Fix MatMulBnFusion to exclude cases when tensors are not 2D tensors (#22762)

### Description
Fixes #22512, MatMul, Add can be fused into a single Gemm even if
tensors dimensions are > 2. The PR excludes that cases.



### Motivation and Context
ORT crashes on valid models due to that unexpected fusion.

onnxruntime/core/optimizer/matmul_bn_fusion.cc

@@ -107,6 +107,22 @@ bool MatmulBNFusion::SatisfyCondition(const Graph& graph, const Node& node, cons
     return false;
   }
 
+  // Checks the first input of MatMul has 2 dimensions.
+  // The test for the second input is done in method Apply as it accesses the constant.
+  if (node.InputDefs()[0] == nullptr) {
+    // This should never happen but just in case.
+    return false;
+  }
+  auto shape_a = node.InputDefs()[0]->Shape();
+  if (shape_a == nullptr) {
+    // We cannot shape the rank. It is better to avoid fusing.
+    return false;
+  }
+  if (shape_a->dim_size() != 2) {
+    // Gemm only supports 2D tensors.
+    return false;
+  }
+
   // First output from BN is required. Others are optional. If any optional outputs exist we can't fuse.
   const auto& output_defs = batch_norm_node->OutputDefs();
   if (output_defs.size() > 1) {
@@ -165,6 +181,7 @@ Status MatmulBNFusion::Apply(Graph& graph, Node& matmul_node, RewriteRuleEffect&
       bias_tensor->dims_size() != 1 ||
       mean_tensor->dims_size() != 1 ||
       var_tensor->dims_size() != 1 ||
+      matmul_b_tensor->dims_size() != 2 ||
       scale_tensor->dims(0) != matmul_b_tensor->dims(1) ||
       bias_tensor->dims(0) != matmul_b_tensor->dims(1) ||
       mean_tensor->dims(0) != matmul_b_tensor->dims(1) ||

onnxruntime/test/optimizer/graph_transform_test.cc

@@ -1764,6 +1764,35 @@ TEST_F(GraphTransformationTests, FuseMatmulBNDirectly) {
   }
 }
 
+TEST_F(GraphTransformationTests, DoNotApplyFuseMatmulBNDirectly) {
+  constexpr const ORTCHAR_T* model_uri = MODEL_FOLDER "fusion/fuse-matmul-bn-directly-dont-fuse.onnx";
+
+  std::shared_ptr<Model> p_model;
+  ASSERT_STATUS_OK(Model::Load(model_uri, p_model, nullptr, *logger_));
+  Graph& graph = p_model->MainGraph();
+
+  std::string expected_output_name;
+  GraphViewer graphViewer(graph);
+  for (auto& node_index : graphViewer.GetNodesInTopologicalOrder()) {
+    auto& node = *graph.GetNode(node_index);
+    if (node.OpType() == "BatchNormalization") {
+      expected_output_name = node.OutputDefs()[0]->Name();
+    }
+  }
+
+  onnxruntime::GraphTransformerManager graph_transformation_mgr{5};
+  auto rule_transformer_L1 = std::make_unique<RuleBasedGraphTransformer>("RuleTransformerL1");
+  ASSERT_STATUS_OK(rule_transformer_L1->Register(std::make_unique<MatmulBNFusion>()));
+  ASSERT_STATUS_OK(graph_transformation_mgr.Register(std::move(rule_transformer_L1), TransformerLevel::Level1));
+
+  ASSERT_STATUS_OK(graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level1, *logger_));
+
+  std::map<std::string, int> op_to_count = CountOpsInGraph(graph);
+  ASSERT_EQ(op_to_count["BatchNormalization"], 1);
+  ASSERT_EQ(op_to_count["MatMul"], 1);
+  ASSERT_EQ(op_to_count["Gemm"], 0);
+}
+
 TEST_F(GraphTransformationTests, FuseMatmulBNWithOnlyReshape) {
   constexpr const ORTCHAR_T* model_uri = MODEL_FOLDER "fusion/fuse-matmul-bn-only-reshape.onnx";