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### Description <!-- Describe your changes. --> Add bfloat16 support for `MatMulBnb4` contrib op. This is useful for QLoRA fine-tuning. - On GPUs with SM80+ (A100, etc), it uses the native cuda bfloat16 dtype, `nv_bfloat16`. On other GPUs, it uses the onnxruntime `BFloat16` type which uses float for compute. - I have validated the op in a llama2-7b training scenario. The losses match pytorch training and the training throughput is better. - Cannot add a bfloat16 case in the op unit test since casting BFloat16 to and from float multiple times during the test causes the required tolerances to be unachievable. The custom autograd function exporter in onnxruntime-training is updated to support the latest version of bitsandbytes. They changed how the `quant_state` is stored. ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> Enable QLoRA fine-tuning with bfloat16. |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documention and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
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| Windows | ||
| Linux | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| Linux |  |
Data/Telemetry
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Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.