[TF FE] Support Div operation for TensorFlow

src/frontends/tensorflow/src/op_table.cpp

@@ -106,6 +106,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops() {
         {"BitwiseAnd", CreatorFunction(translate_binary_op<opset13::BitwiseAnd>)},
         {"BitwiseOr", CreatorFunction(translate_binary_op<opset13::BitwiseOr>)},
         {"BitwiseXor", CreatorFunction(translate_binary_op<opset13::BitwiseXor>)},
+        {"Div", CreatorFunction(translate_div_op)},
         {"Equal", CreatorFunction(translate_binary_op<opset8::Equal>)},
         {"FloorMod", CreatorFunction(translate_binary_op<opset8::FloorMod>)},
         {"Greater", CreatorFunction(translate_binary_op<opset8::Greater>)},

src/frontends/tensorflow_common/include/common_op_table.hpp

@@ -63,6 +63,7 @@ OP_CONVERTER(translate_crop_and_resize_op);
 OP_CONVERTER(translate_depth_to_space_op);
 OP_CONVERTER(translate_depthwise_conv_2d_native_op);
 OP_CONVERTER(translate_div_no_nan_op);
+OP_CONVERTER(translate_div_op);
 OP_CONVERTER(translate_mul_op);
 OP_CONVERTER(translate_dynamic_partition_op);
 OP_CONVERTER(translate_einsum_op);

src/frontends/tensorflow_common/src/op/div.cpp

@@ -0,0 +1,59 @@
+// Copyright (C) 2018-2023 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "common_op_table.hpp"
+#include "openvino/op/divide.hpp"
+
+using namespace std;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace tensorflow {
+namespace op {
+OutputVector translate_div_op(const NodeContext& node) {
+    default_op_checks(node, 2, {"Div"});
+    auto x = node.get_input(0);
+    auto y = node.get_input(1);
+    // Check if the element type is a signed integer
+    if (x.get_element_type().is_integral_number() && x.get_element_type().is_signed()) {
+        // prepare auxiliary zero constants of the same type as the inputs
+        auto const_zero = create_same_type_const_scalar<int32_t>(x, 0);
+
+        // compute the modulus of x and y
+        auto mod_result = make_shared<v1::Mod>(x, y);
+        // compute a mask to get positions of non-zero values of mod result
+        auto mod_non_zero = make_shared<v1::NotEqual>(mod_result, const_zero);
+
+        // compute the division of x and y
+        auto divide = make_shared<v1::Divide>(x, y);
+        // compute a mask to get positions of negative values of division result
+        auto div_is_neg = make_shared<v1::Less>(divide, const_zero);
+
+        // compute a boolean mask of elements for non-zero values of Mod result and negative values of Divide result
+        auto mask = make_shared<v1::LogicalAnd>(mod_non_zero, div_is_neg);
+
+        // prepare auxiliary one constants of the same type as the inputs
+        auto const_one = create_same_type_const_scalar<int32_t>(x, 1);
+        // add 1 to the divide result
+        auto add_result = make_shared<v1::Add>(divide, const_one);
+
+        // select division results based on the mask
+        // - perform floor division for non-negative values.
+        // - round negative values to the nearest zero.
+        auto div = make_shared<v1::Select>(mask, add_result, divide);
+        set_node_name(node.get_name(), div);
+        return div->outputs();
+    } else {
+        // for other cases (non-signed-integer types)
+        // compute regular division of x and y
+        auto div = make_shared<v1::Divide>(x, y);
+        set_node_name(node.get_name(), div);
+        return div->outputs();
+    }
+}
+}  // namespace op
+}  // namespace tensorflow
+}  // namespace frontend
+}  // namespace ov

tests/layer_tests/tensorflow_tests/test_tf_Div.py

@@ -0,0 +1,52 @@
+# 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
+
+
+class TestDiv(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 = {}
+        # generate x and y
+        inputs_data['x'] = np.random.randint(-10, 10, x_shape).astype(self.input_type)
+        inputs_data['y'] = np.random.randint(1, 10, y_shape)*np.random.choice([-1,1], y_shape)
+        print("input x: \n{}".format(inputs_data['x']))
+        print("input y: \n{}".format(inputs_data['y']))
+        return inputs_data
+
+    def create_div_net(self, input_shape, input_type):
+        self.input_type = input_type
+        tf.compat.v1.reset_default_graph()
+        # Create the graph and model
+        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.Div(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, 20], input_type=np.float32),
+        dict(input_shape=[2, 3, 4], input_type=np.float32),
+        pytest.param(dict(input_shape=[8, 5], input_type=np.int32),
+                              marks=pytest.mark.xfail(condition=platform.system() == 'Windows', reason='Ticket - TBD'))
+        dict(input_shape=[], input_type=np.float32),
+    ]
+
+    @pytest.mark.parametrize("params", test_data_basic)
+    @pytest.mark.precommit_tf_fe
+    @pytest.mark.nightly
+    def test_div_basic(self, params, ie_device, precision, ir_version, temp_dir,
+                       use_new_frontend, use_old_api):
+        self._test(*self.create_div_net(**params),
+                   ie_device, precision, ir_version, temp_dir=temp_dir,
+                   use_new_frontend=use_new_frontend, use_old_api=use_old_api)