A tool for ONNX model:
- Parse and edit: Constant folding; OPs fusion.
- Model profiling: Rapid shape inference; MACs statistics
- Compute Graph and Shape Engine.
- Model memory compression: activation compression and weight compression.
- Quantized models and sparse models are supported.
Supported Models:
- NLP: BERT, T5, GPT, LLaMa, MPT(TransformerModel)
- Diffusion: Stable Diffusion(TextEncoder, VAE, UNET)
- CV: BEVFormer, MobileNet, YOLO, ...
- Audio: sovits, LPCNet
You can load any onnx file by onnx_tool.Model:
Change graph structure with onnx_tool.Graph;
Change op attributes and IO tensors with onnx_tool.Node;
Change tensor data or type with onnx_tool.Tensor.
To apply your changes, just call save_model method of onnx_tool.Model or onnx_tool.Graph.
Please refer benchmark/examples.py.
All profiling data must be built on shape inference result.
ONNX graph with tensor shapes:
Sparse profiling table:
Introduction: data/Profile.md.
pytorch usage: data/PytorchUsage.md.
tensorflow
usage: data/TensorflowUsage.md.
examples: benchmark/examples.py.
From a raw graph to a compute graph:
Remove shape calculation layers(created by ONNX export) to get a Compute Graph. Use Shape Engine to update tensor
shapes at runtime.
Examples: benchmark/shape_regress.py.
benchmark/examples.py.
Integrate Compute Graph and Shape Engine into a cpp inference
engine: data/inference_engine.md
Activation memory also called temporary memory is created by each OP's output. Only the last activation marked as the model's output will be kept. So you don't have to prepare memory space for each activation tensor. They better reuse an optimized memory size.
For large language models and high-resolution CV models, the activation memory compression is a key to save memory.
The compression method achieves 5% memory compression on most models.
For example:
| model | Native Memory Size(MB) | Compressed Memory Size(MB) | Compression Ratio(%) |
|---|---|---|---|
| StableDiffusion(VAE_encoder) | 14,245 | 540 | 3.7 |
| StableDiffusion(VAE_decoder) | 25,417 | 1,140 | 4.48 |
| StableDiffusion(Text_encoder) | 215 | 5 | 2.5 |
| StableDiffusion(UNet) | 36,135 | 2,232 | 6.2 |
| GPT2 | 40 | 2 | 6.9 |
| BERT | 2,170 | 27 | 1.25 |
code example: benchmark/compression.py
A fp32 model with 7B parameters will take 28GB disk space and memory space. You can not even run the model if your device doesn't have that much memory space. So weight compression is critical to run large language models. As a reference, 7B model with int4 symmetric per block(32) quantization(llama.cpp's q4_0 quantization method) only has ~0.156x model size compared with fp32 model.
Current support:
- [fp16]
- [int8]x[symmetric/asymmetric]x[per tensor/per channel/per block]
- [int4]x[symmetric/asymmetric]x[per tensor/per channel/per block]
code examples:benchmark/examples.py.
pip install onnx-tool
OR
pip install --upgrade git+https://github.com/ThanatosShinji/onnx-tool.git
python>=3.6
If pip install onnx-tool failed by onnx's installation, you may try pip install onnx==1.8.1 (a lower version like this) first.
Then pip install onnx-tool again.
- Loop op is not supported
- Sequence type is not supported
Results of ONNX Model Zoo and SOTA models
Some models have dynamic input shapes. The MACs varies from input shapes. The input shapes used in these results are writen to data/public/config.py. These onnx models with all tensors' shape can be downloaded: baidu drive(code: p91k) google drive
|
|