class_docstr:     Callback for valuate the performance of the model.
  Args:
      actor_num_params: The number of parameters of the actor model.
      critic_num_params: The number of parameters of the critic model.
      initial_model_num_params: The number of parameters of the initial model.
      reward_model_num_params: The number of parameters of the reward model.
      enable_grad_checkpoint: Whether to enable gradient checkpointing.
      ignore_episodes: The number of episodes to ignore when calculating the performance.
class_docstr_tok: ["Callback","for","valuate","the","performance","of","the","model",".","Args",":","actor_num_params",":","The","number","of","parameters","of","the","actor","model",".","critic_num_params",":","The","number","of","parameters","of","the","critic","model",".","initial_model_num_params",":","The","number","of","parameters","of","the","initial","model",".","reward_model_num_params",":","The","number","of","parameters","of","the","reward","model",".","enable_grad_checkpoint",":","Whether","to","enable","gradient","checkpointing",".","ignore_episodes",":","The","number","of","episodes","to","ignore","when","calculating","the","performance","."]
code: def on_step_start(self, step: int) -> None:
      self.disable = self.ignore_steps > 0 and step < self.ignore_steps
      if self.disable:
          return
      device_utils.synchronize()
      self.timer.start()
  
file_path: colossalai/examples/language/llama2/performance_evaluator.py
imports: from time import time
  from typing import Optional
  import torch
  import torch.distributed
  from torch import Tensor
  import colossalai.utils.device
  from colossalai.cluster import DistCoordinator
  from colossalai.utils.device import get_current_device
label_desc: Use image node_id 2 for calling the PerformanceEvaluator obj's underlying member method code with example usage: obj.on_step_start(step) without return types