86 lines
4.3 KiB
Markdown
86 lines
4.3 KiB
Markdown
# LoRA Finetuning with BigDL-LLM
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This example ports [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/tree/main) to BigDL-LLM (using [LoRA](https://arxiv.org/abs/2106.09685) algorithm) on [Intel GPU](../../README.md).
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### 0. Requirements
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To run this example with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../README.md#requirements) for more information.
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### 1. Install
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```bash
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conda create -n llm python=3.9
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install transformers==4.34.0 datasets
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pip install fire peft==0.5.0
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pip install oneccl_bind_pt==2.1.100 -f https://developer.intel.com/ipex-whl-stable-xpu # necessary to run distributed finetuning
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pip install accelerate==0.23.0
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pip install bitsandbytes scipy
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```
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### 2. Configures OneAPI environment variables
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. LoRA Finetune
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Here, we provide example usages on different hardware. Please refer to the appropriate script based on your device:
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##### Finetuning LLaMA2-7B on single Arc A770
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```bash
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bash lora_finetune_llama2_7b_arc_1_card.sh
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```
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##### Finetuning LLaMA2-7B on four Intel Data Center GPU Max 1100
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```bash
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bash lora_finetune_llama2_7b_pvc_1100_1_card.sh
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```
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##### Finetuning LLaMA2-7B on single tile of Intel Data Center GPU Max 1550
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```bash
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bash lora_finetune_llama2_7b_pvc_1550_1_tile.sh
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```
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##### Finetuning LLaMA2-7B on four Intel Data Center GPU Max 1550
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```bash
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bash lora_finetune_llama2_7b_pvc_1550_4_card.sh
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```
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### 4. (Optional) Resume Training
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**If you fail to complete the whole finetuning process, it is suggested to resume training from a previously saved checkpoint by specifying `resume_from_checkpoint` to the local checkpoint folder as following:**
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```bash
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python ./alpaca_lora_finetuning.py \
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--base_model "meta-llama/Llama-2-7b-hf" \
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--data_path "yahma/alpaca-cleaned" \
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--output_dir "./bigdl-qlora-alpaca" \
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--resume_from_checkpoint "./bigdl-qlora-alpaca/checkpoint-1100"
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```
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### 5. Sample Output
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```log
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{'loss': 1.9231, 'learning_rate': 2.9999945367033285e-05, 'epoch': 0.0}
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{'loss': 1.8622, 'learning_rate': 2.9999781468531096e-05, 'epoch': 0.01}
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{'loss': 1.9043, 'learning_rate': 2.9999508305687345e-05, 'epoch': 0.01}
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{'loss': 1.8967, 'learning_rate': 2.999912588049185e-05, 'epoch': 0.01}
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{'loss': 1.9658, 'learning_rate': 2.9998634195730358e-05, 'epoch': 0.01}
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{'loss': 1.8386, 'learning_rate': 2.9998033254984483e-05, 'epoch': 0.02}
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{'loss': 1.809, 'learning_rate': 2.999732306263172e-05, 'epoch': 0.02}
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{'loss': 1.8552, 'learning_rate': 2.9996503623845395e-05, 'epoch': 0.02}
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1%|█ | 8/1164 [xx:xx<xx:xx:xx, xx s/it]
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```
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### 6. Merge the adapter into the original model
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```
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python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --adapter_path ./outputs/checkpoint-200 --output_path ./outputs/checkpoint-200-merged
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```
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Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.
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### 7. Troubleshooting
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Please refer to [here](../README.md#troubleshooting) for solutions of common issues during finetuning.
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