[PT FE] Support non boolean inputs for __or__ and __and__ operations (#19268)

* [PT FE] Support non boolean inputs for __or__ and __and__ operations

* Add test for __or__

src/frontends/pytorch/src/op/logical.cpp

@@ -0,0 +1,42 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/pytorch/node_context.hpp"
+#include "openvino/op/logical_and.hpp"
+#include "openvino/op/logical_or.hpp"
+#include "utils.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pytorch {
+namespace op {
+
+using namespace ov::op;
+
+OutputVector translate_or(const NodeContext& context) {
+    num_inputs_check(context, 2, 2);
+    auto x = context.get_input(0);
+    auto y = context.get_input(1);
+    x = context.mark_node(std::make_shared<v0::Convert>(x, element::boolean));
+    y = context.mark_node(std::make_shared<v0::Convert>(y, element::boolean));
+    // TODO: use bitwise op here when will be supported by openvino
+    auto or_node = context.mark_node(std::make_shared<v1::LogicalOr>(x, y));
+    return {or_node};
+};
+
+OutputVector translate_and(const NodeContext& context) {
+    num_inputs_check(context, 2, 2);
+    auto x = context.get_input(0);
+    auto y = context.get_input(1);
+    x = context.mark_node(std::make_shared<v0::Convert>(x, element::boolean));
+    y = context.mark_node(std::make_shared<v0::Convert>(y, element::boolean));
+    // TODO: use bitwise op here when will be supported by openvino
+    auto or_node = context.mark_node(std::make_shared<v1::LogicalAnd>(x, y));
+    return {or_node};
+};
+
+}  // namespace op
+}  // namespace pytorch
+}  // namespace frontend
+}  // namespace ov

src/frontends/pytorch/src/op_table.cpp

@@ -22,6 +22,7 @@ OP_CONVERTER(translate_add);
 OP_CONVERTER(translate_addcmul);
 OP_CONVERTER(translate_addmm);
 OP_CONVERTER(translate_all);
+OP_CONVERTER(translate_and);
 OP_CONVERTER(translate_arange);
 OP_CONVERTER(translate_argmax);
 OP_CONVERTER(translate_argsort);
@@ -111,6 +112,7 @@ OP_CONVERTER(translate_norm);
 OP_CONVERTER(translate_numel);
 OP_CONVERTER(translate_ones);
 OP_CONVERTER(translate_ones_like);
+OP_CONVERTER(translate_or);
 OP_CONVERTER(translate_outer);
 OP_CONVERTER(translate_pad);
 OP_CONVERTER(translate_pairwise_distance);
@@ -202,11 +204,11 @@ OP_CONVERTER(translate_transpose_fx);
 // Supported ops for TorchScript
 const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
     return {
-        {"aten::__and__", op::translate_1to1_match_2_inputs<opset10::LogicalAnd>},  // TODO: cover numerical cases
+        {"aten::__and__", op::translate_and},
         {"aten::__derive_index", op::translate_derive_index},
         {"aten::__getitem__", op::translate_getitem},
         {"aten::__not__", op::translate_1to1_match_1_inputs<opset10::LogicalNot>},
-        {"aten::__or__", op::translate_1to1_match_2_inputs<opset10::LogicalOr>},
+        {"aten::__or__", op::translate_or},
         {"aten::__range_length", op::translate_range_length},
         {"aten::_convolution", op::translate_convolution},
         {"aten::_convolution_mode", op::translate_convolution_mode},

tests/layer_tests/pytorch_tests/test_or.py

@@ -0,0 +1,28 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+from pytorch_layer_test_class import PytorchLayerTest
+
+
+class TestLog(PytorchLayerTest):
+    def _prepare_input(self):
+        import numpy as np
+        return (np.random.randint(0, 255, (20, 30, 40, 50)),)
+
+    def create_model(self):
+        import torch
+
+        class aten_or(torch.nn.Module):
+            def forward(self, x):
+                res = torch.ByteTensor(x.size()).zero_()
+                res[:, :, :, 1:] = res[:, :, :, 1:] | (x[:, :, :, 1:] != x[:, :, :, :-1])
+                res[:, :, :, :-1] = res[:, :, :, :-1] | (x[:, :, :, 1:] != x[:, :, :, :-1])
+                return res.float()
+
+        return aten_or(), None, "aten::__or__"
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_or(self, ie_device, precision, ir_version):
+        self._test(*self.create_model(), ie_device, precision, ir_version, dynamic_shapes=False, trace_model=True)