torch.float_power — PyTorch 2.5 documentation (original) (raw)

torch.float_power(input, exponent, *, out=None) → Tensor¶

Raises input to the power of exponent, elementwise, in double precision. If neither input is complex returns a torch.float64 tensor, and if one or more inputs is complex returns a torch.complex128 tensor.

Note

This function always computes in double precision, unlike torch.pow(), which implements more typical type promotion. This is useful when the computation needs to be performed in a wider or more precise dtype, or the results of the computation may contain fractional values not representable in the input dtypes, like when an integer base is raised to a negative integer exponent.

Parameters

• input (Tensor or Number) – the base value(s)
• exponent (Tensor or Number) – the exponent value(s)

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

a = torch.randint(10, (4,)) a tensor([6, 4, 7, 1]) torch.float_power(a, 2) tensor([36., 16., 49., 1.], dtype=torch.float64)

a = torch.arange(1, 5) a tensor([ 1, 2, 3, 4]) exp = torch.tensor([2, -3, 4, -5]) exp tensor([ 2, -3, 4, -5]) torch.float_power(a, exp) tensor([1.0000e+00, 1.2500e-01, 8.1000e+01, 9.7656e-04], dtype=torch.float64)