diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc
index c0ad959309a7036639c4bc15621a2bd0296526f5..5f5a1f70f2aa5fa382f6fdd0657eb30a2d004823 100644
--- a/paddle/fluid/operators/batch_norm_op.cc
+++ b/paddle/fluid/operators/batch_norm_op.cc
@@ -65,11 +65,21 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
       (data_layout == DataLayout::kNCHW ? x_dims[1]
                                         : x_dims[x_dims.size() - 1]);
 
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], C);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], C);
+  auto scale_dim = ctx->GetInputDim("Scale");
+  auto bias_dim = ctx->GetInputDim("Bias");
 
+  bool check = true;
+  if ((!ctx->IsRuntime()) && (framework::product(scale_dim) <= 0 ||
+                              framework::product(bias_dim) <= 0)) {
+    check = false;
+  }
+
+  if (check) {
+    PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
+    PADDLE_ENFORCE_EQ(scale_dim[0], C);
+    PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
+    PADDLE_ENFORCE_EQ(scale_dim[0], C);
+  }
   ctx->SetOutputDim("Y", x_dims);
   ctx->SetOutputDim("MeanOut", {C});
   ctx->SetOutputDim("VarianceOut", {C});
diff --git a/paddle/fluid/operators/cos_sim_op.cc b/paddle/fluid/operators/cos_sim_op.cc
index 8f3644039f9950a8a70e2fd66c20837a5f52bd7f..920d087e429f5714ee52ebb943e568e34b5f5241 100644
--- a/paddle/fluid/operators/cos_sim_op.cc
+++ b/paddle/fluid/operators/cos_sim_op.cc
@@ -40,17 +40,27 @@ class CosSimOp : public framework::OperatorWithKernel {
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
 
-    PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
-                      "Ranks of Input(X) and Input(Y) must be equal.");
-    PADDLE_ENFORCE_GE(x_dims.size(), 2,
-                      "Rank of Input(X) must not be less than 2.");
-    PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 1, x_dims.size()),
-                      framework::slice_ddim(y_dims, 1, y_dims.size()),
-                      "All dimensions except the 1st of Input(X) and Input(Y) "
-                      "must be equal.");
-    PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
-                   "The 1st dimension of Input(Y) must be equal to Input(X) or"
-                   " just 1 (which will be broadcasted to match Input(X)).");
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
+      check = false;
+    }
+
+    if (check) {
+      PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
+                        "Ranks of Input(X) and Input(Y) must be equal.");
+      PADDLE_ENFORCE_GE(x_dims.size(), 2,
+                        "Rank of Input(X) must not be less than 2.");
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 1, x_dims.size()),
+          framework::slice_ddim(y_dims, 1, y_dims.size()),
+          "All dimensions except the 1st of Input(X) and Input(Y) "
+          "must be equal.");
+      PADDLE_ENFORCE(
+          x_dims[0] == y_dims[0] || y_dims[0] == 1,
+          "The 1st dimension of Input(Y) must be equal to Input(X) or"
+          " just 1 (which will be broadcasted to match Input(X)).");
+    }
 
     // resize tensor
     ctx->SetOutputDim("Out", {x_dims[0], 1});