AI BriefingTransformers ONNX Conversion Guide
Key point
Explains how to convert Transformers models to ONNX format and optimize inference performance using Hugging Face Optimum.
Details
ONNX (Open Neural Network eXchange) is an open standard format that ensures model compatibility across various frameworks. Converting a model to ONNX enables efficient deployment leveraging hardware acceleration through dedicated runtimes such as ONNX Runtime.
Hugging Face Optimum is an extension tool for the Transformers library that provides optimization features to maximize a model's training and inference efficiency. Its key features are as follows:
- Quantization and graph optimization
- Accelerated hardware support (Graphcore IPU, Habana Gaudi, etc.)
- Integration support with the Transformers pipeline
Supported architectures include popular models such as BERT, GPT-2, RoBERTa, T5, ViT. Conversion methods are provided in stages, ranging from a low-level approach using PyTorch's torch.onnx to a high-level approach based on the Optimum library that allows for simple conversion without complex configuration.
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