prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [CI] Test examples in CI
  ### Anything you want to discuss about vllm.
  
  Current scripts in `examples/` directory are not tested in CI. We should run them to ensure passing 
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/llava_example.py`
  Content:
  ```
  1 import argparse
  2 import os
  3 import subprocess
  4 
  5 import torch
  6 
  7 from vllm import LLM
  8 from vllm.sequence import MultiModalData
  9 
  10 # The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.
  11 
  12 
  13 def run_llava_pixel_values():
  14     llm = LLM(
  15         model="llava-hf/llava-1.5-7b-hf",
  16         image_input_type="pixel_values",
  17         image_token_id=32000,
  18         image_input_shape="1,3,336,336",
  19         image_feature_size=576,
  20     )
  21 
  22     prompt = "<image>" * 576 + (
  23         "\nUSER: What is the content of this image?\nASSISTANT:")
  24 
  25     # This should be provided by another online or offline component.
  26     images = torch.load("images/stop_sign_pixel_values.pt")
  27 
  28     outputs = llm.generate(prompt,
  29                            multi_modal_data=MultiModalData(
  30                                type=MultiModalData.Type.IMAGE, data=images))
  31     for o in outputs:
  32         generated_text = o.outputs[0].text
  33         print(generated_text)
  34 
  35 
  36 def run_llava_image_features():
  37     llm = LLM(
  38         model="llava-hf/llava-1.5-7b-hf",
  39         image_input_type="image_features",
  40         image_token_id=32000,
  41         image_input_shape="1,576,1024",
  42         image_feature_size=576,
  43     )
  44 
  45     prompt = "<image>" * 576 + (
  46         "\nUSER: What is the content of this image?\nASSISTANT:")
  47 
  48     # This should be provided by another online or offline component.
  49     images = torch.load("images/stop_sign_image_features.pt")
  50 
  51     outputs = llm.generate(prompt,
  52                            multi_modal_data=MultiModalData(
  53                                type=MultiModalData.Type.IMAGE, data=images))
  54     for o in outputs:
  55         generated_text = o.outputs[0].text
  56         print(generated_text)
  57 
  58 
  59 def main(args):
  60     if args.type == "pixel_values":
  61         run_llava_pixel_values()
  62     else:
  63         run_llava_image_features()
  64 
  65 
  66 if __name__ == "__main__":
  67     parser = argparse.ArgumentParser(description="Demo on Llava")
  68     parser.add_argument("--type",
  69                         type=str,
  70                         choices=["pixel_values", "image_features"],
  71                         default="pixel_values",
  72                         help="image input type")
  73     args = parser.parse_args()
  74     # Download from s3
  75     s3_bucket_path = "s3://air-example-data-2/vllm_opensource_llava/"
  76     local_directory = "images"
  77 
  78     # Make sure the local directory exists or create it
  79     os.makedirs(local_directory, exist_ok=True)
  80 
  81     # Use AWS CLI to sync the directory
  82     subprocess.check_call(
  83         ["aws", "s3", "sync", s3_bucket_path, local_directory])
  84     main(args)
  85
  ```
  --- 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/examples/llava_example.py b/examples/llava_example.py
  --- a/examples/llava_example.py
  +++ b/examples/llava_example.py
  @@ -78,7 +78,13 @@
       # Make sure the local directory exists or create it
       os.makedirs(local_directory, exist_ok=True)
   
  -    # Use AWS CLI to sync the directory
  -    subprocess.check_call(
  -        ["aws", "s3", "sync", s3_bucket_path, local_directory])
  +    # Use AWS CLI to sync the directory, assume anonymous access
  +    subprocess.check_call([
  +        "aws",
  +        "s3",
  +        "sync",
  +        s3_bucket_path,
  +        local_directory,
  +        "--no-sign-request",
  +    ])
       main(args)
  
verification_info: {"golden_diff": "diff --git a/examples/llava_example.py b/examples/llava_example.py\n--- a/examples/llava_example.py\n+++ b/examples/llava_example.py\n@@ -78,7 +78,13 @@\n     # Make sure the local directory exists or create it\n     os.makedirs(local_directory, exist_ok=True)\n \n-    # Use AWS CLI to sync the directory\n-    subprocess.check_call(\n-        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n+    # Use AWS CLI to sync the directory, assume anonymous access\n+    subprocess.check_call([\n+        \"aws\",\n+        \"s3\",\n+        \"sync\",\n+        s3_bucket_path,\n+        local_directory,\n+        \"--no-sign-request\",\n+    ])\n     main(args)\n", "issue": "[CI] Test examples in CI\n### Anything you want to discuss about vllm.\n\nCurrent scripts in `examples/` directory are not tested in CI. We should run them to ensure passing \n", "before_files": [{"content": "import argparse\nimport os\nimport subprocess\n\nimport torch\n\nfrom vllm import LLM\nfrom vllm.sequence import MultiModalData\n\n# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.\n\n\ndef run_llava_pixel_values():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"pixel_values\",\n        image_token_id=32000,\n        image_input_shape=\"1,3,336,336\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_pixel_values.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef run_llava_image_features():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"image_features\",\n        image_token_id=32000,\n        image_input_shape=\"1,576,1024\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_image_features.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef main(args):\n    if args.type == \"pixel_values\":\n        run_llava_pixel_values()\n    else:\n        run_llava_image_features()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Demo on Llava\")\n    parser.add_argument(\"--type\",\n                        type=str,\n                        choices=[\"pixel_values\", \"image_features\"],\n                        default=\"pixel_values\",\n                        help=\"image input type\")\n    args = parser.parse_args()\n    # Download from s3\n    s3_bucket_path = \"s3://air-example-data-2/vllm_opensource_llava/\"\n    local_directory = \"images\"\n\n    # Make sure the local directory exists or create it\n    os.makedirs(local_directory, exist_ok=True)\n\n    # Use AWS CLI to sync the directory\n    subprocess.check_call(\n        [\"aws\", \"s3\", \"sync\", s3_bucket_path, local_directory])\n    main(args)\n", "path": "examples/llava_example.py"}], "after_files": [{"content": "import argparse\nimport os\nimport subprocess\n\nimport torch\n\nfrom vllm import LLM\nfrom vllm.sequence import MultiModalData\n\n# The assets are located at `s3://air-example-data-2/vllm_opensource_llava/`.\n\n\ndef run_llava_pixel_values():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"pixel_values\",\n        image_token_id=32000,\n        image_input_shape=\"1,3,336,336\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_pixel_values.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef run_llava_image_features():\n    llm = LLM(\n        model=\"llava-hf/llava-1.5-7b-hf\",\n        image_input_type=\"image_features\",\n        image_token_id=32000,\n        image_input_shape=\"1,576,1024\",\n        image_feature_size=576,\n    )\n\n    prompt = \"<image>\" * 576 + (\n        \"\\nUSER: What is the content of this image?\\nASSISTANT:\")\n\n    # This should be provided by another online or offline component.\n    images = torch.load(\"images/stop_sign_image_features.pt\")\n\n    outputs = llm.generate(prompt,\n                           multi_modal_data=MultiModalData(\n                               type=MultiModalData.Type.IMAGE, data=images))\n    for o in outputs:\n        generated_text = o.outputs[0].text\n        print(generated_text)\n\n\ndef main(args):\n    if args.type == \"pixel_values\":\n        run_llava_pixel_values()\n    else:\n        run_llava_image_features()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Demo on Llava\")\n    parser.add_argument(\"--type\",\n                        type=str,\n                        choices=[\"pixel_values\", \"image_features\"],\n                        default=\"pixel_values\",\n                        help=\"image input type\")\n    args = parser.parse_args()\n    # Download from s3\n    s3_bucket_path = \"s3://air-example-data-2/vllm_opensource_llava/\"\n    local_directory = \"images\"\n\n    # Make sure the local directory exists or create it\n    os.makedirs(local_directory, exist_ok=True)\n\n    # Use AWS CLI to sync the directory, assume anonymous access\n    subprocess.check_call([\n        \"aws\",\n        \"s3\",\n        \"sync\",\n        s3_bucket_path,\n        local_directory,\n        \"--no-sign-request\",\n    ])\n    main(args)\n", "path": "examples/llava_example.py"}]}