code: def train(
      # model/data params
      base_model: str = "",  # the only required argument
      data_path: str = "./alpaca_data_cleaned.json",
      output_dir: str = "./lora-alpaca",
      # training hyperparams
      batch_size: int = 128,
      micro_batch_size: int = 4,
      num_epochs: int = 3,
      learning_rate: float = 3e-4,
      cutoff_len: int = 256,
      val_set_size: int = 2000,
      # lora hyperparams
      lora_r: int = 8,
      lora_alpha: int = 16,
      lora_dropout: float = 0.05,
      lora_target_modules: List[str] = [
          "q_proj",
          "v_proj",
      ],
      # llm hyperparams
      train_on_inputs: bool = True,  # if False, masks out inputs in loss
      group_by_length: bool = False,  # faster, but produces an odd training loss curve
      # wandb params
      wandb_project: str = "",
      wandb_run_name: str = "",
      wandb_watch: str = "",  # options: false | gradients | all
      wandb_log_model: str = "",  # options: false | true
      resume_from_checkpoint: str = None,  # either training checkpoint or final adapter
      use_ipex: bool = False,
      bf16: bool = False,
      no_cuda: bool = True,
      xpu_backend: str = "ccl",
  ):
      print(
          f"Training Alpaca-LoRA model with params:\n"
          f"base_model: {base_model}\n"
          f"data_path: {data_path}\n"
          f"output_dir: {output_dir}\n"
          f"batch_size: {batch_size}\n"
          f"micro_batch_size: {micro_batch_size}\n"
          f"num_epochs: {num_epochs}\n"
          f"learning_rate: {learning_rate}\n"
          f"cutoff_len: {cutoff_len}\n"
          f"val_set_size: {val_set_size}\n"
          f"lora_r: {lora_r}\n"
          f"lora_alpha: {lora_alpha}\n"
          f"lora_dropout: {lora_dropout}\n"
          f"lora_target_modules: {lora_target_modules}\n"
          f"train_on_inputs: {train_on_inputs}\n"
          f"group_by_length: {group_by_length}\n"
          f"wandb_project: {wandb_project}\n"
          f"wandb_run_name: {wandb_run_name}\n"
          f"wandb_watch: {wandb_watch}\n"
          f"wandb_log_model: {wandb_log_model}\n"
          f"resume_from_checkpoint: {resume_from_checkpoint}\n"
          f"use_ipex: {use_ipex}\n"
          f"bf16: {bf16}\n"
      )
      assert (
          base_model
      ), "Please specify a --base_model, e.g. --base_model='decapoda-research/llama-7b-hf'"
      gradient_accumulation_steps = batch_size // micro_batch_size
  
      device_map = "auto"
      pmi_world_size = int(os.environ.get("PMI_SIZE", -1))
      if pmi_world_size > 0:
          os.environ["WORLD_SIZE"] = str(pmi_world_size)
      else:
          os.environ["WORLD_SIZE"] = str(
              os.environ.get("WORLD_SIZE", 1)
          )
      world_size = int(os.environ.get("WORLD_SIZE", 1))
      print(f"world_size: {world_size}!!")
      ddp = world_size != 1
      local_rank = 0
      if ddp:
          os.environ["RANK"] = str(os.environ.get("PMI_RANK", 0))
          os.environ["LOCAL_RANK"] = str(os.environ.get("PMI_RANK", 0))
          local_rank = str(os.environ.get("PMI_RANK", 0))
          print("PMI_RANK(local_rank): " + local_rank)
          gradient_accumulation_steps = (
              gradient_accumulation_steps // world_size
          )
  
      # Check if parameter passed or if set within environ
      use_wandb = len(wandb_project) > 0 or (
          "WANDB_PROJECT" in os.environ
          and len(os.environ["WANDB_PROJECT"]) > 0
      )
      # Only overwrite environ if wandb param passed
      if len(wandb_project) > 0:
          os.environ["WANDB_PROJECT"] = wandb_project
      if len(wandb_watch) > 0:
          os.environ["WANDB_WATCH"] = wandb_watch
      if len(wandb_log_model) > 0:
          os.environ["WANDB_LOG_MODEL"] = wandb_log_model
  
      model = LlamaForCausalLM.from_pretrained(
          base_model, low_cpu_mem_usage=True
      )
  
      tokenizer = LlamaTokenizer.from_pretrained(base_model)
  
      tokenizer.pad_token_id = (
          0  # unk. we want this to be different from the eos token
      )
      tokenizer.padding_side = "left"  # Allow batched inference
  
      def tokenize(prompt, add_eos_token=True):
          # there's probably a way to do this with the tokenizer settings
          # but again, gotta move fast
          result = tokenizer(
              prompt,
              truncation=True,
              max_length=cutoff_len,
              padding=False,
              return_tensors=None,
          )
          if (
              result["input_ids"][-1] != tokenizer.eos_token_id
              and len(result["input_ids"]) < cutoff_len
              and add_eos_token
          ):
              result["input_ids"].append(tokenizer.eos_token_id)
              result["attention_mask"].append(1)
  
          result["labels"] = result["input_ids"].copy()
  
          return result
  
      def generate_and_tokenize_prompt(data_point):
          full_prompt = generate_prompt(data_point)
          tokenized_full_prompt = tokenize(full_prompt)
          if not train_on_inputs:
              user_prompt = generate_prompt(
                  {**data_point, "output": ""}
              )
              tokenized_user_prompt = tokenize(
                  user_prompt, add_eos_token=False
              )
              user_prompt_len = len(tokenized_user_prompt["input_ids"])
  
              tokenized_full_prompt["labels"] = [
                  -100
              ] * user_prompt_len + tokenized_full_prompt["labels"][
                  user_prompt_len:
              ]  # could be sped up, probably
          return tokenized_full_prompt
  
      model = prepare_model_for_int8_training(model)
  
      config = LoraConfig(
          r=lora_r,
          lora_alpha=lora_alpha,
          target_modules=lora_target_modules,
          lora_dropout=lora_dropout,
          bias="none",
          task_type="CAUSAL_LM",
      )
      model = get_peft_model(model, config)
  
      if data_path.endswith(".json"):  # todo: support jsonl
          data = load_dataset("json", data_files=data_path)
      else:
          data = load_dataset(data_path)
  
      if resume_from_checkpoint:
          # Check the available weights and load them
          checkpoint_name = os.path.join(
              resume_from_checkpoint, "pytorch_model.bin"
          )  # Full checkpoint
          if not os.path.exists(checkpoint_name):
              checkpoint_name = os.path.join(
                  resume_from_checkpoint, "adapter_model.bin"
              )  # only LoRA model - LoRA config above has to fit
              resume_from_checkpoint = (
                  False  # So the trainer won't try loading its state
              )
          # The two files above have a different name depending on how they were saved, but are actually the same.
          if os.path.exists(checkpoint_name):
              print(f"Restarting from {checkpoint_name}")
              adapters_weights = torch.load(checkpoint_name)
              model = set_peft_model_state_dict(model, adapters_weights)
          else:
              print(f"Checkpoint {checkpoint_name} not found")
      model.print_trainable_parameters()  # Be more transparent about the % of trainable params.
  
      if val_set_size > 0:
          print("[INFO] spliting and shuffling dataset...")
          train_val = data["train"].train_test_split(
              test_size=val_set_size, shuffle=True, seed=42
          )
          print("[INFO] shuffling and tokenizing train data...")
          train_data = (
              train_val["train"]
              .shuffle()
              .map(generate_and_tokenize_prompt)
          )
          print("[INFO] shuffling and tokenizing test data...")
          val_data = (
              train_val["test"]
              .shuffle()
              .map(generate_and_tokenize_prompt)
          )
      else:
          train_data = (
              data["train"].shuffle().map(generate_and_tokenize_prompt)
          )
          val_data = None
      print("[INFO] begining the training of transformers...")
  
      args = transformers.TrainingArguments(
          per_device_train_batch_size=micro_batch_size,
          gradient_accumulation_steps=gradient_accumulation_steps,
          warmup_steps=100,
          num_train_epochs=num_epochs,
          learning_rate=learning_rate,
          bf16=bf16,
          logging_steps=10,
          optim="adamw_torch",
          evaluation_strategy="epoch",
          save_strategy="steps",
          local_rank=local_rank,
          output_dir=output_dir,
          save_total_limit=3,
          ddp_find_unused_parameters=False,
          group_by_length=group_by_length,
          report_to="wandb" if use_wandb else None,
          run_name=wandb_run_name if use_wandb else None,
          xpu_backend=xpu_backend,
          no_cuda=no_cuda,
      )
  
      print(
          f"[INFO] Process rank: {args.local_rank}, device: {args.device}"
          + f"distributed training: {args.parallel_mode.value == 'distributed'}"
      )
  
      trainer = transformers.Trainer(
          model=model,
          train_dataset=train_data,
          eval_dataset=val_data,
          args=args,
          data_collator=transformers.DataCollatorForSeq2Seq(
              tokenizer,
              pad_to_multiple_of=8,
              return_tensors="pt",
              padding=True,
          ),
      )
      model.config.use_cache = False
  
      old_state_dict = model.state_dict
      model.state_dict = (
          lambda self, *_, **__: get_peft_model_state_dict(
              self, old_state_dict()
          )
      ).__get__(model, type(model))
  
      start = time.time()
      trainer.train(resume_from_checkpoint=resume_from_checkpoint)
      end = time.time()
      print("training time is: ", end - start)
  
      if int(os.environ.get("PMI_RANK", -1)) == 0:
          model.save_pretrained(output_dir)
      elif int(os.environ.get("PMI_RANK", -1)) == -1:
          model.save_pretrained(output_dir)
  
      print(
          "\n If there's a warning about missing keys above, please disregard :)"
      )
  
file_path: BigDL/docker/llm/finetune/lora/cpu/docker/lora_finetune.py
imports: import os
  import sys
  from typing import List
  import time
  import fire
  import torch
  from torch.utils.data import DataLoader
  import transformers
  from datasets import load_dataset
  from peft import LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict
  from transformers import LlamaForCausalLM, LlamaTokenizer
  from transformers import BitsAndBytesConfig
label_desc: Use image node_id 1 for calling a global function with example usage: train(base_model, data_path, output_dir, batch_size, micro_batch_size, num_epochs, learning_rate, cutoff_len, val_set_size, lora_r, lora_alpha, lora_dropout, lora_target_modules, train_on_inputs, group_by_length, wandb_project, wandb_run_name, wandb_watch, wandb_log_model, resume_from_checkpoint, use_ipex, bf16, no_cuda, xpu_backend) and returns: result, tokenized_full_prompt