Merge pull request #14709 from yihuaxu/develop_4f71a6ee2_conv3d_bias_fusion_mkldnn_impl

Implement the fusion of convolution 3D and bias for mkldnn

paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.cc

@@ -46,14 +46,16 @@ std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
   auto* scope = param_scope();
   PADDLE_ENFORCE(scope);
 
+  std::string type = is_conv3d() ? "conv3d" : "conv2d";
+
   GraphPatternDetector gpd;
   auto* conv_input =
       gpd.mutable_pattern()
           ->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
           ->AsInput()
-          ->assert_is_op_input("conv2d", "Input");
+          ->assert_is_op_input(type, "Input");
   patterns::ConvBias conv_bias_pattern(gpd.mutable_pattern(), name_scope_);
-  conv_bias_pattern(conv_input);
+  conv_bias_pattern(conv_input, is_conv3d());
   int found_conv_bias_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
@@ -109,7 +111,7 @@ std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
       desc.SetInput("Filter", std::vector<std::string>({conv_weight->Name()}));
       desc.SetInput("Bias", std::vector<std::string>({eltwise_bias->Name()}));
       desc.SetOutput("Output", std::vector<std::string>({eltwise_out->Name()}));
-      desc.SetType("conv2d");
+      desc.SetType(type);
 
       for (auto& attr : conv->Op()->GetAttrMap()) {
         desc.SetAttr(attr.first, attr.second);
@@ -135,3 +137,5 @@ std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
 }  // namespace paddle
 REGISTER_PASS(conv_bias_mkldnn_fuse_pass,
               paddle::framework::ir::ConvBiasFusePass);
+REGISTER_PASS(conv3d_bias_mkldnn_fuse_pass,
+              paddle::framework::ir::Conv3DBiasFusePass);

paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.h

@@ -26,11 +26,19 @@ namespace ir {
 class ConvBiasFusePass : public FusePassBase {
  public:
   virtual ~ConvBiasFusePass() {}
+  virtual bool is_conv3d() const { return false; }
 
  protected:
   std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
   const std::string name_scope_{"conv_bias_mkldnn_fuse"};
 };
+/*
+* Fuse the Conv3D and Elementwise_add to a Conv3DBiasOp.
+*/
+class Conv3DBiasFusePass : public ConvBiasFusePass {
+ public:
+  bool is_conv3d() const override { return true; }
+};
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1030,10 +1030,11 @@ PDNode *patterns::ElewiseAddActInplaceGrad::operator()(
 }
 
 PDNode *patterns::ConvBias::operator()(
-    paddle::framework::ir::PDNode *conv_input) {
+    paddle::framework::ir::PDNode *conv_input, bool is_conv3d) {
+  std::string type = is_conv3d ? "conv3d" : "conv2d";
   // Create Operators
-  conv_input->assert_is_op_input("conv2d", "Input");
-  auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d");
+  conv_input->assert_is_op_input(type, "Input");
+  auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op(type);
   auto *eltiwse_op =
       pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add");
   // Create variables
@@ -1041,11 +1042,11 @@ PDNode *patterns::ConvBias::operator()(
   auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
                               ->AsInput()
                               ->assert_is_persistable_var()
-                              ->assert_is_op_input("conv2d", "Filter");
+                              ->assert_is_op_input(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("conv2d")
+                           ->assert_is_only_output_of_op(type)
                            ->assert_is_op_input("elementwise_add");
   // Bias stored in elementwise_add
   auto *eltwise_bias_var = pattern->NewNode(eltwise_bias_repr())

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -623,7 +623,7 @@ struct ElewiseAddActInplaceGrad : public PatternBase {
 struct ConvBias : public PatternBase {
   ConvBias(PDPattern* pattern, const std::string& name_scope)
       : PatternBase(pattern, name_scope, "conv_bias") {}
-  PDNode* operator()(PDNode* conv_input);
+  PDNode* operator()(PDNode* conv_input, bool is_conv3d = false);
   // declare operator node's name
   PATTERN_DECL_NODE(conv);
   PATTERN_DECL_NODE(eltwise);

paddle/fluid/inference/api/paddle_pass_builder.h

@@ -98,9 +98,10 @@ class CpuPassStrategy : public PassStrategy {
     passes_.insert(passes_.begin(), "mkldnn_placement_pass");
 
     for (auto &pass :
-         std::vector<std::string>({"depthwise_conv_mkldnn_pass",  //
-                                   "conv_bias_mkldnn_fuse_pass",  //
-                                   "conv_relu_mkldnn_fuse_pass",  //
+         std::vector<std::string>({"depthwise_conv_mkldnn_pass",    //
+                                   "conv_bias_mkldnn_fuse_pass",    //
+                                   "conv3d_bias_mkldnn_fuse_pass",  //
+                                   "conv_relu_mkldnn_fuse_pass",    //
                                    "conv_elementwise_add_mkldnn_fuse_pass"})) {
       passes_.push_back(pass);
     }

paddle/fluid/inference/tests/api/analyzer_dam_tester.cc

@@ -188,10 +188,14 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 }
 
 // Easy for profiling independently.
-TEST(Analyzer_dam, profile) {
+void profile(bool use_mkldnn = false) {
   contrib::AnalysisConfig cfg;
   SetConfig(&cfg);
 
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
+
   std::vector<PaddleTensor> outputs;
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -209,6 +213,11 @@ TEST(Analyzer_dam, profile) {
   }
 }
 
+TEST(Analyzer_dam, profile) { profile(); }
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_dam, profile_mkldnn) { profile(true /* use_mkldnn */); }
+#endif
+
 // Check the fuse status
 TEST(Analyzer_dam, fuse_statis) {
   contrib::AnalysisConfig cfg;
@@ -222,9 +231,12 @@ TEST(Analyzer_dam, fuse_statis) {
 }
 
 // Compare result of NativeConfig and AnalysisConfig
-TEST(Analyzer_dam, compare) {
-  contrib::AnalysisConfig cfg;
+void compare(bool use_mkldnn = false) {
+  AnalysisConfig cfg;
   SetConfig(&cfg);
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
@@ -233,5 +245,10 @@ TEST(Analyzer_dam, compare) {
       reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
 }
 
+TEST(Analyzer_dam, compare) { compare(); }
+#ifdef PADDLE_WITH_MKLDNN
+TEST(Analyzer_dam, compare_mkldnn) { compare(true /* use_mkldnn */); }
+#endif
+
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/operators/activation_mkldnn_op.cc

@@ -100,8 +100,9 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
   const T *x_data = x->data<T>();
   T *y_data = y->mutable_data<T>(ctx.GetPlace());
 
-  PADDLE_ENFORCE(x->dims().size() == 2 || x->dims().size() == 4,
-                 "Input dim must be with 2 or 4");
+  PADDLE_ENFORCE(
+      x->dims().size() == 2 || x->dims().size() == 3 || x->dims().size() == 4,
+      "Input dim must be with 2, 3 or 4");
 
   std::vector<int> src_tz = framework::vectorize2int(x->dims());