prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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
  
  
  </issue>
  <code>
  [start of applications/ChatGPT/chatgpt/models/base/critic.py]
  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 of applications/ChatGPT/chatgpt/models/base/critic.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: 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
  
verification_info: {"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"}]}