verl-project/verl

[FR] Documentation about Resolving OOM

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#1014 opened on Apr 10, 2025

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call for contributiondocumentationgood first issue

Description

Motivation

There are many issues related to OOM, e.g. #328 . We might need a clear guide about how to resolve OOM.

Plan

A non-exclusive enumeration about related configurations:

  1. Rollout：gpu_memory_utilization
  2. Other Inference：
    1. Liger Kernel
    2. *_max_len_per_gpu / micro_batch_size_per_gpu
  3. Training:
    1. Liger Kernel
    2. Ulysses Sequence Parallelism
    3. gradient checkpointing
    4. offload

TODO

  • Complete the list of related configurations
  • Benchmark the effect & overhead of each configuration

Contributor guide

Tech stack
pythonpytorch
Domain
documentationperformance
Issue type
documentation
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
basic Pythonfamiliarity with PyTorchunderstanding of GPU memory
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70
Research direction
Investigate existing OOM related issues, e.g., #328, to identify common scenarios. For each configuration listed (rollout's gpu memory utilization, Liger Kernel, max len/gpu, gradient checkpointing, etc.), benchmark their effect on memory usage and training performance. Document findings in a structured guide, referencing relevant source files like configuration scripts and training loops.