[Relay] Support dynamic shape searchsorted

python/tvm/relay/op/_algorithm.py

@@ -84,3 +84,28 @@ def topk_shape_func(attrs, inputs, _):
         ret = [indices_out]
 
     return ret
+
+
+@script
+def _searchsorted_shape(sorted_sequence_shape, values_shape):
+    out_shape = output_tensor((values_shape.shape[0],), "int64")
+    if sorted_sequence_shape.shape[0] > 1:
+        assert (
+            sorted_sequence_shape.shape[0] == values_shape.shape[0]
+        ), "Ranks of `sorted_sequence` and values must be the same if `sorted_sequence` is not 1-D."
+    for i in range(values_shape.shape[0]):
+        if sorted_sequence_shape.shape[0] > 1 and i < values_shape.shape[0] - 1:
+            assert (
+                sorted_sequence_shape[i] == values_shape[i]
+            ), "`sorted_sequence and `values` do not have the same shape along outer axes."
+
+        out_shape[i] = values_shape[i]
+    return out_shape
+
+
+@_reg.register_shape_func("searchsorted", False)
+def searchsorted_shape_func(attrs, inputs, _):
+    """
+    Shape func for searchsorted operator.
+    """
+    return [_searchsorted_shape(inputs[0], inputs[1])]

tests/python/relay/test_any.py

@@ -23,6 +23,7 @@
 from tvm import relay, te
 from tvm.relay.loops import while_loop
 from tvm.relay.testing import run_infer_type as infer_type
+from tvm.topi.testing import searchsorted_ref
 
 from utils import ref_funcs
 from utils.assert_diagnostic import DiagnosticTesting
@@ -2086,5 +2087,35 @@ def verify_gather(data_shape, indices_shape, data_shape_np, indices_shape_np, ax
     verify_gather((relay.Any(), relay.Any()), (relay.Any(), relay.Any()), (2, 3), (1, 3), 0)
 
 
+@tvm.testing.uses_gpu
+def test_searchsorted():
+    def verify_searchsorted(
+        sorted_sequence_shape, values_shape, sorted_sequence_shape_np, values_shape_np
+    ):
+        x = relay.var("x", relay.TensorType(sorted_sequence_shape, "float32"))
+        y = relay.var("y", relay.TensorType(values_shape, "float32"))
+        z = relay.searchsorted(x, y)
+
+        mod = tvm.IRModule()
+        mod["main"] = relay.Function([x, y], z)
+
+        x_np = np.sort(np.random.uniform(size=sorted_sequence_shape_np).astype("float32"), axis=-1)
+        y_np = np.random.uniform(size=values_shape_np).astype("float32")
+
+        ref_res = searchsorted_ref(x_np, y_np, False, "int32")
+        check_result([x_np, y_np], mod, [ref_res])
+
+    for shape_np, values_shape_np in zip([(8, 9, 10), (10,), (11,)], [(8, 9, 20), (5,), (8, 9, 7)]):
+        sorted_sequence_shape = (relay.Any(),) * len(shape_np)
+        values_shape = (relay.Any(),) * len(values_shape_np)
+
+        verify_searchsorted(
+            sorted_sequence_shape,
+            values_shape,
+            shape_np,
+            values_shape_np,
+        )
+
+
 if __name__ == "__main__":
     pytest.main([__file__])