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')