[PT FE]: allow example input list with single tensor (#19308)

src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py

@@ -35,6 +35,7 @@ class TorchScriptPythonDecoder (Decoder):
         self.m_decoders = []
         self._input_signature = None
         self._shared_memory = shared_memory
+        self._input_is_list = False
         if graph_element is None:
             try:
                 pt_module = self._get_scripted_model(pt_module, example_input)
@@ -136,11 +137,16 @@ class TorchScriptPythonDecoder (Decoder):
             return {"example_inputs": [inputs[name] for name in ordered_inputs]}, ordered_inputs, wrapped_model
 
         def prepare_example_inputs_and_model(inputs, input_params, model):
+            input_signature = list(input_params)
             if isinstance(inputs, dict):
                 return process_dict_inputs(inputs, input_params, model)
+            if isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], torch.Tensor):
+                if "typing.List" in str(input_params[input_signature[0]].annotation):
+                    inputs = inputs[0].unsqueeze(0)
+                    self._input_is_list = True
+
             if isinstance(inputs, torch.Tensor):
                 inputs = [inputs]
-            input_signature = list(input_params)
             input_signature = input_signature[:len(inputs)]
             return {"example_inputs": inputs}, input_signature, model
 
@@ -164,7 +170,7 @@ class TorchScriptPythonDecoder (Decoder):
                         try:
                             scripted = torch.jit.trace(pt_module, **input_parameters, strict=False)
                         except Exception as te:
-                            raise f"Tracing failed with exception {te}\nScripting failed with exception: {se}"
+                            raise Exception(f"Tracing failed with exception {te}\nScripting failed with exception: {se}")
             skip_freeze = False
             for n in scripted.inlined_graph.nodes():
                 # TODO: switch off freezing for all traced models

tests/layer_tests/ovc_python_api_tests/test_pytorch.py

@@ -3,7 +3,7 @@
 
 import os
 
-from typing import Tuple
+from typing import Tuple, List
 import numpy
 import numpy as np
 import openvino.runtime as ov
@@ -958,6 +958,48 @@ def create_pytorch_module_with_nested_inputs6(tmp_dir):
         "compress_to_fp16": False}
 
 
+def create_pytorch_module_with_nested_list_and_single_input(tmp_dir):
+    class PTModel(torch.nn.Module):
+        def forward(self, x: List[torch.Tensor]):
+            x0 = x[0]
+            x0 = torch.cat([x0, torch.zeros(1, 1)], 1)
+            return x0 + torch.ones((1, 1))
+    
+    net = PTModel()
+    constant_one = ov.opset10.constant(np.ones((1, 1)), dtype=np.float32)
+    const_zero = ov.opset10.constant(0, dtype=np.int32)
+    constant_zeros1 = ov.opset10.constant(np.zeros((1, 1), dtype=np.float32), dtype=np.float32)
+
+    param =  ov.opset10.parameter(PartialShape([-1, -1, -1]), dtype=np.float32)
+    gather = ov.opset10.gather(param, const_zero, const_zero)
+    concat1 = ov.opset10.concat([gather, constant_zeros1], 1)
+    add = ov.opset10.add(concat1, constant_one)
+    ref_model = Model([add], [param], "test")
+    return net, ref_model, {
+        "example_input": [torch.ones((1, 11))],
+        "compress_to_fp16": False}
+
+def create_pytorch_module_with_single_input_as_list(tmp_dir):
+    class PTModel(torch.nn.Module):
+        def forward(self, x):
+            x0 = x[0]
+            x0 = torch.cat([x0, torch.zeros(1)], 0)
+            return x0 + torch.ones(1)
+    
+    net = PTModel()
+    constant_one = ov.opset10.constant(np.ones((1,)), dtype=np.float32)
+    const_zero = ov.opset10.constant(0, dtype=np.int32)
+    constant_zeros1 = ov.opset10.constant(np.zeros((1, ), dtype=np.float32), dtype=np.float32)
+
+    param =  ov.opset10.parameter(PartialShape([-1, -1]), dtype=np.float32)
+    gather = ov.opset10.gather(param, const_zero, const_zero)
+    concat1 = ov.opset10.concat([gather, constant_zeros1], 0)
+    add = ov.opset10.add(concat1, constant_one)
+    ref_model = Model([add], [param], "test")
+    return net, ref_model, {
+        "example_input": [torch.ones((1, 11))],
+        "compress_to_fp16": False}
+
 class TestMoConvertPyTorch(CommonMOConvertTest):
     test_data = [
         create_pytorch_nn_module_case1,
@@ -1006,7 +1048,9 @@ class TestMoConvertPyTorch(CommonMOConvertTest):
         create_pytorch_module_with_nested_inputs3,
         create_pytorch_module_with_nested_inputs4,
         create_pytorch_module_with_nested_inputs5,
-        create_pytorch_module_with_nested_inputs6
+        create_pytorch_module_with_nested_inputs6,
+        create_pytorch_module_with_nested_list_and_single_input,
+        create_pytorch_module_with_single_input_as_list
     ]
 
     @pytest.mark.parametrize("create_model", test_data)

tools/mo/openvino/tools/mo/moc_frontend/pytorch_frontend_utils.py

@@ -78,6 +78,8 @@ def extract_input_info_from_example(args, inputs):
     input_names = None
     if not isinstance(example_inputs, (list, tuple, dict)):
         list_inputs = [list_inputs]
+    if args.input_model._input_is_list:
+        list_inputs[0] = list_inputs[0].unsqueeze(0)
     if args.input_model._input_signature is not None and not is_dict_input:
         input_names = args.input_model._input_signature[1:] if args.input_model._input_signature[0] == "self" else args.input_model._input_signature
         if not is_dict_input:
@@ -156,10 +158,6 @@ def prepare_torch_inputs(example_inputs):
         inputs = example_inputs
         if isinstance(inputs, list):
             inputs = [to_torch_tensor(x) for x in inputs]
-            if len(inputs) == 1:
-                inputs = torch.unsqueeze(inputs[0], 0)
-            else:
-                inputs = inputs
         elif isinstance(inputs, tuple):
             inputs = [to_torch_tensor(x) for x in inputs]
             inputs = tuple(inputs)

tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py

@@ -77,6 +77,8 @@ def extract_input_info_from_example(args, inputs):
     input_names = None
     if not isinstance(example_inputs, (list, tuple, dict)):
         list_inputs = [list_inputs]
+    if args.input_model._input_is_list:
+        list_inputs[0] = list_inputs[0].unsqueeze(0)
     if args.input_model._input_signature is not None and not is_dict_input:
         input_names = args.input_model._input_signature[1:] if args.input_model._input_signature[0] == "self" else args.input_model._input_signature
         if not is_dict_input:
@@ -155,10 +157,6 @@ def prepare_torch_inputs(example_inputs):
         inputs = example_inputs
         if isinstance(inputs, list):
             inputs = [to_torch_tensor(x) for x in inputs]
-            if len(inputs) == 1:
-                inputs = torch.unsqueeze(inputs[0], 0)
-            else:
-                inputs = inputs
         elif isinstance(inputs, tuple):
             inputs = [to_torch_tensor(x) for x in inputs]
             inputs = tuple(inputs)