fix conv_bias_relu fuse bug & x86 conv kernel bug (#18098)

fix conv_bias_relu fuse bug，x86 conv kernel bug

paddle/fluid/lite/core/mir/conv_elementwise_add_relu_fuse_pass.cc

@@ -24,8 +24,11 @@ namespace mir {
 
 void ConvElementwiseAddReLUFusePass::Apply(
     const std::unique_ptr<SSAGraph>& graph) {
-  fusion::ConvElementwiseAddReLUFuser fuser;
+  fusion::ConvElementwiseAddReLUFuser fuser("conv2d");
   fuser(graph.get());
+
+  fusion::ConvElementwiseAddReLUFuser depthwise_fuser("depthwise_conv2d");
+  depthwise_fuser(graph.get());
 }
 
 }  // namespace mir

paddle/fluid/lite/core/mir/conv_elementwise_add_relu_fuse_pass_test.cc

@@ -85,7 +85,7 @@ std::unique_ptr<SSAGraph> BuildGraph(framework::ProgramDesc* program_desc,
   add_1->SetAttr("axis", 1);
 
   relu_1->SetType("relu");
-  relu_1->SetInput("Input", {"add_1_out"});
+  relu_1->SetInput("X", {"add_1_out"});
   relu_1->SetOutput("Out", {"relu_1_out"});
 
   conv2d_2->SetType("conv2d");
@@ -105,7 +105,7 @@ std::unique_ptr<SSAGraph> BuildGraph(framework::ProgramDesc* program_desc,
   add_2->SetAttr("axis", 1);
 
   relu_2->SetType("relu");
-  relu_2->SetInput("Input", {"add_2_out"});
+  relu_2->SetInput("X", {"add_2_out"});
   relu_2->SetOutput("Out", {"out"});
 
   program_desc->Flush();

paddle/fluid/lite/core/mir/fusion/conv_elementwise_add_relu_fuser.cc

@@ -23,21 +23,33 @@ namespace fusion {
 
 void ConvElementwiseAddReLUFuser::BuildPattern() {
   // create input nodes.
-  auto* input = VarNode("input");
-  auto* filter = VarNode("filter");
-  auto* bias = VarNode("bias");
+  auto* input =
+      VarNode("input")->assert_is_op_input(conv_type_, "Input")->AsInput();
+  auto* filter =
+      VarNode("filter")->assert_is_op_input(conv_type_, "Filter")->AsInput();
+  auto* bias =
+      VarNode("bias")->assert_is_op_input("elementwise_add", "Y")->AsInput();
 
   // create op nodes
-  auto* conv2d = OpNode("conv2d", "conv2d");
-  auto* add = OpNode("add", "elementwise_add");
-  auto* relu = OpNode("relu", "relu");
+  auto* conv2d =
+      OpNode("conv2d", conv_type_)->assert_is_op(conv_type_)->AsIntermediate();
+  auto* add = OpNode("add", "elementwise_add")
+                  ->assert_is_op("elementwise_add")
+                  ->AsIntermediate();
+  auto* relu = OpNode("relu", "relu")->assert_is_op("relu")->AsIntermediate();
 
   // create intermediate nodes
-  auto* conv2d_out = VarNode("conv2d_out");
-  auto* add_out = VarNode("add_out");
+  auto* conv2d_out = VarNode("conv2d_out")
+                         ->assert_is_op_output(conv_type_, "Output")
+                         ->assert_is_op_input("elementwise_add", "X")
+                         ->AsIntermediate();
+  auto* add_out = VarNode("add_out")
+                      ->assert_is_op_output("elementwise_add", "Out")
+                      ->assert_is_op_input("relu", "X")
+                      ->AsIntermediate();
 
   // create output node
-  auto* out = VarNode("output");
+  auto* out = VarNode("output")->assert_is_op_output("relu", "Out")->AsOutput();
 
   // create topology.
   std::vector<PMNode*> conv2d_inputs{filter, input};
@@ -45,19 +57,12 @@ void ConvElementwiseAddReLUFuser::BuildPattern() {
   conv2d_inputs >> *conv2d >> *conv2d_out;
   add_inputs >> *add >> *add_out;
   *add_out >> *relu >> *out;
-
-  // Some op specialities.
-  conv2d_out->AsIntermediate();
-  add_out->AsIntermediate();
-  conv2d->AsIntermediate();
-  add->AsIntermediate();
-  relu->AsIntermediate();
 }
 
 void ConvElementwiseAddReLUFuser::InsertNewNode(SSAGraph* graph,
                                                 const key2nodes_t& matched) {
   auto op_desc = GenOpDesc(matched);
-  auto conv_op = LiteOpRegistry::Global().Create("conv2d");
+  auto conv_op = LiteOpRegistry::Global().Create(conv_type_);
   auto conv_old = matched.at("conv2d")->stmt()->op;
   auto* scope = conv_old->scope();
   auto& valid_places = conv_old->valid_places();
@@ -75,7 +80,7 @@ cpp::OpDesc ConvElementwiseAddReLUFuser::GenOpDesc(const key2nodes_t& matched) {
   auto* desc = matched.at("conv2d")->stmt()->op_info();
 
   cpp::OpDesc op_desc;
-  op_desc.SetType("conv2d");
+  op_desc.SetType(conv_type_);
   op_desc.SetInput("Input", {matched.at("input")->arg()->name});
   op_desc.SetInput("Filter", {matched.at("filter")->arg()->name});
   op_desc.SetInput("Bias", {matched.at("bias")->arg()->name});

paddle/fluid/lite/core/mir/fusion/conv_elementwise_add_relu_fuser.h

@@ -25,11 +25,14 @@ namespace fusion {
 
 class ConvElementwiseAddReLUFuser : public FuseBase {
  public:
+  explicit ConvElementwiseAddReLUFuser(const std::string& conv_type)
+      : conv_type_(conv_type) {}
   void BuildPattern() override;
   void InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) override;
 
  private:
   cpp::OpDesc GenOpDesc(const key2nodes_t& matched) override;
+  std::string conv_type_;
 };
 
 }  // namespace fusion

paddle/fluid/lite/core/mir/passes.h

@@ -32,3 +32,4 @@ USE_MIR_PASS(io_copy_kernel_pick_pass);
 USE_MIR_PASS(argument_type_display_pass);
 USE_MIR_PASS(runtime_context_assign_pass);
 USE_MIR_PASS(lite_conv_bn_fuse_pass);
+USE_MIR_PASS(graph_visualze);

paddle/fluid/lite/kernels/x86/conv_compute.cc

@@ -105,7 +105,7 @@ class Conv2dCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
           param.x->raw_tensor().Slice(i, i + 1).Resize(input_shape.data()));
       lite::Tensor out_batch;
       out_batch.ShareDataWith(param.output->raw_tensor().Slice(i, i + 1).Resize(
-          input_shape.data()));
+          output_matrix_shape.data()));
 
       for (int g = 0; g < param.groups; g++) {
         lite::Tensor in_slice;

paddle/fluid/lite/kernels/x86/relu_compute.cc

@@ -51,6 +51,6 @@ class ReluCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
 
 REGISTER_LITE_KERNEL(relu, kX86, kFloat, kNCHW,
                      paddle::lite::kernels::x86::ReluCompute<float>, def)
-    .BindInput("Input", {LiteType::GetTensorTy(TARGET(kX86))})
+    .BindInput("X", {LiteType::GetTensorTy(TARGET(kX86))})
     .BindOutput("Out", {LiteType::GetTensorTy(TARGET(kX86))})
     .Finalize();

paddle/fluid/lite/operators/conv_op.h

@@ -73,19 +73,22 @@ class ConvOpLite : public OpLite {
     std::vector<std::string> input_arg_names = op_desc.InputArgumentNames();
     if (std::find(input_arg_names.begin(), input_arg_names.end(), "Bias") !=
         input_arg_names.end()) {
-      auto bias_var = scope->FindVar(op_desc.Input("Bias").front());
+      auto bias_arguments = op_desc.Input("Bias");
+      if (bias_arguments.size() != 0) {
+        auto bias_var = scope->FindVar(bias_arguments.front());
         if (bias_var != nullptr) {
-        param_.bias =
-            const_cast<lite::Tensor*>(&(bias_var->Get<lite::Tensor>()));
+          param_.bias = &bias_var->Get<lite::Tensor>();
+        }
       }
     }
     if (std::find(input_arg_names.begin(), input_arg_names.end(),
                   "ResidualData") != input_arg_names.end()) {
-      auto residual_data_var =
-          scope->FindVar(op_desc.Input("ResidualData").front());
+      auto res_argument = op_desc.Input("ResidualData");
+      if (res_argument.size() != 0) {
+        auto residual_data_var = scope->FindVar(res_argument.front());
         if (residual_data_var != nullptr) {
-        param_.residualData = const_cast<lite::Tensor*>(
-            &(residual_data_var->Get<lite::Tensor>()));
+          param_.residualData = &residual_data_var->Get<lite::Tensor>();
+        }
       }
     }
 

paddle/fluid/lite/operators/op_params.h

@@ -124,8 +124,8 @@ struct ConcatParam {
 struct ConvParam {
   lite::Tensor* x{};
   lite::Tensor* filter{};
-  lite::Tensor* bias{};
-  lite::Tensor* residualData{};
+  const lite::Tensor* bias{};
+  const lite::Tensor* residualData{};
   lite::Tensor* output{};
   std::vector<int> strides{1, 1};
   std::vector<int> paddings{0, 0};

paddle/fluid/lite/operators/relu_op.cc

@@ -32,7 +32,7 @@ bool ReluOp::InferShape() const {
 
 bool ReluOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
   param_.input = const_cast<lite::Tensor *>(
-      &scope->FindVar(opdesc.Input("Input").front())->Get<lite::Tensor>());
+      &scope->FindVar(opdesc.Input("X").front())->Get<lite::Tensor>());
   param_.output =
       scope->FindVar(opdesc.Output("Out").front())->GetMutable<lite::Tensor>();
   CHECK(param_.input);