search

PKU-YuanGroup / Chat-UniVi

[CVPR 2024 Highlight🔥] Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
https://arxiv.org/abs/2311.08046
Apache License 2.0
760 stars 41 forks source link

Training in 8-bit/4-bit causes error #4

Open Muhammad4hmed opened 9 months ago

Muhammad4hmed commented 9 months ago

Hi,

First of all, great work! I've tested your model on some videos and they are doing perfect! I was trying to pretrain and finetune the model on my custom dataset, the data is well prepared as per the mentioned instructions but since I have less ram (16GB) in my RTX 3080 Ti, I was trying with quantization (8-Bit) for training.

The command I used:

deepspeed \
ChatUniVi/train/train_mem.py \
--model_name_or_path Chat-UniVi/Chat-UniVi \
--version v1 \
--model_use PRETUNE \
--dataset_use "New" \
--vision_tower openai/clip-vit-large-patch14 \
--tune_mm_mlp_adapter True \
--mm_vision_select_layer -2 \
--mm_use_im_start_end False \
--mm_use_im_patch_token False \
--bf16 True \
--output_dir "EXP" \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 24000 \
--save_total_limit 1 \
--learning_rate 2e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--bits 8 \
--tf32 True \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 0 \
--lazy_preprocess True

The reason I removed --deepspeed scripts/zero2.json is because it wasn't reducing memory usage at all. After removing it, the model memory consumption is reduced significantly.

The problem is, I'm getting two errors, first one at:

        model.config.tune_mm_mlp_adapter = training_args.tune_mm_mlp_adapter = model_args.tune_mm_mlp_adapter
        if model_args.tune_mm_mlp_adapter:
            model.requires_grad_(False)
            for p in model.get_model().mm_projector.parameters():
           ---->p.requires_grad = True

Error: RuntimeError: only Tensors of floating point and complex dtype can require gradients

So I commented it out and enabled LoRa optimzation lora_enable: bool = True it adds adapter weights with require_gradient = True

But I faced the following error at training time:

RuntimeError: "addmm_cuda" not implemented for 'Char'

The complete log of last error:

/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.int8 to float16 during quantization
  warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
Traceback (most recent call last):
  File "/home/ahmed/Desktop/WORK/Pioneer/VLM/01_12_23/UniVi/Chat-UniVi/ChatUniVi/train/train_mem.py", line 13, in <module>
    train()
  File "/home/ahmed/Desktop/WORK/Pioneer/VLM/24_11_23/UniVi/Chat-UniVi/ChatUniVi/train/train.py", line 1089, in train
    trainer.train()
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train
    return inner_training_loop(
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/transformers/trainer.py", line 1809, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/transformers/trainer.py", line 2654, in training_step
    loss = self.compute_loss(model, inputs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/transformers/trainer.py", line 2679, in compute_loss
    outputs = model(**inputs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1519, in forward
    else self._run_ddp_forward(*inputs, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1355, in _run_ddp_forward
    return self.module(*inputs, **kwargs)  # type: ignore[index]
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/accelerate/utils/operations.py", line 581, in forward
    return model_forward(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/accelerate/utils/operations.py", line 569, in __call__
    return convert_to_fp32(self.model_forward(*args, **kwargs))
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 16, in decorate_autocast
    return func(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/peft/peft_model.py", line 922, in forward
    return self.base_model(
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/accelerate/hooks.py", line 165, in new_forward
    output = old_forward(*args, **kwargs)
  File "/home/ahmed/Desktop/WORK/Pioneer/VLM/24_11_23/UniVi/Chat-UniVi/ChatUniVi/model/language_model/llama.py", line 54, in forward
    input_ids, attention_mask, past_key_values, inputs_embeds, labels = self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images)
  File "/home/ahmed/Desktop/WORK/Pioneer/VLM/24_11_23/UniVi/Chat-UniVi/ChatUniVi/model/arch.py", line 283, in prepare_inputs_labels_for_multimodal
    cur_image_features = self.project(cur_image_features, input_type="video")
  File "/home/ahmed/Desktop/WORK/Pioneer/VLM/24_11_23/UniVi/Chat-UniVi/ChatUniVi/model/arch.py", line 215, in project
    image_features = self.get_model().mm_projector(image_features)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/peft/tuners/lora.py", line 1064, in forward
    result = super().forward(x)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/bitsandbytes/nn/modules.py", line 441, in forward
    out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 563, in matmul
    return MatMul8bitLt.apply(A, B, out, bias, state)
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/home/ahmed/miniconda3/envs/chatunivi/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py", line 421, in forward
    output += torch.matmul(subA, state.subB)
RuntimeError: "addmm_cuda" not implemented for 'Char'

I'm a little confused here, if I'm doing something wrong or I'm missing anything? Can you please help here? Thanks

jpthu17 commented 9 months ago

Thank you for your interest in our work. I will solve this error as soon as possible.