prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  BUG: Asking to pad but the tokenizer does not have a padding token
  ### Describe the bug
  When using Llama-2 PyTorch model for embedding creation, there's an issue:
  
  ----> 1 model.create_embedding("Once upon a time, there was a very old computer")
  
  File ~/inference/xinference/client.py:324, in RESTfulGenerateModelHandle.create_embedding(self, input)
      322 response = requests.post(url, json=request_body)
      323 if response.status_code != 200:
  --> 324     raise RuntimeError(
      325         f"Failed to create the embeddings, detail: {response.json()['detail']}"
      326     )
      328 response_data = response.json()
      329 return response_data
  
  RuntimeError: Failed to create the embeddings, detail: [address=127.0.0.1:35477, pid=951303] Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.
  
  
  --- 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/model/llm/pytorch/llama_2.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 from typing import Optional
  16 
  17 from ..llm_family import LLMFamilyV1, LLMSpecV1
  18 from .core import PytorchChatModel, PytorchModel, PytorchModelConfig
  19 
  20 
  21 class LlamaPytorchModel(PytorchModel):
  22     def __init__(
  23         self,
  24         model_uid: str,
  25         model_family: "LLMFamilyV1",
  26         model_spec: "LLMSpecV1",
  27         quantization: str,
  28         model_path: str,
  29         pytorch_model_config: Optional[PytorchModelConfig] = None,
  30     ):
  31         super().__init__(
  32             model_uid,
  33             model_family,
  34             model_spec,
  35             quantization,
  36             model_path,
  37             pytorch_model_config=pytorch_model_config,
  38         )
  39 
  40     def _load_model(self, kwargs: dict):
  41         model, tokenizer = super()._load_model(kwargs)
  42         model.config.eos_token_id = tokenizer.eos_token_id
  43         model.config.pad_token_id = tokenizer.pad_token_id
  44         return model, tokenizer
  45 
  46     @classmethod
  47     def match(cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1") -> bool:
  48         if llm_spec.model_format != "pytorch":
  49             return False
  50         if "llama-2" not in llm_family.model_name:
  51             return False
  52         if "generate" not in llm_family.model_ability:
  53             return False
  54         return True
  55 
  56 
  57 class LlamaPytorchChatModel(PytorchChatModel):
  58     def __init__(
  59         self,
  60         model_uid: str,
  61         model_family: "LLMFamilyV1",
  62         model_spec: "LLMSpecV1",
  63         quantization: str,
  64         model_path: str,
  65         pytorch_model_config: Optional["PytorchModelConfig"] = None,
  66     ):
  67         super().__init__(
  68             model_uid,
  69             model_family,
  70             model_spec,
  71             quantization,
  72             model_path,
  73             pytorch_model_config=pytorch_model_config,
  74         )
  75         self._use_fast_tokenizer = False
  76 
  77     def _load_model(self, kwargs: dict):
  78         model, tokenizer = super()._load_model(kwargs)
  79         model.config.eos_token_id = tokenizer.eos_token_id
  80         model.config.pad_token_id = tokenizer.pad_token_id
  81         return model, tokenizer
  82 
  83     @classmethod
  84     def match(cls, llm_family: "LLMFamilyV1", llm_spec: "LLMSpecV1") -> bool:
  85         if llm_spec.model_format != "pytorch":
  86             return False
  87         if "llama-2" not in llm_family.model_name:
  88             return False
  89         if "chat" not in llm_family.model_ability:
  90             return False
  91         return True
  92
  ```
  --- 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/model/llm/pytorch/llama_2.py b/xinference/model/llm/pytorch/llama_2.py
  --- a/xinference/model/llm/pytorch/llama_2.py
  +++ b/xinference/model/llm/pytorch/llama_2.py
  @@ -39,6 +39,9 @@
   
       def _load_model(self, kwargs: dict):
           model, tokenizer = super()._load_model(kwargs)
  +        # Llama has no pad token by default
  +        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125
  +        tokenizer.pad_token = tokenizer.eos_token
           model.config.eos_token_id = tokenizer.eos_token_id
           model.config.pad_token_id = tokenizer.pad_token_id
           return model, tokenizer
  @@ -76,6 +79,9 @@
   
       def _load_model(self, kwargs: dict):
           model, tokenizer = super()._load_model(kwargs)
  +        # Llama has no pad token by default
  +        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125
  +        tokenizer.pad_token = tokenizer.eos_token
           model.config.eos_token_id = tokenizer.eos_token_id
           model.config.pad_token_id = tokenizer.pad_token_id
           return model, tokenizer
  
verification_info: {"golden_diff": "diff --git a/xinference/model/llm/pytorch/llama_2.py b/xinference/model/llm/pytorch/llama_2.py\n--- a/xinference/model/llm/pytorch/llama_2.py\n+++ b/xinference/model/llm/pytorch/llama_2.py\n@@ -39,6 +39,9 @@\n \n     def _load_model(self, kwargs: dict):\n         model, tokenizer = super()._load_model(kwargs)\n+        # Llama has no pad token by default\n+        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125\n+        tokenizer.pad_token = tokenizer.eos_token\n         model.config.eos_token_id = tokenizer.eos_token_id\n         model.config.pad_token_id = tokenizer.pad_token_id\n         return model, tokenizer\n@@ -76,6 +79,9 @@\n \n     def _load_model(self, kwargs: dict):\n         model, tokenizer = super()._load_model(kwargs)\n+        # Llama has no pad token by default\n+        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125\n+        tokenizer.pad_token = tokenizer.eos_token\n         model.config.eos_token_id = tokenizer.eos_token_id\n         model.config.pad_token_id = tokenizer.pad_token_id\n         return model, tokenizer\n", "issue": "BUG: Asking to pad but the tokenizer does not have a padding token\n### Describe the bug\r\nWhen using Llama-2 PyTorch model for embedding creation, there's an issue:\r\n\r\n----> 1 model.create_embedding(\"Once upon a time, there was a very old computer\")\r\n\r\nFile ~/inference/xinference/client.py:324, in RESTfulGenerateModelHandle.create_embedding(self, input)\r\n    322 response = requests.post(url, json=request_body)\r\n    323 if response.status_code != 200:\r\n--> 324     raise RuntimeError(\r\n    325         f\"Failed to create the embeddings, detail: {response.json()['detail']}\"\r\n    326     )\r\n    328 response_data = response.json()\r\n    329 return response_data\r\n\r\nRuntimeError: Failed to create the embeddings, detail: [address=127.0.0.1:35477, pid=951303] Asking to pad but the tokenizer does not have a padding token. Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.\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\nfrom typing import Optional\n\nfrom ..llm_family import LLMFamilyV1, LLMSpecV1\nfrom .core import PytorchChatModel, PytorchModel, PytorchModelConfig\n\n\nclass LlamaPytorchModel(PytorchModel):\n    def __init__(\n        self,\n        model_uid: str,\n        model_family: \"LLMFamilyV1\",\n        model_spec: \"LLMSpecV1\",\n        quantization: str,\n        model_path: str,\n        pytorch_model_config: Optional[PytorchModelConfig] = None,\n    ):\n        super().__init__(\n            model_uid,\n            model_family,\n            model_spec,\n            quantization,\n            model_path,\n            pytorch_model_config=pytorch_model_config,\n        )\n\n    def _load_model(self, kwargs: dict):\n        model, tokenizer = super()._load_model(kwargs)\n        model.config.eos_token_id = tokenizer.eos_token_id\n        model.config.pad_token_id = tokenizer.pad_token_id\n        return model, tokenizer\n\n    @classmethod\n    def match(cls, llm_family: \"LLMFamilyV1\", llm_spec: \"LLMSpecV1\") -> bool:\n        if llm_spec.model_format != \"pytorch\":\n            return False\n        if \"llama-2\" not in llm_family.model_name:\n            return False\n        if \"generate\" not in llm_family.model_ability:\n            return False\n        return True\n\n\nclass LlamaPytorchChatModel(PytorchChatModel):\n    def __init__(\n        self,\n        model_uid: str,\n        model_family: \"LLMFamilyV1\",\n        model_spec: \"LLMSpecV1\",\n        quantization: str,\n        model_path: str,\n        pytorch_model_config: Optional[\"PytorchModelConfig\"] = None,\n    ):\n        super().__init__(\n            model_uid,\n            model_family,\n            model_spec,\n            quantization,\n            model_path,\n            pytorch_model_config=pytorch_model_config,\n        )\n        self._use_fast_tokenizer = False\n\n    def _load_model(self, kwargs: dict):\n        model, tokenizer = super()._load_model(kwargs)\n        model.config.eos_token_id = tokenizer.eos_token_id\n        model.config.pad_token_id = tokenizer.pad_token_id\n        return model, tokenizer\n\n    @classmethod\n    def match(cls, llm_family: \"LLMFamilyV1\", llm_spec: \"LLMSpecV1\") -> bool:\n        if llm_spec.model_format != \"pytorch\":\n            return False\n        if \"llama-2\" not in llm_family.model_name:\n            return False\n        if \"chat\" not in llm_family.model_ability:\n            return False\n        return True\n", "path": "xinference/model/llm/pytorch/llama_2.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\nfrom typing import Optional\n\nfrom ..llm_family import LLMFamilyV1, LLMSpecV1\nfrom .core import PytorchChatModel, PytorchModel, PytorchModelConfig\n\n\nclass LlamaPytorchModel(PytorchModel):\n    def __init__(\n        self,\n        model_uid: str,\n        model_family: \"LLMFamilyV1\",\n        model_spec: \"LLMSpecV1\",\n        quantization: str,\n        model_path: str,\n        pytorch_model_config: Optional[PytorchModelConfig] = None,\n    ):\n        super().__init__(\n            model_uid,\n            model_family,\n            model_spec,\n            quantization,\n            model_path,\n            pytorch_model_config=pytorch_model_config,\n        )\n\n    def _load_model(self, kwargs: dict):\n        model, tokenizer = super()._load_model(kwargs)\n        # Llama has no pad token by default\n        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125\n        tokenizer.pad_token = tokenizer.eos_token\n        model.config.eos_token_id = tokenizer.eos_token_id\n        model.config.pad_token_id = tokenizer.pad_token_id\n        return model, tokenizer\n\n    @classmethod\n    def match(cls, llm_family: \"LLMFamilyV1\", llm_spec: \"LLMSpecV1\") -> bool:\n        if llm_spec.model_format != \"pytorch\":\n            return False\n        if \"llama-2\" not in llm_family.model_name:\n            return False\n        if \"generate\" not in llm_family.model_ability:\n            return False\n        return True\n\n\nclass LlamaPytorchChatModel(PytorchChatModel):\n    def __init__(\n        self,\n        model_uid: str,\n        model_family: \"LLMFamilyV1\",\n        model_spec: \"LLMSpecV1\",\n        quantization: str,\n        model_path: str,\n        pytorch_model_config: Optional[\"PytorchModelConfig\"] = None,\n    ):\n        super().__init__(\n            model_uid,\n            model_family,\n            model_spec,\n            quantization,\n            model_path,\n            pytorch_model_config=pytorch_model_config,\n        )\n        self._use_fast_tokenizer = False\n\n    def _load_model(self, kwargs: dict):\n        model, tokenizer = super()._load_model(kwargs)\n        # Llama has no pad token by default\n        # https://github.com/huggingface/transformers/blob/07998ef39926b76d3f6667025535d0859eed61c3/docs/source/en/llm_tutorial.md?plain=1#L125\n        tokenizer.pad_token = tokenizer.eos_token\n        model.config.eos_token_id = tokenizer.eos_token_id\n        model.config.pad_token_id = tokenizer.pad_token_id\n        return model, tokenizer\n\n    @classmethod\n    def match(cls, llm_family: \"LLMFamilyV1\", llm_spec: \"LLMSpecV1\") -> bool:\n        if llm_spec.model_format != \"pytorch\":\n            return False\n        if \"llama-2\" not in llm_family.model_name:\n            return False\n        if \"chat\" not in llm_family.model_ability:\n            return False\n        return True\n", "path": "xinference/model/llm/pytorch/llama_2.py"}]}