QLinearMatMul#
QLinearMatMul - 10#
Version
name: QLinearMatMul (GitHub)
domain: main
since_version: 10
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 10.
Summary
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for ‘a’ and per column for ‘b’). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, …, v_M] for per row quantization and K element vector of shape [v_1, v_2, …, v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits.
Inputs
a (heterogeneous) - T1: N-dimensional quantized matrix a
a_scale (heterogeneous) - tensor(float): scale of quantized input a
a_zero_point (heterogeneous) - T1: zero point of quantized input a
b (heterogeneous) - T2: N-dimensional quantized matrix b
b_scale (heterogeneous) - tensor(float): scale of quantized input b
b_zero_point (heterogeneous) - T2: zero point of quantized input b
y_scale (heterogeneous) - tensor(float): scale of quantized output y
y_zero_point (heterogeneous) - T3: zero point of quantized output y
Outputs
y (heterogeneous) - T3: Quantized matrix multiply results from a * b
Type Constraints
T1 in ( tensor(int8), tensor(uint8) ): Constrain input a and its zero point data type to 8-bit integer tensor.
T2 in ( tensor(int8), tensor(uint8) ): Constrain input b and its zero point data type to 8-bit integer tensor.
T3 in ( tensor(int8), tensor(uint8) ): Constrain output y and its zero point data type to 8-bit integer tensor.
Examples