diff --git a/csrc/cpu/jit/passes/graph_rewrite_linear.cpp b/csrc/cpu/jit/passes/graph_rewrite_linear.cpp
index f808842a8..00eb17228 100644
--- a/csrc/cpu/jit/passes/graph_rewrite_linear.cpp
+++ b/csrc/cpu/jit/passes/graph_rewrite_linear.cpp
@@ -451,6 +451,23 @@ void fuseLinearMulAdd(std::shared_ptr<Graph>& graph) {
         %res = ipex_prepack::linear_mul_run(%input, %operand, %packed_weight)
         return (%res))";
 
+  auto filter_scalar = [](const Match& match,
+                          const std::unordered_map<std::string, Value*>& vmap) {
+    Node* node = match.anchor;
+    if (utils::is_scalar(node->input(1)) || utils::is_scalar(node->input(0))) {
+      return false;
+    }
+    if (node->input(1)->type()->cast<TensorType>()->dim().has_value() &&
+        node->input(1)->type()->cast<TensorType>()->dim().value() == 0) {
+      return false;
+    }
+    if (node->input(0)->type()->cast<TensorType>()->dim().has_value() &&
+        node->input(0)->type()->cast<TensorType>()->dim().value() == 0) {
+      return false;
+    }
+    return true;
+  };
+
   for (const auto& mul : mul_operators) {
     TemplateEnv env;
     env.s("mul", mul);
@@ -460,8 +477,8 @@ void fuseLinearMulAdd(std::shared_ptr<Graph>& graph) {
         linear_mul_operand_on_the_left_rstring.format(env), linear_mul_fused);
   }
 
-  rewriter_mul_operand_on_the_right.runOnGraph(graph);
-  rewriter_mul_operand_on_the_left.runOnGraph(graph);
+  rewriter_mul_operand_on_the_right.runOnGraph(graph, filter_scalar);
+  rewriter_mul_operand_on_the_left.runOnGraph(graph, filter_scalar);
 
   // linear + mul + add
   // linear_mul   Y
diff --git a/examples/cpu/inference/python/llm/single_instance/run_quantization.py b/examples/cpu/inference/python/llm/single_instance/run_quantization.py
index 1c562f3dc..583ae0d74 100644
--- a/examples/cpu/inference/python/llm/single_instance/run_quantization.py
+++ b/examples/cpu/inference/python/llm/single_instance/run_quantization.py
@@ -653,6 +653,7 @@ def calib_func(prepared_model):
                 op_type_dict=op_type_dict,
                 smoothquant_args=smoothquant_args
             )
+            pathlib.Path(args.output_dir).mkdir(parents=True, exist_ok=True)
             prepared_model.save_qconf_summary(args.output_dir + "/best_configure.json")
 
         else:
diff --git a/tests/cpu/test_jit.py b/tests/cpu/test_jit.py
index f3e559a78..cb9469eb2 100644
--- a/tests/cpu/test_jit.py
+++ b/tests/cpu/test_jit.py
@@ -815,6 +815,19 @@ def forward(self, input):
         return x_l
 
 
+class LinearMulAdd_v2(nn.Module):
+    def __init__(self, in_features, out_features):
+        super(LinearMulAdd_v2, self).__init__()
+        self.linear = torch.nn.Linear(in_features, out_features, bias=False)
+        self.mul_tensor = torch.tensor(1)
+        self.mul_scalar = 0.5
+
+    def forward(self, input):
+        x_add = input
+        result = self.mul_tensor * self.linear(input) * self.mul_scalar
+        return result + (x_add).to(result.dtype)
+
+
 class LinearMul(nn.Module):
     def __init__(self, in_features, num_layers, low_rank):
         super(LinearMul, self).__init__()
@@ -841,6 +854,17 @@ def forward(self, input):
         return x_l
 
 
+class LinearMul_v2(nn.Module):
+    def __init__(self, in_features, out_features):
+        super(LinearMul_v2, self).__init__()
+        self.linear = torch.nn.Linear(in_features, out_features, bias=False)
+        self.mul_tensor = torch.tensor(1)
+        self.mul_scalar = 0.5
+
+    def forward(self, input):
+        return self.mul_scalar * self.linear(input) * self.mul_tensor
+
+
 class Linear_Reshape_Relu(nn.Module):
     def __init__(self, in_channels, out_channels, dest_shape, **kwargs):
         super(Linear_Reshape_Relu, self).__init__()
@@ -4536,6 +4560,25 @@ def test_output_linear_mul_add(self):
             prec=5e-2,
         )
 
+    def test_output_linear_mul_add_v2(self):
+        m = LinearMulAdd_v2(4, 4)
+        x = torch.ones(2, 4)
+        self._test_output(
+            m,
+            x,
+            kind_in_graph="aten::linear",
+            kind_not_in_graph="ipex_prepack::linear_mul_add_run",
+        )
+        self._test_mkl_fp32(m, x, kind_in_graph="ipex_prepack::mkl_sgemm_run")
+        self._test_dnnl_fp32(m, x, kind_in_graph="ipex_prepack::linear_run")
+        self._test_output_lowp(
+            m,
+            x,
+            kind_in_graph="ipex_prepack::linear_run",
+            kind_not_in_graph="ipex_prepack::linear_mul_add_run",
+            prec=5e-2,
+        )
+
     def test_output_linear_mul(self):
         m = LinearMul(4, 2, 8)
         x = torch.ones(2, 4)
@@ -4549,6 +4592,25 @@ def test_output_linear_mul(self):
             prec=5e-2,
         )
 
+    def test_output_linear_mul_v2(self):
+        m = LinearMul_v2(4, 4)
+        x = torch.ones(2, 4)
+        self._test_output(
+            m,
+            x,
+            kind_in_graph="aten::linear",
+            kind_not_in_graph="ipex_prepack::linear_mul_run",
+        )
+        self._test_mkl_fp32(m, x, kind_in_graph="ipex_prepack::mkl_sgemm_run")
+        self._test_dnnl_fp32(m, x, kind_in_graph="ipex_prepack::linear_run")
+        self._test_output_lowp(
+            m,
+            x,
+            kind_in_graph="ipex_prepack::linear_run",
+            kind_not_in_graph="ipex_prepack::linear_mul_run",
+            prec=5e-2,
+        )
+
     def test_output_linear_reshape_relu(self):
         self._test_output(
             Linear_Reshape_Relu(3, 32, (64, 16), bias=True),