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### Description This code change allows for the QlinearConv operator to sync batches into a single parallel section. This allows for the tasks of all the batches to be made available for threads to exercise. This would act alternatively to the existing method which parallelizes the tasks of induvial images separately which forces threads to wait for all an entire image’s tasks to complete before continuing. ### Motivation and Context For int8 convolution models where multiple batches are being utilized, this patch delivers an inference improvement of up-to 41% and 39% for Mobilenet_edtpu (U8S8) and Resnet50(U8S8) respectively on systems with higher core counts. The patch, delivers the highest benefit on systems with higher thread counts and when utilizing large batch sizes. <html> <body> <!--StartFragment--><span style="color: rgb(201, 209, 217); font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", "Noto Sans", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji"; font-size: 14px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(13, 17, 23); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"><style> </style></span> | | Batch 2 | Batch 4 | Batch 8 | Batch 16 | Batch 32 | Batch 64 -- | -- | -- | -- | -- | -- | -- | -- resnet50 | % Gain | 22% | 25% | 32% | 36% | 33% | 32% <!--EndFragment--> </body> </html> |
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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
| System | CPU | GPU | EPs |
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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.