In [13]:
# download code git repository
# 下载代码仓库
!git clone -b dev https://github.com/utensil/Alpaca-CoT.git code
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In [ ]:
!cd code && git pull
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
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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Collecting git+https://github.com/huggingface/transformers.git
Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-h5udkdph
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-h5udkdph
Resolved https://github.com/huggingface/transformers.git to commit dacd34568d1a27b91f84610eab526640ed8f94e0
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In [3]:
!pip install wandb
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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Collecting pathtools
Downloading pathtools-0.1.2.tar.gz (11 kB)
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In [4]:
!wandb login
wandb: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server) wandb: You can find your API key in your browser here: https://wandb.ai/authorize wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit: wandb: Appending key for api.wandb.ai to your netrc file: /root/.netrc
In [5]:
%env WANDB_PROJECT=Alpaca-CoT-Colab
env: WANDB_PROJECT=Alpaca-CoT-Colab
In [15]:
# 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
fatal: destination path 'data' already exists and is not an empty directory.
In [16]:
# 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 /content/data && git config core.sparsecheckout true && git config lfs.fetchinclude alpaca/
!cd /content/data && git lfs pull
!cp /content/data/alpaca/*.json /content/code/data/
!ls -lh /content/code/data/
total 44M -rw-r--r-- 1 root root 22M Apr 18 02:28 alpaca_data_cleaned.json -rw-r--r-- 1 root root 22M Apr 18 02:28 alpaca_data.json drwxr-xr-x 2 root root 4.0K Apr 18 02:26 formatted_cot_data drwxr-xr-x 2 root root 4.0K Apr 18 02:26 origin_cot_data
In [17]:
# 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 /content/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 --report_to wandb
===================================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/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so
/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...
warn(msg)
/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}
warn(msg)
/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}
warn(msg)
/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-30aa7guwwttv1 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}
warn(msg)
/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}
warn(msg)
/usr/local/lib/python3.9/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('//ipykernel.pylab.backend_inline')}
warn(msg)
CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...
/usr/local/lib/python3.9/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: 7.5
CUDA SETUP: Detected CUDA version 118
CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...
2023-04-18 02:28:14.307655: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
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'], report_to='wandb')
Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-619d3a558a064e25/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e...
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Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/json/default-619d3a558a064e25/0.0.0/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/python3.9/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(
wandb: Currently logged in as: utensil. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.14.2
wandb: Run data is saved locally in /content/code/wandb/run-20230418_023339-l81k9fdc
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run cool-flower-1
wandb: ⭐️ View project at https://wandb.ai/utensil/Alpaca-CoT-Colab
wandb: 🚀 View run at https://wandb.ai/utensil/Alpaca-CoT-Colab/runs/l81k9fdc
{'loss': 1.4123, 'learning_rate': 0.00015384615384615382, 'epoch': 0.05}
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{'eval_loss': 1.1518847942352295, 'eval_runtime': 341.0299, 'eval_samples_per_second': 5.865, 'eval_steps_per_second': 0.733, 'epoch': 0.1}
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Traceback (most recent call last):
File "/content/code/uniform_finetune.py", line 348, in <module>
train(args)
File "/content/code/uniform_finetune.py", line 316, in train
trainer.train()
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 1661, in train
return inner_training_loop(
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 2005, in _inner_training_loop
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 2290, in _maybe_log_save_evaluate
self._save_checkpoint(model, trial, metrics=metrics)
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 2347, in _save_checkpoint
self.save_model(output_dir, _internal_call=True)
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 2829, in save_model
self._save(output_dir)
File "/usr/local/lib/python3.9/dist-packages/transformers/trainer.py", line 2872, in _save
state_dict = self.model.state_dict()
File "/content/code/uniform_finetune.py", line 310, in <lambda>
lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1818, in state_dict
module.state_dict(destination=destination, prefix=prefix + name + '.', keep_vars=keep_vars)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1818, in state_dict
module.state_dict(destination=destination, prefix=prefix + name + '.', keep_vars=keep_vars)
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1818, in state_dict
module.state_dict(destination=destination, prefix=prefix + name + '.', keep_vars=keep_vars)
[Previous line repeated 4 more times]
File "/usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py", line 1815, in state_dict
self._save_to_state_dict(destination, prefix, keep_vars)
File "/usr/local/lib/python3.9/dist-packages/bitsandbytes/nn/modules.py", line 268, in _save_to_state_dict
self.weight.data = undo_layout(self.state.CxB, self.state.tile_indices)
File "/usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py", line 100, in undo_layout
return outputs.reshape(rows, cols).contiguous()
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 44.00 MiB (GPU 0; 14.75 GiB total capacity; 13.11 GiB already allocated; 16.81 MiB free; 13.68 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
╭───────────────────── Traceback (most recent call last) ──────────────────────╮
│ /content/code/uniform_finetune.py:348 in <module> │
│ │
│ 345 │ args = parser.parse_args() │
│ 346 │ print(args) │
│ 347 │ │
│ ❱ 348 │ train(args) │
│ 349 │
│ │
│ /content/code/uniform_finetune.py:316 in train │
│ │
│ 313 │ if torch.__version__ >= "2" and sys.platform != "win32": │
│ 314 │ │ model = torch.compile(model) │
│ 315 │ │
│ ❱ 316 │ trainer.train() │
│ 317 │ │
│ 318 │ model.save_pretrained(output_dir) │
│ 319 │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:1661 in train │
│ │
│ 1658 │ │ inner_training_loop = find_executable_batch_size( │
│ 1659 │ │ │ self._inner_training_loop, self._train_batch_size, args.a │
│ 1660 │ │ ) │
│ ❱ 1661 │ │ return inner_training_loop( │
│ 1662 │ │ │ args=args, │
│ 1663 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │
│ 1664 │ │ │ trial=trial, │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:2005 in │
│ _inner_training_loop │
│ │
│ 2002 │ │ │ │ │ self.state.epoch = epoch + (step + 1 + steps_skip │
│ 2003 │ │ │ │ │ self.control = self.callback_handler.on_step_end( │
│ 2004 │ │ │ │ │ │
│ ❱ 2005 │ │ │ │ │ self._maybe_log_save_evaluate(tr_loss, model, tri │
│ 2006 │ │ │ │ else: │
│ 2007 │ │ │ │ │ self.control = self.callback_handler.on_substep_e │
│ 2008 │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:2290 in │
│ _maybe_log_save_evaluate │
│ │
│ 2287 │ │ │ self._report_to_hp_search(trial, self.state.global_step, │
│ 2288 │ │ │
│ 2289 │ │ if self.control.should_save: │
│ ❱ 2290 │ │ │ self._save_checkpoint(model, trial, metrics=metrics) │
│ 2291 │ │ │ self.control = self.callback_handler.on_save(self.args, s │
│ 2292 │ │
│ 2293 │ def _load_rng_state(self, checkpoint): │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:2347 in │
│ _save_checkpoint │
│ │
│ 2344 │ │ │
│ 2345 │ │ run_dir = self._get_output_dir(trial=trial) │
│ 2346 │ │ output_dir = os.path.join(run_dir, checkpoint_folder) │
│ ❱ 2347 │ │ self.save_model(output_dir, _internal_call=True) │
│ 2348 │ │ if self.deepspeed: │
│ 2349 │ │ │ # under zero3 model file itself doesn't get saved since i │
│ 2350 │ │ │ # config `stage3_gather_16bit_weights_on_model_save` is T │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:2829 in │
│ save_model │
│ │
│ 2826 │ │ │ │ │ self.deepspeed.save_checkpoint(output_dir) │
│ 2827 │ │ │
│ 2828 │ │ elif self.args.should_save: │
│ ❱ 2829 │ │ │ self._save(output_dir) │
│ 2830 │ │ │
│ 2831 │ │ # Push to the Hub when `save_model` is called by the user. │
│ 2832 │ │ if self.args.push_to_hub and not _internal_call: │
│ │
│ /usr/local/lib/python3.9/dist-packages/transformers/trainer.py:2872 in _save │
│ │
│ 2869 │ │ # They can then be reloaded using `from_pretrained()` │
│ 2870 │ │ if not isinstance(self.model, PreTrainedModel): │
│ 2871 │ │ │ if state_dict is None: │
│ ❱ 2872 │ │ │ │ state_dict = self.model.state_dict() │
│ 2873 │ │ │ │
│ 2874 │ │ │ if isinstance(unwrap_model(self.model), PreTrainedModel): │
│ 2875 │ │ │ │ unwrap_model(self.model).save_pretrained( │
│ │
│ /content/code/uniform_finetune.py:310 in <lambda> │
│ │
│ 307 │ │
│ 308 │ old_state_dict = model.state_dict │
│ 309 │ model.state_dict = ( │
│ ❱ 310 │ │ lambda self, *_, **__: get_peft_model_state_dict(self, old_sta │
│ 311 │ ).__get__(model, type(model)) │
│ 312 │ │
│ 313 │ if torch.__version__ >= "2" and sys.platform != "win32": │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1818 in │
│ state_dict │
│ │
│ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ ❱ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ 1819 │ │ for hook in self._state_dict_hooks.values(): │
│ 1820 │ │ │ hook_result = hook(self, destination, prefix, local_metad │
│ 1821 │ │ │ if hook_result is not None: │
│ │
│ /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1815 in │
│ state_dict │
│ │
│ 1812 │ │ if hasattr(destination, "_metadata"): │
│ 1813 │ │ │ destination._metadata[prefix[:-1]] = local_metadata │
│ 1814 │ │ │
│ ❱ 1815 │ │ self._save_to_state_dict(destination, prefix, keep_vars) │
│ 1816 │ │ for name, module in self._modules.items(): │
│ 1817 │ │ │ if module is not None: │
│ 1818 │ │ │ │ module.state_dict(destination=destination, prefix=pre │
│ │
│ /usr/local/lib/python3.9/dist-packages/bitsandbytes/nn/modules.py:268 in │
│ _save_to_state_dict │
│ │
│ 265 │ │ │
│ 266 │ │ try: │
│ 267 │ │ │ if reorder_layout: │
│ ❱ 268 │ │ │ │ self.weight.data = undo_layout(self.state.CxB, self.st │
│ 269 │ │ │ │
│ 270 │ │ │ super()._save_to_state_dict(destination, prefix, keep_vars │
│ 271 │
│ │
│ /usr/local/lib/python3.9/dist-packages/bitsandbytes/autograd/_functions.py:1 │
│ 00 in undo_layout │
│ │
│ 97 │ outputs[tile_indices.flatten()] = tensor │
│ 98 │ outputs = outputs.reshape(tile_rows, tile_cols, cols // tile_cols, │
│ 99 │ outputs = outputs.permute(3, 0, 2, 1) # (rows // tile_rows, tile_ │
│ ❱ 100 │ return outputs.reshape(rows, cols).contiguous() │
│ 101 │
│ 102 │
│ 103 class MatMul8bit(torch.autograd.Function): │
╰──────────────────────────────────────────────────────────────────────────────╯
OutOfMemoryError: CUDA out of memory. Tried to allocate 44.00 MiB (GPU 0; 14.75
GiB total capacity; 13.11 GiB already allocated; 16.81 MiB free; 13.68 GiB
reserved in total by PyTorch) If reserved memory is >> allocated memory try
setting max_split_size_mb to avoid fragmentation. See documentation for Memory
Management and PYTORCH_CUDA_ALLOC_CONF
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb:
wandb: Run history:
wandb: eval/loss ▁
wandb: eval/runtime ▁
wandb: eval/samples_per_second ▁
wandb: eval/steps_per_second ▁
wandb: train/epoch ▁█
wandb: train/global_step ▁█
wandb: train/learning_rate ▁
wandb: train/loss ▁
wandb:
wandb: Run summary:
wandb: eval/loss 1.15188
wandb: eval/runtime 341.0299
wandb: eval/samples_per_second 5.865
wandb: eval/steps_per_second 0.733
wandb: train/epoch 0.1
wandb: train/global_step 39
wandb: train/learning_rate 0.00015
wandb: train/loss 1.4123
wandb:
wandb: 🚀 View run cool-flower-1 at: https://wandb.ai/utensil/Alpaca-CoT-Colab/runs/l81k9fdc
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-20230418_023339-l81k9fdc/logs
In [ ]: