[GPU] Convert activation's slope buffer data type for OneDNN fusions (#13444)

src/plugins/intel_gpu/src/graph/graph_optimizer/reorder_inputs.cpp

@@ -789,4 +789,31 @@ void reorder_inputs::run(program& p, layout_optimizer& lo, reorder_factory& rf)
             n->set_preferred_impl_type(preferred_impl);
         }
     }
+
+    // WA for OneDNN PRelu activation fusions: convert activation's slope buffer to expected f32 data type
+    for (auto& node : p.get_processing_order()) {
+        if (node->get_preferred_impl_type() == impl_types::onednn) {
+            auto fused_prims = node->get_fused_primitives();
+            for (auto& fused_desc : fused_prims) {
+                if (!fused_desc.is_type<activation>())
+                    continue;
+
+                auto activation_desc = fused_desc.typed_desc<activation>();
+                if (activation_desc->activation_function == cldnn::activation_func::relu_negative_slope &&
+                    !activation_desc->additional_params_input.empty()) {
+                    const auto expected_dt = data_types::f32;
+                    const auto dep_idx = fused_desc.dep_start_idx;
+                    const auto orig_layout = node->get_dependency(dep_idx).get_output_layout();
+                    if (orig_layout.data_type == expected_dt)
+                        continue;
+
+                    auto new_layout = orig_layout;
+                    new_layout.data_type = expected_dt;
+                    auto new_input = rf.get_reorder(node->get_dependency(dep_idx).id(), orig_layout, new_layout);
+                    if (new_input.first)
+                        p.add_intermediate(new_input.first, *node, dep_idx);
+                }
+            }
+        }
+    }
 }

src/plugins/intel_gpu/tests/fusions/convolution_fusion_test.cpp

@@ -265,6 +265,10 @@ public:
     layout get_per_channel_layout(convolution_test_params& p) {
         return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
     }
+
+    layout get_prelu_slope_layout(convolution_test_params& p) {
+        return layout{ p.default_type, p.default_format, tensor{1, p.out_shape.feature[0], 1, 1} };
+    }
 };
 #endif  // ENABLE_ONEDNN_FOR_GPU
 
@@ -2914,6 +2918,30 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp32_reorder_bfyx_to_fsv32_conv_data_
 }));
 
 #ifdef ENABLE_ONEDNN_FOR_GPU
+class conv_fp16_prelu_onednn : public WeightsPrimitiveFusingTestOneDNN {};
+TEST_P(conv_fp16_prelu_onednn, basic_activation_eltwise) {
+    auto p = GetParam();
+
+    create_topologies(
+        input_layout("input", get_input_layout(p)),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("slope_data", get_mem(get_prelu_slope_layout(p))),
+        data("eltwise_data", get_mem(get_output_layout(p))),
+        convolution("conv_prim", "input", { "weights" }, { "bias" }, p.groups, p.stride, p.pad, p.dilation),
+        activation("activation", "conv_prim", "slope_data", activation_func::relu_negative_slope),
+        eltwise("eltwise", "activation", "eltwise_data", eltwise_mode::sum),
+        reorder("reorder_bfyx", "eltwise", p.default_format, data_types::f32)
+    );
+
+    tolerance = default_tolerance(p.default_type);
+    execute(p);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, conv_fp16_prelu_onednn, ::testing::ValuesIn(std::vector<convolution_test_params>{
+    convolution_test_params{ CASE_CONV_FP16_1, 2, 4 },
+}));
+
 class conv_int8_eltwise_onednn : public WeightsPrimitiveFusingTestOneDNN {};
 TEST_P(conv_int8_eltwise_onednn, u8_eltwise_sum_out) {
     auto p = GetParam();