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### Description <!-- Describe your changes. --> TopK in BeamSearch retrieves top 2*beam next tokens based on logit score, specifically computing top [batch, 2*beam] tokens based on score [batch, beam, vocab_size]. ### 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. --> Current implementation use batch as the grid and each thread block compute top 2*beam from [beam, vocab_size]. It is inefficient because: 1. batch size is usually small( <32) and can not fully leverage GPU's SMs; 2. vocab_size is usually more than 50k. It is inefficient to compute 50k * beam in one thread block. This PR split the topk computation into multiple stages: - for small beam size, split [batch, beam, vocab_size] to [batch, beam, parts_of_vocab, vocab_size_per_part] - 1st stage, each thread block compute top 2*beam from vocab_sizer_per_part and gets [batch, beam, parts_of_vocab, 2*beam] - 2nd stage, each thread block compute top 2*beam from parts_of_vocab *(2*beam} and gets [batch, beam, 2*beam] - last stage, compute [batch, 2*beam] from [batch, beam, 2*beam] - for large beam size, 1st stage computes [batch, beam, 2*beam] from [batch, beam, vocab_size] and 2nd stage computes [batch, 2*beam] from [batch, beam, 2*beam]. With the change, performance improves a lot, it reduces ~100us from 2ms for batch:4, beam:4, vocab_size:~50k. |
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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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
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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.