/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.
wandb: Currently logged in as: 2358044947 (pku_rl). Use `wandb login --relogin` to force relogin

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Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]wandb: Tracking run with wandb version 0.18.1
wandb: Run data is saved locally in /data/sora/Projects/safe-sora/wandb/run-20250112_023625-geujnuui
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run reward-harmlessness-2025-01-12-02-36-24
wandb: ⭐️ View project at https://wandb.ai/pku_rl/Value-gradient
wandb: 🚀 View run at https://wandb.ai/pku_rl/Value-gradient/runs/geujnuui

Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]You are using a model of type llava to instantiate a model of type llava_score. This is not supported for all configurations of models and can yield errors.

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Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:12<00:00,  5.89s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:12<00:00,  6.47s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  5.95s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  6.52s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  5.96s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  6.52s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  5.99s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  6.57s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  5.94s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:13<00:00,  6.51s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:12<00:00,  5.78s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:12<00:00,  6.33s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(

Loading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  4.54s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:09<00:00,  5.00s/it]
Some weights of LlavaLlamaForScore were not initialized from the model checkpoint at /home/juntao/Models/LanguageBind/Video-LLaVA-7B and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:567: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:572: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.
  warnings.warn(
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
/data/sora/Projects/safe-sora/safe_sora/models/video_llava.py:209: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')

  0%|          | 0/1520 [00:00<?, ?it/s]/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                        

  0%|          | 0/1520 [00:18<?, ?it/s]/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.
  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                   

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                    

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                      

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                       

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                       

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                       

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                     

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                     

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.
  return fn(*args, **kwargs)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/torch/utils/checkpoint.py:92: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn(

                                                     

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  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]

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                                               /home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)
/home/juntao/Miniconda3/envs/safe-sora/lib/python3.11/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py:28: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  partition = torch.load(path, map_location=map_location)

                                                     

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