[GPU] Integrate dynamic quantization for onednn (openvinotoolkit#26940)

### Details:
 - Integrated grouped dynamic quantization from onednn
 - Integrated asymmetric per-token dynamic quantization from onednn
 - Those are not enabled by default, yet

### Tickets:
 - 148732, 157869, 157589

src/plugins/intel_gpu/include/intel_gpu/op/fully_connected_compressed.hpp

@@ -22,6 +22,7 @@ class FullyConnectedCompressed : public FullyConnected {
                              const ov::Output<Node> &w_decompression_scale,
                              const ov::Output<Node> &w_decompression_zero_point,
                              const ov::Output<Node> &a_decompression_scale,
+                             const ov::Output<Node> &a_decompression_zero_point,
                              const ov::element::Type output_type = ov::element::undefined);
 
 

src/plugins/intel_gpu/include/intel_gpu/primitives/dynamic_quantize.hpp

@@ -26,16 +26,19 @@ struct dynamic_quantize : public primitive_base<dynamic_quantize> {
     /// @param output_size Output data size of the primitive
     dynamic_quantize(const primitive_id& id,
            const input_info& input,
-           const Attributes& attrs)
+           const Attributes& attrs,
+           const size_t input_size = 3)
            : primitive_base(id, {input})
-           , attrs(attrs) {
+           , attrs(attrs)
+           , input_size(input_size) {
         num_outputs = 2;
         if (attrs.quantization_type == ov::op::internal::DynamicQuantize::QuantizationType::Asymmetric &&
             attrs.output_storage_type == ov::op::internal::DynamicQuantize::OutputStorageType::Planar)
             num_outputs++;
     }
 
     Attributes attrs;
+    size_t input_size;
 
     size_t hash() const override {
         size_t seed = primitive::hash();
@@ -46,6 +49,7 @@ struct dynamic_quantize : public primitive_base<dynamic_quantize> {
         seed = hash_combine(seed, attrs.scale_dt.hash());
         seed = hash_combine(seed, attrs.zp_dt.hash());
         seed = hash_combine(seed, attrs.output_storage_type);
+        seed = hash_combine(seed, input_size);
 
         return seed;
     }
@@ -62,7 +66,8 @@ struct dynamic_quantize : public primitive_base<dynamic_quantize> {
                attrs.quantization_dt == rhs_casted.attrs.quantization_dt &&
                attrs.scale_dt == rhs_casted.attrs.scale_dt &&
                attrs.zp_dt == rhs_casted.attrs.zp_dt &&
-               attrs.quantization_type == rhs_casted.attrs.quantization_type;;
+               attrs.quantization_type == rhs_casted.attrs.quantization_type &&
+               input_size == rhs_casted.input_size;
     }
 
     void save(BinaryOutputBuffer& ob) const override {
@@ -75,6 +80,7 @@ struct dynamic_quantize : public primitive_base<dynamic_quantize> {
         ob << make_data(&attrs.output_storage_type, sizeof(attrs.output_storage_type));
         ob << attrs.scales_zp_output_order;
         ob << attrs.group_sizes;
+        ob << input_size;
     }
 
     void load(BinaryInputBuffer& ib) override {
@@ -87,6 +93,7 @@ struct dynamic_quantize : public primitive_base<dynamic_quantize> {
         ib >> make_data(&attrs.output_storage_type, sizeof(attrs.output_storage_type));
         ib >> attrs.scales_zp_output_order;
         ib >> attrs.group_sizes;
+        ib >> input_size;
     }
 };
 }  // namespace cldnn

src/plugins/intel_gpu/include/intel_gpu/primitives/fully_connected.hpp

@@ -96,6 +96,7 @@ struct fully_connected : public primitive_base<fully_connected> {
           decompression_scale(decompression_scale),
           decompression_zero_point(decompression_zero_point),
           dynamic_quantized_activation(false),
+          dynamic_quantized_activation_zp(false),
           input_size(input_size),
           weights_rank(weights_rank) {
         OPENVINO_ASSERT(!decompression_scale.empty(), "[GPU] Compressed fully connected requires at least decompression scale input");
@@ -109,13 +110,15 @@ struct fully_connected : public primitive_base<fully_connected> {
     /// @param compression_scale Primitive id containing scale factors for weights decompression.
     /// @param compression_zero_point Primitive id containing zero points for weights decompression.
     /// @param activation_scale Primitive id containing scale factor for activation.
+    /// @param activation_zero_point Primitive id containing zero point for activation.
     fully_connected(const primitive_id& id,
                     const input_info& input,
                     const primitive_id& weights,
                     const primitive_id& bias,
                     const primitive_id& decompression_scale,
                     const primitive_id& decompression_zero_point,
                     const input_info& activation_scale,
+                    const input_info& activation_zero_point,
                     const data_types data_type,
                     const size_t input_size = 2,
                     const size_t weights_rank = 2)
@@ -126,11 +129,15 @@ struct fully_connected : public primitive_base<fully_connected> {
           decompression_scale(decompression_scale),
           decompression_zero_point(decompression_zero_point),
           dynamic_quantized_activation(false),
+          dynamic_quantized_activation_zp(false),
           activation_scale(activation_scale),
+          activation_zero_point(activation_zero_point),
           input_size(input_size),
           weights_rank(weights_rank) {
         if (activation_scale.is_valid())
             dynamic_quantized_activation = true;
+        if (activation_zero_point.is_valid())
+            dynamic_quantized_activation_zp = true;
 
         OPENVINO_ASSERT(!decompression_scale.empty(), "[GPU] Compressed fully connected requires at least decompression scale input");
     }
@@ -144,7 +151,9 @@ struct fully_connected : public primitive_base<fully_connected> {
     primitive_id decompression_scale = "";
     primitive_id decompression_zero_point = "";
     bool dynamic_quantized_activation = false;
+    bool dynamic_quantized_activation_zp = false;
     input_info activation_scale = {"", 0};
+    input_info activation_zero_point = {"", 0};
     optional_value<float> decompression_zero_point_scalar = optional_value<float>();
 
     /// @brief Primitive dimension size.
@@ -161,6 +170,7 @@ struct fully_connected : public primitive_base<fully_connected> {
         seed = hash_combine(seed, !decompression_scale.empty());
         seed = hash_combine(seed, !decompression_zero_point.empty());
         seed = hash_combine(seed, activation_scale.is_valid());
+        seed = hash_combine(seed, activation_zero_point.is_valid());
         seed = hash_combine(seed, decompression_zero_point_scalar.has_value());
         seed = hash_combine(seed, decompression_zero_point_scalar.value_or(0.0f));
         return seed;
@@ -179,6 +189,7 @@ struct fully_connected : public primitive_base<fully_connected> {
                decompression_scale.empty() == rhs_casted.decompression_scale.empty() &&
                decompression_zero_point.empty() == rhs_casted.decompression_zero_point.empty() &&
                activation_scale.is_valid() == rhs_casted.activation_scale.is_valid() &&
+               activation_zero_point.is_valid() == rhs_casted.activation_zero_point.is_valid() &&
                decompression_zero_point_scalar.value_or(0.0f) == rhs_casted.decompression_zero_point_scalar.value_or(0.0f);
     }
 
@@ -190,9 +201,11 @@ struct fully_connected : public primitive_base<fully_connected> {
         ob << decompression_scale;
         ob << decompression_zero_point;
         ob << activation_scale;
+        ob << activation_zero_point;
         ob << input_size;
         ob << weights_rank;
         ob << dynamic_quantized_activation;
+        ob << dynamic_quantized_activation_zp;
 
         if (decompression_zero_point_scalar.has_value()) {
             ob << true;
@@ -211,9 +224,11 @@ struct fully_connected : public primitive_base<fully_connected> {
         ib >> decompression_scale;
         ib >> decompression_zero_point;
         ib >> activation_scale;
+        ib >> activation_zero_point;
         ib >> input_size;
         ib >> weights_rank;
         ib >> dynamic_quantized_activation;
+        ib >> dynamic_quantized_activation_zp;
 
         bool has_value;
         ib >> has_value;
@@ -243,6 +258,9 @@ struct fully_connected : public primitive_base<fully_connected> {
         if (activation_scale.is_valid())
             ret.push_back(activation_scale);
 
+        if (activation_zero_point.is_valid())
+            ret.push_back(activation_zero_point);
+
         return ret;
     }
 };

src/plugins/intel_gpu/include/intel_gpu/runtime/debug_configuration.hpp

@@ -146,6 +146,7 @@ class debug_configuration {
     std::vector<std::string> dynamic_quantize_layers_without_onednn;  // Specify Fully-connected layers which enable Dynamic quantization
     int use_kv_cache_compression;                               // Enable KV-cache compression
     int dynamic_quantize_group_size;                            // Enable Dynamic quantization for fully connected primitive by specified group size
+    int dynamic_quantize_asym;                                  // Use asymmetric dynamic quantization
     int disable_horizontal_fc_fusion;                           // Disable fc horizontal fusion
     int disable_fc_swiglu_fusion;                               // Disable swiglu fusion to fc
     std::set<int64_t> dump_iteration;                           // Dump n-th execution of network.

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

@@ -463,7 +463,9 @@ void prepare_primitive_fusing::fuse_bias(program &p) {
                 if (desc->decompression_zero_point_scalar.has_value())
                     fc_with_bias_prim->decompression_zero_point_scalar = desc->decompression_zero_point_scalar.value();
                 fc_with_bias_prim->activation_scale = desc->activation_scale;
+                fc_with_bias_prim->activation_zero_point = desc->activation_zero_point;
                 fc_with_bias_prim->dynamic_quantized_activation = desc->dynamic_quantized_activation;
+                fc_with_bias_prim->dynamic_quantized_activation_zp = desc->dynamic_quantized_activation_zp;
             }
             auto& new_fc_node = p.get_or_create(fc_with_bias_prim);
             fuse_bias_f(fc, new_fc_node, bias_node, eltw_node);

src/plugins/intel_gpu/src/graph/impls/ocl/dynamic_quantize.cpp

@@ -35,6 +35,7 @@ struct dynamic_quantize_impl : typed_primitive_impl_ocl<dynamic_quantize> {
 
     static kernel_params_t get_kernel_params(const kernel_impl_params& impl_param, bool is_shape_agnostic = false) {
         auto params = get_default_params<kernel_selector::dynamic_quantize_params>(impl_param, is_shape_agnostic);
+        const auto& primitive = impl_param.typed_desc<dynamic_quantize>();
         params.outputs.push_back(convert_data_tensor(impl_param.get_output_layout(1)));
 
         // In Some model, the feature size could be dynamic in input0.
@@ -48,6 +49,10 @@ struct dynamic_quantize_impl : typed_primitive_impl_ocl<dynamic_quantize> {
         if (impl_param.output_layouts.size() > 2)
             params.outputs.push_back(convert_data_tensor(impl_param.get_output_layout(2)));
 
+        // Keep 2d data as bf layout
+        if (primitive->input_size == 2)
+            params.outputs[0] = params.outputs[0].FlattenFeatureAndSpatials();
+
         const auto& desc = impl_param.typed_desc<dynamic_quantize>();
         params.group_sizes = desc->attrs.group_sizes;
         params.scales_output_order = desc->attrs.scales_zp_output_order;
@@ -68,7 +73,8 @@ namespace detail {
 attach_dynamic_quantize_impl::attach_dynamic_quantize_impl() {
     auto types = {
         data_types::f16,
-        data_types::i8
+        data_types::i8,
+        data_types::u8
     };
 
     auto formats = {

src/plugins/intel_gpu/src/graph/impls/onednn/fully_connected_onednn.cpp

@@ -83,10 +83,16 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
             if (prim->activation_scale.is_valid()) {
                 auto activation_scale_idx = idx++;
                 auto act_scale_mem = instance.dep_memory_ptr(activation_scale_idx);
-                // TODO: handle group_size here
-                dnnl::memory::desc desc = onednn::layout_to_memory_desc(act_scale_mem->get_layout(), dnnl::memory::format_tag::a, true);
+                dnnl::memory::desc desc = onednn::layout_to_memory_desc(act_scale_mem->get_layout(), dnnl::memory::format_tag::ab, true);
                 args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC_0, act_scale_mem->get_onednn_memory(desc)});
             }
+
+            if (prim->activation_zero_point.is_valid()) {
+                auto activation_zp_idx = idx++;
+                auto act_zp_mem = instance.dep_memory_ptr(activation_zp_idx);
+                dnnl::memory::desc desc = onednn::layout_to_memory_desc(act_zp_mem->get_layout(), dnnl::memory::format_tag::ab, true);
+                args.insert({DNNL_ARG_ATTR_ZERO_POINTS | DNNL_ARG_SRC_0, act_zp_mem->get_onednn_memory(desc)});
+            }
         }
 
         return args;
@@ -245,6 +251,7 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
         ob << has_bias;
         ob << is_compressed;
         ob << prim->dynamic_quantized_activation;
+        ob << prim->dynamic_quantized_activation_zp;
 
         bool has_decompression_scale = !prim->decompression_scale.empty();
         if (has_decompression_scale) {
@@ -271,10 +278,12 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
         bool has_bias = false;
         bool is_compressed = false;
         bool dynamic_quantized_activation;
+        bool dynamic_quantized_activation_zp;
         ib >> input_size;
         ib >> has_bias;
         ib >> is_compressed;
         ib >> dynamic_quantized_activation;
+        ib >> dynamic_quantized_activation_zp;
 
         const kernel_impl_params* impl_params = reinterpret_cast<kernel_impl_params*>(ib.getKernelImplParams());
         auto prim = impl_params->typed_desc<fully_connected>();
@@ -293,11 +302,12 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
 
         bool has_decompression_zp = !prim->decompression_zero_point.empty() || prim->decompression_zero_point_scalar.has_value();
         auto& arg = impl_params->get_program().get_node(impl_params->desc->id).as<fully_connected>();
-        int idx = !arg.bias_term() ? 3 : 4;
+        int idx = !arg.bias_term() ? 2 : 3;
 
         if (has_decompression_zp) {
             ib >> make_data(&_dzp_data_type, sizeof(dnnl::memory::data_type));
-            auto dzp_layout = arg.get_dependency(idx++).get_output_layout();
+            auto decompression_zp_idx = ++idx;
+            auto dzp_layout = arg.get_dependency(decompression_zp_idx).get_output_layout();
 
             if (dzp_layout.count() == 1) {
                 _attrs->set_zero_points(DNNL_ARG_WEIGHTS, COMMON, dnnl::memory::dims{}, _dzp_data_type);
@@ -312,12 +322,17 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
         }
 
         if (dynamic_quantized_activation) {
-            // TODO: it supports per-token activation scale only
+            auto src_scale_idx = ++idx;
             auto partial_shape = impl_params->get_input_layout(0).get_partial_shape();
             auto innermost_len = partial_shape[partial_shape.size() - 1].get_length();
-
-            auto act_scale_data_type = convert_data_type(impl_params->get_input_layout(idx).data_type);
-            _attrs->set_scales(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, innermost_len}, act_scale_data_type);
+            auto& src_scale_shape = impl_params->input_layouts[src_scale_idx].get_partial_shape();
+            int src_scale_ngroups = src_scale_shape[src_scale_shape.size() - 1].get_length();
+            int src_group_size = innermost_len / src_scale_ngroups;
+
+            auto act_scale_data_type = convert_data_type(impl_params->get_input_layout(src_scale_idx).data_type);
+            _attrs->set_scales(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, src_group_size}, act_scale_data_type);
+            if (dynamic_quantized_activation_zp)
+                _attrs->set_zero_points(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, src_group_size}, dnnl::memory::data_type::u8);
         }
 
         if (is_compressed) {
@@ -387,15 +402,21 @@ struct fully_connected_onednn : typed_primitive_onednn_impl<fully_connected> {
             }
 
             if (prim->dynamic_quantized_activation) {
-                // Note: it supports per-token activation scale only
-                ++idx;
-                auto partial_shape = impl_params.input_layouts[0].get_partial_shape();
+                auto src_scale_idx = ++idx;
+                auto& partial_shape = impl_params.input_layouts[0].get_partial_shape();
                 auto innermost_len = partial_shape[partial_shape.size() - 1].get_length();
+                auto& src_scale_shape = impl_params.input_layouts[src_scale_idx].get_partial_shape();
+                int src_scale_ngroups = src_scale_shape[src_scale_shape.size() - 1].get_length();
+                int src_group_size = innermost_len / src_scale_ngroups;
 
-                auto act_scale_data_type = convert_data_type(impl_params.input_layouts[idx].data_type);
-                attr->set_scales(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, innermost_len}, act_scale_data_type);
+                auto act_scale_data_type = convert_data_type(impl_params.input_layouts[src_scale_idx].data_type);
+                attr->set_scales(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, src_group_size}, act_scale_data_type);
+
+                if (prim->activation_zero_point.is_valid())
+                    attr->set_zero_points(DNNL_ARG_SRC, GROUPED, dnnl::memory::dims{1, src_group_size}, dnnl::memory::data_type::u8);
             }
 
+
             auto prim_desc = get_matmul_primitive_descriptor(impl_params, impl_params.prog->get_engine(),
                                                              prim->input_size, !prim->bias.empty(), *attr);
 

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

@@ -48,7 +48,7 @@ struct FullyConnectedImplementationManager : public ImplementationManager {
                          one_of(wei_dt, {data_types::i8, data_types::u8}) &&
                          one_of(out_dt, {data_types::f16, data_types::f32, data_types::i32, data_types::i8, data_types::u8});
         bool compressed_case = fc_prim->compressed_weights &&
-                               one_of(in0_dt, {data_types::f16, data_types::f32, data_types::i8}) &&
+                               one_of(in0_dt, {data_types::f16, data_types::f32, data_types::i8, data_types::u8}) &&
                                one_of(wei_dt, {data_types::u8, data_types::i8, data_types::u4, data_types::i4}) &&
                                one_of(out_dt, {data_types::f16, data_types::f32, data_types::u8, data_types::i8});
         if (!f16f16_case && !f32f32_case && !u8s8_case && !compressed_case)