[cherry-pick] release/2.3 elementwise_mul and matmul mkldnn fix

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -2069,6 +2069,20 @@ PDNode *patterns::Elementwise::operator()(PDNode *x_var, PDNode *y_var,
   return out_var;
 }
 
+PDNode *patterns::ElementwiseOp::operator()(
+    const std::string elementwise_type) {
+  auto elementwise_op =
+      pattern->NewNode(elementwise_op_repr())->assert_is_op(elementwise_type);
+
+  auto out_var = pattern->NewNode(elementwise_out_repr())
+                     ->AsOutput()
+                     ->assert_is_op_output(elementwise_type, "Out");
+
+  elementwise_op->LinksTo({out_var});
+
+  return out_var;
+}
+
 PDNode *patterns::ResidualElementwise::operator()(
     PDNode *op_var, PDNode *residual_var, const std::string elementwise_type,
     bool as_x) {

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1071,6 +1071,19 @@ struct Elementwise : public PatternBase {
   PATTERN_DECL_NODE(elementwise_out);
 };
 
+// Elementwise ops
+// Forward pass for element-wise operators
+// elementwise_out is the result of the operator
+struct ElementwiseOp : public PatternBase {
+  ElementwiseOp(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "elementwise") {}
+
+  PDNode* operator()(const std::string elementwise_type);
+
+  PATTERN_DECL_NODE(elementwise_op);
+  PATTERN_DECL_NODE(elementwise_out);
+};
+
 // Residual Elementwise ops
 // This pattern allows operator output to be X or Y
 // and residual data Y or X, based on as_x flag

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

@@ -819,12 +819,9 @@ void CPUQuantizePass::QuantizeElementwise(
     Graph* graph, const std::string elementwise_type) const {
   GraphPatternDetector gpd;
   auto pattern = gpd.mutable_pattern();
-  patterns::Elementwise elementwise_pattern{pattern, name_scope_};
+  patterns::ElementwiseOp elementwise_pattern{pattern, name_scope_};
 
-  elementwise_pattern(
-      pattern->NewNode(elementwise_pattern.elementwise_x_repr()),
-      pattern->NewNode(elementwise_pattern.elementwise_y_repr()),
-      elementwise_type);
+  elementwise_pattern(elementwise_type);
 
   int quantize_elementwise_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
@@ -839,10 +836,18 @@ void CPUQuantizePass::QuantizeElementwise(
       return;
     }
 
-    GET_IR_NODE_FROM_SUBGRAPH(elementwise_x, elementwise_x,
-                              elementwise_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(elementwise_y, elementwise_y,
-                              elementwise_pattern);
+    auto x_name = elementwise_op->Op()->Input("X");
+    auto y_name = elementwise_op->Op()->Input("Y");
+    Node *elementwise_x, *elementwise_y;
+
+    for (auto& input : elementwise_op->inputs) {
+      if (input->Name() == x_name[0]) elementwise_x = input;
+      if (input->Name() == y_name[0]) elementwise_y = input;
+    }
+    if (!elementwise_x || !elementwise_y) {
+      return;
+    }
+
     GET_IR_NODE_FROM_SUBGRAPH(elementwise_out, elementwise_out,
                               elementwise_pattern);
 

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

@@ -354,10 +354,9 @@ bool QuantDequantMkldnnPass::IsInt8Weight(
   auto* op_desc = op_node->Op();
   auto var_name = op_desc->Input(weight_name)[0];
   auto* var = scope->FindVar(var_name);
-  PADDLE_ENFORCE_NOT_NULL(
-      var, platform::errors::NotFound(
-               "The input persistable [%s] var of [%s] op is not found.",
-               var_name, op_desc->Type()));
+  if (var == nullptr) {
+    return false;
+  }
   auto* weight_tensor = var->GetMutable<LoDTensor>();
   auto* weight_data = weight_tensor->data<float>();
   bool is_int8 = true;