diff options
Diffstat (limited to 'tests/validation/CL/GEMMMatrixMultiplyNative.cpp')
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyNative.cpp | 244 |
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 |