f1044e3b9a
### Description Explore the possible re-use of the logits buffer in `GreedySearch` for cases where sequence length == 1 (Post the first decoding run, the sequence length is guaranteed to be 1). This re-use will ensure that we do not have to make copies of the logits before processing them. Currently, we make a copy of the logits even if the sequence length == 1 which is not necessary as we can directly re-use the logits buffer for the token generation step. A similar optimization exists in `BeamSearch`, but seems lacking in `GreedySearch`. Since, the logits buffer may contain padded data, we need to adjust the pieces consuming the logits buffer directly to account for any padding. A more invasive change (needs changes in a few places) will be to adjust the interfaces of `ProcessLogits()` such that it takes a reference to the logits and not a const reference as (based on my understanding) this is the only place where the logits from the decoder subgraph will ever be used and giving the `ProcessLogits()` method license to mutate/process the underlying buffer of the logits OrtValue seems reasonable (instead of making a copy and then mutating/processing them). The will also remove the ugly `const_cast`(s) seen in this change. |
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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
Build Pipeline Status
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| 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.