[Perf][V1] Fully overlap model execution #23569

benchislett · 2025-08-25T16:00:03Z

Purpose

This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding.

Expected speedup is 5-10% over the current async scheduling.

This PR is pending some light refactoring (see inline comments) and testing but is otherwise ready for review.

Design Analysis

Overview

This PR implements overlapped model execution by allowing the model runner to return a CUDA tensor reference to the sampled token ids, instead of the pythonized token ids. The result is passed via queue to an output worker thread which blocks until the value is ready and then places the pythonized result on the main output queue. This way, the model runner can run ahead to handle new inputs before the GPU has finished processing the previous iteration. This results in an elimination of the CPU overhead of input preparation and sampling.

In order to implement this, the output_token_ids and token_ids_cpu are no longer updated after the sampling step. Instead, a reference to the previous sampled_token_ids is kept and the ids are copied into self.input_ids during the next step's prepare_inputs phase. This means that this approach will not be compatible with ngram speculative decoding (since that requires the output token to be known on the cpu), and will need to be adapted for other speculative decoding (which can [with some modification] receive their inputs from the gpu tensor directly).

Compatibility with Key Features

Currently, this PR is not compatible with:

Pipeline Parallelism
Speculative Decoding
Guided Decoding

I expect that for Speculative Decoding and Guided Decoding, the integration will be straightforward.

For Guided Decoding, an open PR #23224 introduces a refactoring of the structured outputs manager into its own process, allowing the filled bitmask to be received directly by the model runner(s) just before it needs to be applied, enabling overlapped computation of the bitmask.

For Speculative Decoding, work on enabling MLA+MTP #22684 implements a refactor to the speculative decoding runtime to eliminate all gpu->cpu synchronizations. This means that it should nicely integrate into this async execution framework by simply caching the rectangular tensor of sampled token ids which includes the speculated tokens, and copying into input_ids in the same manner as this PR already does.

Drawbacks

This PR's correctness can be enforced by straightforward end-to-end testing with async scheduling enabled. However, it is not so easy to maintain the absence of synchronizations in the execute_model code. Currently, the FlashInfer implementation has two such synchronizations, so Flash Attention is used for benchmarking instead. This is a notable flaw in this design and, if accepted, will require vigilant regression testing for performance degradation due to accidentally introduced synchronization points.

Further, enforcing a fully sync-free execution limits compatibility with features such as n-gram speculative decoding, which inherently require the sampled token id to be serialized to the host. There may be future techniques that limit our ability to effectively maintain zero-synchronizations, and therefore limit the compatibility with this style of async scheduling.

Profile Results

The following is a snapshot of nsys profile of a decode iteration (BS=1) before and after this change. The setup is Llama 3.2 1B-Instruct on 1xB200 with async scheduling and full cudagraph enabled in both cases.

Before:

After:

To reproduce, run:

VLLM_ATTENTION_BACKEND=FLASH_ATTN vllm serve meta-llama/Llama-3.2-1B-Instruct --compilation-config '{"full_cuda_graph": true}' --no-enable-prefix-caching --async-scheduling

Test Plan

Tests are coming soon. See discussion in drawbacks above for test considerations.

Test Result

Signed-off-by: Benjamin Chislett <[email protected]>

mergify · 2025-08-25T16:01:18Z

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

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

Signed-off-by: Benjamin Chislett <[email protected]>

njhill

Thanks @benchislett, the idea looks good to me!

vllm/v1/executor/multiproc_executor.py

vllm/v1/worker/gpu_model_runner.py

Signed-off-by: Benjamin Chislett <[email protected]>

vllm/v1/executor/multiproc_executor.py

vllm/v1/outputs.py

vllm/v1/worker/gpu_model_runner.py

Signed-off-by: Benjamin Chislett <[email protected]>

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]>

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]> Signed-off-by: Yizhou Liu <[email protected]>

Signed-off-by: Benjamin Chislett <[email protected]>

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]>

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

njhill · 2025-09-19T18:55:39Z

@wangqia0309 @JaheimLee could you try again with #25279? It's possible that was the reason for the penalty crash.

JaheimLee · 2025-09-21T13:58:46Z

@wangqia0309 @JaheimLee could you try again with #25279? It's possible that was the reason for the penalty crash.

Still has the problem.

wangqia0309 · 2025-09-22T03:37:29Z

@wangqia0309 @JaheimLee could you try again with #25279? It's possible that was the reason for the penalty crash.

thanks, I have fixed this issue in house, It resulted from asynchronous scheduling that no longer fills the sample output token and uses -1 instead, so it needs to be asynchronously filled with the correct value in another thread

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]>

Signed-off-by: Benjamin Chislett <[email protected]>

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]>

It looks like this must have been missed during refactoring of vllm-project#23569 before it was merged. Signed-off-by: Nick Hill <[email protected]>

Signed-off-by: Benjamin Chislett <[email protected]> Signed-off-by: xuebwang-amd <[email protected]>

lhtin · 2025-10-16T06:49:30Z

@benchislett Currently, [async scheduling] + [ray backend] will crash, can you take a look at the first PR(#25887) to throw a error, and I will submit the second PR to support this feature.

benchislett · 2025-10-16T14:22:28Z

Thanks @wangqia0309 for identifying the source of the issue.

If there is no PR with a fix by next week I will create one myself. This will be fixed soon. If anyone has a local fix they would like to contribute, that would be welcome.

This PR is based on top of [#23569](vllm-project/vllm#23569) and [#24219](vllm-project/vllm#24219). ### What this PR does / why we need it? This PR allows the model runner to function asynchronously when using async scheduling. This allows full overlap of the cpu operations (including prepare_inputs) and the model forward pass. This diff is functional and does not support speculative decoding, PP, or guided decoding. Expected speedup is 5-10% over the current async scheduling. ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? server ``` python -m vllm.entrypoints.openai.api_server --model=Qwen3-32B\ --trust-remote-code --enforce-eager \ --distributed-executor-backend=mp \ -tp=4 \ --port 8006 \ --max-model-len 32000 \ --block-size 128 \ --gpu-memory-utilization 0.99 ``` client ``` python $TEST_PY --backend vllm --trust-remote-code --model Qwen3-32B \ --dataset-name random --random-input-len 2048 --random-output-len 2048 \ --ignore-eos\ --num-prompts 48 --max-concurrency 48 --request-rate inf --temperature 0 \ --metric-percentiles 90 --base-url http://localhost:8006 --save-result \ --result-dir $PROFILER_DIR ``` benchmark test based on Qwen3-32B TPOT result: ||forward async| scheduler async |sync| |-|-|-|-| |avg|41.73|41.86|44.20| |improve0|0.3%|0|0| |improve1|5.58%|0|0| benchmark test based on Qwen2___5-VL-7B-Instruct TPOT result: ||forward async|sync| |-|-|-| |avg|23.22|29.16| |improve|20.3%|0| - vLLM version: main - vLLM main: vllm-project/vllm@e93f4cc Signed-off-by: jiangpeng36 <[email protected]> Signed-off-by: Ronald1995 <[email protected]> Co-authored-by: jiangpeng36 <[email protected]> Co-authored-by: Ronald1995 <[email protected]>

njhill · 2025-10-22T02:25:41Z

I found a bug: when asynchronous scheduling is enabled, vLLM crashes if the request includes a frequency_penalty (greater than 0).

/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:163: operator(): block: [0,0,0], thread: [0,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "scatter gather kernel index out of bounds"` failed.
torch.AcceleratorError: CUDA error: device-side assert triggered

Stack trace:
  File "/usr/local/lib/python3.12/dist-packages/vllm/v1/sample/ops/penalties.py", line 24, in apply_all_penalties
    return apply_penalties(logits, prompt_token_ids, output_tokens_t,
  File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/utils.py", line 73, in apply_penalties
    output_bin_counts, output_mask = get_token_bin_counts_and_mask(
  File "/usr/local/lib/python3.12/dist-packages/vllm/model_executor/layers/utils.py", line 45, in get_token_bin_counts_and_mask
    bin_counts.scatter_add_(1, tokens, torch.ones_like(tokens))

Same problem. I think it's pretty important because many quantitative models cannot be used without it. @njhill

@wangqia0309 @JaheimLee you may have already seen but the async scheduling + penalties incompatibility has now been addressed: #26467.

Signed-off-by: Benjamin Chislett <[email protected]> Signed-off-by: xuebwang-amd <[email protected]>

benchislett added 4 commits August 24, 2025 15:11

first prototype, working for BS=1

95b1b7d

Signed-off-by: Benjamin Chislett <[email protected]>

wip for batched

5bd9851

Signed-off-by: Benjamin Chislett <[email protected]>

fix bs > 1

9a59696

Signed-off-by: Benjamin Chislett <[email protected]>

add back removed code

a24d715

Signed-off-by: Benjamin Chislett <[email protected]>

mergify bot added the v1 label Aug 25, 2025

mergify bot added the needs-rebase label Aug 25, 2025

minor perf optimization

51b6169

Signed-off-by: Benjamin Chislett <[email protected]>

benchislett added the performance Performance-related issues label Aug 25, 2025

benchislett marked this pull request as ready for review August 25, 2025 21:02

benchislett requested review from WoosukKwon, alexm-redhat, comaniac, njhill, robertgshaw2-redhat and ywang96 as code owners August 25, 2025 21:02

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benchislett added 3 commits August 26, 2025 15:32

improvements

2832e37

Signed-off-by: Benjamin Chislett <[email protected]>

remove old prints

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Merge branch 'main' into overlap-model-execution

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mergify bot removed the needs-rebase label Aug 26, 2025

fix precommit

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Merge branch 'main' into overlap-model-execution

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benchislett added 3 commits August 27, 2025 20:18

misc cleanup

752ccf9

Signed-off-by: Benjamin Chislett <[email protected]>

refactor prepare_input_ids

9f28326

Signed-off-by: Benjamin Chislett <[email protected]>

tiny refactor to reorder some ops

15d7b31

Signed-off-by: Benjamin Chislett <[email protected]>

benchislett requested a review from njhill August 27, 2025 20:58

skyloevil pushed a commit to skyloevil/vllm that referenced this pull request Sep 13, 2025

[Perf][V1] Fully overlap model execution (vllm-project#23569)

226ced9

Signed-off-by: Benjamin Chislett <[email protected]>

Ronald1995 mentioned this pull request Sep 13, 2025

[Core] Async Scheduling X Spec Decoding Compatibility #24799

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5 tasks

xuechendi added a commit to vllm-project/vllm-gaudi that referenced this pull request Sep 15, 2025

Fully overlap model execution (#134)

dca6719

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

nvjullin mentioned this pull request Sep 17, 2025

[BugFix] Make FlashInferMetadataBuilder non-blocking #25040

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kdamaszk pushed a commit to kdamaszk/vllm-gaudi that referenced this pull request Sep 18, 2025

Fully overlap model execution (vllm-project#134)

a84a19c

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

slokesha pushed a commit to slokesha/vllm-gaudi that referenced this pull request Sep 24, 2025

Fully overlap model execution (vllm-project#134)

e04d6aa

Dependent on vllm-project/vllm#23569 --------- Signed-off-by: Tianmu Li <[email protected]> Co-authored-by: Chendi.Xue <[email protected]>

FeiDaLI pushed a commit to FeiDaLI/vllm that referenced this pull request Sep 25, 2025

[Perf][V1] Fully overlap model execution (vllm-project#23569)

8bb7422

Signed-off-by: Benjamin Chislett <[email protected]>

woodlgz mentioned this pull request Sep 29, 2025

【Spec Decode】support async scheduling with eagle speculative decoding #25872

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njhill mentioned this pull request Oct 9, 2025

[Core] Remove unused prev_sampled_token_ids_invalid_indices input batch field #26514

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xuebwang-amd pushed a commit to xuebwang-amd/vllm that referenced this pull request Oct 10, 2025

[Perf][V1] Fully overlap model execution (vllm-project#23569)

9568137

Signed-off-by: Benjamin Chislett <[email protected]> Signed-off-by: xuebwang-amd <[email protected]>

benchislett deleted the overlap-model-execution branch October 22, 2025 19:07

xuebwang-amd pushed a commit to xuebwang-amd/vllm that referenced this pull request Oct 24, 2025

[Perf][V1] Fully overlap model execution (vllm-project#23569)

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Signed-off-by: Benjamin Chislett <[email protected]> Signed-off-by: xuebwang-amd <[email protected]>

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