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

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([12], dtype=np.uint8)

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

b_zero_point = np.array([0], 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",
)