Support Fine-Tune a Quantized LLM

Ray Libraries (Data, Train, Tune, Serve) Ray Data
1

it works without ray.
but if I use ray, I got below error:
RuntimeError: Expected to mark a variable ready only once. This error is caused by one of the following reasons: 1) Use of a module parameter outside the forward function. Please make sure model parameters are not shared across multiple concurrent forward-backward passes. or try to use _set_static_graph() as a workaround if this module graph does not change during training loop.2) Reused parameters in multiple reentrant backward passes. For example, if you use multiple checkpoint functions to wrap the same part of your model, it would result in the same set of parameters been used by different reentrant backward passes multiple times, and hence marking a variable ready multiple times. DDP does not support such use cases in default. You can try to use _set_static_graph() as a workaround if your module graph does not change over iterations.
Parameter at index 319 with name base_model.model.model.layers.31.mlp.gate_proj.lora_B.default.weight has been marked as ready twice. This means that multiple autograd engine hooks have fired for this particular parameter during this iteration.

here is the code:

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
import os,torch, platform, warnings
from datasets import load_dataset
from trl import SFTTrainer
from huggingface_hub import notebook_login

import ray.train
from ray.train.huggingface.transformers import prepare_trainer, RayTrainReportCallback

import ray.train.huggingface.transformers
from ray.train import ScalingConfig
from ray.train.torch import TorchTrainer

def train_func(config):
    base_model, dataset_name, new_model = "mistralai/Mistral-7B-v0.1" , "gathnex/Gath_baize", "gathnex/Gath_mistral_7b"
    
    #Importing the dataset
    dataset = load_dataset(dataset_name, split="train")
    
    
    # Load base model(Mistral 7B)
    bnb_config = BitsAndBytesConfig(
        load_in_4bit= True,
        bnb_4bit_quant_type= "nf4",
        bnb_4bit_compute_dtype= torch.bfloat16,
        bnb_4bit_use_double_quant= False,
    )
    model = AutoModelForCausalLM.from_pretrained(
       base_model,
        quantization_config=bnb_config,
        device_map={"": 0}
    )
    model.config.use_cache = False # silence the warnings. Please re-enable for inference!
    #model.config.pretraining_tp = 1
    
    # Load tokenizer
    tokenizer = AutoTokenizer.from_pretrained(base_model,model_max_length=512,trust_remote_code=True)
    tokenizer.padding_side = 'right'
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.add_eos_token = True
    tokenizer.add_bos_token, tokenizer.add_eos_token
    
    #Adding the adapters in the layers
    model.gradient_checkpointing_enable()
    model = prepare_model_for_kbit_training(model)
    peft_config = LoraConfig(
            r=8,
            lora_alpha=16,
            lora_dropout=0.05,
            bias="none",
            task_type="CAUSAL_LM",
            target_modules=["q_proj", "k_proj", "v_proj", "o_proj","gate_proj"]
        )
    model = get_peft_model(model, peft_config)
    
    #Hyperparamter
    training_arguments = TrainingArguments(
        output_dir= "results",
        num_train_epochs= 2,
        per_device_train_batch_size= 8,
        gradient_accumulation_steps= 2,
        optim = "paged_adamw_8bit",
        save_steps= 100,
        logging_steps= 30,
        learning_rate= 2e-4,
        weight_decay= 0.001,
        bf16= True,
        max_grad_norm= 0.3,
        max_steps= -1,
        warmup_ratio= 0.3,
        group_by_length= True,
        lr_scheduler_type= "constant",
    )
    # Hugging Face Trainer
    training_arguments = TrainingArguments(
        output_dir="test_trainer",
        evaluation_strategy="epoch",
        save_strategy="epoch",
        report_to="none",
    )
    
    # Setting sft parameters
    trainer = SFTTrainer(
        model=model,
        train_dataset=dataset,
        peft_config=peft_config,
        max_seq_length= None,
        dataset_text_field="chat_sample",
        tokenizer=tokenizer,
        args=training_arguments,
        packing= False,
    )
    trainer.add_callback(RayTrainReportCallback())
    trainer = prepare_trainer(trainer)

    print("Starting training")
    trainer.train()

trainer = TorchTrainer(
        train_func,
        #train_loop_config=config,
        #datasets=ray_datasets,
        #dataset_config=DataConfig(datasets_to_split=["train", "validation"]),
        scaling_config=ScalingConfig(num_workers=4, resources_per_worker={
            "CPU": 4,
            "GPU": 1,
        },
        trainer_resources={
          "CPU": 0,
          "GPU": 0,
        },use_gpu=True),
        # If running in a multi-node cluster, this is where you
        # should configure the run's persistent storage that is accessible
        # across all worker nodes.
        run_config=ray.train.RunConfig(storage_path="/data/ray/storage/"),
    )

result = trainer.fit()