diff --git a/src/tir/transforms/lower_thread_allreduce.cc b/src/tir/transforms/lower_thread_allreduce.cc
index c1566936c531..97a34a6ede1f 100644
--- a/src/tir/transforms/lower_thread_allreduce.cc
+++ b/src/tir/transforms/lower_thread_allreduce.cc
@@ -333,8 +333,8 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
       {
         PrimExpr mask = Call(mask_dtype, builtin::tvm_warp_activemask(), {});
         if (group_extent > 1) {
-          mask = mask &
-                 (((1 << reduce_extent) - 1) << (reduce_extent * cast(mask_dtype, group_index)));
+          mask = mask & (make_const(mask_dtype, (1ll << reduce_extent) - 1)
+                         << (reduce_extent * cast(mask_dtype, group_index)));
         }
         seq.emplace_back(BufferStore(mask_buffer, mask, zero_indices));
         // Push the buffer description.  Later this will have an
@@ -392,7 +392,7 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
         // During the sub-warp reduction, values from inactive threads could be read,
         // which is an undefined behavior according to the cuda document.
         //
-        // In practise, the return value are usually 0, which does no harm to sum reduction.
+        // In practice, the return value are usually 0, which does no harm to sum reduction.
         // However, the result can be incorrect in max or prod reduction.
         // Therefore an additional range check has to be performed to ensure the correctness.
         if (offset * 2 > reduce_extent) {
@@ -405,7 +405,7 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
 
       // Broadcast the reduction result from lane 0 to all other lanes.
       // This avoids to emit predicated stores, as all threads are
-      // uniformly writting the same result.
+      // uniformly writing the same result.
       //
       for (size_t i = 0; i < size; ++i) {
         Buffer buf = shared_bufs[i];
@@ -669,7 +669,7 @@ class ThreadAllreduceBuilder final : public StmtExprMutator {
       return false;
     }
 
-    // whether reduce_extent and group_extent are vaild for warp reduction.
+    // whether reduce_extent and group_extent are valid for warp reduction.
     if (target_->kind->name == "rocm") {
       return reduce_extent == warp_size_;
     } else {  // target_->kind->name == "cuda"
diff --git a/tests/python/unittest/test_tir_transform_lower_thread_all_reduce.py b/tests/python/unittest/test_tir_transform_lower_thread_all_reduce.py
index f20d11ffb401..c9e6136ca8d7 100644
--- a/tests/python/unittest/test_tir_transform_lower_thread_all_reduce.py
+++ b/tests/python/unittest/test_tir_transform_lower_thread_all_reduce.py
@@ -235,5 +235,70 @@ def expected(A: T.Buffer((128, 128), "float32"), B: T.Buffer(128, "float32")):
                 B[i] = reduce[0]
 
 
+class TestMultiGroupMask(BaseCompare):
+    @T.prim_func
+    def before(A: T.Buffer((32, 32), "float32"), B: T.Buffer((32,), "float32")):
+        T.func_attr({"target": T.target("cuda", host="llvm")})
+        threadIdx_y = T.launch_thread("threadIdx.y", 32)
+        cross_thread_B = T.allocate([1], "float32", "local")
+        threadIdx_x = T.launch_thread("threadIdx.x", 32)
+        cross_thread_B_1 = T.Buffer((1,), data=cross_thread_B, scope="local")
+        with T.attr(
+            T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]),
+            "reduce_scope",
+            T.reinterpret("handle", T.uint64(0)),
+        ):
+            A_1 = T.Buffer((1024,), data=A.data)
+            T.tvm_thread_allreduce(
+                T.uint32(1),
+                A_1[threadIdx_y * 32 + threadIdx_x],
+                T.bool(True),
+                cross_thread_B_1[0],
+                threadIdx_x,
+            )
+        if threadIdx_x == 0:
+            B_1 = T.Buffer((32,), data=B.data)
+            B_1[threadIdx_y] = cross_thread_B_1[0]
+
+    @T.prim_func
+    def expected(A: T.Buffer((32, 32), "float32"), B: T.Buffer((32,), "float32")):
+        T.func_attr({"target": T.target("cuda", host="llvm")})
+        threadIdx_y = T.launch_thread("threadIdx.y", 32)
+        red_buf0 = T.allocate([1], "float32", "local")
+        threadIdx_x = T.launch_thread("threadIdx.x", 32)
+        red_buf0_1 = T.Buffer((1,), data=red_buf0, scope="local")
+        with T.attr(
+            T.comm_reducer(lambda x0, y0: x0 + y0, [T.float32(0)]),
+            "reduce_scope",
+            T.reinterpret("handle", T.uint64(0)),
+        ):
+            mask = T.allocate([1], "uint32", "local")
+            t0 = T.allocate([1], "float32", "local")
+            A_1 = T.Buffer((1024,), data=A.data)
+            red_buf0_1[0] = A_1[threadIdx_y * 32 + threadIdx_x]
+
+            mask_1 = T.Buffer((1,), "uint32", data=mask, scope="local")
+            mask_1[0] = T.bitwise_and(
+                T.tvm_warp_activemask(),
+                T.shift_left(T.uint32(4294967295), T.uint32(32) * T.Cast("uint32", threadIdx_y)),
+            )
+
+            t0_1 = T.Buffer((1,), data=t0, scope="local")
+            t0_1[0] = T.tvm_warp_shuffle_down(mask_1[0], red_buf0_1[0], 16, 32, 32)
+            red_buf0_1[0] = red_buf0_1[0] + t0_1[0]
+            t0_1[0] = T.tvm_warp_shuffle_down(mask_1[0], red_buf0_1[0], 8, 32, 32)
+            red_buf0_1[0] = red_buf0_1[0] + t0_1[0]
+            t0_1[0] = T.tvm_warp_shuffle_down(mask_1[0], red_buf0_1[0], 4, 32, 32)
+            red_buf0_1[0] = red_buf0_1[0] + t0_1[0]
+            t0_1[0] = T.tvm_warp_shuffle_down(mask_1[0], red_buf0_1[0], 2, 32, 32)
+            red_buf0_1[0] = red_buf0_1[0] + t0_1[0]
+            t0_1[0] = T.tvm_warp_shuffle_down(mask_1[0], red_buf0_1[0], 1, 32, 32)
+            red_buf0_1[0] = red_buf0_1[0] + t0_1[0]
+            red_buf0_1[0] = T.tvm_warp_shuffle(mask_1[0], red_buf0_1[0], 32 * threadIdx_y, 32, 32)
+        if threadIdx_x == 0:
+            B_1 = T.Buffer((32,), data=B.data)
+            B_1[threadIdx_y] = red_buf0_1[0]
+
+
 if __name__ == "__main__":
     tvm.testing.main()