chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
3151 changed files with 974126 additions and 0 deletions
@@ -0,0 +1,133 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name
+"""searchsorted operator"""
+
+from tvm.script.ir_builder import IRBuilder
+from tvm.script.ir_builder import tirx as T
+
+from . import te, utils
+from .math import cast
+
+
+def binary_search(sequence_offset, search_range, sorted_sequence, value, right, out_dtype):
+    """Common IR generator for binary search used by CPU and GPU backends.
+
+    Must be called within an active IRBuilder context.
+
+    `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_buf = T.decl_buffer([1], out_dtype, scope="local")
+    hi_buf = T.decl_buffer([1], out_dtype, scope="local")
+    lo = T.buffer_proxy(lo_buf)
+    hi = T.buffer_proxy(hi_buf)
+
+    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 T.While(lo[0] < hi[0]):
+        mid = lo[0] + (hi[0] - lo[0] >> 1)
+        with T.If(condition(sorted_sequence[sequence_offset + mid], value)):
+            with T.Then():
+                lo[0] = mid + 1
+            with T.Else():
+                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):
+        with IRBuilder() as ib:
+            sorted_sequence_shape = sorted_sequence.shape
+            values_shape = values.shape
+            num_search = utils.prod(values_shape)
+            search_range = sorted_sequence_shape[-1]
+
+            sorted_sequence = T.buffer_proxy(sorted_sequence)
+            values = T.buffer_proxy(values)
+            indices = T.buffer_proxy(indices)
+
+            with T.parallel(0, num_search) 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(
+                    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,
+    )