MatMulInteger#
MatMulInteger - 10#
Version
name: MatMulInteger (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. The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
Inputs
Between 2 and 4 inputs.
A (heterogeneous) - T1: N-dimensional matrix A
B (heterogeneous) - T2: N-dimensional matrix B
a_zero_point (optional, heterogeneous) - T1: Zero point tensor for input ‘A’. It’s optional and default value is 0. It could be a scalar or N-D tensor. Scalar refers to per tensor quantization whereas N-D refers to per row quantization. If the input is 2D of shape [M, K] then zero point tensor may be an M element vector [zp_1, zp_2, …, zp_M]. If the input is N-D tensor with shape [D1, D2, M, K] then zero point tensor may have shape [D1, D2, M, 1].
b_zero_point (optional, heterogeneous) - T2: Zero point tensor for input ‘B’. It’s optional and default value is 0. It could be a scalar or a N-D tensor, Scalar refers to per tensor quantization whereas N-D refers to per col quantization. If the input is 2D of shape [K, N] then zero point tensor may be an N element vector [zp_1, zp_2, …, zp_N]. If the input is N-D tensor with shape [D1, D2, K, N] then zero point tensor may have shape [D1, D2, 1, N].
Outputs
Y (heterogeneous) - T3: Matrix multiply results from A * B
Type Constraints
T1 in ( tensor(int8), tensor(uint8) ): Constrain input A data type to 8-bit integer tensor.
T2 in ( tensor(int8), tensor(uint8) ): Constrain input B data type to 8-bit integer tensor.
T3 in ( tensor(int32) ): Constrain output Y data type as 32-bit integer tensor.
Examples