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

#
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# 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 nvtripy import export
from nvtripy.frontend.ops import utils as op_utils
from nvtripy.trace.ops.unary import Relu
from nvtripy.frontend import wrappers
from nvtripy.common import datatype
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.int32, dt.int64, dt.int8]
    ),
    output_guarantees=GetReturn(0).dtype == GetInput("input").dtype,
)
def relu(input: "nvtripy.Tensor") -> "nvtripy.Tensor":
    r"""
    Applies Rectified Linear Unit (RELU) function
    to each element of the input tensor:

    :math:`\text{relu}(x) = \max(0,x)`

    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.])
        output = tp.relu(input)

        t = torch.tensor([1, 2, 3, 4], dtype=torch.float32) # doc: omit
        assert tp.allclose(output, tp.Tensor(torch.nn.functional.relu(t)))

    """
    from nvtripy.frontend.ops.binary.maximum import maximum

    if issubclass(input.dtype, datatype.integer):
        # Activation in TensorRT does not support integral types.
        return maximum(input, 0)

    return op_utils.create_op(Relu, [input])