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### Description - 4-bit QuantizeLinear(21). **Blocked quantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit DequantizeLinear(21). **Blocked dequantization still missing (i.e., do not support the new `block_size` attribute)** - 4-bit Transpose(21). - Update quantization tool with int4 types. - Disable QDQ fusions for 4-bit types. See: https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/core/optimizer/qdq_transformer/selectors_actions/qdq_selector_action_transformer.cc - MLAS 4-bit quantization kernels for intel, neon, powerpc. ##### Notes To calculate a tensor's storage size, we normally get the number of elements from the shape (i.e., `tensor_shape.Size()`) and multiply by the size of a single element. This does not directly work for sub-byte elements like int4 as each element in a `Tensor<Int4x2>` stores **two** packed int4 elements in a byte. The `Tensor:: CalculateTensorStorageSize` should be called to perform the correct calculation for any tensor element type. ### Motivation and Context ONNX 1.16 added the int4 and uint4 types. This initial PR adds the int4 type to ORT and adds int4 implementations for the Quant, Dequant, and Transpose ops on CPU EP. We still need to add int4 support for many ops and execution providers. See the ONNX 1.16 release notes: https://github.com/onnx/onnx/releases. |
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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.