40 lines
1.9 KiB
Markdown
40 lines
1.9 KiB
Markdown
# Run BigDL-LLM on Multiple Intel GPUs using DeepSpeed AutoTP
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This example demonstrates how to run BigDL-LLM optimized low-bit model on multiple [Intel GPUs](../README.md) by leveraging DeepSpeed AutoTP.
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## 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#recommended-requirements) for more information. For this particular example, you will need at least two GPUs on your machine.
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## Example:
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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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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu_2.1] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install oneccl_bind_pt==2.1.100 -f https://developer.intel.com/ipex-whl-stable-xpu
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# configures OneAPI environment variables
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source /opt/intel/oneapi/setvars.sh
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pip install git+https://github.com/microsoft/DeepSpeed.git@4fc181b0
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pip install git+https://github.com/intel/intel-extension-for-deepspeed.git@ec33277
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pip install mpi4py
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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```
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> **Important**: IPEX 2.1.10+xpu requires Intel® oneAPI Base Toolkit's version == 2024.0. Please make sure you have installed the correct version.
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### 2. Run tensor parallel inference on multiple GPUs
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Here, we provide example usages on different models and different hardwares. Please refer to the appropriate script based on your model and device:
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#### Llama2 series
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<details><summary>Show LLaMA2-70B example</summary>
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Run LLaMA2-70B on four Intel Data Center GPU Max 1550
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```
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bash run_llama2_70b_pvc_1550_4_card.sh
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```
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</details>
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> **Note**:If you may want to select only part of GPUs on your machine, please change `ZE_AFFINITY_MASK` and `NUM_GPUS` to your prefer value.
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