5 Exploring ONNX

The text introduces ONNX as an open standard for machine learning interoperability, emphasizing its role in making models portable across frameworks, hardware platforms, and deployment environments. It explains that ONNX consists of a common model format and a runtime system, allowing trained models from frameworks such as PyTorch, TensorFlow, Keras, and others to be converted, optimized, quantized, and executed consistently. A key idea is that ONNX helps avoid framework or hardware lock-in by representing models as directed computational graphs composed of typed tensors and operators.

The text then explains how ONNX models are built and executed. Operators such as matrix multiplication, addition, reshaping, softmax, normalization, and quantization form the basic building blocks of ONNX graphs, while strong typing and tensor shapes define the inputs and outputs of each operation. A simple linear regression example shows how to construct a model graph programmatically, validate it, serialize it, reload it, and inspect its structure. The ONNX Runtime is presented as the production-oriented execution engine, supporting inference sessions, input and output handling, and hardware-specific execution providers for CPUs, GPUs, mobile devices, edge platforms, and specialized accelerators.

The text concludes by applying ONNX to language models, showing how transformer-based models such as BERT and GPT-2 can be exported from PyTorch, validated, optimized, and run through ONNX Runtime on both CPU and GPU. It highlights practical performance benefits, including faster CPU inference after conversion and additional gains from transformer-specific graph optimization. For GPU deployments, it explains the importance of choosing the correct execution provider and reducing unnecessary CPU-GPU data transfers through I/O binding. Overall, ONNX is presented as a central tool for making small and large language models more portable, efficient, and deployment-ready across diverse environments.

ONNX overview.

The list of organizations supporting the ONNX initiative.

All the possible operations on ML/DL models in ONNX.

Visualization of a Linear Regression model implemented in ONNX using the Netron library.

The ONNX Runtime execution model.

Part of ONNX graph of the BERT uncased converted model (one attention block only)

• ONNX provides an open standard format for machine learning model interoperability across different frameworks and hardware platforms.
• ONNX runtime enables efficient execution of ONNX models on various hardware configurations including CPUs, GPUs, and edge devices.
• ONNX operators define the basic building blocks that describe virtually any machine learning model from various frameworks.
• ONNX supports multiple data types including float32, int8, int16, int32, int64, and specialized formats like bfloat16.
• ONNX runtime execution providers optimize model performance by leveraging specific hardware capabilities like CUDA for NVIDIA GPUs.
• Model optimization in ONNX can be performed offline during conversion or online during inference session initialization.
• Graph optimization levels in ONNX range from basic constant folding to extended node fusions and layout transformations.
• IOBinding in ONNX runtime eliminates data copying between CPU and GPU by keeping data on the target execution device.
• ONNX converted models typically achieve faster inference times compared to their original framework implementations.
• Validation of ONNX models requires checking both schema consistency and numerical output equivalence with original models.
• ONNX runtime supports multiple programming language bindings but Python provides the most comprehensive API access.
• ONNX models can be optimized specifically for transformer architectures using the onnxruntime.transformers.optimizer module.
• Converting language models to ONNX format preserves model functionality while enabling deployment across diverse production environments.

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