create memory descriptors based on the tensor dimensions

Arm Compute Library(ACL)backend requires explicit memory format tag
iniatilization to decide wether the tensor can be computed with the
ACL kernels. Hence, the src, weights and dst memroy descriptor format is
set based on the tensor dimensions instead of using the format::any
tag.

onnxruntime/core/providers/dnnl/subgraph/dnnl_matmul.cc

@@ -12,6 +12,25 @@
 namespace onnxruntime {
 namespace ort_dnnl {
 
+inline static dnnl::memory::format_tag get_default_format(const dnnl::memory::dims& tensor_dims) {
+    switch (tensor_dims.size()) {
+      case 1:
+        return dnnl::memory::format_tag::a;
+      case 2:
+        return dnnl::memory::format_tag::ab;
+      case 3:
+        return dnnl::memory::format_tag::abc;
+      case 4:
+        return dnnl::memory::format_tag::abcd;
+      case 5:
+        return dnnl::memory::format_tag::abcde;
+      case 6:
+        return dnnl::memory::format_tag::abcdef;
+      default:
+        return dnnl::memory::format_tag::undef;
+    }
+}
+
 DnnlMatMul::DnnlMatMul() {}
 
 // This handles ONNX defined "MatMul" as well as two other variations of MatMul
@@ -139,14 +158,14 @@ void DnnlMatMul::CreatePrimitive(DnnlSubgraphPrimitive& sp, DnnlNode& node) {
   if (transA || transBatchA) {
     src_md = transposedA_md;
   } else {
-    src_md = dnnl::memory::desc(src_dims, node.Input(IN_A).Type(), dnnl::memory::format_tag::any);
+    src_md = dnnl::memory::desc(src_dims, node.Input(IN_A).Type(),  get_default_format(src_dims));
   }
 
   dnnl::memory::desc weights_md;
   if (transB || transBatchB) {
     weights_md = transposedB_md;
   } else {
-    weights_md = dnnl::memory::desc(weights_dims, node.Input(IN_B).Type(), dnnl::memory::format_tag::any);
+    weights_md = dnnl::memory::desc(weights_dims, node.Input(IN_B).Type(), get_default_format(weights_dims));
   }
 
   auto output_shape = src_dims;
@@ -241,7 +260,7 @@ void DnnlMatMul::CreatePrimitive(DnnlSubgraphPrimitive& sp, DnnlNode& node) {
     attr.set_scales_mask(DNNL_ARG_SRC, 0);
   }
 
-  auto dst_md = dnnl::memory::desc(output_shape, node.Output(OUT_Y).Type(), dnnl::memory::format_tag::any);
+  auto dst_md = dnnl::memory::desc(output_shape, node.Output(OUT_Y).Type(), get_default_format(output_shape));
 
   auto matmul_pd = dnnl::matmul::primitive_desc(eng, src_md, weights_md, dst_md, attr);