Mod

Mod - 13

Version

  • name: Mod (GitHub)

  • domain: main

  • since_version: 13

  • function: False

  • support_level: SupportType.COMMON

  • shape inference: True

This version of the operator has been available since version 13.

Summary

Performs an element-wise binary modulo operation. The semantics and supported data types depend on the value of the fmod attribute which must be 0 (default), or 1.

If the fmod attribute is set to 0, T is constrained to integer data types and the semantics follow that of the Python %-operator. The sign of the result is that of the divisor.

If fmod is set to 1, the behavior of this operator follows that of the fmod function in C and T is constrained to floating point data types. The result of this operator is the remainder of the division operation x / y where x and y are respective elements of A and B. The result is exactly the value x - n * y, where n is x / y with its fractional part truncated. The returned value has the same sign as x (except if x is -0) and is less or equal to |y| in magnitude. The following special cases apply when fmod is set to 1:

  • If x is -0 and y is greater than zero, either +0 or -0 may be returned.

  • If x is ±∞ and y is not NaN, NaN is returned.

  • If y is ±0 and x is not NaN, NaN should be returned.

  • If y is ±∞ and x is finite, x is returned.

  • If either argument is NaN, NaN is returned.

This operator supports multidirectional (i.e., NumPy-style) broadcasting; for more details please check Broadcasting in ONNX.

Attributes

  • fmod - INT (default is '0'):

    Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment

Inputs

  • A (heterogeneous) - T:

    Dividend tensor

  • B (heterogeneous) - T:

    Divisor tensor

Outputs

  • C (heterogeneous) - T:

    Remainder tensor

Type Constraints

  • T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to high-precision numeric tensors.

Mod - 10

Version

  • name: Mod (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

Performs element-wise binary modulus (with Numpy-style broadcasting support). The sign of the remainder is the same as that of the Divisor.

Mod operator can also behave like C fmod() or numpy.fmod. In this case, the sign of the remainder however, will be the same as the Dividend (in contrast to integer mod). To force a behavior like numpy.fmod() an ‘fmod’ Attribute is provided. This attribute is set to 0 by default causing the behavior to be like integer mod. Setting this attribute to 1 causes the remainder to be calculated similar to that of numpy.fmod().

If the input type is floating point, then fmod attribute must be set to 1.

In case of dividend being zero, the results will be platform dependent.

This operator supports multidirectional (i.e., Numpy-style) broadcasting; for more details please check Broadcasting in ONNX.

Attributes

  • fmod - INT (default is '0'):

    Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment

Inputs

  • A (heterogeneous) - T:

    Dividend tensor

  • B (heterogeneous) - T:

    Divisor tensor

Outputs

  • C (heterogeneous) - T:

    Remainder tensor

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):

    Constrain input and output types to high-precision numeric tensors.