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### Description 1. added kernel to quantize matmul B tensor to q4, and store in the same shape as original tensor. scales and zero points are calculated as well. scales and zero points have the same shape. 2. added kernel to transpose q4 B tensor to B tensor in MatMulNBits. Scales and zero points are transposed as well. #### Benchmark <1024 x 4096 input, 64 quant block, 8 threads>: - quantize: 23035923 ns - transpose: 718635 ns <1024 x 4095 input, 64 quant block, 8 threads>: - quantize: 26759319 ns - transpose: 1279064 ns ### Motivation and Context The MatMulNbits tool chain current only supports converting a MatMul op direct to MatMulNBits op. MatMulNbits op is not an ONNX standard op. Therefore, we need the tool chain to support converting MatMul to Q/DQ format, and later in the transform step converts DQ + MatMul to MatMulNBits. The tensors stored in DQ are the quantized constants and will be stored in the MatMulNBits. |
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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 | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.