[PT FE]: extend logical operations support

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

@@ -4,7 +4,9 @@
 
 #include "openvino/frontend/pytorch/node_context.hpp"
 #include "openvino/op/logical_and.hpp"
+#include "openvino/op/logical_not.hpp"
 #include "openvino/op/logical_or.hpp"
+#include "openvino/op/logical_xor.hpp"
 #include "utils.hpp"
 
 namespace ov {
@@ -15,25 +17,57 @@ namespace op {
 using namespace ov::op;
 
 OutputVector translate_or(const NodeContext& context) {
-    num_inputs_check(context, 2, 2);
+    num_inputs_check(context, 2, 3);
     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));
+    if (!context.input_is_none(2)) {
+        context.mutate_input(2, or_node);
+    }
     return {or_node};
 };
 
 OutputVector translate_and(const NodeContext& context) {
-    num_inputs_check(context, 2, 2);
+    num_inputs_check(context, 2, 3);
     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};
+    auto and_node = context.mark_node(std::make_shared<v1::LogicalAnd>(x, y));
+    if (!context.input_is_none(2)) {
+        context.mutate_input(2, and_node);
+    }
+    return {and_node};
+};
+
+OutputVector translate_not(const NodeContext& context) {
+    num_inputs_check(context, 1, 2);
+    auto x = context.get_input(0);
+    x = context.mark_node(std::make_shared<v0::Convert>(x, element::boolean));
+    // TODO: use bitwise op here when will be supported by openvino
+    auto not_node = context.mark_node(std::make_shared<v1::LogicalNot>(x));
+    if (!context.input_is_none(1)) {
+        context.mutate_input(1, not_node);
+    }
+    return {not_node};
+};
+
+OutputVector translate_xor(const NodeContext& context) {
+    num_inputs_check(context, 2, 3);
+    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 xor_node = context.mark_node(std::make_shared<v1::LogicalXor>(x, y));
+    if (!context.input_is_none(2)) {
+        context.mutate_input(2, xor_node);
+    }
+    return {xor_node};
 };
 
 }  // namespace op

src/frontends/pytorch/src/op_table.cpp

@@ -109,6 +109,7 @@ OP_CONVERTER(translate_new_zeros);
 OP_CONVERTER(translate_nms);
 OP_CONVERTER(translate_nonzero);
 OP_CONVERTER(translate_norm);
+OP_CONVERTER(translate_not);
 OP_CONVERTER(translate_numel);
 OP_CONVERTER(translate_one_hot);
 OP_CONVERTER(translate_ones);
@@ -185,6 +186,7 @@ OP_CONVERTER(translate_quantized_cat);
 OP_CONVERTER(translate_quantized_convnd);
 OP_CONVERTER(translate_quantized_convnd_relu);
 OP_CONVERTER(translate_quantized_linear);
+OP_CONVERTER(translate_xor);
 // Torch FX Translations
 OP_CONVERTER(translate_arange_fx);
 OP_CONVERTER(translate_batch_norm_fx);
@@ -340,6 +342,10 @@ const std::map<std::string, CreatorFunction> get_supported_ops_ts() {
         {"aten::linspace", op::translate_linspace},
         {"aten::log", op::translate_log},
         {"aten::log_", op::inplace_op<op::translate_log>},
+        {"aten::logical_and", op::translate_and},
+        {"aten::logical_or", op::translate_or},
+        {"aten::logical_not", op::translate_not},
+        {"aten::logical_xor", op::translate_xor},
         {"aten::log_softmax", op::translate_log_softmax},
         {"aten::log2", op::translate_log2},
         {"aten::log2_", op::inplace_op<op::translate_log2>},

tests/layer_tests/pytorch_tests/test_logical_ops.py

@@ -0,0 +1,60 @@
+import numpy as np
+import pytest
+from pytorch_layer_test_class import PytorchLayerTest
+
+class TestLogicalOp(PytorchLayerTest):
+
+    def _prepare_input(self, out, unary):
+        x = np.random.randint(0, 2, (1, 10)).astype(bool)
+        if unary:
+            return (x, ) if not out else (x, np.zeros_like(x).astype(bool))
+        y = np.random.randint(0, 2, (1, 10)).astype(bool)
+        if not out:
+            return x, y
+        return x, y, np.zeros_like(x).astype(bool)
+
+    def create_model(self, op_name, out):
+        import torch
+
+        ops = {
+            "and": torch.logical_and,
+            "or": torch.logical_or,
+            "xor": torch.logical_xor,
+            "not": torch.logical_not
+        }
+        op = ops[op_name]
+        class aten_logical(torch.nn.Module):
+
+            def __init__(self, op, out) -> None:
+                super().__init__()
+                self.op = op
+                if op == torch.logical_not:
+                    self.forward = self.forward_not
+                if out:
+                    self.forward = self.forward_out if not op == torch.logical_not else self.forward_not_out
+
+            def forward(self, tensor_a, tensor_b):
+                return self.op(tensor_a, tensor_b)
+
+
+            def forward_out(self, tensor_a, tensor_b, out):
+                return self.op(tensor_a, tensor_b, out=out), out
+
+            def forward_not(self, tensor_a):
+                return self.op(tensor_a)
+
+            def forward_not_out(self, tensor_a, out):
+                return self.op(tensor_a, out=out), out
+
+        ref_net = None
+
+        return aten_logical(op, out), ref_net, f"aten::logical_{op_name}"
+
+
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.parametrize("op_type", ["and", "or", "not", "xor"])
+    @pytest.mark.parametrize("out", [True, False])
+    def test_and_tensor(self, op_type, out, ie_device, precision, ir_version):
+        self._test(*self.create_model(op_type, out),
+                   ie_device, precision, ir_version, kwargs_to_prepare_input={"out": out, "unary": op_type == "not"})