[FRONTEND] Remove unnecessary Reshapes in PyTorch Conv1D conversion (o…

…penvinotoolkit#26960)

[FRONTEND] Remove unnecessary Reshapes in PyTorch Conv1D conversion

PyTorch's Conv1D operation utilizes additional reshaping in its
implementation because torch.addmm() doesn't support tensors with rank
higher than 2.

Remove unnecessary Reshapes in PyTorch's Conv1D conversion as OpenVINO's
MatMul supports tensors with rank higher than 2. This should reduce the
number of nodes in a graph and potentially improve performance.

- Tickets:
	* CVS-150872

src/frontends/pytorch/src/op/addmm.cpp

@@ -73,17 +73,8 @@ OutputVector translate_conv1d_ext(const NodeContext& context) {
     auto bias = context.get_input(2);
     bias = context.mark_node(std::make_shared<ov::op::v1::ConvertLike>(bias, x));
 
-    auto neg_one = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {-1}));
-    auto zero = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {0}));
-    auto shape_x = context.mark_node(std::make_shared<v3::ShapeOf>(x, element::i32));
-    auto x_last_dim = context.mark_node(std::make_shared<v8::Gather>(shape_x, neg_one, zero));
-    auto x_new_shape = context.mark_node(std::make_shared<v0::Concat>(OutputVector{neg_one, x_last_dim}, 0));
-
-    auto x_new = context.mark_node(std::make_shared<v1::Reshape>(x, x_new_shape, false));
-    auto mm = context.mark_node(std::make_shared<v0::MatMul>(x_new, weight));
-    auto addmm = context.mark_node(std::make_shared<v1::Add>(bias, mm));
-    auto size_out = context.mark_node(std::make_shared<v12::ScatterElementsUpdate>(shape_x, neg_one, neg_one, zero));
-    return {context.mark_node(std::make_shared<v1::Reshape>(addmm, size_out, false))};
+    auto mm = context.mark_node(std::make_shared<v0::MatMul>(x, weight));
+    return {context.mark_node(std::make_shared<v1::Add>(mm, bias))};
 };
 
 }  // namespace op

tests/model_hub_tests/pytorch/test_llm.py

@@ -100,21 +100,24 @@ def load_model(self, name, type):
             config = {}
         model_kwargs = {"torchscript": True, "trust_remote_code": True}
         is_gptq = is_gptq_model(config)
+        is_gpt2 = name == "openai-community/gpt2"
+
         if is_gptq:
             self.cuda_available, self.gptq_postinit = patch_gptq()
             model_kwargs["torch_dtype"] = torch.float32
             self.ov_config = {"DYNAMIC_QUANTIZATION_GROUP_SIZE": "0"}
+        elif is_gpt2:
+            model_kwargs["torch_dtype"] = torch.float16
         else:
             model_kwargs["torch_dtype"] = "auto"
-            pass
 
         t = AutoTokenizer.from_pretrained(name, trust_remote_code=True)
         self.model = AutoModelForCausalLM.from_pretrained(name, **model_kwargs)
         if is_gptq:
             model = self.model
         else:
             assert self.model.config.torch_dtype in [
-                torch.float16, torch.bfloat16]
+                torch.float16, torch.bfloat16] or is_gpt2
             model = copy.deepcopy(self.model).float()
 
         example = t("Some input text to verify that model works.",
@@ -188,6 +191,7 @@ def get_pkv(model, tokenizer):
     @pytest.mark.parametrize("type,name", [
         ("opt_gptq", "katuni4ka/opt-125m-gptq"),
         ("llama", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"),
+        ("gpt2", "openai-community/gpt2")
     ])
     @pytest.mark.precommit
     @pytest.mark.nightly