Bump up the version to v0.1.6 (#989)

Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>

tests/async_engine/api_server_async_engine.py

@@ -40,8 +40,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngineWithStats.from_engine_args(engine_args,
+    engine = AsyncLLMEngineWithStats.from_engine_args(engine_args)
-                                                      start_engine_loop=False)
     vllm.entrypoints.api_server.engine = engine
     uvicorn.run(
         app,

vllm/__init__.py

@@ -8,7 +8,7 @@ from vllm.entrypoints.llm import LLM
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 
-__version__ = "0.1.5"
+__version__ = "0.1.6"
 
 __all__ = [
     "LLM",

vllm/engine/async_llm_engine.py

@@ -230,6 +230,8 @@ class AsyncLLMEngine:
             async frontend will be executed in a separate process as the
             model workers.
         log_requests: Whether to log the requests.
+        start_engine_loop: If True, the background task to run the engine
+            will be automatically started in the generate call.
         *args, *kwargs: Arguments for LLMEngine.
     """
 
@@ -240,7 +242,7 @@ class AsyncLLMEngine:
                  engine_use_ray: bool,
                  *args,
                  log_requests: bool = True,
-                 start_engine_loop: bool = False,
+                 start_engine_loop: bool = True,
                  **kwargs) -> None:
         self.worker_use_ray = worker_use_ray
         self.engine_use_ray = engine_use_ray
@@ -249,8 +251,7 @@ class AsyncLLMEngine:
 
         self.request_tracker: RequestTracker = RequestTracker()
         self.background_loop = None
-        if start_engine_loop:
+        self.start_engine_loop = start_engine_loop
-            self.start_background_loop()
 
     @property
     def is_running(self) -> bool:
@@ -330,11 +331,14 @@ class AsyncLLMEngine:
                         f"prompt token ids: {prompt_token_ids}.")
 
         if not self.is_running:
-            raise AsyncEngineDeadError(
+            if self.start_engine_loop:
-                "Background loop is not running. If it was running, "
+                self.start_background_loop()
-                "inspect the output to find the stacktrace of the "
+            else:
-                "error that caused the background loop to stop "
+                raise AsyncEngineDeadError(
-                "(AsyncEngineDeadError).")
+                    "Background loop is not running. If it was running, "
+                    "inspect the output to find the stacktrace of the "
+                    "error that caused the background loop to stop "
+                    "(AsyncEngineDeadError).")
 
         stream = self.request_tracker.add_request(
             request_id,
@@ -426,7 +430,7 @@ class AsyncLLMEngine:
     @classmethod
     def from_engine_args(cls,
                          engine_args: AsyncEngineArgs,
-                         start_engine_loop: bool = False) -> "AsyncLLMEngine":
+                         start_engine_loop: bool = True) -> "AsyncLLMEngine":
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
         engine_configs = engine_args.create_engine_configs()

vllm/entrypoints/api_server.py

@@ -32,9 +32,6 @@ async def generate(request: Request) -> Response:
     sampling_params = SamplingParams(**request_dict)
     request_id = random_uuid()
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     results_generator = engine.generate(prompt, sampling_params, request_id)
 
     # Streaming case
@@ -80,8 +77,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngine.from_engine_args(engine_args,
+    engine = AsyncLLMEngine.from_engine_args(engine_args)
-                                             start_engine_loop=False)
 
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/api_server.py

@@ -192,9 +192,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
     """
     logger.info(f"Received chat completion request: {request}")
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     error_check_ret = await check_model(request)
     if error_check_ret is not None:
         return error_check_ret
@@ -367,9 +364,6 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
     """
     logger.info(f"Received completion request: {request}")
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     error_check_ret = await check_model(request)
     if error_check_ret is not None:
         return error_check_ret
@@ -627,8 +621,7 @@ if __name__ == "__main__":
         served_model = args.model
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngine.from_engine_args(engine_args,
+    engine = AsyncLLMEngine.from_engine_args(engine_args)
-                                             start_engine_loop=False)
     engine_model_config = asyncio.run(engine.get_model_config())
     max_model_len = engine_model_config.get_max_model_len()
 

vllm/model_executor/layers/attention.py

@@ -259,8 +259,9 @@ class PagedAttentionWithRoPE(PagedAttention):
         self.is_neox_style = is_neox_style
 
         # Create the cos and sin cache.
-        inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
+        inv_freq = 1.0 / (base**(
-        t = torch.arange(max_position).float()
+            torch.arange(0, rotary_dim, 2, device="cuda") / rotary_dim))
+        t = torch.arange(max_position, device="cuda").float()
         freqs = torch.einsum("i,j -> ij", t, inv_freq.float())
         cos = freqs.cos()
         sin = freqs.sin()