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>

1 files changed, 1 insertions, 243 deletions

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