AI BriefingHugging Face Improves TensorFlow Model Performance
Key point
The performance of TensorFlow models in Hugging Face Transformers has been improved, becoming up to 10% faster than Google's implementation.
Details
The Hugging Face team made updates to make TensorFlow models in the Transformers library more robust and faster. This improvement focuses on enhancing the computational performance of BERT, RoBERTa, ELECTRA, MPNet models and enabling efficient deployment via TensorFlow Serving.
Key Performance Improvements
- Improved Computational Performance: For the BERT model, performance was recorded at up to about 10% faster than Google's official implementation. This is about 2x faster compared to the previous version (v4.1.1).
- General Benefits: Performance improvements appear across Graph/Eager mode, TF Serving, and all CPU/GPU/TPU device environments.
TensorFlow Serving Deployment Support To enable efficient model deployment in production environments, SavedModel format is supported. Starting with Transformers v4.2.0, the following features were added when creating SavedModels:
- Ability to freely change sequence length between runs
- Ability to use all model inputs during inference
hidden statesandattentionoutputs provided together as a single group
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