[Benchmark] Add --async-engine option to benchmark_throughput.py (#…

…7964)

benchmarks/benchmark_throughput.py

@@ -6,13 +6,16 @@
 from typing import List, Optional, Tuple
 
 import torch
+import uvloop
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
 
-from vllm.engine.arg_utils import EngineArgs
+from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
+from vllm.entrypoints.openai.api_server import (
+    build_async_engine_client_from_engine_args)
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
-from vllm.utils import FlexibleArgumentParser
+from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
 def sample_requests(
@@ -135,6 +138,93 @@ def run_vllm(
     return end - start
 
 
+async def run_vllm_async(
+    requests: List[Tuple[str, int, int]],
+    model: str,
+    tokenizer: str,
+    quantization: Optional[str],
+    tensor_parallel_size: int,
+    seed: int,
+    n: int,
+    use_beam_search: bool,
+    trust_remote_code: bool,
+    dtype: str,
+    max_model_len: Optional[int],
+    enforce_eager: bool,
+    kv_cache_dtype: str,
+    quantization_param_path: Optional[str],
+    device: str,
+    enable_prefix_caching: bool,
+    enable_chunked_prefill: bool,
+    max_num_batched_tokens: int,
+    distributed_executor_backend: Optional[str],
+    gpu_memory_utilization: float = 0.9,
+    num_scheduler_steps: int = 1,
+    use_v2_block_manager: bool = False,
+    download_dir: Optional[str] = None,
+    load_format: str = EngineArgs.load_format,
+    disable_async_output_proc: bool = False,
+    disable_frontend_multiprocessing: bool = False,
+) -> float:
+    from vllm import SamplingParams
+    engine_args = AsyncEngineArgs(
+        model=model,
+        tokenizer=tokenizer,
+        quantization=quantization,
+        tensor_parallel_size=tensor_parallel_size,
+        seed=seed,
+        trust_remote_code=trust_remote_code,
+        dtype=dtype,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        enforce_eager=enforce_eager,
+        kv_cache_dtype=kv_cache_dtype,
+        quantization_param_path=quantization_param_path,
+        device=device,
+        enable_prefix_caching=enable_prefix_caching,
+        download_dir=download_dir,
+        enable_chunked_prefill=enable_chunked_prefill,
+        max_num_batched_tokens=max_num_batched_tokens,
+        distributed_executor_backend=distributed_executor_backend,
+        load_format=load_format,
+        num_scheduler_steps=num_scheduler_steps,
+        use_v2_block_manager=use_v2_block_manager,
+        disable_async_output_proc=disable_async_output_proc,
+        worker_use_ray=False,
+        engine_use_ray=False,
+        disable_log_requests=True,
+    )
+
+    async with build_async_engine_client_from_engine_args(
+            engine_args, disable_frontend_multiprocessing) as llm:
+
+        # Add the requests to the engine.
+        prompts: List[str] = []
+        sampling_params: List[SamplingParams] = []
+        for prompt, _, output_len in requests:
+            prompts.append(prompt)
+            sampling_params.append(
+                SamplingParams(
+                    n=n,
+                    temperature=0.0 if use_beam_search else 1.0,
+                    top_p=1.0,
+                    use_beam_search=use_beam_search,
+                    ignore_eos=True,
+                    max_tokens=output_len,
+                ))
+
+        generators = []
+        start = time.perf_counter()
+        for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
+            generator = llm.generate(prompt, sp, request_id=f"test{i}")
+            generators.append(generator)
+        all_gens = merge_async_iterators(*generators)
+        async for i, res in all_gens:
+            pass
+        end = time.perf_counter()
+        return end - start
+
+
 def run_hf(
     requests: List[Tuple[str, int, int]],
     model: str,
@@ -230,7 +320,7 @@ def main(args: argparse.Namespace):
                                    args.output_len)
 
     if args.backend == "vllm":
-        elapsed_time = run_vllm(
+        run_args = [
             requests, args.model, args.tokenizer, args.quantization,
             args.tensor_parallel_size, args.seed, args.n, args.use_beam_search,
             args.trust_remote_code, args.dtype, args.max_model_len,
@@ -240,7 +330,14 @@ def main(args: argparse.Namespace):
             args.max_num_batched_tokens, args.distributed_executor_backend,
             args.gpu_memory_utilization, args.num_scheduler_steps,
             args.use_v2_block_manager, args.download_dir, args.load_format,
-            args.disable_async_output_proc)
+            args.disable_async_output_proc
+        ]
+
+        if args.async_engine:
+            run_args.append(args.disable_frontend_multiprocessing)
+            elapsed_time = uvloop.run(run_vllm_async(*run_args))
+        else:
+            elapsed_time = run_vllm(*run_args)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -426,6 +523,14 @@ def main(args: argparse.Namespace):
         action='store_true',
         default=False,
         help="Disable async output processor for vLLM backend.")
+    parser.add_argument("--async-engine",
+                        action='store_true',
+                        default=False,
+                        help="Use vLLM async engine rather than LLM class.")
+    parser.add_argument("--disable-frontend-multiprocessing",
+                        action='store_true',
+                        default=False,
+                        help="Disable decoupled async engine frontend.")
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

vllm/entrypoints/openai/api_server.py

@@ -67,7 +67,7 @@
 
 
 def model_is_embedding(model_name: str, trust_remote_code: bool,
-                       quantization: str) -> bool:
+                       quantization: Optional[str]) -> bool:
     return ModelConfig(model=model_name,
                        tokenizer=model_name,
                        tokenizer_mode="auto",
@@ -96,13 +96,6 @@ async def _force_log():
 @asynccontextmanager
 async def build_async_engine_client(
         args: Namespace) -> AsyncIterator[Optional[AsyncEngineClient]]:
-    """
-    Create AsyncEngineClient, either:
-        - in-process using the AsyncLLMEngine Directly
-        - multiprocess using AsyncLLMEngine RPC
-
-    Returns the Client or None if the creation failed.
-    """
 
     # Context manager to handle async_engine_client lifecycle
     # Ensures everything is shutdown and cleaned up on error/exit
@@ -112,14 +105,37 @@ async def build_async_engine_client(
     # Backend itself still global for the silly lil' health handler
     global async_engine_client
 
+    async with build_async_engine_client_from_engine_args(
+            engine_args, args.disable_frontend_multiprocessing) as engine:
+
+        async_engine_client = engine  # type: ignore[assignment]
+        yield engine
+
+
+@asynccontextmanager
+async def build_async_engine_client_from_engine_args(
+    engine_args: AsyncEngineArgs,
+    disable_frontend_multiprocessing: bool = False,
+) -> AsyncIterator[Optional[AsyncEngineClient]]:
+    """
+    Create AsyncEngineClient, either:
+        - in-process using the AsyncLLMEngine Directly
+        - multiprocess using AsyncLLMEngine RPC
+
+    Returns the Client or None if the creation failed.
+    """
+
     # If manually triggered or embedding model, use AsyncLLMEngine in process.
     # TODO: support embedding model via RPC.
-    if (model_is_embedding(args.model, args.trust_remote_code,
-                           args.quantization)
-            or args.disable_frontend_multiprocessing):
-        async_engine_client = AsyncLLMEngine.from_engine_args(
+    if (model_is_embedding(engine_args.model, engine_args.trust_remote_code,
+                           engine_args.quantization)
+            or disable_frontend_multiprocessing):
+        engine_client = AsyncLLMEngine.from_engine_args(
             engine_args, usage_context=UsageContext.OPENAI_API_SERVER)
-        yield async_engine_client
+        try:
+            yield engine_client
+        finally:
+            engine_client.shutdown_background_loop()
         return
 
     # Otherwise, use the multiprocessing AsyncLLMEngine.
@@ -148,7 +164,6 @@ async def build_async_engine_client(
         # NOTE: Actually, this is not true yet. We still need to support
         # embedding models via RPC (see TODO above)
         rpc_client = AsyncEngineRPCClient(rpc_path)
-        async_engine_client = rpc_client  # type: ignore
 
         # Start RPCServer in separate process (holds the AsyncLLMEngine).
         context = multiprocessing.get_context("spawn")
@@ -174,7 +189,7 @@ async def build_async_engine_client(
                         yield None
                         return
 
-            yield async_engine_client
+            yield rpc_client  # type: ignore[misc]
         finally:
             # Ensure rpc server process was terminated
             rpc_server_process.terminate()

vllm/entrypoints/openai/rpc/client.py

@@ -7,6 +7,7 @@
 import cloudpickle
 import zmq
 import zmq.asyncio
+from zmq import Frame  # type: ignore[attr-defined]
 from zmq.asyncio import Socket
 
 from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
@@ -214,6 +215,7 @@ async def _send_get_data_rpc_request(self, request: RPCUtilityRequest,
 
             # Await the data from the Server.
             frame = await socket.recv(copy=False)
+            assert isinstance(frame, Frame)
             data = pickle.loads(frame.buffer)
 
         if isinstance(data, Exception):
@@ -247,6 +249,7 @@ async def do_rpc_call(socket: Socket, request: RPC_REQUEST_TYPE):
                                    f"{self._data_timeout} ms")
 
             frame = await socket.recv(copy=False)
+            assert isinstance(frame, Frame)
             return pickle.loads(frame.buffer)
 
         # Make a new socket connection.
@@ -395,6 +398,7 @@ async def generate(
                 # Stream back the results from the RPC Server.
                 while not finished:
                     message = await socket.recv(copy=False)
+                    assert isinstance(message, Frame)
                     request_output = pickle.loads(message.buffer)
 
                     if isinstance(request_output, Exception):