llava-llama3-8b llm+llm adapter merge error

[ztfmars]

• description i follow the turial for llama3 ft :https://github.com/SmartFlowAI/Llama3-Tutorial/blob/main/docs/llava.md

i use llava_llama3_8b_instruct_qlora_clip_vit_large_p14_336_lora_e1_finetune.py to fineture on my dataset, and want to get a llava-llama38b multimodal model on my datasets. after training and pth -> hf,
i got llm adapter, visual encoder adapter ,project.

but i can't merge llm +llm adapter together and can'get the LLM weights as turial https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/llama3_8b_instruct_clip_vit_large_p14_336

the error can be listed as following:

/llava_train_20240506$ xtuner convert merge /home/fusionai/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-                Instruct /home/fusionai/project/internllm_demo/llama3/llama3-ft/llava_train_20240506/iter_1000_hf/llm_adapter /home/fusionai/project/internllm_demo/llama3/llama3-ft/llava_train_                20240506/iter_1000_llava
[2024-05-08 09:51:48,946] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1
 [WARNING]  using untested triton version (2.1.0), only 1.0.0 is known to be compatible
[2024-05-08 09:51:53,816] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
 [WARNING]  async_io requires the dev libaio .so object and headers but these were not found.
 [WARNING]  async_io: please install the libaio-dev package with apt
 [WARNING]  If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
 [WARNING]  Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
 [WARNING]  sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.1
 [WARNING]  using untested triton version (2.1.0), only 1.0.0 is known to be compatible
Loading checkpoint shards:  75%|██████████████████████████████████████████████████████████████████████████████████████▎                            |                                             Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████|                                             Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████|                                             4/4 [00:05<00:00,  1.25s/it]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Traceback (most recent call last):
  File "/home/fusionai/project/internllm/xtuner/xtuner/tools/model_converters/merge.py", line 73, in <module>
    main()
  File "/home/fusionai/project/internllm/xtuner/xtuner/tools/model_converters/merge.py", line 56, in main
    model_unmerged = PeftModel.from_pretrained(
  File "/home/fusionai/anaconda3/envs/llama3/lib/python3.10/site-packages/peft/peft_model.py", line 324, in from_pretrained
    config = PEFT_TYPE_TO_CONFIG_MAPPING[
  File "/home/fusionai/anaconda3/envs/llama3/lib/python3.10/site-packages/peft/config.py", line 151, in from_pretrained
    return cls.from_peft_type(**kwargs)
  File "/home/fusionai/anaconda3/envs/llama3/lib/python3.10/site-packages/peft/config.py", line 118, in from_peft_type
    return config_cls(**kwargs)
TypeError: LoraConfig.__init__() got an unexpected keyword argument 'layer_replication'

additon description:

• merge cmd

  xtuner convert merge /home/fusionai/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct \
  /home/fusionai/project/internllm_demo/llama3/llama3-ft/llava_train_20240506/iter_1000_hf/llm_adapter \
  /home/fusionai/project/internllm_demo/llama3/llama3-ft/llava_train_20240506/iter_1000_llava

• training configs

  
  # Copyright (c) OpenMMLab. All rights reserved.
  import torch
  from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                          LoggerHook, ParamSchedulerHook)
  from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
  from peft import LoraConfig
  from torch.optim import AdamW
  from transformers import (AutoModelForCausalLM, AutoTokenizer,
                        BitsAndBytesConfig, CLIPImageProcessor,
                        CLIPVisionModel)

from xtuner.dataset import LLaVADataset from xtuner.dataset.collate_fns import default_collate_fn from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory from xtuner.dataset.samplers import LengthGroupedSampler from xtuner.engine.hooks import DatasetInfoHook, EvaluateChatHook from xtuner.engine.runner import TrainLoop from xtuner.model import LLaVAModel from xtuner.utils import PROMPT_TEMPLATE

#######################################################################

PART 1 Settings

#######################################################################

Model

llm_name_or_path = '/home/fusionai/.cache/modelscope/hub/LLM-Research/Meta-Llama-3-8B-Instruct' visual_encoder_name_or_path = '/home/fusionai/.cache/modelscope/hub/AI-ModelScope/clip-vit-large-patch14-336'

Specify the pretrained pth

pretrained_pth = '/home/fusionai/project/internllm_demo/llama3/pretrained-model/llama3-llava-iter_2181.pth' # noqa: E501

Data

data_root = '/home/fusionai/project/datasets/llama3_test001/' data_path = data_root + 'repeated_data.json' image_folder = data_root prompt_template = PROMPT_TEMPLATE.llama3_chat max_length = int(2048 - (336 / 14)**2)

Scheduler & Optimizer

batch_size = 1 # per_device accumulative_counts = 1 dataloader_num_workers = 0 max_epochs = 1 optim_type = AdamW lr = 2e-4 betas = (0.9, 0.999) weight_decay = 0 max_norm = 1 # grad clip warmup_ratio = 0.03

Save

save_steps = 500 save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited)

Evaluate the generation performance during the training

evaluation_freq = 500 SYSTEM = '' evaluation_images = '/home/fusionai/project/datasets/llama3_test001/imgs/test0001.png' evaluation_inputs = ['此图表示什么逻辑？','图中都有哪些逻辑符号？']

#######################################################################

PART 2 Model & Tokenizer & Image Processor

####################################################################### tokenizer = dict( type=AutoTokenizer.from_pretrained, pretrained_model_name_or_path=llm_name_or_path, trust_remote_code=True, padding_side='right')

image_processor = dict( type=CLIPImageProcessor.from_pretrained, pretrained_model_name_or_path=visual_encoder_name_or_path, trust_remote_code=True)

model = dict( type=LLaVAModel, freeze_llm=True, freeze_visual_encoder=True, pretrained_pth=pretrained_pth, llm=dict( type=AutoModelForCausalLM.from_pretrained, pretrained_model_name_or_path=llm_name_or_path, trust_remote_code=True, torch_dtype=torch.float16, quantization_config=dict( type=BitsAndBytesConfig, load_in_4bit=True, load_in_8bit=False, llm_int8_threshold=6.0, llm_int8_has_fp16_weight=False, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type='nf4')), llm_lora=dict( type=LoraConfig, r=512, lora_alpha=256, lora_dropout=0.05, bias='none', task_type='CAUSAL_LM'), visual_encoder=dict( type=CLIPVisionModel.from_pretrained, pretrained_model_name_or_path=visual_encoder_name_or_path), visual_encoder_lora=dict( type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05, bias='none'))

#######################################################################

PART 3 Dataset & Dataloader

####################################################################### llava_dataset = dict( type=LLaVADataset, data_path=data_path, image_folder=image_folder, tokenizer=tokenizer, image_processor=image_processor, dataset_map_fn=llava_map_fn, template_map_fn=dict( type=template_map_fn_factory, template=prompt_template), max_length=max_length, pad_image_to_square=True)

train_dataloader = dict( batch_size=batch_size, num_workers=dataloader_num_workers, dataset=llava_dataset, sampler=dict( type=LengthGroupedSampler, length_property='modality_length', per_device_batch_size=batch_size * accumulative_counts), collate_fn=dict(type=default_collate_fn))

#######################################################################

PART 4 Scheduler & Optimizer

#######################################################################

optimizer

optim_wrapper = dict( type=AmpOptimWrapper, optimizer=dict( type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), accumulative_counts=accumulative_counts, loss_scale='dynamic', dtype='float16')

learning policy

More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501

param_scheduler = [ dict( type=LinearLR, start_factor=1e-5, by_epoch=True, begin=0, end=warmup_ratio max_epochs, convert_to_iter_based=True), dict( type=CosineAnnealingLR, eta_min=0.0, by_epoch=True, begin=warmup_ratio max_epochs, end=max_epochs, convert_to_iter_based=True) ]

train, val, test setting

train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

#######################################################################

PART 5 Runtime

#######################################################################

Log the dialogue periodically during the training process, optional

custom_hooks = [ dict(type=DatasetInfoHook, tokenizer=tokenizer), dict( type=EvaluateChatHook, tokenizer=tokenizer, image_processor=image_processor, every_n_iters=evaluation_freq, evaluation_inputs=evaluation_inputs, evaluation_images=evaluation_images, system=SYSTEM, prompt_template=prompt_template) ]

configure default hooks

default_hooks = dict(

record the time of every iteration.

timer=dict(type=IterTimerHook),
# print log every 10 iterations.
logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
# enable the parameter scheduler.
param_scheduler=dict(type=ParamSchedulerHook),
# save checkpoint per `save_steps`.
checkpoint=dict(
    type=CheckpointHook,
    by_epoch=False,
    interval=save_steps,
    max_keep_ckpts=save_total_limit),
# set sampler seed in distributed evrionment.
sampler_seed=dict(type=DistSamplerSeedHook),

)

configure environment

env_cfg = dict(

whether to enable cudnn benchmark

cudnn_benchmark=False,
# set multi process parameters
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
# set distributed parameters
dist_cfg=dict(backend='nccl'),

)

set visualizer

visualizer = None

set log level

log_level = 'INFO'

load from which checkpoint

load_from = None

whether to resume training from the loaded checkpoint

resume = False

Defaults to use random seed and disable deterministic

randomness = dict(seed=None, deterministic=False)

set log processor

log_processor = dict(by_epoch=False)

**how to solve this problem, waiting for help! 
thx**

[LZHgrla]

@ztfmars Hi

This issues is caused by the mismatch between the version of transformers and peft.

This PR https://github.com/huggingface/peft/pull/1368/files supports the layer_replication for LoraConfig, so we recommend that you can update your peft to v0.10.0 and re-run your merge script.

[ztfmars]

@ztfmars Hi

This issues is caused by the mismatch between the version of transformers and peft.

This PR https://github.com/huggingface/peft/pull/1368/files supports the layer_replication for LoraConfig, so we recommend that you can update your peft to v0.10.0 and re-run your merge script.

yes, it works! but it have obvious version conflicts between xtuner and lmdeploy on peft, i will try to install another venv env for lmdeploy again and continue.

thx very much!