prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  RayWorker import error
  When import `vllm` I got following error
  ```
  Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
    File "/code/vllm/vllm/__init__.py", line 4, in <module>
      from vllm.engine.async_llm_engine import AsyncLLMEngine
    File "/code/vllm/vllm/engine/async_llm_engine.py", line 7, in <module>
      from vllm.engine.llm_engine import LLMEngine
    File "/code/vllm/vllm/engine/llm_engine.py", line 9, in <module>
      from vllm.engine.ray_utils import initialize_cluster, ray, RayWorker
  ImportError: cannot import name 'RayWorker' from 'vllm.engine.ray_utils' 
  ```
  It seems `ray` requires `pandas` I haven't installed it.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `vllm/engine/ray_utils.py`
  Content:
  ```
  1 import socket
  2 from typing import Optional, Tuple, TYPE_CHECKING
  3 
  4 from vllm.config import ParallelConfig
  5 
  6 try:
  7     import ray
  8     from ray.air.util.torch_dist import TorchDistributedWorker
  9 
  10     class RayWorker(TorchDistributedWorker):
  11         """Ray wrapper for vllm.worker.Worker, allowing Worker to be
  12         lazliy initialized after Ray sets CUDA_VISIBLE_DEVICES."""
  13 
  14         def __init__(self) -> None:
  15             self.worker = None
  16 
  17         def init_worker(self, worker_init_fn):
  18             self.worker = worker_init_fn()
  19 
  20         def __getattr__(self, name):
  21             return getattr(self.worker, name)
  22 
  23         def execute_method(self, method, *args, **kwargs):
  24             executor = getattr(self, method)
  25             return executor(*args, **kwargs)
  26 
  27 except ImportError:
  28     ray = None
  29     TorchDistributedWorker = None
  30 
  31 if TYPE_CHECKING:
  32     from ray.util.placement_group import PlacementGroup
  33 
  34 
  35 def get_open_port():
  36     with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
  37         s.bind(("", 0))
  38         return s.getsockname()[1]
  39 
  40 
  41 def initialize_cluster(
  42     parallel_config: ParallelConfig,
  43     engine_use_ray: bool = False,
  44     ray_address: Optional[str] = None,
  45 ) -> Tuple[str, Optional["PlacementGroup"]]:
  46     """Initialize the distributed cluster probably with Ray.
  47 
  48     Args:
  49         parallel_config: The configurations for parallel execution.
  50         engine_use_ray: Whether to use Ray for async engine.
  51         ray_address: The address of the Ray cluster. If None, uses
  52             the default Ray cluster address.
  53 
  54     Returns:
  55         A tuple of (`distributed_init_method`, `all_stage_devices`). The
  56         `distributed_init_method` is the address for initializing the
  57         distributed backend. `all_stage_devices` includes device IDs for
  58         each worker in each pipeline stage. Each device ID is a tuple of
  59         (rank, node resource, device id).
  60     """
  61     if parallel_config.worker_use_ray or engine_use_ray:
  62         if ray is None:
  63             raise ImportError(
  64                 "Ray is not installed. Please install Ray to use distributed "
  65                 "serving.")
  66         # Connect to a ray cluster.
  67         ray.init(address=ray_address, ignore_reinit_error=True)
  68 
  69     if not parallel_config.worker_use_ray:
  70         # Initialize cluster locally.
  71         port = get_open_port()
  72         # We need to setup the distributed init method to make sure
  73         # the distributed megatron code (e.g., get world size) works correctly.
  74         distributed_init_method = f"tcp://localhost:{port}"
  75         return distributed_init_method, None
  76 
  77     current_placement_group = ray.util.get_current_placement_group()
  78     if current_placement_group:
  79         # We are in a placement group
  80         bundles = current_placement_group.bundle_specs
  81         # Verify that we can use the placement group.
  82         gpu_bundles = 0
  83         for bundle in bundles:
  84             bundle_gpus = bundle.get("GPU", 0)
  85             if bundle_gpus > 1:
  86                 raise ValueError(
  87                     "Placement group bundle cannot have more than 1 GPU.")
  88             if bundle_gpus:
  89                 gpu_bundles += 1
  90         if parallel_config.world_size > gpu_bundles:
  91             raise ValueError(
  92                 "The number of required GPUs exceeds the total number of "
  93                 "available GPUs in the placement group.")
  94     else:
  95         num_gpus_in_cluster = ray.cluster_resources().get("GPU", 0)
  96         if parallel_config.world_size > num_gpus_in_cluster:
  97             raise ValueError(
  98                 "The number of required GPUs exceeds the total number of "
  99                 "available GPUs in the cluster.")
  100         # Create a new placement group
  101         current_placement_group = ray.util.placement_group([{
  102             "GPU": 1
  103         }] * parallel_config.world_size)
  104         # Wait until PG is ready - this will block until all
  105         # requested resources are available, and will timeout
  106         # if they cannot be provisioned.
  107         ray.get(current_placement_group.ready(), timeout=1800)
  108 
  109     return None, current_placement_group
  110
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/vllm/engine/ray_utils.py b/vllm/engine/ray_utils.py
  --- a/vllm/engine/ray_utils.py
  +++ b/vllm/engine/ray_utils.py
  @@ -27,6 +27,7 @@
   except ImportError:
       ray = None
       TorchDistributedWorker = None
  +    RayWorker = None  # pylint: disable=invalid-name
   
   if TYPE_CHECKING:
       from ray.util.placement_group import PlacementGroup
  
verification_info: {"golden_diff": "diff --git a/vllm/engine/ray_utils.py b/vllm/engine/ray_utils.py\n--- a/vllm/engine/ray_utils.py\n+++ b/vllm/engine/ray_utils.py\n@@ -27,6 +27,7 @@\n except ImportError:\n     ray = None\n     TorchDistributedWorker = None\n+    RayWorker = None  # pylint: disable=invalid-name\n \n if TYPE_CHECKING:\n     from ray.util.placement_group import PlacementGroup\n", "issue": "RayWorker import error\nWhen import `vllm` I got following error\r\n```\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"/code/vllm/vllm/__init__.py\", line 4, in <module>\r\n    from vllm.engine.async_llm_engine import AsyncLLMEngine\r\n  File \"/code/vllm/vllm/engine/async_llm_engine.py\", line 7, in <module>\r\n    from vllm.engine.llm_engine import LLMEngine\r\n  File \"/code/vllm/vllm/engine/llm_engine.py\", line 9, in <module>\r\n    from vllm.engine.ray_utils import initialize_cluster, ray, RayWorker\r\nImportError: cannot import name 'RayWorker' from 'vllm.engine.ray_utils' \r\n```\r\nIt seems `ray` requires `pandas` I haven't installed it.\r\n\n", "before_files": [{"content": "import socket\nfrom typing import Optional, Tuple, TYPE_CHECKING\n\nfrom vllm.config import ParallelConfig\n\ntry:\n    import ray\n    from ray.air.util.torch_dist import TorchDistributedWorker\n\n    class RayWorker(TorchDistributedWorker):\n        \"\"\"Ray wrapper for vllm.worker.Worker, allowing Worker to be\n        lazliy initialized after Ray sets CUDA_VISIBLE_DEVICES.\"\"\"\n\n        def __init__(self) -> None:\n            self.worker = None\n\n        def init_worker(self, worker_init_fn):\n            self.worker = worker_init_fn()\n\n        def __getattr__(self, name):\n            return getattr(self.worker, name)\n\n        def execute_method(self, method, *args, **kwargs):\n            executor = getattr(self, method)\n            return executor(*args, **kwargs)\n\nexcept ImportError:\n    ray = None\n    TorchDistributedWorker = None\n\nif TYPE_CHECKING:\n    from ray.util.placement_group import PlacementGroup\n\n\ndef get_open_port():\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        s.bind((\"\", 0))\n        return s.getsockname()[1]\n\n\ndef initialize_cluster(\n    parallel_config: ParallelConfig,\n    engine_use_ray: bool = False,\n    ray_address: Optional[str] = None,\n) -> Tuple[str, Optional[\"PlacementGroup\"]]:\n    \"\"\"Initialize the distributed cluster probably with Ray.\n\n    Args:\n        parallel_config: The configurations for parallel execution.\n        engine_use_ray: Whether to use Ray for async engine.\n        ray_address: The address of the Ray cluster. If None, uses\n            the default Ray cluster address.\n\n    Returns:\n        A tuple of (`distributed_init_method`, `all_stage_devices`). The\n        `distributed_init_method` is the address for initializing the\n        distributed backend. `all_stage_devices` includes device IDs for\n        each worker in each pipeline stage. Each device ID is a tuple of\n        (rank, node resource, device id).\n    \"\"\"\n    if parallel_config.worker_use_ray or engine_use_ray:\n        if ray is None:\n            raise ImportError(\n                \"Ray is not installed. Please install Ray to use distributed \"\n                \"serving.\")\n        # Connect to a ray cluster.\n        ray.init(address=ray_address, ignore_reinit_error=True)\n\n    if not parallel_config.worker_use_ray:\n        # Initialize cluster locally.\n        port = get_open_port()\n        # We need to setup the distributed init method to make sure\n        # the distributed megatron code (e.g., get world size) works correctly.\n        distributed_init_method = f\"tcp://localhost:{port}\"\n        return distributed_init_method, None\n\n    current_placement_group = ray.util.get_current_placement_group()\n    if current_placement_group:\n        # We are in a placement group\n        bundles = current_placement_group.bundle_specs\n        # Verify that we can use the placement group.\n        gpu_bundles = 0\n        for bundle in bundles:\n            bundle_gpus = bundle.get(\"GPU\", 0)\n            if bundle_gpus > 1:\n                raise ValueError(\n                    \"Placement group bundle cannot have more than 1 GPU.\")\n            if bundle_gpus:\n                gpu_bundles += 1\n        if parallel_config.world_size > gpu_bundles:\n            raise ValueError(\n                \"The number of required GPUs exceeds the total number of \"\n                \"available GPUs in the placement group.\")\n    else:\n        num_gpus_in_cluster = ray.cluster_resources().get(\"GPU\", 0)\n        if parallel_config.world_size > num_gpus_in_cluster:\n            raise ValueError(\n                \"The number of required GPUs exceeds the total number of \"\n                \"available GPUs in the cluster.\")\n        # Create a new placement group\n        current_placement_group = ray.util.placement_group([{\n            \"GPU\": 1\n        }] * parallel_config.world_size)\n        # Wait until PG is ready - this will block until all\n        # requested resources are available, and will timeout\n        # if they cannot be provisioned.\n        ray.get(current_placement_group.ready(), timeout=1800)\n\n    return None, current_placement_group\n", "path": "vllm/engine/ray_utils.py"}], "after_files": [{"content": "import socket\nfrom typing import Optional, Tuple, TYPE_CHECKING\n\nfrom vllm.config import ParallelConfig\n\ntry:\n    import ray\n    from ray.air.util.torch_dist import TorchDistributedWorker\n\n    class RayWorker(TorchDistributedWorker):\n        \"\"\"Ray wrapper for vllm.worker.Worker, allowing Worker to be\n        lazliy initialized after Ray sets CUDA_VISIBLE_DEVICES.\"\"\"\n\n        def __init__(self) -> None:\n            self.worker = None\n\n        def init_worker(self, worker_init_fn):\n            self.worker = worker_init_fn()\n\n        def __getattr__(self, name):\n            return getattr(self.worker, name)\n\n        def execute_method(self, method, *args, **kwargs):\n            executor = getattr(self, method)\n            return executor(*args, **kwargs)\n\nexcept ImportError:\n    ray = None\n    TorchDistributedWorker = None\n    RayWorker = None  # pylint: disable=invalid-name\n\nif TYPE_CHECKING:\n    from ray.util.placement_group import PlacementGroup\n\n\ndef get_open_port():\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        s.bind((\"\", 0))\n        return s.getsockname()[1]\n\n\ndef initialize_cluster(\n    parallel_config: ParallelConfig,\n    engine_use_ray: bool = False,\n    ray_address: Optional[str] = None,\n) -> Tuple[str, Optional[\"PlacementGroup\"]]:\n    \"\"\"Initialize the distributed cluster probably with Ray.\n\n    Args:\n        parallel_config: The configurations for parallel execution.\n        engine_use_ray: Whether to use Ray for async engine.\n        ray_address: The address of the Ray cluster. If None, uses\n            the default Ray cluster address.\n\n    Returns:\n        A tuple of (`distributed_init_method`, `all_stage_devices`). The\n        `distributed_init_method` is the address for initializing the\n        distributed backend. `all_stage_devices` includes device IDs for\n        each worker in each pipeline stage. Each device ID is a tuple of\n        (rank, node resource, device id).\n    \"\"\"\n    if parallel_config.worker_use_ray or engine_use_ray:\n        if ray is None:\n            raise ImportError(\n                \"Ray is not installed. Please install Ray to use distributed \"\n                \"serving.\")\n        # Connect to a ray cluster.\n        ray.init(address=ray_address, ignore_reinit_error=True)\n\n    if not parallel_config.worker_use_ray:\n        # Initialize cluster locally.\n        port = get_open_port()\n        # We need to setup the distributed init method to make sure\n        # the distributed megatron code (e.g., get world size) works correctly.\n        distributed_init_method = f\"tcp://localhost:{port}\"\n        return distributed_init_method, None\n\n    current_placement_group = ray.util.get_current_placement_group()\n    if current_placement_group:\n        # We are in a placement group\n        bundles = current_placement_group.bundle_specs\n        # Verify that we can use the placement group.\n        gpu_bundles = 0\n        for bundle in bundles:\n            bundle_gpus = bundle.get(\"GPU\", 0)\n            if bundle_gpus > 1:\n                raise ValueError(\n                    \"Placement group bundle cannot have more than 1 GPU.\")\n            if bundle_gpus:\n                gpu_bundles += 1\n        if parallel_config.world_size > gpu_bundles:\n            raise ValueError(\n                \"The number of required GPUs exceeds the total number of \"\n                \"available GPUs in the placement group.\")\n    else:\n        num_gpus_in_cluster = ray.cluster_resources().get(\"GPU\", 0)\n        if parallel_config.world_size > num_gpus_in_cluster:\n            raise ValueError(\n                \"The number of required GPUs exceeds the total number of \"\n                \"available GPUs in the cluster.\")\n        # Create a new placement group\n        current_placement_group = ray.util.placement_group([{\n            \"GPU\": 1\n        }] * parallel_config.world_size)\n        # Wait until PG is ready - this will block until all\n        # requested resources are available, and will timeout\n        # if they cannot be provisioned.\n        ray.get(current_placement_group.ready(), timeout=1800)\n\n    return None, current_placement_group\n", "path": "vllm/engine/ray_utils.py"}]}