llmexport: A toolkit to export llm to onnx or mnn.
Project description
llm-export
llm-export是一个llm模型导出工具,能够将llm模型导出为onnx和mnn模型。
- 🚀 优化原始代码,支持动态形状
- 🚀 优化原始代码,减少常量部分
- 🚀 使用OnnxSlim优化onnx模型,性能提升约5%; by @inisis
- 🚀 支持将lora权重导出为onnx和mnn
- 🚀 Onnx推理代码OnnxLLM
安装
# pip install
pip install llmexport
# git install
pip install git+https://github.com/wangzhaode/llm-export@master
# local install
git clone https://github.com/wangzhaode/llm-export && cd llm-export/
pip install .
用法
- 下载模型
git clone https://huggingface.co/Qwen/Qwen2-1.5B-Instruct
# 如果huggingface下载慢可以使用modelscope
git clone https://modelscope.cn/qwen/Qwen2-1.5B-Instruct.git
- 测试模型
# 测试文本输入
llmexport --path Qwen2-1.5B-Instruct --test "你好"
# 测试图像文本
llmexport --path Qwen2-VL-2B-Instruct --test "<img>https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg</img>介绍一下图片里的内容"
- 导出模型
# 将Qwen2-1.5B-Instruct导出为onnx模型
llmexport --path Qwen2-1.5B-Instruct --export onnx
# 将Qwen2-1.5B-Instruct导出为mnn模型, 量化参数为4bit, blokc-wise = 128
llmexport --path Qwen2-1.5B-Instruct --export mnn --quant_bit 4 --quant_block 128
功能
- 支持将模型为onnx或mnn模型,使用
--export onnx
或--export mnn
- 支持对模型进行对话测试,使用
--test $query
会返回llm的回复内容 - 默认会使用onnx-slim对onnx模型进行优化,跳过该步骤使用
--skip_slim
- 支持合并lora权重后导出,指定lora权重的目录使用
--lora_path
- 制定量化bit数使用
--quant_bit
;量化的block大小使用--quant_block
- 使用
--lm_quant_bit
来制定lm_head层权重的量化bit数,不指定则使用--quant_bit
的量化bit数 - 支持使用自己编译的
MNNConvert
,使用--mnnconvert
参数
usage: llmexport.py [-h] --path PATH [--type TYPE] [--lora_path LORA_PATH] [--dst_path DST_PATH] [--test TEST] [--export EXPORT]
[--skip_slim] [--quant_bit QUANT_BIT] [--quant_block QUANT_BLOCK] [--lm_quant_bit LM_QUANT_BIT]
[--mnnconvert MNNCONVERT]
llm_exporter
options:
-h, --help show this help message and exit
--path PATH path(`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model like `THUDM/chatglm-6b`. [TODO]
- A path to a *directory* clone from repo like `../chatglm-6b`.
--type TYPE type(`str`, *optional*):
The pretrain llm model type.
--lora_path LORA_PATH
lora path, defaut is `None` mean not apply lora.
--dst_path DST_PATH export onnx/mnn model to path, defaut is `./model`.
--test TEST test model inference with query `TEST`.
--export EXPORT export model to an onnx/mnn model.
--skip_slim Whether or not to skip onnx-slim.
--quant_bit QUANT_BIT
mnn quant bit, 4 or 8, default is 4.
--quant_block QUANT_BLOCK
mnn quant block, default is 0 mean channle-wise.
--lm_quant_bit LM_QUANT_BIT
mnn lm_head quant bit, 4 or 8, default is `quant_bit`.
--mnnconvert MNNCONVERT
local mnnconvert path, if invalid, using pymnn.
支持模型
- llama/llama2/llama3/llama3.2/tinyllama
- qwen/qwen1.5/qwen2/qwen-vl/qwen2-vl/qwen2.5
- baichuan2/phi-2/internlm/yi/deepseek
- chatglm/codegeex/chatglm2/chatglm3
- phi-2/gemma-2
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