[GPU] Improve kv cache memory allocation efficiency (openvinotoolkit#…

…25580)

### Details:
 - Fixed two issues 
- 1) KV cache was allocating redundant memory when it requires new
memory
- 2) At a new inference, KV cache was setting a padding value as the one
used in the previous execution (last token for the previous generation),
which caused memory usage inefficiency.
- After fixing above issues, in some cases, memory is more frequently
allocated because
- 1) switching shape 1024 => 32 : happens reclaiming (previously due to
the wrong padding, it is not reclaimed.)
- 2) switching shape 32 => 1024 : new alloc needed at the first infer,
but shape history is not tracked yet. So during 3 iteration, it is
allocating new memory.
- Additional fix to resolve above issues: 
- 1) For initial allocation of kv cache, enforce prealloc with custom
prealloc count (known value of 128 + id%64) for sequence axis
- 2) For reclaiming kv cache : use prealloc size as the required memory
size

Memalloc count with PR

![image](https://github.com/user-attachments/assets/c65b3335-c849-46f8-b9fe-140c3a0fbccb)

### Tickets:
 - 146930

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

@@ -34,25 +34,31 @@ struct ShapePredictor {
         static_assert(_max_deque_size >= 2, "[GPU] Deque is supposed to contain at least 2 elements for prediction");
     }
 
-
-/// \brief Predicts the next possible shapes sizes based on history collected by previous
-///        predict_preallocation_shape() calls.
-///        This function works in two modes: by default it tries to predict shape for the next
-///        `_next_iters_preallocation_count` iterations, in case if per-iteration buffer size is less than
-///        `_max_per_iter_size` and difference between shapes is less than `_max_per_dim_diff`; the second
-///        operation mode is percentage preallocation - this mode can be configured with
-///        ov::intel_gpu::buffers_preallocation_ratio property, it increases buffer size by
-///        `_buffers_preallocation_ratio` value unconditionally.
-/// \param id Primitive id.
-/// \param layout Primitive's layout on current iteration.
-/// \param can_reuse_buffer Specifies if current memory buffer is enough to store data.
-/// \return The result of shape size prediction as std::pair<bool, ov::Shape>, where the first element
-///         says if shape is successfully predicted and can be preallocated, and the second element is ov::Shape itself.
+    /// \brief Predicts the next possible shapes sizes based on history collected by previous
+    ///        predict_preallocation_shape() calls.
+    ///        This function works in two modes: by default it tries to predict shape for the next
+    ///        `_next_iters_preallocation_count` iterations, in case if per-iteration buffer size is less than
+    ///        `_max_per_iter_size` and difference between shapes is less than `_max_per_dim_diff`; the second
+    ///        operation mode is percentage preallocation - this mode can be configured with
+    ///        ov::intel_gpu::buffers_preallocation_ratio property, it increases buffer size by
+    ///        `_buffers_preallocation_ratio` value unconditionally.
+    /// \param id Primitive id.
+    /// \param layout Primitive's layout on current iteration.
+    /// \param can_reuse_buffer Specifies if current memory buffer is enough to store data.
+    /// \param out_idx output index of multiple outputs
+    /// \param custom_next_iters_prealloc_couunt If it is specified, enforce prealloc size as the specified value
+    /// \param custom_prealloc_dim If both custom_next_iters_prealloc_count and custom_prealloc_dim are specified,
+    ///         increase custom_prealloc_dim with custom_next_iters_prealloc_count without checking shape history (e.g.,
+    ///         used for first inference of kv cache)
+    /// \return The result of shape size prediction as std::pair<bool, ov::Shape>, where
+    ///         the first element says if shape is successfully predicted and can be preallocated, and the second
+    ///         element is ov::Shape itself.
     std::pair<bool, ov::Shape> predict_preallocation_shape(const std::string& id,
                                                            const cldnn::layout& layout,
                                                            bool can_reuse_buffer,
                                                            const size_t out_idx = 0,
-                                                           int32_t next_iters_prealloc_count = -1);
+                                                           int32_t custom_next_iters_prealloc_count = -1,
+                                                           int32_t custom_prealloc_dim = -1);
 
     bool can_preallocate(size_t desired_buffer_size);
 

src/plugins/intel_gpu/src/graph/primitive_inst.cpp

@@ -664,9 +664,32 @@ event::ptr primitive_inst::realloc_if_needed() {
         updated_layouts[0] = layout(current_buf_shape, updated_layouts[0].data_type, updated_layouts[0].format);
     }
 
+    int32_t tmp_prealloc_count = get_prealloc_iter_num();
+    GPU_DEBUG_IF(debug_config->mem_preallocation_params.is_initialized) {
+        // If debug config is set, repsect the config most
+        tmp_prealloc_count = -1;
+    }
+
     // If we allocated too large memory, reclaim the memory.
     for (size_t i = 0; i < updated_layouts.size(); ++i) {
-        if (updated_layouts[i].get_buffer_size().count() * 10 < _max_output_layout_count[i]) {
+        bool reclaim = 0;
+        size_t required_buffer_size = 0;
+        if (_node->is_type<kv_cache>() && i == 0) {
+            // Relax reclaiming condition for kv cache
+            const auto& desc = _node->as<kv_cache>().get_primitive();
+            auto prealloc_shape = updated_layouts[i].get_shape();
+            const auto shape_rank = prealloc_shape.size();
+            auto seq_axis =
+                static_cast<int32_t>(desc->concat_axis >= 0 ? desc->concat_axis : shape_rank + desc->concat_axis);
+            prealloc_shape[seq_axis] += tmp_prealloc_count;
+            required_buffer_size = std::accumulate(prealloc_shape.begin(), prealloc_shape.end(), size_t(1), std::multiplies<size_t>());
+        } else {
+            required_buffer_size = (updated_layouts[i].get_buffer_size().count());
+        }
+        if (required_buffer_size * 10 < _max_output_layout_count[i]) {
+            reclaim = true;
+        }
+        if (reclaim) {
             GPU_DEBUG_TRACE_DETAIL << id() << ": Updated output[" << i << "] size " << updated_layouts[i].get_buffer_size().count()
                                    << " is much smaller than current memory size! " << _max_output_layout_count[i]
                                    << "Reset memory of output " << i << std::endl;
@@ -681,31 +704,51 @@ event::ptr primitive_inst::realloc_if_needed() {
         return ev;
     }
 
-    int32_t tmp_prealloc_count = get_prealloc_iter_num();
-    GPU_DEBUG_IF(debug_config->mem_preallocation_params.is_initialized) {
-        // If debug config is set, repsect the config most
-        tmp_prealloc_count = -1;
-    }
-
     for (size_t i = 0; i < actual_layouts.size(); ++i) {
         bool can_reuse_buffer = (_outputs[i] && updated_layouts[i].get_buffer_size().count() <= _max_output_layout_count[i]);
-        std::pair<bool, ov::Shape> prealloc_info =
-            sp.predict_preallocation_shape(id(), updated_layouts[i], can_reuse_buffer, i, tmp_prealloc_count);
+        std::pair<bool, ov::Shape> prealloc_info;
+        if (_node->is_type<kv_cache>() && i == 0) {
+            const auto& desc = _node->as<kv_cache>().get_primitive();
+            auto shape_rank = updated_layouts[i].get_shape().size();
+            auto seq_axis =
+                static_cast<int32_t>(desc->concat_axis >= 0 ? desc->concat_axis : shape_rank + desc->concat_axis);
+            prealloc_info = sp.predict_preallocation_shape(id(), updated_layouts[i], false, i, tmp_prealloc_count, seq_axis);
+        } else {
+            prealloc_info = sp.predict_preallocation_shape(id(), updated_layouts[i], can_reuse_buffer, i, tmp_prealloc_count);
+        }
         if (prealloc_info.first && sp.can_preallocate(ov::shape_size(prealloc_info.second) * (dt_sizes_in_B[i]))) {
             auto new_layout = updated_layouts[i];
             new_layout.set_partial_shape(prealloc_info.second);
             updated_params.output_layouts[i] = new_layout;
         }
-
         if (updated_params.output_layouts[i].get_buffer_size().count() < updated_layouts[i].get_buffer_size().count()) {
             updated_params.output_layouts[i] = updated_layouts[i];
         }
 
         if (can_reuse_buffer) {
-            GPU_DEBUG_TRACE_DETAIL << id() << ": reuse previously allocated output buffer - "
+            GPU_DEBUG_TRACE_DETAIL << id() << ": reuse previously allocated output buffer[" << i << "] - "
                                    << actual_layouts[i].get_buffer_size().count() << "/" << _max_output_layout_count[i]
                                    << std::endl;
-            _outputs[i] = _network.get_engine().reinterpret_buffer(*_outputs[i], actual_layouts[i]);
+            if (_node->is_type<kv_cache>() && (i == 0)) {
+                // kv_cache has already assigned memory.
+                // No need to reinterpret output memory but need to update padding
+                const auto& desc = _node->as<kv_cache>().get_primitive();
+                auto& present_layout = _impl_params->output_layouts[i];
+                const auto present_layout_rank = present_layout.get_partial_shape().size();
+                const auto sequence_axis =
+                    desc->concat_axis >= 0 ? desc->concat_axis : present_layout_rank + desc->concat_axis;
+
+                const auto sequence_axis_legacy = kv_cache_inst::get_sequence_axis_legacy(sequence_axis, present_layout_rank);
+                auto max_pad = kv_cache_inst::get_max_pad(present_layout,
+                                                          _max_output_layout_count[i],
+                                                          sequence_axis_legacy,
+                                                          "present_layout");
+                kv_cache_inst::update_pad(present_layout, max_pad, sequence_axis_legacy);
+                GPU_DEBUG_TRACE_DETAIL << _impl_params->output_layouts[i].to_string() << std::endl;
+                set_shape_change();
+            } else {
+                _outputs[i] = _network.get_engine().reinterpret_buffer(*_outputs[i], actual_layouts[i]);
+            }
             // TODO: check need_reset_output_memory per output
             if (need_reset_output_memory() && !can_be_optimized()) {
                 GPU_DEBUG_TRACE_DETAIL << id() << " : Need reset output memory considering user" << std::endl;
@@ -740,7 +783,7 @@ event::ptr primitive_inst::realloc_if_needed() {
 
     // Set variable memory same as output memory
     if (_node->is_type<kv_cache>()) {
-        auto desc = _node->as<kv_cache>().get_primitive();
+        const auto& desc = _node->as<kv_cache>().get_primitive();
         auto& variable = get_network().get_variable(desc->variable_info.variable_id);
         auto present_layout = _impl_params->output_layouts[0];
         auto present_layout_rank = present_layout.get_partial_shape().size();
@@ -760,7 +803,7 @@ event::ptr primitive_inst::realloc_if_needed() {
         if (present_layout.data_padding.get_dynamic_pad_dims().sizes()[sequence_axis_legacy] == 1) {
             // Apply padding of variable to make it be optimized in the next iteration
             auto max_pad = kv_cache_inst::get_max_pad(present_layout,
-                                                      updated_params.output_layouts[0].get_buffer_size().count(),
+                                                      _max_output_layout_count[0],
                                                       sequence_axis_legacy,
                                                       "present_layout");
             if (max_pad > 0) {
@@ -783,7 +826,7 @@ event::ptr primitive_inst::realloc_if_needed() {
                 GPU_DEBUG_TRACE_DETAIL << id() << ": Update variable " << variable.get_name()
                                        << "'s layout with allocated kv cache output: " << present_layout.to_short_string()
                                        << " (is_set  = " << variable.is_set() << ") " << std::endl;
-                variable.set_layout(present_layout);
+                variable.set_memory(_outputs[0], present_layout);
             }
         } else {
             GPU_DEBUG_TRACE_DETAIL << id() << ": Update variable " << variable.get_name()
@@ -1036,8 +1079,10 @@ void primitive_inst::update_paddings() {
     auto reset_pad = [](kernel_impl_params& params, const program_node* node) {
         params.output_layouts[0].data_padding = node->get_output_layout(0).data_padding;
     };
-    if (_node->is_type<read_value>()) {
-        auto& variable = get_network().get_variable(_node->as<read_value>().get_primitive()->variable_id);
+    if (_node->is_type<read_value>() || _node->is_type<kv_cache>()) {
+        auto variable_id = _node->is_type<read_value>() ? (_node->as<read_value>().get_primitive()->variable_id)
+                                                        : (_node->as<kv_cache>().get_primitive()->variable_info.variable_id);
+        auto& variable = get_network().get_variable(variable_id);
         // Reset paddings for read_value and users with dynamic pad when variable is reset
         // to avoid wrong pad used for some nodes due to pad propagation logic (which uses previous iter pad values)
         if (!variable.is_set()) {
@@ -1054,6 +1099,7 @@ void primitive_inst::update_paddings() {
         }
         return;
     }
+
     if (_node->is_type<gather>() && _impl_params->output_layouts[0].data_padding.get_dynamic_pad_dims() != tensor(0)) {
         if (can_be_optimized())
             _impl_params->output_layouts[0] = _impl_params->input_layouts[0];
@@ -1141,7 +1187,7 @@ void primitive_inst::do_runtime_in_place_kv_cache() {
     if (_impl_params->get_input_layout(0).count() == 0) {
         return;
     }
-    auto desc = _node->as<kv_cache>().get_primitive();
+    const auto& desc = _node->as<kv_cache>().get_primitive();
     auto& past_layout = _impl_params->input_layouts[0];
     auto& present_layout = _impl_params->output_layouts[0];
     const auto& sequence_axis = desc->concat_axis;

src/plugins/intel_gpu/src/runtime/shape_predictor.cpp

@@ -60,7 +60,8 @@ std::pair<bool, ov::Shape> ShapePredictor::predict_preallocation_shape(const std
                                                                        const cldnn::layout& layout,
                                                                        bool can_reuse_buffer,
                                                                        const size_t out_idx,
-                                                                       int32_t custom_next_iters_prealloc_count) {
+                                                                       int32_t custom_next_iters_prealloc_count,
+                                                                       int32_t custom_prealloc_dim) {
     size_t next_iters_prealloc_count = custom_next_iters_prealloc_count > 0
                                            ? static_cast<size_t>(custom_next_iters_prealloc_count)
                                            : _next_iters_preallocation_count;
@@ -79,6 +80,13 @@ std::pair<bool, ov::Shape> ShapePredictor::predict_preallocation_shape(const std
     if (can_reuse_buffer)
         return {false, {}};
 
+    // If both prealloc dim and prealloc count are specified, dont predict and just use the given info
+    if (custom_prealloc_dim >= 0 && custom_next_iters_prealloc_count > 0) {
+        auto new_shape = current_shape;
+        new_shape[custom_prealloc_dim] += custom_next_iters_prealloc_count;
+        return {true, new_shape};
+    }
+
     // Check if there is enough data for prediction
     const auto& shapes = _shapes_info[id_record];
     const auto shapes_num = shapes.size();