torch.fmin — PyTorch 2.7 documentation (original) (raw)

torch.fmin(input, other, *, out=None) → Tensor¶

Computes the element-wise minimum of input and other.

This is like torch.minimum() except it handles NaNs differently: if exactly one of the two elements being compared is a NaN then the non-NaN element is taken as the minimum. Only if both elements are NaN is NaN propagated.

This function is a wrapper around C++’s std::fmin and is similar to NumPy’s fmin function.

Supports broadcasting to a common shape,type promotion, and integer and floating-point inputs.

Parameters

• input (Tensor) – the input tensor.
• other (Tensor) – the second input tensor

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

a = torch.tensor([2.2, float('nan'), 2.1, float('nan')]) b = torch.tensor([-9.3, 0.1, float('nan'), float('nan')]) torch.fmin(a, b) tensor([-9.3000, 0.1000, 2.1000, nan])