Source code for nvtripy.frontend.ops.unary.sigmoid

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from nvtripy import export
from nvtripy.utils import wrappers
from nvtripy.trace.ops.unary import Sigmoid
from nvtripy.frontend.ops import utils as op_utils




@export.public_api(document_under="operations/functions")
@wrappers.interface(
    dtype_constraints={"input": "T1", wrappers.RETURN_VALUE: "T1"},
    dtype_variables={
        "T1": ["float32", "float16", "bfloat16", "int8"],
    },
)
def sigmoid(input: "nvtripy.Tensor") -> "nvtripy.Tensor":
    r"""
    Applies a logistic sigmoid function to each element of the input tensor:

    :math:`\text{sigmoid}(x)_i = \frac{1}{1 + \exp{-x_i}}`

    Args:
        input: The input tensor.

    Returns:
        A tensor of the same shape as the input.

    .. code-block:: python
        :linenos:

        input = tp.Tensor([1., 2., 3., 4.], dtype=tp.float32)
        output = tp.sigmoid(input)

        t = torch.tensor([1, 2, 3, 4], dtype=torch.float32) # doc: omit
        assert tp.allclose(output, tp.Tensor(torch.nn.functional.sigmoid(t)))
    """
    return op_utils.create_op(Sigmoid, [input])