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/ChatGPT/chatgpt/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     """
19 
20     def __init__(self,
21                  pretrained: Optional[str] = None,
22                  config: Optional[GPT2Config] = None,
23                  checkpoint: bool = False,
24                  **kwargs) -> None:
25         if pretrained is not None:
26             model = GPT2Model.from_pretrained(pretrained)
27         elif config is not None:
28             model = GPT2Model(config)
29         else:
30             model = GPT2Model(GPT2Config())
31         if checkpoint:
32             model.gradient_checkpointing_enable()
33         value_head = nn.Linear(model.config.n_embd, 1)
34         super().__init__(model, value_head, **kwargs)
35
```

Path: `applications/ChatGPT/chatgpt/models/opt/opt_critic.py`
Content:
```
1 from typing import Optional
2 
3 import torch.nn as nn
4 from transformers.models.opt.configuration_opt import OPTConfig
5 from transformers.models.opt.modeling_opt import OPTModel
6 
7 from ..base import Critic
8 
9 
10 class OPTCritic(Critic):
11     """
12     OPT Critic model.
13 
14     Args:
15         pretrained (str): Pretrained model name or path.
16         config (OPTConfig): Model config.
17         checkpoint (bool): Enable gradient checkpointing.
18         lora_rank (int): Rank of the low-rank approximation.
19         lora_train_bias (str): LoRA bias training mode.
20     """
21 
22     def __init__(self,
23                  pretrained: Optional[str] = None,
24                  config: Optional[OPTConfig] = None,
25                  checkpoint: bool = False,
26                  lora_rank: int = 0,
27                  lora_train_bias: str = 'none',
28                  **kwargs) -> None:
29         if pretrained is not None:
30             model = OPTModel.from_pretrained(pretrained)
31         elif config is not None:
32             model = OPTModel(config)
33         else:
34             model = OPTModel(OPTConfig())
35         if checkpoint:
36             model.gradient_checkpointing_enable()
37         value_head = nn.Linear(model.config.hidden_size, 1)
38         super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)
39
```

Path: `applications/ChatGPT/chatgpt/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     """
18 
19     def __init__(self,
20                  pretrained: Optional[str] = None,
21                  config: Optional[GPT2Config] = None,
22                  checkpoint: bool = False) -> None:
23         if pretrained is not None:
24             model = GPT2LMHeadModel.from_pretrained(pretrained)
25         elif config is not None:
26             model = GPT2LMHeadModel(config)
27         else:
28             model = GPT2LMHeadModel(GPT2Config())
29         if checkpoint:
30             model.gradient_checkpointing_enable()
31         super().__init__(model)
32
```
--- 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/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py
--- a/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py
+++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py
@@ -14,12 +14,16 @@
         pretrained (str): Pretrained model name or path.
         config (GPT2Config): Model config.
         checkpoint (bool): Enable gradient checkpointing.
+        lora_rank (int): Rank of the LoRa layer.
+        lora_train_bias (str): Bias training strategy for the LoRa layer.
     """
 
     def __init__(self,
                  pretrained: Optional[str] = None,
                  config: Optional[GPT2Config] = None,
-                 checkpoint: bool = False) -> None:
+                 checkpoint: bool = False,
+                 lora_rank: int = 0,
+                 lora_train_bias: str = 'none') -> None:
         if pretrained is not None:
             model = GPT2LMHeadModel.from_pretrained(pretrained)
         elif config is not None:
@@ -28,4 +32,4 @@
             model = GPT2LMHeadModel(GPT2Config())
         if checkpoint:
             model.gradient_checkpointing_enable()
-        super().__init__(model)
+        super().__init__(model, lora_rank, lora_train_bias)
diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py
--- a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py
+++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py
@@ -15,13 +15,16 @@
         pretrained (str): Pretrained model name or path.
         config (GPT2Config): Model config.
         checkpoint (bool): Enable gradient checkpointing.
+        lora_rank (int): Rank of the LO-RA decomposition.
+        lora_train_bias (str): LoRA bias training mode.
     """
 
     def __init__(self,
                  pretrained: Optional[str] = None,
                  config: Optional[GPT2Config] = None,
                  checkpoint: bool = False,
-                 **kwargs) -> None:
+                 lora_rank: int = 0,
+                 lora_train_bias: str = 'none') -> None:
         if pretrained is not None:
             model = GPT2Model.from_pretrained(pretrained)
         elif config is not None:
@@ -31,4 +34,4 @@
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.n_embd, 1)
-        super().__init__(model, value_head, **kwargs)
+        super().__init__(model, value_head, lora_rank, lora_train_bias)
diff --git a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py b/applications/ChatGPT/chatgpt/models/opt/opt_critic.py
--- a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py
+++ b/applications/ChatGPT/chatgpt/models/opt/opt_critic.py
@@ -34,5 +34,5 @@
             model = OPTModel(OPTConfig())
         if checkpoint:
             model.gradient_checkpointing_enable()
-        value_head = nn.Linear(model.config.hidden_size, 1)
+        value_head = nn.Linear(model.config.word_embed_proj_dim, 1)
         super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)

{"golden_diff": "diff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py\n--- a/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py\n+++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py\n@@ -14,12 +14,16 @@\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) -> 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@@ -28,4 +32,4 @@\n             model = GPT2LMHeadModel(GPT2Config())\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n-        super().__init__(model)\n+        super().__init__(model, lora_rank, lora_train_bias)\ndiff --git a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py b/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py\n--- a/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py\n+++ b/applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py\n@@ -15,13 +15,16 @@\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-                 **kwargs) -> None:\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@@ -31,4 +34,4 @@\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n         value_head = nn.Linear(model.config.n_embd, 1)\n-        super().__init__(model, value_head, **kwargs)\n+        super().__init__(model, value_head, lora_rank, lora_train_bias)\ndiff --git a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py b/applications/ChatGPT/chatgpt/models/opt/opt_critic.py\n--- a/applications/ChatGPT/chatgpt/models/opt/opt_critic.py\n+++ b/applications/ChatGPT/chatgpt/models/opt/opt_critic.py\n@@ -34,5 +34,5 @@\n             model = OPTModel(OPTConfig())\n         if checkpoint:\n             model.gradient_checkpointing_enable()\n-        value_head = nn.Linear(model.config.hidden_size, 1)\n+        value_head = nn.Linear(model.config.word_embed_proj_dim, 1)\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\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    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False,\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, **kwargs)\n", "path": "applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.opt.configuration_opt import OPTConfig\nfrom transformers.models.opt.modeling_opt import OPTModel\n\nfrom ..base import Critic\n\n\nclass OPTCritic(Critic):\n    \"\"\"\n    OPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (OPTConfig): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the low-rank approximation.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[OPTConfig] = 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 = OPTModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = OPTModel(config)\n        else:\n            model = OPTModel(OPTConfig())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.hidden_size, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/ChatGPT/chatgpt/models/opt/opt_critic.py"}, {"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    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[GPT2Config] = None,\n                 checkpoint: bool = False) -> 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)\n", "path": "applications/ChatGPT/chatgpt/models/gpt/gpt_actor.py"}], "after_files": [{"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/ChatGPT/chatgpt/models/gpt/gpt_critic.py"}, {"content": "from typing import Optional\n\nimport torch.nn as nn\nfrom transformers.models.opt.configuration_opt import OPTConfig\nfrom transformers.models.opt.modeling_opt import OPTModel\n\nfrom ..base import Critic\n\n\nclass OPTCritic(Critic):\n    \"\"\"\n    OPT Critic model.\n\n    Args:\n        pretrained (str): Pretrained model name or path.\n        config (OPTConfig): Model config.\n        checkpoint (bool): Enable gradient checkpointing.\n        lora_rank (int): Rank of the low-rank approximation.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 pretrained: Optional[str] = None,\n                 config: Optional[OPTConfig] = 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 = OPTModel.from_pretrained(pretrained)\n        elif config is not None:\n            model = OPTModel(config)\n        else:\n            model = OPTModel(OPTConfig())\n        if checkpoint:\n            model.gradient_checkpointing_enable()\n        value_head = nn.Linear(model.config.word_embed_proj_dim, 1)\n        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)\n", "path": "applications/ChatGPT/chatgpt/models/opt/opt_critic.py"}, {"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/ChatGPT/chatgpt/models/gpt/gpt_actor.py"}]}