Conv bn fuse fix (#30830)

* fixed compilation error on gcc 4.8.x due to the usage of isfinite (#30733)

* modified conv+bn fuse pass to fix wrong mask in mask rcnn (#30704)

paddle/fluid/framework/ir/conv_bn_fuse_pass.cc

@@ -95,13 +95,28 @@ void recompute_bias_and_weights(const Scope* scope,
   variance_array += epsilon;
   variance_array = variance_array.sqrt();
   variance_array = scale_array / variance_array;
-
+  for (int i = 0; i < variance_tensor->numel(); i++) {
+    PADDLE_ENFORCE_EQ(std::isfinite(variance_array[i]), true,
+                      platform::errors::InvalidArgument(
+                          "The inverse of Fused batch norm variance "
+                          "should be finite. Found nonfinite values! "
+                          "Please check %s ",
+                          bn_variance.Name()));
+  }
   EigenVectorArrayMap eltwise_y_in_array(
       eltwise_y_in_tensor->mutable_data<float>(platform::CPUPlace()),
       eltwise_y_in_tensor->numel(), 1);
 
   eltwise_y_in_array =
       ((eltwise_y_in_array - mean_array) * variance_array) + bn_bias_array;
+  for (int i = 0; i < eltwise_y_in_tensor->numel(); i++) {
+    PADDLE_ENFORCE_EQ(std::isfinite(eltwise_y_in_array[i]), true,
+                      platform::errors::InvalidArgument(
+                          "Fused batch norm bias should be "
+                          "finite. Found nonfinite values! "
+                          "Please check %s and related variables.",
+                          bn_variance.Name()));
+  }
 
   // Re-compute weight of conv2d from BN
   auto* weights = scope->FindVar(conv_weight->Name())->GetMutable<LoDTensor>();

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -824,22 +824,25 @@ PDNode *patterns::ConvBN::operator()(paddle::framework::ir::PDNode *conv_input,
 
   auto *bn_mean_out_var = pattern->NewNode(bn_mean_out_repr())
                               ->AsOutput()
-                              ->assert_is_op_output("batch_norm", "MeanOut");
+                              ->assert_is_op_output("batch_norm", "MeanOut")
+                              ->assert_has_n_outputs(0);
 
   auto *bn_variance_out_var =
       pattern->NewNode(bn_variance_out_repr())
           ->AsOutput()
-          ->assert_is_op_output("batch_norm", "VarianceOut");
+          ->assert_is_op_output("batch_norm", "VarianceOut")
+          ->assert_has_n_outputs(0);
 
-  auto *bn_saved_mean_var =
-      pattern->NewNode(bn_saved_mean_repr())
-          ->AsOutput()
-          ->assert_is_op_output("batch_norm", "SavedMean");
+  auto *bn_saved_mean_var = pattern->NewNode(bn_saved_mean_repr())
+                                ->AsOutput()
+                                ->assert_is_op_output("batch_norm", "SavedMean")
+                                ->assert_has_n_outputs(0);
 
   auto *bn_saved_variance_var =
       pattern->NewNode(bn_saved_variance_repr())
           ->AsOutput()
-          ->assert_is_op_output("batch_norm", "SavedVariance");
+          ->assert_is_op_output("batch_norm", "SavedVariance")
+          ->assert_has_n_outputs(0);
 
   conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});