onnx-tool

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onnx-tool

A comprehensive toolkit for analyzing, optimizing, and transforming ONNX models with advanced capabilities for LLMs, diffusion models, and computer vision architectures.

• LLM Optimization: Build and profile large language models with KV cache analysis (example)
• Graph Transformation:
  • Constant folding (docs)
  • Operator fusion (docs)
• Advanced Profiling:
  • Rapid shape inference
  • MACs/parameter statistics with sparsity awareness
• Compute Graph Engine: Runtime shape computation with minimal overhead (details)
• Memory Compression:
  • Activation memory optimization (up to 95% reduction)
  • Weight quantization (FP16, INT8/INT4 with per-tensor/channel/block schemes)
• Quantization & Sparsity: Full support for quantized and sparse model analysis

🤖 Supported Model Architectures

Domain	Models
NLP	BERT, T5, GPT, LLaMa, MPT (TransformerModel)
Diffusion	Stable Diffusion (TextEncoder, VAE, UNet)
CV	Detic, BEVFormer, SSD300_VGG16, ConvNeXt, Mask R-CNN, Silero VAD
Audio	Sovits, LPCNet

⚡ Build & Profile LLMs in Seconds

Profile 10 Hugging Face models in under one second. Export ONNX models with llama.cpp-like simplicity (code).

Model Statistics (1k token input)

model name(1k input)	MACs(G)	Parameters(G)	KV Cache(G)
gpt-j-6b	6277	6.05049	0.234881
yi-1.5-34B	35862	34.3889	0.125829
microsoft/phi-2	2948	2.77944	0.167772
Phi-3-mini-4k	4083	3.82108	0.201327
Phi-3-small-8k-instruct	7912	7.80167	0.0671089
Phi-3-medium-4k-instruct	14665	13.9602	0.104858
Llama3-8B	8029	8.03026	0.0671089
Llama-3.1-70B-Japanese-Instruct-2407	72888	70.5537	0.167772
QWen-7B	7509	7.61562	0.0293601
Qwen2_72B_Instruct	74895	72.7062	0.167772

Latency Estimation (4-bit weights, 16-bit KV cache)

model_type_4bit_kv16bit	memory_size(GB)	Ultra-155H_TTFT	Ultra-155H_TPOT	Arc-A770_TTFT	Arc-A770_TPOT	H100-PCIe_TTFT	H100-PCIe_TPOT
gpt-j-6b	3.75678	1.0947	0.041742	0.0916882	0.00670853	0.0164015	0.00187839
yi-1.5-34B	19.3369	5.77095	0.214854	0.45344	0.0345302	0.0747854	0.00966844
microsoft/phi-2	1.82485	0.58361	0.0202761	0.0529628	0.00325866	0.010338	0.000912425
Phi-3-mini-4k	2.49649	0.811173	0.0277388	0.0745356	0.00445802	0.0147274	0.00124825
Phi-3-small-8k-instruct	4.2913	1.38985	0.0476811	0.117512	0.00766303	0.0212535	0.00214565
Phi-3-medium-4k-instruct	7.96977	2.4463	0.088553	0.198249	0.0142317	0.0340576	0.00398489
Llama3-8B	4.35559	1.4354	0.0483954	0.123333	0.00777784	0.0227182	0.00217779
Llama-3.1-70B-Japanese-Instruct-2407	39.4303	11.3541	0.438114	0.868475	0.0704112	0.137901	0.0197151
QWen-7B	4.03576	1.34983	0.0448417	0.11722	0.00720671	0.0218461	0.00201788
Qwen2_72B_Instruct	40.5309	11.6534	0.450343	0.890816	0.0723766	0.14132	0.0202654

💡 Latencies computed from hardware specs – no actual inference required

🔧 Basic Parsing & Editing

Intuitive API for model manipulation:

from onnx_tool import Model

model = Model('model.onnx')          # Load any ONNX file
graph = model.graph                  # Access computation graph
node = graph.nodemap['Conv_0']       # Modify operator attributes
tensor = graph.tensormap['weight']   # Edit tensor data/types
model.save_model('modified.onnx')    # Persist changes

See comprehensive examples in benchmark/examples.py.

📊 Shape Inference & Profiling

All profiling relies on precise shape inference:

Profiling Capabilities

• Standard profiling: MACs, parameters, memory footprint
• Sparse-aware profiling: Quantify sparsity impact on compute

📚 Learn more:

• Profiling Guide
• PyTorch Integration
• TensorFlow Integration

⚙️ Compute Graph & Shape Engine

Transform exported ONNX graphs into efficient Compute Graphs by removing shape-calculation overhead:

• Compute Graph: Minimal graph containing only compute operations
• Shape Engine: Runtime shape resolver for dynamic models

Use Cases:

• Integration with custom inference engines (guide)
• Shape regression testing (example)

💾 Memory Compression

Activation Memory Compression

Reuses temporary buffers to minimize peak memory usage – critical for LLMs and high-res CV models.

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

✅ Typical models achieve >90% activation memory reduction
📌 Implementation: benchmark/compression.py

Weight Compression

Essential for deploying large models on memory-constrained devices:

Quantization Scheme	Size vs FP32	Example (7B model)
FP32 (baseline)	1.00×	28 GB
FP16	0.50×	14 GB
INT8 (per-channel)	0.25×	7 GB
INT4 (block=32, symmetric) – llama.cpp	0.156×	4.4 GB

Supported schemes:

• ✅ FP16
• ✅ INT8: symmetric/asymmetric × per-tensor/channel/block
• ✅ INT4: symmetric/asymmetric × per-tensor/channel/block

📌 See benchmark/examples.py for implementation examples.

🚀 Installation

# PyPI (recommended)
pip install onnx-tool

# Latest development version
pip install --upgrade git+https://github.com/ThanatosShinji/onnx-tool.git

Requirements: Python ≥ 3.6

⚠️ Troubleshooting: If ONNX installation fails, try:

pip install onnx==1.8.1 && pip install onnx-tool

Known Issues

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

📈 Model Zoo Results

Comprehensive profiling of ONNX Model Zoo and SOTA models. Input shapes defined in data/public/config.py.

📥 Download pre-profiled models (with full tensor shapes):

• Baidu Drive (code: p91k)
• Google Drive

Model Params(M) MACs(M) GPT-J 1 layer 464 173,398 MPT 1 layer 261 79,894 text_encoder 123.13 6,782 UNet2DCondition 859.52 888,870 VAE_encoder 34.16 566,371 VAE_decoder 49.49 1,271,959 SqueezeNet 1.0 1.23 351 AlexNet 60.96 665 GoogleNet 6.99 1,606 googlenet_age 5.98 1,605 LResNet100E-IR 65.22 12,102 BERT-Squad 113.61 22,767 BiDAF 18.08 9.87 EfficientNet-Lite4 12.96 1,361 Emotion 12.95 877 Mask R-CNN 46.77 92,077

Model Params(M) MACs(M) LLaMa 1 layer 618 211,801 BEVFormer Tiny 33.7 210,838 rvm_mobilenetv3 3.73 4,289 yolov4 64.33 3,319 ConvNeXt-L 229.79 34,872 edgenext_small 5.58 1,357 SSD 19.98 216,598 RealESRGAN 16.69 73,551 ShuffleNet 2.29 146 GPT-2 137.02 1,103 T5-encoder 109.62 686 T5-decoder 162.62 1,113 RoBERTa-BASE 124.64 688 Faster R-CNN 44.10 46,018 FCN ResNet-50 35.29 37,056 ResNet50 25 3,868

🤝 Contributing

Contributions are welcome! Please open an issue or PR for:

• Bug reports
• Feature requests
• Documentation improvements
• New model support