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### Description Add blocked quantization to QuantizeLinear op kernel. If the quantize axis is not the last axis, block the tensor using 1x128 blocks. Blocks are dispatched to multiple threads for concurrently processing. Currently only support scalar instructions. If the quantize axis is the last axis, block the tensor using 1 x quant_block_size blocks. Blocks are dispatched to multiple threads for concurrent processing. If output type is int types, call mlas kernel to use the SIMD instructions in each block. #### Benchmark data 20 core 2GHz CPU, RelWithDebInfo config, 196 x 4096 tensor, quantize float to int4x2 Quantize before last axis: * single thread, scalar instruction: 31380900 ns * 8 thread, scalar instruction: 5098620 ns Quantize last axis: * single thread, scalar instruction: 27927900 ns * 8 thread, SIMD instruction: 102261 ns more thread, SIMD instruction, larger block size helps ### Motivation and Context ONNX added blocked quantization to QuantizeLinear in optset 21 |
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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 |
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| Windows | ||
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Third-party Pipeline Status
| System | Inference | Training |
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| 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.