prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  [tensor] fix some unittests
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `applications/Chat/coati/models/gpt/gpt_actor.py`
  Content:
  ```
  1 from typing import Optional
  2 
  3 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  4 from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
  5 
  6 from ..base import Actor
  7 
  8 
  9 class GPTActor(Actor):
  10     """
  11     GPT Actor model.
  12 
  13     Args:
  14         pretrained (str): Pretrained model name or path.
  15         config (GPT2Config): Model config.
  16         checkpoint (bool): Enable gradient checkpointing.
  17         lora_rank (int): Rank of the LoRa layer.
  18         lora_train_bias (str): Bias training strategy for the LoRa layer.
  19     """
  20 
  21     def __init__(self,
  22                  pretrained: Optional[str] = None,
  23                  config: Optional[GPT2Config] = None,
  24                  checkpoint: bool = False,
  25                  lora_rank: int = 0,
  26                  lora_train_bias: str = 'none') -> None:
  27         if pretrained is not None:
  28             model = GPT2LMHeadModel.from_pretrained(pretrained)
  29         elif config is not None:
  30             model = GPT2LMHeadModel(config)
  31         else:
  32             model = GPT2LMHeadModel(GPT2Config())
  33         if checkpoint:
  34             model.gradient_checkpointing_enable()
  35         super().__init__(model, lora_rank, lora_train_bias)
  36
  ```
  
  Path: `applications/Chat/coati/models/gpt/gpt_critic.py`
  Content:
  ```
  1 from typing import Optional
  2 
  3 import torch.nn as nn
  4 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
  5 from transformers.models.gpt2.modeling_gpt2 import GPT2Model
  6 
  7 from ..base import Critic
  8 
  9 
  10 class GPTCritic(Critic):
  11     """
  12     GPT Critic model.
  13 
  14     Args:
  15         pretrained (str): Pretrained model name or path.
  16         config (GPT2Config): Model config.
  17         checkpoint (bool): Enable gradient checkpointing.
  18         lora_rank (int): Rank of the LO-RA decomposition.
  19         lora_train_bias (str): LoRA bias training mode.
  20     """
  21 
  22     def __init__(self,
  23                  pretrained: Optional[str] = None,
  24                  config: Optional[GPT2Config] = None,
  25                  checkpoint: bool = False,
  26                  lora_rank: int = 0,
  27                  lora_train_bias: str = 'none') -> None:
  28         if pretrained is not None:
  29             model = GPT2Model.from_pretrained(pretrained)
  30         elif config is not None:
  31             model = GPT2Model(config)
  32         else:
  33             model = GPT2Model(GPT2Config())
  34         if checkpoint:
  35             model.gradient_checkpointing_enable()
  36         value_head = nn.Linear(model.config.n_embd, 1)
  37         super().__init__(model, value_head, lora_rank, lora_train_bias)
  38
  ```
  --- 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/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py
  --- a/applications/Chat/coati/models/gpt/gpt_actor.py
  +++ b/applications/Chat/coati/models/gpt/gpt_actor.py
  @@ -23,7 +23,8 @@
                    config: Optional[GPT2Config] = None,
                    checkpoint: bool = False,
                    lora_rank: int = 0,
  -                 lora_train_bias: str = 'none') -> None:
  +                 lora_train_bias: str = 'none',
  +                 **kwargs) -> None:
           if pretrained is not None:
               model = GPT2LMHeadModel.from_pretrained(pretrained)
           elif config is not None:
  @@ -32,4 +33,4 @@
               model = GPT2LMHeadModel(GPT2Config())
           if checkpoint:
               model.gradient_checkpointing_enable()
  -        super().__init__(model, lora_rank, lora_train_bias)
  +        super().__init__(model, lora_rank, lora_train_bias, **kwargs)
  diff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py
  --- a/applications/Chat/coati/models/gpt/gpt_critic.py
  +++ b/applications/Chat/coati/models/gpt/gpt_critic.py
  @@ -24,7 +24,8 @@
                    config: Optional[GPT2Config] = None,
                    checkpoint: bool = False,
                    lora_rank: int = 0,
  -                 lora_train_bias: str = 'none') -> None:
  +                 lora_train_bias: str = 'none',
  +                 **kwargs) -> None:
           if pretrained is not None:
               model = GPT2Model.from_pretrained(pretrained)
           elif config is not None:
  @@ -34,4 +35,4 @@
           if checkpoint:
               model.gradient_checkpointing_enable()
           value_head = nn.Linear(model.config.n_embd, 1)
  -        super().__init__(model, value_head, lora_rank, lora_train_bias)
  +        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
  
verification_info: {"golden_diff": "diff --git a/applications/Chat/coati/models/gpt/gpt_actor.py b/applications/Chat/coati/models/gpt/gpt_actor.py\n--- a/applications/Chat/coati/models/gpt/gpt_actor.py\n+++ b/applications/Chat/coati/models/gpt/gpt_actor.py\n@@ -23,7 +23,8 @@\n                  config: Optional[GPT2Config] = None,\n                  checkpoint: bool = False,\n                  lora_rank: int = 0,\n-                 lora_train_bias: str = 'none') -> None:\n+                 lora_train_bias: str = 'none',\n+                 **kwargs) -> None:\n         if pretrained is not None:\n             model = GPT2LMHeadModel.from_pretrained(pretrained)\n         elif config is not None:\n@@ -32,4 +33,4 @@\n             model = GPT2LMHeadModel(GPT2Config())\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n-        super().__init__(model, lora_rank, lora_train_bias)\n+        super().__init__(model, lora_rank, lora_train_bias, **kwargs)\ndiff --git a/applications/Chat/coati/models/gpt/gpt_critic.py b/applications/Chat/coati/models/gpt/gpt_critic.py\n--- a/applications/Chat/coati/models/gpt/gpt_critic.py\n+++ b/applications/Chat/coati/models/gpt/gpt_critic.py\n@@ -24,7 +24,8 @@\n                  config: Optional[GPT2Config] = None,\n                  checkpoint: bool = False,\n                  lora_rank: int = 0,\n-                 lora_train_bias: str = 'none') -> None:\n+                 lora_train_bias: str = 'none',\n+                 **kwargs) -> None:\n         if pretrained is not None:\n             model = GPT2Model.from_pretrained(pretrained)\n         elif config is not None:\n@@ -34,4 +35,4 @@\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n         value_head = nn.Linear(model.config.n_embd, 1)\n-        super().__init__(model, value_head, lora_rank, lora_train_bias)\n+        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "issue": "[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from typing import Optional\n\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel\n\nfrom ..base import Actor\n\n\nclass GPTActor(Actor):\n    \"\"\"\n    GPT Actor model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LoRa layer.\n        lora_train_bias (str): Bias training strategy for the LoRa layer.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n        if pretrained is not None:\n            model = GPT2LMHeadModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2LMHeadModel(config)\n        else:\n            model = GPT2LMHeadModel(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        super().__init__(model, lora_rank, lora_train_bias)\n", "path": "applications/Chat/coati/models/gpt/gpt_actor.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2Model\n\nfrom ..base import Critic\n\n\nclass GPTCritic(Critic):\n    \"\"\"\n    GPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LO-RA decomposition.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n        if pretrained is not None:\n            model = GPT2Model.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2Model(config)\n        else:\n            model = GPT2Model(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.n_embd, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias)\n", "path": "applications/Chat/coati/models/gpt/gpt_critic.py"}], "after_files": [{"content": "from typing import Optional\n\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel\n\nfrom ..base import Actor\n\n\nclass GPTActor(Actor):\n    \"\"\"\n    GPT Actor model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LoRa layer.\n        lora_train_bias (str): Bias training strategy for the LoRa layer.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none',\n                 **kwargs) -> None:\n        if pretrained is not None:\n            model = GPT2LMHeadModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2LMHeadModel(config)\n        else:\n            model = GPT2LMHeadModel(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        super().__init__(model, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/Chat/coati/models/gpt/gpt_actor.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.gpt2.configuration_gpt2 import GPT2Config\nfrom transformers.models.gpt2.modeling_gpt2 import GPT2Model\n\nfrom ..base import Critic\n\n\nclass GPTCritic(Critic):\n    \"\"\"\n    GPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (GPT2Config): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the LO-RA decomposition.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none',\n                 **kwargs) -> None:\n        if pretrained is not None:\n            model = GPT2Model.from_pretrained(pretrained)\n        elif config is not None:\n            model = GPT2Model(config)\n        else:\n            model = GPT2Model(GPT2Config())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.n_embd, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/Chat/coati/models/gpt/gpt_critic.py"}]}