diff --git a/python/tvm/topi/cuda/pooling.py b/python/tvm/topi/cuda/pooling.py
index f2a6aadb659f..7ddbea27c174 100644
--- a/python/tvm/topi/cuda/pooling.py
+++ b/python/tvm/topi/cuda/pooling.py
@@ -20,6 +20,8 @@
 from tvm import te
 from .. import tag
 from ..utils import traverse_inline
+from .reduction import _schedule_reduce
+from .injective import schedule_injective_from_existing
 
 
 def schedule_adaptive_pool(outs, layout="NCHW"):
@@ -39,12 +41,7 @@ def schedule_adaptive_pool(outs, layout="NCHW"):
     outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
     s = te.create_schedule([x.op for x in outs])
 
-    def _schedule(Pool):
-        num_thread = 8
-        block_x = te.thread_axis("blockIdx.x")
-        block_y = te.thread_axis("blockIdx.y")
-        thread_x = te.thread_axis((0, num_thread), "threadIdx.x")
-        thread_y = te.thread_axis((0, num_thread), "threadIdx.y")
+    def _schedule_non_global(Pool):
         if Pool.op in s.outputs:
             Out = Pool
             OL = s.cache_write(Pool, "local")
@@ -52,16 +49,12 @@ def _schedule(Pool):
             Out = outs[0].op.output(0)
             s[Pool].set_scope("local")
 
-        by, ty = s[Out].split(s[Out].op.axis[0], factor=num_thread)
-        if layout == "NHWC":
-            bx, tx = s[Out].split(s[Out].op.axis[3], factor=num_thread)
-        else:
-            bx, tx = s[Out].split(s[Out].op.axis[1], factor=num_thread)
-        s[Out].reorder(by, bx, ty, tx)
-        s[Out].bind(ty, thread_y)
-        s[Out].bind(tx, thread_x)
-        s[Out].bind(by, block_y)
-        s[Out].bind(bx, block_x)
+        max_threads = int(tvm.target.Target.current(allow_none=False).max_num_threads)
+        fused_axis = s[Out].fuse(*s[Out].op.axis)
+        bx, tx = s[Out].split(fused_axis, factor=max_threads)
+        s[Out].bind(bx, te.thread_axis("blockIdx.x"))
+        s[Out].bind(tx, te.thread_axis("threadIdx.x"))
+
         if Pool.op in s.outputs:
             s[OL].compute_at(s[Out], tx)
         else:
@@ -81,7 +74,16 @@ def traverse(OP):
         # schedule global_pool
         elif OP.tag.startswith("adaptive_pool"):
             Pool = OP.output(0)
-            _schedule(Pool)
+            oshape = Pool.shape
+            if (layout == "NCHW" and oshape[2] == 1 and oshape[3] == 1) or (
+                layout == "NHWC" and oshape[1] == 1 and oshape[2] == 1
+            ):
+                _schedule_reduce(OP, s)
+                if OP != outs[0].op:
+                    # the final division for adaptive pool or fused elemwise ops
+                    schedule_injective_from_existing(s, outs[0])
+            else:
+                _schedule_non_global(Pool)
         else:
             raise RuntimeError("Unsupported operator: %s" % OP.tag)