Fix cpu_bfloat16_pass (#28730)

* Fix cpu_bfloat16_pass

* Add output_format

* Fix incorrect SetOutput

* Change fromating

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -2181,6 +2181,36 @@ PDNode *patterns::FirstBfloat16Ops::operator()() {
   return op;
 }
 
+PDNode *patterns::DuplicatedInputs::operator()() {
+  auto op = pattern->NewNode(op_repr())->assert_is_ops({"concat", "sum"});
+  op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "bfloat16";
+  });
+  return op;
+}
+
+PDNode *patterns::UnnecessaryReorders::operator()() {
+  auto prev_op = pattern->NewNode(prev_op_repr())->assert_is_op();
+  prev_op->assert_more([&](Node *node) {
+    return node->Op()->GetAttrIfExists<std::string>("mkldnn_data_type") ==
+           "bfloat16";
+  });
+
+  auto *quant_in = pattern->NewNode(quant_in_repr())
+                       ->assert_is_op_input("quantize", "Input");
+
+  auto *quant_op = pattern->NewNode(quant_op_repr())->assert_is_op("quantize");
+
+  auto *quant_out = pattern->NewNode(quant_out_repr())
+                        ->assert_is_op_output("quantize", "Output");
+
+  prev_op->LinksTo({quant_in});
+  quant_op->LinksFrom({quant_in}).LinksTo({quant_out});
+
+  return quant_out;
+}
+
 PDNode *patterns::MKLDNNInPlace::operator()() {
   const std::unordered_set<std::string> &supported_op_types = {
       "abs",

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1273,6 +1273,26 @@ struct FirstBfloat16Ops : public PatternBase {
   PATTERN_DECL_NODE(op);
 };
 
+struct DuplicatedInputs : public PatternBase {
+  DuplicatedInputs(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "many_inputs_op") {}
+
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(op);
+};
+
+struct UnnecessaryReorders : public PatternBase {
+  UnnecessaryReorders(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "unnecessary_reorders") {}
+  PDNode* operator()();
+
+  PATTERN_DECL_NODE(prev_op);
+  PATTERN_DECL_NODE(quant_in);
+  PATTERN_DECL_NODE(quant_op);
+  PATTERN_DECL_NODE(quant_out);
+};
+
 // Pattern used for enforcing inplace computation for in-place computation
 // supporting DNNL ops. softmax, batch_norm and layer_norm
 struct MKLDNNInPlace : public PatternBase {

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass.cc

@@ -33,58 +33,157 @@ void UnlinkNodes(ir::Node* a, ir::Node* b) {
                   b->inputs.end());
 }
 
-void CPUBFloat16Pass::SetInputDataType(ir::Graph* graph) const {
+void AddQuantize(Graph* g, ir::Node* op, ir::Node* op_in,
+                 int* quantize_counter) {
+  VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
+  auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
+
+  OpDesc q_desc;
+  q_desc.SetType("quantize");
+  q_desc.SetInput("Input", std::vector<std::string>({op_in->Name()}));
+  q_desc.SetOutput("Output",
+                   std::vector<std::string>({quantize_out_node->Name()}));
+  q_desc.SetAttr("Scale", 1.f);
+  q_desc.SetAttr("bfloat16", true);
+  q_desc.SetAttr("output_format", op->Op()->HasAttr("data_layout")
+                                      ? op->Op()->GetAttr("data_layout")
+                                      : std::string("NCHW"));
+  auto quantize_op = g->CreateOpNode(&q_desc);
+
+  std::vector<std::string> input_names;
+  for (auto name : op->Op()->InputNames()) {
+    for (auto input_name : op->Op()->Input(name)) {
+      if (input_name == op_in->Name()) input_names.push_back(name);
+    }
+  }
+
+  PADDLE_ENFORCE_NE(
+      input_names.empty(), true,
+      platform::errors::NotFound(
+          "Operator before operator should have input as op output"));
+
+  for (auto name = input_names.begin(); name < input_names.end(); name++)
+    op->Op()->SetInput(*name,
+                       std::vector<std::string>({quantize_out_node->Name()}));
+
+  UnlinkNodes(op_in, op);
+  IR_NODE_LINK_TO(op_in, quantize_op);
+  IR_NODE_LINK_TO(quantize_op, quantize_out_node);
+  IR_NODE_LINK_TO(quantize_out_node, op);
+  (*quantize_counter)++;
+}
+
+void AddQuantizes(Graph* g, ir::Node* op, int* quantize_counter) {
+  auto inputs = op->inputs;
+  PADDLE_ENFORCE_GE(inputs.size(), 1,
+                    platform::errors::InvalidArgument(
+                        "OP(%s)'s inputs(%d) must be equal or greater than 1.",
+                        op->Name(), inputs.size()));
+  PADDLE_ENFORCE_EQ(op->outputs.size(), 1,
+                    platform::errors::InvalidArgument(
+                        "OP(%s)'s outputs(%d) must be equal to 1.", op->Name(),
+                        op->outputs.size()));
+
+  OpDesc q_desc;
+  q_desc.SetType("quantize");
+
+  std::vector<Node*> quantize_out_nodes(inputs.size());
+  std::vector<std::string> quantize_out_node_names(inputs.size());
+
+  for (size_t i = 0; i < inputs.size(); i++) {
+    VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
+    quantize_out_nodes[i] = g->CreateVarNode(&quantize_out_desc);
+    quantize_out_node_names[i] = quantize_out_nodes[i]->Name();
+
+    q_desc.SetInput("Input", std::vector<std::string>({inputs[i]->Name()}));
+    q_desc.SetOutput("Output",
+                     std::vector<std::string>({quantize_out_node_names[i]}));
+    q_desc.SetAttr("Scale", 1.f);
+    q_desc.SetAttr("bfloat16", true);
+    q_desc.SetAttr("output_format", op->Op()->HasAttr("data_layout")
+                                        ? op->Op()->GetAttr("data_layout")
+                                        : std::string("NCHW"));
+    auto quantize_op = g->CreateOpNode(&q_desc);
+
+    UnlinkNodes(inputs[i], op);
+    IR_NODE_LINK_TO(inputs[i], quantize_op);
+    IR_NODE_LINK_TO(quantize_op, quantize_out_nodes[i]);
+    IR_NODE_LINK_TO(quantize_out_nodes[i], op);
+    (*quantize_counter)++;
+  }
+
+  op->Op()->SetInput("X", quantize_out_node_names);
+}
+
+void AddReoderBeforeDuplicatedInputs(ir::Graph* graph, int* quantize_counter) {
+  GraphPatternDetector gpd;
+  patterns::DuplicatedInputs duplicated_inputs{gpd.mutable_pattern(),
+                                               "duplicated_inputs"};
+  duplicated_inputs();
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(op, op, duplicated_inputs);
+    AddQuantizes(g, op, quantize_counter);
+  };
+  gpd(graph, handler);
+}
+
+void RemoveUnnecessaryReorders(ir::Graph* graph, int* quantize_counter) {
+  GraphPatternDetector gpd;
+  patterns::UnnecessaryReorders unnecessary_reorders{gpd.mutable_pattern(),
+                                                     "unnecessary_reorders"};
+  unnecessary_reorders();
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, unnecessary_reorders);
+    GET_IR_NODE_FROM_SUBGRAPH(quant_in, quant_in, unnecessary_reorders);
+    GET_IR_NODE_FROM_SUBGRAPH(quant_op, quant_op, unnecessary_reorders);
+    GET_IR_NODE_FROM_SUBGRAPH(quant_out, quant_out, unnecessary_reorders);
+
+    std::string op_output_name;
+    for (auto name : prev_op->Op()->OutputNames())
+      for (auto output_name : prev_op->Op()->Output(name))
+        if (output_name == quant_in->Name()) op_output_name = name;
+
+    PADDLE_ENFORCE_NE(
+        op_output_name.empty(), true,
+        platform::errors::NotFound(
+            "Operator before operator should have input as op output"));
+
+    prev_op->Op()->SetOutput(op_output_name,
+                             std::vector<std::string>({quant_out->Name()}));
+
+    IR_NODE_LINK_TO(prev_op, quant_out);
+    GraphSafeRemoveNodes(graph, {quant_in, quant_op});
+    (*quantize_counter)--;
+  };
+  gpd(graph, handler);
+}
+
+void AddReoderBeforeSingleInputs(ir::Graph* graph, int* quantize_counter) {
   GraphPatternDetector gpd;
   patterns::FirstBfloat16Ops bfloat16_ops{gpd.mutable_pattern(),
                                           "first_bfloat16_ops"};
   bfloat16_ops();
-  int quantize_counter = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
     GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, bfloat16_ops);
     GET_IR_NODE_FROM_SUBGRAPH(op_in, op_in, bfloat16_ops);
     GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
-
-    if (op->Op()->Type() != "conv2d" && prev_op->Op()->Type() != "quantize") {
-      VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
-      auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
-
-      // create a quantize op node
-      OpDesc q_desc;
-      q_desc.SetType("quantize");
-      q_desc.SetInput("Input", std::vector<std::string>({op_in->Name()}));
-      q_desc.SetOutput("Output",
-                       std::vector<std::string>({quantize_out_node->Name()}));
-      q_desc.SetAttr("Scale", 1.f);
-      q_desc.SetAttr("bfloat16", true);
-      q_desc.SetAttr("output_format", Has("data_layout")
-                                          ? Get<std::string>("data_layout")
-                                          : "NCHW");
-      auto quantize_op = g->CreateOpNode(&q_desc);  // OpDesc will be copied.
-
-      std::string op_input_name;
-      for (auto name : op->Op()->InputNames()) {
-        for (auto input_name : op->Op()->Input(name)) {
-          if (input_name == op_in->Name()) op_input_name = name;
-        }
-      }
-
-      PADDLE_ENFORCE_NE(
-          op_input_name.empty(), true,
-          platform::errors::NotFound(
-              "Operator before operator should have input as op output"));
-
-      op->Op()->SetInput(op_input_name,
-                         std::vector<std::string>({quantize_out_node->Name()}));
-
-      UnlinkNodes(op_in, op);
-      IR_NODE_LINK_TO(op_in, quantize_op);
-      IR_NODE_LINK_TO(quantize_op, quantize_out_node);
-      IR_NODE_LINK_TO(quantize_out_node, op);
-      quantize_counter++;
+    auto prev_op_type = prev_op->Op()->Type();
+    if (op->Op()->Type() != "conv2d" && prev_op_type != "quantize" &&
+        prev_op_type != "sum" && prev_op_type != "concat") {
+      AddQuantize(g, op, op_in, quantize_counter);
     }
   };
   gpd(graph, handler);
+}
+
+void CPUBFloat16Pass::SetInputDataType(ir::Graph* graph) const {
+  int quantize_counter = 0;
+  AddReoderBeforeDuplicatedInputs(graph, &quantize_counter);
+  RemoveUnnecessaryReorders(graph, &quantize_counter);
+  AddReoderBeforeSingleInputs(graph, &quantize_counter);
   PrettyLogDetail("---    added %d quantize op before bfloat16 op",
                   quantize_counter);
 }
@@ -101,45 +200,42 @@ void CPUBFloat16Pass::SetOutputDataType(ir::Graph* graph) const {
     GET_IR_NODE_FROM_SUBGRAPH(op, op, bfloat16_ops);
     GET_IR_NODE_FROM_SUBGRAPH(op_out, op_out, bfloat16_ops);
     GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, bfloat16_ops);
-
     if ((op->Op()->HasAttr("force_fp32_output") ||
          op->Op()->HasProtoAttr("force_fp32_output")) &&
         !op->Op()->GetAttrIfExists<bool>("fuse_residual_connection")) {
       op->Op()->SetAttr("force_fp32_output", true);
       force_fp32_counter++;
     } else if (op->Op()->Type() != "prior_box") {
-      // Create dequantize input variable
-      VarDesc dequantize_in_desc(patterns::PDNodeName("dequantize", "in"));
-      auto* dequantize_in_node = g->CreateVarNode(&dequantize_in_desc);
+      VarDesc dequantize_out_desc(patterns::PDNodeName("dequantize", "out"));
+      auto* dequantize_out_node = g->CreateVarNode(&dequantize_out_desc);
 
-      // create a dequantize op node for output.
       OpDesc deq_desc;
       deq_desc.SetType("dequantize");
-      deq_desc.SetInput("Input",
-                        std::vector<std::string>({dequantize_in_node->Name()}));
-      deq_desc.SetOutput("Output", std::vector<std::string>({op_out->Name()}));
+      deq_desc.SetInput("Input", std::vector<std::string>({op_out->Name()}));
+      deq_desc.SetOutput(
+          "Output", std::vector<std::string>({dequantize_out_node->Name()}));
       deq_desc.SetAttr("Scale", 1.0f);
       auto dequantize_op = g->CreateOpNode(&deq_desc);
 
-      std::string op_output_name;
-      for (auto name : op->Op()->OutputNames()) {
-        for (auto output_name : op->Op()->Output(name)) {
-          if (output_name == op_out->Name()) op_output_name = name;
+      std::string next_op_input_name;
+      for (auto name : next_op->Op()->InputNames()) {
+        for (auto input_name : next_op->Op()->Input(name)) {
+          if (input_name == op_out->Name()) next_op_input_name = name;
         }
       }
 
       PADDLE_ENFORCE_NE(
-          op_output_name.empty(), true,
+          next_op_input_name.empty(), true,
           platform::errors::NotFound(
-              "Operator after operator should have input as op output"));
-
-      op->Op()->SetOutput(op_output_name, std::vector<std::string>(
-                                              {dequantize_in_node->Name()}));
+              "Operator before operator should have input as op output"));
 
-      UnlinkNodes(op, op_out);
-      IR_NODE_LINK_TO(op, dequantize_in_node);
-      IR_NODE_LINK_TO(dequantize_in_node, dequantize_op);
-      IR_NODE_LINK_TO(dequantize_op, op_out);
+      next_op->Op()->SetInput(
+          next_op_input_name,
+          std::vector<std::string>({dequantize_out_node->Name()}));
+      UnlinkNodes(op_out, next_op);
+      IR_NODE_LINK_TO(op_out, dequantize_op);
+      IR_NODE_LINK_TO(dequantize_op, dequantize_out_node);
+      IR_NODE_LINK_TO(dequantize_out_node, next_op);
       dequantize_counter++;
     }
   };

paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_pass_tester.cc

@@ -42,60 +42,45 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
              type == "dropout") {
     op->SetInput("X", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
-    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+    if (type != "dropout") op->SetAttr("mkldnn_data_type", mkldnn_data_type);
   } else if (type == "fc") {
     op->SetInput("Input", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
     op->SetAttr("mkldnn_data_type", mkldnn_data_type);
-  } else if (type == "concat") {
+    op->SetAttr("force_fp32_output", force_fp32_output);
+  } else if (type == "concat" || type == "sum") {
     op->SetInput("X", inputs);
     op->SetOutput("Out", outputs);
     op->SetAttr("mkldnn_data_type", mkldnn_data_type);
-  } else if (type == "matmul" || type == "elementwise_add") {
+  } else if (type == "matmul" || type == "elementwise_add" ||
+             type == "elementwise_mul") {
     op->SetInput("X", {inputs[0]});
     if (inputs.size() > 1) op->SetInput("Y", {inputs[1]});
     op->SetOutput("Out", {outputs[0]});
     op->SetAttr("mkldnn_data_type", mkldnn_data_type);
+    if (type == "matmul") op->SetAttr("force_fp32_output", force_fp32_output);
+  } else if (type == "layer_norm") {
+    op->SetInput("X", {inputs[0]});
+    op->SetOutput("Y", {outputs[0]});
+    op->SetAttr("mkldnn_data_type", mkldnn_data_type);
   }
 }
 
+static const std::initializer_list<std::string> variable_names{
+    "z", "a", "b", "c", "d", "e", "f", "g", "h", "i"};
+
 void PreparePass(std::unique_ptr<ir::Graph>* graph, const ProgramDesc& prog,
                  const std::initializer_list<std::string> variable_names,
                  int* original_nodes_num, int* current_nodes_num) {
   auto pass = PassRegistry::Instance().Get("cpu_bfloat16_pass");
 
-  graph->reset(pass->Apply(graph->release()));
-
   *original_nodes_num = (*graph)->Nodes().size();
   (*graph).reset(pass->Apply((*graph).release()));
   *current_nodes_num = (*graph)->Nodes().size();
 }
 
-static const std::initializer_list<std::string> variable_names{
-    "z", "a", "b", "c", "d", "e", "f", "g", "h", "i"};
-
-ProgramDesc BuildProgramDesc(bool use_mkldnn) {
-  ProgramDesc prog;
-  for (auto& v : variable_names) {
-    prog.MutableBlock(0)->Var(v);
-  }
-  SetOp(&prog, "dropout", "Dropout1", {"z"}, {"a"}, use_mkldnn, "float32");
-  SetOp(&prog, "conv2d", "Conv1", {"a"}, {"b"}, use_mkldnn, "bfloat16");
-  SetOp(&prog, "pool2d", "Pool1", {"b"}, {"c"}, use_mkldnn, "bfloat16");
-  SetOp(&prog, "conv2d", "Conv1", {"c"}, {"d"}, use_mkldnn, "bfloat16");
-  SetOp(&prog, "dropout", "Dropout2", {"d"}, {"e"}, use_mkldnn, "float32");
-  SetOp(&prog, "transpose2", "Transpose1", {"e"}, {"f"}, use_mkldnn,
-        "bfloat16");
-  SetOp(&prog, "reshape2", "Reshape1", {"f"}, {"g"}, use_mkldnn, "bfloat16");
-  SetOp(&prog, "concat", "Concat1", {"g"}, {"h"}, use_mkldnn, "bfloat16");
-  SetOp(&prog, "dropout", "Dropout3", {"h"}, {"i"}, use_mkldnn, "float32");
-
-  return prog;
-}
-
-void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
-              int transpose_count, int quant_count, int dequant_count,
-              int added_nodes_count) {
+void MainTest(const ProgramDesc& prog, int quant_count, int dequant_count,
+              int force_fp32_count, int added_nodes_count) {
   std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
   int original_nodes_num, current_nodes_num;
   PreparePass(&graph, prog, variable_names, &original_nodes_num,
@@ -103,39 +88,114 @@ void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
 
   int quantize_nodes_count = 0;
   int dequantize_nodes_count = 0;
-  int conv2d_nodes_count = 0;
-  int pool2d_nodes_count = 0;
-  int transpose2_nodes_count = 0;
-
+  int force_fp32_nodes_count = 0;
   for (auto* node : graph->Nodes()) {
     if (node->IsOp()) {
       auto* op = node->Op();
-      if (op->Type() == "conv2d") {
-        conv2d_nodes_count++;
-      } else if (op->Type() == "pool2d") {
-        pool2d_nodes_count++;
-      } else if (op->Type() == "transpose2") {
-        transpose2_nodes_count++;
-      } else if (op->Type() == "quantize") {
+      if (op->Type() == "quantize") {
         quantize_nodes_count++;
       } else if (op->Type() == "dequantize") {
         dequantize_nodes_count++;
+      } else if (op->Type() == "conv2d" || op->Type() == "matmul" ||
+                 op->Type() == "fc") {
+        if (op->GetAttrIfExists<bool>("force_fp32_output"))
+          force_fp32_nodes_count++;
       }
     }
   }
-  EXPECT_EQ(conv2d_nodes_count, conv_count);
-  EXPECT_EQ(pool2d_nodes_count, pool_count);
-  EXPECT_EQ(transpose2_nodes_count, transpose_count);
   EXPECT_EQ(quantize_nodes_count, quant_count);
   EXPECT_EQ(dequantize_nodes_count, dequant_count);
+  EXPECT_EQ(force_fp32_nodes_count, force_fp32_count);
   EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num);
 }
 
-TEST(CpuQuantizePass, quantize) {
+ProgramDesc BuildProgramDescConv(bool use_mkldnn) {
+  ProgramDesc prog;
+  for (auto& v : variable_names) {
+    prog.MutableBlock(0)->Var(v);
+  }
+  SetOp(&prog, "dropout", "Dropout", {"a"}, {"b"}, use_mkldnn, "float32");
+  SetOp(&prog, "conv2d", "Conv1", {"b"}, {"c"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "pool2d", "Pool", {"c"}, {"d"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "conv2d", "Conv2", {"d"}, {"e"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "transpose2", "Transpose", {"e"}, {"f"}, use_mkldnn, "float32");
+
+  return prog;
+}
+
+TEST(CpuBfloat16Pass, convolution) {
+  bool use_mkldnn = true;
+  // 0 added + 1 force_fp32_output
+  int added_nodes = 0;
+  MainTest(BuildProgramDescConv(use_mkldnn), 0, 0, 1, added_nodes);
+}
+
+ProgramDesc BuildProgramDescDoubleInput(bool use_mkldnn) {
+  ProgramDesc prog;
+  for (auto& v : variable_names) {
+    prog.MutableBlock(0)->Var(v);
+  }
+  SetOp(&prog, "dropout", "Dropout", {"a"}, {"b"}, use_mkldnn, "float32");
+  SetOp(&prog, "matmul", "Matmul", {"b", "b"}, {"c"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "transpose2", "Transpose", {"d"}, {"e"}, use_mkldnn, "float32");
+  SetOp(&prog, "elementwise_add", "ElemetwiseAdd", {"c", "e"}, {"f"},
+        use_mkldnn, "bfloat16");
+  SetOp(&prog, "reshape2", "Reshape", {"f"}, {"g"}, use_mkldnn, "bfloat16");
+
+  return prog;
+}
+
+TEST(CpuBfloat16Pass, double_input_ops) {
+  bool use_mkldnn = true;
+  // 2 quant + 2 quant out
+  int added_nodes = 4;
+  MainTest(BuildProgramDescDoubleInput(use_mkldnn), 2, 0, 0, added_nodes);
+}
+
+ProgramDesc BuildProgramDescDuplicatedInput(bool use_mkldnn) {
+  ProgramDesc prog;
+  for (auto& v : variable_names) {
+    prog.MutableBlock(0)->Var(v);
+  }
+  SetOp(&prog, "dropout", "Dropout1", {"a"}, {"b"}, use_mkldnn, "float32");
+  SetOp(&prog, "dropout", "Dropout2", {"c"}, {"d"}, use_mkldnn, "float32");
+  SetOp(&prog, "concat", "Concat", {"b", "d"}, {"e"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "transpose2", "Transpose", {"f"}, {"g"}, use_mkldnn, "float32");
+  SetOp(&prog, "sum", "Sum", {"e", "g"}, {"h"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "reshape2", "Reshape", {"h"}, {"i"}, use_mkldnn, "bfloat16");
+
+  return prog;
+}
+
+TEST(CpuBfloat16Pass, duplicated_input_ops) {
+  bool use_mkldnn = true;
+  // 3 quant + 3 quant out
+  int added_nodes = 6;
+  MainTest(BuildProgramDescDuplicatedInput(use_mkldnn), 3, 0, 0, added_nodes);
+}
+
+ProgramDesc BuildProgramDescDoubleOutputs(bool use_mkldnn) {
+  ProgramDesc prog;
+  for (auto& v : variable_names) {
+    prog.MutableBlock(0)->Var(v);
+  }
+  SetOp(&prog, "layer_norm", "LayerNorm1", {"a"}, {"b"}, use_mkldnn,
+        "bfloat16");
+  SetOp(&prog, "dropout", "Dropout1", {"b"}, {"c"}, use_mkldnn, "float32");
+  SetOp(&prog, "transpose2", "Transpose", {"b"}, {"d"}, use_mkldnn, "bfloat16");
+  SetOp(&prog, "layer_norm", "LayerNorm2", {"d"}, {"e"}, use_mkldnn,
+        "bfloat16");
+  SetOp(&prog, "reshape2", "Reshape", {"e"}, {"f"}, use_mkldnn, "float32");
+  SetOp(&prog, "dropout", "Dropout2", {"e"}, {"g"}, use_mkldnn, "float32");
+
+  return prog;
+}
+
+TEST(CpuBfloat16Pass, double_outputs_ops) {
   bool use_mkldnn = true;
-  // 1 quantize + 1 dequantize
-  int added_nodes = 2;
-  MainTest(BuildProgramDesc(use_mkldnn), 2, 1, 1, 1, 2, added_nodes);
+  // 3 dequant + 3 dequant out
+  int added_nodes = 6;
+  MainTest(BuildProgramDescDoubleOutputs(use_mkldnn), 0, 3, 0, added_nodes);
 }
 
 }  // namespace ir