69 lines
2 KiB
Markdown
69 lines
2 KiB
Markdown
# Serve IPEX-LLM on Multiple Intel GPUs in multi-stage pipeline parallel fashion
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This example demonstrates how to run IPEX-LLM serving on multiple [Intel GPUs](../README.md) with Pipeline Parallel.
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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 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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### 2. Run pipeline parallel serving on multiple GPUs
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```bash
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# Need to set MODEL_PATH in run.sh first
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bash run.sh
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```
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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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#### generate()
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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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### 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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```
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