29400e2e75
* feat: change oneccl * fix: restore llama-70b * fix: remove tab * fix: remove extra blank * small fix * add comments * fix: add a blank space
91 lines
No EOL
5.2 KiB
Markdown
91 lines
No EOL
5.2 KiB
Markdown
# 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](../README.md) 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](../README.md#recommended-requirements) 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](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html), follow [Install Intel GPU Driver](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-intel-gpu-driver) and [Install oneAPI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-oneapi) to install GPU driver and Intel® oneAPI Base Toolkit 2024.0.
|
|
|
|
### 1. Install
|
|
|
|
```bash
|
|
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
|
|
wget https://sourceforge.net/projects/oneccl-wks/files/2024.0.0.5.1-release/oneccl_wks_installer_2024.0.0.5.1.sh
|
|
bash oneccl_wks_installer_2024.0.0.5.1.sh
|
|
# 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 *X*B model saved in 16-bit will requires approximately 0.5*X* GB total GPU memory in the whole process. For example, if you select to use two GPUs, 0.25*X* 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
|
|
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
|
|
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
|
|
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
|
|
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
|
|
|
|
```bash
|
|
[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](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/dev/benchmark/all-in-one). 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. |