     PADDLE_ENFORCE_EQ(lod[0].back(), (size_t)in->numel(),
                       "The actual size mismatches with the LoD information.");
     auto tokens = ctx.Attr<std::vector<T>>("tokens");
-    auto tokens_len = tokens.size();
     auto in_len = in->numel();
     auto in_dat = in->data<T>();
-    auto lod0 = lod[0];
+    // Copy tokens to GPU
+    thrust::device_vector<T> dev_tokens =
+        set_device_vector<T, std::vector<T>>(tokens);
+    T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
 
-    thrust::host_vector<T> host_tokens(tokens_len);
-    for (size_t i = 0; i < tokens.size(); ++i) {
-      host_tokens[i] = tokens[i];
-    }
-    thrust::device_vector<T> dev_tokens = host_tokens;
+    // Count number of elements to be erased
     thrust::device_vector<int> num_erased(in_len + 1);
-
-    T* dev_tokens_ptr = thrust::raw_pointer_cast(dev_tokens.data());
     int* num_erased_ptr = thrust::raw_pointer_cast(num_erased.data());
-
     auto stream = ctx.cuda_device_context().stream();
     LabelErasedIdx<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                      PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
-        in_dat, in_len, dev_tokens_ptr, tokens_len, num_erased_ptr);
+        in_dat, in_len, dev_tokens_ptr, tokens.size(), num_erased_ptr);
     thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
                            num_erased.begin() + 1);
 
-    // Calc LoD
+    // Copy LoD to GPU
+    auto lod0 = lod[0];
     auto lod_len = lod0.size();
-    thrust::host_vector<int> host_lod(lod_len);
-    for (size_t i = 0; i < lod_len; ++i) {
-      host_lod[i] = lod0[i];
-    }
-    thrust::device_vector<int> dev_in_lod = host_lod;
-    thrust::device_vector<int> dev_out_lod(lod_len);
-    int* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data());
-    int* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
+    thrust::device_vector<size_t> dev_in_lod =
+        set_device_vector<size_t, paddle::framework::Vector<size_t>>(lod0);
+    size_t* dev_in_lod_ptr = thrust::raw_pointer_cast(dev_in_lod.data());
+
+    // Calc output LoD
+    thrust::device_vector<size_t> dev_out_lod(lod_len);
+    size_t* dev_out_lod_ptr = thrust::raw_pointer_cast(dev_out_lod.data());
     GetOutLod<<<(lod_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                 PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
         num_erased_ptr, dev_in_lod_ptr, lod_len, dev_out_lod_ptr);
-    thrust::host_vector<int> host_out_lod = dev_out_lod;
-    std::vector<int> out_lod0(lod_len, 0);
-    for (size_t i = 0; i < lod_len; i++) {
-      out_lod0[i] = host_out_lod[i];
-    }
+
+    // Set LoD for output
+    std::vector<size_t> out_lod0 = get_std_vector<size_t>(dev_out_lod);
     framework::LoD out_lod;
     out_lod.push_back(out_lod0);
     out->set_lod(out_lod);
 
     // Set output
-    out->Resize({out_lod0.back(), 1});
+    out->Resize({static_cast<int64_t>(out_lod0.back()), 1});
     auto out_dat = out->mutable_data<T>(ctx.GetPlace());
     SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                 PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len,

}

template <typename T>
__global__ void GetOutLod(const T* num_erased, const int* in_lod,
                          const int lod_len, int* out_lod0) {
  int index = blockIdx.x * blockDim.x + threadIdx.x;
  if (index < lod_len) {
    out_lod0[index] = in_lod[index] - num_erased[in_lod[index]];

  }
}

template <typename T, typename Vector>
thrust::device_vector<T> set_device_vector(Vector& vector) {
  thrust::host_vector<T> host_vec(vector.size());
  for (size_t i = 0; i < vector.size(); ++i) {
    host_vec[i] = vector[i];
  }
  thrust::device_vector<T> dev_vec = host_vec;
  return dev_vec;
}

template <typename T>
std::vector<T> get_std_vector(thrust::device_vector<T>& dev_vec) {
  thrust::host_vector<T> host_vec = dev_vec;
  std::vector<T> std_vec(host_vec.size(), 0);
  for (size_t i = 0; i < host_vec.size(); ++i) {
    std_vec[i] = host_vec[i];
  }
  return std_vec;
}

template <typename T>
class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
 public:
