[GPU] Save use_onednn attribute in the blob (#27097)

### Details:
- This is needed to have correct runtime impl selection of imported
model

### Tickets:
 - *CVS-154891*

Author: vladimir-paramuzov

src/plugins/intel_gpu/src/graph/program.cpp

@@ -1776,6 +1776,7 @@ void program::save(cldnn::BinaryOutputBuffer& ob) const {
 
     ob << _is_body_program;
     ob << _can_be_optimized;
+    ob << get_layout_optimizer().get_optimization_attributes().use_onednn_impls;
     processing_order.save(ob);
 
     {
@@ -1895,6 +1896,9 @@ void program::load(cldnn::BinaryInputBuffer& ib) {
 
     ib >> _is_body_program;
     ib >> _can_be_optimized;
+    int32_t use_onednn_attr = 0;
+    ib >> use_onednn_attr;
+    get_layout_optimizer().set_optimization_attribute(layout_optimizer::optimization_attributes_type::use_onednn_impls, use_onednn_attr);
     _loaded_from_cache = true;
 
     processing_order.load(ib, *this);

src/plugins/intel_gpu/tests/unit/test_cases/gemm_gpu_test.cpp

@@ -15,6 +15,7 @@
 #include "intel_gpu/runtime/compilation_context.hpp"
 #include "gemm_inst.h"
 #include "permute_inst.h"
+#include "layout_optimizer.h"
 
 #include <cstddef>
 #include <vector>
@@ -625,7 +626,7 @@ class gemm_gpu_tests: public ::testing::Test {
             topology topology;
             topology.add(input_layout("input1", in1_layout),
                         input_layout("input2", in2_layout),
-                        gemm("gemm_ref", { input_info("input1"), input_info("input2") }, data_types::f16, 
+                        gemm("gemm_ref", { input_info("input1"), input_info("input2") }, data_types::f16,
                              {0, 2, 1, 3}, {0, 2, 3, 1}, {0, 1, 2, 3})
             );
 
@@ -652,7 +653,7 @@ class gemm_gpu_tests: public ::testing::Test {
         topology topology;
         topology.add(input_layout("input1", in1_layout),
                      input_layout("input2", in2_layout),
-                     gemm("gemm", { input_info("input1"), input_info("input2") }, data_types::f16, 
+                     gemm("gemm", { input_info("input1"), input_info("input2") }, data_types::f16,
                              {0, 2, 1, 3}, {0, 2, 3, 1}, {0, 1, 2, 3})
         );
 
@@ -2789,7 +2790,7 @@ INSTANTIATE_TEST_SUITE_P(gemm_gpu, gemm_onednn_ndims, ::testing::ValuesIn(std::v
 
 class gemm_onednn: public ::testing::Test {
 public:
-    void test_impl_replacement_with_cldnn() {
+    void test_impl_replacement_with_cldnn(bool is_caching_test) {
         auto& engine = get_test_engine();
 
         if (!engine.get_device_info().supports_immad)
@@ -2828,16 +2829,34 @@ class gemm_onednn: public ::testing::Test {
                              ov::intel_gpu::optimize_data(true),
                              ov::intel_gpu::allow_new_shape_infer(true) };
 
-        network network(engine, topology, cfg);
-        network.set_input_data("input1", input1);
-        network.set_input_data("input2", input2);
+        cldnn::network::ptr network;
+        if (is_caching_test) {
+            membuf mem_buf;
+            {
+                std::ostream out_mem(&mem_buf);
+                BinaryOutputBuffer ob = BinaryOutputBuffer(out_mem);
+                ob.set_stream(get_test_stream_ptr().get());
+                program::build_program(engine, topology, cfg)->save(ob);
+            }
+            {
+                std::istream in_mem(&mem_buf);
+                BinaryInputBuffer ib = BinaryInputBuffer(in_mem, engine);
+                auto imported_prog = std::make_shared<cldnn::program>(engine, cfg);
+                imported_prog->load(ib);
+                network = std::make_shared<cldnn::network>(imported_prog);
+            }
+        } else {
+            network = std::make_shared<cldnn::network>(engine, topology, cfg);
+        }
+        network->set_input_data("input1", input1);
+        network->set_input_data("input2", input2);
 
-        auto inst = network.get_primitive("gemm");
+        auto inst = network->get_primitive("gemm");
         auto impl = inst->get_impl();
         ASSERT_TRUE(impl != nullptr);
         ASSERT_TRUE(impl->is_dynamic());
 
-        auto outputs = network.execute();
+        auto outputs = network->execute();
 
         auto output = outputs.at("gemm").get_memory();
         cldnn::mem_lock<ov::float16> output_ptr(output, get_test_stream());
@@ -2847,12 +2866,15 @@ class gemm_onednn: public ::testing::Test {
             ASSERT_FLOAT_EQ(output_ptr[i], out_data[i]);
         }
 
-        // WA: Call wait_all() to wait for all queued kernels compilation finish
-        network.get_program()->get_compilation_context().wait_all();
+        // Call wait_all() to wait for all queued kernels compilation finish
+        network->get_program()->get_compilation_context().wait_all();
+
+        auto& lo = network->get_program()->get_layout_optimizer();
+        ASSERT_TRUE(lo.get_optimization_attributes().use_onednn_impls);
 
         // Check if OneDNN's impl is used for the next execute() call
-        network.execute();
-        inst = network.get_primitive("gemm");
+        network->execute();
+        inst = network->get_primitive("gemm");
         impl = inst->get_impl();
         ASSERT_TRUE(impl != nullptr);
         ASSERT_FALSE(impl->is_dynamic());
@@ -3214,7 +3236,10 @@ class gemm_onednn: public ::testing::Test {
 };
 
 TEST_F(gemm_onednn, impl_replacement_with_cldnn) {
-    this->test_impl_replacement_with_cldnn();
+    this->test_impl_replacement_with_cldnn(false);
+}
+TEST_F(gemm_onednn, impl_replacement_with_cldnn_cached) {
+    this->test_impl_replacement_with_cldnn(true);
 }
 
 // Check gemm_onednn transpose_format() can accept transpose white list format (byfx/bxfy)