ad9d2e2e89
* Add BeamSearch op schema * Add ONNX conversion for beams search * remove attention_mask and change input order * add option to run baseline * add check data type NULL * applies VerifyNodeAndOpMatch to subgraph * update input_ids shape * Add node name for Cast node * expose API for topk * parse parameters * Add beam search scorer * output results * fix typo * use c++ template and format python * fix build pipeline errors * symbolic shape infer of input onnx * output scores * add kernel def hash * Handle vocab_mask; move CheckSubgraph * undo insert_cast_transformer.cc and fusion_utils.py * fix typo * fix merge * update doc * add repetition penalty * refactoring: add GptSubgraph class * move BeamSearchState from .h to .cc file * adjust logits processor order * add batch generation example * fix repetition penalty for dup words in sequence * Add test * Add no repeat ngram processor * refactoring: move logits processor to classes * fix build warning * show latency * use allocator in beam state * use allocator in sequences * fix build error * move next_positions to beam state * Changes for prefix matching * removing debugs * removing more debugs * clean up * clean up * cpu doc updated * Updated docs * updated prefix_vocab_mask dimension in convert script * changes to support bxs prefix_vocab_mask in beamsearchop kernel * doc update * OperatorKernels.md updated * matching docs from artifacts * minor change in logits processor * Addressing comments * Updated the prefix vocab mask usage properly Co-authored-by: Tianlei Wu <tlwu@microsoft.com> |
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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
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.