[PT FE]: fix aten::embedding realization for integer-like indicies an… (#15721)

* [PT FE]: fix aten::embedding realization for integer-like indicies and add tests

* more comments

* Update src/frontends/pytorch/src/op/embedding.cpp

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

---------

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

src/frontends/pytorch/src/op/embedding.cpp

@@ -4,6 +4,7 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/constant.hpp"
+#include "openvino/op/convert.hpp"
 #include "openvino/op/gather.hpp"
 #include "utils.hpp"
 
@@ -13,13 +14,17 @@ namespace pytorch {
 namespace op {
 
 OutputVector translate_embedding(NodeContext& context) {
+    // aten::embedding(Tensor weight, Tensor indices, SymInt padding_idx=-1, bool scale_grad_by_freq=False, bool
+    // sparse=False)
     num_inputs_check(context, 5, 5);
     auto data = context.get_input(0);
     auto indices = context.get_input(1);
-    // TODO: find out the meaning of input idx 2
-    FRONT_END_OP_CONVERSION_CHECK(
-        context.const_input<bool>(3) == false && context.const_input<bool>(4) == false,
-        "Only False is supported on inputs with indexes 3 and 4 for aten::embedding translation");
+    indices = context.mark_node(std::make_shared<ov::op::v0::Convert>(indices, element::i64));
+    // skip parameters 2, 3, 4 used only during trainig:
+    // padding_idx - if specified, the entries at padding_idx do not contribute to the gradient
+    // scale_grad_by_freq - if given, this will scale gradients by the inverse of frequency of
+    //                      the words in the mini-batch.
+    // sparse - if True, gradient will be represented as sparse tensor
     auto axis_0 = context.mark_node(ov::op::v0::Constant::create(element::i64, Shape{}, {0}));
     return {context.mark_node(std::make_shared<ov::op::v8::Gather>(data, indices, axis_0))};
 };

tests/layer_tests/pytorch_tests/test_embedding.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 TestExp(PytorchLayerTest):
+    def _prepare_input(self, indicies_size, indicies_dtype):
+        import numpy as np
+        return (np.random.randint(0, 9, size=indicies_size).astype(indicies_dtype), np.random.randn(10, 10))
+
+    def create_model(self):
+        import torch
+        import torch.nn.functional as F
+
+        class aten_embedding(torch.nn.Module):
+
+            def forward(self, indicies, weight):
+                return F.embedding(indicies, weight)
+
+        ref_net = None
+
+        return aten_embedding(), ref_net, "aten::embedding"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("indicies_size", [1, 2, 3, 4])
+    @pytest.mark.parametrize("indicies_dtype", ["int", "int32"])
+    def test_exp(self, ie_device, precision, ir_version, indicies_size, indicies_dtype):
+        self._test(*self.create_model(), ie_device, precision, ir_version,
+         kwargs_to_prepare_input={"indicies_size": indicies_size, "indicies_dtype": indicies_dtype})