See https://github.com/onnx/onnx/blob/main/docs/Operators.md#QLinearConv.

• y = saturate ((x / y_scale) + y_zero)
• Bias must be quantized using scale = x_scale * w_scale and zero = 0

Computation

• x = (qx - qx_zero) * qx_scale
• bias = qbias * x_scale * w_scale
• y = x*w + bias =>
• (qy-qy_zero)*qy_scale = (qx-qx_zero)*qx_scale * (qw-qw_zero)*qw_scale + qbias*qx_scale*qw_scale = [(qx-qx_zero) * (qw-qw_zero) + qbias] * qx_scale*qw_scale
• qy = qy_zero + [(qx-qx_zero)*(qw-qw_zero) + qbias] * qx_scale*qw_scale/qy_scale

Implementation

• only precompute -qx_zero*(qw_qw_zero), rounding is done before adding qy_zero
• int32 bias: -qx_zero*(qw_zero): k*int8*int8 > 16bits
• int8 shifted qy_zero: shift added into acc
• int16 scale: saturated to 8 bits
• each m kernel of shape (1,C_PER_M,K,K) applied on input of shape (1,C_PER_M,H,W) for kernels that allow GROUP != 1