ipex-llm/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models

Run HuggingFace transformers Models with Pipeline Optimization on Intel NPU

In this directory, you will find examples on how to directly run HuggingFace transformers models with pipeline optimization on Intel NPUs. See the table blow for verified models.

Verified Models

Model	Model Link
Llama2	meta-llama/Llama-2-7b-chat-hf
Llama3	meta-llama/Meta-Llama-3-8B-Instruct
Qwen2	Qwen/Qwen2-1.5B-Instruct
Qwen2.5	Qwen/Qwen2.5-7b-Instruct
Baichuan2	baichuan-inc/Baichuan2-7B-Chat
MiniCPM	openbmb/MiniCPM-1B-sft-bf16, openbmb/MiniCPM-2B-sft-bf16

0. Requirements

To run these examples 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]

2. Runtime Configurations

Following environment variables are required:

set BIGDL_USE_NPU=1

3. Run Models

In the example generate.py, we show a basic use case for a Llama2 model to predict the next N tokens using generate() API, with IPEX-LLM INT4 optimizations on Intel NPUs.

:: to run Llama-2-7b-chat-hf
python llama2.py

:: to run Meta-Llama-3-8B-Instruct
python llama3.py

:: to run Qwen2.5-7b-Instruct
python qwen.py

:: to run Qwen2-1.5b-Instruct
python qwen.py --repo-id-or-model-path "Qwen/Qwen2-1.5B-Instruct" --load_in_low_bit "sym_int8"

:: to run Baichuan2-7B-Chat
python baichuan2.py

:: to run MiniCPM-1B-sft-bf16
python minicpm.py

:: to run MiniCPM-2B-sft-bf16
python minicpm.py --repo-id-or-model-path "openbmb/MiniCPM-2B-sft-bf16"

Arguments info:

• --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) to be downloaded, or the path to the huggingface checkpoint folder.
• --lowbit-path LOWBIT_MODEL_PATH: argument defining the path to save/load lowbit version of the model. If it is an empty string, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded. If it is an existing path, the lowbit model in LOWBIT_MODEL_PATH will be loaded. If it is a non-existing path, the original pretrained model specified by REPO_ID_OR_MODEL_PATH will be loaded, and the converted lowbit version will be saved into LOWBIT_MODEL_PATH. It is default to be '', i.e. an empty string.
• --prompt PROMPT: argument defining the prompt to be infered. It is default to be What is AI?.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 32.
• --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 512.
• --disable-transpose-value-cache: Disable the optimization of transposing value cache.

Sample Output

meta-llama/Llama-2-7b-chat-hf

 Number of input tokens: 28
 Generated tokens: 32
 First token generation time: xxxx s
 Generation average latency: xxxx ms, (xxxx token/s)
 Generation time: xxxx s

Inference time: xxxx s
-------------------- Input --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

What is AI? [/INST]
-------------------- Output --------------------
<s><s> [INST] <<SYS>>

<</SYS>>

What is AI? [/INST]  AI (Artificial Intelligence) is a field of computer science and technology that focuses on the development of intelligent machines that can perform