diff --git a/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc
index 4230d180385b49a4b7dbc13d5563a592d0dd8ea2..30e788bb395a4c9076195f6742d5df87f254c90a 100644
--- a/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc
+++ b/paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc
@@ -403,26 +403,36 @@ class FCPrimitiveFactory {
   // scaled with its own scales, this data needs to be divided by
   // those scales to normalise them back to what their floating-point range
   // was. Then we multiply them by desired output scale we want on the output.
-  std::vector<float> ComputeOutputShiftScale(const ExecutionContext& ctx) {
+  std::tuple<std::vector<float>, float> ComputeOutputShiftScale(
+      const ExecutionContext& ctx) {
     auto scale_in_data = ctx.Attr<float>("Scale_in");
     auto scale_weights_data = ctx.Attr<std::vector<float>>("Scale_weights");
+
     // If the output will be in floats, we don't multiply by scale_out.
-    auto scale_out_data = ctx.Attr<bool>("force_fp32_output")
-                              ? 1.0f
-                              : ctx.Attr<float>("Scale_out");
+    float activation_scale = 1.0f;
+    float inner_scale = 1.0f;
+    if (!ctx.Attr<bool>("force_fp32_output")) {
+      // if has activation use it's scale, otherwise use inner scale.
+      if (!ctx.Attr<std::string>("activation_type").empty()) {
+        activation_scale = ctx.Attr<float>("Scale_out");
+      } else {
+        inner_scale = ctx.Attr<float>("Scale_out");
+      }
+    }
+
     const size_t weight_scales_num = scale_weights_data.size();
     std::vector<float> output_shift_scale(weight_scales_num);
 
 #pragma omp parallel for
     for (size_t i = 0; i < weight_scales_num; i++) {
       if (scale_weights_data[i] == 0.0)
-        output_shift_scale[i] = scale_out_data;
+        output_shift_scale[i] = inner_scale;
       else
         output_shift_scale[i] =
-            scale_out_data / (scale_in_data * scale_weights_data[i]);
+            inner_scale / (scale_in_data * scale_weights_data[i]);
     }
 
-    return output_shift_scale;
+    return make_tuple(output_shift_scale, activation_scale);
   }
 
   // Computing MKL-DNN's scaling mask which determines along which dimension
@@ -449,48 +459,43 @@ class FCPrimitiveFactory {
     dnnl::primitive_attr attributes;
     dnnl::post_ops post_operations;
 
-    auto output_shift_scale = ComputeOutputShiftScale(ctx);
+    std::vector<float> output_shift_scale;
+    float scale;
+    std::tie(output_shift_scale, scale) = ComputeOutputShiftScale(ctx);
     int mask = CreateMask(1, output_shift_scale.size() > 1);
     attributes.set_output_scales(mask, output_shift_scale);
 
     if (ctx.Attr<std::string>("activation_type") == "relu") {
-      constexpr float scale = 1.0f;
       constexpr float negative_slope = 0.0f;
       constexpr float placeholder = 1.0f;  // beta
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_relu,
                                      negative_slope, placeholder);
     } else if (ctx.Attr<std::string>("activation_type") == "gelu") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_gelu,
                                      alpha, beta);
     } else if (ctx.Attr<std::string>("activation_type") == "gelu_tanh") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_gelu_tanh,
                                      alpha, beta);
     } else if (ctx.Attr<std::string>("activation_type") == "gelu_erf") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_gelu_erf,
                                      alpha, beta);
     } else if (ctx.Attr<std::string>("activation_type") == "tanh") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_tanh,
                                      alpha, beta);
     } else if (ctx.Attr<std::string>("activation_type") == "sigmoid") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_logistic,
                                      alpha, beta);
     } else if (ctx.Attr<std::string>("activation_type") == "hard_swish") {
-      constexpr float scale = 1.0f;
       constexpr float alpha = 0.0f;
       constexpr float beta = 0.0f;
       post_operations.append_eltwise(scale, dnnl::algorithm::eltwise_hardswish,
diff --git a/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py b/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
index 97c41443e04a3306a111d58878436204b4b53954..3e5db06a86a374699a90c5c3da133037a1d8a4ed 100644
--- a/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
+++ b/python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py
@@ -410,6 +410,9 @@ class Quant2Int8MkldnnPass(object):
         graph = self._apply_pass(graph, 'seq_concat_fc_fuse_pass')
         graph = self._apply_pass(graph, 'squared_mat_sub_fuse_pass')
         graph = self._apply_pass(graph, 'is_test_pass')
+        graph = self._apply_pass(graph, 'map_matmul_v2_to_mul_pass')
+        graph = self._apply_pass(graph, 'map_matmul_v2_to_matmul_pass')
+        graph = self._apply_pass(graph, 'map_matmul_to_mul_pass')
         graph = self._apply_pass(graph, 'mkldnn_placement_pass',
                                  ['mkldnn_enabled_op_types'], [set()])
         graph = self._apply_pass(graph, 'depthwise_conv_mkldnn_pass')
@@ -426,7 +429,9 @@ class Quant2Int8MkldnnPass(object):
                                  ['use_gpu', 'use_fc_padding'], [False, False])
         graph = self._apply_pass(graph, 'repeated_fc_relu_fuse_pass')
         if self._is_fc_quantized(graph):
+            # Disabled due to topology-dependent speed-up
             graph = self._apply_pass(graph, 'fc_mkldnn_pass')
+            graph = self._apply_pass(graph, 'fc_act_mkldnn_fuse_pass')
         graph = self._apply_pass(graph, 'matmul_transpose_reshape_fuse_pass')
         graph = self._apply_pass(graph, 'matmul_v2_transpose_reshape_fuse_pass')
         # the following pass should be the last one since it will work on all fused ops.