Fix vLLM worker

[aIbrahiim]

Fixes: #30513
Successful run: https://github.com/apache/beam/actions/runs/23814494318

Fixes Dataflow postcommit vllmTests failures caused by vLLM exiting during engine startup on NVIDIA T4 workers. The failure was CUDA OOM during vLLM V1 engine initialization. The example now passes memory-aware vLLM server flags via the existing vllm_server_kwargs pattern

as after investigation Task :sdks:python:test-suites:dataflow:py312:vllmTests failed and job dataflow logs showed:

Exception: Failed to start vLLM server, polling process exited with code 1.

Starting service with ['/opt/apache/beam-venv/beam-venv-worker-sdk-0-0/bin/python' '-m'
'vllm.entrypoints.openai.api_server' '--model' 'facebook/opt-125m' '--port' '…']

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 100.00 MiB.
GPU 0 has a total capacity of 14.58 GiB of which 33.56 MiB is free.
… 13.62 GiB is allocated by PyTorch …

vLLM then raised:

RuntimeError: CUDA out of memory occurred when warming up sampler with 256 dummy requests.
Please try lowering max_num_seqs or gpu_memory_utilization when initializing the engine.

So this PR :

Uses vllm_server_kwargs (same pattern as other vLLM examples, e.g. vllm_gemma_batch.py) to pass --max-num-seqs and --gpu-memory-utilization with conservative defaults suited to ~16 GiB GPUs.
Adds --vllm_max_num_seqs and --vllm_gpu_memory_utilization so larger GPUs can override.

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Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a critical issue where the vLLM worker in the text completion example could fail to initialize on GPUs with approximately 16GB of memory due to CUDA out-of-memory errors. By introducing configurable parameters for max-num-seqs and gpu-memory-utilization, the changes allow users to fine-tune vLLM's memory footprint, ensuring the example runs reliably on a wider range of hardware. The update includes both the implementation of these configuration options and clear documentation to guide users in optimizing their vLLM deployments.

Highlights

• Memory Optimization for vLLM: Introduced new command-line arguments --vllm_max_num_seqs and --vllm_gpu_memory_utilization to the vllm_text_completion example to prevent CUDA out-of-memory errors on GPUs with limited memory (e.g., 16GiB NVIDIA T4).
• Enhanced Example Configuration: The vllm_text_completion.py example now allows users to explicitly configure vLLM server parameters, providing greater control over memory usage during engine startup and inference.
• Documentation Update: Updated the README.md for the vLLM example to explain the purpose and usage of the new memory configuration parameters, guiding users on how to avoid common OOM issues.
• Model Handler Parameterization: Modified VLLMCompletionsModelHandler and VLLMChatModelHandler instantiation in the example to accept and apply the new vllm_server_kwargs.

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[aIbrahiim]

@damccorm @Abacn