MKLDNN conv + elementwise_add fusion: fix for crash when bias is not present

paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h"
 #include <functional>
+#include <utility>
 
 #include "paddle/fluid/framework/ir/graph_traits.h"
 
@@ -67,11 +68,32 @@ graph_ptr ConvElementwiseAddMKLDNNFusePass::ApplyImpl(graph_ptr graph) const {
 
   conv_output->AsIntermediate();
 
+  auto conv_op_has_bias = [](const Node& conv_op,
+                             const Scope& scope) -> std::pair<bool, Node*> {
+    auto bias_input_names = conv_op.Op()->Inputs();
+    auto bias_it = bias_input_names.find("Bias");
+
+    if (bias_it != std::end(bias_input_names)) {
+      bool has_bias = !bias_it->second.empty();
+
+      if (has_bias) {
+        auto conv_bias_names = bias_it->second;
+        auto conv_bias_names_it =
+            std::find_if(std::begin(conv_op.inputs), std::end(conv_op.inputs),
+                         [&conv_bias_names](Node* n) -> bool {
+                           return n->Name() == conv_bias_names[0];
+                         });
+        return std::make_pair(has_bias, *conv_bias_names_it);
+      }
+    }
+
+    return std::make_pair(false, nullptr);
+  };
+
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
     GET_IR_NODE_FROM_SUBGRAPH(conv_op, conv_op, conv_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(conv_input, conv_input, conv_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(conv_bias, conv_bias, conv_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(conv_filter, conv_filter, conv_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(conv_output, conv_output, conv_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_op, elementwise_add_op,
@@ -81,17 +103,25 @@ graph_ptr ConvElementwiseAddMKLDNNFusePass::ApplyImpl(graph_ptr graph) const {
     GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_out, elementwise_add_out,
                               elementwise_add_pattern);
 
-    if (FindFuseOption(conv_op, elementwise_add_op) != FUSE_MKLDNN) return;
+    if (FindFuseOption(*conv_op, *elementwise_add_op) != FUSE_MKLDNN) return;
 
     OpDesc op_desc;
     op_desc.SetType("conv2d");
 
     op_desc.SetInput("Input", {conv_input->Name()});
-    op_desc.SetInput("Bias", {conv_bias->Name()});
     op_desc.SetInput("Filter", {conv_filter->Name()});
     op_desc.SetInput("ResidualData", {elementwise_add_x->Name()});
     op_desc.SetOutput("Output", {conv_output->Name()});
 
+    bool has_bias;
+    Node* conv_bias;
+
+    std::tie(has_bias, conv_bias) = conv_op_has_bias(*conv_op, *param_scope());
+
+    if (has_bias) {
+      op_desc.SetInput("Bias", {conv_bias->Name()});
+    }
+
     for (const auto& attr : conv_op->Op()->GetAttrMap()) {
       op_desc.SetAttr(attr.first, attr.second);
     }
@@ -101,11 +131,14 @@ graph_ptr ConvElementwiseAddMKLDNNFusePass::ApplyImpl(graph_ptr graph) const {
     auto fused_conv_op = g->CreateOpNode(&op_desc);
 
     IR_NODE_LINK_TO(conv_input, fused_conv_op);
-    IR_NODE_LINK_TO(conv_bias, fused_conv_op);
     IR_NODE_LINK_TO(conv_filter, fused_conv_op);
     IR_NODE_LINK_TO(elementwise_add_x, fused_conv_op);
     IR_NODE_LINK_TO(fused_conv_op, conv_output);
 
+    if (has_bias) {
+      IR_NODE_LINK_TO(conv_bias, fused_conv_op);
+    }
+
     CorrectGraphEdges(g, elementwise_add_out, conv_output);
     GraphSafeRemoveNodes(g, {elementwise_add_out, conv_op, elementwise_add_op});
   };

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1006,10 +1006,6 @@ PDNode *patterns::Conv::operator()() {
                        ->AsInput()
                        ->assert_is_op_input("conv2d", "Input");
 
-  auto bias_var = pattern->NewNode(conv_bias_repr())
-                      ->AsInput()
-                      ->assert_is_op_input("conv2d", "Bias");
-
   auto filter_var = pattern->NewNode(conv_filter_repr())
                         ->AsInput()
                         ->assert_is_op_input("conv2d", "Filter");
@@ -1018,7 +1014,7 @@ PDNode *patterns::Conv::operator()() {
                         ->AsOutput()
                         ->assert_is_op_output("conv2d", "Output");
 
-  conv_op->LinksFrom({input_var, bias_var, filter_var});
+  conv_op->LinksFrom({input_var, /*bias_var,*/ filter_var});
   conv_op->LinksTo({output_var});
 
   return output_var;