f4edf9bb58
### Description
- Extends the QDQPropagationTransformer to propagate DQs (forward)
across operators with multiple consumers (previously only supported 1
consumer).
- Adds Slice to the list of operators that the QDQPropagationTransformer
can propagate DQ/Q ops across.
- Supports QDQ propagation for opset 21.
- Correctly copies Q or DQ attributes when creating new nodes.
### Motivation and Context
The QDQPropagationTransformer fixes up QDQ node units for certain "data
movement" ops (e.g., Transpose) by inserting Q -> DQ sequences where
necessary. For example, the sequence `DQ -> Transpose -> Sigmoid` is
transformed to `DQ -> Transpose -> Q -> DQ -> Sigmoid`.
However, this fix-up does not currently support data movement ops with
multiple consumers, as in:
```
DQ -> Transpose --+--> Sigmoid ->
|
+--> Relu ->
|
+-> graph_output
```
With the updates in this PR, the above model can be transformed to:
```
DQ -> Transpose -> Q --+--> DQ -> Sigmoid ->
|
+--> DQ -> Relu ->
|
+--> DQ -> graph_output
```
This update allows QNN EP to support quantized models created with tools
that do not wrap data movement ops in Q/DQ ops.
---------
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
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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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
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|---|---|---|
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| Other |
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| System | Inference | Training |
|---|---|---|
| Linux |  |
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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.