diff --git a/paddle/phi/kernels/fusion/gpu/fused_dropout_add_grad_kernel.cu b/paddle/phi/kernels/fusion/gpu/fused_dropout_add_grad_kernel.cu
index e6fb4b97d66e153ea38d50e0f12dab95ecddda75..513e3d035c130e75551c06dd8bf866b830e355b5 100644
--- a/paddle/phi/kernels/fusion/gpu/fused_dropout_add_grad_kernel.cu
+++ b/paddle/phi/kernels/fusion/gpu/fused_dropout_add_grad_kernel.cu
@@ -91,9 +91,9 @@ struct NoMaskBwFunctor {
 template <typename T, typename Functor>
 __global__ void VectorizedDropoutBackward(const size_t n,
                                           uint64_t seed,
-                                          T* src,
-                                          T* res,
-                                          const T* dst,
+                                          T* x,
+                                          T* y,
+                                          const T* out_grad,
                                           uint64_t increment,
                                           size_t main_offset,
                                           Functor functor) {
@@ -112,44 +112,38 @@ __global__ void VectorizedDropoutBackward(const size_t n,
 #endif
 
   float rands[kCount];
-  T src_res[kCount * 2];
-  T res_grad[kCount];
+  T x_y[kCount * 2];
 
   using Rand = phi::funcs::uniform_distribution<float>;
   using Cast = kps::IdentityFunctor<T>;
 
   int deal_size = BLOCK_NUM_X * kCount;
   size_t fix = idx * kCount;
+
   for (; fix < main_offset; fix += stride) {
-    kps::ReadData<T, kCount, 1, false>(&src_res[0], dst, deal_size);
+    kps::ReadData<T, kCount, 1, false>(&x_y[0], out_grad + fix, deal_size);
     kps::ElementwiseRandom<SType, float, kCount, Rand>(
         &rands[0], Rand(), &state);
-    // x_grad
     kps::OperatorTernary<T, float, T, Functor>(
-        &src_res[0], &src_res[0], &rands[0], functor, kCount);
-    kps::WriteData<T, kCount, 1, false>(src + fix, &src_res[0], deal_size);
-    // res
-    kps::ElementwiseUnary<T, T, kCount, 1, Cast>(
-        &res_grad[0], &src_res[kCount], Cast());
-    kps::WriteData<T, kCount, 1, false>(res + fix, &res_grad[0], deal_size);
+        &x_y[0], &x_y[0], &rands[0], functor, kCount);
+
+    kps::WriteData<T, kCount, 1, false>(x + fix, &x_y[0], deal_size);
+    kps::WriteData<T, kCount, 1, false>(y + fix, &x_y[kCount], deal_size);
     if (fix > idx * kCount + 1) {
       __syncthreads();
     }
   }
+
   int remainder = n - fix;
   if (remainder > 0) {
-    kps::ReadData<T, kCount, 1, true>(&src_res[0], dst + fix, remainder);
+    kps::ReadData<T, kCount, 1, true>(&x_y[0], out_grad + fix, remainder);
     kps::ElementwiseRandom<SType, float, kCount, Rand>(
         &rands[0], Rand(), &state);
-    // x_grad
     kps::OperatorTernary<T, float, T, Functor>(
-        &src_res[0], &src_res[0], &rands[0], functor, kCount);
-    kps::WriteData<T, kCount, 1, true>(src + fix, &src_res[0], remainder);
+        &x_y[0], &x_y[0], &rands[0], functor, kCount);
 
-    // res
-    kps::ElementwiseUnary<T, T, kCount, 1, Cast>(
-        &res_grad[0], &src_res[kCount], Cast());
-    kps::WriteData<T, kCount, 1, true>(res + fix, &res_grad[0], remainder);
+    kps::WriteData<T, kCount, 1, true>(x + fix, &x_y[0], remainder);
+    kps::WriteData<T, kCount, 1, true>(y + fix, &x_y[kCount], remainder);
     __syncthreads();
   }
 }
@@ -201,7 +195,6 @@ void FusedDropoutAddGradKernel(const Context& dev_ctx,
     size_t block_size = random_prop[1];
     size_t offset = random_prop[2];
     size_t main_offset = random_prop[3];
-
     auto functor = upscale_in_train
                        ? NoMaskBwFunctor<T, float>(1.0f - dropout_rate)
                        : NoMaskBwFunctor<T, float>(1.0f - dropout_rate, 1.0f);
diff --git a/python/paddle/fluid/tests/unittests/test_fused_dropout_add_op.py b/python/paddle/fluid/tests/unittests/test_fused_dropout_add_op.py
index 562765c1c2652287ebba30da327b1eefa13a85dc..792f0b2e877d088057701259aa8e63c5d7518ca9 100644
--- a/python/paddle/fluid/tests/unittests/test_fused_dropout_add_op.py
+++ b/python/paddle/fluid/tests/unittests/test_fused_dropout_add_op.py
@@ -34,9 +34,9 @@ def paddle_dropout_add(x, y, p=0.5, training=True, mode="upscale_in_train"):
 )
 class TestFusedDropoutAdd(unittest.TestCase):
     def setUp(self):
-        self.shape = (2, 10, 10, 2)
-        self.dtype = 'float64'
-        self.dropout_rate = 0.9
+        self.shape = [2, 1024, 2, 1]
+        self.dtype = 'float16'
+        self.dropout_rate = 0.5
         self.training = True
         self.mode = "upscale_in_train"
         self.seed = 1027
@@ -66,9 +66,8 @@ class TestFusedDropoutAdd(unittest.TestCase):
                 mode=self.mode,
             )
             fw.append(out)
-
-        loss = paddle.mean(out)
-        loss.backward()
+        out_g = paddle.randn(self.shape, self.dtype)
+        paddle.autograd.backward([out], [out_g], True)
         for i in range(count):
             bw.append(data[i].grad)
         return fw, bw
@@ -95,7 +94,7 @@ def create_test_class(parent, dtype, mode, training, p, seed):
     )
     class TestFusedDropoutAddCase(parent):
         def setUp(self):
-            self.shape = (2, 10, 10, 2)
+            self.shape = (2, 1024, 1, 1)
             self.dtype = dtype
             self.dropout_rate = p
             self.training = training
@@ -168,7 +167,7 @@ class TestFusedDropoutAddStatic(unittest.TestCase):
         y = paddle.randn(self.shape, self.dtype)
         fused_d_a = FusedDropoutAdd(p=0.5)
         d = paddle.nn.Dropout(p=0.5)
-        print(d)
+        print(d.extra_repr())
         paddle.seed(2048)
         fused_out = fused_d_a(x, y)
         paddle.seed(2048)