Source code for tripy.frontend.ops.softmax

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# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
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# http://www.apache.org/licenses/LICENSE-2.0
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from tripy import export, constraints




@export.public_api(document_under="operations/functions")
@constraints.dtypes(
    constraints={"input": "T1", constraints.RETURN_VALUE: "T1"},
    variables={
        "T1": ["float32", "float16", "bfloat16"],
    },
)
def softmax(input: "tripy.Tensor", dim: int = None) -> "tripy.Tensor":
    r"""
    Applies the softmax function to the input tensor:

    :math:`\text{softmax}(x_{i}) = \Large \frac{e^{x_{i}}}{\sum_{j=1}^N e^{x_{j}}} \normalsize for\ i=1,2,\dots,N`

    where :math:`x_{i}` is the :math:`i^{th}` element along dimension ``dim``
    and :math:`N` is the size of the dimension.

    Effectively, for each slice along ``dim``, elements are scaled such that they
    lie in the range :math:`[0, 1]` and sum to 1.

    Args:
        input: The input tensor.
        dim: The dimension along which softmax will be computed.
            If this is ``None``, softmax is applied over the whole input array.

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

    .. code-block:: python
        :linenos:
        :caption: Example

        input = tp.iota([2, 2], dtype=tp.float32)
        output = tp.softmax(input, dim=0)

        assert tp.allclose(output, tp.Tensor(torch.Tensor([[0., 0.], [1., 1.]]).softmax(0)))
    """
    from tripy.frontend.trace.ops.reduce import max, sum
    from tripy.frontend.trace.ops.unary_elementwise import exp

    exp_inp = exp(input - max(input, dim, keepdim=True))
    return exp_inp / sum(exp_inp, dim, keepdim=True)