.. _l-onnx-doccom.microsoft-QLinearAdd: ========================== com.microsoft - QLinearAdd ========================== .. contents:: :local: .. _l-onnx-opcom-microsoft-qlinearadd-1: QLinearAdd - 1 (com.microsoft) ============================== **Version** * **name**: `QLinearAdd (GitHub) `_ * **domain**: **com.microsoft** * **since_version**: **1** * **function**: * **support_level**: * **shape inference**: This version of the operator has been available **since version 1 of domain com.microsoft**. **Summary** Performs element-wise binary addition on 8 bit data types (with Numpy-style broadcasting support). C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_point **Inputs** Between 7 and 8 inputs. * **A** (heterogeneous) - **T**: First operand. * **A_scale** (heterogeneous) - **tensor(float)**: Input A's scale. It's a scalar, which means a per-tensor/layer quantization. * **A_zero_point** (optional, heterogeneous) - **T**: Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. * **B** (heterogeneous) - **T**: Second operand. * **B_scale** (heterogeneous) - **tensor(float)**: Input B's scale. It's a scalar, which means a per-tensor/layer quantization. * **B_zero_point** (optional, heterogeneous) - **T**: Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. * **C_scale** (heterogeneous) - **tensor(float)**: Output scale. It's a scalar, which means a per-tensor/layer quantization. * **C_zero_point** (optional, heterogeneous) - **T**: Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. **Outputs** * **C** (heterogeneous) - **T**: Result, has same element type as two inputs **Examples**