84 lines
3.3 KiB
Markdown
84 lines
3.3 KiB
Markdown
# Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi
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This example demonstrates how to run IPEX-LLM serving on multiple [Intel GPUs](../README.md) by leveraging DeepSpeed AutoTP.
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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. Install
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```bash
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conda create -n llm python=3.11
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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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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install oneccl_bind_pt==2.1.100 --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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pip install git+https://github.com/microsoft/DeepSpeed.git@ed8aed5
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pip install git+https://github.com/intel/intel-extension-for-deepspeed.git@0eb734b
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pip install mpi4py fastapi uvicorn
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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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When we run the model in a distributed manner across two GPUs, the memory consumption of each GPU is only half of what it was originally, and the GPUs can work simultaneously during inference computation.
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We provide example usage for `Llama-2-7b-chat-hf` model running on Arc A770
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Run Llama-2-7b-chat-hf on two Intel Arc A770:
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```bash
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# Before run this script, you should adjust the YOUR_REPO_ID_OR_MODEL_PATH in last line
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# If you want to change server port, you can set port parameter in last line
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bash run_llama2_7b_chat_hf_arc_2_card.sh
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```
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If you successfully run the serving, you can get output like this:
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```bash
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[0] INFO: Started server process [120071]
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[0] INFO: Waiting for application startup.
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[0] INFO: Application startup complete.
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[0] INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
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```
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> **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`.
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### 3. Sample Input and Output
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We can use `curl` to test serving api
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```bash
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# Set http_proxy and https_proxy to null to ensure that requests are not forwarded by a proxy.
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export http_proxy=
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export https_proxy=
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curl -X 'POST' \
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'http://127.0.0.1:8000/generate/' \
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-H 'accept: application/json' \
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-H 'Content-Type: application/json' \
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-d '{
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"prompt": "What is AI?",
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"n_predict": 32
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}'
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```
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And you should get output like this:
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```json
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{
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"generated_text": "What is AI? Artificial intelligence (AI) refers to the development of computer systems able to perform tasks that would normally require human intelligence, such as visual perception, speech",
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"generate_time": "0.45149803161621094s"
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}
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```
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**Important**: The first token latency is much larger than rest token latency, you could use [our benchmark tool](https://github.com/intel-analytics/ipex-llm/blob/main/python/llm/dev/benchmark/README.md) to obtain more details about first and rest token latency.
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