Support aten::tile op (#19645)

src/frontends/pytorch/src/op_table.cpp

@@ -435,6 +435,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::tanh", op::translate_1to1_match_1_inputs_with_fp32_type_alignment<opset10::Tanh>},
         {"aten::tanh_", op::inplace_op<op::translate_1to1_match_1_inputs<opset10::Tanh>>},
         {"aten::tensor", op::translate_as_tensor},
+        {"aten::tile", op::translate_1to1_match_2_inputs<opset10::Tile>},
         {"aten::to", op::translate_to},
         {"aten::topk", op::translate_topk},
         {"aten::transpose", op::quantizable_op<op::translate_transpose>},

tests/layer_tests/pytorch_tests/test_tile.py

@@ -0,0 +1,33 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestTile(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randn(1, 3, 224, 224).astype(np.float32),)
+
+    def create_model(self, dims):
+        import torch
+
+        class aten_tile(torch.nn.Module):
+            def __init__(self, dims):
+                super(aten_tile, self).__init__()
+                self.dims = dims
+
+            def forward(self, x):
+                return torch.tile(x, self.dims)
+
+        ref_net = None
+
+        return aten_tile(dims), ref_net, "aten::tile"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("dims", [(2, 2), (1, 1), (1, 2, 3, 4)])
+    def test_tile(self, dims, ie_device, precision, ir_version):
+        self._test(*self.create_model(dims), ie_device, precision, ir_version)