code: def parse_args():
      # basic settings
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--model_name",
          type=str,
          default="base",
          choices=["base", "8b", "test"],
          help="Path to pretrained model or model identifier from huggingface.co/models.",
      )
      parser.add_argument(
          "--plugin",
          type=str,
          default="hybrid",
          choices=["ep", "ep_zero", "hybrid"],
          help="Parallel methos. ep_zero is recommended for general cases. ep can provides least memory consumption and hybrid suits large scale training.",
      )
      parser.add_argument(
          "--output_path",
          type=str,
          default="./outputs",
          help="The path of your saved model after finetuning.",
      )
      parser.add_argument(
          "--num_epoch", type=int, default=1, help="Number of epochs."
      )
      parser.add_argument(
          "--batch_size",
          type=int,
          default=1,
          help="Batch size (per dp group) for the training dataloader.",
      )
      parser.add_argument(
          "--save_interval",
          type=int,
          default=1000,
          help=" The interval (steps) of saving checkpoints.",
      )
      parser.add_argument(
          "--precision",
          type=str,
          default="bf16",
          choices=["fp32", "bf16", "fp16"],
          help="The mixed precision training.",
      )
      parser.add_argument(
          "--max_length",
          type=int,
          default=2048,
          help="Max sequence length.",
      )
      parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="A seed for reproducible training.",
      )
      parser.add_argument(
          "--dataset",
          type=str,
          default="yizhongw/self_instruct",
          help="dataset name from `datasets` repo.",
      )
      parser.add_argument(
          "--task_name",
          type=str,
          default="super_natural_instructions",
          help="task of corresponding dataset.",
      )
  
      # optim
      parser.add_argument(
          "--lr", type=float, default=1e-5, help="Learning rate."
      )
      parser.add_argument(
          "--weight_decay",
          type=float,
          default=0.0,
          help="Weight decay to use.",
      )
  
      # zero stage for all plugins
      parser.add_argument(
          "--zero_stage", type=int, default=2, help="zero stage."
      )
      # ep_zero plugin
      parser.add_argument(
          "--extra_dp_size",
          type=int,
          default=1,
          help="ep_zero plugin's moe dp size. Recommended to be 2 or 4.",
      )
      # hybrid plugin
      parser.add_argument(
          "--pp_size",
          type=int,
          default=2,
          help="pp size for hybrid plugin",
      )
      parser.add_argument(
          "--dp_size",
          type=int,
          default=1,
          help="dp size for hybrid plugin",
      )
      parser.add_argument(
          "--ep_size",
          type=int,
          default=2,
          help="ep size for hybrid plugin",
      )
      parser.add_argument(
          "--microbatch_size",
          type=int,
          default=1,
          help="Microbatch size in pipeline for hybrid plugin",
      )
  
      # kernel
      parser.add_argument(
          "--use_kernel",
          action="store_true",
          help="Use kernel optim. Need to install flash attention and triton to enable all kernel optimizations. Skip if not installed.",
      )
      parser.add_argument(
          "--use_layernorm_kernel",
          action="store_true",
          help="Use layernorm kernel. Need to install apex. Raise error if not installed.",
      )
  
      # loss
      parser.add_argument(
          "--router_aux_loss_factor",
          type=float,
          default=0.01,
          help="Moe router z loss. You can refer to STMoE for details.",
      )
      parser.add_argument(
          "--router_z_loss_factor",
          type=float,
          default=0.0001,
          help="Moe router aux loss. You can refer to STMoE for details.",
      )
      parser.add_argument(
          "--label_smoothing",
          type=float,
          default=0.0,
          help="Label smoothing.",
      )
      parser.add_argument(
          "--z_loss_factor",
          type=float,
          default=0.0001,
          help="The final outputs' classification z loss factor.",
      )
  
      # load balance
      parser.add_argument(
          "--load_balance",
          action="store_true",
          help="Expert load balance. Defaults to False. Recommend to enable.",
      )
      parser.add_argument(
          "--load_balance_interval",
          type=int,
          default=1000,
          help="Expert load balance interval.",
      )
      # communicate overlap
      parser.add_argument(
          "--comm_overlap",
          action="store_true",
          help="Use communication overlap for MoE. Recommended to enable for muiti-node training.",
      )
      # hierarchical all-to-all
      parser.add_argument(
          "--hierarchical_alltoall",
          action="store_true",
          help="Use hierarchical all-to-all for MoE. Recommended to enable for muiti-node training.",
      )
  
      args = parser.parse_args()
      return args
  
imports: import argparse
  import os
  from functools import partial
  from typing import Dict
  import torch
  import torch.distributed
  from datasets import load_dataset
  from huggingface_hub import snapshot_download
  from model.modeling_openmoe import OpenMoeForCausalLM, set_openmoe_args
  from model.openmoe_policy import OpenMoeForCausalLMPolicy
  from torch.utils.data import Dataset
  from tqdm import tqdm
  from transformers import T5Tokenizer
  from transformers.models.llama import LlamaConfig
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin.moe_hybrid_parallel_plugin import MoeHybridParallelPlugin
  from colossalai.cluster import DistCoordinator
  from colossalai.moe.layers import apply_load_balance
  from colossalai.moe.manager import MOE_MANAGER
  from colossalai.moe.utils import skip_init
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.utils import get_current_device
label_desc: Use image node_id 4 for calling a global function with example usage: parse_args() and returns: args