[Doc] Update Ray Data distributed offline inference example

[Yard1]

This PR makes use of the new ray_remote_args_fn API added to Ray Data to allow for tensor parallelism when conducting batch inference with vLLM and Ray Data.

FIX #4410

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[Yard1]

cc @c21 @richardliaw

[c21]

LGTM

[stikkireddy]

@Yard1 did you happen to test this with tp > 1? IIRC with tp > 1 vllm also calls ray init and does that impact this at all?

[Yard1]

@stikkireddy yes, I tested it with multiple TP factors and both in single and multi-node setups. ray.init() can be called multiple times without issues.

[comaniac]

LGTM

[YQ-Wang]

Why do we need to set CPU: 1 here?

[Yard1]

it's not directly necessary (it will work fine without that), but it shows that we will still be running a CPU process per each worker

[chenhongyu2048]

If my understanding is correct, is there data parallelism between nodes and tensor parallelism inside nodes in the documentation offline_inference_distributed.py?

[c21]

If my understanding is correct, is there data parallelism between nodes and tensor parallelism inside nodes in the documentation offline_inference_distributed.py?

Yes. Data is sharded across nodes, and each node does inference on a portion of data. Each node supports tensor parallelism acorss multiple GPUs.