Describe the bug
We currently set seed for each vllm instance at init and underneath vllm would propagate the rng state. This is wrong since 1 run of 4hrs will not be the same as 4 runs of 1hrs where the seed will be reset everytime init is called.
Steps/Code to reproduce bug
Please list minimal steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
Expected behavior
A clear and concise description of what you expected to happen.
Environment overview (please complete the following information)
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide
docker pull & docker run commands used
Environment details
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
Additional context
Add any other context about the problem here.
Example: GPU model
Describe the bug
We currently set seed for each vllm instance at init and underneath vllm would propagate the rng state. This is wrong since 1 run of 4hrs will not be the same as 4 runs of 1hrs where the seed will be reset everytime init is called.
Steps/Code to reproduce bug
Please list minimal steps or code snippet for us to be able to reproduce the bug.
A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
Expected behavior
A clear and concise description of what you expected to happen.
Environment overview (please complete the following information)
docker pull&docker runcommands usedEnvironment details
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
Additional context
Add any other context about the problem here.
Example: GPU model