prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG]: Error during running chatgpt train_rm.sh 
  ### 🐛 Describe the bug
  
  I run into a runtime error when running `train_rm.sh` under examples of chatgpt.
  ```
  Traceback (most recent call last):
    File "/root/ColossalAI/applications/ChatGPT/examples/train_reward_model.py", line 53, in <module>
      train(args)
    File "/root/ColossalAI/applications/ChatGPT/examples/train_reward_model.py", line 36, in train
      trainer.fit(use_lora=args.lora_rank)
    File "/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/chatgpt/trainer/rm.py", line 57, in fit
      loss.backward()
    File "/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/torch/_tensor.py", line 488, in backward
      torch.autograd.backward(
    File "/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/torch/autograd/__init__.py", line 197, in backward
      Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
  ```
  
  
  The model is downloaded from Huggingface `bigscience/bloom-560m`.  `lora_rank` is `16`. Is there any hints on this?
  
  
  ### Environment
  
  Environment:
  torch                    1.13.1+cu113
  
  ------------ Environment ------------
  Colossal-AI version: 0.2.4
  PyTorch version: 1.13.1
  CUDA version: 11.3
  CUDA version required by PyTorch: 11.7
  
  Note:
  1. The table above checks the versions of the libraries/tools in the current environment
  2. If the CUDA version is N/A, you can set the CUDA_HOME environment variable to locate it
  
  ------------ CUDA Extensions AOT Compilation ------------
  Found AOT CUDA Extension: x
  PyTorch version used for AOT compilation: N/A
  CUDA version used for AOT compilation: N/A
  
  Note:
  1. AOT (ahead-of-time) compilation of the CUDA kernels occurs during installation when the environment varialbe CUDA_EXT=1 is set
  2. If AOT compilation is not enabled, stay calm as the CUDA kernels can still be built during runtime
  
  ------------ Compatibility ------------
  PyTorch version match: N/A
  System and PyTorch CUDA version match: x
  System and Colossal-AI CUDA version match: N/A
  
  Note:
  1. The table above checks the version compatibility of the libraries/tools in the current environment
     - PyTorch version mistach: whether the PyTorch version in the current environment is compatible with the PyTorch version used for AOT compilation
     - System and PyTorch CUDA version match: whether the CUDA version in the current environment is compatible with the CUDA version required by PyTorch
     - System and Colossal-AI CUDA version match: whether the CUDA version in the current environment is compatible with the CUDA version used for AOT compilation
  [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/trainer/rm.py`
  Content:
  ```
  1 from abc import ABC
  2 
  3 import loralib as lora
  4 from chatgpt.dataset import RewardDataset
  5 from chatgpt.nn import PairWiseLoss
  6 from torch.optim import Adam
  7 from torch.utils.data import DataLoader
  8 from tqdm import tqdm
  9 
  10 
  11 class RewardModelTrainer(ABC):
  12     """
  13         Trainer to use while training reward model.
  14 
  15     Args:
  16         model (torch.nn.Module): the model to train
  17         train_dataset (RewardDataset): the dataset to use for training
  18         eval_dataset (RewardDataset): the dataset to use for evaluation
  19         batch_size (int, defaults to 1): the batch size while training
  20         num_epochs (int, defaults to 2): the number of epochs to train
  21         optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer
  22     """
  23 
  24     def __init__(self,
  25                  model,
  26                  train_dataset: RewardDataset,
  27                  eval_dataset: RewardDataset,
  28                  batch_size: int = 1,
  29                  num_epochs: int = 2,
  30                  optim_kwargs: dict = {'lr': 1e-4}) -> None:
  31         super().__init__()
  32         self.model = model
  33         self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size)
  34         self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size)
  35         self.loss_fn = PairWiseLoss()
  36         self.optimizer = Adam(self.model.parameters(), **optim_kwargs)
  37         self.epochs = num_epochs
  38 
  39     def fit(self, use_lora):
  40         epoch_bar = tqdm(range(self.epochs), desc='Train epoch')
  41         for epoch in range(self.epochs):
  42             step_bar = tqdm(range(self.train_dataloader.__len__()), desc='Train step of epoch %d' % epoch)
  43             # train
  44             if use_lora > 0:
  45                 print("Using Lora")
  46                 lora.mark_only_lora_as_trainable(self.model)
  47             else:
  48                 self.model.train()
  49             for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:
  50                 chosen_ids = chosen_ids.squeeze(1).cuda()
  51                 c_mask = c_mask.squeeze(1).cuda()
  52                 reject_ids = reject_ids.squeeze(1).cuda()
  53                 r_mask = r_mask.squeeze(1).cuda()
  54                 chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
  55                 reject_reward = self.model(reject_ids, attention_mask=r_mask)
  56                 loss = self.loss_fn(chosen_reward, reject_reward)
  57                 loss.backward()
  58                 self.optimizer.step()
  59                 self.optimizer.zero_grad()
  60                 step_bar.update()
  61                 step_bar.set_postfix({'loss': loss.item()})
  62 
  63             # eval
  64             self.model.eval()
  65             for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader:
  66                 dist = 0
  67                 chosen_ids = chosen_ids.squeeze(1).cuda()
  68                 c_mask = c_mask.squeeze(1).cuda()
  69                 reject_ids = reject_ids.squeeze(1).cuda()
  70                 r_mask = r_mask.squeeze(1).cuda()
  71                 chosen_reward = self.model(chosen_ids, attention_mask=c_mask)
  72                 reject_reward = self.model(reject_ids, attention_mask=r_mask)
  73                 dist += (chosen_reward - reject_reward)
  74             dist_mean = dist / self.eval_dataloader.__len__()
  75             epoch_bar.update()
  76             step_bar.set_postfix({'loss': loss.item(), 'dist_mean': dist_mean.item()})
  77             step_bar.close()
  78
  ```
  --- 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/ChatGPT/chatgpt/trainer/rm.py b/applications/ChatGPT/chatgpt/trainer/rm.py
  --- a/applications/ChatGPT/chatgpt/trainer/rm.py
  +++ b/applications/ChatGPT/chatgpt/trainer/rm.py
  @@ -43,7 +43,7 @@
               # train
               if use_lora > 0:
                   print("Using Lora")
  -                lora.mark_only_lora_as_trainable(self.model)
  +                lora.mark_only_lora_as_trainable(self.model.model)
               else:
                   self.model.train()
               for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:
  
verification_info: {"golden_diff": "diff --git a/applications/ChatGPT/chatgpt/trainer/rm.py b/applications/ChatGPT/chatgpt/trainer/rm.py\n--- a/applications/ChatGPT/chatgpt/trainer/rm.py\n+++ b/applications/ChatGPT/chatgpt/trainer/rm.py\n@@ -43,7 +43,7 @@\n             # train\n             if use_lora > 0:\n                 print(\"Using Lora\")\n-                lora.mark_only_lora_as_trainable(self.model)\n+                lora.mark_only_lora_as_trainable(self.model.model)\n             else:\n                 self.model.train()\n             for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:\n", "issue": "[BUG]: Error during running chatgpt train_rm.sh \n### \ud83d\udc1b Describe the bug\n\nI run into a runtime error when running `train_rm.sh` under examples of chatgpt.\r\n```\r\nTraceback (most recent call last):\r\n  File \"/root/ColossalAI/applications/ChatGPT/examples/train_reward_model.py\", line 53, in <module>\r\n    train(args)\r\n  File \"/root/ColossalAI/applications/ChatGPT/examples/train_reward_model.py\", line 36, in train\r\n    trainer.fit(use_lora=args.lora_rank)\r\n  File \"/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/chatgpt/trainer/rm.py\", line 57, in fit\r\n    loss.backward()\r\n  File \"/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/torch/_tensor.py\", line 488, in backward\r\n    torch.autograd.backward(\r\n  File \"/root/miniconda3/envs/chatgpt/lib/python3.10/site-packages/torch/autograd/__init__.py\", line 197, in backward\r\n    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\r\nRuntimeError: element 0 of tensors does not require grad and does not have a grad_fn\r\n```\r\n\r\n\r\nThe model is downloaded from Huggingface `bigscience/bloom-560m`.  `lora_rank` is `16`. Is there any hints on this?\r\n\n\n### Environment\n\nEnvironment:\r\ntorch                    1.13.1+cu113\r\n\r\n------------ Environment ------------\r\nColossal-AI version: 0.2.4\r\nPyTorch version: 1.13.1\r\nCUDA version: 11.3\r\nCUDA version required by PyTorch: 11.7\r\n\r\nNote:\r\n1. The table above checks the versions of the libraries/tools in the current environment\r\n2. If the CUDA version is N/A, you can set the CUDA_HOME environment variable to locate it\r\n\r\n------------ CUDA Extensions AOT Compilation ------------\r\nFound AOT CUDA Extension: x\r\nPyTorch version used for AOT compilation: N/A\r\nCUDA version used for AOT compilation: N/A\r\n\r\nNote:\r\n1. AOT (ahead-of-time) compilation of the CUDA kernels occurs during installation when the environment varialbe CUDA_EXT=1 is set\r\n2. If AOT compilation is not enabled, stay calm as the CUDA kernels can still be built during runtime\r\n\r\n------------ Compatibility ------------\r\nPyTorch version match: N/A\r\nSystem and PyTorch CUDA version match: x\r\nSystem and Colossal-AI CUDA version match: N/A\r\n\r\nNote:\r\n1. The table above checks the version compatibility of the libraries/tools in the current environment\r\n   - PyTorch version mistach: whether the PyTorch version in the current environment is compatible with the PyTorch version used for AOT compilation\r\n   - System and PyTorch CUDA version match: whether the CUDA version in the current environment is compatible with the CUDA version required by PyTorch\r\n   - System and Colossal-AI CUDA version match: whether the CUDA version in the current environment is compatible with the CUDA version used for AOT compilation\n[tensor] fix some unittests\n\n[tensor] fix some unittests\n\n", "before_files": [{"content": "from abc import ABC\n\nimport loralib as lora\nfrom chatgpt.dataset import RewardDataset\nfrom chatgpt.nn import PairWiseLoss\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\n\nclass RewardModelTrainer(ABC):\n    \"\"\"\n        Trainer to use while training reward model.\n\n    Args:\n        model (torch.nn.Module): the model to train\n        train_dataset (RewardDataset): the dataset to use for training\n        eval_dataset (RewardDataset): the dataset to use for evaluation\n        batch_size (int, defaults to 1): the batch size while training\n        num_epochs (int, defaults to 2): the number of epochs to train\n        optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer\n    \"\"\"\n\n    def __init__(self,\n                 model,\n                 train_dataset: RewardDataset,\n                 eval_dataset: RewardDataset,\n                 batch_size: int = 1,\n                 num_epochs: int = 2,\n                 optim_kwargs: dict = {'lr': 1e-4}) -> None:\n        super().__init__()\n        self.model = model\n        self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size)\n        self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size)\n        self.loss_fn = PairWiseLoss()\n        self.optimizer = Adam(self.model.parameters(), **optim_kwargs)\n        self.epochs = num_epochs\n\n    def fit(self, use_lora):\n        epoch_bar = tqdm(range(self.epochs), desc='Train epoch')\n        for epoch in range(self.epochs):\n            step_bar = tqdm(range(self.train_dataloader.__len__()), desc='Train step of epoch %d' % epoch)\n            # train\n            if use_lora > 0:\n                print(\"Using Lora\")\n                lora.mark_only_lora_as_trainable(self.model)\n            else:\n                self.model.train()\n            for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:\n                chosen_ids = chosen_ids.squeeze(1).cuda()\n                c_mask = c_mask.squeeze(1).cuda()\n                reject_ids = reject_ids.squeeze(1).cuda()\n                r_mask = r_mask.squeeze(1).cuda()\n                chosen_reward = self.model(chosen_ids, attention_mask=c_mask)\n                reject_reward = self.model(reject_ids, attention_mask=r_mask)\n                loss = self.loss_fn(chosen_reward, reject_reward)\n                loss.backward()\n                self.optimizer.step()\n                self.optimizer.zero_grad()\n                step_bar.update()\n                step_bar.set_postfix({'loss': loss.item()})\n\n            # eval\n            self.model.eval()\n            for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader:\n                dist = 0\n                chosen_ids = chosen_ids.squeeze(1).cuda()\n                c_mask = c_mask.squeeze(1).cuda()\n                reject_ids = reject_ids.squeeze(1).cuda()\n                r_mask = r_mask.squeeze(1).cuda()\n                chosen_reward = self.model(chosen_ids, attention_mask=c_mask)\n                reject_reward = self.model(reject_ids, attention_mask=r_mask)\n                dist += (chosen_reward - reject_reward)\n            dist_mean = dist / self.eval_dataloader.__len__()\n            epoch_bar.update()\n            step_bar.set_postfix({'loss': loss.item(), 'dist_mean': dist_mean.item()})\n            step_bar.close()\n", "path": "applications/ChatGPT/chatgpt/trainer/rm.py"}], "after_files": [{"content": "from abc import ABC\n\nimport loralib as lora\nfrom chatgpt.dataset import RewardDataset\nfrom chatgpt.nn import PairWiseLoss\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\n\nclass RewardModelTrainer(ABC):\n    \"\"\"\n        Trainer to use while training reward model.\n\n    Args:\n        model (torch.nn.Module): the model to train\n        train_dataset (RewardDataset): the dataset to use for training\n        eval_dataset (RewardDataset): the dataset to use for evaluation\n        batch_size (int, defaults to 1): the batch size while training\n        num_epochs (int, defaults to 2): the number of epochs to train\n        optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer\n    \"\"\"\n\n    def __init__(self,\n                 model,\n                 train_dataset: RewardDataset,\n                 eval_dataset: RewardDataset,\n                 batch_size: int = 1,\n                 num_epochs: int = 2,\n                 optim_kwargs: dict = {'lr': 1e-4}) -> None:\n        super().__init__()\n        self.model = model\n        self.train_dataloader = DataLoader(train_dataset, batch_size=batch_size)\n        self.eval_dataloader = DataLoader(eval_dataset, batch_size=batch_size)\n        self.loss_fn = PairWiseLoss()\n        self.optimizer = Adam(self.model.parameters(), **optim_kwargs)\n        self.epochs = num_epochs\n\n    def fit(self, use_lora):\n        epoch_bar = tqdm(range(self.epochs), desc='Train epoch')\n        for epoch in range(self.epochs):\n            step_bar = tqdm(range(self.train_dataloader.__len__()), desc='Train step of epoch %d' % epoch)\n            # train\n            if use_lora > 0:\n                print(\"Using Lora\")\n                lora.mark_only_lora_as_trainable(self.model.model)\n            else:\n                self.model.train()\n            for chosen_ids, c_mask, reject_ids, r_mask in self.train_dataloader:\n                chosen_ids = chosen_ids.squeeze(1).cuda()\n                c_mask = c_mask.squeeze(1).cuda()\n                reject_ids = reject_ids.squeeze(1).cuda()\n                r_mask = r_mask.squeeze(1).cuda()\n                chosen_reward = self.model(chosen_ids, attention_mask=c_mask)\n                reject_reward = self.model(reject_ids, attention_mask=r_mask)\n                loss = self.loss_fn(chosen_reward, reject_reward)\n                loss.backward()\n                self.optimizer.step()\n                self.optimizer.zero_grad()\n                step_bar.update()\n                step_bar.set_postfix({'loss': loss.item()})\n\n            # eval\n            self.model.eval()\n            for chosen_ids, c_mask, reject_ids, r_mask in self.eval_dataloader:\n                dist = 0\n                chosen_ids = chosen_ids.squeeze(1).cuda()\n                c_mask = c_mask.squeeze(1).cuda()\n                reject_ids = reject_ids.squeeze(1).cuda()\n                r_mask = r_mask.squeeze(1).cuda()\n                chosen_reward = self.model(chosen_ids, attention_mask=c_mask)\n                reject_reward = self.model(reject_ids, attention_mask=r_mask)\n                dist += (chosen_reward - reject_reward)\n            dist_mean = dist / self.eval_dataloader.__len__()\n            epoch_bar.update()\n            step_bar.set_postfix({'loss': loss.item(), 'dist_mean': dist_mean.item()})\n            step_bar.close()\n", "path": "applications/ChatGPT/chatgpt/trainer/rm.py"}]}