sgl-project/sglang

[Feature] Reduce constrained-decoding overhead in TP

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#13,809 opened on Nov 23, 2025

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good first issue

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Description

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Motivation

Currently, when tensor-parallelism (TP) and constrained-decoding are enabled, each TP worker will compile the same grammar across different TP ranks. This will incur non-trivial CPU overhead (for TP $n$, the overhead is $n \times$. In fact, we only need to compile the grammar and sample the result on the first rank.

A possible implementation can be:

  1. Only apply grammar token mask on the rank 0.
  2. Broadcast the next tokens id from rank 0 when there're grammars in batch.
  3. Keep the old code path when there's no grammar in batch (i.e. no extra all-reduce).

https://github.com/sgl-project/sglang/blob/5c2915494c83f076a11afb2c3382eeb8a41f1974/python/sglang/srt/layers/sampler.py#L205-L217

Related resources

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Contributor guide

Research direction
Implement the proposed approach: compile grammar only on rank 0, broadcast next token ids when grammars are present, and keep current code path when no grammars. Refer to the sampler.py code linked.
Tech stack
python
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
35