Retiarii graph and code generation test (#3231)

nni/retiarii/converter/op_types.py

@@ -30,6 +30,10 @@ BasicOpsPT = {
     'aten::size': 'Size',
     'aten::view': 'View',
     'aten::eq': 'Eq',
+    'aten::Bool': 'Bool',
+    'aten::empty': 'Empty',
+    'aten::zeros': 'Zeros',
+    'aten::chunk': 'Chunk',
     'aten::add_': 'Add_'  # %out.3 : Tensor = aten::add_(%out.1, %connection.1, %4)
 }
 

nni/retiarii/operation.py

@@ -121,6 +121,8 @@ class PyTorchOperation(Operation):
             return f'{output} = {value}'
         elif self.type == 'prim::ListConstruct':
             return f'{output} = [{", ".join(inputs)}]'
+        elif self.type == 'prim::GetAttr':
+            return f"{output} = {self.parameters['input']}.{self.parameters['name']}"
         elif self.type == 'aten::mean':
             return f'{output} = torch.mean({inputs[0]}, {", ".join(inputs[1:-1])}, out={inputs[-1]})'
         elif self.type == 'aten::__getitem__':
@@ -133,8 +135,7 @@ class PyTorchOperation(Operation):
             assert len(inputs) == 2
             return f'{output} = torch.cat({inputs[0]}, dim={inputs[1]})'
         elif self.type == 'aten::add':
-            assert len(inputs) == 2
-            return f'{output} = {inputs[0]} + {inputs[1]}'
+            return f'{output} = ' + ' + '.join(inputs)
         elif self.type == OpTypeName.MergedSlice:
             assert (len(inputs) - 1) % 4 == 0
             slices = []
@@ -151,6 +152,8 @@ class PyTorchOperation(Operation):
             return f'{output} = {inputs[0]}.view({inputs[1]})'
         elif self.type == 'aten::slice':
             raise RuntimeError('not supposed to have aten::slice operation')
+        elif self.type == 'aten::Bool':
+            return f'{output} = bool({inputs[0]})'
         else:
             raise RuntimeError(f'unsupported operation type: {self.type} ? {self._to_class_name()}')
 

nni/retiarii/utils.py

@@ -27,6 +27,11 @@ def get_records():
     return _records
 
 
+def clear_records():
+    global _records
+    _records = {}
+
+
 def add_record(key, value):
     """
     """
@@ -56,7 +61,7 @@ def _blackbox_cls(cls, module_name, register_format=None):
             # eject un-serializable arguments
             for k in list(full_args.keys()):
                 # The list is not complete and does not support nested cases.
-                if not isinstance(full_args[k], (int, float, str, dict, list)):
+                if not isinstance(full_args[k], (int, float, str, dict, list, tuple)):
                     if not (register_format == 'full' and k == 'model'):
                         # no warning if it is base model in trainer
                         warnings.warn(f'{cls} has un-serializable arguments {k} whose value is {full_args[k]}. \