support MulNoNan for TF (#21459)

* support MulNoNan for TF

* update test_tf_MulNoNan

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* update zeros const method

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* Update src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

* Update tests/layer_tests/tensorflow_tests/test_tf_MulNoNan.py

---------

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>

src/frontends/tensorflow/src/op_table.cpp

@@ -232,6 +232,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"MaxPoolWithArgmax", CreatorFunction(translate_max_pool_with_argmax)},
         {"Merge", CreatorFunction(translate_merge_op)},
         {"MirrorPad", CreatorFunction(translate_mirror_pad_op)},
+        {"MulNoNan", CreatorFunction(translate_mul_no_nan_op)},
         {"MutableHashTable", CreatorFunction(translate_hash_table_op)},
         {"MutableHashTableV2", CreatorFunction(translate_hash_table_op)},
         {"NonMaxSuppression", CreatorFunction(translate_non_max_suppression_op)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -101,6 +101,7 @@ OP_CONVERTER(translate_matrix_diag_op);
 OP_CONVERTER(translate_max_pool_op);
 OP_CONVERTER_NAMED(translate_max_pool_with_argmax);
 OP_CONVERTER(translate_mirror_pad_op);
+OP_CONVERTER(translate_mul_no_nan_op);
 OP_CONVERTER_NAMED(translate_non_max_suppression_op);
 OP_CONVERTER(translate_parallel_dynamic_stitch_op);
 OP_CONVERTER(translate_placeholder_op);

src/frontends/tensorflow_common/src/op/mul_no_nan.cpp

@@ -0,0 +1,43 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/equal.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/select.hpp"
+#include "utils.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_mul_no_nan_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"MulNoNan"});
+
+    // first = x, second = y
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+
+    // prepare zero constant of the same type as the inputs
+    auto const_zero = create_same_type_const_scalar<int32_t>(x, 0);
+
+    // get mask where y equals 0
+    auto is_zero = make_shared<v1::Equal>(y, const_zero);
+
+    // replace all values in x at is_zero mask with zeros
+    auto x_zeros = make_shared<v1::Select>(is_zero, const_zero, x);
+
+    // multiply y with the updated x
+    auto mul_no_nan = make_shared<v1::Multiply>(x_zeros, y);
+
+    set_node_name(node.get_name(), mul_no_nan);
+    return mul_no_nan->outputs();
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_MulNoNan.py

@@ -0,0 +1,47 @@
+# Copyright (C) 2018-2023 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
+from common.utils.tf_utils import mix_array_with_value
+
+class TestMulNoNan(CommonTFLayerTest):
+    def _prepare_input(self, inputs_info):
+        assert 'x' in inputs_info
+        assert 'y' in inputs_info
+        x_shape = inputs_info['x']
+        y_shape = inputs_info['y']
+        inputs_data = {}
+        inputs_data['x'] = np.random.randint(-10, 10, x_shape).astype(self.input_type)
+        inputs_data['x'] = mix_array_with_value(inputs_data['x'], np.inf)
+        inputs_data['x'] = mix_array_with_value(inputs_data['x'], np.nan)
+        inputs_data['y'] = np.random.randint(-10, 10, y_shape).astype(self.input_type) * \
+                           np.random.choice([0.0], y_shape).astype(self.input_type)
+        return inputs_data
+
+    def create_mul_no_nan_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        with tf.compat.v1.Session() as sess:
+            x = tf.compat.v1.placeholder(input_type, input_shape, 'x')
+            y = tf.compat.v1.placeholder(input_type, input_shape, 'y')
+            tf.raw_ops.MulNoNan(x=x, y=y)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+        return tf_net, None
+
+    test_data_basic = [
+        dict(input_shape=[10, 10], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_mul_no_nan_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                              use_new_frontend, use_old_api):
+        self._test(*self.create_mul_no_nan_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)