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* fix: remove BIGDL_LLM_XMX_DISABLED in mddocs * fix: remove set SYCL_CACHE_PERSISTENT=1 in example * fix: remove BIGDL_LLM_XMX_DISABLED in workflows * fix: merge igpu and A-series Graphics * fix: remove set BIGDL_LLM_XMX_DISABLED=1 in example * fix: remove BIGDL_LLM_XMX_DISABLED in workflows * fix: merge igpu and A-series Graphics * fix: textual adjustment * fix: textual adjustment * fix: textual adjustment
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7.2 KiB
Markdown
246 lines
No EOL
7.2 KiB
Markdown
# Running Lightweight Serving using IPEX-LLM on one Intel GPU
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## Requirements
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To run this example with IPEX-LLM on one Intel GPU, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
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## Example
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### 1. Install
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#### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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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 fastapi uvicorn openai
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pip install gradio # for gradio web UI
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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# for internlm-xcomposer2-vl-7b
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pip install transformers==4.31.0
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pip install accelerate timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops
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# for whisper-large-v3
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pip install transformers==4.36.2
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pip install datasets soundfile librosa # required by audio processing
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```
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#### 1.2 Installation on Windows
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11 libuv
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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 fastapi uvicorn openai
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pip install gradio # for gradio web UI
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conda install -c conda-forge -y gperftools=2.10 # to enable tcmalloc
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# for glm-4v-9b
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pip install transformers==4.42.4 "trl<0.12.0"
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# for internlm-xcomposer2-vl-7b
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pip install transformers==4.31.0
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pip install accelerate timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops
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# for whisper-large-v3
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pip install transformers==4.36.2
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pip install datasets soundfile librosa # required by audio processing
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```
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### 2. Configures OneAPI environment variables for Linux
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> [!NOTE]
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> Skip this step if you are running on Windows.
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This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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<details>
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<summary>For Intel iGPU</summary>
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```bash
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU and Intel Arc™ A-Series Graphics</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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> [!NOTE]
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> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
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### 4. Running example
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```
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python ./lightweight_serving.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --low-bit LOW_BIT --port PORT
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```
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Arguments info:
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- `--repo-id-or-model-path REPO_ID_OR_MODEL_PATH`: argument defining the huggingface repo id for the model (e.g. `meta-llama/Llama-2-7b-chat-hf` and `meta-llama/Llama-2-13b-chat-hf`) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be `'meta-llama/Llama-2-7b-chat-hf'`.
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- `--low-bit LOW_BIT`: Sets the low bit optimizations (such as 'sym_int4', 'fp16', 'fp8' and 'fp6') for the model. It is default to be `sym_int4`.
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- `--port PORT`: The serving access port. It is default to be `8000`.
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### 5. Sample Input and Output
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We can use `curl` to test serving api. And need to set no_proxy to ensure that requests are not forwarded by a proxy. `export no_proxy=localhost,127.0.0.1`
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#### /generate
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{
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"inputs": "What is AI?",
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"parameters": {
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"max_new_tokens": 32,
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"min_new_tokens": 32,
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"repetition_penalty": 1.0,
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"temperature": 1.0,
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"do_sample": false,
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"top_k": 5,
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"tok_p": 1.0
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},
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"stream": false
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}' http://localhost:8000/generate
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```
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#### /generate_stream
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```bash
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curl -X POST -H "Content-Type: application/json" -d '{
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"inputs": "What is AI?",
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"parameters": {
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"max_new_tokens": 32,
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"min_new_tokens": 32,
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"repetition_penalty": 1.0,
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"temperature": 1.0,
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"do_sample": false,
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"top_k": 5,
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"tok_p": 1.0
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},
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"stream": false
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}' http://localhost:8000/generate_stream
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```
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#### /v1/chat/completions
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```bash
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Llama-2-7b-chat-hf",
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"messages": [{"role": "user", "content": "Hello! What is your name?"}],
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"stream": false
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}'
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```
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##### Image input
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image input only supports [internlm-xcomposer2-vl-7b](https://huggingface.co/internlm/internlm-xcomposer2-vl-7b) and [glm-4v-9b](https://huggingface.co/THUDM/glm-4v-9b) now. And they should both install specific transformers version to run.
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```bash
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "internlm-xcomposer2-vl-7b",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What'\''s in this image?"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "http://farm6.staticflickr.com/5268/5602445367_3504763978_z.jpg"
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}
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}
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]
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}
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],
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"max_tokens": 128
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}'
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```
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#### /v1/completions
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```bash
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curl http://localhost:8000/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Llama-2-7b-chat-hf",
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"prompt": "Once upon a time",
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"max_tokens": 32,
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"stream": false
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}'
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```
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#### v1/audio/transcriptions
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ASR only supports [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) now. And `whisper-large-v3` just can be used to transcription audio. The audio file_type should be supported by `librosa.load`.
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```bash
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curl http://localhost:8000/v1/audio/transcriptions \
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-H "Content-Type: multipart/form-data" \
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-F file="@/llm/test.mp3" \
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-F model="whisper-large-v3" \
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-F languag="zh"
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```
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### 6. Benchmark with wrk
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Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#4-benchmark-with-wrk) for more details
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## 7. Using the `benchmark.py` Script
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Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#5-using-the-benchmarkpy-script) for more details
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## 8. Gradio Web UI
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Please refer to [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Pipeline-Parallel-Serving#6-gradio-web-ui) for more details |