diff --git a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
index 8569dd63478529fda885ce94fb28d626fe78eaa3..7c5af43816c4481400e08f1c2808513daa0b63ad 100644
--- a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
@@ -228,7 +228,7 @@ class ElementwiseTensorOpConverter : public OpConverter {
       }
     };
 
-    if (CheckDims(dims_x, dims_y)) {
+    if (dims_x.nbDims == dims_y.nbDims) {
       // The two input tensor should have the same dims
       VLOG(3) << "Convert a fluid elementwise op to TensorRT IElementWiseLayer";
       nvinfer1::IElementWiseLayer* elet_layer =
diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
index 992e0353837bc2f839353bd5b81595fc3530ba57..b54b923d3b0867cc41cd13db631d1c436e5265a1 100644
--- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
@@ -317,21 +317,28 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
         input1_shape_list = [[4, 32], [2, 4, 32], [4, 2, 4, 32]]
         input2_shape1_list = [[32], [4, 32], [2, 4, 32]]
         input2_shape2_list = [[4, 1], [2, 4, 1], [4, 2, 4, 1]]
-        input2_shape3_list = [[32], [2, 1, 1], [4, 2, 1, 1]]
-        input2_shape4_list = [[32], [4, 32], [4, 1, 1, 1]]
+        input2_shape3_list = [[32], [2, 1, 1], [4, 2, 1, 32]]
+        input2_shape4_list = [[32], [4, 32], [4, 1, 4, 32]]
+        input2_shape5_list = [[32], [2, 1, 32], [4, 1, 1, 32]]
+        input2_shape6_list = [[1, 32], [1, 32], [1, 1, 1, 32]]
         input2_shape_list = [
             input2_shape1_list, input2_shape2_list, input2_shape3_list,
-            input2_shape4_list
+            input2_shape4_list, input2_shape5_list, input2_shape6_list
         ]
         axis1_list = [[-1], [1, -1], [1, -1]]
         axis2_list = [[-1], [0], [0]]
         axis3_list = [[-1], [0], [0]]
         axis4_list = [[-1], [-1], [0]]
-        axis_list = [axis1_list, axis2_list, axis3_list, axis4_list]
+        axis5_list = [[-1, 1], [-1, 0], [-1, 0]]
+        axis6_list = [[-1, 0], [-1, 1], [-1, 0]]
+        axis_list = [
+            axis1_list, axis2_list, axis3_list, axis4_list, axis5_list,
+            axis6_list
+        ]
 
         for i in range(3):
             input1_shape = input1_shape_list[i]
-            for j in range(4):
+            for j in range(6):
                 input2_shape = input2_shape_list[j][i]
                 for op_type in ["elementwise_add", "elementwise_mul"]:
                     for axis in axis_list[j][i]: