code: def train(args, accelerator):
      set_seed(0)
      accelerator.print(f"Using {accelerator.num_processes} GPUs")
  
      plm, tokenizer, model_config, WrapperClass = load_plm(
          args.model, args.model_name_or_path
      )
  
      mytemplate = MixedTemplate(
          model=plm, tokenizer=tokenizer
      ).from_file("./scripts/UltraChat/template.txt")
  
      with accelerator.main_process_first():
          processor = UltraChatProcessor()
          dataset = processor.get_examples(args.data_file)
  
          train_dataset, val_dataset = train_test_split(
              dataset, test_size=0.2, random_state=0
          )
  
      # wrapped_example = mytemplate.wrap_one_example(dataset[1])
      # print(wrapped_example)
  
      train_dataloader = PromptDataLoader(
          dataset=train_dataset,
          template=mytemplate,
          tokenizer=tokenizer,
          tokenizer_wrapper_class=WrapperClass,
          max_seq_length=1024,
          decoder_max_length=1024,
          batch_size=2,
          shuffle=True,
          teacher_forcing=True,
          predict_eos_token=True,  # be sure to pass predict_eos_token=True if your template doesn't contain one, or you model may fail to stop generation.
          truncate_method="head",
      ).dataloader
  
      val_dataloader = PromptDataLoader(
          dataset=val_dataset,
          template=mytemplate,
          tokenizer=tokenizer,
          tokenizer_wrapper_class=WrapperClass,
          max_seq_length=1024,
          decoder_max_length=1024,
          batch_size=5,
          shuffle=False,
          teacher_forcing=True,
          predict_eos_token=True,  # be sure to pass predict_eos_token=True if your template doesn't contain one, or you model may fail to stop generation.
          truncate_method="head",
      ).dataloader
  
      # load the pipeline model PromptForGeneration.
      prompt_model = PromptForGeneration(
          plm=plm, template=mytemplate, tokenizer=tokenizer
      )
  
      device = accelerator.device
      prompt_model.to(device)
  
      optimizer = AdamW(
          [p for p in prompt_model.parameters() if p.requires_grad],
          lr=args.lr,
          eps=1e-8,
      )
  
      scheduler = get_linear_schedule_with_warmup(
          optimizer,
          num_warmup_steps=200,
          num_training_steps=len(train_dataloader) * args.epochs,
      )
  
      (
          prompt_model,
          optimizer,
          train_dataloader,
          val_dataloader,
          scheduler,
      ) = accelerator.prepare(
          prompt_model,
          optimizer,
          train_dataloader,
          val_dataloader,
          scheduler,
      )
  
      accelerator.register_for_checkpointing(scheduler)
  
      train_loss = MeanMetric().to(prompt_model.device)
  
      # training and generation.
      global_step = 0
      for epoch in range(args.epochs):
          for step, inputs in tqdm(enumerate(train_dataloader)):
              prompt_model.train()
              loss = prompt_model(inputs["input_ids"])
              accelerator.backward(loss)
              optimizer.step()
              scheduler.step()
              optimizer.zero_grad()
  
              loss_values = accelerator.gather_for_metrics(
                  {"loss": loss.detach()}
              )
              train_loss.update(loss_values["loss"])
              global_step += 1
  
              if global_step % 50 == 0:
                  accelerator.save_state(
                      f"ultrachat_{args.model}/step_{global_step}"
                  )
  
                  val_loss = evaluate(
                      args, prompt_model, val_dataloader, accelerator
                  )
  
                  log_train = {"train_loss": train_loss.compute()}
                  log_val = {"val_loss": val_loss.compute()}
  
                  accelerator.print(
                      f"Current LR: {scheduler.get_last_lr()[0]}"
                  )
                  accelerator.print(
                      format_metrics(
                          log_train, "train", f" step {global_step} "
                      )
                  )
                  accelerator.print(
                      format_metrics(
                          log_val, "val", f" step {global_step} "
                      )
                  )
  
                  train_loss.reset()
  
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
          torch.save(
              accelerator.get_state_dict(prompt_model),
              f"ultrachat_{args.model}/final",
          )
  
file_path: OpenPrompt/tutorial/9_UltraChat.py
imports: import argparse
  import torch
  from openprompt import plms
  from openprompt.plms import *
  from transformers import GPTJConfig, GPTJModel, GPTJForCausalLM, GPT2Tokenizer
  from openprompt.plms import load_plm
  from openprompt.prompts import MixedTemplate
  from openprompt.data_utils.conditional_generation_dataset import UltraChatProcessor
  from transformers import AdamW
  from openprompt import PromptDataLoader
  from openprompt import PromptForGeneration
  from transformers.optimization import get_linear_schedule_with_warmup
  from accelerate import Accelerator
  from torchmetrics import MeanMetric
  from sklearn.model_selection import train_test_split
  from tqdm import tqdm
  from accelerate.utils import set_seed
label_desc: Use image node_id 3 for calling a global function with example usage: train(args, accelerator) without return types