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()

```

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

{"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"}]}