Fix symbolic shape infer empty value_info (#15842)

### Description
When node output is optional, symbolic shape infer might add an empty
value_info item. Add some checking to avoid this.

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
- 
Stable diffusion optimized model reported invalid data type 0 during
inference.

Author: tianleiwu

onnxruntime/python/tools/symbolic_shape_infer.py

@@ -491,12 +491,13 @@ def _onnx_infer_single_node(self, node):
 
         for i_o in range(len(node.output)):
             o = node.output[i_o]
-            vi = self.out_mp_.graph.value_info.add()
-            if not skip_infer:
-                vi.CopyFrom(self.tmp_mp_.graph.output[i_o])
-            else:
-                vi.name = o
-            self.known_vi_[o] = vi
+            if o:  # skip optional output
+                vi = self.out_mp_.graph.value_info.add()
+                if not skip_infer:
+                    vi.CopyFrom(self.tmp_mp_.graph.output[i_o])
+                else:
+                    vi.name = o
+                self.known_vi_[o] = vi
 
     def _onnx_infer_subgraph(self, node, subgraph, use_node_input=True, inc_subgraph_id=True):
         if self.verbose_ > 2:

onnxruntime/python/tools/transformers/onnx_model.py

@@ -644,12 +644,17 @@ def convert_float_to_float16(self, use_symbolic_shape_infer=True, **kwargs):
                 if model_with_shape is not None:
                     name_vi = {}
                     for vi in model_with_shape.graph.value_info:
-                        vi_copy = ValueInfoProto()
-                        vi_copy.CopyFrom(vi)
-                        if hasattr(vi_copy.type, "tensor_type") and hasattr(vi_copy.type.tensor_type, "shape"):
-                            vi_copy.type.tensor_type.ClearField("shape")
-                        name_vi[vi.name] = vi_copy
-
+                        if (
+                            hasattr(vi.type, "tensor_type")
+                            and hasattr(vi.type.tensor_type, "elem_type")
+                            and vi.type.tensor_type.elem_type != TensorProto.UNDEFINED
+                            and vi.name
+                        ):
+                            vi_copy = ValueInfoProto()
+                            vi_copy.CopyFrom(vi)
+                            if hasattr(vi_copy.type.tensor_type, "shape"):
+                                vi_copy.type.tensor_type.ClearField("shape")
+                            name_vi[vi.name] = vi_copy
                     for vi in model.graph.value_info:
                         if vi.name in name_vi:
                             del name_vi[vi.name]