len(self.free_pages) > 0:
-                    page_idx = self.free_pages.pop()
-                    results[i] = (False, page_idx)
+                    page_index = self.free_pages.pop()
                 else:
-                    results[i] = (False, -1)
+                    page_index = self.key_to_index.popitem(last=False)[1]
+
+                results[i] = (False, page_index)
 
             return results
 
@@ -68,6 +72,7 @@ class RankMetadata:
         with self.lock:
             for key, page_index in written_keys_to_confirm:
                 self.key_to_index[key] = page_index
+                self.key_to_index.move_to_end(key)
 
             for page_index in pages_to_release:
                 if page_index not in self.free_pages:
@@ -94,7 +99,14 @@ class RankMetadata:
     def get_page_indices(self, keys: List[str]) -> List[Optional[int]]:
         """Get page indices for keys."""
         with self.lock:
-            return [self.key_to_index.get(key) for key in keys]
+            results = []
+            for key in keys:
+                if key in self.key_to_index:
+                    results.append(self.key_to_index[key])
+                    self.key_to_index.move_to_end(key)
+                else:
+                    results.append(None)
+            return results
 
 
 class GlobalMetadataState:
@@ -182,7 +194,8 @@ class Hf3fsMetadataServer:
 
     def __init__(self, persistence_path: Optional[str] = None, save_interval: int = 60):
         self.state = GlobalMetadataState(persistence_path, save_interval)
-        self.app = FastAPI()
+        self.app = FastAPI(default_response_class=ORJSONResponse)
+
         self._setup_routes()
 
     def _setup_routes(self):
@@ -199,17 +212,25 @@ class Hf3fsMetadataServer:
 
     def get_rank_metadata(self, rank: int) -> RankMetadata:
         """Get rank metadata with proper error handling."""
-        with self.state.global_lock:
         if rank not in self.state.ranks:
             raise HTTPException(
                 status_code=404,
-                    detail=f"Rank {rank} not initialized. Please call /{{rank}}/initialize first.",
+                detail=f"Rank {rank} not initialized. Please call /{rank}/initialize first.",
             )
         return self.state.ranks[rank]
 
+    async def _read_json(self, request: Request) -> dict:
+        """Parse request JSON using orjson if available."""
+        body = await request.body()
+        return orjson.loads(body)
+
+    def _json_response(self, content: dict):
+        """Return ORJSONResponse when available to bypass jsonable_encoder."""
+        return ORJSONResponse(content)
+
     async def initialize(self, rank: int, request: Request):
         """Initialize a rank with specified number of pages."""
-        data = await request.json()
+        data = await self._read_json(request)
         num_pages = data["num_pages"]
         with self.state.global_lock:
             if rank in self.state.ranks:
@@ -223,57 +244,55 @@ class Hf3fsMetadataServer:
             else:
                 logging.info(f"Initializing new Rank {rank} with {num_pages} pages.")
                 self.state.ranks[rank] = RankMetadata(num_pages)
-        return {"message": f"Rank {rank} is ready."}
+        return Response(status_code=204)
 
     async def exists(self, rank: int, request: Request):
         """Check if keys exist in metadata."""
-        data = await request.json()
+        data = await self._read_json(request)
         keys = data["keys"]
         metadata = self.get_rank_metadata(rank)
         results = metadata.exists_keys(keys)
-        return {"exists": results}
+        return self._json_response({"exists": results})
 
     async def reserve_and_allocate_page_indices(self, rank: int, request: Request):
         """Reserve and allocate page indices for keys."""
-        data = await request.json()
+        data = await self._read_json(request)
         metadata = self.get_rank_metadata(rank)
         keys = data["keys"]
         results = metadata.reserve_and_allocate_page_indices(keys)
-        return {"indices": results}
+        return self._json_response({"indices": results})
 
     async def confirm_write(self, rank: int, request: Request):
         """Confirm write operations and release pages."""
-        data = await request.json()
+        data = await self._read_json(request)
         metadata = self.get_rank_metadata(rank)
         success_written_keys = data.get("written_keys_to_confirm", [])
         released_pages = data.get("pages_to_release", [])
 
         metadata.confirm_write(success_written_keys, released_pages)
 
-        return {
-            "message": f"Rank {rank}: Write confirmed for {len(success_written_keys)} keys. {len(released_pages)} pages released."
-        }
+        return Response(status_code=204)
 
     async def delete_keys(self, rank: int, request: Request):
         """Delete keys from metadata."""
-        data = await request.json()
+        data = await self._read_json(request)
         metadata = self.get_rank_metadata(rank)
         count = metadata.delete_keys(data["keys"])
-        return {"message": f"Rank {rank}: {count} keys deleted."}
+        return Response(status_code=204)
 
     async def clear(self, rank: int):
         """Clear all metadata for a rank."""
         metadata = self.get_rank_metadata(rank)
         metadata.clear_all()
-        return {"message": f"Rank {rank}: Metadata cleared."}
+        return Response(status_code=204)
 
     async def get_page_indices(self, rank: int, request: Request):
         """Get page indices for keys."""
-        data = await request.json()
+        data = await self._read_json(request)
         metadata = self.get_rank_metadata(rank)
         keys = data["keys"]
         results = metadata.get_page_indices(keys)
-        return {"indices": results}
+        return self._json_response({"indices": results})
 
     def run(self, host: str = "0.0.0.0", port: int = 18000):
         """Run the metadata server."""
@@ -309,14 +328,22 @@ class Hf3fsGlobalMetadataClient(Hf3fsMetadataInterface):
             status_forcelist=[500, 502, 503, 504],
             allowed_methods=["GET", "POST"],
         )
-        adapter = HTTPAdapter(max_retries=retry_strategy)
+        adapter = HTTPAdapter(
+            max_retries=retry_strategy, pool_connections=256, pool_maxsize=256
+        )
         self._session.mount("http://", adapter)
 
     def _post(self, endpoint: str, json_data: dict) -> dict:
         try:
-            response = self._session.post(f"{self.base_url}/{endpoint}", json=json_data)
+            url = f"{self.base_url}/{endpoint}"
+            headers = {"Content-Type": "application/json"}
+            payload = orjson.dumps(json_data)  # type: ignore[union-attr]
+            response = self._session.post(url, data=payload, headers=headers)
             response.raise_for_status()
-            return response.json()
+
+            if response.status_code == 204 or not response.content:
+                return {}
+            return orjson.loads(response.content)  # type: ignore[union-attr]
         except requests.exceptions.RequestException as e:
             logging.error(f"Failed to POST to {endpoint} after retries: {e}")
             raise RuntimeError(f"Failed to connect to metadata server: {e}") from e

python/sglang/srt/mem_cache/storage/hf3fs/storage_hf3fs.py

@@ -113,6 +113,8 @@ def synchronized():
 
 
 class HiCacheHF3FS(HiCacheStorage):
+    """HiCache backend that stores KV cache pages in HF3FS files."""
+
     default_env_var: str = "SGLANG_HICACHE_HF3FS_CONFIG_PATH"
 
     def __init__(
@@ -176,15 +178,32 @@ class HiCacheHF3FS(HiCacheStorage):
         dtype: torch.dtype,
         storage_config: HiCacheStorageConfig = None,
     ) -> "HiCacheHF3FS":
+        """Create a HiCacheHF3FS instance from environment configuration.
+
+        Environment:
+            - Uses env var stored in `HiCacheHF3FS.default_env_var` to locate a JSON config.
+            - Falls back to a local single-machine config when the env var is not set.
+
+        Raises:
+            ValueError: If MLA Model is requested without global metadata server or required keys are missing.
+        """
         from sglang.srt.mem_cache.storage.hf3fs.mini_3fs_metadata_server import (
             Hf3fsGlobalMetadataClient,
             Hf3fsLocalMetadataClient,
         )
 
-        rank = storage_config.tp_rank if storage_config is not None else 0
+        if storage_config is not None:
+            rank, is_mla_model = storage_config.tp_rank, storage_config.is_mla_model
+        else:
+            rank, is_mla_model = 0, False
+
+        mla_unsupported_msg = f"MLA model is not supported without global metadata server, please refer to https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/mem_cache/storage/hf3fs/docs/deploy_sglang_3fs_multinode.md"
 
         config_path = os.getenv(HiCacheHF3FS.default_env_var)
         if not config_path:
+            if is_mla_model:
+                raise ValueError(mla_unsupported_msg)
+
             return HiCacheHF3FS(
                 rank=rank,
                 file_path=f"/data/hicache.{rank}.bin",
@@ -214,25 +233,27 @@ class HiCacheHF3FS(HiCacheStorage):
             raise ValueError(f"Missing required keys in config: {missing_keys}")
 
         # Choose metadata client based on configuration
-        is_mla_model = False
-        if "metadata_server_url" in config and config["metadata_server_url"]:
+        if config.get("metadata_server_url"):
             # Use global metadata client to connect to metadata server
             metadata_server_url = config["metadata_server_url"]
             metadata_client = Hf3fsGlobalMetadataClient(metadata_server_url)
 
-            # Enable MLA optimization only when using the global metadata client
-            is_mla_model = storage_config.is_mla_model if storage_config else False
             logger.info(
                 f"Using global metadata client with server url: {metadata_server_url}"
             )
         else:
+            # Enable MLA optimization only when using the global metadata client
+            if is_mla_model:
+                raise ValueError(mla_unsupported_msg)
+
             # Use local metadata client for single-machine deployment
             metadata_client = Hf3fsLocalMetadataClient()
 
+        rank_for_path = 0 if is_mla_model else rank
         return HiCacheHF3FS(
             rank=rank,
             # Let all ranks use the same file path for MLA model
-            file_path=f"{config['file_path_prefix']}.{rank if not is_mla_model else 0}.bin",
+            file_path=f"{config['file_path_prefix']}.{rank_for_path}.bin",
             file_size=int(config["file_size"]),
             numjobs=int(config["numjobs"]),
             bytes_per_page=bytes_per_page,