[PT FE] Add more operators for FX graph (openvinotoolkit#23698)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

Author: mvafin

src/frontends/pytorch/src/op/full.cpp

@@ -107,6 +107,8 @@ OutputVector translate_full_like_fx(const NodeContext& context) {
     if (context.has_attribute("dtype")) {
         auto dtype = context.get_attribute<element::Type>("dtype");
         filled_tensor = context.mark_node(std::make_shared<v0::Convert>(filled_tensor, dtype));
+    } else {
+        filled_tensor = context.mark_node(std::make_shared<v1::ConvertLike>(filled_tensor, input));
     }
     return {filled_tensor};
 };
@@ -200,6 +202,8 @@ OutputVector translate_zeros_like_fx(const NodeContext& context) {
     if (context.has_attribute("dtype")) {
         auto dtype = context.get_attribute<element::Type>("dtype");
         filled_tensor = context.mark_node(std::make_shared<v0::Convert>(filled_tensor, dtype));
+    } else {
+        filled_tensor = context.mark_node(std::make_shared<v1::ConvertLike>(filled_tensor, input));
     }
     return {filled_tensor};
 };
@@ -280,6 +284,8 @@ OutputVector translate_ones_like_fx(const NodeContext& context) {
     if (context.has_attribute("dtype")) {
         auto dtype = context.get_attribute<element::Type>("dtype");
         filled_tensor = context.mark_node(std::make_shared<v0::Convert>(filled_tensor, dtype));
+    } else {
+        filled_tensor = context.mark_node(std::make_shared<v1::ConvertLike>(filled_tensor, input));
     }
     return {filled_tensor};
 };

src/frontends/pytorch/src/op_table.cpp

@@ -829,6 +829,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_fx() {
         {"aten.hardtanh.default", op::translate_hardtanh},
         {"aten.hardtanh_.default", op::inplace_op<op::translate_hardtanh>},
         {"aten.index.Tensor", op::translate_index_fx},
+        // aten.index_put.default - Supported in limited set of patterns
         {"aten.index_select.default", op::translate_index_select},
         {"aten.isfinite.default", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::IsFinite>>},
         {"aten.isinf.default", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::IsInf>>},
@@ -844,6 +845,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_fx() {
         {"aten.log10.default", op::translate_log10},
         {"aten.log1p.default", op::translate_log1p},
         {"aten.log2.default", op::translate_log2},
+        {"aten.logical_and.default", op::translate_and},
         {"aten.logsumexp.default", op::translate_logsumexp},
         {"aten.lt.Scalar", op::translate_1to1_match_2_inputs_align_types<opset10::Less>},
         {"aten.lt.Tensor", op::translate_1to1_match_2_inputs_align_types<opset10::Less>},
@@ -898,6 +900,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_fx() {
         {"aten.scalar_tensor.default", op::translate_scalar_tensor_fx},
         {"aten.scatter.src", op::translate_scatter},
         {"aten.scatter.value", op::translate_scatter},
+        {"aten.scatter_add.default", op::translate_scatter_add},
         {"aten.select.int", op::translate_select},
         {"aten.select_scatter.default", op::translate_select_scatter_fx},
         {"aten.sigmoid.default", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Sigmoid>},

src/frontends/pytorch/src/transforms/aten_index_put_replacer.cpp

@@ -49,7 +49,10 @@ AtenIndexPutReplacer::AtenIndexPutReplacer() {
     ov::matcher_pass_callback callback = [](ov::pass::pattern::Matcher& m) {
         auto index_op = cast_fw_node(m.get_match_root(), "aten::index_put_");
         if (!index_op) {
-            return false;
+            index_op = cast_fw_node(m.get_match_root(), "aten.index_put.default");
+            if (!index_op) {
+                return false;
+            }
         }
         NodeVector rt_copy_from;
         ov::pass::NodeRegistry rg;

tests/layer_tests/pytorch_tests/pytorch_layer_test_class.py

@@ -95,17 +95,12 @@ def numpy_to_torch_recursively(x):
             if self.use_torch_export():
                 from openvino import convert_model
                 from torch.export import export
-                from torch.fx.experimental.proxy_tensor import make_fx
 
                 em = export(model, tuple(torch_inputs))
                 if version.parse(torch.__version__) >= version.parse("2.3"):
                     em = em.run_decompositions()
-                print(em.graph_module.code)
-
-                try:
-                    gm = make_fx(em)(*torch_inputs)
-                except:
-                    gm = make_fx(em, tracing_mode='symbolic')(*torch_inputs)
+                gm = em.module()
+                print(gm.code)
 
                 input_shapes = []
                 input_types = []

tests/layer_tests/pytorch_tests/test_scatter.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 import torch
-from pytorch_layer_test_class import PytorchLayerTest
+from pytorch_layer_test_class import PytorchLayerTest, skip_if_export
 
 
 class TestScatter(PytorchLayerTest):
@@ -256,6 +256,7 @@ def forward(self, x: torch.Tensor):
 
     @pytest.mark.nightly
     @pytest.mark.precommit
+    @pytest.mark.precommit_torch_export
     @pytest.mark.parametrize("dim", [1, -1, 0])
     @pytest.mark.parametrize(
         "index",
@@ -267,7 +268,7 @@ def forward(self, x: torch.Tensor):
     )
     @pytest.mark.parametrize("src", [torch.arange(1, 26).reshape(5, 5)])
     @pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
-    @pytest.mark.parametrize("inplace", [True, False])
+    @pytest.mark.parametrize("inplace", [skip_if_export(True), False])
     def test_scatter_add(self, dim, index, src, dtype, inplace, ie_device, precision, ir_version):
         if isinstance(src, torch.Tensor):
             src = src.to(getattr(torch, dtype))

tests/model_hub_tests/pytorch/hf_transformers_models

@@ -219,7 +219,7 @@ liamcripwell/pgdyn-plan,context-roberta,skip,Load problem
 linhdo/graphdoc,graphdoc,skip,Load problem
 LinkSoul/Chinese-LLaVA-Baichuan,llava,skip,Load problem
 LinkSoul/LLaSM-Cllama2,llaaa,skip,Load problem
-lintang/t5-v2-xl-flan,umt5
+lintang/pile-t5-base-flan,umt5
 liuhaotian/LLaVA-Lightning-MPT-7B-preview,llava_mpt,skip,Load problem
 liya0121/my_finetune_0121,progen,skip,Load problem
 lucadiliello/BLEURT-20,bleurt,skip,Load problem

tests/model_hub_tests/pytorch/torch_utils.py

@@ -64,7 +64,6 @@ def prepare_inputs(self, inputs_info):
 
     def convert_model_impl(self, model_obj):
         if hasattr(self, "mode") and self.mode == "export":
-            from torch.fx.experimental.proxy_tensor import make_fx, get_isolated_graphmodule
             from torch.export import export
             from packaging import version
             from openvino.frontend.pytorch.fx_decoder import TorchFXPythonDecoder
@@ -90,17 +89,7 @@ def convert_model_impl(self, model_obj):
             if version.parse(torch.__version__) >= version.parse("2.2"):
                 graph = graph.run_decompositions()
 
-            if isinstance(self.example, dict):
-              try:
-                gm = get_isolated_graphmodule(graph, tuple(), self.example)
-              except:
-                gm = get_isolated_graphmodule(graph, tuple(), self.example, tracing_mode='symbolic')
-            else:
-              try:
-                gm = make_fx(graph)(*self.example)
-              except:
-                gm = make_fx(graph, tracing_mode='symbolic')(*self.example)
-
+            gm = graph.module()
             print(gm.code)
 
             decoder = TorchFXPythonDecoder(gm, gm, input_shapes=input_shapes, input_types=input_types)

tests/model_hub_tests/pytorch/torchbench_models

@@ -66,7 +66,7 @@ pyhpc_equation_of_state,None,xfail,Accuracy validation failed
 pyhpc_isoneutral_mixing,None,xfail,Accuracy validation failed
 pyhpc_turbulent_kinetic_energy,None,xfail,Unsupported op aten::empty_like
 pytorch_CycleGAN_and_pix2pix,None
-pytorch_stargan,None,xfail,CPU plugin error: Unsupported operation of type: BatchNormInference
+pytorch_stargan,None
 pytorch_unet,None
 #resnet152,None - Already tested by torchvision tests
 #resnet18,None - Already tested by torchvision tests