963a5c8d79
* test * test * fix last kv cache * add xpu readme * remove numactl for xpu example * fix link error * update max_num_batched_tokens logic * add explaination * add xpu environement version requirement * refine gpu memory * fix * fix style
94 lines
3.6 KiB
Markdown
94 lines
3.6 KiB
Markdown
# vLLM continuous batching on Intel CPUs (experimental support)
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This example demonstrates how to serve a LLaMA2-7B model using vLLM continuous batching on Intel CPU (with BigDL-LLM 4 bits optimizations).
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The code shown in the following example is ported from [vLLM](https://github.com/vllm-project/vllm/tree/v0.2.1.post1).
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## Example: Serving LLaMA2-7B using Xeon CPU
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In this example, we will run Llama2-7b model using 48 cores in one socket and provide `OpenAI-compatible` interface for users.
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### 1. Install
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To run vLLM continuous batching on Intel CPUs, install the dependencies as follows:
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```bash
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# First create an conda environment
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conda create -n bigdl-vllm python==3.9
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conda activate bigdl-vllm
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# Install dependencies
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pip3 install numpy
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pip3 install --pre --upgrade bigdl-llm[all]
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pip3 install psutil
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pip3 install sentencepiece # Required for LLaMA tokenizer.
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pip3 install "torch==2.0.1"
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pip3 install "transformers>=4.33.1" # Required for Code Llama.
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pip3 install fastapi
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pip3 install "uvicorn[standard]"
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pip3 install "pydantic<2" # Required for OpenAI server.
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```
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### 2. Configure recommended environment variables
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```bash
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source bigdl-llm-init -t
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```
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### 3. Offline inference/Service
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#### Offline inference
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To run offline inference using vLLM for a quick impression, use the following example:
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```bash
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#!/bin/bash
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# Please first modify the MODEL_PATH in offline_inference.py
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numactl -C 48-95 -m 1 python offline_inference.py
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```
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#### Service
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To fully utilize the continuous batching feature of the `vLLM`, you can send requests to the service using curl or other similar methods. The requests sent to the engine will be batched at token level. Queries will be executed in the same `forward` step of the LLM and be removed when they are finished instead of waiting for all sequences to be finished.
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```bash
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#!/bin/bash
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# You may also want to adjust the `--max-num-batched-tokens` argument, it indicates the hard limit
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# of batched prompt length the server will accept
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numactl -C 48-95 -m 1 python -m bigdl.llm.vllm.entrypoints.openai.api_server \
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--model /MODEL_PATH/Llama-2-7b-chat-hf-bigdl/ --port 8000 \
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--load-format 'auto' --device cpu --dtype bfloat16 \
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--max-num-batched-tokens 4096
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```
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Then you can access the api server as follows:
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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": "/MODEL_PATH/Llama-2-7b-chat-hf-bigdl/",
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"prompt": "San Francisco is a",
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"max_tokens": 128,
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"temperature": 0
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}' &
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```
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### 4. (Optional) Add a new model
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Currently we have only supported LLaMA family model (including `llama`, `vicuna`, `llama-2`, etc.). To use aother model, you may need add some adaptions.
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#### 4.1 Add model code
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Create or clone the Pytorch model code to `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models`.
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#### 4.2 Rewrite the forward methods
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Refering to `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/models/bigdl_llama.py`, it's necessary to maintain a `kv_cache`, which is a nested list of dictionary that maps `req_id` to a three-dimensional tensor **(the structure may vary from models)**. Before the model's actual `forward` method, you could prepare a `past_key_values` according to current `req_id`, and after that you need to update the `kv_cache` with `output.past_key_values`. The clearence will be executed when the request is finished.
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#### 4.3 Register new model
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Finally, register your `*ForCausalLM` class to the _MODEL_REGISTRY in `BigDL/python/llm/src/bigdl/llm/vllm/model_executor/model_loader.py`.
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