megengine.functional.mod¶

Element-wise \(\operatorname{mod}(x, y)\) function.

Returns the remainder of division for each element \(x_i\) of the input tensor \(x\) and the respective element \(y_i\) of the input tensor \(y\).

Note

In general, similar to Python’s % operator, this function is not recommended for floating-point operands as semantics do not follow IEEE 754. That this function is specified to accept floating-point operands is primarily for reasons of backward compatibility.
mod is an alias of remainder in NumPy.

See also

div / floor_div

Parameters

x (Tensor) – dividend input tensor. Should have a numeric data type.
y (Tensor) – divisor input tensor. Must be compatible with \(x\) (see Broadcasting mechanism and rules ). Should have a numeric data type.

Return type

Tensor

Returns

A tensor containing the element-wise results. The returned tensor must have a data type determined by Type promotion rules.

Special cases

For floating-point operands,

If either \(x_i\) or \(y_i\) is NaN, the result is NaN.
If \(x_i\) is either +infinity or -infinity and \(y_i\) is either +infinity or -infinity, the result is NaN.
If \(x_i\) is either +0 or -0 and \(y_i\) is either +0 or -0, the result is NaN.
If \(x_i\) is +0 and \(y_i\) is greater than 0, the result is +0.
If \(x_i\) is -0 and \(y_i\) is greater than 0, the result is +0.
If \(x_i\) is +0 and \(y_i\) is less than 0, the result is -0.
If \(x_i\) is -0 and \(y_i\) is less than 0, the result is -0.
If \(x_i\) is greater than 0 and \(y_i\) is +0, the result is NaN.
If \(x_i\) is greater than 0 and \(y_i\) is -0, the result is NaN.
If \(x_i\) is less than 0 and \(y_i\) is +0, the result is NaN.
If \(x_i\) is less than 0 and \(y_i\) is -0, the result is NaN.
If \(x_i\) is +infinity and \(y_i\) is a positive (i.e., greater than 0) finite number, the result is NaN.
If \(x_i\) is +infinity and \(y_i\) is a negative (i.e., less than 0) finite number, the result is NaN.
If \(x_i\) is -infinity and \(y_i\) is a positive (i.e., greater than 0) finite number, the result is NaN.
If \(x_i\) is -infinity and \(y_i\) is a negative (i.e., less than 0) finite number, the result is NaN.
If \(x_i\) is a positive (i.e., greater than 0) finite number and \(y_i\) is +infinity, the result is \(x_i\). (note: this result matches Python behavior.)
If \(x_i\) is a positive (i.e., greater than 0) finite number and \(y_i\) is -infinity, the result is \(y_i\). (note: this result matches Python behavior.)
If \(x_i\) is a negative (i.e., less than 0) finite number and \(y_i\) is +infinity, the result is \(y_i\). (note: this results matches Python behavior.)
If \(x_i\) is a negative (i.e., less than 0) finite number and \(y_i\) is -infinity, the result is \(x_i\). (note: this result matches Python behavior.)
In the remaining cases, the result must match that of the Python % operator.

Examples

>>> F.mod(8, 3)
Tensor(2, dtype=int32, device=xpux:0)

Element-wise mod:

>>> x = Tensor([1, 2, 3, 4, 5])
>>> y = Tensor([1, 2, 1, 2, 1])
>>> F.mod(x, y)
Tensor([0 0 0 0 0], dtype=int32, device=xpux:0)

Broadcasting:

>>> x = Tensor([1, 2, 3, 4, 5])
>>> F.mod(x, 3)
Tensor([1 2 0 1 2], dtype=int32, device=xpux:0)

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