Source code for nvtripy.frontend.ops.masked_fill

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# SPDX-License-Identifier: Apache-2.0
#
# 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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import numbers

from nvtripy import export
from nvtripy.frontend import wrappers

from nvtripy.common import datatype as dt
from nvtripy.frontend.constraints import GetInput, GetReturn, OneOf




@export.public_api(document_under="operations/functions")
@wrappers.interface(
    input_requirements=OneOf(
        GetInput("input").dtype,
        [dt.float32, dt.float16, dt.bfloat16, dt.int4, dt.int8, dt.int32, dt.int64],
    )
    & (GetInput("mask").dtype == dt.bool),
    output_guarantees=GetReturn(0).dtype == GetInput("input").dtype,
)
def masked_fill(input: "nvtripy.Tensor", mask: "nvtripy.Tensor", value: numbers.Number) -> "nvtripy.Tensor":
    r"""
    Returns a new tensor filled with ``value`` where ``mask`` is ``True`` and elements from
    the input tensor otherwise.

    Args:
        input: The input tensor.
        mask: The mask tensor.
        value: the value to fill with. This will be casted to match the data type of the input tensor.

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

    .. code-block:: python
        :linenos:

        mask = tp.Tensor([[True, False], [True, True]])
        input = tp.zeros([2, 2])
        output = tp.masked_fill(input, mask, -1.0)

        assert np.array_equal(cp.from_dlpack(output).get(), np.array([[-1, 0], [-1, -1]], dtype=np.float32))
    """
    from nvtripy.frontend.ops.full import full_like
    from nvtripy.frontend.ops.where import where

    fill_tensor = full_like(input, value)
    return where(mask, fill_tensor, input)