[Chore] Separate out vllm.utils.async_utils (#26913)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/lora/test_add_lora.py

@@ -12,7 +12,7 @@ from vllm.entrypoints.openai.api_server import (
 from vllm.inputs import TextPrompt
 from vllm.lora.request import LoRARequest
 from vllm.sampling_params import SamplingParams
-from vllm.utils import merge_async_iterators
+from vllm.utils.async_utils import merge_async_iterators
 
 MODEL_PATH = "zai-org/chatglm3-6b"
 LORA_RANK = 64

tests/utils_/test_async_utils.py

@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import asyncio
+from collections.abc import AsyncIterator
+
+import pytest
+
+from vllm.utils.async_utils import merge_async_iterators
+
+
+async def _mock_async_iterator(idx: int):
+    try:
+        while True:
+            yield f"item from iterator {idx}"
+            await asyncio.sleep(0.1)
+    except asyncio.CancelledError:
+        print(f"iterator {idx} cancelled")
+
+
+@pytest.mark.asyncio
+async def test_merge_async_iterators():
+    iterators = [_mock_async_iterator(i) for i in range(3)]
+    merged_iterator = merge_async_iterators(*iterators)
+
+    async def stream_output(generator: AsyncIterator[tuple[int, str]]):
+        async for idx, output in generator:
+            print(f"idx: {idx}, output: {output}")
+
+    task = asyncio.create_task(stream_output(merged_iterator))
+    await asyncio.sleep(0.5)
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    for iterator in iterators:
+        try:
+            await asyncio.wait_for(anext(iterator), 1)
+        except StopAsyncIteration:
+            # All iterators should be cancelled and print this message.
+            print("Iterator was cancelled normally")
+        except (Exception, asyncio.CancelledError) as e:
+            raise AssertionError() from e

tests/utils_/test_utils.py

@@ -2,14 +2,12 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # ruff: noqa
 
-import asyncio
 import hashlib
 import json
 import os
 import pickle
 import socket
 import tempfile
-from collections.abc import AsyncIterator
 from pathlib import Path
 from unittest.mock import patch
 
@@ -37,7 +35,6 @@ from vllm.utils import (
     make_zmq_path,
     make_zmq_socket,
     memory_profiling,
-    merge_async_iterators,
     sha256,
     split_host_port,
     split_zmq_path,
@@ -48,39 +45,6 @@ from vllm.utils import (
 from ..utils import create_new_process_for_each_test
 
 
-@pytest.mark.asyncio
-async def test_merge_async_iterators():
-    async def mock_async_iterator(idx: int):
-        try:
-            while True:
-                yield f"item from iterator {idx}"
-                await asyncio.sleep(0.1)
-        except asyncio.CancelledError:
-            print(f"iterator {idx} cancelled")
-
-    iterators = [mock_async_iterator(i) for i in range(3)]
-    merged_iterator = merge_async_iterators(*iterators)
-
-    async def stream_output(generator: AsyncIterator[tuple[int, str]]):
-        async for idx, output in generator:
-            print(f"idx: {idx}, output: {output}")
-
-    task = asyncio.create_task(stream_output(merged_iterator))
-    await asyncio.sleep(0.5)
-    task.cancel()
-    with pytest.raises(asyncio.CancelledError):
-        await task
-
-    for iterator in iterators:
-        try:
-            await asyncio.wait_for(anext(iterator), 1)
-        except StopAsyncIteration:
-            # All iterators should be cancelled and print this message.
-            print("Iterator was cancelled normally")
-        except (Exception, asyncio.CancelledError) as e:
-            raise AssertionError() from e
-
-
 def test_get_open_port(monkeypatch: pytest.MonkeyPatch):
     with monkeypatch.context() as m:
         m.setenv("VLLM_PORT", "5678")

vllm/benchmarks/throughput.py

@@ -34,7 +34,7 @@ from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.lora.request import LoRARequest
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
-from vllm.utils import merge_async_iterators
+from vllm.utils.async_utils import merge_async_iterators
 
 
 def run_vllm(

vllm/entrypoints/openai/serving_completion.py

@@ -34,7 +34,8 @@ from vllm.logprobs import Logprob
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import as_list, merge_async_iterators
+from vllm.utils import as_list
+from vllm.utils.async_utils import merge_async_iterators
 
 logger = init_logger(__name__)
 

vllm/entrypoints/openai/serving_embedding.py

@@ -40,6 +40,7 @@ from vllm.outputs import (
 )
 from vllm.pooling_params import PoolingParams
 from vllm.utils import chunk_list
+from vllm.utils.async_utils import merge_async_iterators
 
 logger = init_logger(__name__)
 
@@ -387,8 +388,6 @@ class EmbeddingMixin(OpenAIServing):
                 )
                 generators.append(generator)
 
-            from vllm.utils import merge_async_iterators
-
             ctx.result_generator = merge_async_iterators(*generators)
 
             return None

vllm/entrypoints/openai/serving_engine.py

@@ -90,14 +90,13 @@ from vllm.tracing import (
     log_tracing_disabled_warning,
 )
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import (
+from vllm.utils import is_list_of, random_uuid
+from vllm.utils.async_utils import (
     AsyncMicrobatchTokenizer,
     collect_from_async_generator,
-    is_list_of,
+    make_async,
     merge_async_iterators,
-    random_uuid,
 )
-from vllm.utils.func import make_async
 from vllm.v1.engine import EngineCoreRequest
 
 logger = init_logger(__name__)

vllm/entrypoints/openai/serving_pooling.py

@@ -36,7 +36,7 @@ from vllm.entrypoints.utils import _validate_truncation_size
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
 from vllm.tasks import SupportedTask
-from vllm.utils import merge_async_iterators
+from vllm.utils.async_utils import merge_async_iterators
 
 logger = init_logger(__name__)
 

vllm/entrypoints/openai/serving_score.py

@@ -37,8 +37,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import merge_async_iterators
-from vllm.utils.func import make_async
+from vllm.utils.async_utils import make_async, merge_async_iterators
 
 logger = init_logger(__name__)
 

vllm/entrypoints/renderer.py

@@ -17,7 +17,7 @@ from vllm.inputs.data import TextPrompt as EngineTextPrompt
 from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
 from vllm.inputs.parse import get_prompt_components, parse_raw_prompts
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import AsyncMicrobatchTokenizer
+from vllm.utils.async_utils import AsyncMicrobatchTokenizer
 
 
 @dataclass(frozen=True)

vllm/executor/executor_base.py

@@ -17,7 +17,7 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.sequence import ExecuteModelRequest
 from vllm.tasks import SupportedTask
-from vllm.utils.func import make_async
+from vllm.utils.async_utils import make_async
 from vllm.v1.outputs import SamplerOutput
 from vllm.v1.worker.worker_base import WorkerBase
 

vllm/executor/ray_distributed_executor.py

@@ -20,12 +20,11 @@ from vllm.platforms import current_platform
 from vllm.ray.ray_env import get_env_vars_to_copy
 from vllm.sequence import ExecuteModelRequest
 from vllm.utils import (
-    _run_task_with_lock,
     get_distributed_init_method,
     get_ip,
     get_open_port,
 )
-from vllm.utils.func import make_async
+from vllm.utils.async_utils import make_async
 from vllm.v1.outputs import SamplerOutput
 
 if ray is not None:
@@ -748,3 +747,9 @@ class RayDistributedExecutor(DistributedExecutorBase):
         # Assume that the Ray workers are healthy.
         # TODO: check the health of the Ray workers
         return
+
+
+async def _run_task_with_lock(task: Callable, lock: asyncio.Lock, *args, **kwargs):
+    """Utility function to run async task in a lock"""
+    async with lock:
+        return await task(*args, **kwargs)

vllm/utils/__init__.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import asyncio
 import contextlib
 import datetime
 import enum
@@ -38,10 +37,8 @@ from argparse import (
     RawDescriptionHelpFormatter,
     _ArgumentGroup,
 )
-from asyncio import FIRST_COMPLETED, AbstractEventLoop, Task
 from collections import UserDict, defaultdict
 from collections.abc import (
-    AsyncGenerator,
     Callable,
     Collection,
     Generator,
@@ -51,7 +48,6 @@ from collections.abc import (
     Mapping,
     Sequence,
 )
-from concurrent.futures import ThreadPoolExecutor
 from concurrent.futures.process import ProcessPoolExecutor
 from dataclasses import dataclass, field
 from functools import cache, lru_cache, partial, wraps
@@ -82,7 +78,6 @@ import zmq.asyncio
 from packaging import version
 from packaging.version import Version
 from torch.library import Library
-from transformers.tokenization_utils_base import BatchEncoding
 from typing_extensions import Never, TypeIs, assert_never
 
 import vllm.envs as envs
@@ -223,278 +218,12 @@ def random_uuid() -> str:
     return str(uuid.uuid4().hex)
 
 
-class AsyncMicrobatchTokenizer:
-    """Asynchronous tokenizer with micro-batching.
-
-    Pulls pending encode/decode requests from a queue and batches them
-    up to reduce overhead. A single-thread ThreadPoolExecutor is used
-    so the event loop stays responsive.
-    """
-
-    def __init__(
-        self,
-        tokenizer,
-        max_batch_size: int = 32,
-        batch_wait_timeout_s: float = 0.002,
-    ) -> None:
-        self.tokenizer = tokenizer
-        self.max_batch_size = max_batch_size
-        self.batch_wait_timeout_s = batch_wait_timeout_s
-
-        self._loop = asyncio.get_running_loop()
-        self._queues: dict[
-            tuple,
-            asyncio.Queue[
-                tuple[str, dict, asyncio.Future] | tuple[list[int], asyncio.Future]
-            ],
-        ] = {}
-        self._batcher_tasks: list[asyncio.Task] = []
-
-        # Single-thread executor for blocking tokenizer calls.
-        self._executor = ThreadPoolExecutor(max_workers=1)
-
-    # === Public async API ===
-    async def __call__(self, prompt, **kwargs):
-        result_future: asyncio.Future = self._loop.create_future()
-        key = self._queue_key("encode", kwargs)
-        queue = self._get_queue(self._loop, key)
-        await queue.put((prompt, kwargs, result_future))
-        return await result_future
-
-    async def decode(self, token_ids, **kwargs):
-        result_future: asyncio.Future = self._loop.create_future()
-        key = self._queue_key("decode", kwargs)
-        queue = self._get_queue(self._loop, key)
-        await queue.put((token_ids, result_future))
-        return await result_future
-
-    # === Internal helpers ===
-    def _get_queue(
-        self, loop: asyncio.AbstractEventLoop, key: tuple
-    ) -> asyncio.Queue[
-        tuple[str, dict, asyncio.Future] | tuple[list[int], asyncio.Future]
-    ]:
-        """Get the request queue for the given operation key, creating a new
-        queue and batcher task if needed."""
-        queue = self._queues.get(key)
-        if queue is None:
-            self._queues[key] = queue = asyncio.Queue()
-            if key[0] == "encode":
-                can_batch = key[1] != "other"
-                coro = self._batch_encode_loop(queue, can_batch)
-            else:
-                assert key[0] == "decode", f"Unknown operation type: {key[0]}."
-                coro = self._batch_decode_loop(queue)
-            self._batcher_tasks.append(loop.create_task(coro))
-        return queue
-
-    async def _batch_encode_loop(self, queue: asyncio.Queue, can_batch: bool):
-        """Batch incoming encode requests for efficiency."""
-        while True:
-            prompt, kwargs, result_future = await queue.get()
-            prompts = [prompt]
-            kwargs_list = [kwargs]
-            result_futures = [result_future]
-            deadline = self._loop.time() + self.batch_wait_timeout_s
-
-            while len(prompts) < self.max_batch_size:
-                timeout = deadline - self._loop.time()
-                if timeout <= 0:
-                    break
-                try:
-                    prompt, kwargs, result_future = await asyncio.wait_for(
-                        queue.get(), timeout
-                    )
-                    prompts.append(prompt)
-                    result_futures.append(result_future)
-                    if not can_batch:
-                        kwargs_list.append(kwargs)
-                except asyncio.TimeoutError:
-                    break
-
-            try:
-                # If every request uses identical kwargs we can run a single
-                # batched tokenizer call for a big speed-up.
-                if can_batch and len(prompts) > 1:
-                    batch_encode_fn = partial(self.tokenizer, prompts, **kwargs)
-                    results = await self._loop.run_in_executor(
-                        self._executor, batch_encode_fn
-                    )
-
-                    for i, fut in enumerate(result_futures):
-                        if not fut.done():
-                            data = {k: v[i] for k, v in results.items()}
-                            fut.set_result(BatchEncoding(data))
-                else:
-                    encode_fn = lambda prompts=prompts, kwargs=kwargs_list: [
-                        self.tokenizer(p, **kw) for p, kw in zip(prompts, kwargs)
-                    ]
-                    results = await self._loop.run_in_executor(
-                        self._executor, encode_fn
-                    )
-
-                    for fut, res in zip(result_futures, results):
-                        if not fut.done():
-                            fut.set_result(res)
-            except Exception as e:
-                for fut in result_futures:
-                    if not fut.done():
-                        fut.set_exception(e)
-
-    async def _batch_decode_loop(self, queue: asyncio.Queue):
-        """Batch incoming decode requests for efficiency."""
-        while True:
-            token_ids, result_future = await queue.get()
-            token_ids_list = [token_ids]
-            result_futures = [result_future]
-            deadline = self._loop.time() + self.batch_wait_timeout_s
-
-            while len(token_ids_list) < self.max_batch_size:
-                timeout = deadline - self._loop.time()
-                if timeout <= 0:
-                    break
-                try:
-                    token_ids, result_future = await asyncio.wait_for(
-                        queue.get(), timeout
-                    )
-                    token_ids_list.append(token_ids)
-                    result_futures.append(result_future)
-                except asyncio.TimeoutError:
-                    break
-
-            try:
-                # Perform a single batched decode call for all requests
-                results = await self._loop.run_in_executor(
-                    self._executor, self.tokenizer.batch_decode, token_ids_list
-                )
-                for fut, res in zip(result_futures, results):
-                    if not fut.done():
-                        fut.set_result(res)
-            except Exception as e:
-                for fut in result_futures:
-                    if not fut.done():
-                        fut.set_exception(e)
-
-    def _queue_key(self, op: str, kwargs: dict) -> tuple:
-        """
-        Return a normalized key describing operation + kwargs.
-
-        - `add_special_tokens`: {True/False}
-        - `truncation`: {True/False}
-          - If `truncation` is False (`max_length` is None),
-            returns a key for a can_batch queue.
-          - If `truncation` is True and `max_length` is None or equals
-            `tokenizer.model_max_length`, returns a key for a can_batch queue.
-          - Otherwise, returns a key for a cannot_batch queue.
-
-        Examples:
-          - Decode: ("decode",)
-          - Encode typical:
-            ("encode", add_special_tokens, bool_truncation, max_length_label)
-          - Fallback: ("encode", "other")
-        """
-
-        if op == "decode":
-            return ("decode",)
-
-        add_special_tokens = kwargs.get("add_special_tokens", True)
-        truncation = kwargs.get("truncation", False)
-        max_length = kwargs.get("max_length")
-
-        if not truncation:
-            return "encode", add_special_tokens, False, None
-
-        model_max = getattr(self.tokenizer, "model_max_length", None)
-        if max_length is None or (model_max is not None and max_length == model_max):
-            return "encode", add_special_tokens, True, "model_max"
-
-        return "encode", "other"
-
-    def __del__(self):
-        if (
-            (tasks := getattr(self, "_batcher_tasks", None))
-            and (loop := getattr(self, "_loop", None))
-            and not loop.is_closed()
-        ):
-
-            def cancel_tasks():
-                for task in tasks:
-                    task.cancel()
-
-            loop.call_soon_threadsafe(cancel_tasks)
-
-
-def cancel_task_threadsafe(task: Task):
-    if task and not task.done():
-        run_in_loop(task.get_loop(), task.cancel)
-
-
 def close_sockets(sockets: Sequence[zmq.Socket | zmq.asyncio.Socket]):
     for sock in sockets:
         if sock is not None:
             sock.close(linger=0)
 
 
-def run_in_loop(loop: AbstractEventLoop, function: Callable, *args):
-    if in_loop(loop):
-        function(*args)
-    elif not loop.is_closed():
-        loop.call_soon_threadsafe(function, *args)
-
-
-def in_loop(event_loop: AbstractEventLoop) -> bool:
-    try:
-        return asyncio.get_running_loop() == event_loop
-    except RuntimeError:
-        return False
-
-
-async def merge_async_iterators(
-    *iterators: AsyncGenerator[T, None],
-) -> AsyncGenerator[tuple[int, T], None]:
-    """Merge multiple asynchronous iterators into a single iterator.
-
-    This method handle the case where some iterators finish before others.
-    When it yields, it yields a tuple (i, item) where i is the index of the
-    iterator that yields the item.
-    """
-    if len(iterators) == 1:
-        # Fast-path single iterator case.
-        async for item in iterators[0]:
-            yield 0, item
-        return
-
-    loop = asyncio.get_running_loop()
-
-    awaits = {loop.create_task(anext(it)): (i, it) for i, it in enumerate(iterators)}
-    try:
-        while awaits:
-            done, _ = await asyncio.wait(awaits.keys(), return_when=FIRST_COMPLETED)
-            for d in done:
-                pair = awaits.pop(d)
-                try:
-                    item = await d
-                    i, it = pair
-                    awaits[loop.create_task(anext(it))] = pair
-                    yield i, item
-                except StopAsyncIteration:
-                    pass
-    finally:
-        # Cancel any remaining iterators
-        for f, (_, it) in awaits.items():
-            with contextlib.suppress(BaseException):
-                f.cancel()
-                await it.aclose()
-
-
-async def collect_from_async_generator(iterator: AsyncGenerator[T, None]) -> list[T]:
-    """Collect all items from an async generator into a list."""
-    items = []
-    async for item in iterator:
-        items.append(item)
-    return items
-
-
 def get_ip() -> str:
     host_ip = envs.VLLM_HOST_IP
     if "HOST_IP" in os.environ and "VLLM_HOST_IP" not in os.environ:
@@ -1803,12 +1532,6 @@ class FlexibleArgumentParser(ArgumentParser):
         return processed_args
 
 
-async def _run_task_with_lock(task: Callable, lock: asyncio.Lock, *args, **kwargs):
-    """Utility function to run async task in a lock"""
-    async with lock:
-        return await task(*args, **kwargs)
-
-
 # Using dynamo with vLLM doesn't really work well with PyTorch versions < 2.4.0.
 # In particular, the FakeScalarType is not supported for earlier versions of
 # PyTorch which breaks dynamo for any ops registered using ScalarType.

vllm/utils/async_utils.py

@@ -0,0 +1,299 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Contains helpers related to asynchronous code."""
+
+import asyncio
+import contextlib
+from asyncio import FIRST_COMPLETED, AbstractEventLoop, Future, Task
+from collections.abc import AsyncGenerator, Awaitable, Callable
+from concurrent.futures import Executor, ThreadPoolExecutor
+from functools import partial
+from typing import TypeVar
+
+from transformers.tokenization_utils_base import BatchEncoding
+from typing_extensions import ParamSpec
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+
+class AsyncMicrobatchTokenizer:
+    """Asynchronous tokenizer with micro-batching.
+
+    Pulls pending encode/decode requests from a queue and batches them
+    up to reduce overhead. A single-thread ThreadPoolExecutor is used
+    so the event loop stays responsive.
+    """
+
+    def __init__(
+        self,
+        tokenizer,
+        max_batch_size: int = 32,
+        batch_wait_timeout_s: float = 0.002,
+    ) -> None:
+        self.tokenizer = tokenizer
+        self.max_batch_size = max_batch_size
+        self.batch_wait_timeout_s = batch_wait_timeout_s
+
+        self._loop = asyncio.get_running_loop()
+        self._queues: dict[
+            tuple,
+            asyncio.Queue[tuple[str, dict, Future] | tuple[list[int], Future]],
+        ] = {}
+        self._batcher_tasks: list[Task] = []
+
+        # Single-thread executor for blocking tokenizer calls.
+        self._executor = ThreadPoolExecutor(max_workers=1)
+
+    # === Public async API ===
+    async def __call__(self, prompt, **kwargs):
+        result_future: Future = self._loop.create_future()
+        key = self._queue_key("encode", kwargs)
+        queue = self._get_queue(self._loop, key)
+        await queue.put((prompt, kwargs, result_future))
+        return await result_future
+
+    async def decode(self, token_ids, **kwargs):
+        result_future: Future = self._loop.create_future()
+        key = self._queue_key("decode", kwargs)
+        queue = self._get_queue(self._loop, key)
+        await queue.put((token_ids, result_future))
+        return await result_future
+
+    # === Internal helpers ===
+    def _get_queue(
+        self, loop: asyncio.AbstractEventLoop, key: tuple
+    ) -> asyncio.Queue[tuple[str, dict, Future] | tuple[list[int], Future]]:
+        """Get the request queue for the given operation key, creating a new
+        queue and batcher task if needed."""
+        queue = self._queues.get(key)
+        if queue is None:
+            self._queues[key] = queue = asyncio.Queue()
+            if key[0] == "encode":
+                can_batch = key[1] != "other"
+                coro = self._batch_encode_loop(queue, can_batch)
+            else:
+                assert key[0] == "decode", f"Unknown operation type: {key[0]}."
+                coro = self._batch_decode_loop(queue)
+            self._batcher_tasks.append(loop.create_task(coro))
+        return queue
+
+    async def _batch_encode_loop(self, queue: asyncio.Queue, can_batch: bool):
+        """Batch incoming encode requests for efficiency."""
+        while True:
+            prompt, kwargs, result_future = await queue.get()
+            prompts = [prompt]
+            kwargs_list = [kwargs]
+            result_futures = [result_future]
+            deadline = self._loop.time() + self.batch_wait_timeout_s
+
+            while len(prompts) < self.max_batch_size:
+                timeout = deadline - self._loop.time()
+                if timeout <= 0:
+                    break
+                try:
+                    prompt, kwargs, result_future = await asyncio.wait_for(
+                        queue.get(), timeout
+                    )
+                    prompts.append(prompt)
+                    result_futures.append(result_future)
+                    if not can_batch:
+                        kwargs_list.append(kwargs)
+                except asyncio.TimeoutError:
+                    break
+
+            try:
+                # If every request uses identical kwargs we can run a single
+                # batched tokenizer call for a big speed-up.
+                if can_batch and len(prompts) > 1:
+                    batch_encode_fn = partial(self.tokenizer, prompts, **kwargs)
+                    results = await self._loop.run_in_executor(
+                        self._executor, batch_encode_fn
+                    )
+
+                    for i, fut in enumerate(result_futures):
+                        if not fut.done():
+                            data = {k: v[i] for k, v in results.items()}
+                            fut.set_result(BatchEncoding(data))
+                else:
+                    encode_fn = lambda prompts=prompts, kwargs=kwargs_list: [
+                        self.tokenizer(p, **kw) for p, kw in zip(prompts, kwargs)
+                    ]
+                    results = await self._loop.run_in_executor(
+                        self._executor, encode_fn
+                    )
+
+                    for fut, res in zip(result_futures, results):
+                        if not fut.done():
+                            fut.set_result(res)
+            except Exception as e:
+                for fut in result_futures:
+                    if not fut.done():
+                        fut.set_exception(e)
+
+    async def _batch_decode_loop(self, queue: asyncio.Queue):
+        """Batch incoming decode requests for efficiency."""
+        while True:
+            token_ids, result_future = await queue.get()
+            token_ids_list = [token_ids]
+            result_futures = [result_future]
+            deadline = self._loop.time() + self.batch_wait_timeout_s
+
+            while len(token_ids_list) < self.max_batch_size:
+                timeout = deadline - self._loop.time()
+                if timeout <= 0:
+                    break
+                try:
+                    token_ids, result_future = await asyncio.wait_for(
+                        queue.get(), timeout
+                    )
+                    token_ids_list.append(token_ids)
+                    result_futures.append(result_future)
+                except asyncio.TimeoutError:
+                    break
+
+            try:
+                # Perform a single batched decode call for all requests
+                results = await self._loop.run_in_executor(
+                    self._executor, self.tokenizer.batch_decode, token_ids_list
+                )
+                for fut, res in zip(result_futures, results):
+                    if not fut.done():
+                        fut.set_result(res)
+            except Exception as e:
+                for fut in result_futures:
+                    if not fut.done():
+                        fut.set_exception(e)
+
+    def _queue_key(self, op: str, kwargs: dict) -> tuple:
+        """
+        Return a normalized key describing operation + kwargs.
+
+        - `add_special_tokens`: {True/False}
+        - `truncation`: {True/False}
+          - If `truncation` is False (`max_length` is None),
+            returns a key for a can_batch queue.
+          - If `truncation` is True and `max_length` is None or equals
+            `tokenizer.model_max_length`, returns a key for a can_batch queue.
+          - Otherwise, returns a key for a cannot_batch queue.
+
+        Examples:
+          - Decode: ("decode",)
+          - Encode typical:
+            ("encode", add_special_tokens, bool_truncation, max_length_label)
+          - Fallback: ("encode", "other")
+        """
+
+        if op == "decode":
+            return ("decode",)
+
+        add_special_tokens = kwargs.get("add_special_tokens", True)
+        truncation = kwargs.get("truncation", False)
+        max_length = kwargs.get("max_length")
+
+        if not truncation:
+            return "encode", add_special_tokens, False, None
+
+        model_max = getattr(self.tokenizer, "model_max_length", None)
+        if max_length is None or (model_max is not None and max_length == model_max):
+            return "encode", add_special_tokens, True, "model_max"
+
+        return "encode", "other"
+
+    def __del__(self):
+        if (
+            (tasks := getattr(self, "_batcher_tasks", None))
+            and (loop := getattr(self, "_loop", None))
+            and not loop.is_closed()
+        ):
+
+            def cancel_tasks():
+                for task in tasks:
+                    task.cancel()
+
+            loop.call_soon_threadsafe(cancel_tasks)
+
+
+def cancel_task_threadsafe(task: Task):
+    if task and not task.done():
+        run_in_loop(task.get_loop(), task.cancel)
+
+
+def make_async(
+    func: Callable[P, T],
+    executor: Executor | None = 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
+    asyncio event loop.
+    The code in this function needs to be thread safe.
+    """
+
+    def _async_wrapper(*args: P.args, **kwargs: P.kwargs) -> Future[T]:
+        loop = asyncio.get_event_loop()
+        p_func = partial(func, *args, **kwargs)
+        return loop.run_in_executor(executor=executor, func=p_func)
+
+    return _async_wrapper
+
+
+def run_in_loop(loop: AbstractEventLoop, function: Callable, *args):
+    if in_loop(loop):
+        function(*args)
+    elif not loop.is_closed():
+        loop.call_soon_threadsafe(function, *args)
+
+
+def in_loop(event_loop: AbstractEventLoop) -> bool:
+    try:
+        return asyncio.get_running_loop() == event_loop
+    except RuntimeError:
+        return False
+
+
+async def merge_async_iterators(
+    *iterators: AsyncGenerator[T, None],
+) -> AsyncGenerator[tuple[int, T], None]:
+    """Merge multiple asynchronous iterators into a single iterator.
+
+    This method handle the case where some iterators finish before others.
+    When it yields, it yields a tuple (i, item) where i is the index of the
+    iterator that yields the item.
+    """
+    if len(iterators) == 1:
+        # Fast-path single iterator case.
+        async for item in iterators[0]:
+            yield 0, item
+        return
+
+    loop = asyncio.get_running_loop()
+
+    awaits = {loop.create_task(anext(it)): (i, it) for i, it in enumerate(iterators)}
+    try:
+        while awaits:
+            done, _ = await asyncio.wait(awaits.keys(), return_when=FIRST_COMPLETED)
+            for d in done:
+                pair = awaits.pop(d)
+                try:
+                    item = await d
+                    i, it = pair
+                    awaits[loop.create_task(anext(it))] = pair
+                    yield i, item
+                except StopAsyncIteration:
+                    pass
+    finally:
+        # Cancel any remaining iterators
+        for f, (_, it) in awaits.items():
+            with contextlib.suppress(BaseException):
+                f.cancel()
+                await it.aclose()
+
+
+async def collect_from_async_generator(iterator: AsyncGenerator[T, None]) -> list[T]:
+    """Collect all items from an async generator into a list."""
+    items = []
+    async for item in iterator:
+        items.append(item)
+    return items

vllm/utils/func.py

@@ -6,12 +6,10 @@ Contains helpers that are applied to functions.
 This is similar in concept to the `functools` module.
 """
 
-import asyncio
-import concurrent.futures
 import inspect
 import threading
 import warnings
-from collections.abc import Awaitable, Callable, Mapping
+from collections.abc import Callable, Mapping
 from functools import lru_cache, partial, wraps
 from typing import Any, TypeVar
 
@@ -32,26 +30,6 @@ def identity(value: T, **kwargs) -> T:
     return value
 
 
-def make_async(
-    func: Callable[P, T],
-    executor: concurrent.futures.Executor | None = 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
-    asyncio event loop.
-    The code in this function needs to be thread safe.
-    """
-
-    def _async_wrapper(*args: P.args, **kwargs: P.kwargs) -> asyncio.Future[T]:
-        loop = asyncio.get_event_loop()
-        p_func = partial(func, *args, **kwargs)
-        return loop.run_in_executor(executor=executor, func=p_func)
-
-    return _async_wrapper
-
-
 def run_once(f: Callable[P, None]) -> Callable[P, None]:
     def wrapper(*args: P.args, **kwargs: P.kwargs) -> None:
         if wrapper.has_run:  # type: ignore[attr-defined]

vllm/v1/engine/async_llm.py

@@ -29,7 +29,8 @@ from vllm.tracing import init_tracer
 from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
 from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import Device, as_list, cancel_task_threadsafe, cdiv
+from vllm.utils import Device, as_list, cdiv
+from vllm.utils.async_utils import cancel_task_threadsafe
 from vllm.utils.func import deprecate_kwargs
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.core_client import EngineCoreClient

vllm/v1/engine/core_client.py

@@ -27,9 +27,9 @@ from vllm.utils import (
     close_sockets,
     get_open_port,
     get_open_zmq_inproc_path,
-    in_loop,
     make_zmq_socket,
 )
+from vllm.utils.async_utils import in_loop
 from vllm.v1.engine import (
     EngineCoreOutputs,
     EngineCoreRequest,