runpod

image.png

based on https://colab.research.google.com/drive/1wfrKqyPkz5BGD1Gkij_cvbUeweIDdRav?usp=sharing

In [1]:
!git clone https://github.com/PhoebusSi/Alpaca-CoT.git code
Cloning into 'code'...
remote: Enumerating objects: 750, done.
remote: Counting objects: 100% (86/86), done.
remote: Compressing objects: 100% (70/70), done.
remote: Total 750 (delta 47), reused 28 (delta 16), pack-reused 664
Receiving objects: 100% (750/750), 127.12 MiB | 16.22 MiB/s, done.
Resolving deltas: 100% (401/401), done.
In [2]:
# install basic python package
# 安装必要的python库
!python -m pip install bitsandbytes
!python -m pip install datasets
!python -m pip install git+https://github.com/huggingface/transformers.git
!python -m pip install peft
!python -m pip install sentencepiece
!python -m pip install gradio
Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.38.1)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.11.0)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.0)
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Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.14.0)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
Collecting git+https://github.com/huggingface/transformers.git
  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-w7rcp87u
  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-w7rcp87u
  Resolved https://github.com/huggingface/transformers.git to commit abbc96a2148da0c91fa078bd021984b2cc10ef85
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.29.0.dev0) (2.28.2)
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Building wheels for collected packages: transformers
  Building wheel for transformers (pyproject.toml) ... done
  Created wheel for transformers: filename=transformers-4.29.0.dev0-py3-none-any.whl size=6929166 sha256=2b2f738ad74a9abef609bb009f6f2e1ed9accb56c423b2b7c56056b72106beba
  Stored in directory: /tmp/pip-ephem-wheel-cache-p8msirxh/wheels/e7/9c/5b/e1a9c8007c343041e61cc484433d512ea9274272e3fcbe7c16
Successfully built transformers
Installing collected packages: transformers
  Attempting uninstall: transformers
    Found existing installation: transformers 4.28.1
    Uninstalling transformers-4.28.1:
      Successfully uninstalled transformers-4.28.1
Successfully installed transformers-4.29.0.dev0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.3.0.dev0)
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Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->transformers->peft) (2.8)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.97)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
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WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: python -m pip install --upgrade pip
In [3]:
# download data git repository
# 拉取数据仓库
!GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/QingyiSi/Alpaca-CoT data
Cloning into 'data'...
remote: Enumerating objects: 2167, done.
remote: Counting objects: 100% (2160/2160), done.
remote: Compressing objects: 100% (1971/1971), done.
remote: Total 2167 (delta 145), reused 2055 (delta 133), pack-reused 7
Receiving objects: 100% (2167/2167), 21.86 MiB | 18.96 MiB/s, done.
Resolving deltas: 100% (145/145), done.
In [5]:
# pull specify data and move it,change <alpaca/> to your preference
# 从仓库中拉取指定文件放入训练目录下,你可以在 https://huggingface.co/datasets/QingyiSi/Alpaca-CoT 查看数据详情
!cd /workspace/data && git config core.sparsecheckout true && git config lfs.fetchinclude alpaca/
!cd /workspace/data && git lfs pull
!cp /workspace/data/alpaca/*.json /workspace/code/data/
!ls -lh /workspace/code/data/
total 44Mng LFS objects: 100% (2/2), 46 MB | 20 MB/s                            
-rw-r--r-- 1 root root  22M Apr 17 13:56 alpaca_data.json
-rw-r--r-- 1 root root  22M Apr 17 13:56 alpaca_data_cleaned.json
drwxr-xr-x 2 root root 4.0K Apr 17 13:54 formatted_cot_data
drwxr-xr-x 2 root root 4.0K Apr 17 13:54 origin_cot_data
In [6]:
# instruction finetuning 
# 进行指令微调,如果你想直接体验已有checkpoint,可忽略此步
!cd /workspace/code && python uniform_finetune.py --model_type llama --model_name_or_path decapoda-research/llama-7b-hf --data alpaca --lora_target_modules q_proj v_proj --per_gpu_train_batch_size 4 --learning_rate 3e-4 --epochs 1 
===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please run

python -m bitsandbytes

 and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda116.so
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib'), PosixPath('/usr/local/nvidia/lib64')}
  warn(msg)
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...
  warn(msg)
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//matplotlib_inline.backend_inline')}
  warn(msg)
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('PygmalionAI/pygmalion-6b')}
  warn(msg)
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('noninteractiveSHELL=/bin/bash')}
  warn(msg)
CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...
/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.
Either way, this might cause trouble in the future:
If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.
  warn(msg)
CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 116
CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda116.so...
Namespace(size=None, data='alpaca', local_rank=-1, model_type='llama', model_name_or_path='decapoda-research/llama-7b-hf', per_gpu_train_batch_size=4, gradient_accumulation_steps=32, epochs=1, learning_rate=0.0003, cutoff_len=512, lora_r=8, lora_alpha=16, lora_dropout=0.05, val_set_size=2000, lora_target_modules=['q_proj', 'v_proj'])
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-f8a343789e1df39c/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e...
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Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-f8a343789e1df39c/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e. Subsequent calls will reuse this data.
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DatasetDict({
    train: Dataset({
        features: ['instruction', 'input', 'output'],
        num_rows: 51942
    })
})
Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.
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The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization. 
The tokenizer class you load from this checkpoint is 'LLaMATokenizer'. 
The class this function is called from is 'LlamaTokenizer'.
trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199
***** Running training *****                                                    
  Num Epochs = 1
  Instantaneous batch size per GPU = 4
  Gradient Accumulation steps = 32
  Total train batch size (w. parallel, distributed & accumulation) = 128
  Total optimization steps = 390
  Saving steps = 39
/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
{'loss': 1.4186, 'learning_rate': 0.00015384615384615382, 'epoch': 0.05}        
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{'eval_loss': 1.1525402069091797, 'eval_runtime': 108.4713, 'eval_samples_per_second': 18.438, 'eval_steps_per_second': 2.305, 'epoch': 0.1}
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{'loss': 1.1665, 'learning_rate': 0.00029914529914529915, 'epoch': 0.1}         
{'loss': 1.1148, 'learning_rate': 0.00028205128205128203, 'epoch': 0.15}        
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{'eval_loss': 1.1168040037155151, 'eval_runtime': 108.7236, 'eval_samples_per_second': 18.395, 'eval_steps_per_second': 2.299, 'epoch': 0.2}

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{'loss': 1.1018, 'learning_rate': 0.00026495726495726497, 'epoch': 0.21}        
{'loss': 1.0935, 'learning_rate': 0.00024786324786324785, 'epoch': 0.26}        
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{'eval_loss': 1.1057891845703125, 'eval_runtime': 108.7439, 'eval_samples_per_second': 18.392, 'eval_steps_per_second': 2.299, 'epoch': 0.3}

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{'loss': 1.0929, 'learning_rate': 0.00023076923076923076, 'epoch': 0.31}        
{'loss': 1.0894, 'learning_rate': 0.00021367521367521365, 'epoch': 0.36}        
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{'eval_loss': 1.1005803346633911, 'eval_runtime': 108.781, 'eval_samples_per_second': 18.386, 'eval_steps_per_second': 2.298, 'epoch': 0.4}

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{'loss': 1.0781, 'learning_rate': 0.00019658119658119659, 'epoch': 0.41}        
{'loss': 1.1028, 'learning_rate': 0.00017948717948717947, 'epoch': 0.46}        
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{'eval_loss': 1.0975703001022339, 'eval_runtime': 108.7398, 'eval_samples_per_second': 18.393, 'eval_steps_per_second': 2.299, 'epoch': 0.5}

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{'loss': 1.0724, 'learning_rate': 0.00016239316239316238, 'epoch': 0.51}        
{'loss': 1.0923, 'learning_rate': 0.0001452991452991453, 'epoch': 0.56}         
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{'eval_loss': 1.093505859375, 'eval_runtime': 108.7239, 'eval_samples_per_second': 18.395, 'eval_steps_per_second': 2.299, 'epoch': 0.6}
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{'loss': 1.0856, 'learning_rate': 0.00012820512820512818, 'epoch': 0.62}        
{'loss': 1.0944, 'learning_rate': 0.00011111111111111109, 'epoch': 0.67}        
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{'eval_loss': 1.091776967048645, 'eval_runtime': 108.7779, 'eval_samples_per_second': 18.386, 'eval_steps_per_second': 2.298, 'epoch': 0.7}

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{'loss': 1.0697, 'learning_rate': 9.4017094017094e-05, 'epoch': 0.72}           
{'loss': 1.0753, 'learning_rate': 7.692307692307691e-05, 'epoch': 0.77}         
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{'eval_loss': 1.0898607969284058, 'eval_runtime': 108.7363, 'eval_samples_per_second': 18.393, 'eval_steps_per_second': 2.299, 'epoch': 0.8}

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{'loss': 1.0732, 'learning_rate': 5.9829059829059824e-05, 'epoch': 0.82}        
{'loss': 1.0808, 'learning_rate': 4.2735042735042735e-05, 'epoch': 0.87}        
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{'eval_loss': 1.0888385772705078, 'eval_runtime': 108.7345, 'eval_samples_per_second': 18.393, 'eval_steps_per_second': 2.299, 'epoch': 0.9}

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{'loss': 1.0532, 'learning_rate': 2.564102564102564e-05, 'epoch': 0.92}         
{'loss': 1.0649, 'learning_rate': 8.547008547008546e-06, 'epoch': 0.97}         
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{'eval_loss': 1.0883216857910156, 'eval_runtime': 108.7281, 'eval_samples_per_second': 18.395, 'eval_steps_per_second': 2.299, 'epoch': 1.0}

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{'train_runtime': 12496.3489, 'train_samples_per_second': 3.997, 'train_steps_per_second': 0.031, 'train_loss': 1.1050885347219614, 'epoch': 1.0}
100%|███████████████████████████████████████| 390/390 [3:28:16<00:00, 32.04s/it]

 If there's a warning about missing keys above, please disregard :)
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