diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.cc b/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.cc
index 931201319b5..6d35bf7274c 100644
--- a/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.cc
+++ b/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.cc
@@ -302,6 +302,21 @@ Status LerpInfo::InferTensorMap() {
   return SUCCESS;
 }
 
+Status LerpInfo::InferMirrorOps() {
+  if (OperatorInfo::InferMirrorOps() != SUCCESS) {
+    return FAILED;
+  }
+  // No need to insert mirror ops
+  if (mirror_ops_.empty()) {
+    return SUCCESS;
+  }
+  if (mirror_ops_.size() == kSizeTwo) {
+    // Push empty mirror op for value
+    (void)mirror_ops_.emplace_back(OperatorVector());
+  }
+  return SUCCESS;
+}
+
 std::vector<StrategyPtr> LerpInfo::GenerateOpStrategies(int64_t stage_id) {
   if (inputs_size_ == 2) {
     return ArithmeticBase::GenerateOpStrategies(stage_id);
diff --git a/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h b/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h
index 9aafef15ce6..0dae6a2a68c 100644
--- a/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h
+++ b/mindspore/ccsrc/frontend/parallel/ops_info/arithmetic_info.h
@@ -322,6 +322,7 @@ class LerpInfo : public ArithmeticBase {
   Status CheckStrategy(const StrategyPtr &strategy) override;
   Status InferDevMatrixShape() override;
   Status InferTensorMap() override;
+  Status InferMirrorOps() override;
 
  private:
   size_t inputs_size_ = 0;
diff --git a/mindspore/ccsrc/frontend/parallel/step_parallel.cc b/mindspore/ccsrc/frontend/parallel/step_parallel.cc
index b2feba4bb9a..1e3858fc2aa 100644
--- a/mindspore/ccsrc/frontend/parallel/step_parallel.cc
+++ b/mindspore/ccsrc/frontend/parallel/step_parallel.cc
@@ -973,8 +973,8 @@ static bool CheckInsertMirrorOps(const MirrorOps &mirror_ops, const CNodePtr &no
   }
 
   if (mirror_ops.size() != node_size - 1) {
-    MS_LOG(EXCEPTION) << "Mirrorops's size is wrong! mirror_ops size is " << mirror_ops.size() << ", node_size is "
-                      << (node_size - 1);
+    MS_LOG(EXCEPTION) << "For " << node->fullname_with_scope() << ", the Mirrorops's size is wrong! mirror_ops size is "
+                      << mirror_ops.size() << ", node_size is " << (node_size - 1);
   }
   return true;
 }
diff --git a/tests/ut/python/parallel/test_lerp.py b/tests/ut/python/parallel/test_lerp.py
index bd7ae785d17..5685085c7cd 100644
--- a/tests/ut/python/parallel/test_lerp.py
+++ b/tests/ut/python/parallel/test_lerp.py
@@ -3,14 +3,16 @@ import pytest
 
 from mindspore import Tensor, context
 from mindspore.nn import Cell
+from mindspore.ops import composite as C
 import mindspore.ops as ops
 
-from parallel.utils.utils import compile_net
+from parallel.utils.utils import compile_net, ParallelValidator
 
 
 def setup_function():
     context.set_auto_parallel_context(dataset_strategy="full_batch")
 
+
 input_start_ = Tensor(np.random.normal(size=[8, 8, 8]).astype(np.float32))
 input_end_ = Tensor(np.random.normal(size=[8]).astype(np.float32))
 input_weight_tensor_ = Tensor(np.random.normal(size=[8, 8]).astype(np.float32))
@@ -27,6 +29,27 @@ class Net(Cell):
         return output
 
 
+class NetWithWeightFloat(Cell):
+    def __init__(self, weight, strategy=None):
+        super(NetWithWeightFloat, self).__init__()
+        self.weight = weight
+        self.lerp = ops.Lerp().shard(strategy)
+
+    def construct(self, *inputs):
+        output = self.lerp(*inputs, self.weight)
+        return output
+
+
+class GradWrap(Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+        self.grad_op = C.GradOperation()
+
+    def construct(self, *inputs):
+        return self.grad_op(self.network)(*inputs)
+
+
 def test_lerp_auto_parallel_with_weight_tensor():
     """
     Feature: test Lerp auto parallel
@@ -45,8 +68,8 @@ def test_lerp_auto_parallel_with_weight_float():
     Expectation: compile success
     """
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
-    net = Net()
-    compile_net(net, input_start_, input_end_, input_weight_float_)
+    net = NetWithWeightFloat(input_weight_float_)
+    compile_net(net, input_start_, input_end_)
 
 
 def test_lerp_model_parallel_with_weight_tensor():
@@ -69,8 +92,8 @@ def test_lerp_model_parallel_with_weight_float():
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy = ((2, 2, 2), (2,))
-    net = Net(strategy)
-    compile_net(net, input_start_, input_end_, input_weight_float_)
+    net = NetWithWeightFloat(input_weight_float_, strategy)
+    compile_net(net, input_start_, input_end_)
 
 
 def test_lerp_model_parallel_repeated_cal_with_weight_tensor():
@@ -81,8 +104,10 @@ def test_lerp_model_parallel_repeated_cal_with_weight_tensor():
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy = ((1, 2, 2), (2,), (2, 2))
-    net = Net(strategy)
-    compile_net(net, input_start_, input_end_, input_weight_tensor_)
+    net = GradWrap(Net(strategy))
+    phase = compile_net(net, input_start_, input_end_, input_weight_tensor_)
+    validator = ParallelValidator(net, phase)
+    assert validator.check_node_inputs("Lerp-0", ["_VirtualDiv-0", "_VirtualDiv-1", "_VirtualDiv-2"])
 
 
 def test_lerp_model_parallel_repeated_cal_with_weight_float():
@@ -93,8 +118,10 @@ def test_lerp_model_parallel_repeated_cal_with_weight_float():
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy = ((1, 2, 2), (2,))
-    net = Net(strategy)
-    compile_net(net, input_start_, input_end_, input_weight_float_)
+    net = GradWrap(NetWithWeightFloat(input_weight_float_, strategy))
+    phase = compile_net(net, input_start_, input_end_)
+    validator = ParallelValidator(net, phase)
+    assert validator.check_node_inputs("Lerp-0", ["_VirtualDiv-0", "_VirtualDiv-1", input_weight_float_])
 
 
 def test_lerp_data_parallel_with_weight_tensor():
@@ -104,8 +131,7 @@ def test_lerp_data_parallel_with_weight_tensor():
     Expectation: compile success
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
-    strategy = ((8, 1, 1), (1,), (1, 1))
-    net = Net(strategy)
+    net = Net()
     compile_net(net, input_start_, input_end_, input_weight_tensor_)
 
 
@@ -116,9 +142,8 @@ def test_lerp_data_parallel_with_weight_float():
     Expectation: compile success
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
-    strategy = ((8, 1, 1), (1,))
-    net = Net(strategy)
-    compile_net(net, input_start_, input_end_, input_weight_float_)
+    net = NetWithWeightFloat(input_weight_float_)
+    compile_net(net, input_start_, input_end_)
 
 
 def test_lerp_strategy_error_with_weight_tensor():
@@ -142,6 +167,6 @@ def test_lerp_strategy_error_with_weight_float():
     """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy = ((4, 1, 2), (1,))
-    net = Net(strategy)
+    net = NetWithWeightFloat(input_weight_float_, strategy)
     with pytest.raises(RuntimeError):
-        compile_net(net, input_start_, input_end_, input_weight_float_)
+        compile_net(net, input_start_, input_end_)