python/tvm/topi/searchsorted.py

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# pylint: disable=invalid-name
"""searchsorted operator"""
from . import utils
from . import te
from ..tir import ir_builder
from .math import cast


def binary_search(ib, sequence_offset, search_range, sorted_sequence, value, right, out_dtype):
    """Common IR generator for binary search used by CPU and GPU backends.

    `sorted_sequence` is a N-D Buffer whose innermost dimension we want to search for `value`,
    and `search_range` is the size of the innermost dimension. `sequence_offset` is
    a 1-D linearlized offset specifying which of innermost sequences to search.

    So the search for `value` is performed over
    `sorted_sequence[sequence_offset:(sequence_offset + search_range)]`.
    Note that we index N-D Buffer by 1-D linearlized indices.

    """
    lo = ib.allocate(out_dtype, (1,), name="lo", scope="local")
    hi = ib.allocate(out_dtype, (1,), name="hi", scope="local")

    lo[0] = cast(0, out_dtype)
    hi[0] = cast(search_range, out_dtype)

    # Reference: pytorch/aten/src/ATen/native/cuda/Bucketization.cu
    def condition(current_val, target_val):
        if right:
            return current_val <= target_val
        return current_val < target_val

    with ib.while_loop(lo[0] < hi[0]):
        mid = lo[0] + (hi[0] - lo[0] >> 1)
        with ib.if_scope(condition(sorted_sequence[sequence_offset + mid], value)):
            lo[0] = mid + 1
        with ib.else_scope():
            hi[0] = mid

    return lo[0]


def searchsorted(sorted_sequence, values, right=False, out_dtype="int64"):
    """Find indices where elements should be inserted to maintain order.
       If `sorted_sequence` is N-dimensional, the innermost dimension of
       `values` are searched in the corresponding dimension of `sorted_sequence`.

    Parameters
    ----------
    sorted_sequence : te.Tensor
        N-D or 1-D Tensor, containing monotonically increasing sequence
        on the innermost dimension.

    values : te.Tensor
        N-D Tensor containing the search values. When `sorted_sequence` is 1-D,
        the shape of `values` can be arbitrary. Otherwise, ranks of `sorted_sequence`
        and `values` must be the same, and outer N-1 axes must have the same size.

    right : bool, optional
        Controls which index is returned if a value lands exactly on one of sorted values. If
        False, the index of the first suitable location found is given. If true, return the
        last such index. If there is no suitable index, return either 0 or N (where N is the
        size of the innermost dimension).

    dtype : string, optional
        The data type of the output indices.

    Returns
    -------
    indices : te.Tensor
        Tensor with same shape as values, representing the indices of
        elements of `values` if they are inserted in `sorted_sequence`.
    """

    def ir(sorted_sequence, values, indices):
        ib = ir_builder.create()
        sorted_sequence_shape = sorted_sequence.shape
        values_shape = values.shape
        num_search = utils.prod(values_shape)
        search_range = sorted_sequence_shape[-1]

        sorted_sequence = ib.buffer_ptr(sorted_sequence)
        values = ib.buffer_ptr(values)
        indices = ib.buffer_ptr(indices)

        with ib.for_range(0, num_search, name="i", kind="parallel") as i:
            if len(sorted_sequence_shape) == 1:
                sequence_offset = 0
            else:
                sequence_id = i // values_shape[-1]
                sequence_offset = sequence_id * search_range

            indices[i] = binary_search(
                ib,
                sequence_offset,
                search_range,
                sorted_sequence,
                values[i],
                right,
                out_dtype,
            )

        return ib.get()

    return te.extern(
        values.shape,
        [sorted_sequence, values],
        lambda ins, outs: ir(ins[0], ins[1], outs[0]),
        name="searchsorted",
        dtype=out_dtype,
    )