model_rpc style improvement

python/sglang/backend/openai.py

@@ -9,8 +9,9 @@
 from sglang.lang.ir import SglSamplingParams
 
 try:
-    import openai
     import tiktoken
+
+    import openai
 except ImportError as e:
     openai = tiktoken = e
 

python/sglang/srt/managers/router/model_rpc.py

@@ -6,7 +6,6 @@
 from concurrent.futures import ThreadPoolExecutor
 from typing import List
 
-import numpy as np
 import rpyc
 import torch
 from rpyc.utils.classic import obtain
@@ -36,8 +35,8 @@
 logger = logging.getLogger("model_rpc")
 
 
-class ModelRpcServer(rpyc.Service):
-    def exposed_init_model(
+class ModelRpcServer:
+    def __init__(
         self,
         tp_rank: int,
         server_args: ServerArgs,
@@ -608,14 +607,19 @@ def handle_finished_requests(self, batch: Batch):
                 batch.reqs = []
 
 
+class ModelRpcService(rpyc.Service):
+    exposed_ModelRpcServer = ModelRpcServer
+
+
 class ModelRpcClient:
     def __init__(self, server_args: ServerArgs, port_args: PortArgs):
         tp_size = server_args.tp_size
 
         if tp_size == 1:
             # Init model
-            self.model_server = ModelRpcServer()
-            self.model_server.exposed_init_model(0, server_args, port_args)
+            self.model_server = ModelRpcService().exposed_ModelRpcServer(
+                0, server_args, port_args
+            )
 
             # Wrap functions
             def async_wrap(f):
@@ -629,14 +633,16 @@ async def _func(*args, **kwargs):
             with ThreadPoolExecutor(tp_size) as executor:
                 # Launch model processes
                 rets = executor.map(start_model_process, port_args.model_rpc_ports)
-                self.model_servers = [x[0] for x in rets]
+                self.remote_services = [x[0] for x in rets]
                 self.procs = [x[1] for x in rets]
 
                 # Init model
                 def init_model(i):
-                    return self.model_servers[i].init_model(i, server_args, port_args)
+                    return self.remote_services[i].ModelRpcServer(
+                        i, server_args, port_args
+                    )
 
-                rets = [obtain(x) for x in executor.map(init_model, range(tp_size))]
+                self.model_servers = executor.map(init_model, range(tp_size))
 
             # Wrap functions
             def async_wrap(func_name):
@@ -654,7 +660,7 @@ async def _func(*args, **kwargs):
 
 def _init_service(port):
     t = ThreadedServer(
-        ModelRpcServer(),
+        ModelRpcService(),
         port=port,
         protocol_config={"allow_pickle": True, "sync_request_timeout": 1800},
     )

python/sglang/srt/managers/router/model_runner.py

@@ -1,10 +1,10 @@
 import importlib
-import logging
+import importlib.resources
 import inspect
+import logging
+import pkgutil
 from dataclasses import dataclass
 from functools import lru_cache
-from pathlib import Path
-import importlib.resources
 
 import numpy as np
 import torch
@@ -18,11 +18,6 @@
 from vllm.model_executor.model_loader import _set_default_torch_dtype
 from vllm.model_executor.parallel_utils.parallel_state import initialize_model_parallel
 
-import importlib
-import pkgutil
-
-import sglang
-
 QUANTIONCONFIG_MAPPING = {"awq": AWQConfig, "gptq": GPTQConfig, "marlin": MarlinConfig}
 
 logger = logging.getLogger("model_runner")
@@ -37,7 +32,7 @@ def import_model_classes():
     model_arch_name_to_cls = {}
     package_name = "sglang.srt.models"
     package = importlib.import_module(package_name)
-    for finder, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + '.'):
+    for _, name, ispkg in pkgutil.iter_modules(package.__path__, package_name + "."):
         if not ispkg:
             module = importlib.import_module(name)
             if hasattr(module, "EntryClass"):
@@ -144,9 +139,12 @@ def init_flashinfer_args(self, tp_size):
 
             # flashinfer >= 0.0.3
             # FIXME: Drop this when flashinfer updates to 0.0.4
-            if len(inspect.signature(self.prefill_wrapper.begin_forward).parameters) == 7:
+            if (
+                len(inspect.signature(self.prefill_wrapper.begin_forward).parameters)
+                == 7
+            ):
                 args.append(self.model_runner.model_config.head_dim)
-            
+
             self.prefill_wrapper.begin_forward(*args)
         else:
             self.decode_wrapper = BatchDecodeWithPagedKVCacheWrapper(
@@ -307,9 +305,11 @@ def load_model(self):
                     hf_quant_method = hf_quant_config["quant_method"]
 
                     # compat: autogptq uses is_marlin_format within quant config
-                    if (hf_quant_method == "gptq"
-                            and "is_marlin_format" in hf_quant_config
-                            and hf_quant_config["is_marlin_format"]):
+                    if (
+                        hf_quant_method == "gptq"
+                        and "is_marlin_format" in hf_quant_config
+                        and hf_quant_config["is_marlin_format"]
+                    ):
                         hf_quant_method = "marlin"
                     quant_config_class = QUANTIONCONFIG_MAPPING.get(hf_quant_method)