[Feature]: Simple Data Parallelism in vLLM

[simon-mo]

🚀 The feature, motivation and pitch

It is common to have a scenario where folks want to deploy multiple vLLM instances on a single machine due to the machine have several GPUs (commonly 8 GPUs). The work can then be sharded across replicated instances. This issue describes the intended UX for such feature. Notably we might not want to tackle large distributed settings (100s of parallel vLLM instances), which should be better handled by a higher layers.

• Offline use case, for the LLM class, a new argument data_parallel_size and support dispatching requests to one engine per GPU (or per tensor parallel size).

from vllm import LLM

llm = LLM(model="...", data_parallel_size=X) # spawn X number of engine processes and shard the work among them
llm = LLM(model="...", data_parallel_size=X, tensor_parallel_size=Y) # this is supported if X*Y <= total number of GPUs

For the server, same argument, route requests to different engine processes, we can start with simple round robin load balancing, but a good stretch goal is session affinity or prefix aware routing

vllm serve ... --data-parallel-size X

Alternatives

• LiteLLM + manually creating replicas
• Using ray.data or ray serve to scale out

Additional context

No response

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[archthegit]

Hi, I'll be working on this issue

[BKitor]

I like this idea, providing flexibility around deployment is great! Being able to deploy multiple types of parallelism would be ideal, e.g. -pp 2 -dp 4 -dp 2 across 16 GPUs would be benificial. Would also be good to have fine-grained control of process-to-device topology mapping, but that might be a stretch goal for a different ticket...

[andoorve]

Related issue: https://github.com/vllm-project/vllm/issues/9198

[Imss27]

Previously, I encountered an issue: When running vLLM(older version) on a machine with multiple GPUs(a CI environment) without specifying CUDA_VISIBLE_DEVICE, it keeps allocating memory on one single GPU which then causes OOM error. This might be related?

@archthegit and I chated a little and plan to investigate and potentially work together on this.😄

[noooop]

Specifying CUDA_VISIBLE_DEVICE before starting vllm can use different cards for multiple vllm instances started on one machine. But multiple vllm instances may have port conflicts. So the best way to use multiple vllm instances on a single machine is to use docker

Now implementation of pp/tp is coupled with layers, it is very difficult to implement dp. If you don't use pp/tp, you can patch GroupCoordinator to implement dp, but without asynchronous scheduling, there is no meaning to implementing dp. Therefore, asynchronous scheduling should be implemented first before implementing dp

If you have many GPUs doing decoder-only model inference, using "Disaggregating Prefill and Decoding architecture", compare with use dp, has higher throughput , refer to DistServe Mooncake

[noooop]

8452

A prototype, first implement asynchronous scheduling, then by patching GroupCoordinator to implement dp.

[tensorflowt]

8452

A prototype, first implement asynchronous scheduling, then by patching GroupCoordinator to implement dp.

What is patching group coordinator ? Can I verify the performance of dp through benchmark_throughput.py?