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Merged
merged 11 commits into from
Apr 23, 2025

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Chenyaaang
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@Chenyaaang Chenyaaang commented Apr 11, 2025

#11104

Enable structured decoding on V1 TPU.
The workflow changes from model forward -> select hidden states -> sample from hidden (compute logits + sampler) to
model forward -> select hidden states -> compute logits -> structured decode -> sampler

The change has been verified no recompilation.

pre-compile time

Adding structured deocoding takes around 25 seconds to compile additional 10 xla graphs.
previous:

stage #xla graphs time(s)
backbone 24 103.87
select_hidden_states 52 12.10
sample_from_hidden 20 16.53
now:

stage #xla graphs time(s)
backbone 24 103.83
select_hidden_states 52 12.19
compute_logits 10 2.31
structured_decoding 10 25.12
sample_from_logits 15 14.12

How to run structured deocoding

unit test

pytest -s tests/v1/entrypoints/llm/test_struct_output_generate.py::test_structured_output

benchmark

server: VLLM_USE_V1=1 VLLM_XLA_CHECK_RECOMPILATION=1 VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests --gpu-memory-utilization 0.98 --max-num-batched-tokens 2048 --max-num-seqs 128 --max-model-len 4096 --tensor-parallel-size 1 --no-enable-prefix-caching
client: python benchmarks/benchmark_serving_structured_output.py --backend vllm --model meta-llama/Llama-3.1-8B-Instruct --dataset choice --structured-output-ratio 1.0 --structured-output-backend auto --request-rate 10 --num-prompts 500

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@mergify mergify bot added ci/build v1 tpu Related to Google TPUs labels Apr 11, 2025
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This pull request has merge conflicts that must be resolved before it can be
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@mgoin mgoin self-requested a review April 11, 2025 21:19
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Thanks for your contribution!

What's the impact on the execution time when splitting sample_from_hidden to two parts?

How does the performance compare with and without structured decoding?

Comment on lines +918 to +919
logger.info(
"Compiling structured_decoding with different input shapes.")
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@russellb russellb Apr 16, 2025

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Do you want to keep this and other logs in this method? They seem more like debug logs to me, so perhaps logger.debug if you want to keep them?

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@Chenyaaang Chenyaaang Apr 16, 2025

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actually I'm fine with both, but since _get_token_paddings is also using logger.info, so I'd like to keep it as it is. In my understanding, it logs the preparation steps.

@Chenyaaang Chenyaaang closed this Apr 16, 2025
@Chenyaaang Chenyaaang reopened this Apr 16, 2025
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Chenyaaang commented Apr 16, 2025

What's the impact on the execution time when splitting sample_from_hidden to two parts?

The benchmark_serving results are almost the same (qps, latency).

How does the performance compare with and without structured decoding?

I ran benchmark_serving_structured_output.py and benchmark_serving.pyon both TPU and GPU to compute performance degradation

server: VLLM_USE_V1=1 VLLM_XLA_CHECK_RECOMPILATION=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests --gpu-memory-utilization 0.98 --max-num-batched-tokens 2048 --max-num-seqs 128 --max-model-len 4096 --tensor-parallel-size 1 --no-enable-prefix-caching

benchmark_serving: python benchmarks/benchmark_serving.py --model meta-llama/Llama-3.1-8B-Instruct --dataset-name sonnet --dataset-path benchmarks/sonnet_4x.txt --sonnet-input-len 1101 --sonnet-output-len 1 --ignore_eos --percentile-metrics ttft,tpot,itl,e2el

benchmark_serving_structured_output: python benchmarks/benchmark_serving_structured_output.py --backend vllm --model meta-llama/Llama-3.1-8B-Instruct --dataset choice --structured-output-ratio 1.0 --structured-output-backend auto --num-prompts 1000 --percentile-metrics ttft,tpot,itl,e2el

  w/o structured decoding w structured decoding
H100 Qps: 38.41mean e2el: 13.16s Qps: 33.87Mean e2el: 15.24s
V6e Qps: 24.44mean e2el: 21.03s Qps: 21.82Mean e2el: 23.45s

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I ran benchmark_serving_structured_output.py and benchmark_serving.py, both using input-len 1100 and output-len 1, qps is 7.15 vs 8.19, mean e2el is 70s vs 61s

I think the performance drop is acceptable. Although there might be some performance optimization chances but we can handle it in the following PRs.

@Chenyaaang Chenyaaang force-pushed the tpu-structured-decoding1 branch from c0774ba to 48adba8 Compare April 17, 2025 00:06
Signed-off-by: Chenyaaang <[email protected]>
@Chenyaaang Chenyaaang force-pushed the tpu-structured-decoding1 branch from 48adba8 to 2a8c28b Compare April 17, 2025 00:11
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LGTM, thanks for the contribution.

Final nit: Per the contributing guide, could you change the title to
[Core][V1][TPU] Enable structured decoding?

@Chenyaaang Chenyaaang changed the title [Core] Enable structured decoding on V1 TPU [Core][V1][TPU] Enable structured decoding on TPU V1 Apr 17, 2025
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@yaochengji yaochengji added the ready ONLY add when PR is ready to merge/full CI is needed label Apr 19, 2025
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@WoosukKwon WoosukKwon requested a review from russellb April 21, 2025 16:07
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Nice work! Left some comments, take a look when you find the time.

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@Chenyaaang Chenyaaang force-pushed the tpu-structured-decoding1 branch from 7309f4a to fe043e4 Compare April 22, 2025 17:01
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LGTM, great work!

@mgoin mgoin merged commit 83d9337 into vllm-project:main Apr 23, 2025
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frieda-huang pushed a commit to frieda-huang/vllm that referenced this pull request Apr 23, 2025
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Signed-off-by: Frieda (Jingying) Huang <[email protected]>
gshtras added a commit to ROCm/vllm that referenced this pull request Apr 25, 2025
* [BugFix] Remove default multiproc executor `collective_rpc` timeout (vllm-project#17000)

Signed-off-by: Nick Hill <[email protected]>

* [Core][V1][TPU] Enable structured decoding on TPU V1 (vllm-project#16499)

Signed-off-by: Chenyaaang <[email protected]>

* [Bugfix] validate urls object for multimodal content parts (vllm-project#16990)

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* add Dockerfile build vllm against torch nightly (vllm-project#16936)

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* [Kernel][ROCM] Upstream prefix prefill speed up for vLLM V1 (vllm-project#13305)

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* [Misc] Improve readability of get_open_port function. (vllm-project#17024)

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* [Perf]Optimize rotary_emb implementation to use Triton operator for improved inference performance (vllm-project#16457)

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* [Bugfix] remove fallback in guided_json (int range, patterns) (vllm-project#16725)

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* [Quantization][FP8] Add support for FP8 models with input_scale for output projection and QK quantization (vllm-project#15734)

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* [Doc] Add headings to improve gptqmodel.md (vllm-project#17164)

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* Only turn on FastIncrementalDetokenizer when tokenizers >= 0.21.1 (vllm-project#17158)

* [Doc] Add two links to disagg_prefill.md (vllm-project#17168)

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* [Doc] Move todo out of beam search docstring (vllm-project#17183)

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* [Bugfix] Fix mistral model tests (vllm-project#17181)

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* [Bugfix] Fix Mistral ChatCompletionRequest Body Exception (vllm-project#16769)

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* Fix API typo and remove FP8 on V1 restriction

---------

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Co-authored-by: 조상연[플레이스 AI] <[email protected]>
Co-authored-by: rasmith <[email protected]>
Co-authored-by: Luka Govedič <[email protected]>
Co-authored-by: Lu Fang <[email protected]>
Co-authored-by: Alex Brooks <[email protected]>
Co-authored-by: Cyrus Leung <[email protected]>
Co-authored-by: Jasmond L <[email protected]>
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ci/build ready ONLY add when PR is ready to merge/full CI is needed structured-output tpu Related to Google TPUs v1
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