Fixed single GPU issue without setting up mp. Added toggles for serve…

…r request batching parameters (opendatahub-io#114)

* Fixed single GPU issue without setting up mp. Added toggles for server request batching parameters

* Adding HTTP headers

vllm/entrypoints/fast_sync_llm.py

@@ -2,9 +2,12 @@
 from queue import Empty
 from typing import Union
 
+from vllm import envs
 from vllm.distributed.communication_op import broadcast_tensor_dict
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.llm_engine import LLMEngine
+from vllm.executor.multiproc_gpu_executor import MultiprocessingGPUExecutor
+from vllm.executor.ray_gpu_executor import RayGPUExecutor
 from vllm.inputs import PromptInputs, TextTokensPrompt
 from vllm.logger import init_logger
 from vllm.pooling_params import PoolingParams
@@ -49,7 +52,9 @@ def _poll_requests(self):
             if not self.llm_engine.has_unfinished_requests():
                 logger.info("No unfinished requests. Waiting...")
                 (request_id, prompt, sampling_params) = self.input_queue.get()
-                if self.need_restart:
+                if self.need_restart and isinstance(
+                        self.llm_engine.model_executor,
+                        MultiprocessingGPUExecutor):
                     logger.info("Restarting worker loops")
                     for worker in self.llm_engine.model_executor.workers:
                         worker.execute_method("start_worker_execution_loop")
@@ -66,13 +71,24 @@ def _poll_requests(self):
     def run_engine(self):
         self.llm_engine = LLMEngine.from_engine_args(
             self.engine_args, usage_context=UsageContext.LLM_CLASS)
+        assert not isinstance(
+            self.llm_engine.model_executor,
+            RayGPUExecutor), "Ray is not supported in sync openai mode"
 
         self.result_queue.put(("Ready", None, None))
         request_stats = {}
         log_interval = 100
+        poll_interval = envs.VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS
         try:
             while True:
-                self._poll_requests()
+                poll_interval -= 1
+                if (self.input_queue.qsize() >=
+                        envs.VLLM_SYNC_SERVER_ACCUM_REQUESTS
+                        or poll_interval <= 0
+                        or not self.llm_engine.has_unfinished_requests()):
+                    self._poll_requests()
+                    poll_interval = \
+                        envs.VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS
                 step_outputs = self.llm_engine.step()
                 log_interval -= 1
                 if log_interval == 0:

vllm/entrypoints/sync_openai/api_server.py

@@ -8,6 +8,7 @@
 
 import uvicorn
 from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import StreamingResponse
 from fastapi.routing import Mount
 from prometheus_client import make_asgi_app
@@ -178,7 +179,8 @@ async def completions(request: CompletionRequest, raw_request: Request):
         created_time = int(time.time())
         return StreamingResponse(content=completion_generator(
             request.model, result_queue, choices, created_time, ids),
-                                 media_type="text/event-stream")
+                                 media_type="text/event-stream",
+                                 headers={"Access-Control-Allow-Origin": "*"})
     while True:
         request_id, token, stats = await result_queue.get()
         choice_idx = choices[request_id]
@@ -207,4 +209,13 @@ def parse_args():
     args = parse_args()
     engine_args = EngineArgs.from_cli_args(args)
     runner.set_engine_args(engine_args)
+
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=args.allowed_origins,
+        allow_credentials=args.allow_credentials,
+        allow_methods=args.allowed_methods,
+        allow_headers=args.allowed_headers,
+    )
+
     uvicorn.run(app, port=args.port, host=args.host)

vllm/envs.py

@@ -38,6 +38,8 @@
     VLLM_INSTALL_PUNICA_KERNELS: bool = False
     CMAKE_BUILD_TYPE: Optional[str] = None
     VERBOSE: bool = False
+    VLLM_SYNC_SERVER_ACCUM_REQUESTS: int = 1
+    VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS: int = 1
 
 # The begin-* and end* here are used by the documentation generator
 # to extract the used env vars.
@@ -219,6 +221,14 @@
     # Both spawn and fork work
     "VLLM_WORKER_MULTIPROC_METHOD":
     lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn"),
+
+    # Try to accumulate this many requests before proceeding
+    "VLLM_SYNC_SERVER_ACCUM_REQUESTS":
+    lambda: int(os.getenv("VLLM_SYNC_SERVER_ACCUM_REQUESTS", "1")),
+
+    # Poll for new requests every this many steps
+    "VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS":
+    lambda: int(os.getenv("VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS", "1")),
 }
 
 # end-env-vars-definition