Fixed bug with inlined inputs (#24448)

### Details:
- Fixed bug with inlined inputs for fx decoder which were being added as
parameters and are not needed.

### Tickets:
 - (https://jira.devtools.intel.com/browse/CVS-116702)

---------

Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>

src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp

@@ -83,15 +83,7 @@ public:
 
     Any get_values_from_const_input(int index) const override;
 
-    // TODO: upstream to base class
-    OutputVector inputs() const {
-        OutputVector res;
-        for (auto input : m_decoder_inputs) {
-            FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding index: ", input, " exist.");
-            res.push_back(m_tensor_map->at(input));
-        }
-        return res;
-    }
+    OutputVector inputs() const;
 
     Any get_input_type(size_t index) const {
         return m_decoder->get_input_type(index);

src/frontends/pytorch/src/node_context.cpp

@@ -147,6 +147,24 @@ std::shared_ptr<ov::Model> NodeContext::convert_subgraph(size_t index) const {
     return model;
 }
 
+OutputVector NodeContext::inputs() const {
+    OutputVector res;
+    for (size_t i = 0; i < m_decoder_inputs.size(); i++) {
+        auto input = m_decoder_inputs.at(i);
+        if (input == 0) {
+            // Case when input can be inlined (possible only for fx decoder)
+            if (m_decoder->is_input_inlined(i)) {
+                auto inlined_input = m_decoder->inlined_input(i);
+                FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, "Incorrect inlined input with index:", i);
+                res.push_back(inlined_input[0]);
+            }
+        }
+        FRONT_END_GENERAL_CHECK(m_tensor_map->count(input), "No tensor corresponding input: ", input, " exist.");
+        res.push_back(m_tensor_map->at(input));
+    }
+    return res;
+}
+
 bool NodeContext::input_is_none(size_t index) const {
     bool res = index >= m_inputs_is_none.size() || m_inputs_is_none.at(index);
     if (!res) {

src/frontends/pytorch/src/translate_session.cpp

@@ -129,6 +129,10 @@ std::shared_ptr<Model> TranslateSession::convert_pytorch_model(
             auto raw_inputs = node->inputs();
             for (size_t i = 0; i < raw_inputs.size(); ++i) {
                 auto input = raw_inputs.at(i);
+                // If inputs are inlined (possible only for fx decoder) we shouldn't add a Parameter for it
+                if (input == 0 && node->is_input_inlined(i)) {
+                    continue;
+                }
                 if (tensor_map->find(input) == tensor_map->end()) {
                     // Input refers value in the outer scope, need to create a new Parameter in the current scope
                     // Linkage to external scope will be performed on the level of the parent operation (if or loop)

src/frontends/pytorch/src/utils.cpp

@@ -18,9 +18,9 @@ namespace frontend {
 namespace pytorch {
 
 void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs) {
-    auto inputs = context.inputs();
-    FRONT_END_OP_CONVERSION_CHECK(inputs.size() >= min_inputs, "Got less inputs than expected");
-    for (auto i = max_inputs; i < inputs.size(); i++) {
+    auto num_inputs = context.get_input_size();
+    FRONT_END_OP_CONVERSION_CHECK(num_inputs >= min_inputs, "Got less inputs than expected");
+    for (auto i = max_inputs; i < num_inputs; i++) {
         FRONT_END_OP_CONVERSION_CHECK(context.input_is_none(i), "Got more inputs than expected.");
     }
 }

tests/layer_tests/py_frontend_tests/test_torch_frontend.py

@@ -720,7 +720,7 @@ def test_patched_16bit_model_converts():
     res_fp16 = cm_fp16([x.numpy() for x in example])
     np.testing.assert_allclose(res_fp16[0], res_ref[0].numpy(), atol=1e-2)
     np.testing.assert_allclose(res_fp16[1], res_ref[1].numpy(), atol=1e-2)
-    
+
     model_bf16 = copy.deepcopy(model_ref).bfloat16()
     patch_model.__make_16bit_traceable(model_bf16)
     # the approach with patching only works for node with no grad
@@ -731,3 +731,18 @@ def test_patched_16bit_model_converts():
     res_bf16 = cm_bf16([x.numpy() for x in example])
     np.testing.assert_allclose(res_bf16[0], res_ref[0].numpy(), atol=1e-2)
     np.testing.assert_allclose(res_bf16[1], res_ref[1].numpy(), atol=1e-2)
+
+
+class InlinedInputsModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self):
+        return torch.arange(2048)
+
+
+def test_inlined_inputs():
+    model = InlinedInputsModel()
+    model.eval()
+    model = torch.compile(model, backend="openvino", options={"testing": 1})
+    model()

tests/layer_tests/pytorch_tests/pytorch_layer_test_class.py

@@ -17,9 +17,19 @@ import torch
 from packaging import version
 import pytest
 
+
+def skip_check(param):
+    return skip_if_export(param) if PytorchLayerTest.use_torch_export() else skip_if_fx(param)
+
+
 def skip_if_export(param, reason="Unsupported on torch.export"):
     return pytest.param(param, marks=pytest.mark.skipif(PytorchLayerTest.use_torch_export(), reason=reason))
 
+
+def skip_if_fx(param, reason="Unsupported on torch.fx"):
+    return pytest.param(param, marks=pytest.mark.skipif(PytorchLayerTest.use_torch_compile_backend(), reason=reason))
+
+
 class PytorchLayerTest:
     _type_map = {
         "float64": Type.f64,

tests/layer_tests/pytorch_tests/test_arange.py

@@ -3,7 +3,7 @@
 
 import pytest
 
-from pytorch_layer_test_class import PytorchLayerTest, skip_if_export
+from pytorch_layer_test_class import PytorchLayerTest, skip_check
 
 
 class TestArange(PytorchLayerTest):
@@ -112,7 +112,7 @@ class TestArange(PytorchLayerTest):
     @pytest.mark.precommit_fx_backend
     @pytest.mark.parametrize("dtype", [None, "float32", "float64", "int32", "int64", "int8", "uin8"])
     @pytest.mark.parametrize("end", [1, 2, 3])
-    @pytest.mark.parametrize("use_out", [skip_if_export(True), False])
+    @pytest.mark.parametrize("use_out", [skip_check(True), False])
     def test_arange_end_only(self, dtype, end, use_out, ie_device, precision, ir_version):
         self._test(*self.create_model(dtype, 1, use_out), ie_device, precision, ir_version,
                    kwargs_to_prepare_input={"end": end})