[PT FE]: multiple fixes for models from optimum testing scope (#18501)

* [PT FE]: multiple fixes for models from optimum testing scope

* Update src/frontends/pytorch/src/op_table.cpp

src/frontends/pytorch/src/op/round.cpp

@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/op/round.hpp"
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/convert.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_round(const NodeContext& context) {
+    // aten::round(Tensor self) -> Tensor
+    // aten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+    // aten::round.int(int a) -> float
+    // aten::round.float(float a) -> float
+    // aten::round.Scalar(Scalar a) -> Scalar
+    num_inputs_check(context, 1, 2);
+    auto data = context.get_input(0);
+    auto data_rank = data.get_partial_shape().rank();
+    auto is_scalar = data_rank.is_static() && data_rank.get_length() == 0;
+    auto is_integer = !data.get_element_type().is_dynamic() && data.get_element_type().is_integral();
+    if (is_scalar && is_integer) {
+        data = context.mark_node(std::make_shared<v0::Convert>(data, element::f32));
+    }
+    auto res = context.mark_node(std::make_shared<v5::Round>(data, v5::Round::RoundMode::HALF_TO_EVEN));
+    if (!context.input_is_none(1)) {
+        context.mutate_input(1, res);
+    }
+    return {res};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -116,6 +116,7 @@ OP_CONVERTER(translate_reshape);
 OP_CONVERTER(translate_reshape_as);
 OP_CONVERTER(translate_roi_align);
 OP_CONVERTER(translate_roll);
+OP_CONVERTER(translate_round);
 OP_CONVERTER(translate_rsqrt);
 OP_CONVERTER(translate_rsub);
 OP_CONVERTER(translate_scaled_dot_product_attention);
@@ -280,6 +281,10 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::leaky_relu", op::translate_1to1_match_2_inputs<opset10::PRelu>},
         {"aten::leaky_relu_", op::inplace_op<op::translate_1to1_match_2_inputs<opset10::PRelu>>},
         {"aten::len", op::translate_len},
+        // lift op is torchscript specific op responsible for tensors coping with guarantee of new memory allocation
+        {"aten::lift", op::skip_node},
+        {"aten::lift_fresh", op::skip_node},
+        {"aten::lift_fresh_copy", op::skip_node},
         {"aten::linalg_norm", op::translate_linalg_norm},
         {"aten::linalg_matrix_norm", op::translate_linalg_matrix_norm},
         {"aten::linalg_vector_norm", op::translate_linalg_vector_norm},
@@ -303,6 +308,8 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::mm", op::translate_1to1_match_2_inputs<opset10::MatMul>},
         {"aten::mul", op::translate_1to1_match_2_inputs_align_types<opset10::Multiply>},
         {"aten::mul_", op::inplace_op<op::translate_1to1_match_2_inputs_align_types<opset10::Multiply>>},
+        {"aten::multiply", op::translate_1to1_match_2_inputs_align_types<opset10::Multiply>},
+        {"aten::multiply_", op::inplace_op<op::translate_1to1_match_2_inputs_align_types<opset10::Multiply>>},
         {"aten::narrow", op::translate_narrow},
         {"aten::ne", op::translate_1to1_match_2_inputs_align_types<opset10::NotEqual>},
         {"aten::neg", op::translate_neg},
@@ -329,6 +336,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"aten::reshape", op::translate_reshape},
         {"aten::reshape_as", op::translate_reshape_as},
         {"aten::roll", op::translate_roll},
+        {"aten::round", op::translate_round},
         {"aten::rsqrt", op::translate_rsqrt},
         {"aten::rsub", op::translate_rsub},
         {"aten::ScalarImplicit", op::skip_node},

tests/layer_tests/pytorch_tests/test_round.py

@@ -0,0 +1,84 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestRound(PytorchLayerTest):
+    def _prepare_input(self, out=False, dtype="float32"):
+        import numpy as np
+        input = np.random.randn(1, 3, 224, 224).astype(dtype)
+        if not out:
+            return (input, )
+        return (input, np.zeros_like(input))
+
+    def create_model(self, out=False):
+        import torch
+
+        class aten_round(torch.nn.Module):
+            def __init__(self, out):
+                super(aten_round, self).__init__()
+                if out:
+                    self.forward = self.forward_out
+                 
+
+            def forward(self, x):
+                return torch.round(x)
+
+            def forward_out(self, x, y):
+                return torch.round(x, out=y), y
+
+        ref_net = None
+
+        return aten_round(out), ref_net, "aten::round"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("out", [True, False])
+    @pytest.mark.parametrize("dtype", ["float32", "float64", "int32", "int64"])
+    def test_round(self, out, dtype, ie_device, precision, ir_version):
+        self._test(*self.create_model(out), ie_device, precision, ir_version, kwargs_to_prepare_input={"out": out, "dtype": dtype})
+
+
+class TestRoundScalar(PytorchLayerTest):
+    def _prepare_input_int(self):
+        import numpy as np
+        return (np.array(np.random.randint(low=-5, high=5)), )
+
+    def _prepare_input_float(self):
+        import numpy as np
+        return (np.array(np.random.uniform(low=-5, high=5)), )
+
+    def create_model(self, input_type="float"):
+        import torch
+
+        class aten_round(torch.nn.Module):
+            def __init__(self, input_type):
+                super(aten_round, self).__init__()
+                if input_type == "int":
+                    self.forward = self.forward_int
+                else:
+                    self.forward = self.forward_float
+                 
+
+            def forward_int(self, x:int):
+                return torch.round(x)
+
+            def forward_float(self, x:float):
+                return torch.round(x)
+
+        ref_net = None
+
+        return aten_round(input_type), ref_net, "aten::round"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("input_type", ["int", "float"])
+    def test_round(self, input_type, ie_device, precision, ir_version):
+        if input_type == "int":
+            self._prepare_input = self._prepare_input_int
+        else:
+            self._prepare_input = self._prepare_input_float
+        self._test(*self.create_model(input_type), ie_device, precision, ir_version, trace_model=True)