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C++ Example of running LLM on Intel NPU using IPEX-LLM

In this directory, you will find a C++ example on how to run LLM models on Intel NPUs using IPEX-LLM (leveraging Intel NPU Acceleration Library). See the table blow for verified models.

Verified Models

Model Model Link
Qwen2 Qwen/Qwen2-7B-Instruct, Qwen/Qwen2-1.5B-Instruct
Qwen2.5 Qwen/Qwen2.5-7B-Instruct, Qwen/Qwen2.5-3B-Instruct
Llama2 meta-llama/Llama-2-7b-chat-hf
Llama3 meta-llama/Meta-Llama-3-8B-Instruct
MiniCPM openbmb/MiniCPM-1B-sft-bf16, openbmb/MiniCPM-2B-sft-bf16
Llama3.2 meta-llama/Llama-3.2-1B-Instruct, meta-llama/Llama-3.2-3B-Instruct

0. Requirements

To run this C++ example with IPEX-LLM on Intel NPUs, make sure to install the newest driver version of Intel NPU. Go to https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html to download and unzip the driver. Then go to Device Manager, find Neural Processors -> Intel(R) AI Boost. Right click and select Update Driver -> Browse my computer for drivers. And then manually select the unzipped driver folder to install.

1. Install

1.1 Installation on Windows

We suggest using conda to manage environment:

conda create -n llm python=3.10
conda activate llm

:: install ipex-llm with 'npu' option
pip install --pre --upgrade ipex-llm[npu]

:: [optional] for Llama-3.2-1B-Instruct & Llama-3.2-3B-Instruct
pip install transformers==4.45.0 accelerate==0.33.0

2. Convert Model

We provide a convert script under current directory, by running it, you can obtain the whole weights and configuration files which are required to run C++ example.

:: to convert Qwen2.5-7B-Instruct
python convert.py --repo-id-or-model-path Qwen/Qwen2.5-7B-Instruct --save-directory <converted_model_path>

:: to convert Qwen2-1.5B-Instruct
python convert.py --repo-id-or-model-path Qwen/Qwen2-1.5B-Instruct --save-directory <converted_model_path>

:: to convert Qwen2.5-3B-Instruct
python convert.py --repo-id-or-model-path Qwen/Qwen2.5-3B-Instruct --save-directory <converted_model_path> --low_bit "sym_int8"

:: to convert Llama-2-7b-chat-hf
python convert.py --repo-id-or-model-path meta-llama/Llama-2-7b-chat-hf --save-directory <converted_model_path>

:: to convert Meta-Llama-3-8B-Instruct
python convert.py --repo-id-or-model-path meta-llama/Meta-Llama-3-8B-Instruct --save-directory <converted_model_path>

:: to convert MiniCPM-1B-sft-bf16
python convert.py --repo-id-or-model-path openbmb/MiniCPM-1B-sft-bf16 --save-directory <converted_model_path>

:: to convert MiniCPM-2B-sft-bf16
python convert.py --repo-id-or-model-path openbmb/MiniCPM-2B-sft-bf16 --save-directory <converted_model_path>

:: to convert Llama-3.2-1B-Instruct
python convert.py --repo-id-or-model-path meta-llama/Llama-3.2-1B-Instruct --save-directory <converted_model_path>

:: to convert Llama-3.2-3B-Instruct
python convert.py --repo-id-or-model-path meta-llama/Llama-3.2-3B-Instruct --save-directory <converted_model_path>

Arguments info:

  • --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the model (e.g. Qwen/Qwen2.5-7B-Instruct) to be downloaded, or the path to the huggingface checkpoint folder.
  • --save-directory SAVE_DIRECTORY: argument defining the path to save converted model. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, and the converted model will be saved into SAVE_DIRECTORY.
  • --max-context-len MAX_CONTEXT_LEN: Defines the maximum sequence length for both input and output tokens. It is default to be 1024.
  • --max-prompt-len MAX_PROMPT_LEN: Defines the maximum number of tokens that the input prompt can contain. It is default to be 960.
  • --low_bit LOW_BIT: Defines the low bit precision to quantize the model. It is default to be sym_int4.
  • --disable-transpose-value-cache: Disable the optimization of transposing value cache.

3. Build C++ Example llm-npu-cli

You can run below cmake script in cmd to build llm-npu-cli, don't forget to replace below conda env dir with your own path.

:: under current directory
:: please replace below conda env dir with your own path
set CONDA_ENV_DIR=C:\Users\arda\miniforge3\envs\llm\Lib\site-packages
mkdir build
cd build
cmake ..
cmake --build . --config Release -j
cd Release

4. Run llm-npu-cli

With built llm-npu-cli, you can run the example with specified paramaters. For example,

# Run simple text completion
llm-npu-cli.exe -m <converted_model_path> -n 64 "AI是什么?"

# Run in conversation mode
llm-npu-cli.exe -m <converted_model_path> -cnv

Arguments info:

  • -m : argument defining the path of saved converted model.
  • -cnv : argument to enable conversation mode.
  • -n : argument defining how many tokens will be generated.
  • Last argument is your input prompt.

5. Sample Output

Qwen/Qwen2.5-7B-Instruct

Text Completion
Input:
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
AI是什么?<|im_end|>
<|im_start|>assistant


Prefill 22 tokens cost xxxx ms.

Decode 63 tokens cost xxxx ms (avg xx.xx ms each token).
Output:
AI是"人工智能"的缩写，它是一门研究计算机如何能够完成与人类智能相关任务的学科，包括学习、推理、自我修正等能力。简而言之，人工智能就是让计算机模拟或执行人类智能行为的理论、技术和方法。

它涵盖了机器学习、深度学习、自然
Conversation
User:你好
Assistant:你好！很高兴能为你提供帮助。有什么问题或需要聊天可以找我哦。
User:AI是什么?
Assistant: AI代表的是"Artificial Intelligence"，中文翻译为人工智能。它是指由计算机或信息技术实现的智能行为。广义的人工智能可以指任何表现出智能行为的计算机或软件系统。狭义的人工智能则指的是模拟、学习、推理、理解自然语言以及自我生成的人工智能系统。

简而言之，人工智能是一种利用计算机和机器来模仿、模拟或扩展人类智能的技术或系统。它包括机器学习、深度学习、自然语言处理等多个子领域。
User:exit

Troubleshooting

Program crash with Chinese prompt

If you run CPP examples on Windows and find that your program raises below error when accepting Chinese prompts, you can search for region in the Windows search bar and go to Region->Administrative->Change System locale.., tick Beta: Use Unicode UTF-8 for worldwide language support option and then restart your computer.

thread '<unnamed>' panicked at src\lib.rs:151:91:
called `Result::unwrap()` on an `Err` value: Utf8Error { valid_up_to: 77, error_len: Some(1) }

For detailed instructions on how to do this, see this issue.