| /* |
| * Copyright (c) 2019-2021 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/KernelDescriptors.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" |
| #include "src/gpu/cl/kernels/ClGemmMatrixMultiplyNativeKernel.h" |
| #include "tests/CL/CLAccessor.h" |
| #include "tests/CL/Helper.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/ShapeDatasets.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/GEMMFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| using namespace arm_compute::misc::shape_calculator; |
| using namespace arm_compute::opencl::kernels; |
| |
| // Create function for ClGemmMatrixMultiplyNativeKernel |
| using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyNativeKernel>; |
| |
| // Fixture for CLGEMMMatrixMultiplyNative |
| 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>; |
| |
| namespace |
| { |
| // *INDENT-OFF* |
| // clang-format off |
| RelativeTolerance<float> rel_tolerance_f32(0.001f); |
| constexpr float abs_tolerance_f32(0.0001f); |
| |
| /** Alpha values to test - Precommit */ |
| const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} ); |
| |
| /** Beta values to test - Precommit */ |
| const auto beta_values = framework::dataset::make("beta", {-0.75f, 0.0f} ); |
| |
| /** M values to test */ |
| const auto m_values = framework::dataset::make("M", 37); |
| |
| /** M_W values to test */ |
| const auto m_w_values = framework::dataset::make("M_W", 5); |
| |
| /** M_H values to test */ |
| const auto m_h_values = framework::dataset::make("M_H", 7); |
| |
| /** N values to test */ |
| const auto n_values = framework::dataset::make("N", 51); |
| |
| /** K values to test */ |
| const auto k_values = framework::dataset::make("K", 23); |
| |
| /** Batch size values to test */ |
| const auto b_values = framework::dataset::make("batch_size", 1, 3); |
| |
| /** Activation values to test */ |
| const auto act_values = framework::dataset::make("Activation", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f), |
| }); |
| |
| /** M0 values to test - Precommit */ |
| const auto m0_values_precommit = framework::dataset::make("M0", { 4, 6 }); |
| |
| /** N0 values to test - Precommit */ |
| const auto n0_values_precommit = framework::dataset::make("N0", { 4 }); |
| |
| /** K0 values to test - Precommit */ |
| const auto k0_values_precommit = framework::dataset::make("K0", { 4 }); |
| |
| /** H0 values to test - Precommit */ |
| const auto h0_values_precommit = framework::dataset::make("H0", 1, 3); |
| |
| /** M0 values to test - Nightly */ |
| const auto m0_values_nightly = framework::dataset::make("M0", 1, 8); |
| |
| /** N0 values to test - Nightly */ |
| const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 }); |
| |
| /** K0 values to test - Nightly */ |
| const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 }); |
| |
| /** Broadcast bias from vector to matrix */ |
| const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { false, true } ); |
| |
| /** Boundary handling cases for testing partial/non-partial (full) block dimensions, resulting from different combinations |
| * of M, M0, N and N0 values. |
| * M0 and N0 are kept constant, while the different test cases need to vary M and N. |
| * |
| * Eg. M = 64 and N = 33 result in a block dimension that has no partial blocks (all full blocks) in Y dimension and |
| * parital blocks in X dimension. |
| */ |
| const auto boundary_handling_cases = combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| // Large k to force potential out-of-bound reads on input0 |
| framework::dataset::make("K", 315), |
| // Batch size == 1 to force potential out-of-bound reads on input0 |
| framework::dataset::make("batch_size", 1)), |
| framework::dataset::make("M0", 4)), |
| framework::dataset::make("N0", 4)), |
| framework::dataset::make("K0", 4)), |
| // Only need to test F32 as F16 shares identical boundary handling logics |
| framework::dataset::make("DataType", DataType::F32)), |
| framework::dataset::make("alpha", -0.75f )), |
| framework::dataset::make("beta", -0.35f )), |
| 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; |
| } |
| |
| /** Different Post Op Lists */ |
| const auto post_op_lists = framework::dataset::make("post_op_lists", { |
| post_ops_1(), |
| post_ops_2(), |
| post_ops_3(), |
| } ); |
| |
| 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) |
| { |
| const unsigned int M = m_value; |
| const unsigned int N = n_value; |
| const unsigned int K = k_value; |
| |
| GEMMLHSMatrixInfo lhs_info; |
| lhs_info.m0 = m0_value; |
| lhs_info.k0 = k0_value; |
| |
| GEMMRHSMatrixInfo rhs_info; |
| rhs_info.n0 = n0_value; |
| rhs_info.k0 = k0_value; |
| |
| GEMMKernelInfo kernel_info; |
| kernel_info.m = M; |
| kernel_info.n = N; |
| kernel_info.k = K; |
| kernel_info.broadcast_bias = broadcast_bias; |
| kernel_info.activation_info = act_info; |
| |
| const TensorShape lhs_shape(K, M, b_value); |
| const TensorShape rhs_shape(N, K, b_value); |
| const TensorShape bias_shape(N, |
| broadcast_bias? 1 : M, |
| broadcast_bias? 1 : b_value); |
| const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type), |
| TensorInfo(rhs_shape, 1, data_type), |
| kernel_info); |
| |
| // Create tensors |
| CLTensor lhs = create_tensor<CLTensor>(lhs_shape, data_type); |
| CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type); |
| CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type); |
| CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type); |
| |
| ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Create and configure function |
| CLGEMMMatrixMultiplyNative gemm; |
| gemm.configure(lhs.info(), rhs.info(), bias.info(), dst.info(), 1.0f, 1.0f, lhs_info, rhs_info, kernel_info); |
| } |
| } // namespace |
| |
| 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( |
| m_values, |
| n_values), |
| k_values), |
| framework::dataset::make("batch_size", 1)), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| broadcast_bias_values), |
| act_values), |
| m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, act_value) |
| { |
| validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, broadcast_bias, DataType::F32, act_value); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingPartialInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 3), |
| framework::dataset::make("N", 1)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingPartialInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 64), |
| framework::dataset::make("N", 51)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXFullInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 64), |
| framework::dataset::make("N", 32)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmallBoundaryHandlingFullInXPartialInY, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| combine(combine( |
| framework::dataset::make("M", 37), |
| framework::dataset::make("N", 32)), |
| boundary_handling_cases)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL, |
| 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("DataType", DataType::F32)), |
| a_values), |
| beta_values), |
| broadcast_bias_values), |
| act_values)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::DISABLED, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| m_values, |
| n_values), |
| k_values), |
| b_values), |
| m0_values_nightly), |
| n0_values_nightly), |
| k0_values_nightly), |
| framework::dataset::make("DataType", DataType::F32)), |
| a_values), |
| beta_values), |
| broadcast_bias_values), |
| act_values)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| m_w_values, |
| m_h_values), |
| n_values), |
| k_values), |
| b_values), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| framework::dataset::make("DataType", DataType::F32)), |
| a_values), |
| beta_values), |
| act_values)) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::DISABLED, |
| combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( |
| m_w_values, |
| m_h_values), |
| n_values), |
| k_values), |
| b_values), |
| m0_values_nightly), |
| n0_values_nightly), |
| k0_values_nightly), |
| framework::dataset::make("DataType", DataType::F32)), |
| a_values), |
| beta_values), |
| act_values)) |
| { |
| // Validate output |
| 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 |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |