[IR][fix] Save value info for initializers

onnxscript/ir/serde.py

@@ -627,32 +627,43 @@
 
     # Initialize the values dictionary for this graph scope with the inputs and initializers
     values: dict[str, _core.Value] = {v.name: v for v in inputs}  # type: ignore[misc]
+
+    # Enter the graph scope by pushing the values for this scope to the stack
     scoped_values.append(values)
+
     initializer_values = []
-    for tensor in initializer_tensors:
-        if tensor.name in values:
+    for i, tensor in enumerate(initializer_tensors):
+        initializer_name = tensor.name
+        if not initializer_name:
+            logger.warning(
+                "Initializer tensor must have a name but the %s-th initializer does not. Skipping this initializer.",
+                i,
+            )
+            continue
+        if initializer_name in values:
             # The initializer is for an input
-            initializer_value = values[tensor.name]
+            initializer_value = values[initializer_name]
             initializer_value.const_value = tensor
         else:
             # The initializer is for some other value. Create this value first
             initializer_value = _core.Value(
                 None,
                 index=None,
-                name=tensor.name,
-                # TODO(justinchuby): Fix type hinting for shape and dtype
-                shape=tensor.shape,  # type: ignore
+                name=initializer_name,
+                # Include shape and type even if the shape or type is not provided as ValueInfoProto.
+                # Users expect initialized values to have shape and type information.
                 type=_core.TensorType(tensor.dtype),
+                shape=tensor.shape,  # type: ignore[arg-type]
                 const_value=tensor,
             )
             if initializer_value.name in quantization_annotations:
                 _deserialize_quantization_annotation(
                     quantization_annotations[initializer_value.name], initializer_value
                 )
-            values[tensor.name] = initializer_value  # type: ignore[index]
+            values[initializer_name] = initializer_value
         initializer_values.append(initializer_value)
 
-    # Add ValueInfos for this graph scope
+    # Build the value info dictionary to allow for quick lookup for this graph scope
     value_info = {info.name: info for info in proto.value_info}
 
     # Deserialize nodes with all known values
@@ -663,7 +674,10 @@
 
     # Fill in values for graph outputs
     outputs = [deserialize_value_info_proto(info, values[info.name]) for info in proto.output]
+
+    # Exit the graph scope by popping the values for this scope from the stack
     scoped_values.pop()
+
     return _core.Graph(
         inputs,
         outputs,
@@ -1204,24 +1218,24 @@
         opset_ids.add(domain=domain, version=version)
 
 
-def _serialize_metadata_props_into(
+def _serialize_string_string_maps(
     string_string_entries: proto_containers.RepeatedCompositeFieldContainer[
         onnx.StringStringEntryProto
     ],
     from_: Mapping[str, str],
 ) -> None:
-    """Serialize metadata properties into a repeated field of string-string entries.
+    """Serialize a <str, str> mapping into a repeated field of string-string entries.
 
     Args:
         string_string_entries: The repeated field to serialize into.
-        from_: The mapping of metadata properties to serialize.
+        from_: The mapping of a <str, str> mapping to serialize.
     """
     # Sort names for deterministic serialization
     for key in sorted(from_):
         string_string_entries.add(key=key, value=from_[key])
 
 
-_serialize_string_string_maps = _serialize_metadata_props_into
+_serialize_metadata_props_into = _serialize_string_string_maps
 
 
 def _maybe_add_quantization_annotation(
@@ -1284,18 +1298,21 @@
             # TODO(justinchuby): We should add a method is_initializer() on Value when
             # the initializer list is tracked
             _maybe_add_quantization_annotation(graph_proto, input_)
+    input_names = {input_.name for input_ in from_.inputs}
     # TODO(justinchuby): Support sparse_initializer
-    for initializer in from_.initializers.values():
-        _maybe_add_quantization_annotation(graph_proto, initializer)
-        if initializer.const_value is None:
+    for value in from_.initializers.values():
+        _maybe_add_quantization_annotation(graph_proto, value)
+        if _should_create_value_info_for_value(value) and value.name not in input_names:
+            # Serialize information about all initializers into value_info,
+            # except for those that are also graph inputs
+            serialize_value_into(graph_proto.value_info.add(), value)
+        if value.const_value is None:
             # Skip initializers without constant values
-            logger.warning(
-                "Initializer '%s' does not have a constant value set.", initializer.name
-            )
+            logger.warning("Initializer '%s' does not have a constant value set.", value.name)
             continue
         # Make sure the tensor's name is the same as the value's name
-        initializer.const_value.name = initializer.name
-        serialize_tensor_into(graph_proto.initializer.add(), from_=initializer.const_value)
+        value.const_value.name = value.name
+        serialize_tensor_into(graph_proto.initializer.add(), from_=value.const_value)
     for node in from_:
         serialize_node_into(graph_proto.node.add(), from_=node)
         for node_output in node.outputs: