# MatMulInteger#

## MatMulInteger - 10#

Version

• 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

default

```node = onnx.helper.make_node('MatMulInteger',
inputs=['A', 'B', 'a_zero_point', 'b_zero_point'],
outputs=['Y'],)

A = np.array([[11, 7, 3],
[10, 6, 2],
[9, 5, 1],
[8, 4, 0], ], dtype=np.uint8)

a_zero_point = np.array(, dtype=np.uint8)

B = np.array([[1, 4],
[2, 5],
[3, 6], ], dtype=np.uint8)

b_zero_point = np.array(, dtype=np.uint8)

output = np.array([[-38, -83],
[-44, -98],
[-50, -113],
[-56, -128], ], dtype=np.int32)

expect(node, inputs=[A, B, a_zero_point, b_zero_point], outputs=[output],
name='test_matmulinteger')
```