add input0(cond) broadcast in select (#14267)

src/plugins/intel_gpu/src/graph/select.cpp

@@ -25,6 +25,9 @@ layout select_inst::calc_output_layout(select_node const& node, kernel_impl_para
         auto input1_size = impl_param.get_input_layout(1).get_tensor();
         auto input2_size = impl_param.get_input_layout(2).get_tensor();
         output_size = tensor::max(input1_size, input2_size);
+        // Cond input0 also can be broadcasted.
+        auto input0_size = impl_param.get_input_layout(0).get_tensor();
+        output_size = tensor::max(input0_size, output_size);
     }
 
     return layout(in_layout.data_type, in_layout.format, output_size);
@@ -139,6 +142,10 @@ select_inst::typed_primitive_inst(network& network, select_node const& node) : p
         auto dep1_size = deps[1]->get_output_layout().get_tensor();
         auto dep2_size = deps[2]->get_output_layout().get_tensor();
         cldnn::tensor output_tensor = tensor::max(dep1_size, dep2_size);
+        // Cond input0 also can be broadcasted.
+        auto dep0_size = deps[0]->get_output_layout().get_tensor();
+        output_tensor = tensor::max(dep0_size, output_tensor);
+
         auto max_dim_count = output_tensor.raw.size();
 
         for (size_t i = 0; i < deps.size(); i++) {

src/plugins/intel_gpu/tests/test_cases/select_gpu_test.cpp

@@ -2240,3 +2240,57 @@ TEST(select_gpu_u8, select_basic_mask_i8_1x1x2x2) {
         EXPECT_EQ(answers[i], output_ptr[i]);
     }
 }
+
+TEST(select_gpu_fp32, select_numpy_broadcast_mask_u8_1x1x3) {
+    auto& engine = get_test_engine();
+
+    auto input = engine.allocate_memory({ data_types::f32, format::bfyx, { 1, 3, 1, 1 } });
+    auto input2 = engine.allocate_memory({ data_types::f32, format::bfyx, { 3, 1, 1, 1 } });
+    auto mask = engine.allocate_memory({ data_types::u8, format::bfyx, { 1, 1, 3, 1 } });
+
+    topology topology;
+    topology.add(input_layout("input", input->get_layout()));
+    topology.add(input_layout("input2", input2->get_layout()));
+    topology.add(input_layout("mask", mask->get_layout()));
+    topology.add(cldnn::select("select", "mask", "input", "input2"));
+
+    set_values(input, {
+        1.f,    0.f,    2.f
+    });
+
+    set_values(input2, {
+        0.5f,    2.5f,    5.f
+    });
+
+    set_values<unsigned char>(mask, {
+        1,   0,   1
+    });
+
+    network network(engine, topology);
+
+    network.set_input_data("input", input);
+    network.set_input_data("input2", input2);
+    network.set_input_data("mask", mask);
+    auto outputs = network.execute();
+
+    auto output = outputs.at("select").get_memory();
+
+    float answers[27] = {
+        1.f, 0.5f, 1.f,
+        0.f, 0.5f, 0.f,
+        2.f, 0.5f, 2.f,
+        1.f, 2.5f, 1.f,
+        0.f, 2.5f, 0.f,
+        2.f, 2.5f, 2.f,
+        1.f, 5.f, 1.f,
+        0.f, 5.f, 0.f,
+        2.f, 5.f, 2.f
+    };
+
+    cldnn::mem_lock<float> output_ptr(output, get_test_stream());
+
+    for (int i = 0; i < 27; i++)
+    {
+        EXPECT_EQ(answers[i], output_ptr[i]);
+    }
+}

src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/select.cpp

@@ -53,7 +53,8 @@ const std::vector<std::vector<std::vector<size_t>>> numpyShapes = {
     {{1, 3, 1}, {8, 2, 3, 1}, {3, 9}},
     {{5, 1, 8}, {2, 1, 9, 8}, {2, 5, 9, 8}},
     {{6, 1, 1, 8}, {6, 7, 1, 8}, {2, 1}},
-    {{5, 1, 1, 1}, {5, 7, 8, 6}, {1, 8, 6}}
+    {{5, 1, 1, 1}, {5, 7, 8, 6}, {1, 8, 6}},
+    {{1, 1, 3}, {1, 3, 1}, {3, 1, 1}}
 };
 
 const auto numpyCases = ::testing::Combine(