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* Change to 'pip install .. --extra-index-url' for readthedocs * Change to 'pip install .. --extra-index-url' for examples * Change to 'pip install .. --extra-index-url' for remaining files * Fix URL for ipex * Add links for ipex US and CN servers * Update ipex cpu url * remove readme * Update for github actions * Update for dockerfiles
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3.1 KiB
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78 lines
No EOL
3.1 KiB
Markdown
# Run IPEX-LLM on Multiple Intel GPUs in pipeline parallel fashion
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This example demonstrates how to run IPEX-LLM optimized low-bit model vertically partitioned on two [Intel GPUs](../README.md).
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## Requirements
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To run this example with IPEX-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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### 1.1 Install IPEX-LLM
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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 ipex-llm[xpu_2.1] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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# configures OneAPI environment variables
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source /opt/intel/oneapi/setvars.sh
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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```
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### 1.2 Build and install patched version of Intel Extension for PyTorch (IPEX)
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```bash
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conda activate llm
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source /opt/intel/oneapi/setvars.sh
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git clone https://github.com/intel/intel-extension-for-pytorch.git
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cd intel-extension-for-pytorch
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git checkout v2.1.10+xpu
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git submodule update --init --recursive
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git cherry-pick be8ea24078d8a271e53d2946ac533383f7a2aa78
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export USE_AOT_DEVLIST='ats-m150,pvc'
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python setup.py install
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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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### 3. Run
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For optimal performance on Arc, it is recommended to set several environment variables.
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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```
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```
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python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the Llama2 model (e.g. `meta-llama/Llama-2-7b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
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- `--prompt PROMPT`: argument defining the prompt to be infered (with integrated prompt format for chat). It is default to be `'What is AI?'`.
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- `--n-predict N_PREDICT`: argument defining the max number of tokens to predict. It is default to be `32`.
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#### Sample Output
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#### [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
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```log
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Inference time: xxxx s
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-------------------- Prompt --------------------
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<s>[INST] <<SYS>>
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<</SYS>>
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What is AI? [/INST]
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-------------------- Output --------------------
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[INST] <<SYS>>
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<</SYS>>
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What is AI? [/INST] Artificial intelligence (AI) is the broader field of research and development aimed at creating machines that can perform tasks that typically require human intelligence,
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``` |