sy-xuan / pink Goto Github PK
View Code? Open in Web Editor NEWPink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs
pink's Introduction
pink's People
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pink's Issues
Code and Data
When will the code and data released?
Training time for both stages
Hi, I noticed that the paper mentions 8 A100 GPUs are used for training, and I'm curious to know how long does it take to train the model in both stages?
The data download link is invalid
COCO dataset link is invalid, and How can I obtain images files corresponding to *.json in LLaVA-158K? The same problem applies to other images as well.
Is the environment installation package missing?
你好，我按照你给的环境安装流程进行了安装，但是执行stages1.sh无法运行，环境是否对呢？我把此处的llava改成pink，还是无法运行，感觉环境漏了很多东西，可以列举一下吗
stage1.sh
export PYTHONPATH=$PYTHONPATH:./
output_dir=./dir_satge1
if [ -d ${output_dir} ];then
echo "dir already exists"
else
mkdir ${output_dir}
fi
export CUDA_LAUNCH_BLOCKING=1
llama_path=./Llama-2-7b-chat-hf
llava_cc3m_pretrain_data_path=./LLaVA-CC3M-Pretrain-595K/chat.json
llava_cc3m_pretrain_base_path=./LLaVA-CC3M-Pretrain-595K/images
llava_cc3m_pretrain_image_folder=./LLaVA-CC3M-Pretrain-595K/images
OMP_NUM_THREADS=1 torchrun --nnodes=1 --nproc_per_node=4 --master_port=25002 \
pink/train/train.py \
--model_name_or_path ${llama_path} \
--llama_path ${llama_path} \
--dataset_name LLaVACaptionDataset \
--data_path ${llava_cc3m_pretrain_data_path} \
--image_folder ${llava_cc3m_pretrain_image_folder} \
--base_path ${llava_cc3m_pretrain_base_path} \
--vision_tower ./clip-vit-large-patch14 \
--tune_mm_mlp_adapter True \
--mm_vision_select_layer -2 \
--conversation_template llamav2 \
--freeze_llm True \
--llm_adapter_enable False \
--visual_adapter_enable False \
--freeze_vit True \
--bf16 True \
--output_dir ${output_dir} \
--num_train_epochs 1 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 16 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 2400000 \
--save_total_limit 1 \
--learning_rate 2e-3 \
--dataloader_num_workers 4 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing False \
--report_to tensorboard
这是我的stage1.sh的内容但是会有报错
WARNING:accelerate.utils.other:Detected kernel version 3.10.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.
0%| | 0/9302 [00:00<?, ?it/s][rank3]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[rank0]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[rank2]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
[rank1]:[W reducer.cpp:1360] Warning: find_unused_parameters=True was specified in DDP constructor, but did not find any unused parameters in the forward pass. This flag results in an extra traversal of the autograd graph every iteration, which can adversely affect performance. If your model indeed never has any unused parameters in the forward pass, consider turning this flag off. Note that this warning may be a false positive if your model has flow control causing later iterations to have unused parameters. (function operator())
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demo
sorry, I couldn't find some files and I have some difficulties understanding the demo operation. What and where is the "pink/Pink-chat" in the file "demo.py"?
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