📄 简体中文 | ✨ New Project: AI-Enhancement-Filter (powered by 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)
• Inference Engine: PyTorch-backed graph inference with memory pool (details)
  • 40+ registered op kernels (Conv, Add, Relu, Gemm, etc.)
  • Up to 11.3x faster than PyTorch in dynamic resolution scenarios
  • 54% memory reduction via two-pass compression algorithm
• 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, Qwen3, Qwen3.5 (Dense & MoE), DeepSeek-V4 (Flash/Pro, MLA+MoE), MiniMax-M2.7 (MoE) (TransformerModel)
Diffusion	Stable Diffusion (TextEncoder, VAE, UNet)
CV	Detic, BEVFormer, SSD300_VGG16, ConvNeXt, Mask R-CNN, Silero VAD
Audio	Sovits, LPCNet

🆕 Qwen3.5 Series: Full support for Qwen3.5 hybrid architecture including:

• Gated DeltaNet (GDN) layers with linear attention
• QKV Gating (Q projection with built-in gate, applied before O-projection)
• Sparse Mixture-of-Experts (MoE) with routed + shared experts
• Mixed layer types (linear_attention / full_attention) per config
• Multimodal Vision Encoder (ViT + MLP Projector) with resolution-aware profiling

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⚡ 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)
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
Qwen3.5-4B-Instruct 🆕	4807	4.651	0.067109
Qwen3.5-35B-A3B-Instruct 🆕 (MoE)	3574	34.705	0.041943
DeepSeek-V4-Flash 🆕 (MoE/MLA)	15681	283.811	0.045089
DeepSeek-V4-Pro 🆕 (MoE/MLA)	55701	1571.742	0.063963
MiniMax-M2.7 🆕 (MoE)	12554	230.315	0.130023

Multimodal: Vision Encoder vs LLM MACs 🆕

Vision encoder (ViT-24L + MLP Projector) vs LLM backbone. LLM input seq_len = vision patches + 1K text tokens. Both models share the same ViT architecture. See benchmark/vision_llm_compare.py.

Resolution	Patches	Total Tokens	Vision(G)	4B LLM(G)	4B Vis/LLM(%)	35B LLM(G)	35B Vis/LLM(%)
224×224	256	1,280	84	6,080	1.4	4,557	1.8
448×448	1,024	2,048	389	10,070	3.9	7,719	5.0
672×672	2,304	3,328	1,061	17,292	6.1	13,702	7.7
896×896	4,096	5,120	2,350	28,599	8.2	23,576	9.9
1344×896	6,144	7,168	4,317	43,233	10.0	37,000	11.6
1344×1344	9,216	10,240	8,259	68,607	12.0	61,414	13.4

💡 Vision MACs scale with O(p²), LLM MACs with O(t²) where t = patches + text. At 448×448, vision is ~4-5% of LLM; at 1344×1344, ~12-13%. Vision is always the minor component.

MoE Activated Parameters vs Sequence Length

Activated parameters = sum of all nodes' static_params (weights actually accessed during forward pass). S=32: Qwen3.5-35B-A3B & MiniMax-M2.7 experts fully activated (gap ≈ embedding); DeepSeek-V4 not yet saturated (top-6 × 32 = 192 < num_experts).

model	Total(G)	S=1	S=2	S=4	S=8	S=16	S=32
Qwen3.5-35B-A3B (MoE)	34.3	2.44	3.44	5.46	9.48	17.54	33.64
MiniMax-M2.7 (MoE)	230.3	10.42	17.44	31.48	59.56	115.73	228.08
DeepSeek-V4-Flash (MoE)	283.8	12.75	19.24	32.23	58.20	110.14	214.03
DeepSeek-V4-Pro (MoE)	1571.7	47.60	71.77	120.13	216.84	410.27	797.12

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

Prefill Throughput (tokens/s, 1k input)

model	Ultra-358H	Arc-B70	RTX-4090	RTX-5090
Phi-3-mini-4k	6658.9	21279.8	19896.0	25567.0
Phi-3-small-8k-instruct	3599.5	11316.9	10429.8	13334.3
Phi-3-medium-4k-instruct	1964.8	6151.8	5649.1	7213.2
Llama3-8B	3499.9	11053.2	10226.7	13092.7
Llama-3.1-70B-Japanese-Instruct-2407	401.2	1249.3	1141.8	1455.5
Qwen3.5-4B-Instruct 🆕	5705.0	18162.0	16923.1	21720.3
Qwen3.5-35B-A3B-Instruct 🆕 (MoE)	4660.9	18458.3	21970.0	29367.3
MiniMax-M2.7 🆕 (MoE)	970.5	4285.1	6090.4	9083.3

Decode Throughput (tokens/s)

model	Ultra-358H	Arc-B70	RTX-4090	RTX-5090
Phi-3-mini-4k	56.4	267.9	444.2	789.7
Phi-3-small-8k-instruct	33.4	158.5	262.8	467.2
Phi-3-medium-4k-instruct	18.0	85.4	141.6	251.8
Llama3-8B	32.9	156.1	258.9	460.2
Llama-3.1-70B-Japanese-Instruct-2407	3.6	17.3	28.7	51.0
Qwen3.5-4B-Instruct 🆕	56.5	268.4	444.9	791.0
Qwen3.5-35B-A3B-Instruct 🆕 (MoE)	79.0	375.0	621.8	1105.4
MiniMax-M2.7 🆕 (MoE)	23.4	111.0	184.1	327.3

💡 Latencies computed from hardware specs – no actual inference required. Uses BF16/FP16 compute with FP32 accumulate as the standard.

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🔧 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.

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📊 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

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⚙️ 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)

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💾 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

The compress_memory() algorithm has been patched with two improvements (see onnx_tool/graph.py):

1. Size-sorted allocation: New tensors within each node are allocated in descending size order, reducing fragmentation
2. Tail compression: Unused space at the end of the memory pool is trimmed
3. List reference fix: Each tensor's [offset, size] is stored as an independent copy, preventing accidental cross-tensor aliasing

Benchmark results across models:

model	Native(MB)	Compressed(MB)	Ratio(%)
VAE encoder	11,313.6	512.0	4.53
VAE decoder	19,816.2	896.1	4.52
Text encoder	172.5	3.7	2.12
GPT2	381.1	16.1	4.23
ResNet50	279.3	10.7	3.84

✅ Optimized algorithm achieves up to 54% additional pool reduction (ResNet50: 21.4→10.7 MB vs original)

Inference Engine

The inference/ module provides a complete PyTorch-backed inference engine built on the compressed memory pool:

• MemoryPool: Zero-copy tensor views into a pre-allocated contiguous buffer
• Kernel Registry: 40+ registered op kernels (Conv, Add, Relu, Gemm, MatMul, etc.)
• GraphInfer: Graph-level inference with shape engine integration

Performance highlights (ResNet18 on Intel XPU):

Metric	PyTorch	GraphInfer	Improvement
Single inference (1080p)	0.0151s	0.0167s	0.91x (on par)
Sequential 7 resolutions	1820ms	162ms	11.3x faster
Peak XPU memory (4K)	2338 MB	1926 MB	17.6% less
Memory pool (4K)	—	1012 MB	Fixed size

📌 See inference/README.md for full benchmark details

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.

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🚀 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

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Known Issues

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

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📈 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