diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
index 23419d5b9e0a20adcb6245a5a5aa4c5c4b5f3a34..aa0979b4be64ae3ccebbd7cc82abcf4a4712527a 100644
--- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
+++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc
@@ -19,6 +19,7 @@
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/platform/errors.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
 #include "paddle/fluid/string/pretty_log.h"
 
 namespace paddle {
@@ -54,7 +55,7 @@ void LogQuantizationDisabled(Node* op) {
   std::stringstream msg_ss;
   VLOG(4) << "Qantization skipped for operator " << op->Name()
           << " (type: " << op->Op()->Type() << ", id: " << op->id()
-          << "). Attribute use_quantizer = false.";
+          << "). Attribute mkldnn_data_type != \"int8\".";
 }
 
 }  // namespace
@@ -228,12 +229,12 @@ double CPUQuantizePass::GetScaleValueForNode(const Node* node,
 
 bool CPUQuantizePass::IsOpDequantized(const Node* node) const {
   return node->Op()->Type() == "dequantize" ||
-         node->Op()->GetAttrIfExists<bool>("use_quantizer");
+         platform::HasOpINT8DataType(node->Op());
 }
 
 bool CPUQuantizePass::IsOpQuantized(const Node* node) const {
   return node->Op()->Type() == "quantize" ||
-         node->Op()->GetAttrIfExists<bool>("use_quantizer");
+         platform::HasOpINT8DataType(node->Op());
 }
 
 void CPUQuantizePass::QuantizeConv(Graph* graph,
@@ -248,10 +249,9 @@ void CPUQuantizePass::QuantizeConv(Graph* graph,
                      Graph* g) {
     VLOG(4) << "Quantize conv2d op";
     GET_IR_NODE_FROM_SUBGRAPH(conv_op, conv_op, conv_pattern);
-    auto* conv_op_desc = conv_op->Op();
 
     // skip if should not be quantized
-    if (!conv_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(conv_op->Op())) {
       LogQuantizationDisabled(conv_op);
       return;
     }
@@ -353,14 +353,13 @@ void CPUQuantizePass::QuantizeFc(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize fc op";
     GET_IR_NODE_FROM_SUBGRAPH(fc, fc, fc_pattern);
-    auto* fc_op_desc = fc->Op();
 
     // skip if should not be quantized
-    if (!fc_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(fc->Op())) {
       LogQuantizationDisabled(fc);
       return;
     }
-    if (!fc_op_desc->GetAttrIfExists<bool>("use_mkldnn")) {
+    if (!fc->Op()->GetAttrIfExists<bool>("use_mkldnn")) {
       return;
     }
 
@@ -420,10 +419,9 @@ void CPUQuantizePass::QuantizePool(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize pool2d op";
     GET_IR_NODE_FROM_SUBGRAPH(pool_op, pool_op, pool_pattern);
-    auto* pool_op_desc = pool_op->Op();
 
     // skip if should not be quantized
-    if (!pool_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(pool_op->Op())) {
       LogQuantizationDisabled(pool_op);
       return;
     }
@@ -465,10 +463,9 @@ void CPUQuantizePass::QuantizeConcat(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize concat op";
     GET_IR_NODE_FROM_SUBGRAPH(concat_op, concat_op, concat_pattern);
-    auto* concat_op_desc = concat_op->Op();
 
     // skip if should not be quantized
-    if (!concat_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(concat_op->Op())) {
       LogQuantizationDisabled(concat_op);
       return;
     }
@@ -511,10 +508,9 @@ void CPUQuantizePass::QuantizePriorBox(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize prior_box op";
     GET_IR_NODE_FROM_SUBGRAPH(prior_box_op, prior_box_op, prior_box_pattern);
-    auto* prior_box_op_desc = prior_box_op->Op();
 
     // skip if should not be quantized
-    if (!prior_box_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(prior_box_op->Op())) {
       LogQuantizationDisabled(prior_box_op);
       return;
     }
@@ -554,10 +550,9 @@ void CPUQuantizePass::QuantizeTranspose(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize transpose op";
     GET_IR_NODE_FROM_SUBGRAPH(transpose_op, transpose_op, transpose_pattern);
-    auto* transpose_op_desc = transpose_op->Op();
 
     // skip if should not be quantized
-    if (!transpose_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(transpose_op->Op())) {
       LogQuantizationDisabled(transpose_op);
       return;
     }
@@ -609,10 +604,9 @@ void CPUQuantizePass::QuantizeReshape(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize reshape op";
     GET_IR_NODE_FROM_SUBGRAPH(reshape_op, reshape_op, reshape_pattern);
-    auto* reshape_op_desc = reshape_op->Op();
 
     // skip if should not be quantized
-    if (!reshape_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(reshape_op->Op())) {
       LogQuantizationDisabled(reshape_op);
       return;
     }
@@ -662,10 +656,9 @@ void CPUQuantizePass::QuantizeMatmul(Graph* graph) const {
                      Graph* g) {
     VLOG(4) << "Quantize matmul op";
     GET_IR_NODE_FROM_SUBGRAPH(matmul_op, matmul_op, matmul_pattern);
-    auto* matmul_op_desc = matmul_op->Op();
 
     // skip if should not be quantized
-    if (!matmul_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(matmul_op->Op())) {
       LogQuantizationDisabled(matmul_op);
       return;
     }
@@ -732,10 +725,9 @@ void CPUQuantizePass::QuantizeElementwiseAdd(Graph* graph) const {
     VLOG(4) << "Quantize elementwise_add op";
     GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_op, elementwise_add_op,
                               elementwise_add_pattern);
-    auto* elementwise_add_op_desc = elementwise_add_op->Op();
 
     // skip if should not be quantized
-    if (!elementwise_add_op_desc->GetAttrIfExists<bool>("use_quantizer")) {
+    if (!platform::HasOpINT8DataType(elementwise_add_op->Op())) {
       LogQuantizationDisabled(elementwise_add_op);
       return;
     }
diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
index 395b419cac13d6d76b6e30579e52a1957b548bab..a66e9f0e9389843267d56d09d3de23add3d34a7f 100644
--- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
+++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc
@@ -26,7 +26,7 @@ namespace ir {
 void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
            const std::vector<std::string>& inputs,
            const std::vector<std::string>& outputs, bool use_mkldnn,
-           bool use_quantizer = false) {
+           const std::string& mkldnn_data_type = "float32") {
   auto* op = prog->MutableBlock(0)->AppendOp();
   op->SetType(type);
   op->SetAttr("use_mkldnn", use_mkldnn);
@@ -47,14 +47,14 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
       op->SetAttr("fuse_residual_connection", false);
     }
     op->SetOutput("Output", {outputs[0]});
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
     op->SetAttr("Scale_in", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
     op->SetAttr("Scale_weights", std::vector<float>{1.0f});
   } else if (type == "pool2d" || type == "transpose2" || type == "reshape2") {
     op->SetInput("X", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
   } else if (type == "dropout") {
     op->SetInput("X", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
@@ -63,14 +63,14 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     if (inputs.size() > 1) op->SetInput("W", {inputs[1]});
     if (inputs.size() > 2) op->SetInput("Bias", {inputs[2]});
     op->SetOutput("Out", {outputs[0]});
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
     op->SetAttr("Scale_in", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
     op->SetAttr("Scale_weights", std::vector<float>{1.0f});
   } else if (type == "concat") {
     op->SetInput("X", inputs);
     op->SetOutput("Out", outputs);
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
   } else if (type == "dequantize") {
     op->SetInput("Input", {inputs[0]});
     op->SetOutput("Output", {outputs[0]});
@@ -79,7 +79,7 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     op->SetInput("X", {inputs[0]});
     if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
     op->SetOutput("Out", {outputs[0]});
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
     op->SetAttr("Scale_x", 1.0f);
     op->SetAttr("Scale_y", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
@@ -87,7 +87,7 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     op->SetInput("X", {inputs[0]});
     if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
     op->SetOutput("Out", {outputs[0]});
-    op->SetAttr("use_quantizer", use_quantizer);
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
     op->SetAttr("Scale_x", 1.0f);
     op->SetAttr("Scale_y", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
@@ -142,7 +142,8 @@ static const std::initializer_list<std::string> variable_names{
 // d->Dropout1->g and (g, w5, b3)->Fc1->h and (h,w3,b1,i)->Conv3->j
 //
 // (d,w4, b2)->Conv4->i
-ProgramDesc BuildProgramDesc(bool use_mkldnn, bool use_quantizer) {
+ProgramDesc BuildProgramDesc(bool use_mkldnn,
+                             const std::string& mkldnn_data_type) {
   ProgramDesc prog;
   for (auto& v : variable_names) {
     auto* var = prog.MutableBlock(0)->Var(v);
@@ -152,21 +153,21 @@ ProgramDesc BuildProgramDesc(bool use_mkldnn, bool use_quantizer) {
   }
 
   SetOp(&prog, "conv2d", "Conv1", {"a", "w1"}, {"c"}, use_mkldnn,
-        use_quantizer);
-  SetOp(&prog, "pool2d", "Pool1", {"c"}, {"d"}, use_mkldnn, use_quantizer);
+        mkldnn_data_type);
+  SetOp(&prog, "pool2d", "Pool1", {"c"}, {"d"}, use_mkldnn, mkldnn_data_type);
 
   SetOp(&prog, "conv2d", "Conv2", {"d", "w2"}, {"e"}, use_mkldnn,
-        use_quantizer);
-  SetOp(&prog, "pool2d", "Pool2", {"e"}, {"f"}, use_mkldnn, use_quantizer);
+        mkldnn_data_type);
+  SetOp(&prog, "pool2d", "Pool2", {"e"}, {"f"}, use_mkldnn, mkldnn_data_type);
 
   SetOp(&prog, "dropout", "Dropout1", {"d"}, {"g"}, use_mkldnn);
   SetOp(&prog, "fc", "Fc1", {"g", "w5", "b3"}, {"h"}, use_mkldnn,
-        use_quantizer);
+        mkldnn_data_type);
   SetOp(&prog, "conv2d", "Conv3", {"h", "w3", "b1", "i"}, {"j"}, use_mkldnn,
-        use_quantizer);
+        mkldnn_data_type);
 
   SetOp(&prog, "conv2d", "Conv4", {"c", "w4", "b2"}, {"i"}, use_mkldnn,
-        use_quantizer);
+        mkldnn_data_type);
 
   return prog;
 }
@@ -215,7 +216,7 @@ void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
 
 TEST(CpuQuantizePass, quantize) {
   bool use_mkldnn = true;
-  bool use_quantizer = true;
+  std::string mkldnn_data_type = "int8";
   // (a->QUANT1->IN1,w1)->Conv1->OUT1->DEQUANT1->c and
   // c->QUANT2->IN2->Pool1->OUT2->DEQUANT2->d
   //
@@ -228,16 +229,16 @@ TEST(CpuQuantizePass, quantize) {
   // (d->QUANT7->IN7,w4, b2)->Conv4->DEQUANT6->OUT6->i
   // Insert nodes: 8 Quant + 8 IN + 7 OUT + 7 DEQUANT
   int added_nodes = 8 + 8 + 7 + 7;
-  MainTest(BuildProgramDesc(use_mkldnn, use_quantizer), 4, 2, 8, 7, added_nodes,
-           2.0f * 127);
+  MainTest(BuildProgramDesc(use_mkldnn, mkldnn_data_type), 4, 2, 8, 7,
+           added_nodes, 2.0f * 127);
 }
 
 TEST(CpuQuantizePass, do_not_quantize) {
   bool use_mkldnn = true;
-  bool use_quantizer = false;
+  std::string mkldnn_data_type = "float32";
   int added_nodes = 0;
-  MainTest(BuildProgramDesc(use_mkldnn, use_quantizer), 4, 2, 0, 0, added_nodes,
-           1.0f);
+  MainTest(BuildProgramDesc(use_mkldnn, mkldnn_data_type), 4, 2, 0, 0,
+           added_nodes, 1.0f);
 }
 
 static const std::initializer_list<std::string> variable_names_concat = {
@@ -250,10 +251,10 @@ static const std::initializer_list<std::string> variable_names_concat = {
 ProgramDesc BuildProgramDescConcat() {
   ProgramDesc prog;
 
-  SetOp(&prog, "pool2d", "Pool1", {"a1"}, {"b1"}, true, false);
-  SetOp(&prog, "pool2d", "Pool2", {"a2"}, {"b2"}, true, false);
-  SetOp(&prog, "concat", "Concat", {"b1", "b2"}, {"c"}, true, true);
-  SetOp(&prog, "pool2d", "Pool3", {"c"}, {"d"}, true, false);
+  SetOp(&prog, "pool2d", "Pool1", {"a1"}, {"b1"}, true, "float32");
+  SetOp(&prog, "pool2d", "Pool2", {"a2"}, {"b2"}, true, "float32");
+  SetOp(&prog, "concat", "Concat", {"b1", "b2"}, {"c"}, true, "int8");
+  SetOp(&prog, "pool2d", "Pool3", {"c"}, {"d"}, true, "float32");
 
   return prog;
 }
@@ -321,11 +322,11 @@ ProgramDesc BuildProgramDescTranspose() {
     }
   }
 
-  SetOp(&prog, "conv2d", "Conv1", {"a", "w1"}, {"b"}, true, true);
-  SetOp(&prog, "transpose2", "Transpose1", {"b"}, {"c"}, true, true);
-  SetOp(&prog, "conv2d", "Conv1", {"c", "w2"}, {"d"}, true, true);
-  SetOp(&prog, "transpose2", "Transpose2", {"d"}, {"e"}, true, true);
-  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, false);
+  SetOp(&prog, "conv2d", "Conv1", {"a", "w1"}, {"b"}, true, "int8");
+  SetOp(&prog, "transpose2", "Transpose1", {"b"}, {"c"}, true, "int8");
+  SetOp(&prog, "conv2d", "Conv1", {"c", "w2"}, {"d"}, true, "int8");
+  SetOp(&prog, "transpose2", "Transpose2", {"d"}, {"e"}, true, "int8");
+  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, "float32");
 
   return prog;
 }
@@ -400,8 +401,8 @@ ProgramDesc BuildProgramDescReshape() {
     prog.MutableBlock(0)->Var(v);
   }
   SetOp(&prog, "dequantize", "Dequantize1", {"a"}, {"b"}, true);
-  SetOp(&prog, "reshape2", "Reshape2", {"b"}, {"c"}, true, true);
-  SetOp(&prog, "dropout", "Dropout", {"c"}, {"d"}, true, false);
+  SetOp(&prog, "reshape2", "Reshape2", {"b"}, {"c"}, true, "int8");
+  SetOp(&prog, "dropout", "Dropout", {"c"}, {"d"}, true, "float32");
 
   return prog;
 }
@@ -415,9 +416,9 @@ ProgramDesc BuildProgramDescReshapeBetweenNonQuantizedOp() {
     prog.MutableBlock(0)->Var(v);
   }
 
-  SetOp(&prog, "transpose2", "Transpose2", {"a"}, {"b"}, true, false);
-  SetOp(&prog, "reshape2", "Reshape2", {"b"}, {"c"}, true, true);
-  SetOp(&prog, "dropout", "Dropout", {"c"}, {"d"}, true, false);
+  SetOp(&prog, "transpose2", "Transpose2", {"a"}, {"b"}, true, "float32");
+  SetOp(&prog, "reshape2", "Reshape2", {"b"}, {"c"}, true, "int8");
+  SetOp(&prog, "dropout", "Dropout", {"c"}, {"d"}, true, "float32");
 
   return prog;
 }
@@ -505,8 +506,8 @@ ProgramDesc BuildProgramDescMatmul() {
   }
   SetOp(&prog, "dequantize", "Dequantize1", {"a"}, {"b"}, true);
   SetOp(&prog, "dequantize", "Dequantize2", {"c"}, {"d"}, true);
-  SetOp(&prog, "matmul", "Matmul", {"b", "d"}, {"e"}, true, true);
-  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, false);
+  SetOp(&prog, "matmul", "Matmul", {"b", "d"}, {"e"}, true, "int8");
+  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, "float32");
 
   return prog;
 }
@@ -518,8 +519,8 @@ ProgramDesc BuildProgramDescMatmulNotQuantized() {
   }
   SetOp(&prog, "dropout", "Dropout", {"a"}, {"b"}, false);
   SetOp(&prog, "dequantize", "Dequantize", {"c"}, {"d"}, true);
-  SetOp(&prog, "matmul", "Matmul", {"b", "d"}, {"e"}, true, true);
-  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, false);
+  SetOp(&prog, "matmul", "Matmul", {"b", "d"}, {"e"}, true, "int8");
+  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, "float32");
 
   return prog;
 }
@@ -590,8 +591,8 @@ ProgramDesc BuildProgramDescElementwiseAdd() {
   SetOp(&prog, "dequantize", "Dequantize1", {"a"}, {"b"}, true);
   SetOp(&prog, "dequantize", "Dequantize2", {"c"}, {"d"}, true);
   SetOp(&prog, "elementwise_add", "ElementwiseAdd", {"b", "d"}, {"e"}, true,
-        true);
-  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, false);
+        "int8");
+  SetOp(&prog, "dropout", "Dropout", {"e"}, {"f"}, true, "float32");
 
   return prog;
 }
diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc
index d570d885c6ebe7a6ff2c7a9047959a2e773541a5..6be4ce566e01e9bcb89a38cbdc2bbd11551a065e 100644
--- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc
+++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc
@@ -32,11 +32,19 @@ void CPUQuantizePlacementPass::ApplyImpl(ir::Graph* graph) const {
                     n->id()) != excluded_ids_list.end())
         continue;
       auto* op = n->Op();
-      if (op->HasAttr("use_quantizer") || op->HasProtoAttr("use_quantizer")) {
+      if (op->HasAttr("mkldnn_data_type") ||
+          op->HasProtoAttr("mkldnn_data_type")) {
+        // use_quantizer is no longer used
+        // assign value for compatibility
+        if (op->GetAttrIfExists<bool>("use_quantizer")) {
+          op->SetAttr("mkldnn_data_type", std::string("int8"));
+        }
         if (op_types_list.empty()) {
+          op->SetAttr("mkldnn_data_type", std::string("int8"));
           op->SetAttr("use_quantizer", true);
         } else if (std::find(op_types_list.begin(), op_types_list.end(),
                              op->Type()) != op_types_list.end()) {
+          op->SetAttr("mkldnn_data_type", std::string("int8"));
           op->SetAttr("use_quantizer", true);
         }
       }
diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass_tester.cc
index 027d3e2861b506003c4ce1cbe8bd7ee02e0c9c2d..95e321e5b7190499f98c9df3dbef217310abcfcd 100644
--- a/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass_tester.cc
+++ b/paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass_tester.cc
@@ -15,7 +15,7 @@
 #include "paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.h"
 
 #include <gtest/gtest.h>
-#include <boost/logic/tribool.hpp>
+#include "paddle/fluid/platform/mkldnn_helper.h"
 
 namespace paddle {
 namespace framework {
@@ -24,13 +24,11 @@ namespace ir {
 void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
            const std::vector<std::string>& inputs,
            const std::vector<std::string>& outputs,
-           boost::tribool use_quantizer) {
+           const std::string& mkldnn_data_type = "float32") {
   auto* op = prog->MutableBlock(0)->AppendOp();
 
   op->SetType(type);
-
-  if (!boost::indeterminate(use_quantizer))
-    op->SetAttr("use_quantizer", use_quantizer);
+  op->SetAttr("mkldnn_data_type", mkldnn_data_type);
 
   if (type == "conv2d") {
     op->SetAttr("name", name);
@@ -50,7 +48,7 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
   op->SetOutput("Out", {outputs[0]});
 }
 
-// operator                      use_quantizer
+// operator                      mkldnn_data_type
 // ---------------------------------------
 // (a,b)->concat->c              none
 // (c,weights,bias)->conv->f     false
@@ -71,19 +69,19 @@ ProgramDesc BuildProgramDesc() {
     }
   }
 
-  SetOp(&prog, "concat", "concat1", {"a", "b"}, {"c"}, boost::indeterminate);
-  SetOp(&prog, "conv2d", "conv1", {"c", "weights", "bias"}, {"f"}, false);
-  SetOp(&prog, "relu", "relu1", {"f"}, {"g"}, boost::indeterminate);
-  SetOp(&prog, "pool2d", "pool1", {"g"}, {"h"}, false);
-  SetOp(&prog, "conv2d", "conv2", {"h", "weights2", "bias2"}, {"k"}, false);
-  SetOp(&prog, "pool2d", "pool2", {"k"}, {"l"}, false);
+  SetOp(&prog, "concat", "concat1", {"a", "b"}, {"c"}, "float32");
+  SetOp(&prog, "conv2d", "conv1", {"c", "weights", "bias"}, {"f"}, "float32");
+  SetOp(&prog, "relu", "relu1", {"f"}, {"g"}, "float32");
+  SetOp(&prog, "pool2d", "pool1", {"g"}, {"h"}, "float32");
+  SetOp(&prog, "conv2d", "conv2", {"h", "weights2", "bias2"}, {"k"}, "float32");
+  SetOp(&prog, "pool2d", "pool2", {"k"}, {"l"}, "float32");
 
   return prog;
 }
 
 void MainTest(std::initializer_list<std::string> quantize_enabled_op_types,
               std::initializer_list<int> quantize_excluded_op_ids,
-              unsigned expected_use_quantizer_true_count) {
+              unsigned expected_int8_data_type_count) {
   auto prog = BuildProgramDesc();
 
   std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
@@ -96,38 +94,34 @@ void MainTest(std::initializer_list<std::string> quantize_enabled_op_types,
 
   graph.reset(pass->Apply(graph.release()));
 
-  unsigned use_quantizer_true_count = 0;
+  unsigned int8_data_type_count = 0;
 
   for (auto* node : graph->Nodes()) {
     if (node->IsOp()) {
-      auto* op = node->Op();
-      if (op->HasAttr("use_quantizer") &&
-          BOOST_GET_CONST(bool, op->GetAttr("use_quantizer"))) {
-        ++use_quantizer_true_count;
+      if (platform::HasOpINT8DataType(node->Op())) {
+        ++int8_data_type_count;
       }
     }
   }
 
-  EXPECT_EQ(use_quantizer_true_count, expected_use_quantizer_true_count);
+  EXPECT_EQ(int8_data_type_count, expected_int8_data_type_count);
 }
 
-void DefaultAttrTest(unsigned expected_use_quantizer_true_count) {
+void DefaultAttrTest(unsigned expected_int8_data_type_count) {
   auto prog = BuildProgramDesc();
   std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
   auto pass = PassRegistry::Instance().Get("cpu_quantize_placement_pass");
   graph.reset(pass->Apply(graph.release()));
 
-  unsigned use_quantizer_true_count = 0;
+  unsigned int8_data_type_count = 0;
   for (auto* node : graph->Nodes()) {
     if (node->IsOp()) {
-      auto* op = node->Op();
-      if (op->HasAttr("use_quantizer") &&
-          BOOST_GET_CONST(bool, op->GetAttr("use_quantizer"))) {
-        ++use_quantizer_true_count;
+      if (platform::HasOpINT8DataType(node->Op())) {
+        ++int8_data_type_count;
       }
     }
   }
-  EXPECT_EQ(use_quantizer_true_count, expected_use_quantizer_true_count);
+  EXPECT_EQ(int8_data_type_count, expected_int8_data_type_count);
 }
 
 TEST(QuantizerPlacementPass, enabled_pool) { MainTest({"pool2d"}, {}, 2); }
@@ -137,13 +131,13 @@ TEST(QuantizerPlacementPass, enabled_conv_excluded_one) {
 }
 
 TEST(QuantizerPlacementPass, excluded_none) {
-  // 2 conv + 2 pool
-  MainTest({}, {}, 4);
+  // all operators quantized
+  MainTest({}, {}, 6);
 }
 
 TEST(QuantizerPlacementPass, default_attr_value) {
-  // 2 conv + 2 pool
-  DefaultAttrTest(4);
+  //  all operators quantized
+  DefaultAttrTest(6);
 }
 
 }  // namespace ir
diff --git a/paddle/fluid/inference/api/mkldnn_quantizer.cc b/paddle/fluid/inference/api/mkldnn_quantizer.cc
index 0dddbf158722181664f3c982cde8b77145eece3c..9be12ff309acff681da75f7f13e317a408a9552a 100644
--- a/paddle/fluid/inference/api/mkldnn_quantizer.cc
+++ b/paddle/fluid/inference/api/mkldnn_quantizer.cc
@@ -27,6 +27,7 @@
 #include "paddle/fluid/framework/type_defs.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/api/analysis_predictor.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
 #include "paddle/fluid/platform/place.h"
 #include "paddle/fluid/string/pretty_log.h"
 
@@ -50,8 +51,7 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() {
   using VariableNameMap = std::map<std::string, std::vector<std::string>>;
   std::map<std::string, std::map<std::string, LoDTensor>> gathered_data;
   for (const auto* op : predictor_.inference_program_->Block(0).AllOps()) {
-    if (op->HasAttr("use_quantizer") &&
-        BOOST_GET_CONST(bool, op->GetAttr("use_quantizer"))) {
+    if (platform::HasOpINT8DataType(op)) {
       const VariableNameMap& connections_in = op->Inputs();
       const VariableNameMap& connections_out = op->Outputs();
 
diff --git a/paddle/fluid/operators/concat_op.cc b/paddle/fluid/operators/concat_op.cc
index 060f5412f28e3704e64d33d9a3081a2ca934e918..4f337c03599a548ac3d95ddd06c726be30d7c13f 100644
--- a/paddle/fluid/operators/concat_op.cc
+++ b/paddle/fluid/operators/concat_op.cc
@@ -122,12 +122,16 @@ class ConcatOpMaker : public framework::OpProtoAndCheckerMaker {
              "It has higher priority than Attr(axis). "
              "The shape of AxisTensor must be [1].")
         .AsDispensable();
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
     AddComment(R"DOC(
 Concat Operator.
 
diff --git a/paddle/fluid/operators/conv_op.cc b/paddle/fluid/operators/conv_op.cc
index 57a5db88c53a393e80204ef94acc9fbc3472e2f7..a8c4107add1beeb9a7a5aedad9be982b6d8b6aac 100644
--- a/paddle/fluid/operators/conv_op.cc
+++ b/paddle/fluid/operators/conv_op.cc
@@ -279,12 +279,16 @@ void Conv2DOpMaker::Make() {
   AddAttr<bool>("use_mkldnn",
                 "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
-  AddAttr<bool>("use_quantizer",
-                "(bool, default false) "
-                "Set to true for operators that should be quantized and use "
-                "int8 kernel. "
-                "Only used on CPU.")
+  AddAttr<bool>(
+      "use_quantizer",
+      "(bool, default false) "
+      "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
       .SetDefault(false);
+  AddAttr<std::string>(
+      "mkldnn_data_type",
+      "(string, default \"float32\"). Data type of mkldnn kernel")
+      .SetDefault("float32")
+      .InEnum({"float32", "int8", "bfloat16"});
   AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
   AddAttr<bool>("fuse_brelu",
diff --git a/paddle/fluid/operators/detection/prior_box_op.cc b/paddle/fluid/operators/detection/prior_box_op.cc
index bd584d660f7d4f76f9ea30354f3b0c2696b6d048..0d293bb964b615c68891be516534a05cc2277426 100644
--- a/paddle/fluid/operators/detection/prior_box_op.cc
+++ b/paddle/fluid/operators/detection/prior_box_op.cc
@@ -14,6 +14,8 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/detection/prior_box_op.h"
 
+#include <string>
+
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #endif
@@ -218,12 +220,16 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>("use_mkldnn",
                   "(bool, default false) Only used in mkldnn kernel")
         .SetDefault(false);
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
     AddComment(R"DOC(
 Prior box operator
 Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
diff --git a/paddle/fluid/operators/elementwise/elementwise_op.h b/paddle/fluid/operators/elementwise/elementwise_op.h
index f32086c94a99d1267939a421b1c73a858f877556..de202ecf88cacbb5877f8aa226409b65e819d3c6 100644
--- a/paddle/fluid/operators/elementwise/elementwise_op.h
+++ b/paddle/fluid/operators/elementwise/elementwise_op.h
@@ -140,12 +140,17 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault("");
     AddAttr<std::string>("y_data_format", "This parameter is no longer used.")
         .SetDefault("");
-    /* int8 parameters */
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
+    /* int8 parameters */
     AddAttr<float>("Scale_x",
                    "(float, default 1.0f), The quantize scale of X tensor")
         .SetDefault(1.0f);
diff --git a/paddle/fluid/operators/fc_op.cc b/paddle/fluid/operators/fc_op.cc
index f81ed3096238b9da84655c86828040062c96106d..847b24f4f0b0b529cdb36b5823a789c97e0a9d84 100644
--- a/paddle/fluid/operators/fc_op.cc
+++ b/paddle/fluid/operators/fc_op.cc
@@ -142,13 +142,17 @@ class FCOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
                   "Skip calling InferShape() function in the runtime.")
         .SetDefault(true);
-    /* int8 parameters */
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
+    /* int8 parameters */
     AddAttr<float>("Scale_in",
                    "(float, default 1.0f), The quantize scale of input data")
         .SetDefault(1.0f);
diff --git a/paddle/fluid/operators/matmul_op.cc b/paddle/fluid/operators/matmul_op.cc
index 98277df454e09acf82af69d4de5f430ae4f25e57..809164df2056cb4f4856a0b70ea5076351603199 100644
--- a/paddle/fluid/operators/matmul_op.cc
+++ b/paddle/fluid/operators/matmul_op.cc
@@ -535,13 +535,17 @@ class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
         R"DOC(When MKLDNN MatMul_transpose_reshape fuse activated, "
               "it's a axis atribute of fused transpose for `Out` output.)DOC")
         .SetDefault({});
-    /* int8 parameters */
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
+    /* int8 parameters */
     AddAttr<float>("Scale_x",
                    "(float, default 1.0f), The quantize scale of X tensor")
         .SetDefault(1.0f);
diff --git a/paddle/fluid/operators/pool_op.cc b/paddle/fluid/operators/pool_op.cc
index 8ff3192ca24e193dfbddebdd1ce79ce91f08dbb2..9900120e6c590f5d0c454fdba3ee4e936c2c409b 100644
--- a/paddle/fluid/operators/pool_op.cc
+++ b/paddle/fluid/operators/pool_op.cc
@@ -306,12 +306,16 @@ void Pool2dOpMaker::Make() {
   AddAttr<bool>("use_mkldnn",
                 "(bool) Only used in mkldnn kernel. Default False")
       .SetDefault(false);
-  AddAttr<bool>("use_quantizer",
-                "(bool) "
-                "Set to true for operators that should be quantized and use "
-                "int8 kernel. "
-                "Only used on CPU. Default False")
+  AddAttr<bool>(
+      "use_quantizer",
+      "(bool, default false) "
+      "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
       .SetDefault(false);
+  AddAttr<std::string>(
+      "mkldnn_data_type",
+      "(string, default \"float32\"). Data type of mkldnn kernel")
+      .SetDefault("float32")
+      .InEnum({"float32", "int8", "bfloat16"});
   AddAttr<std::string>(
       "data_format",
       "(string, default NCHW) Only used in "
diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc
index fee0f045825591d548350c289f3f290d5dd1d723..01a33a46521cd81d084f8971c47741b28a105d41 100644
--- a/paddle/fluid/operators/reshape_op.cc
+++ b/paddle/fluid/operators/reshape_op.cc
@@ -431,13 +431,16 @@ class Reshape2OpMaker : public ReshapeOpMaker {
               "XShape is just used to store the shape and lod of X, which will "
               "be used in FlattenGradOp.")
         .AsIntermediate();
-    /* int8 parameters */
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Used only on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
   }
 };
 
diff --git a/paddle/fluid/operators/transpose_op.cc b/paddle/fluid/operators/transpose_op.cc
index 6849bd739501cc73e7142e6462a3f627f445bd22..946fa6305d737363dab3cea2e2b581f2e5659cfd 100644
--- a/paddle/fluid/operators/transpose_op.cc
+++ b/paddle/fluid/operators/transpose_op.cc
@@ -108,13 +108,17 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
         "Defaults to \"NHWC\". Specify the data format of the output data, "
         "the input will be transformed automatically. ")
         .SetDefault("AnyLayout");
-    /* int8 parameters */
-    AddAttr<bool>("use_quantizer",
-                  "(bool, default false) "
-                  "Set to true for operators that should be quantized and use "
-                  "int8 kernel. "
-                  "Only used on CPU.")
+    AddAttr<bool>(
+        "use_quantizer",
+        "(bool, default false) "
+        "This parameter is no longer used. Use 'mkldnn_data_type' instead.")
         .SetDefault(false);
+    AddAttr<std::string>(
+        "mkldnn_data_type",
+        "(string, default \"float32\"). Data type of mkldnn kernel")
+        .SetDefault("float32")
+        .InEnum({"float32", "int8", "bfloat16"});
+    /* int8 parameters */
     AddComment(R"DOC(
 Transpose Operator.
 
diff --git a/paddle/fluid/platform/mkldnn_helper.h b/paddle/fluid/platform/mkldnn_helper.h
index 60588d89db803fe6421d16b48ec98e1f02ad522c..c74c47b7d84820f089d4e657f8bddccc5de8d727 100644
--- a/paddle/fluid/platform/mkldnn_helper.h
+++ b/paddle/fluid/platform/mkldnn_helper.h
@@ -422,6 +422,11 @@ inline std::vector<std::vector<int64_t>> ToMkldnnPadding(
   }
 }
 
+inline bool HasOpINT8DataType(const paddle::framework::OpDesc* op) {
+  return (op->GetAttrIfExists<std::string>("mkldnn_data_type") == "int8" ||
+          op->GetAttrIfExists<bool>("use_quantizer"));
+}
+
 enum class RNNReorderType { PP_NTC, PP_TNC, NTC_PP, TNC_PP };
 
 }  // namespace platform
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 1ef15ca4733518949a3d1856c20a0e5f70820554..ae42b3bbdf0ada9ab19dc1ad6cc2f1e09def214d 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -11969,7 +11969,7 @@ for func in [
         ],
         skip_attrs_set={
             "x_data_format", "y_data_format", "axis", "use_quantizer",
-            "Scale_x", "Scale_y", "Scale_out"
+            "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out"
         }) + """\n""" + str(func.__doc__)
 
 for func in []:
diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py
index 9b1d7ec3a542c471d2f960b6f0b0b2b7f3509b99..c531eb7f5a49fc5eb054ea75c87591031958830e 100644
--- a/python/paddle/tensor/math.py
+++ b/python/paddle/tensor/math.py
@@ -609,7 +609,7 @@ for func in [
         op_proto,
         additional_args_lines=additional_args_lines,
         skip_attrs_set={"x_data_format", "y_data_format", "axis",
-            "use_quantizer", "Scale_x", "Scale_y", "Scale_out"
+            "use_quantizer", "mkldnn_data_type", "Scale_x", "Scale_y", "Scale_out"
         }) + """\n""" + str(func.__doc__)
 
 def sum(input, dim=None, dtype=None, keep_dim=False, name=None):