[Frontend] Make tokenizer batch wait timeout configurable

[Jraaay]

Purpose

AsyncMicrobatchTokenizer currently uses a fixed batch_wait_timeout_s=0.002.
This is useful for microbatching, but it also adds about 2ms of frontend latency
for low-concurrency, small-model, single-token-output workloads.

This PR makes the tokenizer batch wait timeout configurable through
--tokenizer-batch-wait-timeout-s, while preserving the existing default value
of 0.002.

Users who prioritize low latency over tokenizer microbatching can now set:

--tokenizer-batch-wait-timeout-s 0

Related frontend/tokenization context:

• [Feature] microbatch tokenization #19334 introduced async tokenizer microbatching.
• [Frontend] Offload blocking preprocessing & postprocessing ops to thread pool for pooling entrypoints. #39763 discussed frontend preprocessing overhead and the tokenizer wait impact.
• [Frontend] Speed up online server preprocess by using sync tokenizer. #27407 is related frontend latency investigation context.

Duplicate check:

• Searched open PRs for batch_wait_timeout_s.
• Searched open PRs for AsyncMicrobatchTokenizer.
• Searched open PRs for tokenizer batch wait timeout.
• Found no open PR that adds the same configurable tokenizer batch wait timeout.

AI assistance was used to help inspect the code path, implement the change, and
prepare test/benchmark commands. I reviewed the changed lines and validated the
behavior.

Test Plan

Unit tests:

.venv/bin/python -m pytest \
  tests/engine/test_arg_utils.py::test_tokenizer_batch_wait_timeout_cli_arg \
  tests/renderers/test_completions.py::TestRenderPrompt::test_async_tokenizer_uses_configured_batch_wait_timeout \
  -v

OpenAI endpoint regression tests:

.venv/bin/python -m pytest \
  tests/entrypoints/openai/completion/test_completion_error.py \
  tests/entrypoints/openai/chat_completion/test_chat_error.py \
  tests/entrypoints/serve/tokenize/test_serving_tokenization.py \
  -v

Lint:

.venv/bin/pre-commit run ruff-check --files \
  vllm/config/model.py \
  vllm/engine/arg_utils.py \
  vllm/renderers/base.py \
  tests/engine/test_arg_utils.py \
  tests/renderers/test_completions.py \
  tests/entrypoints/openai/completion/test_completion_error.py \
  tests/entrypoints/openai/completion/test_lora_resolvers.py \
  tests/entrypoints/openai/chat_completion/test_serving_chat.py

Online smoke test:

CUDA_VISIBLE_DEVICES=1 .venv/bin/python -m vllm.entrypoints.cli.main serve \
  Qwen/Qwen3-0.6B \
  --served-model-name test-model \
  --max-model-len 1024 \
  --host 127.0.0.1 \
  --port 43395 \
  --tokenizer-batch-wait-timeout-s 0 \
  --gpu-memory-utilization 0.80

Then verified both endpoints:

• /v1/completions
• /v1/chat/completions

Test Result

Unit tests:

2 passed

OpenAI endpoint regression tests:

23 passed

Lint:

ruff-check: Passed

Online smoke test:

Server started successfully with tokenizer_batch_wait_timeout_s: 0.0
/v1/completions returned HTTP 200
/v1/chat/completions returned HTTP 200

Benchmark setup:

• Model: Qwen/Qwen3-0.6B
• Input length: 16 tokens
• Output length: 1 token
• Requests: 200
• Warmups: 20
• Request rate: inf
• Compared default 0.002s vs configured 0s

E2E TTFT results:

Endpoint	Max concurrency	0.002s mean TTFT	0s mean TTFT	Delta
/v1/completions	1	8.66 ms	6.45 ms	-2.20 ms
/v1/chat/completions	1	8.84 ms	6.51 ms	-2.33 ms
/v1/completions	2	11.87 ms	10.13 ms	-1.73 ms
/v1/chat/completions	2	13.18 ms	11.29 ms	-1.88 ms
/v1/completions	4	14.03 ms	10.45 ms	-3.58 ms
/v1/chat/completions	4	15.73 ms	11.37 ms	-4.36 ms

Render-only latency results:

Endpoint	Concurrency	0.002s mean	0s mean	Delta
/v1/completions/render	1	3.01 ms	0.90 ms	-2.12 ms
/v1/chat/completions/render	1	3.25 ms	1.00 ms	-2.25 ms
/v1/completions/render	2	3.50 ms	1.35 ms	-2.16 ms
/v1/chat/completions/render	2	4.06 ms	1.66 ms	-2.40 ms
/v1/completions/render	4	4.20 ms	2.58 ms	-1.63 ms
/v1/chat/completions/render	4	5.31 ms	3.62 ms	-1.70 ms

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[DarkLight1337]

@njhill do you have time to review this?

[mergify]

Hi @Jraaay, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Tip

Is mypy failing?

mypy is run differently in CI. If the failure is related to this check, please use the following command to run it locally:

# For mypy (substitute "3.10" with the failing version if needed)
pre-commit run --hook-stage manual mypy-3.10

[Jraaay]

Hi @njhill @DarkLight1337, just checking in on this PR.

The latest commit passes pre-run-check and pre-commit, and the change is intentionally small:
it keeps the default tokenizer microbatch wait timeout at 0.002s while allowing latency-sensitive users to set it to 0 via --tokenizer-batch-wait-timeout-s.

Could you please take a look when you have time? If there are concerns about the API surface or naming, I’m happy to adjust.

[DarkLight1337]

I wonder, in this case would it be better to just run the sync tokenizer instead of batching? Since you already set the timeout to 0. cc @noooop

[noooop]

I finally know where the 2ms latency comes from.

(╯‵□′)╯︵┻━┻

I don't think AsyncMicrobatchTokenizer will be of much help. Can we remove it?

[noooop]

You can see that the batch acceleration of tokenizer.encode comes entirely from multithreading; without TOKENIZERS_PARALLELISM, the speed does not improve.

We are already using a thread pool in the renderer, which is more general-purpose, so AsyncMicrobatchTokenizer doesn't have much effect.

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

import time

from transformers import AutoTokenizer

model_name = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
prompt = "hello " * 10000

n_tasks = 1024

for bs in [1, 2, 4, 8, 16,32]:
    start = time.perf_counter()

    for _ in range(n_tasks // bs):
        tokenizer.encode([prompt] * bs)

    end = time.perf_counter()

    e2e = end - start
    print(f"tokenizer_encode batch size: {bs}, e2e: {e2e} ms")

with TOKENIZERS_PARALLELISM

tokenizer_encode batch size: 1, e2e: 8.075238061002892 ms
tokenizer_encode batch size: 2, e2e: 4.354615904998354 ms
tokenizer_encode batch size: 4, e2e: 2.6432245899995905 ms
tokenizer_encode batch size: 8, e2e: 1.986106992000714 ms
tokenizer_encode batch size: 16, e2e: 1.711663033998775 ms
tokenizer_encode batch size: 32, e2e: 1.7132872410002165 ms

without TOKENIZERS_PARALLELISM
tokenizer_encode batch size: 1, e2e: 7.97959671100034 ms
tokenizer_encode batch size: 2, e2e: 8.035094329999993 ms
tokenizer_encode batch size: 4, e2e: 8.010271022998495 ms
tokenizer_encode batch size: 8, e2e: 8.016586410001764 ms
tokenizer_encode batch size: 16, e2e: 8.042091774001165 ms
tokenizer_encode batch size: 32, e2e: 8.068442815998424 ms

[Jraaay]

Thanks @DarkLight1337 and @noooop, that makes sense.

I agree that setting batch_wait_timeout_s=0 only removes the explicit 2ms wait, but it still keeps the AsyncMicrobatchTokenizer machinery in the path. If the sync tokenizer path is generally faster / simpler for this use case, then using the sync tokenizer directly would be a cleaner solution than exposing this timeout as a user-facing knob.

My original intention with this PR was to make a small, backward-compatible mitigation:

• preserve the current default behavior (0.002s)
• allow latency-sensitive low-concurrency users to remove the fixed wait
• avoid changing the default serving path in this PR

But I’m happy to pivot if maintainers prefer a cleaner direction.

Would you prefer that I:

1. keep this PR as the minimal configurable-timeout mitigation, or
2. rework it to use the sync tokenizer path / remove AsyncMicrobatchTokenizer where appropriate?

If option 2 is preferred, I can update this PR or open a separate PR depending on the desired scope.

[noooop]

We have decided to remove AsyncMicrobatchTokenizer.

#45759

Thanks for your testing and contributions.