Benchmark Comparation with Verl
- Dominant language
- Python
- Stars
- 3.4k
- Forks
- 312
- Avg merge
- 1h 2m
- Merged PRs (30d)
- 2
Description
Thank you so much for your awesome code implementation!😘😘
Have we compare the performance with Verl? Since Verl also supports VLM.🥹🥹
I do some DYI experiments: In 4 GPUs, 3B VL model; batch size = 8; response length = 1024
I find that the average time of one step in ROLL is 17s; the average time of one step in VerL is 30s
I wonder why ROLL is much faster than verl.
By the way, I find ROLL is also easier OOM than verl.🥹🥹
Below is the detailed script of ROLL and verl:
[roll.txt](https://github.com/user-attachments/files/21254169/roll.txt)
[verl.txt](https://github.com/user-attachments/files/21254168/verl.txt)
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reviewing the attached roll.txt and verl.txt scripts and compare their 4-GPU, 3B VLM, batch-size 8, and response-length 1024 configurations. Reproduce the reported step times and out-of-memory behavior, then document the cause of the performance and memory difference with supporting measurements.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100