[GPU] add format::b_fs_yx_fsv4 to onednn::conv support list (#32435)

### Description of the issue(symptom, root-cause, how it was resolved)
- This issue can be reproduced on the machine which the onednn is
working.
- It cannot create the onednn primitive with this error msg: "Check
'shape_consistent' failed at
src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp:264:
[GPU] Input shape and output shape of weight reorder should be same."
- root-cause: At add_required_reorders pass, b_fs_fsv4 is chosen for
input0 via find_data_format(). b_fs_fsv4 is not in supported list for
onednn::conv. it chooses bfzyx for in/out of conv via test_format().

#### The code and line that caused this issue (if it is not changed
directly)
- src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.cpp
(test_format)

#### Reproduction step and snapshot (if applicable. Do not attach for
customer model)
- reproducer is attached in the ticket.

#### Problematic graph
cldnn_program_1_15_prepare_buffer_fusing.graph
<img width="388" height="209" alt="image"
src="https://github.com/user-attachments/assets/99bbeaf8-3c46-4a08-858e-5da6127b3fb2"
/>

cldnn_program_1_16_add_required_reorders.graph
<img width="388" height="301" alt="image"
src="https://github.com/user-attachments/assets/deb64b52-af65-4203-a9ce-b536171ba2a4"
/>


#### Checklist
 - [x] Is it a proper fix? (not a workaround)
 - [x] Did you include test case for this fix, if necessary?
- [x] Did you review existing test that can be extended to cover this
scenario? Which test did you review?
-> In add_required_reorder pass, this convolution should be chosen wrong
format via test_format() to reproduce the issue.
### Tickets:
 - 174951

Author: sungeunk

src/plugins/intel_gpu/src/graph/impls/onednn/convolution_onednn.hpp

@@ -47,6 +47,7 @@ struct ConvolutionImplementationManager : public ImplementationManager {
             format::bfzyx,
             format::byxf,
             format::bzyxf,
+            format::b_fs_yx_fsv4,
             format::b_fs_yx_fsv8,
             format::b_fs_zyx_fsv8,
             format::b_fs_yx_fsv16,

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

@@ -10845,6 +10845,45 @@ TEST(convolution_gpu_onednn, grouped_runtime_weights) {
     ASSERT_EQ(output_layout.get_shape(), ov::Shape({1, 256, 25, 25}));
 }
 
+TEST(convolution_gpu_onednn, grouped_conv_fsv4) {
+    auto& engine = get_test_engine();
+
+    if (!engine.get_device_info().supports_immad)
+        return;
+
+    tests::random_generator rg(GET_SUITE_NAME);
+
+    int64_t input_b = 1, input_f = 4, input_y = 3, input_x = 3;
+    auto input_size = ov::PartialShape{ input_b, input_f, input_y, input_x };
+    auto input_data = rg.generate_random_4d<ov::float16>(input_b, input_f, input_y, input_x, -1, 1);
+    auto input_data_byxf = flatten_4d(format::bfyx, input_data);
+    auto input_mem = engine.allocate_memory({ input_size, data_types::f16, format::bfyx });
+    set_values(input_mem, input_data_byxf);
+
+    int64_t weights_b = 2, weights_f = 2048, weights_y = 2, weights_x = 1;
+    auto weights_size = ov::PartialShape{ weights_b, weights_f, weights_y, weights_x };
+    auto weights_data = rg.generate_random_4d<ov::float16>(weights_b, weights_f, weights_y, weights_x, -1, 1);
+    auto weights_data_bfyx = flatten_4d(format::bfyx, weights_data);
+    auto weights_mem = engine.allocate_memory({ weights_size, data_types::f16, format::bfyx });
+    set_values(weights_mem, weights_data_bfyx);
+
+    topology topology(
+        input_layout("input", input_mem->get_layout()),
+        reorder("input_fsv", input_info("input"), { input_size, data_types::f16, format::b_fs_yx_fsv4 }),
+        input_layout("weights", weights_mem->get_layout()),
+        reshape("reshaped_weights", input_info("weights"), true, { 2, 2048, 2, 1, 1 }, { 2, 2048, 2, 1, 1 }),
+        convolution("conv", input_info("input_fsv"), "reshaped_weights", no_bias, 2, { 1, 1 }, { 1, 1 }, { 0, 0 }, { 0, 0 }, true),
+        reorder("reorder", input_info("conv"), { data_types::f32, format::bfyx, { 1, 4096, 3, 3 } }));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::optimize_data(true));
+
+    network network(engine, topology, config);
+    network.set_input_data("input", input_mem);
+    network.set_input_data("weights", weights_mem);
+    network.execute();
+}
+
 TEST(convolution_gpu_onednn, dyn_conv4d_reshape6d_pattern) {
     tests::random_generator rg(GET_SUITE_NAME);
     auto& engine = get_test_engine();