Remove legacy PostOps code

PostOps was the experimental interface for Dynamic Fusion. It is now
replaced by the new Dynamic Fusion interface with code generation using
the Compute Kernel Writer.

Resolves: COMPMID-6190

Change-Id: I813b48facef2fd6f3aee332588886b4f9b3d33d8
Signed-off-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10219
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 23 insertions, 1227 deletions

diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index bced540d2a..986d76708d 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -22,7 +22,6 @@
  * SOFTWARE.
  */
 #include "arm_compute/core/Types.h"
-#include "arm_compute/core/experimental/PostOps.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
@@ -69,53 +68,30 @@ constexpr float                     tolerance_num = 0.07f;                /**< T
 const auto CNNDataTypes = framework::dataset::make("DataType",
 {
     DataType::F16,
-    DataType::F32,
-    DataType::QASYMM8,
-    DataType::QASYMM8_SIGNED,
+             DataType::F32,
+             DataType::QASYMM8,
+             DataType::QASYMM8_SIGNED,
 });
 
 /** Grouped CNN data types */
 const auto GroupedCNNDataTypes = framework::dataset::make("DataType",
 {
     DataType::F16,
-    DataType::F32
+             DataType::F32
 });
 
-const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+const auto ActivationFunctionsDataset      = framework::dataset::make("ActivationInfo",
 {
     ActivationLayerInfo(),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f),
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
 });
 const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo",
 {
     ActivationLayerInfo(),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
 });
-
-bool is_post_op_list_valid_in_gemmconv(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &output_shape, DataType data_type, DataLayout data_layout,
-                                       const PadStrideInfo &conv_info, const experimental::PostOpList<ITensorInfo *> &post_ops)
-{
-    const int idx_width   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
-    const int idx_height  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
-    const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES);
-
-    const auto         dilation   = Size2D(1U, 1U);
-    const unsigned int num_groups = 1U;
-
-    TensorInfo input_info(input_shape, 1, data_type, data_layout);
-    TensorInfo weights_info(weights_shape, 1, data_type, data_layout);
-
-    TensorInfo output_info(output_shape, 1, data_type, data_layout);
-
-    WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels));
-
-    const auto status = CLGEMMConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true),
-                                                         &weights_info.clone()->set_is_resizable(true), nullptr, &output_info.clone()->set_is_resizable(true),
-                                                         conv_info, w_info, dilation, ActivationLayerInfo(), num_groups, post_ops);
-    return bool(status);
-}
 } // namespace
 
 TEST_SUITE(CL)
@@ -207,72 +183,6 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z
                                                                             enable_fast_math);
     ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
 }
-
-DATA_TEST_CASE(ValidatePostOpSupportInConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
-                                          framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC),            // Select GEMM
-                                                                                  TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32, DataLayout::NCHW),           // Select WINOGRAD
-                                                                                  TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW),           // Select Direct
-                                                                                  TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW),           // Select FFT
-                                          }),
-                                          framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(2U, 1U, 1U, 19U), 1, DataType::F32, DataLayout::NHWC),
-                                                                                    TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32, DataLayout::NCHW),
-                                                                                    TensorInfo(TensorShape(5U, 5U, 48U, 128U), 1, DataType::F32, DataLayout::NCHW),
-                                                                                    TensorInfo(TensorShape(11U, 11U, 48U, 24), 1, DataType::F32, DataLayout::NCHW),
-                                          })),
-                                          framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(19U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC),
-                                                                                   TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, DataLayout::NCHW),
-                                                                                   TensorInfo(TensorShape(27U, 27U, 128U), 1, DataType::F32, DataLayout::NCHW),
-                                                                                   TensorInfo(TensorShape(27U, 27U, 24U), 1, DataType::F32, DataLayout::NCHW),
-                                          })),
-                                          framework::dataset::make("ConvInfo", { PadStrideInfo(1U, 1U, 0U, 0U),
-                                                                                 PadStrideInfo(1U, 1U, 2U, 2U),
-                                                                                 PadStrideInfo(1U, 1U, 2U, 2U),
-                                                                                 PadStrideInfo(1U, 1U, 5U, 5U),
-                                          })),
-                                         framework::dataset::make("EnableFastMath", { false, true, false, false})),
-                                         framework::dataset::make("ExpectedMethod",{ ConvolutionMethod::GEMM,
-                                                                                     ConvolutionMethod::WINOGRAD,
-                                                                                     ConvolutionMethod::DIRECT,
-                                                                                     ConvolutionMethod::FFT,
-                                         })),
-                                         framework::dataset::make("PostOpSupported",{ true, false, false, false
-                                         })),
-                                         input_info, weights_info, output_info, conv_info, enable_fast_math, expected_method, post_op_supported)
-{
-    const int idx_width  = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::WIDTH);
-    const int idx_height = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::HEIGHT);
-    const int idx_kernels = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::BATCHES);
-
-    const auto dilation = Size2D(1U, 1U);
-    const unsigned int num_groups = 1U;
-
-    WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels));
-
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<ITensorInfo*>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-
-    ConvolutionMethod actual_method = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true),
-                                                                            &weights_info.clone()->set_is_resizable(true),
-                                                                            &output_info.clone()->set_is_resizable(true), conv_info,
-                                                                            WeightsInfo(),
-                                                                            ActivationLayerInfo(),
-                                                                            GPUTarget::BIFROST,
-                                                                            dilation,
-                                                                            enable_fast_math);
-    ARM_COMPUTE_EXPECT(actual_method == expected_method, framework::LogLevel::ERRORS);
-    const auto is_valid = CLConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true),
-                                                                            &weights_info.clone()->set_is_resizable(true),
-                                                                            nullptr,
-                                                                            &output_info.clone()->set_is_resizable(true),
-                                                                            conv_info,
-                                                                            w_info,
-                                                                            dilation,
-                                                                            ActivationLayerInfo(),
-                                                                            enable_fast_math,
-                                                                            num_groups,
-                                                                            post_ops);
-    ARM_COMPUTE_EXPECT( bool(is_valid) == post_op_supported, framework::LogLevel::ERRORS);
-}
 // clang-format on
 // *INDENT-ON*
 TEST_SUITE_END() // ConvolutionLayer
@@ -285,167 +195,11 @@ using CLGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixtur
 template <typename T>
 using CLConvolutionValidationWithPaddingFixture = ConvolutionValidationWithPaddingFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>;
 
-TEST_SUITE(ValidateFusedPostOpsConfigs)
-TEST_SUITE(Invalid)
-TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 1U, 1U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    const TensorShape post_op_arg0_shape(output_shape);
-    TensorInfo        post_op_arg_info(post_op_arg0_shape, 1, data_type);
-    auto              post_op_arg1_info = post_op_arg_info.clone();
-
-    // Unsupported sequence of post ops
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          post_op_arg1_info.get(),
-                                                                          0,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OnlyNHWCIsSupported, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NCHW;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(14U, 12U, 16U, 2U);
-    const auto weights_shape = TensorShape(1U, 1U, 16U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    const TensorShape post_op_arg0_shape(output_shape);
-    TensorInfo        post_op_arg_info(post_op_arg0_shape, 1, data_type);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OnlyFloatingTypeIsSupported, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::QASYMM8;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 1U, 1U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    const TensorShape post_op_arg0_shape(output_shape);
-    TensorInfo        post_op_arg_info(post_op_arg0_shape, 1, data_type);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedKernelSize, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 3U, 3U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    const TensorShape post_op_arg0_shape(output_shape);
-    TensorInfo        post_op_arg_info(post_op_arg0_shape, 1, data_type);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedStride, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(3, 3, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 1U, 1U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    const TensorShape post_op_arg0_shape(output_shape);
-    TensorInfo        post_op_arg_info(post_op_arg0_shape, 1, data_type);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Invalid
-TEST_SUITE(Valid)
-TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 1U, 1U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(SupportedPostOps, framework::DatasetMode::ALL)
-{
-    const auto data_type     = DataType::F32;
-    const auto data_layout   = DataLayout::NHWC;
-    const auto conv_info     = PadStrideInfo(1, 1, 0, 0);
-    const auto input_shape   = TensorShape(16U, 14U, 12U, 2U);
-    const auto weights_shape = TensorShape(16U, 1U, 1U, 24U);
-
-    const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info);
-
-    TensorShape post_op_arg0_shape(output_shape);
-    post_op_arg0_shape[1] = 1; // Broadcast in "Y" (second) dimension
-    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
-
-    experimental::PostOpList<ITensorInfo *> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>(
-                                                                          &post_op_arg_info,
-                                                                          1,
-                                                                          ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Valid
-TEST_SUITE_END() // ValidateFusedPostOps
 TEST_SUITE(Float)
 TEST_SUITE(FP16)
 
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
-                                                                                                                   framework::dataset::make("ReshapeWeights", { true })),
-                                                                                                                   framework::dataset::make("DataType",
-                                                                                                                           DataType::F16)),
-                                                                                                                   framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
+                                                                                                                   framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
                                                                                                            ActivationFunctionsSmallDataset))
 {
     // Validate output
@@ -456,10 +210,7 @@ TEST_SUITE_END() // FP16
 TEST_SUITE(FP32)
 
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
-                                                                                                                    framework::dataset::make("ReshapeWeights", { true })),
-                                                                                                                    framework::dataset::make("DataType",
-                                                                                                                            DataType::F32)),
-                                                                                                                    framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
+                                                                                                                    framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
                                                                                                             ActivationFunctionsSmallDataset))
 {
     // Validate output
@@ -503,16 +254,16 @@ using CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidat
 template <typename T>
 using CLGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, int8_t>;
 
-const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+const auto QuantizedActivationFunctionsDataset      = framework::dataset::make("ActivationInfo",
 {
     ActivationLayerInfo(),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
 });
 const auto QuantizedActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo",
 {
     ActivationLayerInfo(),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
+                        ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
 });
 
 TEST_SUITE(Quantized)
@@ -520,8 +271,8 @@ TEST_SUITE(Quantized)
 const auto QuantizationData = framework::dataset::make("QuantizationInfo",
 {
     QuantizationInfo(0.5f, 10),
-    QuantizationInfo(0.3f, 3),
-    QuantizationInfo(1.1f, 10),
+                     QuantizationInfo(0.3f, 3),
+                     QuantizationInfo(1.1f, 10),
 });
 TEST_SUITE(QASYMM8)
 
@@ -637,9 +388,7 @@ TEST_SUITE(Float)
 TEST_SUITE(FP32)
 
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(),
-                                                                                                                   framework::dataset::make("ReshapeWeights", { true })),
-                                                                                                                   framework::dataset::make("DataType", DataType::F32)),
-                                                                                                                   framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                                                                                                   framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })),
                                                                                                                    ActivationFunctionsSmallDataset))
 {
     // Validate output
@@ -661,9 +410,7 @@ TEST_SUITE_END() // FP32
 TEST_SUITE(FP16)
 
 FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(),
-                                                                                                                  framework::dataset::make("ReshapeWeights", { true })),
-                                                                                                                  framework::dataset::make("DataType", DataType::F16)),
-                                                                                                                  framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                                                                                                  framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NCHW })),
                                                                                                                   ActivationFunctionsSmallDataset))
 {
     // Validate output
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
index 7f63a03371..0ddf43766f 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2022 Arm Limited.
+ * Copyright (c) 2019-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -53,11 +53,6 @@ using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyNati
 template <typename T>
 using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
 
-// Fixture for CLGEMMMatrixMultiplyNative with post ops
-template <typename T>
-using CLGEMMMatrixMultiplyNativeWithPostOpsFixture =
-    GEMMMatrixMultiplyNativeWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
-
 // Fixture for CLGEMMMatrixMultiplyNative3D
 template <typename T>
 using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
@@ -146,105 +141,6 @@ const auto boundary_handling_cases = combine(combine(combine(combine(combine(com
                                     broadcast_bias_values),
                                     framework::dataset::make("Activation", ActivationLayerInfo()));
 
-/** Post Ops */
-using PostOpArgBroadcast =  CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>::PostOpArgBroadcast;
-experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(true, true, false),   // If broadcast in dims 0, 1 and 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, true, true),   // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    // post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, false),  // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    return post_ops;
-}
-// To test that the output of the main op is the first parameter in prelu post op
-experimental::PostOpList<PostOpArgBroadcast> post_ops_4()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, true),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param
-experimental::PostOpList<PostOpArgBroadcast> post_ops_5()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, false),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-/** Different Post Op Lists */
-const auto post_op_lists = framework::dataset::make("post_op_lists", {
-    post_ops_1(),
-    post_ops_2(),
-    post_ops_3(),
-    post_ops_4(),
-    post_ops_5()
- } );
-
-bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
-{
-    const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
-    const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
-
-    // Create TensorInfo for post op arguments
-    TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
-    TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
-    TensorInfo input2_info(TensorShape(n), 1, data_type);
-    TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
-
-    GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
-             false /**< reinterpret the input as 3D */,
-             true  /**< Flag used to broadcast the bias addition */,
-             false /**< wider accumm */,
-             false /**< has pad y */,
-           ActivationLayerInfo::ActivationFunction::IDENTITY,
-             1   /**< Multiplication factor for the width of the 1xW transposed block */,
-             1   /**< Multiplication factor for the height of the 4x4 interleaved block */,
-             lhs_info,
-             rhs_info,
-             0  /**< Offset to be added to each element of the matrix A */,
-             0 /**< Offset to be added to each element of the matrix B */,
-             post_ops);
-    return bool(ClGemmMatrixMultiplyNativeKernel::validate(&input0_info.clone()->set_is_resizable(true),
-                                                          &input1_info.clone()->set_is_resizable(true),
-                                                          &input2_info.clone()->set_is_resizable(true),
-                                                          &output_info.clone()->set_is_resizable(true),1.f,1.f,
-                                                          lhs_info,
-                                                          rhs_info,
-                                                          gemm_info));
-}
-
 /** Configuration test */
 void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info)
 {
@@ -295,119 +191,6 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned
 
 TEST_SUITE(CL)
 TEST_SUITE(GEMMMatrixMultiplyNative)
-TEST_SUITE(ValidateFusedPostOpsConfigs)
-TEST_SUITE(Invalid)
-TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 17;
-    const unsigned int n = 1;
-    const unsigned int k = 13;
-    const unsigned int batch = 2;
-    TensorShape post_op_arg0_shape(n, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
-    auto post_op_arg1_info = post_op_arg_info.clone();
-
-    // Unsupported sequence of post ops
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        &post_op_arg_info,
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        post_op_arg1_info.get(),
-        0,
-        ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors "widen" the output tensor
-    const auto data_type = DataType::F32;
-    const unsigned int m = 1;
-    const unsigned int n = 18;
-    const unsigned int k = 13;
-    const unsigned int batch = 2;
-    TensorShape post_op_arg_shape(n, m + 1, batch); // output's Y dimension (m) is "widened", which is not allowed
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Invalid
-TEST_SUITE(Valid)
-TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(n, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, 1);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Valid
-TEST_SUITE_END() // ValidateFusedPostOps
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
 DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine(
@@ -541,31 +324,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<float>, f
     validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
 }
 
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   framework::dataset::make("M0", { 4 })),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   framework::dataset::make("DataType", DataType::F32)),
-                                                                   framework::dataset::make("alpha", {1.0f} )),
-                                                                   framework::dataset::make("beta", {1.0f} )),
-                                                                   framework::dataset::make("broadcast_bias", { false, true } )),
-                                                                   framework::dataset::make("Activation", { ActivationLayerInfo() })),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output
-    validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
-}
-
-TEST_SUITE_END() //  FusedPostOps
-
 TEST_SUITE_END() // FP32
 TEST_SUITE_END() // Float
 TEST_SUITE_END() // GEMMMatrixMulipltyNative
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
index cdd89670fa..b06e4bf213 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2022 Arm Limited.
+ * Copyright (c) 2018-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -23,7 +23,6 @@
  */
 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/Types.h"
-#include "arm_compute/core/experimental/PostOps.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
@@ -62,21 +61,11 @@ using CLGEMMMatrixMultiplyReshaped = CLSynthetizeOperator<ClGemmMatrixMultiplyRe
 template <typename T>
 using CLGEMMMatrixMultiplyReshapedFixture = GEMMMatrixMultiplyReshapedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
 
-// Fixture for CLGEMMMatrixMultiplyReshaped with post ops
-template <typename T>
-using CLGEMMMatrixMultiplyReshapedWithPostOpsFixture =
-    GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
-
 // Fixture for CLGEMMMatrixMultiplyReshaped mixed precision
 template <typename T>
 using CLGEMMMatrixMultiplyReshapedMixedPrecisionFixture =
     GEMMMatrixMultiplyReshapedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped, true>;
 
-// Fixture for CLGEMMMatrixMultiplyReshaped mixed precision with post ops
-template <typename T>
-using CLGEMMMatrixMultiplyReshapedMixedPrecisionWithPostOpsFixture =
-    GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped, true>;
-
 // Fixture for CLGEMMMatrixMultiplyReshaped3D
 template <typename T>
 using CLGEMMMatrixMultiplyReshaped3DFixture = GEMMMatrixMultiplyReshaped3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>;
@@ -184,108 +173,6 @@ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", {
 /** LHS transposed values */
 const auto lhs_transpose_values = framework::dataset::make("lhs_transpose", { false, true } );
 
-/** Post Ops */
-using PostOpArgBroadcast =  CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>::PostOpArgBroadcast;
-experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(true, true, false),   // If broadcast in dims 0, 1 and 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, true, true),   // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, true),  // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    return post_ops;
-}
-// To test that the output of the main op is the first parameter in prelu post op
-experimental::PostOpList<PostOpArgBroadcast> post_ops_4()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, true),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param
-experimental::PostOpList<PostOpArgBroadcast> post_ops_5()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, false),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-/** Different Post Op Lists */
-const auto post_op_lists = framework::dataset::make("post_op_lists", {
-    post_ops_1(),
-    post_ops_2(),
-    post_ops_3(),
-    post_ops_4(),
-    post_ops_5()
- } );
-
-bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
-{
-    const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
-    const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
-
-    // Create TensorInfo for post op arguments
-    TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
-    TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
-    TensorInfo input2_info(TensorShape(n), 1, data_type);
-    TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
-
-    const TensorInfo reshaped_input0_info = input0_info.clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(input0_info, lhs_info));
-    const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info));
-
-    GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
-             false /**< reinterpret the input as 3D */,
-             true  /**< Flag used to broadcast the bias addition */,
-             false /**< wider accumm */,
-             false /**< has pad y */,
-           ActivationLayerInfo::ActivationFunction::IDENTITY,
-             1   /**< Multiplication factor for the width of the 1xW transposed block */,
-             1   /**< Multiplication factor for the height of the 4x4 interleaved block */,
-             lhs_info,
-             rhs_info,
-             0  /**< Offset to be added to each element of the matrix A */,
-             0 /**< Offset to be added to each element of the matrix B */,
-             post_ops);
-    return bool(ClGemmMatrixMultiplyReshapedKernel::validate(&reshaped_input0_info.clone()->set_is_resizable(true),
-                                                          &reshaped_input1_info.clone()->set_is_resizable(true),
-                                                          &input2_info.clone()->set_is_resizable(true),
-                                                          &output_info.clone()->set_is_resizable(true),1.f,1.f,
-                                                          lhs_info,
-                                                          rhs_info,
-                                                          gemm_info));
-}
-
 } // namespace
 
 TEST_SUITE(CL)
@@ -450,119 +337,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi
                                                           rhs_info,
                                                           gemm_info)) == expected, framework::LogLevel::ERRORS);
 }
-TEST_SUITE(ValidateFusedPostOpsConfigs)
-TEST_SUITE(Invalid)
-TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 17;
-    const unsigned int n = 1;
-    const unsigned int k = 13;
-    const unsigned int batch = 2;
-    TensorShape post_op_arg0_shape(n, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
-    auto post_op_arg1_info = post_op_arg_info.clone();
-
-    // Unsupported sequence of post ops
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        &post_op_arg_info,
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        post_op_arg1_info.get(),
-        0,
-        ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors "widen" the output tensor
-    const auto data_type = DataType::F32;
-    const unsigned int m = 17;
-    const unsigned int n = 1;
-    const unsigned int k = 13;
-    const unsigned int batch = 2;
-    TensorShape post_op_arg_shape(n + 4, m, batch); // output's X dimension (n) is "widened", which is not allowed
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Invalid
-TEST_SUITE(Valid)
-TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(n, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, 1);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Valid
-TEST_SUITE_END() // ValidateFusedPostOps
+
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
 
@@ -697,44 +472,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>,
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   v0_values_precommit),
-                                                                   h0_values_precommit),
-                                                                   framework::dataset::make("interleave_lhs", { false })),
-                                                                   framework::dataset::make("interleave_rhs", { false })),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", false)),
-                                                                   framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values_precommit),
-                                                                   beta_values_precommit),
-                                                                   framework::dataset::make("broadcast_bias", { true } )),
-                                                                   lhs_transpose_values),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
 
 TEST_SUITE(ExportToCLImage)
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
@@ -1002,44 +739,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>,
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   v0_values_precommit),
-                                                                   h0_values_precommit),
-                                                                   framework::dataset::make("interleave_lhs", { false })),
-                                                                   framework::dataset::make("interleave_rhs", { false })),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", true)),
-                                                                   framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values_precommit),
-                                                                   beta_values_precommit),
-                                                                   framework::dataset::make("broadcast_bias", { true } )),
-                                                                   lhs_transpose_values),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output only if validate() is successful
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
 
 TEST_SUITE_END() // ExportToCLImage
 TEST_SUITE_END() // FP32
@@ -1178,45 +877,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>,
     }
 }
 
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   v0_values_precommit),
-                                                                   h0_values_precommit),
-                                                                   framework::dataset::make("interleave_lhs", { false })),
-                                                                   framework::dataset::make("interleave_rhs", { false })),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", false)),
-                                                                   framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values_precommit),
-                                                                   beta_values_precommit),
-                                                                   framework::dataset::make("broadcast_bias", { true } )),
-                                                                   lhs_transpose_values),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
-
 TEST_SUITE(ExportToCLImage)
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
                framework::dataset::make("Input0Info", { TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16),  // OK or incorrect if cl_khr_image2d_from_buffer not supported
@@ -1483,44 +1143,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>,
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   v0_values_precommit),
-                                                                   h0_values_precommit),
-                                                                   framework::dataset::make("interleave_lhs", { false })),
-                                                                   framework::dataset::make("interleave_rhs", { false })),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", true)),
-                                                                   framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values_precommit),
-                                                                   beta_values_precommit),
-                                                                   framework::dataset::make("broadcast_bias", { true } )),
-                                                                   lhs_transpose_values),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output only if validate() is successful
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
 
 TEST_SUITE_END() // ExportToCLImage
 TEST_SUITE_END() // FP16
@@ -1659,45 +1281,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DMixedPrecisionF
     }
 }
 
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedMixedPrecisionWithPostOpsFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   v0_values_precommit),
-                                                                   h0_values_precommit),
-                                                                   framework::dataset::make("interleave_lhs", { false })),
-                                                                   framework::dataset::make("interleave_rhs", { false })),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", { true, false })),
-                                                                   framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values_precommit),
-                                                                   beta_values_precommit),
-                                                                   framework::dataset::make("broadcast_bias", { true } )),
-                                                                   lhs_transpose_values),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() // FusedPostOps
-
 TEST_SUITE_END() // MixedPrecision
 TEST_SUITE_END() // Float
 TEST_SUITE_END() // GEMMMatrixMultiplyReshaped
diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
index 53038c8177..dafc8dc5ec 100644
--- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
+++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2022 Arm Limited.
+ * Copyright (c) 2019-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -23,7 +23,6 @@
  */
 #include "arm_compute/core/KernelDescriptors.h"
 #include "arm_compute/core/Types.h"
-#include "arm_compute/core/experimental/PostOps.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTensorAllocator.h"
@@ -62,11 +61,6 @@ using CLGEMMMatrixMultiplyReshapedOnlyRHSFixture = GEMMMatrixMultiplyReshapedOnl
 template <typename T>
 using CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture = GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>;
 
-// Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS with post ops
-template <typename T>
-using CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture =
-    GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>;
-
 namespace
 {
 // *INDENT-OFF*
@@ -164,106 +158,6 @@ const auto boundary_handling_cases = combine(combine(combine(combine(combine(com
                                     broadcast_bias_values),
                                     framework::dataset::make("Activation", ActivationLayerInfo()));
 
-/** Post Ops */
-using PostOpArgBroadcast =  CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>::PostOpArgBroadcast;
-experimental::PostOpList<PostOpArgBroadcast> post_ops_1()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(true, true, false),   // If broadcast in dims 0, 1 and 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_2()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, true, true),   // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-experimental::PostOpList<PostOpArgBroadcast> post_ops_3()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, true),  // If broadcast in dims 0, 1 and 2
-        1,
-        ConvertPolicy::SATURATE);
-    return post_ops;
-}
-// To test that the output of the main op is the first parameter in prelu post op
-experimental::PostOpList<PostOpArgBroadcast> post_ops_4()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, true),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        0,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param
-experimental::PostOpList<PostOpArgBroadcast> post_ops_5()
-{
-    experimental::PostOpList<PostOpArgBroadcast> post_ops{};
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F});
-    post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>(
-        std::make_tuple(false, false, false),   // If true, broadcast in corresponding dim: 0, 1 or 2
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F});
-    return post_ops;
-}
-/** Different Post Op Lists */
-const auto post_op_lists = framework::dataset::make("post_op_lists", {
-    post_ops_1(),
-    post_ops_2(),
-    post_ops_3(),
-    post_ops_4(),
-    post_ops_5()
- } );
-
- bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops)
-{
-    const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true);
-    const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false);
-
-    // Create TensorInfo for post op arguments
-    TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type);
-    TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type);
-    TensorInfo input2_info(TensorShape(n), 1, data_type);
-    TensorInfo output_info(TensorShape(n, m, batch), 1, data_type);
-
-    const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info));
-
-    GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */,
-             false /**< reinterpret the input as 3D */,
-             true  /**< Flag used to broadcast the bias addition */,
-             false /**< wider accumm */,
-             false /**< has pad y */,
-           ActivationLayerInfo::ActivationFunction::IDENTITY,
-             1   /**< Multiplication factor for the width of the 1xW transposed block */,
-             1   /**< Multiplication factor for the height of the 4x4 interleaved block */,
-             lhs_info,
-             rhs_info,
-             0  /**< Offset to be added to each element of the matrix A */,
-             0 /**< Offset to be added to each element of the matrix B */,
-             post_ops);
-    return bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(&input0_info.clone()->set_is_resizable(true),
-                                                          &reshaped_input1_info.clone()->set_is_resizable(true),
-                                                          &input2_info.clone()->set_is_resizable(true),
-                                                          &output_info.clone()->set_is_resizable(true),1.f,1.f,
-                                                          lhs_info,
-                                                          rhs_info,
-                                                          gemm_info));
-}
 /** Configuration test */
 bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value,
                             unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, unsigned int h0_value,
@@ -370,119 +264,6 @@ b_value, m0_value, n0_value, k0_value, broadcast_bias, input_as_3d, depth_output
     ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS);
 }
 
-TEST_SUITE(ValidateFusedPostOpsConfigs)
-TEST_SUITE(Invalid)
-TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 17;
-    const unsigned int n = 1;
-    const unsigned int k = 13;
-    const unsigned int batch = 2;
-    TensorShape post_op_arg0_shape(n, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type);
-    auto post_op_arg1_info = post_op_arg_info.clone();
-
-    // Unsupported sequence of post ops
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        &post_op_arg_info,
-        1,
-        ConvertPolicy::SATURATE);
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>(
-        post_op_arg1_info.get(),
-        0,
-        ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(OutputWidened, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors "widen" the output tensor
-    const auto data_type = DataType::F32;
-    const unsigned int m = 17;
-    const unsigned int n = 1;
-    const unsigned int k = 1;
-    const unsigned int batch = 1;
-    TensorShape post_op_arg_shape(n, m, batch + 4); // output's batch dimension is "widened", which is not allowed
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL)
-{
-    // Invalid broadcast: post op tensors broadcast in the first dimension (X) only
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, m, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Invalid
-TEST_SUITE(Valid)
-TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(n, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, batch);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL)
-{
-    const auto data_type = DataType::F32;
-    const unsigned int m = 22;
-    const unsigned int n = 16;
-    const unsigned int k = 15;
-    const unsigned int batch = 3;
-    TensorShape post_op_arg_shape(1, 1, 1);
-    TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type);
-    experimental::PostOpList<ITensorInfo*> post_ops{};
-    post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE);
-
-    ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS);
-}
-TEST_SUITE_END() // Valid
-TEST_SUITE_END() // ValidateFusedPostOps
 TEST_SUITE(Float)
 TEST_SUITE(FP32)
 
@@ -684,43 +465,6 @@ FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixtur
     }
 }
 
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   framework::dataset::make("H0", {1})),
-                                                                   framework::dataset::make("interleave_rhs", { true })),
-                                                                   t_values_rhs),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", {false, true})),
-                                                                   framework::dataset::make("DataType", DataType::F32)),
-                                                                   a_values),
-                                                                   beta_values),
-                                                                   framework::dataset::make("broadcast_bias", { false } )),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
-
 TEST_SUITE_END() // FP32
 
 TEST_SUITE(FP16)
@@ -849,42 +593,6 @@ FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixtur
         framework::ARM_COMPUTE_PRINT_INFO();
     }
 }
-TEST_SUITE(FusedPostOps)
-
-FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<half>, framework::DatasetMode::ALL,
-                combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(
-                                                                   m_values,
-                                                                   n_values),
-                                                                   k_values),
-                                                                   b_values),
-                                                                   m0_values_precommit),
-                                                                   n0_values_precommit),
-                                                                   k0_values_precommit),
-                                                                   framework::dataset::make("H0", {1})),
-                                                                   framework::dataset::make("interleave_rhs", { true })),
-                                                                   t_values_rhs),
-                                                                   framework::dataset::make("export_to_cl_image_rhs", true)),
-                                                                   framework::dataset::make("DataType", DataType::F16)),
-                                                                   a_values),
-                                                                   beta_values),
-                                                                   framework::dataset::make("broadcast_bias", { false } )),
-                                                                   act_values),
-                                                                   post_op_lists)
-                                                                   )
-{
-    // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension
-    if(validate_result)
-    {
-        validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16);
-    }
-    else
-    {
-        ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
-        framework::ARM_COMPUTE_PRINT_INFO();
-    }
-}
-
-TEST_SUITE_END() //  FusedPostOps
 
 TEST_SUITE_END() // FP16