try to remove onnx fallback

Author: eaidova

optimum/exporters/openvino/convert.py

@@ -415,80 +415,43 @@ def export_pytorch(
         dummy_inputs = config.rename_ambiguous_inputs(dummy_inputs)
         dummy_inputs, dict_inputs = remove_none_from_dummy_inputs(dummy_inputs)
 
-        try:
-            # 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([value if not isinstance(value, list) else tuple(value) for value in outputs.values()])
-
-            patcher.patched_forward = ts_patched_forward
-
-            ts_decoder_kwargs = {}
-            if library_name == "diffusers" and is_openvino_version(">=", "2025.0"):
-                ts_decoder_kwargs["trace_kwargs"] = {"check_trace": False}
-
-            with patcher:
-                if patch_16bit_model:
-                    from openvino.frontend.pytorch.patch_model import __make_16bit_traceable
-
-                    __make_16bit_traceable(model)
-                check_dummy_inputs_are_allowed(model, dummy_inputs)
-                input_info = _get_input_info(model, config, dummy_inputs)
-                ts_decoder = TorchScriptPythonDecoder(model, example_input=dummy_inputs, **ts_decoder_kwargs)
-                ov_model = convert_model(
-                    ts_decoder,
-                    example_input=dummy_inputs,
-                    input=[(item.shape, item.type) for item in input_info],
-                )
-
-        except Exception as ex:
-            logger.warning(f"Export model to OpenVINO directly failed with: \n{ex}.\nModel will be exported to ONNX")
-
-            if stateful:
-                # cannot raise because stateful is enabled by default and it would break backward compatibility for models that couldn't convert to OV directly
-                # TODO: Implement stateful for ONNX path as well, not doing it right now because of lack of validation
-                logger.warning(
-                    "[ WARNING ] Making stateful models is not supported when exporting to ONNX as an intermediate step. "
-                    "A stateless model will be exported instead. It may result in sub-optimal inference performance."
-                    "Provide a model that can be converted to OpenVINO without fallback to ONNX conversion path."
-                )
-
+        # 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([value if not isinstance(value, list) else tuple(value) for value in outputs.values()])
+
+        patcher.patched_forward = ts_patched_forward
+
+        ts_decoder_kwargs = {}
+        if library_name == "diffusers" and is_openvino_version(">=", "2025.0"):
+            ts_decoder_kwargs["trace_kwargs"] = {"check_trace": False}
+
+        with patcher:
             if patch_16bit_model:
-                from openvino.frontend.pytorch.patch_model import unpatch_model
-
-                unpatch_model(model, "_openvino_module_extension_patch_orig_forward")
-                for m in model.modules():
-                    if any(p.dtype in [torch.float16, torch.bfloat16] for p in m.parameters(False)) or any(
-                        b.dtype in [torch.float16, torch.bfloat16] for b in m.buffers(False)
-                    ):
-                        m.float()
-
-            return export_pytorch_via_onnx(
-                model,
-                config,
-                opset,
-                output,
-                device,
-                input_shapes,
-                model_kwargs,
-                ov_config=ov_config,
-                library_name=library_name,
+                from openvino.frontend.pytorch.patch_model import __make_16bit_traceable
+
+                __make_16bit_traceable(model)
+            check_dummy_inputs_are_allowed(model, dummy_inputs)
+            input_info = _get_input_info(model, config, dummy_inputs)
+            ts_decoder = TorchScriptPythonDecoder(model, example_input=dummy_inputs, **ts_decoder_kwargs)
+            ov_model = convert_model(
+                ts_decoder,
+                example_input=dummy_inputs,
+                input=[(item.shape, item.type) for item in input_info],
             )
 
-        ov_model.validate_nodes_and_infer_types()  # TODO: remove as unnecessary validation?
-
         output_names = list(config.outputs.keys())
         for idx, out_tensor in enumerate(ov_model.outputs):
             if idx < len(output_names):