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### Description - Adds a utility to the QNN quantization scripts that "fixes" an initial set of tensor quantization overrides for mixed-precision QDQ models. Follow-up to https://github.com/microsoft/onnxruntime/pull/19925 - Moves existing overrides for QNN compatibility (matmul, layernorm, sigmoid, tanh) to separate functions. PR adds missing unit tests for these. - Adds `weight_symmetric=None` parameter to the `get_qnn_qdq_config()` function to enable user specification (instead of always using default behavior). - If weight_symmetric is set to `None`, it will be set to `weight_symmetric = weight_type in (QUInt8, QUInt16)`. - Otherwise, the user's value is used. #### Example Float model: ``` input_0 --> Op1 --> Op3 --> Op5 --> Op6 --> output_0 ^ | input_1 --> Op2 -+-> Op4 ----+ | +-> Op7 --> output_1 | +-> Op8 --> output_2 ``` If we'd like to quantize this model to uint8 precision, but would like to make sure tensor "Op4_out" is quantized to 16-bit, then we would specify the following initial tensor quantization overrides: ```python # Op4_out could be an inaccurate tensor that should be upgraded to 16bit initial_overrides = {"Op4_out": [{"quant_type": QuantType.QUInt16}]} ``` These initial overrides may not create a valid model because Op4 and Op5 may require both the input and output to be the same type (e.g., uint16). This helper fixes the overrides so that input/output data types are valid: ```python qnn_config = get_qnn_qdq_config( float_model_path, data_reader, activation_type=QuantType.QUInt8, weight_type=QuantType.QUInt8, init_overrides=initial_overrides, # These initial overrides will be "fixed" ) ``` The above snippet generates the following "fixed" overrides (get via `qnn_config.extra_options["TensorQuantOverrides"]`): ```python { "Op2_out": [{"quant_type": QUInt8, "convert": {"quant_type": QUInt16, "recv_nodes": {"Op4"}}}], "Op3_out": [{"quant_type": QUInt8, "convert": {"quant_type": QUInt16, "recv_nodes": {"Op5"}}}], "Op4_out": [{"quant_type": QUInt16}], "Op5_out": [{"quant_type": QUInt16, "convert": {"quant_type": QUInt8, "recv_nodes": {"Op6"}}}] } ``` How to interpret the fixed overrides: - Op2's output is consumed by Op4, Op7, and Op8. Op4 consumes the converted u16 type, but Op7 and Op8 consume the original u8 type. - Op3's output is converted from u8 to u16. Op5 consumes the converted u16 type. - Op4's output is just u16 (not converted). All consumers of Op4_out get the u16 type. - Op5's output is converted from u16 to u8. Op6 consumes the u8 type. ### Motivation and Context Generating mixed-precision quantization overrides is currently a manual process. This PR adds an utility that helps generate valid overrides. |
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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
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For feature requests or bug reports, please file a GitHub Issue.
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License
This project is licensed under the MIT License.