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onnx-tool

A tool for ONNX model:

• Build LLM model and profile
• 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: Detic, BEVFormer, SSD300_VGG16, ...
• Audio: sovits, LPCNet

Build LLM model and profile

<a id="build-profile"></a> Profile 10 hugging face models within one second. Save the ONNX models as simple as llama.cpp's. code ref

Get first-token latency and next-token latency from hardware specs.

Basic Parse and Edit

<a id="basic-parse-edit"></a> 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.

Shape Inference & Profile Model

<a id="shapeinfer-profile"></a> All profiling data must be built on shape inference result.
ONNX graph with tensor shapes:

Introduction: data/Profile.md.
pytorch usage: data/PytorchUsage.md.
tensorflow usage: data/TensorflowUsage.md.
examples: benchmark/examples.py.

Compute Graph with Shape Engine

<a id="compute_graph-header"></a> 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

Memory Compression

<a id="memory-compression"></a>

Activation Compression

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:

code example: benchmark/compression.py

Weight Compression

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.

How to install

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.

Known Issues

• Loop op is not supported
• Sequence type is not supported

Results of ONNX Model Zoo and SOTA models

<a id='models'></a> 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