Change reorder implementation to be vector length agnostic for OHWIo8 reorder

As the reorder kernel is called with WeightFormat OHWIo8
for hardware that does not support it e.g. vector length 128,
adapt the test case and add kernel implementation for this edge case.

This fixes the mismatching values that appear when OHWIo8 fixture
was run with 128 vector length.

Resolves: ONCPUML-1523, COMPMID-6281

Signed-off-by: Radu Salavat <radu.salavat@arm.com>
Change-Id: Iaa1a3b486d1725a2d6031051aa544082c1bbe913
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11421
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 21 insertions, 6 deletions

diff --git a/tests/validation/fixtures/ReorderFixture.h b/tests/validation/fixtures/ReorderFixture.h
index 36e62696bc..8e28484c48 100644
--- a/tests/validation/fixtures/ReorderFixture.h
+++ b/tests/validation/fixtures/ReorderFixture.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023 Arm Limited.
+ * Copyright (c) 2023-2024 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,8 +21,8 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#ifndef ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE
-#define ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE
+#ifndef ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H
+#define ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H
 
 #include "arm_compute/core/TensorShape.h"
 #include "arm_compute/core/Types.h"
@@ -32,6 +32,7 @@
 #include "tests/framework/Asserts.h"
 #include "tests/framework/Fixture.h"
 #include "tests/validation/reference/Reorder.h"
+#include "src/core/NEON/kernels/arm_gemm/utils.hpp"
 
 namespace arm_compute
 {
@@ -44,10 +45,23 @@ template <typename TensorType, typename AccessorType, typename FunctionType, typ
 class ReorderValidationFixture : public framework::Fixture
 {
 public:
+    void check_hardware_supports(WeightFormat output_wf){
+        if(!Scheduler::get().cpu_info().has_sve() && output_wf!=WeightFormat::OHWIo4){
+            _hardware_supports = false;
+        }
+        if (Scheduler::get().cpu_info().has_sve() && arm_gemm::utils::get_vector_length<float>() != 8 && output_wf==WeightFormat::OHWIo8)
+        {
+            _hardware_supports = false;
+        }
+    }
+
     void setup(TensorShape input_shape, TensorShape output_shape, WeightFormat input_wf, WeightFormat output_wf, DataType data_type)
     {
-        _target    = compute_target(input_shape, output_shape, input_wf, output_wf, data_type);
-        _reference = compute_reference(input_shape, output_shape, output_wf, data_type);
+        check_hardware_supports(output_wf);
+        if (_hardware_supports){
+            _target    = compute_target(input_shape, output_shape, input_wf, output_wf, data_type);
+            _reference = compute_reference(input_shape, output_shape, output_wf, data_type);
+        }
     }
 
     protected:
@@ -98,6 +112,7 @@ public:
         return reference::reorder_layer<T>(src, output_shape, output_wf);
     }
 
+    bool _hardware_supports = true;
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };
@@ -105,4 +120,4 @@ public:
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
-#endif /* ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE */
+#endif // ACL_TESTS_VALIDATION_FIXTURES_REORDERFIXTURE_H