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https://github.com/ThanatosShinji/onnx-tool

A parser, editor and profiler tool for ONNX models.
https://github.com/ThanatosShinji/onnx-tool
onnx python
Last synced: 3 months ago
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A parser, editor and profiler tool for ONNX models.
Host: GitHub
URL: https://github.com/ThanatosShinji/onnx-tool
Owner: ThanatosShinji
License: mit
Created: 2022-06-28T15:28:35.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2024-03-18T15:55:29.000Z (3 months ago)
Last Synced: 2024-03-18T17:22:46.752Z (3 months ago)
Topics: onnx, python
Language: Python
Homepage: https://pypi.org/project/onnx-tool/
Size: 1.15 MB
Stars: 295
Watchers: 5
Forks: 38
Open Issues: 16
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists

awesome-cs-tutorial - https://github.com/ThanatosShinji/onnx-tool
README

        简体中文

# onnx-tool

**A tool for ONNX model:**

* *[Parse and edit](#basic-parse-edit): [Constant folding](data/ConstantFolding.md); [OPs fusion](data/GraphFusion.md).*

* *[Model profiling](#shapeinfer-profile): Rapid shape inference; MACs statistics*

* *[Compute Graph and Shape Engine](#compute_graph-header).*

* *[Model memory compression](#memory-compression): activation compression and weight compression.*

* *[Quantized models and sparse models](#models) are supported.*

Supported Models:

* NLP: BERT, T5, GPT, LLaMa, MPT([TransformerModel](benchmark/transfomer_models.py))

* Diffusion: Stable Diffusion(TextEncoder, VAE, UNET)

* CV: [Detic](https://github.com/ThanatosShinji/onnx-tool/issues/63), [BEVFormer](benchmark/compression.py), [SSD300_VGG16](https://github.com/ThanatosShinji/onnx-tool/issues/66), ...

* Audio: sovits, LPCNet

---

## Basic Parse and Edit



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](benchmark/examples.py).

---

## Shape Inference & Profile Model



All profiling data must be built on shape inference result.  

ONNX graph with tensor shapes:

  

  

  

Regular model profiling table:  



  







Sparse profiling table:



  





  

Introduction: [data/Profile.md](data/Profile.md).  

pytorch usage: [data/PytorchUsage.md](data/PytorchUsage.md).  

tensorflow

usage: [data/TensorflowUsage.md](data/TensorflowUsage.md).  

examples: [benchmark/examples.py](benchmark/examples.py).

---

## Compute Graph with Shape Engine



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/shape_regress.py).

[benchmark/examples.py](benchmark/examples.py).  

Integrate *Compute Graph* and *Shape Engine* into a cpp inference

engine: [data/inference_engine.md](data/inference_engine.md)

---

## Memory Compression



### 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:

 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](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](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](https://github.com/onnx/models) 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](data/public/config.py).

These onnx models with all tensors' shape can be downloaded: [baidu drive](https://pan.baidu.com/s/1eebBP-n-wXvOhSmIH-NUZQ 

)(code: p91k) [google drive](https://drive.google.com/drive/folders/1H-ya1wTvjIMg2pMcMITWDIfWNSnjYxTn?usp=sharing)





Model | Params(M) | MACs(M)

---|---|---

GPT-J 1 layer | 464 | 173,398  

MPT 1 layer | 261 | 79,894

[text_encoder](https://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx/tree/main)| 123.13 | 6,782

[UNet2DCondition](https://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx/tree/main)| 859.52 | 888,870

[VAE_encoder](https://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx/tree/main) | 34.16 | 566,371

[VAE_decoder](https://huggingface.co/bes-dev/stable-diffusion-v1-4-onnx/tree/main) | 49.49 | 1,271,959

[SqueezeNet 1.0](https://github.com/onnx/models/tree/main/vision/classification/squeezenet) | 1.23 | 351

[AlexNet](https://github.com/onnx/models/tree/main/vision/classification/alexnet) | 60.96 | 665

[GoogleNet](https://github.com/onnx/models/tree/main/vision/classification/inception_and_googlenet/googlenet) | 6.99 | 1,606

[googlenet_age](https://github.com/onnx/models/tree/main/vision/body_analysis/age_gender) | 5.98 | 1,605

[LResNet100E-IR](https://github.com/onnx/models/tree/main/vision/body_analysis/arcface) | 65.22 | 12,102

[BERT-Squad](https://github.com/onnx/models/tree/main/text/machine_comprehension/bert-squad) | 113.61 | 22,767

[BiDAF](https://github.com/onnx/models/tree/main/text/machine_comprehension/bidirectional_attention_flow) | 18.08 | 9.87

[EfficientNet-Lite4](https://github.com/onnx/models/tree/main/vision/classification/efficientnet-lite4) | 12.96 | 1,361

[Emotion](https://github.com/onnx/models/tree/main/vision/body_analysis/emotion_ferplus) | 12.95 | 877

[Mask R-CNN](https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/mask-rcnn) | 46.77 | 92,077

Model | Params(M) | MACs(M)

---|-----------|---

LLaMa 1 layer | 618       | 211,801  

[BEVFormer Tiny](https://github.com/DerryHub/BEVFormer_tensorrt) | 33.7      | 210,838

[rvm_mobilenetv3](https://github.com/PeterL1n/RobustVideoMatting) | 3.73      | 4,289

[yolov4](https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/yolov4) | 64.33     | 3,319

[ConvNeXt-L](https://github.com/facebookresearch/ConvNeXt) | 229.79    | 34,872

[edgenext_small](https://github.com/mmaaz60/EdgeNeXt) | 5.58      | 1,357

[SSD](https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/ssd) | 19.98     | 216,598

[RealESRGAN](https://github.com/xinntao/Real-ESRGAN) | 16.69     | 73,551

[ShuffleNet](https://github.com/onnx/models/tree/main/vision/classification/shufflenet) | 2.29      | 146

[GPT-2](https://github.com/onnx/models/tree/main/text/machine_comprehension/gpt-2) | 137.02    | 1,103

[T5-encoder](https://github.com/onnx/models/tree/main/text/machine_comprehension/t5) | 109.62    | 686

[T5-decoder](https://github.com/onnx/models/tree/main/text/machine_comprehension/t5) | 162.62    | 1,113

[RoBERTa-BASE](https://github.com/onnx/models/tree/main/text/machine_comprehension/roberta) | 124.64    | 688

[Faster R-CNN](https://github.com/onnx/models/blob/main/vision/object_detection_segmentation/faster-rcnn) | 44.10     | 46,018

[FCN ResNet-50](https://github.com/onnx/models/tree/main/vision/object_detection_segmentation/fcn) | 35.29     | 37,056

[ResNet50](https://github.com/onnx/models/tree/main/vision/classification/resnet) | 25        | 3,868