fix dpo finetune (#12774)

python/llm/example/GPU/LLM-Finetuning/DPO/README.md

@@ -17,11 +17,9 @@ conda create -n llm python=3.11
 conda activate llm
 # below command will install intel_extension_for_pytorch==2.1.10+xpu as default
 pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
-pip install datasets
+pip install transformers==4.45.0 "trl<0.12.0" datasets
 pip install peft==0.10.0
-pip install 'trl<0.9'
-# Note, if you don't want to reinstall BNBs dependencies, append the `--no-deps` flag!
-pip install --no-deps --force-reinstall 'https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-manylinux_2_24_x86_64.whl'
+pip install bitsandbytes==0.45.1
 ```
 
 ### 2. Configures OneAPI environment variables

python/llm/example/GPU/LLM-Finetuning/DPO/dpo_finetuning.py

@@ -37,10 +37,10 @@ import torch
 from ipex_llm.transformers.qlora import get_peft_model, prepare_model_for_kbit_training
 from ipex_llm.transformers import AutoModelForCausalLM
 import transformers
-from transformers import AutoTokenizer, TrainingArguments, BitsAndBytesConfig
+from transformers import AutoTokenizer, BitsAndBytesConfig
 from datasets import load_dataset
 from peft import LoraConfig
-from trl import DPOTrainer
+from trl import DPOConfig, DPOTrainer
 import argparse
 
 
@@ -83,7 +83,7 @@ if __name__ == "__main__":
     dataset_path = args.dataset
     output_path = args.output_path
     gradient_checkpointing = args.gradient_checkpointing
-    
+
     # Load dataset
     dataset = load_dataset(dataset_path)['train']
 
@@ -143,12 +143,15 @@ if __name__ == "__main__":
     ref_model = ref_model.to('xpu')
 
     # Training arguments
-    training_args = TrainingArguments(
+    training_args = DPOConfig(
         per_device_train_batch_size=4,
         gradient_accumulation_steps=4,
         gradient_checkpointing=gradient_checkpointing,
         learning_rate=5e-5,
         lr_scheduler_type="cosine",
+        beta=0.1,
+        max_prompt_length=1024,
+        max_length=1536,
         max_steps=200,
         save_strategy="no",
         logging_steps=1,
@@ -166,9 +169,6 @@ if __name__ == "__main__":
         args=training_args,
         train_dataset=dataset,
         tokenizer=tokenizer,
-        beta=0.1,
-        max_prompt_length=1024,
-        max_length=1536,
     )
 
     # Fine-tune model with DPO