 }
 
 template <typename T>
-__global__ void GetOutLod(const T* num_erased, const int* in_lod,
-                          const int lod_len, int* out_lod0) {
+__global__ void GetOutLod(const T* num_erased, const size_t* in_lod,
+                          const int lod_len, size_t* 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]];

    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];

    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;
    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);
    thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
                           num_erased.begin() + 1);

    // Calc LoD
    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());
    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];
    }
    framework::LoD out_lod;
    out_lod.push_back(out_lod0);
    out->set_lod(out_lod);

    // Set output
    out->Resize({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, 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:
