9a3a21e4fc
* axolotl_quickstart.md * benchmark_quickstart.md * bigdl_llm_migration.md * chatchat_quickstart.md * continue_quickstart.md * deepspeed_autotp_fastapi_quickstart.md * dify_quickstart.md * fastchat_quickstart.md * adjust tab style * fix link * fix link * add video preview * Small fixes * Small fix --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
104 lines
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3.8 KiB
Markdown
104 lines
No EOL
3.8 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](../../../python/llm/example/GPU/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](../../../python/llm/example/GPU/README.md#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]
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> 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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# To avoid GPU OOM, you could adjust --max-num-seqs and --max-num-batched-tokens parameters in below script
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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]
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> 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]
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> The first token latency is much larger than rest token latency, you could use [our benchmark tool](../../../python/llm/dev/benchmark/README.md) to obtain more details about first and rest token latency.
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### 4. Benchmark with wrk
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We use wrk for testing end-to-end throughput, check [here](https://github.com/wg/wrk).
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You can install by:
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```bash
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sudo apt install wrk
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```
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Please change the test url accordingly.
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```bash
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# set t/c to the number of concurrencies to test full throughput.
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wrk -t1 -c1 -d5m -s ./wrk_script_1024.lua http://127.0.0.1:8000/generate/ --timeout 1m
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``` |