prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  ENH: Add the option to use CPU to inference even there is GPU device
  ### Is your feature request related to a problem? Please describe
  There is a GPU in my server, but when load some LLM model, I need load it into my memory because the model size is bigger
  than GPU memory.
  
  However, when I launch the model from web page, the N-GPU setting only contains auto, 0, 1 options, if I select 0, system will complain the following error:
  
  > Server error: 400 - [address=0.0.0.0:19270, pid=2063850] The parameter `n_gpu` must be greater than 0 and not greater than the number of GPUs: 1 on the machine.
  
  ### Describe the solution you'd like
  I think when the N GPU setting is set to 0, it should use CPU as inference device.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `xinference/device_utils.py`
  Content:
  ```
  1 # Copyright 2022-2023 XProbe Inc.
  2 #
  3 # Licensed under the Apache License, Version 2.0 (the "License");
  4 # you may not use this file except in compliance with the License.
  5 # You may obtain a copy of the License at
  6 #
  7 #      http://www.apache.org/licenses/LICENSE-2.0
  8 #
  9 # Unless required by applicable law or agreed to in writing, software
  10 # distributed under the License is distributed on an "AS IS" BASIS,
  11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  12 # See the License for the specific language governing permissions and
  13 # limitations under the License.
  14 
  15 import os
  16 
  17 import torch
  18 from typing_extensions import Literal, Union
  19 
  20 DeviceType = Literal["cuda", "mps", "xpu", "cpu"]
  21 
  22 
  23 def is_xpu_available() -> bool:
  24     return hasattr(torch, "xpu") and torch.xpu.is_available()
  25 
  26 
  27 def get_available_device() -> DeviceType:
  28     if torch.cuda.is_available():
  29         return "cuda"
  30     elif torch.backends.mps.is_available():
  31         return "mps"
  32     elif is_xpu_available():
  33         return "xpu"
  34     return "cpu"
  35 
  36 
  37 def is_device_available(device: str) -> bool:
  38     if device == "cuda":
  39         return torch.cuda.is_available()
  40     elif device == "mps":
  41         return torch.backends.mps.is_available()
  42     elif device == "xpu":
  43         return is_xpu_available()
  44     elif device == "cpu":
  45         return True
  46 
  47     return False
  48 
  49 
  50 def move_model_to_available_device(model):
  51     device = get_available_device()
  52 
  53     if device == "cpu":
  54         return model
  55 
  56     return model.to(device)
  57 
  58 
  59 def get_device_preferred_dtype(device: str) -> Union[torch.dtype, None]:
  60     if device == "cpu":
  61         return torch.float32
  62     elif device == "cuda" or device == "mps":
  63         return torch.float16
  64     elif device == "xpu":
  65         return torch.bfloat16
  66 
  67     return None
  68 
  69 
  70 def is_hf_accelerate_supported(device: str) -> bool:
  71     return device == "cuda" or device == "xpu"
  72 
  73 
  74 def empty_cache():
  75     if torch.cuda.is_available():
  76         torch.cuda.empty_cache()
  77     if torch.backends.mps.is_available():
  78         torch.mps.empty_cache()
  79     if is_xpu_available():
  80         torch.xpu.empty_cache()
  81 
  82 
  83 def gpu_count():
  84     if torch.cuda.is_available():
  85         cuda_visible_devices_env = os.getenv("CUDA_VISIBLE_DEVICES", None)
  86 
  87         if cuda_visible_devices_env is None:
  88             return torch.cuda.device_count()
  89 
  90         cuda_visible_devices = (
  91             cuda_visible_devices_env.split(",") if cuda_visible_devices_env else []
  92         )
  93 
  94         return min(torch.cuda.device_count(), len(cuda_visible_devices))
  95     elif torch.backends.mps.is_available():
  96         return 1
  97     elif is_xpu_available():
  98         return torch.xpu.device_count()
  99     else:
  100         return 0
  101
  ```
  --- 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/xinference/device_utils.py b/xinference/device_utils.py
  --- a/xinference/device_utils.py
  +++ b/xinference/device_utils.py
  @@ -92,8 +92,6 @@
           )
   
           return min(torch.cuda.device_count(), len(cuda_visible_devices))
  -    elif torch.backends.mps.is_available():
  -        return 1
       elif is_xpu_available():
           return torch.xpu.device_count()
       else:
  
verification_info: {"golden_diff": "diff --git a/xinference/device_utils.py b/xinference/device_utils.py\n--- a/xinference/device_utils.py\n+++ b/xinference/device_utils.py\n@@ -92,8 +92,6 @@\n         )\n \n         return min(torch.cuda.device_count(), len(cuda_visible_devices))\n-    elif torch.backends.mps.is_available():\n-        return 1\n     elif is_xpu_available():\n         return torch.xpu.device_count()\n     else:\n", "issue": "ENH: Add the option to use CPU to inference even there is GPU device\n### Is your feature request related to a problem? Please describe\r\nThere is a GPU in my server, but when load some LLM model, I need load it into my memory because the model size is bigger\r\nthan GPU memory.\r\n\r\nHowever, when I launch the model from web page, the N-GPU setting only contains auto, 0, 1 options, if I select 0, system will complain the following error:\r\n\r\n> Server error: 400 - [address=0.0.0.0:19270, pid=2063850] The parameter `n_gpu` must be greater than 0 and not greater than the number of GPUs: 1 on the machine.\r\n\r\n### Describe the solution you'd like\r\nI think when the N GPU setting is set to 0, it should use CPU as inference device.\r\n\n", "before_files": [{"content": "# Copyright 2022-2023 XProbe Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nimport torch\nfrom typing_extensions import Literal, Union\n\nDeviceType = Literal[\"cuda\", \"mps\", \"xpu\", \"cpu\"]\n\n\ndef is_xpu_available() -> bool:\n    return hasattr(torch, \"xpu\") and torch.xpu.is_available()\n\n\ndef get_available_device() -> DeviceType:\n    if torch.cuda.is_available():\n        return \"cuda\"\n    elif torch.backends.mps.is_available():\n        return \"mps\"\n    elif is_xpu_available():\n        return \"xpu\"\n    return \"cpu\"\n\n\ndef is_device_available(device: str) -> bool:\n    if device == \"cuda\":\n        return torch.cuda.is_available()\n    elif device == \"mps\":\n        return torch.backends.mps.is_available()\n    elif device == \"xpu\":\n        return is_xpu_available()\n    elif device == \"cpu\":\n        return True\n\n    return False\n\n\ndef move_model_to_available_device(model):\n    device = get_available_device()\n\n    if device == \"cpu\":\n        return model\n\n    return model.to(device)\n\n\ndef get_device_preferred_dtype(device: str) -> Union[torch.dtype, None]:\n    if device == \"cpu\":\n        return torch.float32\n    elif device == \"cuda\" or device == \"mps\":\n        return torch.float16\n    elif device == \"xpu\":\n        return torch.bfloat16\n\n    return None\n\n\ndef is_hf_accelerate_supported(device: str) -> bool:\n    return device == \"cuda\" or device == \"xpu\"\n\n\ndef empty_cache():\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n    if torch.backends.mps.is_available():\n        torch.mps.empty_cache()\n    if is_xpu_available():\n        torch.xpu.empty_cache()\n\n\ndef gpu_count():\n    if torch.cuda.is_available():\n        cuda_visible_devices_env = os.getenv(\"CUDA_VISIBLE_DEVICES\", None)\n\n        if cuda_visible_devices_env is None:\n            return torch.cuda.device_count()\n\n        cuda_visible_devices = (\n            cuda_visible_devices_env.split(\",\") if cuda_visible_devices_env else []\n        )\n\n        return min(torch.cuda.device_count(), len(cuda_visible_devices))\n    elif torch.backends.mps.is_available():\n        return 1\n    elif is_xpu_available():\n        return torch.xpu.device_count()\n    else:\n        return 0\n", "path": "xinference/device_utils.py"}], "after_files": [{"content": "# Copyright 2022-2023 XProbe Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nimport torch\nfrom typing_extensions import Literal, Union\n\nDeviceType = Literal[\"cuda\", \"mps\", \"xpu\", \"cpu\"]\n\n\ndef is_xpu_available() -> bool:\n    return hasattr(torch, \"xpu\") and torch.xpu.is_available()\n\n\ndef get_available_device() -> DeviceType:\n    if torch.cuda.is_available():\n        return \"cuda\"\n    elif torch.backends.mps.is_available():\n        return \"mps\"\n    elif is_xpu_available():\n        return \"xpu\"\n    return \"cpu\"\n\n\ndef is_device_available(device: str) -> bool:\n    if device == \"cuda\":\n        return torch.cuda.is_available()\n    elif device == \"mps\":\n        return torch.backends.mps.is_available()\n    elif device == \"xpu\":\n        return is_xpu_available()\n    elif device == \"cpu\":\n        return True\n\n    return False\n\n\ndef move_model_to_available_device(model):\n    device = get_available_device()\n\n    if device == \"cpu\":\n        return model\n\n    return model.to(device)\n\n\ndef get_device_preferred_dtype(device: str) -> Union[torch.dtype, None]:\n    if device == \"cpu\":\n        return torch.float32\n    elif device == \"cuda\" or device == \"mps\":\n        return torch.float16\n    elif device == \"xpu\":\n        return torch.bfloat16\n\n    return None\n\n\ndef is_hf_accelerate_supported(device: str) -> bool:\n    return device == \"cuda\" or device == \"xpu\"\n\n\ndef empty_cache():\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n    if torch.backends.mps.is_available():\n        torch.mps.empty_cache()\n    if is_xpu_available():\n        torch.xpu.empty_cache()\n\n\ndef gpu_count():\n    if torch.cuda.is_available():\n        cuda_visible_devices_env = os.getenv(\"CUDA_VISIBLE_DEVICES\", None)\n\n        if cuda_visible_devices_env is None:\n            return torch.cuda.device_count()\n\n        cuda_visible_devices = (\n            cuda_visible_devices_env.split(\",\") if cuda_visible_devices_env else []\n        )\n\n        return min(torch.cuda.device_count(), len(cuda_visible_devices))\n    elif is_xpu_available():\n        return torch.xpu.device_count()\n    else:\n        return 0\n", "path": "xinference/device_utils.py"}]}