diff --git "a/train_video.log" "b/train_video.log"
new file mode 100644--- /dev/null
+++ "b/train_video.log"
@@ -0,0 +1,688 @@
+[2023-10-13 02:59:14,478] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2023-10-13 02:59:16,541] [WARNING] [runner.py:196:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
+[2023-10-13 02:59:16,541] [INFO] [runner.py:555:main] cmd = /usr/local/miniconda3/envs/llava/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMV19 --master_addr=127.0.0.1 --master_port=29500 --enable_each_rank_log=None llava/train/train_mem_video.py --deepspeed ./scripts/zero2.json --lora_enable True --model_name_or_path /hy-tmp/vicuna-7b-v1.3 --version v1 --data_path ./data/avsd_train_omni.json --video_folder /hy-tmp/Charades_v1_480 --vision_tower /hy-tmp/clip-vit-large-patch14 --pretrain_mm_mlp_adapter /hy-tmp/llava-pretrain-vicuna-7b-v1.3/mm_projector.bin --mm_vision_select_layer -2 --mm_use_im_start_end False --mm_use_im_patch_token False --bf16 True --output_dir /hy-tmp/checkpoints/omni-vicuna-7b-v1.3-finetune_lora --num_train_epochs 8 --per_device_train_batch_size 8 --per_device_eval_batch_size 4 --gradient_accumulation_steps 8 --evaluation_strategy no --save_strategy steps --save_steps 100 --save_total_limit 3 --learning_rate 2e-5 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type cosine --logging_steps 1 --tf32 True --model_max_length 2048 --gradient_checkpointing True --lazy_preprocess True --dataloader_num_workers 8 --report_to wandb
+[2023-10-13 02:59:17,802] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2023-10-13 02:59:19,574] [INFO] [launch.py:138:main] 0 NCCL_P2P_LEVEL=NVL
+[2023-10-13 02:59:19,574] [INFO] [launch.py:145:main] WORLD INFO DICT: {'localhost': [0, 1]}
+[2023-10-13 02:59:19,574] [INFO] [launch.py:151:main] nnodes=1, num_local_procs=2, node_rank=0
+[2023-10-13 02:59:19,574] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1]})
+[2023-10-13 02:59:19,574] [INFO] [launch.py:163:main] dist_world_size=2
+[2023-10-13 02:59:19,574] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0,1
+[2023-10-13 02:59:22,389] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2023-10-13 02:59:22,433] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)
+[2023-10-13 02:59:22,977] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented
+[2023-10-13 02:59:22,977] [INFO] [comm.py:594:init_distributed] cdb=None
+[2023-10-13 02:59:22,977] [INFO] [comm.py:625:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
+[2023-10-13 02:59:23,051] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented
+[2023-10-13 02:59:23,051] [INFO] [comm.py:594:init_distributed] cdb=None
+You are using a model of type llama to instantiate a model of type omni. This is not supported for all configurations of models and can yield errors.
+You are using a model of type llama to instantiate a model of type omni. This is not supported for all configurations of models and can yield errors.
+
Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
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Loading checkpoint shards: 100%|██████████| 2/2 [00:24<00:00, 12.07s/it]
+
Loading checkpoint shards: 100%|██████████| 2/2 [00:35<00:00, 16.58s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:35<00:00, 17.72s/it]
+Adding LoRA adapters...
+You are using the legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at https://github.com/huggingface/transformers/pull/24565
+You are using the legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at https://github.com/huggingface/transformers/pull/24565
+Formatting inputs...Skip in lazy mode
+Rank: 0 partition count [2, 2] and sizes[(82444288, False), (2176, False)] 
+Rank: 1 partition count [2, 2] and sizes[(82444288, False), (2176, False)] 
+wandb: Currently logged in as: wanghao-cst. Use `wandb login --relogin` to force relogin
+wandb: Tracking run with wandb version 0.15.12
+wandb: Run data is saved locally in /root/Omni-LLM/wandb/run-20231013_030309-30lhy90r
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run fiery-dew-9
+wandb: ⭐️ View project at https://wandb.ai/wanghao-cst/huggingface
+wandb: 🚀 View run at https://wandb.ai/wanghao-cst/huggingface/runs/30lhy90r
+
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{'loss': 2.123, 'learning_rate': 1.9768151948859705e-05, 'epoch': 0.78}
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{'loss': 2.0356, 'learning_rate': 1.9756750997357738e-05, 'epoch': 0.79}
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{'loss': 2.0142, 'learning_rate': 1.9745079864546184e-05, 'epoch': 0.81}
+
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{'loss': 2.061, 'learning_rate': 1.97331388736189e-05, 'epoch': 0.82}
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{'loss': 2.0508, 'learning_rate': 1.972092835524257e-05, 'epoch': 0.83}
+
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{'loss': 2.0171, 'learning_rate': 1.9708448647547575e-05, 'epoch': 0.84}
+
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{'loss': 2.1284, 'learning_rate': 1.9695700096118594e-05, 'epoch': 0.86}
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{'loss': 2.0166, 'learning_rate': 1.9682683053985073e-05, 'epoch': 0.87}
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{'loss': 2.062, 'learning_rate': 1.966939788161142e-05, 'epoch': 0.88}
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{'loss': 2.0142, 'learning_rate': 1.9655844946887035e-05, 'epoch': 0.9}
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{'loss': 2.0103, 'learning_rate': 1.9642024625116117e-05, 'epoch': 0.91}
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{'loss': 1.9956, 'learning_rate': 1.9627937299007286e-05, 'epoch': 0.92}
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{'loss': 1.9868, 'learning_rate': 1.961358335866296e-05, 'epoch': 0.94}
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{'loss': 2.0435, 'learning_rate': 1.959896320156857e-05, 'epoch': 0.95}
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{'loss': 2.0112, 'learning_rate': 1.958407723258156e-05, 'epoch': 0.96}
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{'loss': 2.0908, 'learning_rate': 1.9568925863920155e-05, 'epoch': 0.97}
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{'loss': 1.9795, 'learning_rate': 1.955350951515195e-05, 'epoch': 0.99}
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{'loss': 2.0112, 'learning_rate': 1.9537828613182314e-05, 'epoch': 1.0}
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{'loss': 2.0459, 'learning_rate': 1.9521883592242537e-05, 'epoch': 1.01}
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{'loss': 2.0117, 'learning_rate': 1.950567489387783e-05, 'epoch': 1.03}
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{'loss': 2.0156, 'learning_rate': 1.9489202966935084e-05, 'epoch': 1.04}
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{'loss': 2.0547, 'learning_rate': 1.947246826755044e-05, 'epoch': 1.05}
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{'loss': 1.9639, 'learning_rate': 1.945547125913667e-05, 'epoch': 1.06}
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{'loss': 2.019, 'learning_rate': 1.943821241237034e-05, 'epoch': 1.08}
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{'loss': 1.9771, 'learning_rate': 1.9420692205178753e-05, 'epoch': 1.09}
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{'loss': 1.9492, 'learning_rate': 1.9402911122726756e-05, 'epoch': 1.1}
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{'loss': 1.9702, 'learning_rate': 1.9384869657403277e-05, 'epoch': 1.12}
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{'loss': 1.9946, 'learning_rate': 1.9366568308807685e-05, 'epoch': 1.13}
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{'loss': 1.9854, 'learning_rate': 1.9348007583735985e-05, 'epoch': 1.14}
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{'loss': 1.959, 'learning_rate': 1.9329187996166747e-05, 'epoch': 1.16}
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{'loss': 1.9722, 'learning_rate': 1.9310110067246905e-05, 'epoch': 1.17}
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{'loss': 2.0376, 'learning_rate': 1.9290774325277305e-05, 'epoch': 1.18}
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{'loss': 1.9834, 'learning_rate': 1.9271181305698084e-05, 'epoch': 1.19}
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{'loss': 2.0049, 'learning_rate': 1.9251331551073843e-05, 'epoch': 1.21}
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{'loss': 1.9824, 'learning_rate': 1.923122561107861e-05, 'epoch': 1.22}
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{'loss': 1.9624, 'learning_rate': 1.9210864042480645e-05, 'epoch': 1.23}
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{'loss': 1.9395, 'learning_rate': 1.9190247409126993e-05, 'epoch': 1.25}
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{'loss': 1.9746, 'learning_rate': 1.916937628192789e-05, 'epoch': 1.26}
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{'loss': 1.9507, 'learning_rate': 1.9148251238840947e-05, 'epoch': 1.27}
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{'loss': 2.0054, 'learning_rate': 1.9126872864855142e-05, 'epoch': 1.29}
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{'loss': 1.9409, 'learning_rate': 1.9105241751974624e-05, 'epoch': 1.3}
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+  warnings.warn(
+/usr/local/miniconda3/envs/llava/lib/python3.10/site-packages/torch/nn/modules/module.py:1802: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
+  warnings.warn(
+
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{'loss': 1.9912, 'learning_rate': 1.9083358499202323e-05, 'epoch': 1.31}
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{'loss': 1.9404, 'learning_rate': 1.9061223712523352e-05, 'epoch': 1.32}
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{'loss': 1.9102, 'learning_rate': 1.903883800488824e-05, 'epoch': 1.34}
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{'loss': 1.9248, 'learning_rate': 1.9016201996195943e-05, 'epoch': 1.35}
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{'loss': 1.8984, 'learning_rate': 1.8993316313276694e-05, 'epoch': 1.36}
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{'loss': 1.9331, 'learning_rate': 1.8970181589874637e-05, 'epoch': 1.38}
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{'loss': 1.9561, 'learning_rate': 1.894679846663027e-05, 'epoch': 1.39}
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{'loss': 1.8901, 'learning_rate': 1.8923167591062723e-05, 'epoch': 1.4}
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{'loss': 1.9922, 'learning_rate': 1.8899289617551803e-05, 'epoch': 1.42}
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{'loss': 1.9277, 'learning_rate': 1.8875165207319902e-05, 'epoch': 1.43}
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{'loss': 1.9185, 'learning_rate': 1.8850795028413658e-05, 'epoch': 1.44}
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{'loss': 1.9575, 'learning_rate': 1.882617975568547e-05, 'epoch': 1.45}
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{'loss': 1.957, 'learning_rate': 1.880132007077482e-05, 'epoch': 1.47}
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{'loss': 1.8984, 'learning_rate': 1.8776216662089373e-05, 'epoch': 1.48}
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{'loss': 1.9429, 'learning_rate': 1.875087022478594e-05, 'epoch': 1.49}
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{'loss': 1.8701, 'learning_rate': 1.8725281460751198e-05, 'epoch': 1.51}
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{'loss': 1.9497, 'learning_rate': 1.869945107858228e-05, 'epoch': 1.52}
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{'loss': 1.8921, 'learning_rate': 1.867337979356715e-05, 'epoch': 1.53}
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{'loss': 1.8569, 'learning_rate': 1.8647068327664774e-05, 'epoch': 1.55}
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{'loss': 1.8882, 'learning_rate': 1.8620517409485148e-05, 'epoch': 1.56}
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{'loss': 1.8765, 'learning_rate': 1.8593727774269122e-05, 'epoch': 1.57}
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{'loss': 1.9282, 'learning_rate': 1.8566700163868027e-05, 'epoch': 1.58}
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{'loss': 1.8384, 'learning_rate': 1.8539435326723135e-05, 'epoch': 1.6}
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{'loss': 1.9185, 'learning_rate': 1.851193401784495e-05, 'epoch': 1.61}
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{'loss': 1.834, 'learning_rate': 1.848419699879227e-05, 'epoch': 1.62}
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{'loss': 1.8657, 'learning_rate': 1.845622503765113e-05, 'epoch': 1.64}
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{'loss': 1.8457, 'learning_rate': 1.842801890901351e-05, 'epoch': 1.65}
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{'loss': 1.7671, 'learning_rate': 1.8399579393955893e-05, 'epoch': 1.66}
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{'loss': 1.8462, 'learning_rate': 1.837090728001764e-05, 'epoch': 1.68}
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{'loss': 1.8296, 'learning_rate': 1.834200336117918e-05, 'epoch': 1.69}
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{'loss': 1.8262, 'learning_rate': 1.8312868437840002e-05, 'epoch': 1.7}
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{'loss': 1.835, 'learning_rate': 1.8283503316796536e-05, 'epoch': 1.71}
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{'loss': 1.7686, 'learning_rate': 1.6299336311240593e-05, 'epoch': 2.44}
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{'loss': 1.708, 'learning_rate': 1.6217249757884954e-05, 'epoch': 2.47}
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{'loss': 1.604, 'learning_rate': 3.300876342821451e-06, 'epoch': 5.94}
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{'loss': 1.6274, 'learning_rate': 3.2618998542090263e-06, 'epoch': 5.95}
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{'loss': 1.6123, 'learning_rate': 3.1460942176927666e-06, 'epoch': 5.99}
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{'loss': 1.6182, 'learning_rate': 2.9943487315303486e-06, 'epoch': 6.04}
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{'loss': 1.623, 'learning_rate': 2.8457071155262885e-06, 'epoch': 6.09}
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{'loss': 1.5874, 'learning_rate': 2.809039439374878e-06, 'epoch': 6.1}
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{'loss': 1.6362, 'learning_rate': 2.7725708933582785e-06, 'epoch': 6.12}
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{'loss': 1.6221, 'learning_rate': 2.7363024873536093e-06, 'epoch': 6.13}
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{'loss': 1.6416, 'learning_rate': 2.700235225695752e-06, 'epoch': 6.14}
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{'loss': 1.668, 'learning_rate': 2.6643701071495644e-06, 'epoch': 6.16}
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{'loss': 1.6748, 'learning_rate': 2.4881112244893403e-06, 'epoch': 6.22}
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{'loss': 1.6167, 'learning_rate': 2.453479623287909e-06, 'epoch': 6.23}
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{'loss': 1.6763, 'learning_rate': 2.419056998287547e-06, 'epoch': 6.25}
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{'loss': 1.6587, 'learning_rate': 2.3848443027102706e-06, 'epoch': 6.26}
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{'loss': 1.6538, 'learning_rate': 2.3508424839647994e-06, 'epoch': 6.27}
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{'loss': 1.5952, 'learning_rate': 2.3170524836202936e-06, 'epoch': 6.29}
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{'loss': 1.6348, 'learning_rate': 2.2834752373803094e-06, 'epoch': 6.3}
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{'loss': 1.6074, 'learning_rate': 2.250111675056863e-06, 'epoch': 6.31}
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{'loss': 1.6284, 'learning_rate': 2.216962720544703e-06, 'epoch': 6.32}
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{'loss': 1.6143, 'learning_rate': 2.184029291795705e-06, 'epoch': 6.34}
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{'loss': 1.6323, 'learning_rate': 2.151312300793473e-06, 'epoch': 6.35}
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{'loss': 1.6387, 'learning_rate': 2.118812653528077e-06, 'epoch': 6.36}
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{'loss': 1.6016, 'learning_rate': 2.086531249970952e-06, 'epoch': 6.38}
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{'loss': 1.6616, 'learning_rate': 2.0544689840499988e-06, 'epoch': 6.39}
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{'loss': 1.6211, 'learning_rate': 2.022626743624807e-06, 'epoch': 6.4}
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{'loss': 1.6504, 'learning_rate': 1.991005410462089e-06, 'epoch': 6.42}
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{'loss': 1.6748, 'learning_rate': 1.9596058602112533e-06, 'epoch': 6.43}
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{'loss': 1.6597, 'learning_rate': 1.928428962380148e-06, 'epoch': 6.44}
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{'loss': 1.6133, 'learning_rate': 1.8974755803109968e-06, 'epoch': 6.45}
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{'loss': 1.5845, 'learning_rate': 8.7312713514486e-07, 'epoch': 6.96}
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{'loss': 1.624, 'learning_rate': 8.517487611590558e-07, 'epoch': 6.97}
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{'loss': 1.5811, 'learning_rate': 8.306237180721121e-07, 'epoch': 6.99}
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{'loss': 1.5898, 'learning_rate': 8.097525908730108e-07, 'epoch': 7.0}
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{'loss': 1.5542, 'learning_rate': 7.891359575193613e-07, 'epoch': 7.01}
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{'loss': 1.6382, 'learning_rate': 7.687743889213939e-07, 'epoch': 7.03}
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{'loss': 1.6597, 'learning_rate': 7.486684489261609e-07, 'epoch': 7.04}
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{'loss': 1.5918, 'learning_rate': 7.288186943019171e-07, 'epoch': 7.05}
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{'loss': 1.6226, 'learning_rate': 7.092256747226944e-07, 'epoch': 7.06}
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{'loss': 1.563, 'learning_rate': 6.89889932753095e-07, 'epoch': 7.08}
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{'loss': 1.6348, 'learning_rate': 6.708120038332533e-07, 'epoch': 7.09}
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{'loss': 1.6089, 'learning_rate': 6.519924162640168e-07, 'epoch': 7.1}
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{'loss': 1.6143, 'learning_rate': 6.334316911923155e-07, 'epoch': 7.12}
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{'loss': 1.6387, 'learning_rate': 5.970888772732453e-07, 'epoch': 7.14}
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{'loss': 1.6318, 'learning_rate': 5.445287408633304e-07, 'epoch': 7.18}
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{'loss': 1.645, 'learning_rate': 5.27531732449561e-07, 'epoch': 7.19}
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{'loss': 1.6011, 'learning_rate': 4.6217138681769026e-07, 'epoch': 7.25}
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{'loss': 1.6396, 'learning_rate': 3.720627009927158e-07, 'epoch': 7.32}
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{'loss': 1.6553, 'learning_rate': 3.5797537488388326e-07, 'epoch': 7.34}
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{'loss': 1.6431, 'learning_rate': 3.441550531129667e-07, 'epoch': 7.35}
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{'loss': 1.6362, 'learning_rate': 3.3060211838858104e-07, 'epoch': 7.36}
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{'loss': 1.6758, 'learning_rate': 2.790716447574304e-07, 'epoch': 7.42}
+
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{'loss': 1.6689, 'learning_rate': 2.3184805114029872e-07, 'epoch': 7.47}
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{'loss': 1.6035, 'learning_rate': 2.098579474228546e-07, 'epoch': 7.49}
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{'loss': 1.5952, 'learning_rate': 1.9926940415138206e-07, 'epoch': 7.51}
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{'loss': 1.6353, 'learning_rate': 1.4144726332248726e-07, 'epoch': 7.58}
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{'loss': 1.6108, 'learning_rate': 1.327642504218951e-07, 'epoch': 7.6}
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{'loss': 1.6201, 'learning_rate': 1.2435447834476254e-07, 'epoch': 7.61}
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{'loss': 1.6128, 'learning_rate': 1.1621817997222507e-07, 'epoch': 7.62}
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{'loss': 1.6196, 'learning_rate': 1.0835558061245587e-07, 'epoch': 7.64}
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{'loss': 1.6621, 'learning_rate': 1.0076689799442874e-07, 'epoch': 7.65}
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{'loss': 1.6216, 'learning_rate': 9.34523422618916e-08, 'epoch': 7.66}
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{'loss': 1.6289, 'learning_rate': 8.641211596754129e-08, 'epoch': 7.68}
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{'loss': 1.6279, 'learning_rate': 7.964641406742135e-08, 'epoch': 7.69}
+
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{'loss': 1.6187, 'learning_rate': 7.315542391551966e-08, 'epoch': 7.7}
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{'loss': 1.6445, 'learning_rate': 6.693932525857927e-08, 'epoch': 7.71}
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{'loss': 1.6226, 'learning_rate': 6.099829023112236e-08, 'epoch': 7.73}
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{'loss': 1.6025, 'learning_rate': 5.533248335068409e-08, 'epoch': 7.74}
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{'loss': 1.5981, 'learning_rate': 4.994206151325509e-08, 'epoch': 7.75}
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{'loss': 1.6479, 'learning_rate': 4.482717398894165e-08, 'epoch': 7.77}
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{'loss': 1.6025, 'learning_rate': 0.0, 'epoch': 8.0}
+
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{'train_runtime': 34978.7912, 'train_samples_per_second': 2.252, 'train_steps_per_second': 0.018, 'train_loss': 2.115578391335227, 'epoch': 8.0}
+
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+Non lora weights:  dict_keys(['base_model.model.model.mm_projector.weight', 'base_model.model.model.mm_projector.bias', 'base_model.model.model.frames_conv.weight', 'base_model.model.model.frames_conv.bias'])
+Non lora weights:  dict_keys(['base_model.model.model.mm_projector.weight', 'base_model.model.model.mm_projector.bias', 'base_model.model.model.frames_conv.weight', 'base_model.model.model.frames_conv.bias'])
+wandb: Waiting for W&B process to finish... (success).
+[2023-10-13 12:46:18,400] [INFO] [launch.py:347:main] Process 1707 exits successfully.
+wandb: 
+wandb: Run history:
+wandb:                    train/epoch ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
+wandb:              train/global_step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
+wandb:            train/learning_rate ▄▇██████▇▇▇▇▇▆▆▆▆▅▅▅▅▄▄▄▃▃▃▃▂▂▂▂▂▁▁▁▁▁▁▁
+wandb:                     train/loss █▅▃▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
+wandb:               train/total_flos ▁
+wandb:               train/train_loss ▁
+wandb:            train/train_runtime ▁
+wandb: train/train_samples_per_second ▁
+wandb:   train/train_steps_per_second ▁
+wandb: 
+wandb: Run summary:
+wandb:                    train/epoch 8.0
+wandb:              train/global_step 616
+wandb:            train/learning_rate 0.0
+wandb:                     train/loss 1.6025
+wandb:               train/total_flos 1.5114021399418634e+18
+wandb:               train/train_loss 2.11558
+wandb:            train/train_runtime 34978.7912
+wandb: train/train_samples_per_second 2.252
+wandb:   train/train_steps_per_second 0.018
+wandb: 
+wandb: 🚀 View run fiery-dew-9 at: https://wandb.ai/wanghao-cst/huggingface/runs/30lhy90r
+wandb: ️⚡ View job at https://wandb.ai/wanghao-cst/huggingface/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEwNTk0Mjk1MA==/version_details/v2
+wandb: Synced 5 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)
+wandb: Find logs at: ./wandb/run-20231013_030309-30lhy90r/logs
+[2023-10-13 12:46:56,444] [INFO] [launch.py:347:main] Process 1706 exits successfully.