Add deepspeed autotp example readme (#9289)
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# 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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## 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#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==2.0.110+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] -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install oneccl_bind_pt==2.0.100 -f https://developer.intel.com/ipex-whl-stable-xpu
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pip install git+https://github.com/microsoft/DeepSpeed.git@78c518e
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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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```
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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. Run tensor parallel inference on multiple GPUs
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You many want to change some of the parameters in the script such as `NUM_GPUS`` to the number of GPUs you have on your machine.
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```
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bash run.sh
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```
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source bigdl-llm-init -t -g
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export MASTER_ADDR=127.0.0.1
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export CCL_ZE_IPC_EXCHANGE=sockets
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NUM_GPUS=4
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if [[ -n $OMP_NUM_THREADS ]]; then
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    export OMP_NUM_THREADS=$(($OMP_NUM_THREADS / 4))
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    export OMP_NUM_THREADS=$(($OMP_NUM_THREADS / $NUM_GPUS))
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else
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    export OMP_NUM_THREADS=$(($(nproc) / 4))
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    export OMP_NUM_THREADS=$(($(nproc) / $NUM_GPUS))
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fi
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torchrun --standalone \
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         --nnodes=1 \
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         --nproc-per-node 4 \
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         --nproc-per-node $NUM_GPUS \
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         deepspeed_autotp.py --repo-id-or-model-path "meta-llama/Llama-2-7b-hf"
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