[Llama3] Error when multiple GPUs are used

The following issues appear when running the LLM reference implementation. Multiple GPUs issue:

(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231] Exception in worker VllmWorkerProcess while processing method init_device: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method, Traceback (most recent call last):
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]   File "/home/zhihanj/.local/lib/python3.10/site-packages/vllm/executor/multiproc_worker_utils.py", line 224, in _run_worker_process
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]     output = executor(*args, **kwargs)
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]   File "/home/zhihanj/.local/lib/python3.10/site-packages/vllm/worker/worker.py", line 166, in init_device
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]     torch.cuda.set_device(self.device)
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]   File "/home/zhihanj/.local/lib/python3.10/site-packages/torch/cuda/__init__.py", line 420, in set_device
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]     torch._C._cuda_setDevice(device)
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]   File "/home/zhihanj/.local/lib/python3.10/site-packages/torch/cuda/__init__.py", line 300, in _lazy_init
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231]     raise RuntimeError(
(VllmWorkerProcess pid=1795) ERROR 12-03 18:49:03 multiproc_worker_utils.py:231] RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

Reproduce the Llama3 reference implementation with multiple GPUs and capture the reported CUDA multiprocessing error. Start with the referenced vllm/executor/multiproc_worker_utils.py and vllm/worker/worker.py paths, then determine where the worker process initialization conflicts with CUDA. Done means the multi-GPU run initializes workers without the reported forked-subprocess error.