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
import numpy as np
import onnx
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",
)