[TF FE] Decouple tests for Logical binary ops into separate suite and…

… run on all platforms

Signed-off-by: Kazantsev, Roman <[email protected]>

tests/layer_tests/tensorflow_tests/test_tf_BinaryOps.py

@@ -54,17 +54,13 @@ def create_add_placeholder_const_net(self, x_shape, y_shape, op_type):
             'Maximum': tf.raw_ops.Maximum,
             'Minimum': tf.raw_ops.Minimum,
             'Mod': tf.raw_ops.Mod,
-            'LogicalAnd': tf.raw_ops.LogicalAnd,
-            'LogicalOr': tf.raw_ops.LogicalOr,
             'FloorMod': tf.raw_ops.FloorMod,
             'FloorDiv': tf.raw_ops.FloorDiv,
             'Xdivy': tf.raw_ops.Xdivy,
         }
 
         input_type = np.float32
-        if op_type in ["LogicalAnd", "LogicalOr", "LogicalXor"]:
-            input_type = bool
-        elif op_type in ['Pow']:
+        if op_type in ['Pow']:
             input_type = np.int32
         self.input_type = input_type
 
@@ -89,8 +85,7 @@ def create_add_placeholder_const_net(self, x_shape, y_shape, op_type):
     @pytest.mark.parametrize('y_shape', [[4], [2, 3, 4]])
     @pytest.mark.parametrize("op_type",
                              ['Add', 'AddV2', 'Sub', 'Mul', 'Div', 'RealDiv', 'SquaredDifference', 'Pow',
-                              'Maximum', 'Minimum', 'Mod', 'LogicalAnd', 'LogicalOr', 'FloorMod',
-                              'FloorDiv', 'Xdivy'])
+                              'Maximum', 'Minimum', 'Mod', 'FloorMod', 'FloorDiv', 'Xdivy'])
     @pytest.mark.nightly
     @pytest.mark.precommit
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',

tests/layer_tests/tensorflow_tests/test_tf_LogicalBinaryOps.py

@@ -0,0 +1,54 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+import tensorflow as tf
+from common.tf_layer_test_class import CommonTFLayerTest
+
+rng = np.random.default_rng(23345)
+
+
+class TestLogicalBinaryOps(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x:0' in inputs_info, "Test error: inputs_info must contain `x`"
+        assert 'y:0' in inputs_info, "Test error: inputs_info must contain `y`"
+        x_shape = inputs_info['x:0']
+        y_shape = inputs_info['y:0']
+
+        inputs_data = {}
+        inputs_data['x:0'] = rng.choice([True, False], x_shape).astype(bool)
+        inputs_data['y:0'] = rng.choice([True, False], y_shape).astype(bool)
+        return inputs_data
+
+    def create_logical_binary_ops_net(self, x_shape, y_shape, op_type):
+        op_type_map = {
+            'LogicalAnd': tf.raw_ops.LogicalAnd,
+            'LogicalOr': tf.raw_ops.LogicalOr,
+        }
+
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(bool, x_shape, 'x')
+            y = tf.compat.v1.placeholder(bool, y_shape, 'y')
+            op_type_map[op_type](x=x, y=y, name=op_type)
+
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        ref_net = None
+
+        return tf_net, ref_net
+
+    @pytest.mark.parametrize('x_shape', [[], [4], [3, 4], [2, 3, 4]])
+    @pytest.mark.parametrize('y_shape', [[2, 3, 4]])
+    @pytest.mark.parametrize("op_type", ['LogicalAnd', 'LogicalOr'])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    def test_logical_binary_op(self, x_shape, y_shape, op_type,
+                               ie_device, precision, ir_version,
+                               temp_dir, use_legacy_frontend):
+        self._test(*self.create_logical_binary_ops_net(x_shape=x_shape, y_shape=y_shape, op_type=op_type),
+                   ie_device, precision, ir_version,
+                   temp_dir=temp_dir, use_legacy_frontend=use_legacy_frontend)