We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[chatgpt] change critic input as state
> ## 📌 Checklist before creating the PR
> * [x]  I have created an issue for this PR for traceability
> * [x]  The title follows the standard format: `[doc/gemini/tensor/...]: A concise description`
> * [ ]  I have added relevant tags if possible for us to better distinguish different PRs
> 
> ## 🚨 Issue number
> > Link this PR to your issue with words like fixed to automatically close the linked issue upon merge
> > e.g. `fixed #1234`, `closed #1234`, `resolved #1234`
> > fixed #3042
> 
> ## 📝 What does this PR do?
> > Summarize your work here.
> > if you have any plots/diagrams/screenshots/tables, please attach them here.
> 
> This commit fix chatgpt critic input as state according to A2C RL algorithm.
> 
> ## 💥 Checklist before requesting a review
> * [x]  I have linked my PR to an issue ([instruction](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue))
> * [x]  My issue clearly describes the problem/feature/proposal, with diagrams/charts/table/code if possible
> * [x]  I have performed a self-review of my code
> * [ ]  I have added thorough tests.
> * [ ]  I have added docstrings for all the functions/methods I implemented
> 
> ## ⭐️ Do you enjoy contributing to Colossal-AI?
> * [x]  🌝 Yes, I do.
> * [ ]  🌚 No, I don't.
> 
> Tell us more if you don't enjoy contributing to Colossal-AI.


[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/base/critic.py`
Content:
```
1 from typing import Optional
2 
3 import torch
4 import torch.nn as nn
5 
6 from ..lora import LoRAModule
7 from ..utils import masked_mean
8 
9 
10 class Critic(LoRAModule):
11     """
12     Critic model base class.
13 
14     Args:
15         model (nn.Module): Critic model.
16         value_head (nn.Module): Value head to get value.
17         lora_rank (int): LoRA rank.
18         lora_train_bias (str): LoRA bias training mode.
19     """
20 
21     def __init__(self,
22                  model: nn.Module,
23                  value_head: nn.Module,
24                  lora_rank: int = 0,
25                  lora_train_bias: str = 'none') -> None:
26 
27         super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)
28         self.model = model
29         self.value_head = value_head
30         self.convert_to_lora()
31 
32     def forward(self,
33                 sequences: torch.LongTensor,
34                 action_mask: Optional[torch.Tensor] = None,
35                 attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
36         outputs = self.model(sequences, attention_mask=attention_mask)
37         last_hidden_states = outputs['last_hidden_state']
38 
39         values = self.value_head(last_hidden_states).squeeze(-1)[:, :-1]
40 
41         if action_mask is not None:
42             num_actions = action_mask.size(1)
43             values = values[:, -num_actions:]
44             value = masked_mean(values, action_mask, dim=1)
45             return value
46         value = values.mean(dim=1).squeeze(1)
47         return value
48
```
--- 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/base/critic.py b/applications/ChatGPT/chatgpt/models/base/critic.py
--- a/applications/ChatGPT/chatgpt/models/base/critic.py
+++ b/applications/ChatGPT/chatgpt/models/base/critic.py
@@ -36,12 +36,15 @@
         outputs = self.model(sequences, attention_mask=attention_mask)
         last_hidden_states = outputs['last_hidden_state']
 
-        values = self.value_head(last_hidden_states).squeeze(-1)[:, :-1]
+        values = self.value_head(last_hidden_states).squeeze(-1)
 
         if action_mask is not None:
             num_actions = action_mask.size(1)
-            values = values[:, -num_actions:]
-            value = masked_mean(values, action_mask, dim=1)
+            prompt_mask = attention_mask[:, :-num_actions]
+            values = values[:, :-num_actions]
+            value = masked_mean(values, prompt_mask, dim=1)
             return value
+
+        values = values[:, :-1]
         value = values.mean(dim=1).squeeze(1)
         return value

{"golden_diff": "diff --git a/applications/ChatGPT/chatgpt/models/base/critic.py b/applications/ChatGPT/chatgpt/models/base/critic.py\n--- a/applications/ChatGPT/chatgpt/models/base/critic.py\n+++ b/applications/ChatGPT/chatgpt/models/base/critic.py\n@@ -36,12 +36,15 @@\n         outputs = self.model(sequences, attention_mask=attention_mask)\n         last_hidden_states = outputs['last_hidden_state']\n \n-        values = self.value_head(last_hidden_states).squeeze(-1)[:, :-1]\n+        values = self.value_head(last_hidden_states).squeeze(-1)\n \n         if action_mask is not None:\n             num_actions = action_mask.size(1)\n-            values = values[:, -num_actions:]\n-            value = masked_mean(values, action_mask, dim=1)\n+            prompt_mask = attention_mask[:, :-num_actions]\n+            values = values[:, :-num_actions]\n+            value = masked_mean(values, prompt_mask, dim=1)\n             return value\n+\n+        values = values[:, :-1]\n         value = values.mean(dim=1).squeeze(1)\n         return value\n", "issue": "[chatgpt] change critic input as state\n> ## \ud83d\udccc Checklist before creating the PR\r\n> * [x]  I have created an issue for this PR for traceability\r\n> * [x]  The title follows the standard format: `[doc/gemini/tensor/...]: A concise description`\r\n> * [ ]  I have added relevant tags if possible for us to better distinguish different PRs\r\n> \r\n> ## \ud83d\udea8 Issue number\r\n> > Link this PR to your issue with words like fixed to automatically close the linked issue upon merge\r\n> > e.g. `fixed #1234`, `closed #1234`, `resolved #1234`\r\n> > fixed #3042\r\n> \r\n> ## \ud83d\udcdd What does this PR do?\r\n> > Summarize your work here.\r\n> > if you have any plots/diagrams/screenshots/tables, please attach them here.\r\n> \r\n> This commit fix chatgpt critic input as state according to A2C RL algorithm.\r\n> \r\n> ## \ud83d\udca5 Checklist before requesting a review\r\n> * [x]  I have linked my PR to an issue ([instruction](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue))\r\n> * [x]  My issue clearly describes the problem/feature/proposal, with diagrams/charts/table/code if possible\r\n> * [x]  I have performed a self-review of my code\r\n> * [ ]  I have added thorough tests.\r\n> * [ ]  I have added docstrings for all the functions/methods I implemented\r\n> \r\n> ## \u2b50\ufe0f Do you enjoy contributing to Colossal-AI?\r\n> * [x]  \ud83c\udf1d Yes, I do.\r\n> * [ ]  \ud83c\udf1a No, I don't.\r\n> \r\n> Tell us more if you don't enjoy contributing to Colossal-AI.\r\n\r\n\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom ..lora import LoRAModule\nfrom ..utils import masked_mean\n\n\nclass Critic(LoRAModule):\n    \"\"\"\n    Critic model base class.\n\n    Args:\n        model (nn.Module): Critic model.\n        value_head (nn.Module): Value head to get value.\n        lora_rank (int): LoRA rank.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 model: nn.Module,\n                 value_head: nn.Module,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n\n        super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)\n        self.model = model\n        self.value_head = value_head\n        self.convert_to_lora()\n\n    def forward(self,\n                sequences: torch.LongTensor,\n                action_mask: Optional[torch.Tensor] = None,\n                attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:\n        outputs = self.model(sequences, attention_mask=attention_mask)\n        last_hidden_states = outputs['last_hidden_state']\n\n        values = self.value_head(last_hidden_states).squeeze(-1)[:, :-1]\n\n        if action_mask is not None:\n            num_actions = action_mask.size(1)\n            values = values[:, -num_actions:]\n            value = masked_mean(values, action_mask, dim=1)\n            return value\n        value = values.mean(dim=1).squeeze(1)\n        return value\n", "path": "applications/ChatGPT/chatgpt/models/base/critic.py"}], "after_files": [{"content": "from typing import Optional\n\nimport torch\nimport torch.nn as nn\n\nfrom ..lora import LoRAModule\nfrom ..utils import masked_mean\n\n\nclass Critic(LoRAModule):\n    \"\"\"\n    Critic model base class.\n\n    Args:\n        model (nn.Module): Critic model.\n        value_head (nn.Module): Value head to get value.\n        lora_rank (int): LoRA rank.\n        lora_train_bias (str): LoRA bias training mode.\n    \"\"\"\n\n    def __init__(self,\n                 model: nn.Module,\n                 value_head: nn.Module,\n                 lora_rank: int = 0,\n                 lora_train_bias: str = 'none') -> None:\n\n        super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)\n        self.model = model\n        self.value_head = value_head\n        self.convert_to_lora()\n\n    def forward(self,\n                sequences: torch.LongTensor,\n                action_mask: Optional[torch.Tensor] = None,\n                attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:\n        outputs = self.model(sequences, attention_mask=attention_mask)\n        last_hidden_states = outputs['last_hidden_state']\n\n        values = self.value_head(last_hidden_states).squeeze(-1)\n\n        if action_mask is not None:\n            num_actions = action_mask.size(1)\n            prompt_mask = attention_mask[:, :-num_actions]\n            values = values[:, :-num_actions]\n            value = masked_mean(values, prompt_mask, dim=1)\n            return value\n\n        values = values[:, :-1]\n        value = values.mean(dim=1).squeeze(1)\n        return value\n", "path": "applications/ChatGPT/chatgpt/models/base/critic.py"}]}