remove broadcast in sequence_expand

paddle/fluid/operators/sequence_expand_op.h

@@ -53,25 +53,27 @@ struct SequenceExpandFunctor<platform::CPUDeviceContext, T> {
       const framework::Vector<size_t>& ref_lod, /*expand referenced lod*/
       LoDTensor* out) {
     int out_offset = 0;
-    auto& eigen_place = *context.eigen_device();
+    int x_item_length = x.numel() / x.dims()[0];
+    auto out_data = out->data<T>();
+    auto x_data = x.data<T>();
     for (size_t i = 1; i < ref_lod.size(); ++i) {
       int repeat_num = ref_lod[i] - ref_lod[i - 1];
       int x_start = x_lod[i - 1];
       int x_end = x_lod[i];
       int x_seq_len = x_end - x_start;
       if (repeat_num > 0) {
-        auto x_sub_tensor = x.Slice(x_start, x_end);
-        x_sub_tensor.Resize({1, x_sub_tensor.numel()});
         int out_start = out_offset;
         if (out->lod().size() == 1) {
           out_start = out->lod()[0][out_offset];
         }
-        auto out_sub_tensor =
-            out->Slice(out_start, out_start + x_seq_len * repeat_num);
-        out_sub_tensor.Resize({repeat_num, x_sub_tensor.dims()[1]});
-        EigenMatrix<T>::From(out_sub_tensor).device(eigen_place) =
-            EigenMatrix<T>::From(x_sub_tensor)
-                .broadcast(Eigen::array<int, 2>({{repeat_num, 1}}));
+        for (int j = 0; j < repeat_num; j++) {
+          for (int k = 0; k < x_seq_len; k++) {
+            for (int l = 0; l < x_item_length; l++) {
+              out_data[(out_start + j * x_seq_len + k) * x_item_length + l] =
+                  x_data[(x_start + k) * x_item_length + l];
+            }
+          }
+        }
       }
       out_offset += repeat_num;
     }