fix export

optimum/exporters/openvino/convert.py

@@ -14,6 +14,7 @@
 
 import functools
 import gc
+import inspect
 import logging
 import os
 from pathlib import Path
@@ -43,6 +44,7 @@
     clear_class_registry,
     flattenize_inputs,
     get_input_shapes,
+    remove_none_from_dummy_inputs,
 )
 
 
@@ -370,9 +372,29 @@ def export_pytorch(
             )
 
         dummy_inputs = config.rename_ambiguous_inputs(dummy_inputs)
+        dummy_inputs, dict_inputs = remove_none_from_dummy_inputs(dummy_inputs)
 
         try:
-            with config.patch_model_for_export(model, model_kwargs=model_kwargs):
+            # TorchScript used behind OpenVINO conversion. Optimum supports only return_dict=True models for patching,
+            # while TorchScript do not support dictionary with values of mixed types (e.g. Tensor and None) in model input/output
+            # To handle it, additional wrapper on patcher forward applied.
+            # model.config.torchscript = True can not be used for patching, because it overrides return_dict to False
+            patcher = config.patch_model_for_export(model, model_kwargs=model_kwargs)
+            patched_forward = patcher.patched_forward
+
+            @functools.wraps(patched_forward)
+            def ts_patched_forward(*args, **kwargs):
+                for i in range(len(dict_inputs)):
+                    input_name, keys = dict_inputs[i]
+                    tuple_input = kwargs[input_name]
+                    input_dict = dict(zip(keys, tuple_input))
+                    kwargs[input_name] = input_dict
+                outputs = patched_forward(*args, **kwargs)
+                return tuple(outputs.values())
+
+            patcher.patched_forward = ts_patched_forward
+
+            with patcher:
                 check_dummy_inputs_are_allowed(model, dummy_inputs)
                 inputs = config.ordered_inputs(model)
                 input_names = list(inputs.keys())
@@ -404,7 +426,8 @@ def export_pytorch(
                 compression_ratio=compression_ratio,
             )
 
-        ordered_dummy_inputs = {param: dummy_inputs[param] for param in inputs if param in dummy_inputs}
+        sig = inspect.signature(model.forward) if hasattr(model, "forward") else inspect.signature(model.call)
+        ordered_dummy_inputs = {param: dummy_inputs[param] for param in sig.parameters if param in dummy_inputs}
         ordered_input_names = list(inputs)
         flatten_inputs = flattenize_inputs(ordered_dummy_inputs.values())
         ov_model.validate_nodes_and_infer_types()
@@ -418,7 +441,6 @@ def export_pytorch(
             inp_data = flatten_inputs[idx]
             static_shape = PartialShape(inp_data.shape)
             dims = inputs[input_name]
-
             for dim in dims:
                 static_shape[dim] = -1
             inp_tensor.get_node().set_partial_shape(static_shape)