Add default dim_order asserts

backends/xnnpack/runtime/XNNExecutor.cpp

@@ -86,6 +86,11 @@ __ET_NODISCARD Error XNNExecutor::prepare_args(EValue** args) {
     // Reshape runtime inputs
     if (i < input_ids_.size()) {
       size_t num_dims = tensor->dim();
+      ET_CHECK_OR_RETURN_ERROR(
+          is_contiguous_dim_order(tensor->dim_order().data(), tensor->dim()),
+          Internal,
+          "Expecting default dim_order but got a non default dim_order tensor for external input %u",
+          i);
       size_t dims[XNN_MAX_TENSOR_DIMS];
       ET_CHECK_OR_RETURN_ERROR(
           num_dims <= XNN_MAX_TENSOR_DIMS,

backends/xnnpack/xnnpack_preprocess.py

@@ -78,6 +78,22 @@ def generate_node_to_external_map(
     return node_to_external_map
 
 
+def assert_default_dim_order(edge_graph_module: torch.fx.GraphModule) -> None:
+    for node in edge_graph_module.graph.nodes:
+        if node.op != "placeholder":
+            continue
+
+        # We expect the default dim order for all tensor-like inputs i.e. inputs, buffers, and params
+        t = node.meta.get("val", None)
+        if t is not None and getattr(t, "dim_order", None) is not None:
+            default_dim_order = tuple(range(t.dim()))
+            if t.dim_order() != default_dim_order:
+                raise RuntimeError(
+                    f"XNNPACK backend only supports contiguous memory format for inputs."
+                    f"Expecting dim_order: {default_dim_order}, but got {node.meta['val'].dim_order()} for a placeholder node {node}."
+                )
+
+
 @final
 class XnnpackBackend(BackendDetails):
     @staticmethod
@@ -126,6 +142,9 @@ def preprocess(
 
         node_to_external_map = generate_node_to_external_map(ep, graph_module)
 
+        # Make sure all inputs are contiguous_format or NCHW or default dim order
+        assert_default_dim_order(graph_module)
+
         # TODO retrace the graph module to lift the new params may have
         # been added to the graph in passes