NVIDIA / NVIDIA/TensorRT-LLM

[Feature]: Allow ep sharding the MTP draft model (currently disabled as WAR)

Open
#15,168 1 comment 0 reactions 1 assignee View on GitHub

Nobody has claimed this yet.

feature request Speculative Decoding
Dominant language
Python
Stars
14.7k
Forks
2.8k
Avg merge
2d 23h
Merged PRs (30d)
489

Description

🚀 The feature, motivation and pitch

Target and draft share a single process-wide MoeAlltoAll workspace + flag_val counter (singleton in MoeAlltoAll._WORKSPACE), so a misconfigured draft alltoall corrupts the workspace and the subsequent target alltoall calls hang or fault. As a WAR we are currently disabling ep sharding of the draft and replicating it.
See: https://github.com/nv-auto-deploy/TensorRT-LLM/blob/b9ee1dfae3a4c573e7c90f90067f9b371de248ed/tensorrt_llm/_torch/auto_deploy/transform/library/sharding.py#L1236

Basically, this MoE object should be one-per-graph and not one-per-runtime, so we'd probably need to add a dict there

Alternatives

No response

Additional context

No response

Before submitting a new issue...
  • Make sure you already searched for relevant issues, and checked the documentation and examples for answers to frequently asked questions.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Assessment

This issue has not been assessed yet.

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.