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/ChatGPT/chatgpt/trainer/strategies/colossalai.py`
  Content:
  ```
  1 from typing import Optional
  2 
  3 import torch
  4 import torch.distributed as dist
  5 import torch.nn as nn
  6 import torch.optim as optim
  7 
  8 import colossalai
  9 from colossalai.nn.optimizer import CPUAdam, HybridAdam
  10 from colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper
  11 from colossalai.tensor import ProcessGroup, ShardSpec
  12 from colossalai.utils import get_current_device
  13 from colossalai.utils.model.colo_init_context import ColoInitContext
  14 
  15 from .ddp import DDPStrategy
  16 
  17 
  18 class ColossalAIStrategy(DDPStrategy):
  19     """
  20         The strategy for training with ColossalAI.
  21 
  22     Args:
  23         stage(int): The stage to use in ZeRO. Choose in (1, 2, 3)
  24         seed(int): The seed for the random number generator.
  25         shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3.
  26         placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda')
  27                           If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU,
  28                           If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest.
  29         pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3.
  30         force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3.
  31         search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3.
  32         hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3.
  33         min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3.
  34         gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3.
  35         reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2.
  36         overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2.
  37         initial_scale(float): The initial scale for the optimizer.
  38         growth_factor(float): The growth factor for the optimizer.
  39         backoff_factor(float): The backoff factor for the optimizer.
  40         growth_interval(int): The growth interval for the optimizer.
  41         hysteresis(int): The hysteresis for the optimizer.
  42         min_scale(float): The minimum scale for the optimizer.
  43         max_scale(float): The maximum scale for the optimizer.
  44         max_norm(float): The maximum norm for the optimizer.
  45         norm_type(float): The norm type for the optimizer.
  46 
  47     """
  48 
  49     def __init__(
  50             self,
  51             stage: int = 3,
  52             seed: int = 42,
  53             shard_init: bool = True,    # only for stage 3
  54             placement_policy: str = 'cuda',
  55             pin_memory: bool = True,    # only for stage 3
  56             force_outputs_fp32: bool = False,    # only for stage 3
  57             search_range_mb: int = 32,    # only for stage 3
  58             hidden_dim: Optional[int] = None,    # only for stage 3
  59             min_chunk_size_mb: float = 32,    # only for stage 3
  60             gpu_margin_mem_ratio: float = 0.0,    # only for stage 3
  61             reduce_bucket_size: int = 12 * 1024**2,    # only for stage 1&2
  62             overlap_communication: bool = True,    # only for stage 1&2
  63             initial_scale: float = 2**16,
  64             growth_factor: float = 2,
  65             backoff_factor: float = 0.5,
  66             growth_interval: int = 1000,
  67             hysteresis: int = 2,
  68             min_scale: float = 1,
  69             max_scale: float = 2**32,
  70             max_norm: float = 0.0,
  71             norm_type: float = 2.0) -> None:
  72         super().__init__(seed)
  73         assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"'
  74         self.stage = stage
  75         self.shard_init = shard_init
  76         self.gemini_config = dict(device=get_current_device(),
  77                                   placement_policy=placement_policy,
  78                                   pin_memory=pin_memory,
  79                                   force_outputs_fp32=force_outputs_fp32,
  80                                   strict_ddp_mode=shard_init,
  81                                   search_range_mb=search_range_mb,
  82                                   hidden_dim=hidden_dim,
  83                                   min_chunk_size_mb=min_chunk_size_mb)
  84         if stage == 3:
  85             self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio)
  86         else:
  87             self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size,
  88                                           overlap_communication=overlap_communication,
  89                                           cpu_offload=(placement_policy == 'cpu'))
  90         self.optim_kwargs = dict(initial_scale=initial_scale,
  91                                  growth_factor=growth_factor,
  92                                  backoff_factor=backoff_factor,
  93                                  growth_interval=growth_interval,
  94                                  hysteresis=hysteresis,
  95                                  min_scale=min_scale,
  96                                  max_scale=max_scale,
  97                                  max_norm=max_norm,
  98                                  norm_type=norm_type)
  99 
  100     def setup_distributed(self) -> None:
  101         colossalai.launch_from_torch({}, seed=self.seed)
  102 
  103     def model_init_context(self):
  104         if self.stage == 3:
  105             world_size = dist.get_world_size()
  106             shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None
  107             default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None
  108             return ColoInitContext(device=get_current_device(),
  109                                    dtype=torch.half,
  110                                    default_pg=shard_pg,
  111                                    default_dist_spec=default_dist_spec)
  112         return super().model_init_context()
  113 
  114     def setup_model(self, model: nn.Module) -> nn.Module:
  115         return zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config)
  116 
  117     def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer:
  118         assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}'
  119         return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs)
  120 
  121     def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None:
  122         optimizer.backward(loss)
  123 
  124     def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:
  125         optimizer.step()
  126
  ```
  --- 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/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py
  --- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py
  +++ b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py
  @@ -1,3 +1,4 @@
  +import warnings
   from typing import Optional
   
   import torch
  @@ -23,6 +24,7 @@
           stage(int): The stage to use in ZeRO. Choose in (1, 2, 3)
           seed(int): The seed for the random number generator.
           shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3.
  +            This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future.
           placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda')
                             If it is “cpu”, parameters, gradients and optimizer states will be offloaded to CPU,
                             If it is “cuda”, they will not be offloaded, which means max CUDA memory will be used. It is the fastest.
  @@ -50,7 +52,7 @@
               self,
               stage: int = 3,
               seed: int = 42,
  -            shard_init: bool = True,    # only for stage 3
  +            shard_init: bool = False,    # only for stage 3
               placement_policy: str = 'cuda',
               pin_memory: bool = True,    # only for stage 3
               force_outputs_fp32: bool = False,    # only for stage 3
  @@ -72,6 +74,10 @@
           super().__init__(seed)
           assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy "{placement_policy}"'
           self.stage = stage
  +        # TODO(ver217): support shard_init when using from_pretrained()
  +        if shard_init:
  +            warnings.warn(f'Shard init is not supported yet. Ignore.')
  +            shard_init = False
           self.shard_init = shard_init
           self.gemini_config = dict(device=get_current_device(),
                                     placement_policy=placement_policy,
  
verification_info: {"golden_diff": "diff --git a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py\n--- a/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py\n+++ b/applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py\n@@ -1,3 +1,4 @@\n+import warnings\n from typing import Optional\n \n import torch\n@@ -23,6 +24,7 @@\n         stage(int): The stage to use in ZeRO. Choose in (1, 2, 3)\n         seed(int): The seed for the random number generator.\n         shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3.\n+            This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future.\n         placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda')\n                           If it is \u201ccpu\u201d, parameters, gradients and optimizer states will be offloaded to CPU,\n                           If it is \u201ccuda\u201d, they will not be offloaded, which means max CUDA memory will be used. It is the fastest.\n@@ -50,7 +52,7 @@\n             self,\n             stage: int = 3,\n             seed: int = 42,\n-            shard_init: bool = True,    # only for stage 3\n+            shard_init: bool = False,    # only for stage 3\n             placement_policy: str = 'cuda',\n             pin_memory: bool = True,    # only for stage 3\n             force_outputs_fp32: bool = False,    # only for stage 3\n@@ -72,6 +74,10 @@\n         super().__init__(seed)\n         assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy \"{placement_policy}\"'\n         self.stage = stage\n+        # TODO(ver217): support shard_init when using from_pretrained()\n+        if shard_init:\n+            warnings.warn(f'Shard init is not supported yet. Ignore.')\n+            shard_init = False\n         self.shard_init = shard_init\n         self.gemini_config = dict(device=get_current_device(),\n                                   placement_policy=placement_policy,\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\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.optim as optim\n\nimport colossalai\nfrom colossalai.nn.optimizer import CPUAdam, HybridAdam\nfrom colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper\nfrom colossalai.tensor import ProcessGroup, ShardSpec\nfrom colossalai.utils import get_current_device\nfrom colossalai.utils.model.colo_init_context import ColoInitContext\n\nfrom .ddp import DDPStrategy\n\n\nclass ColossalAIStrategy(DDPStrategy):\n    \"\"\"\n        The strategy for training with ColossalAI.\n\n    Args:\n        stage(int): The stage to use in ZeRO. Choose in (1, 2, 3)\n        seed(int): The seed for the random number generator.\n        shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3.\n        placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda')\n                          If it is \u201ccpu\u201d, parameters, gradients and optimizer states will be offloaded to CPU,\n                          If it is \u201ccuda\u201d, they will not be offloaded, which means max CUDA memory will be used. It is the fastest.\n        pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3.\n        force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3.\n        search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3.\n        hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3.\n        min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3.\n        gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3.\n        reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2.\n        overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2.\n        initial_scale(float): The initial scale for the optimizer.\n        growth_factor(float): The growth factor for the optimizer.\n        backoff_factor(float): The backoff factor for the optimizer.\n        growth_interval(int): The growth interval for the optimizer.\n        hysteresis(int): The hysteresis for the optimizer.\n        min_scale(float): The minimum scale for the optimizer.\n        max_scale(float): The maximum scale for the optimizer.\n        max_norm(float): The maximum norm for the optimizer.\n        norm_type(float): The norm type for the optimizer.\n\n    \"\"\"\n\n    def __init__(\n            self,\n            stage: int = 3,\n            seed: int = 42,\n            shard_init: bool = True,    # only for stage 3\n            placement_policy: str = 'cuda',\n            pin_memory: bool = True,    # only for stage 3\n            force_outputs_fp32: bool = False,    # only for stage 3\n            search_range_mb: int = 32,    # only for stage 3\n            hidden_dim: Optional[int] = None,    # only for stage 3\n            min_chunk_size_mb: float = 32,    # only for stage 3\n            gpu_margin_mem_ratio: float = 0.0,    # only for stage 3\n            reduce_bucket_size: int = 12 * 1024**2,    # only for stage 1&2\n            overlap_communication: bool = True,    # only for stage 1&2\n            initial_scale: float = 2**16,\n            growth_factor: float = 2,\n            backoff_factor: float = 0.5,\n            growth_interval: int = 1000,\n            hysteresis: int = 2,\n            min_scale: float = 1,\n            max_scale: float = 2**32,\n            max_norm: float = 0.0,\n            norm_type: float = 2.0) -> None:\n        super().__init__(seed)\n        assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy \"{placement_policy}\"'\n        self.stage = stage\n        self.shard_init = shard_init\n        self.gemini_config = dict(device=get_current_device(),\n                                  placement_policy=placement_policy,\n                                  pin_memory=pin_memory,\n                                  force_outputs_fp32=force_outputs_fp32,\n                                  strict_ddp_mode=shard_init,\n                                  search_range_mb=search_range_mb,\n                                  hidden_dim=hidden_dim,\n                                  min_chunk_size_mb=min_chunk_size_mb)\n        if stage == 3:\n            self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio)\n        else:\n            self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size,\n                                          overlap_communication=overlap_communication,\n                                          cpu_offload=(placement_policy == 'cpu'))\n        self.optim_kwargs = dict(initial_scale=initial_scale,\n                                 growth_factor=growth_factor,\n                                 backoff_factor=backoff_factor,\n                                 growth_interval=growth_interval,\n                                 hysteresis=hysteresis,\n                                 min_scale=min_scale,\n                                 max_scale=max_scale,\n                                 max_norm=max_norm,\n                                 norm_type=norm_type)\n\n    def setup_distributed(self) -> None:\n        colossalai.launch_from_torch({}, seed=self.seed)\n\n    def model_init_context(self):\n        if self.stage == 3:\n            world_size = dist.get_world_size()\n            shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None\n            default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None\n            return ColoInitContext(device=get_current_device(),\n                                   dtype=torch.half,\n                                   default_pg=shard_pg,\n                                   default_dist_spec=default_dist_spec)\n        return super().model_init_context()\n\n    def setup_model(self, model: nn.Module) -> nn.Module:\n        return zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config)\n\n    def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer:\n        assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}'\n        return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs)\n\n    def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None:\n        optimizer.backward(loss)\n\n    def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:\n        optimizer.step()\n", "path": "applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py"}], "after_files": [{"content": "import warnings\nfrom typing import Optional\n\nimport torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport torch.optim as optim\n\nimport colossalai\nfrom colossalai.nn.optimizer import CPUAdam, HybridAdam\nfrom colossalai.nn.parallel import zero_model_wrapper, zero_optim_wrapper\nfrom colossalai.tensor import ProcessGroup, ShardSpec\nfrom colossalai.utils import get_current_device\nfrom colossalai.utils.model.colo_init_context import ColoInitContext\n\nfrom .ddp import DDPStrategy\n\n\nclass ColossalAIStrategy(DDPStrategy):\n    \"\"\"\n        The strategy for training with ColossalAI.\n\n    Args:\n        stage(int): The stage to use in ZeRO. Choose in (1, 2, 3)\n        seed(int): The seed for the random number generator.\n        shard_init(bool): Whether to shard the model parameters during initialization. Only for ZeRO-3.\n            This is not compativle with `from_pretrained()`. We temporarily disable this and will support it in the future.\n        placement_policy(str): The placement policy for gemini. Choose in ('cpu', 'cuda')\n                          If it is \u201ccpu\u201d, parameters, gradients and optimizer states will be offloaded to CPU,\n                          If it is \u201ccuda\u201d, they will not be offloaded, which means max CUDA memory will be used. It is the fastest.\n        pin_memory(bool): Whether to pin the memory for the data loader. Only for ZeRO-3.\n        force_outputs_fp32(bool): Whether to force the outputs to be fp32. Only for ZeRO-3.\n        search_range_mb(int): The search range in MB for the chunk size. Only for ZeRO-3.\n        hidden_dim(optional, int): The hidden dimension for the gemini. Only for ZeRO-3.\n        min_chunk_size_mb(float): The minimum chunk size in MB. Only for ZeRO-3.\n        gpu_margin_mem_ratio(float): The margin memory ratio for the GPU. Only for ZeRO-3.\n        reduce_bugket_size(int): The reduce bucket size in bytes. Only for ZeRO-1 and ZeRO-2.\n        overlap_communication(bool): Whether to overlap communication and computation. Only for ZeRO-1 and ZeRO-2.\n        initial_scale(float): The initial scale for the optimizer.\n        growth_factor(float): The growth factor for the optimizer.\n        backoff_factor(float): The backoff factor for the optimizer.\n        growth_interval(int): The growth interval for the optimizer.\n        hysteresis(int): The hysteresis for the optimizer.\n        min_scale(float): The minimum scale for the optimizer.\n        max_scale(float): The maximum scale for the optimizer.\n        max_norm(float): The maximum norm for the optimizer.\n        norm_type(float): The norm type for the optimizer.\n\n    \"\"\"\n\n    def __init__(\n            self,\n            stage: int = 3,\n            seed: int = 42,\n            shard_init: bool = False,    # only for stage 3\n            placement_policy: str = 'cuda',\n            pin_memory: bool = True,    # only for stage 3\n            force_outputs_fp32: bool = False,    # only for stage 3\n            search_range_mb: int = 32,    # only for stage 3\n            hidden_dim: Optional[int] = None,    # only for stage 3\n            min_chunk_size_mb: float = 32,    # only for stage 3\n            gpu_margin_mem_ratio: float = 0.0,    # only for stage 3\n            reduce_bucket_size: int = 12 * 1024**2,    # only for stage 1&2\n            overlap_communication: bool = True,    # only for stage 1&2\n            initial_scale: float = 2**16,\n            growth_factor: float = 2,\n            backoff_factor: float = 0.5,\n            growth_interval: int = 1000,\n            hysteresis: int = 2,\n            min_scale: float = 1,\n            max_scale: float = 2**32,\n            max_norm: float = 0.0,\n            norm_type: float = 2.0) -> None:\n        super().__init__(seed)\n        assert placement_policy in ('cpu', 'cuda'), f'Unsupported placement policy \"{placement_policy}\"'\n        self.stage = stage\n        # TODO(ver217): support shard_init when using from_pretrained()\n        if shard_init:\n            warnings.warn(f'Shard init is not supported yet. Ignore.')\n            shard_init = False\n        self.shard_init = shard_init\n        self.gemini_config = dict(device=get_current_device(),\n                                  placement_policy=placement_policy,\n                                  pin_memory=pin_memory,\n                                  force_outputs_fp32=force_outputs_fp32,\n                                  strict_ddp_mode=shard_init,\n                                  search_range_mb=search_range_mb,\n                                  hidden_dim=hidden_dim,\n                                  min_chunk_size_mb=min_chunk_size_mb)\n        if stage == 3:\n            self.zero_optim_config = dict(gpu_margin_mem_ratio=gpu_margin_mem_ratio)\n        else:\n            self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size,\n                                          overlap_communication=overlap_communication,\n                                          cpu_offload=(placement_policy == 'cpu'))\n        self.optim_kwargs = dict(initial_scale=initial_scale,\n                                 growth_factor=growth_factor,\n                                 backoff_factor=backoff_factor,\n                                 growth_interval=growth_interval,\n                                 hysteresis=hysteresis,\n                                 min_scale=min_scale,\n                                 max_scale=max_scale,\n                                 max_norm=max_norm,\n                                 norm_type=norm_type)\n\n    def setup_distributed(self) -> None:\n        colossalai.launch_from_torch({}, seed=self.seed)\n\n    def model_init_context(self):\n        if self.stage == 3:\n            world_size = dist.get_world_size()\n            shard_pg = ProcessGroup(tp_degree=world_size) if self.shard_init else None\n            default_dist_spec = ShardSpec([-1], [world_size]) if self.shard_init else None\n            return ColoInitContext(device=get_current_device(),\n                                   dtype=torch.half,\n                                   default_pg=shard_pg,\n                                   default_dist_spec=default_dist_spec)\n        return super().model_init_context()\n\n    def setup_model(self, model: nn.Module) -> nn.Module:\n        return zero_model_wrapper(model, zero_stage=self.stage, gemini_config=self.gemini_config)\n\n    def setup_optimizer(self, optimizer: optim.Optimizer, model: nn.Module) -> optim.Optimizer:\n        assert isinstance(optimizer, (CPUAdam, HybridAdam)), f'Unsupported optimizer {type(optimizer)}'\n        return zero_optim_wrapper(model, optimizer, optim_config=self.zero_optim_config, **self.optim_kwargs)\n\n    def backward(self, loss: torch.Tensor, model: nn.Module, optimizer: optim.Optimizer, **kwargs) -> None:\n        optimizer.backward(loss)\n\n    def optimizer_step(self, optimizer: optim.Optimizer, **kwargs) -> None:\n        optimizer.step()\n", "path": "applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py"}]}