diff --git a/paddle/fluid/operators/dropout_op.cu b/paddle/fluid/operators/dropout_op.cu
index 49ad67bbca353acc4a79c9e8912d7ae5a70c0021..cf90b9eb52b19dd4dc3ca4c04a029fc1fca252ef 100644
--- a/paddle/fluid/operators/dropout_op.cu
+++ b/paddle/fluid/operators/dropout_op.cu
@@ -17,6 +17,7 @@ limitations under the License. */
 #include <thrust/iterator/counting_iterator.h>
 #include <thrust/random.h>
 #include <thrust/transform.h>
+#include <algorithm>
 #include <string>
 #include "paddle/fluid/memory/memcpy.h"
 #include "paddle/fluid/operators/dropout_op.h"
@@ -27,24 +28,18 @@ namespace paddle {
 namespace operators {
 
 template <typename T, typename MaskType>
-__global__ void RandomGenerator(const size_t n, const int seed,
+__global__ void RandomGenerator(const size_t n, uint64_t seed,
                                 const float dropout_prob, const T* src,
                                 MaskType* mask_data, T* dst,
-                                bool is_upscale_in_train) {
+                                bool is_upscale_in_train, uint64_t increment) {
   curandStatePhilox4_32_10_t state;
   int idx = blockDim.x * blockIdx.x + threadIdx.x;
-  int step_size = 0;
+  curand_init(seed, idx, increment, &state);
 
   MaskType mask;
   T dest;
   for (; idx < n; idx += blockDim.x * gridDim.x) {
     T s = src[idx];
-    if (step_size == 0) {
-      curand_init(seed, idx, idx, &state);
-      step_size = blockDim.x * gridDim.x;
-    } else {
-      curand_init(seed, idx, step_size, &state);
-    }
     if (curand_uniform(&state) < dropout_prob) {
       mask = 0;
       dest = 0;
@@ -61,74 +56,49 @@ __global__ void RandomGenerator(const size_t n, const int seed,
   }
 }
 
-template <typename T, typename MaskType>
-__global__ void RandomGeneratorWithSeed(const size_t n, const int* seed,
-                                        const float dropout_prob, const T* src,
-                                        MaskType* mask_data, T* dst,
-                                        bool is_upscale_in_train) {
+template <typename T, typename MaskType, int VecSize>
+__global__ void VectorizedRandomGenerator(const size_t n, uint64_t seed,
+                                          const float dropout_prob,
+                                          const T* src, MaskType* mask_data,
+                                          T* dst, bool is_upscale_in_train,
+                                          uint64_t increment) {
+  int64_t idx = blockDim.x * blockIdx.x + threadIdx.x;
   curandStatePhilox4_32_10_t state;
-  int idx = blockDim.x * blockIdx.x + threadIdx.x;
-  int step_size = 0;
+  curand_init(seed, idx, increment, &state);
 
   MaskType mask;
   T dest;
-  for (; idx < n; idx += blockDim.x * gridDim.x) {
-    T s = src[idx];
-    if (step_size == 0) {
-      curand_init(seed[0], idx, idx, &state);
-      step_size = blockDim.x * gridDim.x;
-    } else {
-      curand_init(seed[0], idx, step_size, &state);
-    }
-    if (curand_uniform(&state) < dropout_prob) {
-      mask = 0;
-      dest = 0;
-    } else {
-      mask = 1;
-      if (is_upscale_in_train) {
-        dest = s / static_cast<T>(1.0f - dropout_prob);
+  using LoadT = AlignedVector<T, VecSize>;
+  using MaskLoadT = AlignedVector<MaskType, VecSize>;
+  T factor = static_cast<T>(1.0f / (1.0f - dropout_prob));
+  for (int i = idx * VecSize; i < n; i += blockDim.x * gridDim.x * VecSize) {
+    T src_vec[VecSize];
+    LoadT* value = reinterpret_cast<LoadT*>(&src_vec);
+    *value = *reinterpret_cast<const LoadT*>(&src[i]);
+    float4 rand = curand_uniform4(&state);
+
+    T dest_vec[VecSize];
+    MaskType mask_vec[VecSize];
+
+#pragma unroll
+    for (int ii = 0; ii < VecSize; ii++) {
+      if ((&rand.x)[ii] < dropout_prob) {
+        dest_vec[ii] = 0;
+        mask_vec[ii] = 0;
       } else {
-        dest = s;
+        if (is_upscale_in_train) {
+          dest_vec[ii] = src_vec[ii] * factor;
+        } else {
+          dest_vec[ii] = src_vec[ii];
+        }
+        mask_vec[ii] = 1;
       }
     }
-    mask_data[idx] = mask;
-    dst[idx] = dest;
-  }
-}
 
-template <typename T, typename MaskType>
-__global__ void RandomGeneratorWithGenerator(const size_t n, uint64_t seed,
-                                             const float dropout_prob,
-                                             const T* src, MaskType* mask_data,
-                                             T* dst, bool is_upscale_in_train,
-                                             uint64_t increment) {
-  curandStatePhilox4_32_10_t state;
-  int idx = blockDim.x * blockIdx.x + threadIdx.x;
-  int step_size = 0;
-
-  MaskType mask;
-  T dest;
-  for (; idx < n; idx += blockDim.x * gridDim.x) {
-    T s = src[idx];
-    if (step_size == 0) {
-      curand_init(seed, idx, increment, &state);
-      step_size = blockDim.x * gridDim.x;
-    } else {
-      curand_init(seed, idx, increment, &state);
-    }
-    if (curand_uniform(&state) < dropout_prob) {
-      mask = 0;
-      dest = 0;
-    } else {
-      mask = 1;
-      if (is_upscale_in_train) {
-        dest = s / static_cast<T>(1.0f - dropout_prob);
-      } else {
-        dest = s;
-      }
-    }
-    mask_data[idx] = mask;
-    dst[idx] = dest;
+    *(reinterpret_cast<LoadT*>(&dst[i])) =
+        *reinterpret_cast<LoadT*>(&dest_vec[0]);
+    *(reinterpret_cast<MaskLoadT*>(&mask_data[i])) =
+        *reinterpret_cast<MaskLoadT*>(&mask_vec[0]);
   }
 }
 
@@ -168,38 +138,61 @@ class GPUDropoutKernel : public framework::OpKernel<T> {
         return;
       }
 
-      int threads = 512;
-      int grid = (x_numel + threads - 1) / threads;
+      const auto& dev_ctx = context.cuda_device_context();
+      platform::GpuLaunchConfig config =
+          platform::GetGpuLaunchConfig1D(dev_ctx, size);
+
+      // increment is used to set the args(offset) of curand_init, which defines
+      // offset in subsequence.
+      // The detail:
+      // https://docs.nvidia.com/cuda/curand/device-api-overview.html
+      // Increment should be at least the number of curand() random numbers used
+      // in each thread to avoid the random number generated this time being the
+      // same as the previous calls.
+      uint64_t seed_data;
+      uint64_t increment;
+      int vec_size = VectorizedSize<T>(x_data);
+      auto offset = ((x_numel - 1) / (config.block_per_grid.x *
+                                      config.thread_per_block.x * vec_size) +
+                     1) *
+                    vec_size;
+      int device_id = BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace())
+                          .GetDeviceId();
+      auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
+
       if (seed && platform::is_gpu_place(seed->place())) {
-        auto seed_gpu_data = seed->data<int>();
-        RandomGeneratorWithSeed<T, uint8_t><<<grid, threads, 0, stream>>>(
-            size, seed_gpu_data, dropout_prob, x_data, mask_data, y_data,
-            upscale_in_train);
-        return;
-      }
-      int seed_data;
-      std::random_device rnd;
-      if (seed) {
-        seed_data = *(seed->data<int>());
+        framework::Tensor seed_cpu_tensor;
+        TensorCopySync(*seed, platform::CPUPlace(), &seed_cpu_tensor);
+        seed_data = static_cast<uint64_t>(seed_cpu_tensor.data<int>()[0]);
+        increment = offset;
+      } else if (gen_cuda->GetIsInitPy() && (!context.Attr<bool>("fix_seed"))) {
+        auto seed_offset = gen_cuda->IncrementOffset(offset);
+        seed_data = seed_offset.first;
+        increment = seed_offset.second;
       } else {
-        seed_data =
-            context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
+        if (seed) {
+          seed_data = *(seed->data<int>());
+        } else {
+          std::random_device rnd;
+          seed_data = context.Attr<bool>("fix_seed") ? context.Attr<int>("seed")
+                                                     : rnd();
+        }
+        increment = offset;
       }
 
-      int device_id = BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace())
-                          .GetDeviceId();
-      auto gen_cuda = framework::GetDefaultCUDAGenerator(device_id);
-      if (gen_cuda->GetIsInitPy() && (!context.Attr<bool>("fix_seed"))) {
-        auto seed_offset = gen_cuda->IncrementOffset(1);
-        RandomGeneratorWithGenerator<T, uint8_t><<<grid, threads, 0, stream>>>(
-            size, seed_offset.first, dropout_prob, x_data, mask_data, y_data,
-            upscale_in_train, seed_offset.second);
-        return;
+      if (vec_size == 4 && size % 4 == 0) {
+        VectorizedRandomGenerator<
+            T, uint8_t,
+            4><<<config.block_per_grid, config.thread_per_block, 0, stream>>>(
+            size, seed_data, dropout_prob, x_data, mask_data, y_data,
+            upscale_in_train, increment);
+      } else {
+        RandomGenerator<T, uint8_t><<<config.block_per_grid,
+                                      config.thread_per_block, 0, stream>>>(
+            size, seed_data, dropout_prob, x_data, mask_data, y_data,
+            upscale_in_train, increment);
       }
 
-      RandomGenerator<T, uint8_t><<<grid, threads, 0, stream>>>(
-          size, seed_data, dropout_prob, x_data, mask_data, y_data,
-          upscale_in_train);
     } else {
       auto X = EigenMatrix<T>::Reshape(*x, 1);
       auto Y = EigenMatrix<T>::Reshape(*y, 1);
diff --git a/paddle/fluid/operators/dropout_op.h b/paddle/fluid/operators/dropout_op.h
index 161c4282ec277a19c19921267eaa4cb46b859900..d77193e48513438515b3fc0696441aaeeaadb07f 100644
--- a/paddle/fluid/operators/dropout_op.h
+++ b/paddle/fluid/operators/dropout_op.h
@@ -17,13 +17,62 @@ limitations under the License. */
 #include <random>
 #include <string>
 
+#include <algorithm>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/generator.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/gpu_launch_config.h"
 
 namespace paddle {
 namespace operators {
 
+// aligned vector generates vectorized load/store on CUDA
+template <typename T, int Size>
+struct alignas(sizeof(T) * Size) AlignedVector {
+  T val[Size];
+};
+
+template <typename T>
+inline int VectorizedSize(const T* pointer) {
+  uint64_t address = reinterpret_cast<uint64_t>(pointer);
+  constexpr int vec4 = std::alignment_of<AlignedVector<T, 4>>::value;  // NOLINT
+  if (address % vec4 == 0) {
+    return 4;
+  }
+  return 1;
+}
+
+#ifdef __NVCC__
+template <typename T, typename MaskType, int VecSize>
+__global__ void DropoutGradCUDAKernel(const T* dout, const MaskType* mask,
+                                      const T factor, const int64_t size,
+                                      T* dx) {
+  int64_t idx = blockDim.x * blockIdx.x + threadIdx.x;
+
+  using LoadT = AlignedVector<T, VecSize>;
+  using MaskLoadT = AlignedVector<MaskType, VecSize>;
+
+  for (int i = idx * VecSize; i < size; i += blockDim.x * gridDim.x * VecSize) {
+    T dout_vec[VecSize];
+    LoadT* dout_value = reinterpret_cast<LoadT*>(&dout_vec);
+    *dout_value = *reinterpret_cast<const LoadT*>(&dout[i]);
+
+    MaskType mask_vec[VecSize];
+    MaskLoadT* mask_value = reinterpret_cast<MaskLoadT*>(&mask_vec);
+    *mask_value = *reinterpret_cast<const MaskLoadT*>(&mask[i]);
+
+    T dx_vec[VecSize];
+
+#pragma unroll
+    for (int ii = 0; ii < VecSize; ii++) {
+      dx_vec[ii] = dout_vec[ii] * static_cast<T>(mask_vec[ii]) * factor;
+    }
+
+    *(reinterpret_cast<LoadT*>(&dx[i])) = *reinterpret_cast<LoadT*>(&dx_vec[0]);
+  }
+}
+#endif
+
 using Tensor = framework::Tensor;
 template <typename T, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
@@ -119,6 +168,7 @@ class DropoutGradKernel : public framework::OpKernel<T> {
     auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
     auto* mask = context.Input<Tensor>("Mask");
     grad_x->mutable_data<T>(context.GetPlace());
+    auto size = grad_x->numel();
 
     auto M = EigenVector<uint8_t>::Flatten(*mask);
     auto dX = EigenVector<T>::Flatten(*grad_x);
@@ -126,7 +176,6 @@ class DropoutGradKernel : public framework::OpKernel<T> {
 
     auto& place =
         *context.template device_context<DeviceContext>().eigen_device();
-
     auto& dropout_implementation =
         context.Attr<std::string>("dropout_implementation");
     if (dropout_implementation == "upscale_in_train") {
@@ -134,8 +183,24 @@ class DropoutGradKernel : public framework::OpKernel<T> {
       if (dropout_prob == 1.0f) {
         dX.device(place) = static_cast<T>(0) * dY;
       } else {
-        dX.device(place) =
-            dY * M.cast<T>() / static_cast<T>(1.0f - dropout_prob);
+        int vec_size = VectorizedSize<T>(grad_y->data<T>());
+        if (platform::is_gpu_place(context.GetPlace()) && vec_size == 4 &&
+            size % 4 == 0) {
+#ifdef __NVCC__
+          auto factor = static_cast<T>(1.0f / (1.0f - dropout_prob));
+          auto stream = context.cuda_device_context().stream();
+          platform::GpuLaunchConfig config = platform::GetGpuLaunchConfig1D(
+              context.cuda_device_context(), size);
+          DropoutGradCUDAKernel<
+              T, uint8_t,
+              4><<<config.block_per_grid, config.thread_per_block, 0, stream>>>(
+              grad_y->data<T>(), mask->data<uint8_t>(), factor, size,
+              grad_x->data<T>());
+#endif
+        } else {
+          dX.device(place) =
+              dY * M.cast<T>() / static_cast<T>(1.0f - dropout_prob);
+        }
       }
     } else {
       dX.device(place) = dY * M.cast<T>();