'TinyLlavaForConditionalGeneration' object has no attribute 'peft_config'

[pspdada]

I followed the instructions in the CUSTOM_FINETUNE.md file to run the Pokémon dataset for LoRA fine-tuning and encountered an issue when using bash scripts/train/custom_finetune.sh. The error message is as follows:

Traceback (most recent call last):
  File "/root/llm-project/TinyLLaVA_Factory/tinyllava/train/custom_finetune.py", line 52, in <module>
    train()
  File "/root/llm-project/TinyLLaVA_Factory/tinyllava/train/custom_finetune.py", line 34, in train
    model = training_recipe(model)
  File "/root/llm-project/TinyLLaVA_Factory/tinyllava/training_recipe/base.py", line 14, in __call__
    model = self.training_model_converse(model)
  File "/root/llm-project/TinyLLaVA_Factory/tinyllava/training_recipe/lora_recipe.py", line 46, in training_model_converse
    if model.peft_config is None:
  File "/root/anaconda3/envs/tinyllava_factory/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1614, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'TinyLlavaForConditionalGeneration' object has no attribute 'peft_config'
[2024-12-04 12:09:01,833] [INFO] [launch.py:316:sigkill_handler] Killing subprocess 73410

The context of this function is as follows:

@register_training_recipe('lora')
class LoRATrainingRecipe(BaseTrainingRecipe):
    def __init__(self, training_arguments):
        super().__init__(training_arguments)
        self.training_arguments = training_arguments
        self.lora_skip_module = ['connector', 'vision_tower', 'language_model']
        
    def training_model_converse(self, model):
        if self.training_arguments.tune_type_connector == 'lora':
            self.lora_skip_module.remove('connector')
        if self.training_arguments.tune_type_llm == 'lora':
            self.lora_skip_module.remove('language_model')
        if self.training_arguments.tune_type_vision_tower == 'lora':
            self.lora_skip_module.remove('vision_tower')
        lora_config = LoraConfig(
            r=self.training_arguments.lora_r,
            lora_alpha=self.training_arguments.lora_alpha,
            target_modules=find_all_linear_names(model, self.lora_skip_module),
            lora_dropout=self.training_arguments.lora_dropout,
            bias=self.training_arguments.lora_bias,
            task_type="CAUSAL_LM",
        )
        if self.training_arguments.bits == 16:
            if self.training_arguments.bf16:
                model.to(torch.bfloat16)
            if self.training_arguments.fp16:
                model.to(torch.float16)
        if model.peft_config is None:  # Error raise here!
            log("Adding LoRA adapters...")
            model = get_peft_model(model, lora_config)  
        return model

My bash scripe is:

DATA_PATH="mydata/text_files/pokemon_blip_captions.json"
IMAGE_PATH="/root/llm-project/TinyLLaVA_Factory/mydata"
MODEL_MAX_LENGTH=3072
OUTPUT_DIR="/root/llm-project/TinyLLaVA_Factory/output/custom-finetune-TinyLLaVA-Phi-2-SigLIP-3.1B-lora"

deepspeed --include localhost:0 --master_port 29501 tinyllava/train/custom_finetune.py \
    --deepspeed ./scripts/zero2.json \
    --data_path  $DATA_PATH \
    --image_folder $IMAGE_PATH \
    --is_multimodal True \
    --conv_version phi \
    --mm_vision_select_layer -2 \
    --image_aspect_ratio square \
    --fp16 True \
    --training_recipe lora \
    --tune_type_llm lora \
    --tune_type_vision_tower frozen \
    --tune_vision_tower_from_layer 0 \
    --tune_type_connector full \
    --lora_r 128 \
    --lora_alpha 256 \
    --group_by_modality_length False \
    --pretrained_model_path "tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B" \
    --output_dir $OUTPUT_DIR \
    --num_train_epochs 1 \
    --per_device_train_batch_size 2 \
    --per_device_eval_batch_size 2 \
    --gradient_accumulation_steps 8 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 50000 \
    --save_total_limit 1 \
    --learning_rate 1e-4 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 False \
    --model_max_length $MODEL_MAX_LENGTH \
    --gradient_checkpointing True \
    --dataloader_num_workers 8 \
    --lazy_preprocess True \
    --report_to tensorboard \
    --tokenizer_use_fast False \
    --run_name custom-finetune-TinyLLaVA-Phi-2-SigLIP-3.1B-lora

My environment details are:

• transformers version: 4.40.1
• Platform: Linux-5.15.0-120-generic-x86_64-with-glibc2.35
• Python version: 3.10.15
• Huggingface_hub version: 0.26.3
• Safetensors version: 0.4.5
• Accelerate version: 0.27.2
• Accelerate config: not found
• PyTorch version (GPU?): 2.0.1+cu117 (True)
• Tensorflow version (GPU?): not installed (NA)
• Flax version (CPU?/GPU?/TPU?): not installed (NA)
• Jax version: not installed
• JaxLib version: not installed
• Using GPU in script?: 1
• Using distributed or parallel set-up in script?: None

[pspdada]

Simply changing if model.peft_config is None: to if not hasattr(model, 'peft_config') or model.peft_config is None: can resolve this issue, and no anomalies were found during the subsequent fine-tuning process.

[AlephZr]

Simply changing if model.peft_config is None: to if not hasattr(model, 'peft_config') or model.peft_config is None: can resolve this issue, and no anomalies were found during the subsequent fine-tuning process.

This works. Thank you.