Fx support for multiple model architectures (#17393)

* Support for Bart and LayoutLM, and partial support for XLNet

* Support for mbart

* A lot of new models supported

* Support for other models

* LayoutLM fix

* Use strings instead of classes

tests/models/swin/test_modeling_swin.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 """ Testing suite for the PyTorch Swin model. """
 
-import copy
 import inspect
 import os
 import pickle
@@ -26,7 +25,7 @@ from transformers.testing_utils import require_torch, require_vision, slow, torc
 from transformers.utils import cached_property, is_torch_available, is_torch_fx_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
 
 
 if is_torch_available():
@@ -45,14 +44,6 @@ if is_torch_fx_available():
     from transformers.utils.fx import symbolic_trace
 
 
-def _config_zero_init(config):
-    configs_no_init = copy.deepcopy(config)
-    for key in configs_no_init.__dict__.keys():
-        if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
-            setattr(configs_no_init, key, 1e-10)
-    return configs_no_init
-
-
 class SwinModelTester:
     def __init__(
         self,
@@ -407,7 +398,9 @@ class SwinModelTest(ModelTesterMixin, unittest.TestCase):
                     input_names = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask"]
                     if labels is not None:
                         input_names.append("labels")
+
                     filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
+                    input_names = list(filtered_inputs.keys())
 
                     model_output = model(**filtered_inputs)
 
@@ -427,7 +420,7 @@ class SwinModelTest(ModelTesterMixin, unittest.TestCase):
                         input_names.append("end_positions")
 
                     filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
-                    input_names = filtered_inputs.keys()
+                    input_names = list(filtered_inputs.keys())
 
                     model_output = model(**filtered_inputs)