[Bugfix] Skip cancelled requests in async tokenizer

[HuskyLYL]

Purpose

This PR avoids wasting CPU tokenizer work on cancelled requests in AsyncMicrobatchTokenizer.

During cancellation-heavy load testing, cancelled requests could remain in tokenizer microbatch queues and still be dispatched to the CPU tokenizer, delaying new requests even after scheduler queues had dropped.

This change filters futures that are already done before tokenizer execution and skips fully-inactive microbatches.

How this was found

I found this issue during a cancellation-heavy load test against the OpenAI-compatible /v1/completions endpoint.

The load test was configured with fire-and-forget and no client-side backpressure. In this mode, workers keep sending requests without waiting for previous requests to finish, so the client can quickly build up a large amount of in-flight work on the server side.

Test setup:

• 10 processes
• 150 workers per process
• 1500 total workers
• fire-and-forget enabled
• no client-side backpressure
• random prompt length: 8 to 20480 tokens
• random max_tokens: 1 to 20480
• request timeout: 300s

The figure shows the scheduler state during the test. The running queue stayed bounded by the engine scheduling limit, while the waiting queue kept growing and reached around 13k requests. After about 5 minutes, many client requests timed out and disconnected around the same time, so the scheduler queues dropped quickly.

However, after this abort storm, new requests could not enter the scheduler promptly for a while. This suggested that the remaining bottleneck was not in the scheduler/KV-cache path. Additional logging showed that cancelled requests were still queued in AsyncMicrobatchTokenizer and were still being dispatched to the CPU tokenizer. In the worst case, a full tokenizer microbatch could contain only cancelled futures, but the tokenizer call still ran.

This PR filters cancelled futures before dispatching tokenizer work to the executor, and skips the tokenizer call entirely when no active requests remain.

Test Plan

Adds tests in tests/utils_/test_async_utils.py:

• test_microbatch_tokenizer_skips_cancelled_encode_requests
• test_microbatch_tokenizer_skips_fully_cancelled_encode_batch
• test_microbatch_tokenizer_skips_cancelled_decode_requests

These tests exercise AsyncMicrobatchTokenizer with a fake tokenizer. They verify that cancelled encode/decode futures are filtered before tokenizer work is dispatched, and that a fully-cancelled microbatch skips the tokenizer call entirely.

I reran the cancellation-heavy /v1/completions load test and verified that, after large client-side aborts, new requests are scheduled promptly because cancelled tokenizer futures are skipped before CPU tokenizer dispatch.

Test Result

python -m pytest tests/utils_/test_async_utils.py -v -p no:cacheprovider
# => 4 passed, 16 warnings

pre-commit run --files vllm/utils/async_utils.py tests/utils_/test_async_utils.py
# => Passed

After applying this patch, I reran the same cancellation-heavy load test.

In the fixed version, the scheduler queues still grow under the same fire-and-forget load, and they still drop sharply when many client-side requests time out and disconnect. However, unlike main, the service is able to accept and schedule new requests again shortly after the abort storm.

In the figure, after the first large disconnect event around 18:18, the scheduler state drops to near zero and then new requests begin entering the scheduler again around 18:19. The same pattern appears again after the second disconnect event around 18:24: the queues drop, then new requests are admitted again shortly afterwards.

This matches the intended behavior of the fix: cancelled tokenizer futures are filtered before dispatching CPU tokenizer work, so the frontend tokenizer queue no longer spends a long time draining already-cancelled microbatches before new requests can progress.

AI Assistance

AI assistance was used while preparing this PR. I reviewed every changed line and ran the tests listed above locally.

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[njhill]

Thanks @HuskyLYL, can you not just add this check in the existing loop above? (same for decode case)

[HuskyLYL]

Thanks for the suggestion! I moved the done() check into the existing encode/decode batch collection loops, including the first request returned by queue.get() in each loop.

[HuskyLYL]

Hi @njhill , just following up on this PR. If you have any further concerns or suggestions, please let me know. I’d be happy to address them.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @HuskyLYL.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @HuskyLYL.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[njhill]

Thanks @HuskyLYL sorry I forgot to come back to this! I guess it's not relevant now with @noooop's latest change.

[HuskyLYL]

@njhill No worries at all! I’ve verified the issue is resolved, so I’ve closed this PR. Could you please take a look at this PR when you have time: #44886. I’m wondering if there might be a better approach to address the problem.