Fix bug in graph converter from Graph_gen (#4092)

nni/retiarii/converter/graph_gen.py

@@ -491,6 +491,17 @@ def handle_single_node(node):
         for node in sm_graph.nodes():
             handle_single_node(node)
 
+        if node_index == {}:
+            # here is an example that the ir_graph is empty
+            # graph(%self : __torch__.torchmodels.googlenet.GoogLeNet,
+            # %x.1 : Tensor): return (%x.1)
+            # add a noop_identity node to handle this situation
+            self.global_seq += 1
+            ni_node = ir_graph.add_node(build_full_name(module_name, 'noop_identity', self.global_seq), 'noop_identity')
+            ir_graph.add_edge(head=(ir_graph.input_node, 0), tail=(ni_node, None))
+            ir_graph.add_edge(head=(ni_node, None), tail=(ir_graph.output_node, None))
+            for _output in sm_graph.outputs():
+                node_index[_output.node()] = ni_node
         return node_index
 
     def merge_aten_slices(self, ir_graph):
@@ -575,9 +586,7 @@ def _convert_module(self, script_module, module, module_name, ir_model):
         # also has LayerChoice or InputChoice or ValueChoice
         original_type_name = script_module.original_name
         m_attrs = None
-        if original_type_name in MODULE_EXCEPT_LIST:
-            pass  # do nothing
-        elif original_type_name == OpTypeName.LayerChoice:
+        if original_type_name == OpTypeName.LayerChoice:
             graph = Graph(ir_model, -100, module_name, _internal=True)  # graph_id is not used now
             candidate_name_list = []
             for cand_name in module.names:
@@ -599,7 +608,9 @@ def _convert_module(self, script_module, module, module_name, ir_model):
             m_attrs = self._handle_valuechoice(module)
         elif original_type_name == OpTypeName.Placeholder:
             m_attrs = get_init_parameters_or_fail(module)
-        elif module.__class__.__module__.startswith('torch.nn') and original_type_name in torch.nn.__dict__:
+        elif module.__class__.__module__.startswith('torch.nn') and \
+            original_type_name in torch.nn.__dict__ and \
+                original_type_name not in MODULE_EXCEPT_LIST:
             # this is a basic module from pytorch, no need to parse its graph
             m_attrs = get_init_parameters_or_fail(module)
         elif getattr(module, '_stop_parsing', False):

nni/retiarii/converter/op_types.py

@@ -3,6 +3,7 @@
 
 from enum import Enum
 
+# except the special case which can not treat as a basic module from pytorch
 MODULE_EXCEPT_LIST = ['Sequential']
 
 

nni/retiarii/nn/pytorch/nn.py

@@ -41,7 +41,7 @@
 
 Module = nn.Module
 
-Sequential = transparent_serialize(nn.Sequential)
+Sequential = nn.Sequential
 ModuleList = transparent_serialize(nn.ModuleList)
 
 Identity = basic_unit(nn.Identity)