d79e3c5791
### Description Previously, MultiHeadAttention supports relative position bias of shape [1, N, S, T] or [B, N, S, T], and DecoderMaskedMultiHeadAttention supports [1, N, S, T]. This will extend the support to allow [1, N, S, T], [B, N, S, T], [B, 1, S, T] and [1, 1, S, T] for CUDA and CPU EPs. - [x] Rename the input of "relative position bias" to "attention bias" because it can also be used for other types of bias, like ALiBi (Attention with Linear Biases) or attention mask. - [x] Update unfused kernel to support broadcasting 2nd dimension of attention bias. - [x] Update efficient attention to support broadcasting 2nd dimension of attention bias. - [x] Update operators (MultiHeadAttention, DecoderMaskedMultiHeadAttention, Attention, PackedAttention, PackedMultiHeadAttention) to support broadcast attention bias on CUDA and CPU EPs. - [x] Update ROCm, DML and WebGPU naming to be consistent. (Note that those EPs do not support broadcasting attention_bias for now). - [x] Add attention bias tests for MultiHeadAttention. - [x] Update operator documents - [x] Update benchmark script Other changes: * Fix some checks in multihead-attention.ts * Add helper functions to dump tensors given dimensions. |
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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 documentation 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 |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
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.