onnxruntime

81796a30 - [QNN EP Quantization] Add fusion preprocessing to QNN quantization (#18719)

[QNN EP Quantization] Add fusion preprocessing to QNN quantization (#18719) ### Description - Adds graph fusions to preprocessing step that can be called before creating a QDQ model for QNN EP. - Fuse Erf sequence to Gelu (adapted from [optimizer.py](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/fusion_gelu.py)). Required by QNN EP. - Fuse ReduceMean sequence to LayerNormaliation (adapted from [optimizer.py](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/fusion_layernorm.py)). Not required by QNN EP. - Fuse ReduceL2 sequence to LpNormalization (new, specific to QNN EP). Required by QNN EP. Example use: ```python3 from quantization.execution_providers.qnn import get_qnn_qdq_config, qnn_preprocess_model # Added by this PR: model_updated = qnn_preprocess_model("model.fp32.onnx", "model.fp32.preprocessed.onnx", fuse_layernorm=True) model_to_quantize = "model.fp32.preprocessed.onnx" if model_updated else "model.fp32.onnx" # Quantize model ... qnn_config = get_qnn_qdq_config(model_to_quantize, data_reader, activation_type=QuantType.QUInt16) quantize(model_to_quantize, "model.qdq.onnx", qnn_config) ``` ### Motivation and Context Allow more models to be quantized for use with QNN EP --------- Signed-off-by: adrianlizarraga <adlizarraga@microsoft.com>