Source code for nvtripy.frontend.ops.reduce.topk

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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from typing import Tuple

from nvtripy import export
from nvtripy.common import datatype as dt
from nvtripy.frontend.constraints import GetInput, GetReturn, OneOf
from nvtripy.frontend.ops.reduce.utils import topk_impl
from nvtripy.trace.ops.topk import TopKMax
from nvtripy.frontend import wrappers




@export.public_api(document_under="operations/functions")
@wrappers.interface(
    input_requirements=OneOf(GetInput("input").dtype, [dt.float32, dt.float16, dt.bfloat16, dt.int32, dt.int64]),
    output_guarantees=(GetReturn(0).dtype == GetInput("input").dtype) & (GetReturn(1).dtype == dt.int32),
)
def topk(input: "nvtripy.Tensor", k: int, dim: int) -> Tuple["nvtripy.Tensor", "nvtripy.Tensor"]:
    """
    Returns the ``k`` largest values in the tensor and their
    indices along the specified dimension.

    Args:
        input: The input tensor.
        k: The number of values to take.
        dim: The dimension along which to find the top-k values.

    Returns:
        The top-k values and indices, in sorted order.

    .. code-block:: python
        :linenos:

        inp = tp.iota((1, 5), dim=1) + 2.5
        values, indices = tp.topk(inp, k=2, dim=1)

        assert tp.equal(values, tp.Tensor([[6.5, 5.5]]))
        assert tp.equal(indices, tp.Tensor([[4, 3]]))
    """
    return topk_impl(TopKMax, input, k=k, dim=dim)