Source code for nvtripy.frontend.ops.pooling.maxpool

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# SPDX-License-Identifier: Apache-2.0
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from typing import Optional, Sequence, Tuple

from nvtripy import export
from nvtripy.frontend.ops import utils as op_utils
from nvtripy.frontend.ops.pooling import utils as pooling_utils
from nvtripy.trace.ops.pooling import MaxPooling
from nvtripy.utils import wrappers




@export.public_api(document_under="operations/functions")
@wrappers.interface(
    dtype_constraints={"input": "T1", wrappers.RETURN_VALUE: "T1"},
    dtype_variables={"T1": ["float32", "bfloat16", "float16", "int8"]},
)
def maxpool(
    input: "nvtripy.Tensor",
    kernel_dims: Sequence[int],
    stride: Optional[Sequence[int]] = None,
    padding: Optional[Sequence[Tuple[int, int]]] = None,
) -> "nvtripy.Tensor":
    r"""
    Applies a max pooling over the input tensor.

    The output's non-spatial dimensions are the same as input. For each input spatial dimension
    :math:`D_{i}`, the corresponding output dimension will be:

    .. math::
        D_{out_i} = \left\lfloor\frac{D_{i} + \text{padding_before[i]} + \text{padding_after[i]} -
                \text{kernel_dims[i]}}{\text{stride[i]}} + 1\right\rfloor

    Args:
        input: The input tensor.
        kernel_dims: The spatial shape of the pooling window. Only 2-D or 3-D ``kernel_dims`` are supported.
            If the input has :class:`int8` datatype, ``kernel_dims`` can only be 2-D.
        stride: A sequence of length :math:`M` indicating the stride of pooling across each spatial dimension,
            where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
            Defaults to all 1.
        padding: A sequence of pairs of integers of length :math:`M` indicating the zero padding
            to apply to the input along each spatial dimension before and after the dimension respectively,
            where :math:`M` is the number of spatial dimensions, i.e. :math:`M = \text{rank(input)} - 2`.
            Defaults to all 0.

    Returns:
        The result tensor after the pooling operation.

    .. code-block:: python
        :linenos:

        input = tp.reshape(tp.arange(16, dtype=tp.float32), (1, 1, 4, 4))
        output = tp.maxpool(input, kernel_dims=(2, 2))

        pool_torch = torch.nn.MaxPool2d((2, 2), stride=1) # doc: omit
        expected = pool_torch(torch.from_dlpack(input).to("cpu")) # doc: omit

        assert torch.allclose(torch.from_dlpack(output).to("cpu"), expected)
    """
    op_utils.check_conv_pooling_args(kernel_dims, stride, padding)
    stride, pre_padding, post_padding = pooling_utils.transform_pooling_params(kernel_dims, stride, padding)

    return op_utils.create_op(MaxPooling, [input], kernel_dims, stride, pre_padding, post_padding)