diff options
Diffstat (limited to 'tests/validation/NEON/GEMM.cpp')
| -rw-r--r-- | tests/validation/NEON/GEMM.cpp | 328 |
1 files changed, 268 insertions, 60 deletions
diff --git a/tests/validation/NEON/GEMM.cpp b/tests/validation/NEON/GEMM.cpp index 500c6029d5..5f6a402204 100644 --- a/tests/validation/NEON/GEMM.cpp +++ b/tests/validation/NEON/GEMM.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -22,12 +22,15 @@ * SOFTWARE. */ #include "arm_compute/core/Types.h" +#include "arm_compute/core/utils/StringUtils.h" #include "arm_compute/runtime/NEON/functions/NEGEMM.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" -#include "src/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h" -#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h" -#include "src/core/NEON/kernels/NEGEMMTranspose1xWKernel.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/cpu/kernels/CpuGemmInterleave4x4Kernel.h" +#include "src/cpu/kernels/CpuGemmMatrixMultiplyKernel.h" +#include "src/cpu/kernels/CpuGemmTranspose1xWKernel.h" +#include "src/cpu/operators/CpuGemm.h" #include "tests/NEON/Accessor.h" #include "tests/NEON/Helper.h" #include "tests/PaddingCalculator.h" @@ -48,6 +51,8 @@ namespace test { namespace validation { +using framework::dataset::make; + namespace { constexpr AbsoluteTolerance<float> tolerance_f(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for FP32 data types */ @@ -57,7 +62,7 @@ const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute constexpr float tolerance_num = 0.07f; /**< Tolerance number for FP16 data types */ #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ /** CNN data types */ -const auto CNNDataTypes = framework::dataset::make("DataType", +const auto CNNDataTypes = make("DataType", { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC DataType::F16, @@ -65,62 +70,210 @@ const auto CNNDataTypes = framework::dataset::make("DataType", DataType::F32, }); -const auto data_interleave = framework::dataset::make("M", 8, 12) * framework::dataset::make("N", 8, 12); -const auto data_transpose = framework::dataset::make("M", 8, 14) * framework::dataset::make("N", 7, 14); +const auto data_interleave = make("M", 8, 12) * make("N", 8, 12); +const auto data_transpose = make("M", 8, 14) * make("N", 7, 14); /** Zero padding test */ template <typename FunctionType> bool validate_zero_padding(unsigned int dim0_value, unsigned int dim1_value) { const TensorShape in_shape(dim0_value, dim1_value); + TensorInfo in(in_shape, 1, DataType::U32); + TensorInfo dst; - // Create tensors - Tensor in = create_tensor<Tensor>(in_shape, DataType::U32); - Tensor dst; - - ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(in.is_resizable(), framework::LogLevel::ERRORS); // Validate zero-padding FunctionType func; func.configure(&in, &dst); - return in.info()->padding().empty(); + return in.padding().empty(); } /* Zero padding test for GEMM kernels */ bool validate_gemm_zero_padding(const TensorShape shape0, const TensorShape shape1) { // Create tensors - Tensor in0 = create_tensor<Tensor>(shape0, DataType::F32); - Tensor in1 = create_tensor<Tensor>(shape1, DataType::F32); - Tensor dst; + TensorInfo in0(shape0, 1, DataType::F32); + TensorInfo in1(shape1, 1, DataType::F32); + TensorInfo dst; // Validate zero-padding - NEGEMMMatrixMultiplyKernel gemm; + cpu::kernels::CpuGemmMatrixMultiplyKernel gemm; gemm.configure(&in0, &in1, &dst, 1.0, false); - return in0.info()->padding().empty() && in1.info()->padding().empty() && dst.info()->padding().empty(); + return in0.padding().empty() && in1.padding().empty() && dst.padding().empty(); } } // namespace TEST_SUITE(NEON) TEST_SUITE(GEMM) +/** Test case for memory injection in @ref cpu::CpuGemm. + * + * Configure the operator once and inject memory at run-time in multiple executions. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MemoryInjection, framework::DatasetMode::ALL) +{ + auto gemm = std::make_unique<cpu::CpuGemm>(); + const auto lhs_info = TensorInfo(TensorShape(3U, 3U), 1, DataType::F32); + const auto rhs_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + const auto c_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + auto dst_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + const auto gemm_info = GEMMInfo{}; + gemm->configure(&lhs_info, &rhs_info, &c_info, &dst_info, 1.f, 1.f, gemm_info); + + // telhs are newly created every call of this lambda function + auto lhs = create_tensor<Tensor>(lhs_info); + auto rhs = create_tensor<Tensor>(rhs_info); + auto c = create_tensor<Tensor>(c_info); + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + c.allocator()->allocate(); + + ITensorPack run_pack{ { TensorType::ACL_SRC_0, &lhs }, { TensorType::ACL_SRC_1, &rhs }, { TensorType::ACL_SRC_2, &c } }; + ITensorPack prep_pack{ { TensorType::ACL_SRC_1, &rhs }, { TensorType::ACL_SRC_2, &c } }; + + auto mg = MemoryGroup{}; + auto ws = manage_workspace<Tensor>(gemm->workspace(), mg, run_pack, prep_pack); + + auto run_conv = [&]() -> Tensor + { + auto dst = create_tensor<Tensor>(dst_info); + dst.allocator()->allocate(); + run_pack.add_tensor(TensorType::ACL_DST, &dst); + + library->fill_tensor_value(Accessor(lhs), 1.f); + library->fill_tensor_value(Accessor(rhs), 2.f); + library->fill_tensor_value(Accessor(c), 3.f); + // This operator is configured once and captured by this lambda. + gemm->prepare(prep_pack); + gemm->run(run_pack); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + +/** Test case for memory injection in @ref NEGEMM. + * + * Make sure @ref NEGEMM still works through injecting the memory at configure time using the old API. + * + * Checks performed in order: + * - Both runs compute the same output + */ +TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) +{ + auto gemm = std::make_unique<NEGEMM>(); + const auto lhs_info = TensorInfo(TensorShape(3U, 3U), 1, DataType::F32); + const auto rhs_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + const auto c_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + auto dst_info = TensorInfo(TensorShape(4U, 3U), 1, DataType::F32); + const auto gemm_info = GEMMInfo{}; + auto run_conv = [&]() + { + auto lhs = create_tensor<Tensor>(lhs_info); + auto rhs = create_tensor<Tensor>(rhs_info); + auto c = create_tensor<Tensor>(c_info); + auto dst = create_tensor<Tensor>(dst_info); + gemm->configure(&lhs, &rhs, &c, &dst, 1.f, 1.f, gemm_info); + lhs.allocator()->allocate(); + rhs.allocator()->allocate(); + c.allocator()->allocate(); + dst.allocator()->allocate(); + library->fill_tensor_value(Accessor(lhs), 1.f); + library->fill_tensor_value(Accessor(rhs), 2.f); + library->fill_tensor_value(Accessor(c), 3.f); + gemm->run(); + return dst; + }; + auto result_0 = run_conv(); + auto result_1 = run_conv(); + for(size_t i = 0; i < result_0.info()->tensor_shape().total_size(); ++i) + { + ARM_COMPUTE_EXPECT(((float *)result_0.buffer())[i] == ((float *)result_1.buffer())[i], framework::LogLevel::ERRORS); + } +} + +// *INDENT-OFF* +// clang-format off +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip( + make("LhsInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::S32), // Unsupported data type + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + }), + make("RhsInfo",{ TensorInfo(TensorShape(8U, 27U), 1, DataType::S32), + TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), + })), + make("OutputInfo",{ TensorInfo(TensorShape(8U, 13U), 1, DataType::S32), + TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), + })), + make("Expected", { false, true })), + lhs_info, rhs_info, output_info, expected) +{ + constexpr float alpha = 1.0; + constexpr float beta = 0.0; + const auto gemm_info = GEMMInfo(); + bool is_valid = bool(NEGEMM::validate(&lhs_info.clone()->set_is_resizable(true), &rhs_info.clone()->set_is_resizable(true), nullptr, &output_info.clone()->set_is_resizable(true), alpha, beta, gemm_info)); + ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); +} +// clang-format on +// *INDENT-ON* +TEST_SUITE(KERNEL_SELECTION) +DATA_TEST_CASE(KernelSelection_mul_and_add, framework::DatasetMode::ALL, + combine(make("CpuExt", std::string("NEON")), + make("DataType", { DataType::F32, + DataType::F16 + })), + cpu_ext, data_type) +{ + using namespace cpu::kernels; + + cpuinfo::CpuIsaInfo cpu_isa{}; + cpu_isa.neon = (cpu_ext == "NEON"); + cpu_isa.fp16 = (data_type == DataType::F16); + + const auto *selected_impl_mul = CpuGemmMatrixMultiplyKernel::get_implementation(DataTypeISASelectorData{ data_type, cpu_isa }, cpu::KernelSelectionType::Preferred); + + ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl_mul); + + std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_gemm_matrix_mul"; + std::string actual = selected_impl_mul->name; + + ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS); + + const auto *selected_impl_add = CpuGemmMatrixAdditionKernel::get_implementation(DataTypeISASelectorData{ data_type, cpu_isa }, cpu::KernelSelectionType::Preferred); + + ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl_add); + + expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_gemm_matrix_add"; + actual = selected_impl_add->name; + + ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS); +} +TEST_SUITE_END() // KERNEL_SELECTION + TEST_SUITE(TRANSPOSE_1XW) -using NEGEMMTranspose1xW = NESynthetizeFunctionWithZeroConstantBorder<NEGEMMTranspose1xWKernel, 4>; +using CpuGemmTranspose1xW = NESynthetizeFunctionWithZeroConstantKernelBorder<cpu::kernels::CpuGemmTranspose1xWKernel>; DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip( - framework::dataset::make("N", { 1, 23, 63, 101 }), - framework::dataset::make("K", { 1, 47, 29, 27 })), + make("N", { 1, 23, 63, 101 }), + make("K", { 1, 47, 29, 27 })), n_value, k_value) { - bool status = validate_zero_padding<NEGEMMTranspose1xWKernel>(n_value, k_value); + bool status = validate_zero_padding<CpuGemmTranspose1xW>(n_value, k_value); ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); } TEST_SUITE(U32) -using NEGEMMTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, NEGEMMTranspose1xW, uint32_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * framework::dataset::make("DataType", DataType::U32)) +using CpuGemmTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, CpuGemmTranspose1xW, uint32_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * make("DataType", DataType::U32)) { // Validate output validate(Accessor(_target), _reference); @@ -128,8 +281,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMTranspose1xWFixture, framework::DatasetMo TEST_SUITE_END() // U32 TEST_SUITE(U16) -using NEGEMMTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, NEGEMMTranspose1xW, uint16_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * framework::dataset::make("DataType", DataType::U16)) +using CpuGemmTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, CpuGemmTranspose1xW, uint16_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * make("DataType", DataType::U16)) { // Validate output validate(Accessor(_target), _reference); @@ -137,8 +290,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMTranspose1xWFixture, framework::DatasetMo TEST_SUITE_END() // U16 TEST_SUITE(U8) -using NEGEMMTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, NEGEMMTranspose1xW, uint8_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * framework::dataset::make("DataType", DataType::U8)) +using CpuGemmTranspose1xWFixture = GEMMTranspose1xWValidationFixture<Tensor, Accessor, CpuGemmTranspose1xW, uint8_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmTranspose1xWFixture, framework::DatasetMode::PRECOMMIT, data_transpose * make("DataType", DataType::U8)) { // Validate output validate(Accessor(_target), _reference); @@ -148,20 +301,20 @@ TEST_SUITE_END() // U8 TEST_SUITE_END() // TRANSPOSE_1XW TEST_SUITE(INTERLEAVE_4X4) -using NEGEMMInterleave4x4 = NESynthetizeFunctionWithZeroConstantBorder<NEGEMMInterleave4x4Kernel, 4>; +using CpuGemmInterleave4x4 = NESynthetizeFunctionWithZeroConstantKernelBorder<cpu::kernels::CpuGemmInterleave4x4Kernel>; DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip( - framework::dataset::make("M", { 1, 23, 63, 101 }), - framework::dataset::make("K", { 1, 47, 29, 27 })), + make("M", { 1, 23, 63, 101 }), + make("K", { 1, 47, 29, 27 })), m_value, k_value) { - bool status = validate_zero_padding<NEGEMMInterleave4x4Kernel>(m_value, k_value); + bool status = validate_zero_padding<cpu::kernels::CpuGemmInterleave4x4Kernel>(m_value, k_value); ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); } TEST_SUITE(U32) -using NEGEMMInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, NEGEMMInterleave4x4, uint32_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * framework::dataset::make("DataType", DataType::U32)) +using CpuGemmInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, CpuGemmInterleave4x4, uint32_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * make("DataType", DataType::U32)) { // Validate output validate(Accessor(_target), _reference); @@ -169,8 +322,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMInterleave4x4Fixture, framework::DatasetM TEST_SUITE_END() // U32 TEST_SUITE(U16) -using NEGEMMInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, NEGEMMInterleave4x4, uint16_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * framework::dataset::make("DataType", DataType::U16)) +using CpuGemmInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, CpuGemmInterleave4x4, uint16_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * make("DataType", DataType::U16)) { // Validate output validate(Accessor(_target), _reference); @@ -178,8 +331,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMInterleave4x4Fixture, framework::DatasetM TEST_SUITE_END() // U16 TEST_SUITE(U8) -using NEGEMMInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, NEGEMMInterleave4x4, uint8_t>; -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * framework::dataset::make("DataType", DataType::QASYMM8)) +using CpuGemmInterleave4x4Fixture = GEMMInterleave4x4ValidationFixture<Tensor, Accessor, CpuGemmInterleave4x4, uint8_t>; +FIXTURE_DATA_TEST_CASE(RunSmall, CpuGemmInterleave4x4Fixture, framework::DatasetMode::PRECOMMIT, data_interleave * make("DataType", DataType::QASYMM8)) { // Validate output validate(Accessor(_target), _reference); @@ -192,17 +345,20 @@ template <typename T> using NEGEMMFixture = GEMMValidationFixture<Tensor, Accessor, NEGEMM, T>; template <typename T> -using NEGEMMFixtureDisabledC = GEMMValidationFixture<Tensor, Accessor, NEGEMM, T, true>; +using NEBatchedMatMulFixture = GEMMValidationFixture<Tensor, Accessor, NEGEMM, T, true, false, false, false, false, true>; + +template <typename T> +using NEGEMMAccumulateFixture = GEMMAccumulateValidationFixture<Tensor, Accessor, NEGEMM, T>; TEST_SUITE(Float) -DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(framework::dataset::make("In0", { TensorShape(21U, 13U), +DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(make("In0", { TensorShape(21U, 13U), TensorShape(31U, 1U), TensorShape(31U, 1U), TensorShape(8U, 2U), TensorShape(38U, 12U), TensorShape(32U, 1U) }), - framework::dataset::make("In1", { TensorShape(33U, 21U), + make("In1", { TensorShape(33U, 21U), TensorShape(23U, 31U), TensorShape(23U, 31U), TensorShape(16U, 8U), @@ -215,59 +371,111 @@ DATA_TEST_CASE(ValidateZeroPadding, framework::DatasetMode::ALL, zip(framework:: ARM_COMPUTE_EXPECT(status, framework::LogLevel::ERRORS); } +DATA_TEST_CASE(ValidateAccumulate, framework::DatasetMode::ALL, combine( + zip(make("In0",{ TensorShape(21U, 13U) }), + make("In1", { TensorShape(33U, 21U) }), + make("Dst", { TensorShape(33U, 13U) })), + zip( + make("alpha", { 1.0, 100.0, 1.0, 1.0 }), + make("beta", { 0.0, 0.0, 1.0, 1.0 }), + make("is_c_null", { false, false, false, true }), + make("Expected", { true, false, false, true }))), + shape_a, shape_b, shape_dst, alpha, beta, is_c_null, expected) +{ + /* Accumulation test for GEMM kernels */ + // Create tensors + TensorInfo in_a(shape_a, 1, DataType::F32); + TensorInfo in_b(shape_b, 1, DataType::F32); + TensorInfo in_c(shape_dst, 1, DataType::F32); + TensorInfo dst(shape_dst, 1, DataType::F32); + + GEMMInfo gemm_info = GEMMInfo(); + gemm_info.set_accumulate(true); + + // Validate accumulation + cpu::CpuGemm gemm; + Status status = gemm.validate(&in_a, &in_b, (is_c_null ? nullptr : &in_c), &dst, alpha, beta, gemm_info); + ARM_COMPUTE_EXPECT((expected == bool(status)), framework::LogLevel::ERRORS); +} + #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallGEMMDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), - framework::dataset::make("DataType", DataType::F16))) + make("ReshapeWeights", { true, false })), + make("DataType", DataType::F16))) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeGEMMDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + make("ReshapeWeights", { true, false })), + make("DataType", DataType::F16))) +{ + // Validate output + validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); +} - framework::dataset::make("DataType", DataType::F16))) +TEST_SUITE(BATCHED_MATMUL) +FIXTURE_DATA_TEST_CASE(RunSmall, NEBatchedMatMulFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallBatchedMatMulDataset(), + make("ReshapeWeights", { false })), + make("DataType", DataType::F16))) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); } -TEST_SUITE_END() +TEST_SUITE_END() // BATCHED_MATMUL + +TEST_SUITE_END() // FP16 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallGEMMDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), - - framework::dataset::make("DataType", DataType::F32))) + make("ReshapeWeights", { true, false })), + make("DataType", DataType::F32))) { // Validate output validate(Accessor(_target), _reference, tolerance_f); } FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeGEMMDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + make("ReshapeWeights", { true, false })), + make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f); +} - framework::dataset::make("DataType", DataType::F32))) +TEST_SUITE(BATCHED_MATMUL) +FIXTURE_DATA_TEST_CASE(RunSmall, NEBatchedMatMulFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallBatchedMatMulDataset(), + make("ReshapeWeights", { false })), + make("DataType", DataType::F32))) { // Validate output validate(Accessor(_target), _reference, tolerance_f); } -TEST_SUITE(DisabledC) -FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMFixtureDisabledC<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallGEMMDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), +TEST_SUITE_END() // BATCHED_MATMUL - framework::dataset::make("DataType", DataType::F32))) +TEST_SUITE(ACCUMULATE) +FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMAccumulateFixture<float>, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallAccumulateGEMMDataset(), + make("ReshapeWeights", { false }), + make("DataType", DataType::F32))) +{ + // Validate output + validate(Accessor(_target), _reference, tolerance_f); +} +FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMAccumulateFixture<float>, framework::DatasetMode::NIGHTLY, combine(datasets::LargeAccumulateGEMMDataset(), + make("ReshapeWeights", { false }), + make("DataType", DataType::F32))) { // Validate output validate(Accessor(_target), _reference, tolerance_f); } -TEST_SUITE_END() +TEST_SUITE_END() // ACCUMULATE -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // FP32 -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // Float +TEST_SUITE_END() // GEMM +TEST_SUITE_END() // NEON } // namespace validation } // namespace test } // namespace arm_compute |