Add generalized Conv+Activation MKLDNN fuse pass creation (#19072)

test=develop

paddle/fluid/framework/ir/CMakeLists.txt

@@ -86,8 +86,7 @@ if(WITH_MKLDNN)
     pass_library(mkldnn_placement_pass base mkldnn)
     pass_library(depthwise_conv_mkldnn_pass base mkldnn)
     pass_library(conv_bias_mkldnn_fuse_pass inference mkldnn)
-    pass_library(conv_relu_mkldnn_fuse_pass inference mkldnn)
+    pass_library(conv_activation_mkldnn_fuse_pass inference mkldnn)
-    pass_library(conv_brelu_mkldnn_fuse_pass inference mkldnn)
     pass_library(conv_concat_relu_mkldnn_fuse_pass inference mkldnn)
     pass_library(conv_elementwise_add_mkldnn_fuse_pass inference mkldnn)
     pass_library(fc_mkldnn_pass inference mkldnn)
@@ -127,8 +126,7 @@ endif()
 if (WITH_MKLDNN)
     cc_test(test_depthwise_conv_mkldnn_pass SRCS mkldnn/depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
     cc_test(test_conv_bias_mkldnn_fuse_pass SRCS mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc DEPS conv_bias_mkldnn_fuse_pass naive_executor)
-    cc_test(test_conv_relu_mkldnn_fuse_pass SRCS mkldnn/conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
+    cc_test(test_conv_activation_mkldnn_fuse_pass SRCS mkldnn/conv_activation_mkldnn_fuse_pass_tester.cc DEPS conv_activation_mkldnn_fuse_pass)
-    cc_test(test_conv_brelu_mkldnn_fuse_pass SRCS mkldnn/conv_brelu_mkldnn_fuse_pass_tester.cc DEPS conv_brelu_mkldnn_fuse_pass)
     cc_test(test_conv_concat_relu_mkldnn_fuse_pass SRCS mkldnn/conv_concat_relu_mkldnn_fuse_pass_tester.cc DEPS conv_concat_relu_mkldnn_fuse_pass)
     cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
     cc_test(test_mkldnn_placement_pass SRCS mkldnn/mkldnn_placement_pass_tester.cc DEPS mkldnn_placement_pass)

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -771,6 +771,35 @@ PDNode *patterns::ConvBN::operator()(paddle::framework::ir::PDNode *conv_input,
   return bn_out_var;
 }
 
+PDNode *patterns::ConvActivation::operator()(
+    paddle::framework::ir::PDNode *conv_input, std::string conv_type,
+    std::string activation_type) {
+  // Create Operators
+  conv_input->assert_is_op_input(conv_type, "Input");
+  auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op(conv_type);
+  auto *activation_op =
+      pattern->NewNode(activation_repr())->assert_is_op(activation_type);
+  // Create variables
+  // Filter
+  auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
+                              ->AsInput()
+                              ->assert_is_persistable_var()
+                              ->assert_is_op_input(conv_type, "Filter");
+  // intermediate variable, will be removed in the IR after fuse.
+  auto *conv_out_var = pattern->NewNode(conv_out_repr())
+                           ->AsIntermediate()
+                           ->assert_is_only_output_of_op(conv_type)
+                           ->assert_is_op_input(activation_type);
+  // output
+  auto *activation_out_var = pattern->NewNode(activation_out_repr())
+                                 ->AsOutput()
+                                 ->assert_is_op_output(activation_type);
+
+  conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});
+  activation_op->LinksFrom({conv_out_var}).LinksTo({activation_out_var});
+  return activation_out_var;
+}
+
 PDNode *patterns::ConvReLU::operator()(
     paddle::framework::ir::PDNode *conv_input) {
   // Create Operators

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -431,6 +431,28 @@ struct ConvBN : public PatternBase {
   PATTERN_DECL_NODE(bn_saved_variance);
 };
 
+// Conv with Activation
+// op: conv + activation
+// named nodes:
+// conv_input, conv_weight,
+// conv_out, conv,
+// activation_out, activation
+struct ConvActivation : public PatternBase {
+  ConvActivation(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "conv_activation") {}
+
+  PDNode* operator()(PDNode* conv_input, std::string conv_type = "conv2d",
+                     std::string activation_type = "relu");
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(conv);
+  PATTERN_DECL_NODE(activation);
+  // declare variable node's name
+  PATTERN_DECL_NODE(conv_weight);
+  PATTERN_DECL_NODE(conv_out);
+  PATTERN_DECL_NODE(activation_out);
+};
+
 // CONV with ReLU
 // op: conv + relu
 // named nodes:

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

@@ -0,0 +1,101 @@
+// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void ConvActivationFusePass::ApplyImpl(ir::Graph* graph) const {
+  PADDLE_ENFORCE_NOT_NULL(graph, "graph cannot be nullptr.");
+  FusePassBase::Init("conv_activation_mkldnn_fuse", graph);
+
+  GraphPatternDetector gpd;
+  auto* conv_input = gpd.mutable_pattern()
+                         ->NewNode("conv_activation_mkldnn_fuse/conv_input")
+                         ->AsInput()
+                         ->assert_is_op_input(conv_type(), "Input");
+  patterns::ConvActivation conv_activation_pattern(
+      gpd.mutable_pattern(), "conv_activation_mkldnn_fuse");
+  conv_activation_pattern(conv_input, conv_type(), activation_type());
+
+  int found_conv_activation_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "handle " + conv_type() + "+" + activation_type() + " fuse";
+    GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
+                              conv_activation_pattern);  // Filter
+    GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out,
+                              conv_activation_pattern);              // tmp
+    GET_IR_NODE_FROM_SUBGRAPH(conv, conv, conv_activation_pattern);  // CONV op
+    GET_IR_NODE_FROM_SUBGRAPH(activation_out, activation_out,
+                              conv_activation_pattern);  // Out
+    GET_IR_NODE_FROM_SUBGRAPH(activation, activation,
+                              conv_activation_pattern);  // Activation op
+
+    // Transform Conv node into ConvActivation node.
+    OpDesc* desc = conv->Op();
+    desc->SetOutput("Output",
+                    std::vector<std::string>({activation_out->Name()}));
+
+    desc->SetAttr("fuse_activation", activation_type());
+
+    // MKLDNN ops use alpha and beta as activation parameters but paddle ops are
+    // not generalized
+    if (activation_type() == "relu6") {
+      desc->SetAttr("fuse_alpha",
+                    boost::get<float>(activation->Op()->GetAttr("threshold")));
+    } else {
+      desc->SetAttr("fuse_alpha",
+                    activation->Op()->HasAttr("alpha")
+                        ? boost::get<float>(activation->Op()->GetAttr("alpha"))
+                        : 0.0f);
+    }
+    desc->SetAttr("fuse_beta",
+                  activation->Op()->HasAttr("beta")
+                      ? boost::get<float>(activation->Op()->GetAttr("beta"))
+                      : 0.0f);
+
+    GraphSafeRemoveNodes(graph, {activation, conv_out});
+
+    PADDLE_ENFORCE_GT(subgraph.count(conv_input), 0UL,
+                      "subgraph has to contain conv_input node.");
+    IR_NODE_LINK_TO(conv, activation_out);
+    found_conv_activation_count++;
+  };
+
+  gpd(graph, handler);
+
+  AddStatis(found_conv_activation_count);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(conv_activation_mkldnn_fuse_pass,
+              paddle::framework::ir::ConvActivationFusePass);
+
+REGISTER_PASS(conv_relu_mkldnn_fuse_pass,
+              paddle::framework::ir::ConvActivationFusePass);
+
+REGISTER_PASS(conv_leaky_relu_mkldnn_fuse_pass,
+              paddle::framework::ir::Conv2DLeakyReLUFusePass);
+
+REGISTER_PASS(conv_relu6_mkldnn_fuse_pass,
+              paddle::framework::ir::Conv2DReLU6FusePass);

paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.h

@@ -0,0 +1,55 @@
+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include <string>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+/*
+ * Fuse Conv and Activation base class.
+ */
+class ConvActivationFusePass : public FusePassBase {
+ public:
+  virtual ~ConvActivationFusePass() {}
+  virtual std::string conv_type() const { return "conv2d"; }
+  virtual std::string activation_type() const { return "relu"; }
+
+ protected:
+  void ApplyImpl(ir::Graph* graph) const override;
+  const std::string name_scope_{"conv_activation_mkldnn_fuse"};
+};
+/*
+ * Fuse Conv and LeakyReLU class
+ */
+class Conv2DLeakyReLUFusePass : public ConvActivationFusePass {
+ public:
+  std::string activation_type() const { return "leaky_relu"; }
+};
+/*
+ * Fuse Conv and BoundedReLU class
+ */
+class Conv2DReLU6FusePass : public ConvActivationFusePass {
+ public:
+  std::string activation_type() const { return "relu6"; }
+};
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

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

@@ -0,0 +1,145 @@
+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/ir/mkldnn/conv_activation_mkldnn_fuse_pass.h"
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/framework/op_proto_maker.h"
+
+namespace paddle {
+namespace framework {
+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 is_activation = false,
+           bool use_mkldnn = false) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetAttr("name", name);
+  if (type == "conv2d") {
+    op->SetAttr("use_mkldnn", use_mkldnn);
+    op->SetInput("Input", {inputs[0]});
+    op->SetInput("Filter", {inputs[1]});
+    op->SetInput("Bias", {inputs[2]});
+  } else if (is_activation) {
+    op->SetAttr("use_mkldnn", use_mkldnn);
+    op->SetInput("X", inputs);
+    if (type == "leaky_relu") {
+      op->SetAttr("alpha", 0.02f);
+    } else if (type == "relu6") {
+      op->SetAttr("threshold", 6.0f);
+    }
+  }
+  op->SetOutput("Out", outputs);
+  op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
+              static_cast<int>(OpRole::kForward));
+}
+
+// a->OP0->b
+// b->OP1->c
+// (c, weights, bias)->conv->f
+// (f)->activation->g
+ProgramDesc BuildProgramDesc(std::string activation) {
+  ProgramDesc prog;
+  for (auto& v :
+       std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
+                                 "h", "weights2", "bias2", "k", "l", "m"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::SELECTED_ROWS);
+    if (v == "weights" || v == "bias" || v == "weights2" || v == "bias2") {
+      var->SetPersistable(true);
+    }
+  }
+
+  SetOp(&prog, "OP0", "op0", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"b"}));
+  SetOp(&prog, "OP1", "op1", std::vector<std::string>({"b"}),
+        std::vector<std::string>({"c"}));
+  // conv+activation, both with MKL-DNN
+  SetOp(&prog, "conv2d", "conv1",
+        std::vector<std::string>({"c", "weights", "bias"}),
+        std::vector<std::string>({"f"}), false, true);
+  SetOp(&prog, activation, "activation1", std::vector<std::string>({"f"}),
+        std::vector<std::string>({"g"}), true, true);
+  SetOp(&prog, "OP3", "op3", std::vector<std::string>({"g"}),
+        std::vector<std::string>({"h"}));
+  // conv+activation, only one with MKL-DNN
+  SetOp(&prog, "conv2d", "conv2",
+        std::vector<std::string>({"h", "weights2", "bias2"}),
+        std::vector<std::string>({"k"}), false, true);
+  SetOp(&prog, "activation", "activation2", std::vector<std::string>({"k"}),
+        std::vector<std::string>({"l"}), true, false);
+  SetOp(&prog, "OP4", "op4", std::vector<std::string>({"l"}),
+        std::vector<std::string>({"m"}));
+
+  return prog;
+}
+
+void MainTest(std::string activation) {
+  auto prog = BuildProgramDesc(activation);
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  auto pass =
+      PassRegistry::Instance().Get("conv_" + activation + "_mkldnn_fuse_pass");
+
+  int original_nodes_num = graph->Nodes().size();
+
+  graph.reset(pass->Apply(graph.release()));
+
+  int current_nodes_num = graph->Nodes().size();
+
+  // Remove 3 Nodes: CONV, activation, conv_out
+  // Add 1 Node: ConvActivation
+  EXPECT_EQ(original_nodes_num - 2, current_nodes_num);
+
+  // Assert conv_activation op in newly generated graph
+  int conv_activation_count = 0;
+
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp() && node->Op()->Type() == "conv2d") {
+      auto* op = node->Op();
+      ASSERT_TRUE(op->HasAttr("use_mkldnn"));
+      EXPECT_TRUE(boost::get<bool>(op->GetAttr("use_mkldnn")));
+      auto op_name = boost::get<std::string>(op->GetAttr("name"));
+      std::string fuse_activation =
+          op->HasAttr("fuse_activation")
+              ? boost::get<std::string>(op->GetAttr("fuse_activation"))
+              : "";
+      if (fuse_activation == activation) {
+        ++conv_activation_count;
+      }
+      // check if only "conv1" convolution is fused
+      if (op_name == "conv1") {
+        ASSERT_TRUE(op->HasAttr("fuse_activation"));
+      } else if (op_name == "conv2") {
+        ASSERT_FALSE(op->HasAttr("fuse_activation"));
+      }
+    }
+  }
+  EXPECT_EQ(conv_activation_count, 1);
+}
+
+TEST(ConvActivationFusePass, conv_relu_fuse_pass) { MainTest("relu"); }
+TEST(ConvActivationFusePass, conv_leaky_relu_fuse_pass) {
+  MainTest("leaky_relu");
+}
+TEST(ConvActivationFusePass, conv_relu6_fuse_pass) { MainTest("relu6"); }
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(conv_activation_mkldnn_fuse_pass);

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

@@ -83,7 +83,7 @@ void ConvConcatReLUFusePass::FuseConvConcatReLU(
 
     // Transform Conv node into ConvReLU node.
     OpDesc* conv_desc = conv_op->Op();
-    conv_desc->SetAttr("fuse_relu", true);
+    conv_desc->SetAttr("fuse_activation", std::string("relu"));
 
     // Remove ReLU when all Convs were transformed.
     auto number_of_unfused_convs_left =

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

@@ -28,7 +28,7 @@ void SetOp(ProgramDesc* prog, const std::string& type,
   op->SetType(type);
   if (type == "conv2d") {
     op->SetAttr("use_mkldnn", use_mkldnn);
-    op->SetAttr("fuse_relu", false);
+    op->SetAttr("fuse_activation", std::string(""));
     op->SetInput("Input", {inputs[0]});
     op->SetInput("Filter", {inputs[1]});
     if (inputs.size() > 2) {
@@ -109,8 +109,9 @@ void MainTest(const ProgramDesc& prog, bool fuse_relu) {
     if (node->IsOp()) {
       auto* op = node->Op();
       if (op->Type() == "conv2d") {
-        ASSERT_TRUE(op->HasAttr("fuse_relu"));
+        ASSERT_TRUE(op->HasAttr("fuse_activation"));
-        bool fuse_relu_attr = boost::get<bool>(op->GetAttr("fuse_relu"));
+        bool fuse_relu_attr =
+            (boost::get<std::string>(op->GetAttr("fuse_activation")) == "relu");
         EXPECT_EQ(fuse_relu, fuse_relu_attr);
       } else if (op->Type() == "relu") {
         relu_count++;

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

@@ -109,8 +109,11 @@ void ResidualConnectionMKLDNNFusePass::IdentityFuseHandle::operator()(
 
   if (!IsReachable(graph, elementwise_add_identity, conv_output)) return;
 
-  auto fuse_relu = HasAttribute<bool>(*conv_op, "fuse_relu");
+  std::string fuse_activation =
-  if (fuse_relu && *fuse_relu) return;
+      conv_op->Op()->HasAttr("fuse_activation")
+          ? boost::get<std::string>(conv_op->Op()->GetAttr("fuse_activation"))
+          : "";
+  if (fuse_activation == "relu" || fuse_activation == "relu6") return;
 
   conv_op->Op()->SetInput("ResidualData", {elementwise_add_identity->Name()});
   conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});
@@ -179,8 +182,12 @@ void ResidualConnectionMKLDNNFusePass::ProjectionFuseHandle::operator()(
     return;
   }
 
-  auto fuse_relu = HasAttribute<bool>(*residual_conv_op, "fuse_relu");
+  std::string fuse_activation =
-  if (fuse_relu && *fuse_relu) return;
+      residual_conv_op->Op()->HasAttr("fuse_activation")
+          ? boost::get<std::string>(
+                residual_conv_op->Op()->GetAttr("fuse_activation"))
+          : "";
+  if (fuse_activation == "relu" || fuse_activation == "relu6") return;
 
   residual_conv_op->Op()->SetInput("ResidualData", {projection_node->Name()});
   residual_conv_op->Op()->SetOutput("Output", {elementwise_add_out->Name()});

paddle/fluid/inference/api/mkldnn_quantizer.cc

@@ -68,10 +68,13 @@ bool AnalysisPredictor::MkldnnQuantizer::CalculateScales() {
             if (is_output) {
               if (op->Type() == "conv2d") {
                 // output of conv2d with relu must be unsigned
-                is_unsigned = (op->HasAttr("fuse_relu") &&
+                std::string fuse_activation =
-                               boost::get<bool>(op->GetAttr("fuse_relu"))) ||
+                    op->HasAttr("fuse_activation")
-                              (op->HasAttr("fuse_brelu") &&
+                        ? boost::get<std::string>(
-                               boost::get<bool>(op->GetAttr("fuse_brelu")));
+                              op->GetAttr("fuse_activation"))
+                        : "";
+                is_unsigned =
+                    (fuse_activation == "relu" || fuse_activation == "relu6");
               } else if (op->Type() == "relu") {
                 is_unsigned = true;
               } else if (op->Type() == "transpose2" ||

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -180,8 +180,9 @@ void CpuPassStrategy::EnableMKLDNN() {
              "conv3d_bias_mkldnn_fuse_pass",  //
              "conv_elementwise_add_mkldnn_fuse_pass",
              "conv_concat_relu_mkldnn_fuse_pass",
-             "conv_relu_mkldnn_fuse_pass",   //
+             "conv_relu_mkldnn_fuse_pass",        //
-             "conv_brelu_mkldnn_fuse_pass",  //
+             "conv_leaky_relu_mkldnn_fuse_pass",  //
+             "conv_relu6_mkldnn_fuse_pass",       //
              // Disabled due to topology-dependent speed-up
              // "fc_mkldnn_pass"
          })) {

paddle/fluid/operators/conv_op.cc

@@ -215,6 +215,14 @@ void Conv2DOpMaker::Make() {
   AddAttr<float>("fuse_brelu_threshold",
                  "(float, default false 6.0) Only used in mkldnn kernel")
       .SetDefault(6.0f);
+  AddAttr<std::string>("fuse_activation",
+                       "(string, default \"\") Only used in mkldnn kernel")
+      .SetDefault("");
+  AddAttr<float>("fuse_alpha",
+                 "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
+  AddAttr<float>("fuse_beta", "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
   AddAttr<bool>("fuse_residual_connection",
                 "(bool, default false) Only used in mkldnn kernel. Used "
                 "whenever convolution output is as an input to residual "
@@ -352,6 +360,14 @@ void Conv3DOpMaker::Make() {
       .SetDefault(false);
   AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
+  AddAttr<std::string>("fuse_activation",
+                       "(string, default \"\") Only used in mkldnn kernel")
+      .SetDefault("");
+  AddAttr<float>("fuse_alpha",
+                 "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
+  AddAttr<float>("fuse_beta", "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
   AddAttr<bool>("fuse_residual_connection",
                 "(bool, default false) Only used in mkldnn kernel. Used "
                 "whenever convolution output is as an input to residual "

paddle/fluid/operators/conv_transpose_op.cc

@@ -170,6 +170,14 @@ void Conv2DTransposeOpMaker::Make() {
       .SetDefault(false);
   AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
+  AddAttr<std::string>("fuse_activation",
+                       "(string, default \"\") Only used in mkldnn kernel")
+      .SetDefault("");
+  AddAttr<float>("fuse_alpha",
+                 "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
+  AddAttr<float>("fuse_beta", "(float, default 0.0) Only used in mkldnn kernel")
+      .SetDefault(0.0f);
   AddAttr<std::string>(
       "data_format",
       "(string, default NCHW) Only used in "

paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc

@@ -71,13 +71,14 @@ inline mkldnn::memory::format GetWeightsFormat(mkldnn::memory::format format,
 
 static mkldnn::memory::data_type GetDstType(bool is_int8,
                                             bool force_fp32_output,
-                                            bool fuse_relu, bool fuse_brelu,
+                                            std::string fuse_activation,
                                             bool fuse_residual_conn,
                                             const Tensor* residual_param) {
   auto dst_dt = mkldnn::memory::data_type::f32;  // uint8_t, int8_t, float
   if (is_int8) {
-    dst_dt = (fuse_relu || fuse_brelu) ? mkldnn::memory::data_type::u8
+    dst_dt = (fuse_activation == "relu" || fuse_activation == "relu6")
-                                       : mkldnn::memory::data_type::s8;
+                 ? mkldnn::memory::data_type::u8
+                 : mkldnn::memory::data_type::s8;
     if (force_fp32_output) {
       dst_dt = mkldnn::memory::data_type::f32;
     }
@@ -100,12 +101,11 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     if (!is_INT8) {
       ComputeFP32(ctx);
     } else {
-      bool fuse_relu = ctx.Attr<bool>("fuse_relu");
+      std::string fuse_activation = ctx.Attr<std::string>("fuse_activation");
       bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
-      bool fuse_brelu = ctx.Attr<bool>("fuse_brelu");
       bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
       auto residual_param = ctx.Input<Tensor>("ResidualData");
-      auto dst_dt = GetDstType(true, force_fp32_output, fuse_relu, fuse_brelu,
+      auto dst_dt = GetDstType(true, force_fp32_output, fuse_activation,
                                fuse_residual_conn, residual_param);
       if (dst_dt == mkldnn::memory::data_type::f32) {
         ComputeINT8<float>(ctx);
@@ -150,16 +150,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
-    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
+    std::string fuse_activation = ctx.Attr<std::string>("fuse_activation");
+    float fuse_alpha = ctx.Attr<float>("fuse_alpha");
+    float fuse_beta = ctx.Attr<float>("fuse_beta");
     bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
-    bool fuse_brelu = false;
-    float fuse_brelu_threshold = 6.0;
     int groups = ctx.Attr<int>("groups");
     bool is_conv3d = strides.size() == 3U;
-    if (!is_conv3d) {
+
-      fuse_brelu = ctx.Attr<bool>("fuse_brelu");
-      fuse_brelu_threshold = ctx.Attr<float>("fuse_brelu_threshold");
-    }
     // TODO(tpatejko): add support for dilation
     PADDLE_ENFORCE(
         is_conv3d
@@ -180,7 +177,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
 
     // Get unique name for storing MKLDNN primitives
     const std::string key = platform::ConvMKLDNNHandler::GetHash(
-        src_tz, weights_tz, fuse_relu, fuse_brelu, strides, paddings, dilations,
+        src_tz, weights_tz, fuse_activation, strides, paddings, dilations,
         groups, ctx.op().Input("Input") + ctx.op().Input("Filter"));
 
     std::vector<primitive> pipeline;
@@ -232,13 +229,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
           bias_tz, platform::MKLDNNGetDataType<T>(), memory::format::x);
       conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
-          fuse_relu, fuse_residual_conn, fuse_brelu, fuse_brelu_threshold,
+          fuse_activation, fuse_alpha, fuse_beta, fuse_residual_conn,
           fwd_prop_kind);
     } else {
       conv_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, boost::none, dst_md, strides, paddings,
-          mkldnn_engine, fuse_relu, fuse_residual_conn, fuse_brelu,
+          mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
-          fuse_brelu_threshold, fwd_prop_kind);
+          fuse_residual_conn, fwd_prop_kind);
     }
 
     // create mkldnn memory from input tensors (data/weights)
@@ -355,12 +352,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
     int groups = ctx.Attr<int>("groups");
-    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
+    std::string fuse_activation = ctx.Attr<std::string>("fuse_activation");
+    float fuse_alpha = ctx.Attr<float>("fuse_alpha");
+    float fuse_beta = ctx.Attr<float>("fuse_beta");
     bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
-    bool fuse_brelu = ctx.Attr<bool>("fuse_brelu");
-    float fuse_brelu_threshold = ctx.Attr<float>("fuse_brelu_threshold");
     bool force_fp32_output = ctx.Attr<bool>("force_fp32_output");
-    bool unsigned_output = fuse_relu || fuse_brelu;
+    bool unsigned_output =
+        (fuse_activation == "relu" || fuse_activation == "relu6");
 
     PADDLE_ENFORCE(!fuse_residual_conn || !force_fp32_output,
                    "residual fusion does not support force output with fp32");
@@ -394,7 +392,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     key.reserve(MaxKeyLength);
     platform::ConvMKLDNNHandler::AppendKey(
         &key, src_tz, weights_tz, strides, paddings, dilations, groups, src_dt,
-        input->format(), fuse_relu, fuse_residual_conn, fuse_brelu,
+        input->format(), fuse_activation, fuse_residual_conn,
         ctx.op().Input("Input") + ctx.op().Input("Filter"));
 
     const std::string key_conv_pd = key + "@conv_pd";
@@ -484,6 +482,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
       handler.reset(
           new platform::ConvMKLDNNHandler(dev_ctx, mkldnn_engine, key));
       // create a conv primitive descriptor and save it for usage in backward
+
+      // TODO(grygielski) if INT8 brelu post-op will be available, just delete
+      // whole if statement
+      if (fuse_activation == "relu6") {
+        fuse_activation = "relu";
+        fuse_alpha = 0.0f;
+      }
+
       auto propagation = is_test ? mkldnn::prop_kind::forward_scoring
                                  : mkldnn::prop_kind::forward_training;
 
@@ -493,13 +499,13 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
                                                mkldnn::memory::format::x);
         conv_pd = handler->AcquireConvolutionPrimitiveDescriptor(
             src_md, weights_md, bias_md, dst_md, strides, paddings,
-            mkldnn_engine, fuse_relu, fuse_residual_conn, fuse_brelu,
+            mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
-            fuse_brelu_threshold, propagation, output_shift_scale, sum_scale);
+            fuse_residual_conn, propagation, output_shift_scale, sum_scale);
       } else {
         conv_pd = handler->AcquireConvolutionPrimitiveDescriptor(
             src_md, weights_md, boost::none, dst_md, strides, paddings,
-            mkldnn_engine, fuse_relu, fuse_residual_conn, fuse_brelu,
+            mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta,
-            fuse_brelu_threshold, propagation, output_shift_scale, sum_scale);
+            fuse_residual_conn, propagation, output_shift_scale, sum_scale);
       }
 
       // create mkldnn memory from input tensors (data/weights)
@@ -681,11 +687,6 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     GetWeightsTz(weights_tz, g, is_conv3d);
     std::vector<int> dst_tz =
         paddle::framework::vectorize2int(output_grad->dims());
-    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
-    bool fuse_brelu = false;
-    if (!is_conv3d) {
-      fuse_brelu = ctx.Attr<bool>("fuse_brelu");
-    }
     auto src_format = input->format();
     mkldnn::memory::format weights_format =
         GetWeightsFormat(filter->format(), g, is_conv3d);
@@ -694,8 +695,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
     // as well as attributes of primitive to be created
     // This name will be used as key when saving info into device context
     const std::string key = platform::ConvMKLDNNHandler::GetHash(
-        src_tz, weights_tz, fuse_relu, fuse_brelu, strides, paddings, dilations,
+        src_tz, weights_tz, "", strides, paddings, dilations, groups,
-        groups, ctx.op().Input("Input") + ctx.op().Input("Filter"));
+        ctx.op().Input("Input") + ctx.op().Input("Filter"));
 
     const std::string key_conv_pd = key + "@conv_pd";
     std::vector<primitive> pipeline;

paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc

@@ -142,7 +142,9 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
     std::string data_format = ctx.Attr<std::string>("data_format");
     auto chosen_memory_format =
         platform::data_format_to_memory_format(data_format);
-    bool fuse_relu = ctx.Attr<bool>("fuse_relu");
+    std::string fuse_activation = ctx.Attr<std::string>("fuse_activation");
+    float fuse_alpha = ctx.Attr<float>("fuse_alpha");
+    float fuse_beta = ctx.Attr<float>("fuse_beta");
 
     auto src_md = platform::MKLDNNMemDesc(
         src_tz, platform::MKLDNNGetDataType<T>(), chosen_memory_format);
@@ -166,11 +168,12 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
           bias_tz, platform::MKLDNNGetDataType<T>(), mkldnn::memory::format::x);
       conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, bias_md, dst_md, strides, paddings, mkldnn_engine,
-          fuse_relu, false, false, 0.0, fwd_prop_kind);
+          fuse_activation, fuse_alpha, fuse_beta, false, fwd_prop_kind);
     } else {
       conv_transpose_pd = handler.AcquireConvolutionPrimitiveDescriptor(
           src_md, weights_md, boost::none, dst_md, strides, paddings,
-          mkldnn_engine, fuse_relu, false, false, 0.0, fwd_prop_kind);
+          mkldnn_engine, fuse_activation, fuse_alpha, fuse_beta, false,
+          fwd_prop_kind);
     }
 
     // create mkldnn memory from input tensors (data/weights)

paddle/fluid/platform/mkldnn_reuse.h

@@ -217,8 +217,8 @@ class MKLDNNHandler {
       const mkldnn::memory::dims& weights_dims, const std::vector<int>& strides,
       const std::vector<int>& paddings, const std::vector<int>& dilations,
       const int& groups, const mkldnn::memory::data_type& srcdt,
-      const mkldnn::memory::format& format, const bool& relu,
+      const mkldnn::memory::format& format, const std::string& fuse_activation,
-      const bool& residual, const bool& brelu, const std::string& suffix) {
+      const bool& residual, const std::string& suffix) {
     AppendKeyDims(key, input_dims);
 
     AppendKeyDims(key, weights_dims);
@@ -232,9 +232,8 @@ class MKLDNNHandler {
     AppendKey(key, std::to_string(groups));
     AppendKey(key, std::to_string(srcdt));
     AppendKey(key, std::to_string(format));
-    AppendKey(key, std::to_string(relu));
+    AppendKey(key, fuse_activation);
     AppendKey(key, std::to_string(residual));
-    AppendKey(key, std::to_string(brelu));
     AppendKey(key, suffix);
   }
 
@@ -1179,9 +1178,8 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
   }
 
   mkldnn::primitive_attr CreatePostOps(
-      bool fuse_relu, bool fuse_residual_conn, bool fuse_brelu,
+      std::string fuse_activation, float fuse_alpha, float fuse_beta,
-      float fuse_brelu_threshold,
+      bool fuse_residual_conn, const std::vector<float> output_shift_scale = {},
-      const std::vector<float> output_shift_scale = {},
       float sum_scale = 1.0f) const {
     mkldnn::primitive_attr conv_attr;
     mkldnn::post_ops post_operations;
@@ -1199,20 +1197,17 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
     }
     // Fusion with ReLU layer is executed through the PostOps feature. Create a
     // PostOps object and configure it to execute an eltwise relu operation.
-    if (fuse_relu) {
+    if (fuse_activation == "relu" || fuse_activation == "leaky_relu") {
       constexpr float scale = 1.0f;
-      constexpr float negative_slope = 0.0f;
-      constexpr float placeholder = 0.0f;
       post_operations.append_eltwise(scale, mkldnn::algorithm::eltwise_relu,
-                                     negative_slope, placeholder);
+                                     fuse_alpha, fuse_beta);
     }
 
-    if (fuse_brelu) {
+    if (fuse_activation == "relu6") {
       constexpr float scale = 1.0f;
-      constexpr float placeholder = 0.0f;
       post_operations.append_eltwise(scale,
                                      mkldnn::algorithm::eltwise_bounded_relu,
-                                     fuse_brelu_threshold, placeholder);
+                                     fuse_alpha, fuse_beta);
     }
     conv_attr.set_post_ops(post_operations);
     return conv_attr;
@@ -1224,9 +1219,8 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
       boost::optional<const mkldnn::memory::desc&> bias,
       const mkldnn::memory::desc& dst, const std::vector<int>& strides,
       const std::vector<int>& paddings, const mkldnn::engine& engine,
-      const bool fuse_relu, const bool fuse_residual_conn,
+      const std::string& fuse_activation, float fuse_alpha, float fuse_beta,
-      const bool fuse_brelu, const float fuse_brelu_threshold,
+      const bool fuse_residual_conn, mkldnn::prop_kind fwd_prop_kind,
-      mkldnn::prop_kind fwd_prop_kind,
       const std::vector<float> output_shift_scale = {},
       const float sum_scale = 1.0f) {
     // Conv PD has to be passed to Grad op that
@@ -1259,8 +1253,8 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
                        padding_dims, mkldnn::padding_kind::zero);
 
         mkldnn::primitive_attr conv_attr =
-            CreatePostOps(fuse_relu, fuse_residual_conn, fuse_brelu,
+            CreatePostOps(fuse_activation, fuse_alpha, fuse_beta,
-                          fuse_brelu_threshold, output_shift_scale, sum_scale);
+                          fuse_residual_conn, output_shift_scale, sum_scale);
 
         conv_pd_.reset(new typename forward_t::primitive_desc(
             conv_desc, conv_attr, engine));
@@ -1343,14 +1337,12 @@ class ConvMKLDNNTemplateHandler : public MKLDNNHandler {
   // TODO(jczaja): Make hashing function more optimial
   static std::string GetHash(mkldnn::memory::dims& input_dims,    // NOLINT
                              mkldnn::memory::dims& weights_dims,  // NOLINT
-                             const bool& fuse_relu,               // NOLINT
+                             const std::string& fuse_activation,  // NOLINT
-                             const bool& fuse_brelu,              // NOLINT
                              std::vector<int>& strides,           // NOLINT
                              std::vector<int>& paddings,          // NOLINT
                              std::vector<int>& dilations,         // NOLINT
                              int groups, const std::string& suffix) {
-    return dims2str(input_dims) + dims2str(weights_dims) +
+    return dims2str(input_dims) + dims2str(weights_dims) + fuse_activation +
-           std::to_string(fuse_relu) + std::to_string(fuse_brelu) +
            dims2str(strides) + dims2str(paddings) + dims2str(dilations) +
            std::to_string(groups) + suffix;
   }

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_int8_mkldnn_op.py

@@ -83,12 +83,12 @@ class TestConv2dInt8Op(TestConv2dOp):
                     input_residual, self.input_residual_size).astype(
                         self.srctype) * (self.scale_out / self.scale_in_eltwise
                                          ))
-                if self.fuse_relu:
+                if self.fuse_activation == "relu":
                     output = np.maximum(output_tmp, 0).astype(self.dsttype)
                 else:
                     output = output_tmp.astype(self.dsttype)
             else:
-                if self.fuse_relu:
+                if self.fuse_activation == "relu":
                     output = np.maximum(np.round(output1 - output2),
                                         0).astype(self.dsttype)
                 else:
@@ -113,12 +113,12 @@ class TestConv2dInt8Op(TestConv2dOp):
                         input_residual, self.input_residual_size).astype(
                             np.int32) * (self.scale_out / self.scale_in_eltwise
                                          ))
-                if self.fuse_relu:
+                if self.fuse_activation == "relu":
                     output = np.maximum(output_tmp_res, 0).astype(self.dsttype)
                 else:
                     output = output_tmp_res.astype(self.dsttype)
             else:
-                if self.fuse_relu:
+                if self.fuse_activation == "relu":
                     output = np.maximum(output1_tmp, 0).astype(self.dsttype)
                 else:
                     output = output1_tmp.astype(self.dsttype)
@@ -145,7 +145,7 @@ class TestConv2dInt8Op(TestConv2dOp):
             'Scale_out': self.scale_out,
             'Scale_weights': self.scale_weights,
             'Scale_in_eltwise': self.scale_in_eltwise,
-            'fuse_relu': self.fuse_relu,
+            'fuse_activation': self.fuse_activation,
             'fuse_residual_connection': self.fuse_residual
         }
         self.outputs = {'Output': output}
@@ -178,7 +178,7 @@ class TestConv2dInt8Op(TestConv2dOp):
         self.dsttype = np.int8
 
     def init_fuse_relu(self):
-        self.fuse_relu = True
+        self.fuse_activation = "relu"
 
     def init_fuse_residual(self):
         self.fuse_residual = True
@@ -262,11 +262,11 @@ class TestWithInput1x1Filter1x1(TestConv2dInt8Op):
         self.groups = 3
 
 
-def init_data_type_with_fusion(self, input_dt, fuse_relu, fuse_residual):
+def init_data_type_with_fusion(self, input_dt, fuse_activation, fuse_residual):
     self.srctype = input_dt
-    self.dsttype = np.uint8 if fuse_relu else np.int8
+    self.dsttype = np.uint8 if fuse_activation == "relu" else np.int8
 
-    self.fuse_relu = fuse_relu
+    self.fuse_activation = fuse_activation
 
     self.fuse_residual = fuse_residual
 
@@ -277,43 +277,43 @@ def create_test_int8_class(parent):
 
     class TestS8U8Case(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.int8, True, False)
+            init_data_type_with_fusion(self, np.int8, "relu", False)
 
     #--------------------test conv2d s8 in and s8 out--------------------
 
     class TestS8S8Case(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.int8, False, False)
+            init_data_type_with_fusion(self, np.int8, "", False)
 
     #--------------------test conv2d u8 in and s8 out--------------------
 
     class TestU8S8Case(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.uint8, False, False)
+            init_data_type_with_fusion(self, np.uint8, "", False)
 
     #--------------------test conv2d u8 in and u8 out without residual fuse--------------------
 
     class TestU8U8Case(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.uint8, True, False)
+            init_data_type_with_fusion(self, np.uint8, "relu", False)
 
     #--------------------test conv2d s8 in and u8 out with residual fuse--------------------
 
     class TestS8U8ResCase(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.int8, True, True)
+            init_data_type_with_fusion(self, np.int8, "relu", True)
 
     #--------------------test conv2d s8 in and s8 out with residual fuse--------------------
 
     class TestS8S8ResCase(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.int8, False, True)
+            init_data_type_with_fusion(self, np.int8, "", True)
 
     #--------------------test conv2d u8 in and s8 out with residual fuse--------------------
 
     class TestU8S8ResCase(parent):
         def init_data_type(self):
-            init_data_type_with_fusion(self, np.uint8, False, True)
+            init_data_type_with_fusion(self, np.uint8, "", True)
 
     cls_name_s8u8 = "{0}_relu_{1}_residual_0".format(parent.__name__, "1")
     cls_name_s8s8 = "{0}_relu_{1}_residual_0".format(parent.__name__, "0")

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py

@@ -56,8 +56,9 @@ class TestConv2dMKLDNNOp(TestConv2dOp):
     def setUp(self):
         self.fuse_bias = False
         self.bias_size = None
-        self.fuse_relu = False
+        self.fuse_activation = ""
-        self.fuse_brelu = False
+        self.fuse_alpha = 0
+        self.fuse_beta = 0
         self.fuse_brelu_threshold = 6.0
         self.fuse_residual_connection = False
         self.input_residual_size = None
@@ -83,18 +84,18 @@ class TestConv2dMKLDNNOp(TestConv2dOp):
             self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype(
                 input_residual)
 
-        if self.fuse_relu:
+        if self.fuse_activation == "relu":
             output = np.maximum(output, 0).astype(self.dsttype)
 
-        if self.fuse_brelu:
+        if self.fuse_activation == "relu6":
-            output = np.minimum(
+            output = np.minimum(np.maximum(output, 0),
-                np.maximum(output, 0),
+                                self.fuse_alpha).astype(self.dsttype)
-                self.fuse_brelu_threshold).astype(self.dsttype)
         output = output.astype(self.dtype)
 
         self.attrs['fuse_bias'] = self.fuse_bias
-        self.attrs['fuse_relu'] = self.fuse_relu
+        self.attrs['fuse_activation'] = self.fuse_activation
-        self.attrs['fuse_brelu'] = self.fuse_brelu
+        self.attrs['fuse_alpha'] = self.fuse_alpha
+        self.attrs['fuse_beta'] = self.fuse_beta
         self.attrs['fuse_brelu_threshold'] = self.fuse_brelu_threshold
         self.attrs['fuse_residual_connection'] = self.fuse_residual_connection
 
@@ -104,8 +105,8 @@ class TestConv2dMKLDNNOp(TestConv2dOp):
 class TestWithbreluFusion(TestConv2dMKLDNNOp):
     def init_test_case(self):
         TestConv2dMKLDNNOp.init_test_case(self)
-        self.fuse_brelu = True
+        self.fuse_activation = "relu6"
-        self.fuse_brelu_threshold = 6.0
+        self.fuse_alpha = 6.0
         self.dsttype = np.float32
 
     def test_check_grad(self):

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_transpose_mkldnn_op.py

@@ -51,7 +51,9 @@ class TestConv2dTransposeMKLDNNOp(TestConv2dTransposeOp):
         self.pad = [0, 0]
         self.fuse_bias = False
         self.bias_size = None
-        self.fuse_relu = False
+        self.fuse_activation = ""
+        self.fuse_alpha = 0.0
+        self.fuse_beta = 0.0
         self.stride = [1, 1]
         self.dilations = [1, 1]
         self.input_size = [2, 3, 5, 5]  # NCHW
@@ -71,11 +73,13 @@ class TestConv2dTransposeMKLDNNOp(TestConv2dTransposeOp):
             self.attrs['fuse_bias'] = self.fuse_bias
             self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias)
 
-        if self.fuse_relu:
+        if self.fuse_activation == "relu":
             output = np.maximum(output, 0).astype(self.dtype)
+        output = output.astype(self.dtype)
 
-        self.attrs['fuse_bias'] = self.fuse_bias
+        self.attrs['fuse_activation'] = self.fuse_activation
-        self.attrs['fuse_relu'] = self.fuse_relu
+        self.attrs['fuse_alpha'] = self.fuse_alpha
+        self.attrs['fuse_beta'] = self.fuse_beta
 
         self.outputs['Output'] = output