f644ff9fc0
### Description
- Adds general support for per-channel quantized weights to QNN EP (HTP
backend).
- Add QNN EP unit tests for per-channel Conv
- Update quantization tool to allow selecting which ops are quantized
per-channel (and which axis) via tensor-level overrides. Currently,
setting `per_channel=True` assumes all Convs, MatMuls, Gemms,
InstanceNormalization, and LayerNormalization ops should be quantized
per-channel using some assumed default axis.
#### Creating QDQ per-channel Conv model example
```python
from onnxruntime.quantization import CalibrationDataReader, QuantType, quantize
from onnxruntime.quantization.execution_providers.qnn import get_qnn_qdq_config, qnn_preprocess_model
class DataReader(CalibrationDataReader):
# TODO: See ONNX Runtime QNN docs for example of a data reader
# https://onnxruntime.ai/docs/execution-providers/QNN-ExecutionProvider.html#generating-a-quantized-model-x64
pass
if __name__ == "__main__":
input_model_path = "model.onnx"
my_data_reader = DataReader(model_to_quantize)
# Pre-process the original float32 model.
preproc_model_path = "model.preproc.onnx"
model_changed = qnn_preprocess_model(input_model_path, preproc_model_path)
model_to_quantize = preproc_model_path if model_changed else input_model_path
# RELEVANT TO THIS PR:
# Make sure Conv's weight input is quantized to int8/symmetric/per-channel with axis == 0.
# The presence of the 'axis' key indicates that this is a per-channel quantized weight.
init_overrides = {'weight': [{'axis': 0, 'quant_type': QuantType.QInt8, 'symmetric': True}]}
qnn_config = get_qnn_qdq_config(model_to_quantize,
my_data_reader,
init_overrides=init_overrides,
activation_type=QuantType.QUInt16, # uint16 activations
weight_type=QuantType.QUInt8) # uint8 weights by default
quantize(model_to_quantize, "model.qdq.onnx", qnn_config)
```
float32 model:
<img width="683" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/19691973/ca650e49-1ad0-47d8-8c46-17fbc224ca39">
QDQ model (per-channel Conv weight):
<img width="748" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/19691973/6bd469f2-968b-4d11-9526-09b3e71f98e7">
### Motivation and Context
Support more models, especially models with int4 quantized weights.
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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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|---|---|---|
| Windows | ||
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| Linux |  |
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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.