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.