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Deepspeed-AutoTP

README.md

Run IPEX-LLM on Multiple Intel GPUs using DeepSpeed AutoTP

This example demonstrates how to run IPEX-LLM optimized low-bit model on multiple Intel GPUs by leveraging DeepSpeed AutoTP.

Requirements

To run this example with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information. For this particular example, you will need at least two GPUs on your machine.

Example:

0. Prerequisites

Please visit the Install IPEX-LLM on Linux with Intel GPU, follow Install Intel GPU Driver and Install oneAPI to install GPU driver and Intel® oneAPI Base Toolkit 2024.0.

1. Install

conda create -n llm python=3.11
conda activate llm
# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
pip install transformers==4.37.0
pip install oneccl_bind_pt==2.1.100 --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
# configures OneAPI environment variables
source /opt/intel/oneapi/setvars.sh
pip install git+https://github.com/microsoft/DeepSpeed.git@ed8aed5
pip install git+https://github.com/intel/intel-extension-for-deepspeed.git@0eb734b
pip install mpi4py
conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc

Important: IPEX 2.1.10+xpu requires Intel® oneAPI Base Toolkit's version == 2024.0. Please make sure you have installed the correct version.

2. Run tensor parallel inference on multiple GPUs

Here, we separate inference process into two stages. First, convert to deepspeed model and apply ipex-llm optimization on CPU. Then, utilize XPU as DeepSpeed accelerator to inference. In this way, a XB model saved in 16-bit will requires approximately 0.5X GB total GPU memory in the whole process. For example, if you select to use two GPUs, 0.25X GB memory is required per GPU.

Please select the appropriate model size based on the capabilities of your machine.

We provide example usages on different models and different hardwares as following:

  • Run LLaMA2-70B on one card of Intel Data Center GPU Max 1550
bash run_llama2_70b_pvc_1550_1_card.sh

Note: You could change ZE_AFFINITY_MASK and NUM_GPUS according to your requirements. And you could also specify other low bit optimizations through --low-bit.

  • Run Vicuna-33B on two Intel Arc A770
bash run_vicuna_33b_arc_2_card.sh

Note: You could change NUM_GPUS to the number of GPUs you have on your machine. And you could also specify other low bit optimizations through --low-bit.

  • Run Qwen1.5-14B-Chat on two Intel Arc A770
bash run_qwen_14b_arc_2_card.sh

Note: You could change NUM_GPUS to the number of GPUs you have on your machine. And you could also specify other low bit optimizations through --low-bit.

  • Run Mistral-7B-Instruct on two cards of Intel Data Center GPU Flex
bash run_mistral_7b_instruct_flex_2_card.sh

Note: You could change NUM_GPUS to the number of GPUs you have on your machine. And you could also specify other low bit optimizations through --low-bit.

3. Sample Output

[0] Inference time of generating 32 tokens: xxx s, average token latency is xxx ms/token.
[0] -------------------- Prompt --------------------
[0] Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun
[0] -------------------- Output --------------------
[0] Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun. She was a curious girl, and she loved to learn new things.
[0] 
[0] One day, she decided to go on a journey to find the legendary

Important: To obtain more details about performance, please use our benchmark tool. You need to specify test_api as "deepspeed_optimize_model_gpu", and modify other configurations based on your requirement in config.yaml. Then just run run-deepspeed-arc.sh or run-deepspeed-pvc.sh according to your device to collect performance data.

Known Issue

  • In our example scripts, tcmalloc is enabled through export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so:${LD_PRELOAD} which speed up inference, but this may raise munmap_chunk(): invalid pointer error after finishing inference.
  • CPU memory OOM during model covert. In this example, multiple processors will loading models into memory at the same time. If model size/rank_num is very large, it will lead to OOM. Please export RANK_WAIT_TIME=xxx. xxx is sleep time in seconds. You can increase RANK_WAIT_TIME to avoid using too much memory.