QLinearMatMul#

QLinearMatMul - 10 #

Version

name: QLinearMatMul (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. It consumes two quantized input tensors, their scales and zero points, scale and zero point of output, and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point). For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details. Scale and zero point must have same shape. They must be either scalar (per tensor) or N-D tensor (per row for ‘a’ and per column for ‘b’). Scalar refers to per tensor quantization whereas N-D refers to per row or per column quantization. If the input is 2D of shape [M, K] then zero point and scale tensor may be an M element vector [v_1, v_2, …, v_M] for per row quantization and K element vector of shape [v_1, v_2, …, v_K] for per column quantization. If the input is N-D tensor with shape [D1, D2, M, K] then zero point and scale tensor may have shape [D1, D2, M, 1] for per row quantization and shape [D1, D2, 1, K] for per column quantization. Production must never overflow, and accumulation may overflow if and only if in 32 bits.

Inputs

a (heterogeneous) - T1: N-dimensional quantized matrix a
a_scale (heterogeneous) - tensor(float): scale of quantized input a
a_zero_point (heterogeneous) - T1: zero point of quantized input a
b (heterogeneous) - T2: N-dimensional quantized matrix b
b_scale (heterogeneous) - tensor(float): scale of quantized input b
b_zero_point (heterogeneous) - T2: zero point of quantized input b
y_scale (heterogeneous) - tensor(float): scale of quantized output y
y_zero_point (heterogeneous) - T3: zero point of quantized output y

Outputs

y (heterogeneous) - T3: Quantized matrix multiply results from a * b

Type Constraints

T1 in ( tensor(int8), tensor(uint8) ): Constrain input a and its zero point data type to 8-bit integer tensor.
T2 in ( tensor(int8), tensor(uint8) ): Constrain input b and its zero point data type to 8-bit integer tensor.
T3 in ( tensor(int8), tensor(uint8) ): Constrain output y and its zero point data type to 8-bit integer tensor.

Examples

default

node = onnx.helper.make_node(
    "QLinearMatMul",
    inputs=[
        "a",
        "a_scale",
        "a_zero_point",
        "b",
        "b_scale",
        "b_zero_point",
        "y_scale",
        "y_zero_point",
    ],
    outputs=["y"],
)

# 2D
a = np.array(
    [
        [208, 236, 0, 238],
        [3, 214, 255, 29],
    ],
    dtype=np.uint8,
)

a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)

b = np.array(
    [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
    dtype=np.uint8,
)

b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)

y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)

output = np.array(
    [
        [168, 115, 255],
        [1, 66, 151],
    ],
    dtype=np.uint8,
)

expect(
    node,
    inputs=[
        a,
        a_scale,
        a_zero_point,
        b,
        b_scale,
        b_zero_point,
        y_scale,
        y_zero_point,
    ],
    outputs=[output],
    name="test_qlinearmatmul_2D",
)

# 3D
a = np.array(
    [
        [[208, 236, 0, 238], [3, 214, 255, 29]],
        [[208, 236, 0, 238], [3, 214, 255, 29]],
    ],
    dtype=np.uint8,
)

a_scale = np.array([0.0066], dtype=np.float32)
a_zero_point = np.array([113], dtype=np.uint8)

b = np.array(
    [
        [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
        [[152, 51, 244], [60, 26, 255], [0, 127, 246], [127, 254, 247]],
    ],
    dtype=np.uint8,
)

b_scale = np.array([0.00705], dtype=np.float32)
b_zero_point = np.array([114], dtype=np.uint8)

y_scale = np.array([0.0107], dtype=np.float32)
y_zero_point = np.array([118], dtype=np.uint8)

output = np.array(
    [[[168, 115, 255], [1, 66, 151]], [[168, 115, 255], [1, 66, 151]]],
    dtype=np.uint8,
)

expect(
    node,
    inputs=[
        a,
        a_scale,
        a_zero_point,
        b,
        b_scale,
        b_zero_point,
        y_scale,
        y_zero_point,
    ],
    outputs=[output],
    name="test_qlinearmatmul_3D",
)

QLinearMatMul#

QLinearMatMul - 10#

QLinearMatMul - 10 #