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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

Signed-off-by: Chenyaaang <chenyangli@google.com>
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@mergify mergify bot added ci/build v1 tpu Related to Google TPUs labels Apr 11, 2025
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Signed-off-by: Chenyaaang <chenyangli@google.com>
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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 <chenyangli@google.com>
@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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Signed-off-by: Chenyaaang <chenyangli@google.com>
@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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This pull request has merge conflicts that must be resolved before it can be
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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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Signed-off-by: Chenyaaang <chenyangli@google.com>
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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: Chenyaaang <chenyangli@google.com>
Signed-off-by: Frieda (Jingying) Huang <jingyingfhuang@gmail.com>
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* [BugFix] Remove default multiproc executor `collective_rpc` timeout (vllm-project#17000)

Signed-off-by: Nick Hill <nhill@redhat.com>

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

Signed-off-by: Chenyaaang <chenyangli@google.com>

* [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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* [V1][DP] More robust DP/EP dummy request coordination (vllm-project#16277)

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* [BugFix] Revert ROCm Custom Paged Attention Env Flag Check (vllm-project#17022)

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* Revert "[Misc] Add S3 environment variables for better support of MinIO." (vllm-project#17021)

* [misc] tune some env vars for GB200 (vllm-project#16992)

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* [INTEL-HPU][v0] Port delayed sampling to upstream (vllm-project#16949)

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* [Bugfix] Triton FA function takes no keyword arguments (vllm-project#16902)

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* [V1] Avoid socket errors during shutdown when requests are in in-flight (vllm-project#16807)

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* [BugFix] llama4 fa3 fix - RuntimeError: scheduler_metadata must have shape (metadata_size) (vllm-project#16998)

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

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* [Bugfix] Fix AssertionError: skip_special_tokens=False is not supported for Mistral tokenizers (vllm-project#16964)

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* [CI] Run v1/test_serial_utils.py in CI (vllm-project#16996)

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* Categorize `tests/kernels/` based on kernel type (vllm-project#16799)

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* [Quantization]add prefix for commandA quantized model (vllm-project#17017)

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* Disable enforce_eager for V1 TPU sampler and structured output tests (vllm-project#17016)

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* Fix OOT registration test (vllm-project#17099)

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* [V1][PP] Optimization: continue scheduling prefill chunks (vllm-project#17080)

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* [Misc] Remove OLMo2 config copy (vllm-project#17066)

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* Improve static type checking in `LoRAModelRunnerMixin` (vllm-project#17104)

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* [V1][Structured Output] Clear xgrammar compiler object when engine core shut down to avoid nanobind leaked warning (vllm-project#16954)

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* [Frontend] Using matryoshka_dimensions control the allowed output dimensions. (vllm-project#16970)

* Add missing rocm_skinny_gemms kernel test to CI (vllm-project#17060)

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* [Misc] refactor example series - structured outputs (vllm-project#17040)

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* [V1][Spec Decoding] Add num_drafts and num_accepted_tokens_per_position metrics (vllm-project#16665)

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* Updating builkite job for IBM Power  (vllm-project#17111)

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* existing torch installation pip command fix for docs (vllm-project#17059)

* Molmo Requirements (vllm-project#17026)

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* Add `:markdownhelp:` to `EngineArgs` docs so markdown docstrings render properly (vllm-project#17124)

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* Add collective_rpc to llm engine (vllm-project#16999)

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* Add chat template for Llama 4 models (vllm-project#16428)

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* [FEAT] [ROCm]: AITER Fused MOE V1 Support (vllm-project#16752)

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* [Attention] FA3 decode perf improvement - single mma warp group support for head dim 128 (vllm-project#16864)

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* fix float16 support for kimi-vl (vllm-project#17156)

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* [Doc] V1 : Update LoRA status (vllm-project#17133)

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* [Docs] Fix True->true in supported_models.md (vllm-project#17141)

* Move missed `SchedulerConfig` args into scheduler config group in `EngineArgs` (vllm-project#17131)

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* [Misc] Clean up redundant code in uniproc_executor.py (vllm-project#16762)

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* [Bugfix][Misc] Use TritonPlaceholderModule to defensively import triton (vllm-project#15099)

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* [Misc] Benchmark Serving Script Support Appending Results (vllm-project#17028)

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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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* Fix API typo and remove FP8 on V1 restriction

---------

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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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