Fix concat to use ngraph shape infer (#16226)

Fix crop to return shape of original rank

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

@@ -8,6 +8,8 @@
 #include "compounds.hpp"
 #include "utils.hpp"
 
+#include <openvino/core/partial_shape.hpp>
+
 #include <map>
 #include <list>
 #include <cstdint>
@@ -652,6 +654,19 @@ public:
         return offset;
     }
 
+    /// @brief Returns tensor as Partial shape of requested rank
+    ov::PartialShape get_partial_shape(size_t rank) const {
+        ov::Shape shape;
+        size_t i = 0;
+        for (; i < std::min(static_cast<size_t>(2), rank); ++i) {
+            shape.push_back(_sizes[i]);
+        }
+        for (; i < rank; ++i) {
+            shape.push_back(_sizes[rank - (i - 2) - 1]);
+        }
+        return ov::PartialShape(shape);
+    }
+
     /// @brief Returns a tensor containing values maximum from @p lhs and @p rhs.
     static tensor max(tensor const& lhs, tensor const& rhs) {
         auto ret = lhs;

src/plugins/intel_gpu/src/graph/concatenation.cpp

@@ -3,6 +3,7 @@
 //
 
 #include "concatenation_inst.h"
+#include "concat_shape_inference.hpp"
 #include "primitive_type_base.h"
 #include "intel_gpu/runtime/error_handler.hpp"
 #include "json_object.h"
@@ -54,18 +55,16 @@ std::vector<layout> concatenation_inst::calc_output_layouts(const concatenation_
     }
 
     auto axis_index = desc->axis;
-
-    auto output_shape = input_layout.get<ShapeType>();
-    output_shape[axis_index] = 0;
+    std::vector<ShapeType> input_shapes;
+    std::vector<ShapeType> output_shapes = {ShapeType{}};
     for (size_t i = 0; i < desc->input.size(); ++i) {
         auto input_shape = impl_param.get_input_layout(i).get<ShapeType>();
-        if (input_shape.is_dynamic()) {
-            return { layout {ov::PartialShape::dynamic(input_shape.size()), output_dt, output_format} };
-        }
-        output_shape[axis_index] += input_shape[axis_index];
+        input_shapes.push_back(input_shape);
     }
-
-    return { layout {output_shape, output_dt, output_format} };
+    ov::op::v0::Concat op;
+    op.set_concatenation_axis(axis_index);
+    ov::op::v0::shape_infer(&op, input_shapes, output_shapes);
+    return { layout {output_shapes[0], output_dt, output_format} };
 }
 
 template std::vector<layout> concatenation_inst::calc_output_layouts<ov::PartialShape>(concatenation_node const& node, const kernel_impl_params& impl_param);

src/plugins/intel_gpu/src/graph/crop.cpp

@@ -102,9 +102,9 @@ std::vector<layout> crop_inst::calc_output_layouts(const crop_node& /*node*/, co
             const auto lt_sizes = offsets.sub({0, 0, 0, 0, 0});
             const auto out_sizes = in_sizes - (rb_sizes + lt_sizes);
 
-            return {layout({in_layout.data_type, in_layout.format, out_sizes})};
+            return {layout{out_sizes.get_partial_shape(in_layout.get_partial_shape().size()), in_layout.data_type, in_layout.format}};
         }
-        return {layout({in_layout.data_type, in_layout.format, ref_in_sizes})};
+        return {layout{ref_in_sizes.get_partial_shape(in_layout.get_partial_shape().size()), in_layout.data_type, in_layout.format}};
     }
 
     bool is_output_static = false;

src/plugins/intel_gpu/tests/shape_infer/concatenation_si_test.cpp

@@ -83,7 +83,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, concatenation_test,
                 layout{ov::PartialShape{2, 2}, data_types::f32, format::bfyx},
             },
             0,
-            layout{ov::PartialShape::dynamic(2), data_types::f32, format::bfyx}
+            layout{ov::PartialShape{ov::Dimension(5, -1), ov::Dimension(2, 2)}, data_types::f32, format::bfyx}
         },
     }));
 

src/plugins/intel_gpu/tests/shape_infer/crop_si_test.cpp

@@ -105,7 +105,7 @@ INSTANTIATE_TEST_SUITE_P(smoke, crop_si_test,
             {tensor({0,0,0,0,1,1,1}),tensor({0,0,0,0,1,1,1})},
             {},
             {{{4},data_types::f32,format::bfyx}},
-            {{{3, 1, 1, 1},data_types::f32,format::bfyx}}, 0 // TODO: update once shape infer impl is fixed and don't do rank extension
+            {{{3},data_types::f32,format::bfyx}}, 0
         },
         {
             tensor({-1,-1,-1,-1,-1,-1,-1}),