[Frontend] don't block event loop in tokenization (preprocess) in Ope…

…nAI compatible server (#10635)

Signed-off-by: Tomer Asida <[email protected]>

tests/entrypoints/openai/test_async_tokenization.py

@@ -0,0 +1,137 @@
+import asyncio
+import contextlib
+import random
+import time
+from typing import Callable
+
+import openai
+import pytest
+import pytest_asyncio
+import requests
+
+from tests.utils import RemoteOpenAIServer
+
+MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
+
+
+@pytest.fixture(scope="module")
+def server():  # noqa: F811
+    args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+        "--max-num-seqs",
+        "128",
+        "--load-format",
+        "dummy",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ids=["completion", "chat"],
+    argnames=["create_func_gen", "content_body"],
+    argvalues=[
+        (lambda x: x.completions.create, {
+            "prompt": " ".join(['A'] * 10_000)
+        }),
+        (lambda x: x.chat.completions.create, {
+            "messages": [{
+                "role": "user",
+                "content": " ".join(['A'] * 10_000)
+            }]
+        }),
+    ],
+)
+async def test_with_and_without_truncate(
+    server: RemoteOpenAIServer,
+    client: openai.AsyncOpenAI,
+    create_func_gen: Callable,
+    content_body: dict,
+):
+    create_func = create_func_gen(client)
+    body = {"model": MODEL_NAME, **content_body, "max_tokens": 10}
+
+    num_requests = 10
+    truncate_prompt_tokens = ([1000] * (num_requests // 2) + [None] *
+                              (num_requests - num_requests // 2))
+    random.shuffle(truncate_prompt_tokens)
+
+    bodies = [{
+        **body, "extra_body": {
+            'truncate_prompt_tokens': t
+        }
+    } for t in truncate_prompt_tokens]
+
+    async def get_status_code(**kwargs):
+        try:
+            await create_func(**kwargs)
+            return 200
+        except openai.APIStatusError as e:
+            return e.status_code
+
+    responses = await asyncio.gather(*[get_status_code(**b) for b in bodies])
+    assert 500 not in responses
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    ids=["single completion", "multiple completions", "chat"],
+    argnames=["create_func_gen", "content_body"],
+    argvalues=[
+        (lambda x: x.completions.create, {
+            "prompt": " ".join(['A'] * 300_000)
+        }),
+        (lambda x: x.completions.create, {
+            "prompt": [" ".join(['A'] * 300_000)] * 2
+        }),
+        (lambda x: x.chat.completions.create, {
+            "messages": [{
+                "role": "user",
+                "content": " ".join(['A'] * 300_000)
+            }]
+        }),
+    ],
+)
+async def test_healthcheck_response_time(
+    server: RemoteOpenAIServer,
+    client: openai.AsyncOpenAI,
+    create_func_gen: Callable,
+    content_body: dict,
+):
+    num_requests = 50
+
+    create_func = create_func_gen(client)
+    body = {"model": MODEL_NAME, **content_body, "max_tokens": 10}
+
+    def get_response_time(url):
+        start_time = time.monotonic()
+        res = requests.get(url)
+        end_time = time.monotonic()
+        assert res.status_code == 200
+        return end_time - start_time
+
+    no_load_response_time = get_response_time(server.url_for("health"))
+    tasks = [
+        asyncio.create_task(create_func(**body)) for _ in range(num_requests)
+    ]
+    await asyncio.sleep(1)  # give the tasks a chance to start running
+    load_response_time = get_response_time(server.url_for("health"))
+
+    with contextlib.suppress(openai.APIStatusError):
+        await asyncio.gather(*tasks)
+
+    assert load_response_time < 100 * no_load_response_time
+    assert load_response_time < 0.1

vllm/entrypoints/openai/serving_completion.py

@@ -101,7 +101,7 @@ async def create_completion(
 
             tokenizer = await self.engine_client.get_tokenizer(lora_request)
 
-            request_prompts, engine_prompts = self._preprocess_completion(
+            request_prompts, engine_prompts = await self._preprocess_completion(
                 request,
                 tokenizer,
                 request.prompt,

vllm/entrypoints/openai/serving_embedding.py

@@ -156,13 +156,14 @@ async def create_embedding(
                     add_special_tokens=request.add_special_tokens,
                 )
             else:
-                request_prompts, engine_prompts = self._preprocess_completion(
-                    request,
-                    tokenizer,
-                    request.input,
-                    truncate_prompt_tokens=truncate_prompt_tokens,
-                    add_special_tokens=request.add_special_tokens,
-                )
+                (request_prompts,
+                 engine_prompts) = await self._preprocess_completion(
+                     request,
+                     tokenizer,
+                     request.input,
+                     truncate_prompt_tokens=truncate_prompt_tokens,
+                     add_special_tokens=request.add_special_tokens,
+                 )
         except ValueError as e:
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))

vllm/entrypoints/openai/serving_engine.py

@@ -1,5 +1,6 @@
 import json
 import pathlib
+from concurrent.futures.thread import ThreadPoolExecutor
 from dataclasses import dataclass
 from http import HTTPStatus
 from typing import (Any, Callable, Dict, Iterable, Iterator, List, Mapping,
@@ -46,7 +47,7 @@
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
                           log_tracing_disabled_warning)
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import AtomicCounter, is_list_of
+from vllm.utils import AtomicCounter, is_list_of, make_async
 
 logger = init_logger(__name__)
 
@@ -140,6 +141,14 @@ def __init__(
         self.request_logger = request_logger
         self.return_tokens_as_token_ids = return_tokens_as_token_ids
 
+        self._tokenizer_executor = ThreadPoolExecutor(max_workers=1)
+
+        self._tokenize_prompt_input_async = make_async(
+            self._tokenize_prompt_input, executor=self._tokenizer_executor)
+        self._tokenize_prompt_input_or_inputs_async = make_async(
+            self._tokenize_prompt_input_or_inputs,
+            executor=self._tokenizer_executor)
+
     async def show_available_models(self) -> ModelList:
         """Show available models. Right now we only have one model."""
         model_cards = [
@@ -368,53 +377,49 @@ def _tokenize_prompt_input_or_inputs(
         input_or_inputs: Union[str, List[str], List[int], List[List[int]]],
         truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
         add_special_tokens: bool = True,
-    ) -> Iterator[TextTokensPrompt]:
+    ) -> List[TextTokensPrompt]:
         """
         Tokenize/detokenize depending on the input format.
 
         According to `OpenAI API <https://platform.openai.com/docs/api-reference/embeddings/create>`_
         , each input can be a string or array of tokens. Note that each request
         can pass one or more inputs.
         """
-        for prompt_input in parse_and_batch_prompt(input_or_inputs):
-            # Although our type checking is based on mypy,
-            # VSCode Pyright extension should still work properly
-            # "is True" is required for Pyright to perform type narrowing
-            # See: https://github.com/microsoft/pyright/issues/7672
-            if prompt_input["is_tokens"] is False:
-                yield self._normalize_prompt_text_to_input(
-                    request,
-                    tokenizer,
-                    prompt=prompt_input["content"],
-                    truncate_prompt_tokens=truncate_prompt_tokens,
-                    add_special_tokens=add_special_tokens,
-                )
-            else:
-                yield self._normalize_prompt_tokens_to_input(
-                    request,
-                    tokenizer,
-                    prompt_ids=prompt_input["content"],
-                    truncate_prompt_tokens=truncate_prompt_tokens,
-                )
+        # Although our type checking is based on mypy,
+        # VSCode Pyright extension should still work properly
+        # "is True" is required for Pyright to perform type narrowing
+        # See: https://github.com/microsoft/pyright/issues/7672
+        return [
+            self._normalize_prompt_text_to_input(
+                request,
+                tokenizer,
+                prompt=prompt_input["content"],
+                truncate_prompt_tokens=truncate_prompt_tokens,
+                add_special_tokens=add_special_tokens)
+            if prompt_input["is_tokens"] is False else
+            self._normalize_prompt_tokens_to_input(
+                request,
+                tokenizer,
+                prompt_ids=prompt_input["content"],
+                truncate_prompt_tokens=truncate_prompt_tokens)
+            for prompt_input in parse_and_batch_prompt(input_or_inputs)
+        ]
 
-    def _preprocess_completion(
+    async def _preprocess_completion(
         self,
         request: CompletionLikeRequest,
         tokenizer: AnyTokenizer,
         input_or_inputs: Union[str, List[str], List[int], List[List[int]]],
         truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
         add_special_tokens: bool = True,
-    ) -> Tuple[Sequence[TextTokensPrompt], List[TokensPrompt]]:
-        request_prompts = [
-            request_prompt
-            for request_prompt in self._tokenize_prompt_input_or_inputs(
-                request,
-                tokenizer,
-                input_or_inputs,
-                truncate_prompt_tokens=truncate_prompt_tokens,
-                add_special_tokens=add_special_tokens,
-            )
-        ]
+    ) -> Tuple[List[TextTokensPrompt], List[TokensPrompt]]:
+        request_prompts = await self._tokenize_prompt_input_or_inputs_async(
+            request,
+            tokenizer,
+            input_or_inputs,
+            truncate_prompt_tokens=truncate_prompt_tokens,
+            add_special_tokens=add_special_tokens,
+        )
 
         engine_prompts = [
             TokensPrompt(prompt_token_ids=request_prompt["prompt_token_ids"])
@@ -493,7 +498,7 @@ async def _preprocess_chat(
                 request=request)
 
         if isinstance(request_prompt, str):
-            prompt_inputs = self._tokenize_prompt_input(
+            prompt_inputs = await self._tokenize_prompt_input_async(
                 request,
                 tokenizer,
                 request_prompt,

vllm/entrypoints/openai/serving_score.py

@@ -15,7 +15,7 @@
 from vllm.logger import init_logger
 from vllm.outputs import EmbeddingRequestOutput
 from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
-from vllm.utils import merge_async_iterators, random_uuid
+from vllm.utils import make_async, merge_async_iterators, random_uuid
 
 logger = init_logger(__name__)
 
@@ -145,9 +145,11 @@ async def create_score(
                 tokenization_kwargs["truncation"] = True
                 tokenization_kwargs["max_length"] = truncate_prompt_tokens
 
-            prompt_inputs = tokenizer(text=q,
-                                      text_pair=t,
-                                      **tokenization_kwargs)
+            tokenize_async = make_async(tokenizer.__call__,
+                                        executor=self._tokenizer_executor)
+            prompt_inputs = await tokenize_async(text=q,
+                                                 text_pair=t,
+                                                 **tokenization_kwargs)
             engine_prompt = TokensPrompt(
                 prompt_token_ids=prompt_inputs["input_ids"],
                 token_type_ids=prompt_inputs.get("token_type_ids"))

vllm/entrypoints/openai/serving_tokenization.py

@@ -81,12 +81,13 @@ async def create_tokenize(
                     add_special_tokens=request.add_special_tokens,
                 )
             else:
-                request_prompts, engine_prompts = self._preprocess_completion(
-                    request,
-                    tokenizer,
-                    request.prompt,
-                    add_special_tokens=request.add_special_tokens,
-                )
+                (request_prompts,
+                 engine_prompts) = await self._preprocess_completion(
+                     request,
+                     tokenizer,
+                     request.prompt,
+                     add_special_tokens=request.add_special_tokens,
+                 )
         except ValueError as e:
             logger.exception("Error in preprocessing prompt inputs")
             return self.create_error_response(str(e))
@@ -134,7 +135,7 @@ async def create_detokenize(
         # Silently ignore prompt adapter since it does not affect tokenization
         # (Unlike in Embeddings API where an error is raised)
 
-        prompt_input = self._tokenize_prompt_input(
+        prompt_input = await self._tokenize_prompt_input_async(
             request,
             tokenizer,
             request.tokens,

vllm/utils.py

@@ -1,5 +1,6 @@
 import argparse
 import asyncio
+import concurrent
 import contextlib
 import datetime
 import enum
@@ -351,7 +352,10 @@ def in_wsl() -> bool:
     return "microsoft" in " ".join(uname()).lower()
 
 
-def make_async(func: Callable[P, T]) -> Callable[P, Awaitable[T]]:
+def make_async(
+    func: Callable[P, T],
+    executor: Optional[concurrent.futures.Executor] = None
+) -> Callable[P, Awaitable[T]]:
     """Take a blocking function, and run it on in an executor thread.
 
     This function prevents the blocking function from blocking the
@@ -362,7 +366,7 @@ def make_async(func: Callable[P, T]) -> Callable[P, Awaitable[T]]:
     def _async_wrapper(*args: P.args, **kwargs: P.kwargs) -> asyncio.Future:
         loop = asyncio.get_event_loop()
         p_func = partial(func, *args, **kwargs)
-        return loop.run_in_executor(executor=None, func=p_func)
+        return loop.run_in_executor(executor=executor, func=p_func)
 
     return _async_wrapper