diff options
Diffstat (limited to 'tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp')
| -rw-r--r-- | tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp | 313 |
1 files changed, 164 insertions, 149 deletions
diff --git a/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp index 38c3a0ca0e..d714a2f70c 100644 --- a/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp +++ b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2023 Arm Limited. + * Copyright (c) 2023-2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,16 +24,15 @@ #ifdef ACL_INTERNAL_TEST_CKW_IN_DF #include "tests/AssetsLibrary.h" #include "tests/CL/CLAccessor.h" -#include "tests/framework/Fixture.h" -#include "tests/framework/Macros.h" -#include "tests/framework/datasets/Datasets.h" #include "tests/datasets/LargeMatMulDataset.h" #include "tests/datasets/SmallMatMulDataset.h" -#include "tests/validation/Validation.h" -#include "tests/validation/reference/Permute.h" -#include "tests/validation/reference/GEMM.h" - +#include "tests/framework/datasets/Datasets.h" +#include "tests/framework/Fixture.h" +#include "tests/framework/Macros.h" #include "tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h" +#include "tests/validation/reference/GEMM.h" +#include "tests/validation/reference/Permute.h" +#include "tests/validation/Validation.h" #include <tuple> @@ -45,35 +44,37 @@ namespace validation { namespace { - RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ -constexpr float abs_tolerance_f32( +RelativeTolerance<float> tolerance_f32( + 0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ +constexpr float abs_tolerance_f32( 0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */ constexpr float abs_tolerance_f16( - 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */ - RelativeTolerance<half_float::half> tolerance_f16(half(0.02)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ -} + 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */ +RelativeTolerance<half_float::half> tolerance_f16(half( + 0.02)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */ +} // namespace /** M0 values to test --precommit*/ -const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 }); +const auto m0_values_precommit = framework::dataset::make("M0", {1, 3}); /** N0 values to test --precommit*/ -const auto n0_values_precommit = framework::dataset::make("N0", { 1, 2, 4 }); +const auto n0_values_precommit = framework::dataset::make("N0", {1, 2, 4}); /** K0 values to test --precommit*/ -const auto k0_values_precommit = framework::dataset::make("K0", { 1, 2, 3 }); +const auto k0_values_precommit = framework::dataset::make("K0", {1, 2, 3}); /** M0 values to test --nightly*/ -const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 }); -const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 }); +const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", {1, 2, 3, 4, 5, 6, 7, 8}); +const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", {1, 2, 3, 4, 8}); /** N0 values to test --nightly*/ -const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 }); -const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 }); +const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", {1, 2, 3, 4, 8, 16}); +const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", {1, 2, 3, 4, 8}); /** K0 values to test --nightly*/ -const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 }); -const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 }); -const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 }); +const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", {1, 2, 3, 4, 8, 16}); +const auto k0_values_nightly_rhs_t = framework::dataset::make("K0", {1, 2, 3, 4, 8}); +const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", {1, 2, 3, 4, 5, 6, 7, 8}); TEST_SUITE(CL) TEST_SUITE(DYNAMIC_FUSION) @@ -85,45 +86,43 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) { using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>; - const std::vector<MatMulConfigurationPair> supported_block_sizes = - { + const std::vector<MatMulConfigurationPair> supported_block_sizes = { // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false) // Lhs not-transposed, Rhs transposed - { MatMulKernelInfo(false, true, 0, 1, 1), false }, // M0 should be > 0 - { MatMulKernelInfo(false, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} - { MatMulKernelInfo(false, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} - { MatMulKernelInfo(false, true, 3, 3, 12), false }, // K0 not in {1, 2, 3, 4, 8, 16} - { MatMulKernelInfo(false, true, 3, 3, 6), false }, // K0 not in {1, 2, 3, 4, 8, 16} - { MatMulKernelInfo(false, true, 5, 1, 2), true }, - { MatMulKernelInfo(false, true, 3, 3, 3), true }, - { MatMulKernelInfo(false, true, 2, 4, 8), true }, + {MatMulKernelInfo(false, true, 0, 1, 1), false}, // M0 should be > 0 + {MatMulKernelInfo(false, true, 3, 11, 1), false}, // N0 not in {1, 2, 3, 4, 8, 16} + {MatMulKernelInfo(false, true, 3, 7, 1), false}, // N0 not in {1, 2, 3, 4, 8, 16} + {MatMulKernelInfo(false, true, 3, 3, 12), false}, // K0 not in {1, 2, 3, 4, 8, 16} + {MatMulKernelInfo(false, true, 3, 3, 6), false}, // K0 not in {1, 2, 3, 4, 8, 16} + {MatMulKernelInfo(false, true, 5, 1, 2), true}, {MatMulKernelInfo(false, true, 3, 3, 3), true}, + {MatMulKernelInfo(false, true, 2, 4, 8), true}, }; // Create a new workload sketch auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - auto context = GpuWorkloadContext{ &cl_compile_ctx }; - GpuWorkloadSketch sketch{ &context }; + auto context = GpuWorkloadContext{&cl_compile_ctx}; + GpuWorkloadSketch sketch{&context}; // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here, // not the shapes themselves. - const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32)); - const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32)); + const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32)); + const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(TensorShape(100U, 100U), 1, DataType::F32)); - for(auto &pair : supported_block_sizes) + for (auto &pair : supported_block_sizes) { - MatMulAttributes matmul_attr {}; + MatMulAttributes matmul_attr{}; matmul_attr.adj_lhs(pair.first.adj_lhs); matmul_attr.adj_rhs(pair.first.adj_rhs); - GpuMatMulSettings matmul_settings {}; + GpuMatMulSettings matmul_settings{}; matmul_settings.m0(pair.first.m0); matmul_settings.n0(pair.first.n0); matmul_settings.k0(pair.first.k0); - Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings); + Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings); ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); } } @@ -132,117 +131,110 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) { // Create a sketch auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - auto context = GpuWorkloadContext{ &cl_compile_ctx }; - GpuWorkloadSketch sketch{ &context }; + auto context = GpuWorkloadContext{&cl_compile_ctx}; + GpuWorkloadSketch sketch{&context}; // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations - using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>; - const std::vector<ShapeConfigurationTuple> shape_configurations = - { - { TensorShape(5U, 1U), TensorShape(3U, 5U), true }, - { TensorShape(10U, 12U), TensorShape(3U, 10U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension - { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension - { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true }, - { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting - { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension + using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>; + const std::vector<ShapeConfigurationTuple> shape_configurations = { + {TensorShape(5U, 1U), TensorShape(3U, 5U), true}, + {TensorShape(10U, 12U), TensorShape(3U, 10U), true}, + {TensorShape(8U, 4U), TensorShape(2U, 8U), true}, + {TensorShape(8U, 4U), TensorShape(2U, 5U), false}, // Mismatch in the K dimension + {TensorShape(5U, 0U), TensorShape(2U, 5U), false}, // Invalid dimension + {TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true}, + {TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false}, // no batch broadcasting + {TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), + false}, // mismatch in batch dimension }; - for(auto &tuple : shape_configurations) + for (auto &tuple : shape_configurations) { const bool expected = std::get<2>(tuple); - for(bool adj_lhs : - { - false - }) + for (bool adj_lhs : {false}) { - for(bool adj_rhs : - { - true - }) + for (bool adj_rhs : {true}) { TensorShape lhs_shape = std::get<0>(tuple); TensorShape rhs_shape = std::get<1>(tuple); - if(adj_lhs) + if (adj_lhs) { permute(lhs_shape, PermutationVector(1U, 0U)); } - if(adj_rhs) + if (adj_rhs) { permute(rhs_shape, PermutationVector(1U, 0U)); } - const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(lhs_shape, 1, DataType::F32)); - const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(rhs_shape, 1, DataType::F32)); + const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(lhs_shape, 1, DataType::F32)); + const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(rhs_shape, 1, DataType::F32)); - MatMulAttributes matmul_attr {}; + MatMulAttributes matmul_attr{}; matmul_attr.adj_lhs(adj_lhs); matmul_attr.adj_rhs(adj_rhs); - GpuMatMulSettings matmul_settings {}; + GpuMatMulSettings matmul_settings{}; matmul_settings.m0(1); matmul_settings.n0(1); matmul_settings.k0(1); - Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings); + Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } } } - TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) { // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>; - const std::vector<DataTypeConfigurationTuple> data_type_configurations = - { - { DataType::F32, DataType::F32, DataType::F32, true }, - { DataType::F16, DataType::F16, DataType::F16, true }, - { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision - { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision - { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types - { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types - { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types - { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types - { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types - { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types - { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types - { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types - { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types - { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types - { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types - { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types - { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types - { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types + const std::vector<DataTypeConfigurationTuple> data_type_configurations = { + {DataType::F32, DataType::F32, DataType::F32, true}, + {DataType::F16, DataType::F16, DataType::F16, true}, + {DataType::F16, DataType::F32, DataType::F32, false}, // no mixed precision + {DataType::F64, DataType::F64, DataType::F64, false}, // no double precision + {DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false}, // no quantized types + {DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false}, // no quantized types + {DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, + false}, // no quantized types + {DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false}, // no quantized types + {DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false}, // no quantized types + {DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false}, // no quantized types + {DataType::S64, DataType::S64, DataType::S64, false}, // no integral types + {DataType::S32, DataType::S32, DataType::S32, false}, // no integral types + {DataType::S16, DataType::S16, DataType::S16, false}, // no integral types + {DataType::S8, DataType::S8, DataType::S8, false}, // no integral types + {DataType::U64, DataType::U64, DataType::U64, false}, // no integral types + {DataType::U32, DataType::U32, DataType::U32, false}, // no integral types + {DataType::U16, DataType::U16, DataType::U16, false}, // no integral types + {DataType::U8, DataType::U8, DataType::U8, false}, // no integral types }; // Create a sketch auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); - auto context = GpuWorkloadContext{ &cl_compile_ctx }; - GpuWorkloadSketch sketch{ &context }; + auto context = GpuWorkloadContext{&cl_compile_ctx}; + GpuWorkloadSketch sketch{&context}; const TensorShape shape = TensorShape(10U, 10U); - MatMulAttributes matmul_attr {}; + MatMulAttributes matmul_attr{}; matmul_attr.adj_lhs(false); matmul_attr.adj_rhs(false); - GpuMatMulSettings matmul_settings {}; + GpuMatMulSettings matmul_settings{}; matmul_settings.m0(1); matmul_settings.n0(1); matmul_settings.k0(1); - for(auto &tuple : data_type_configurations) + for (auto &tuple : data_type_configurations) { const bool expected = std::get<3>(tuple); - const TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<0>(tuple))); - const TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<1>(tuple))); + const ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<0>(tuple))); + const ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape, 1, std::get<1>(tuple))); - Status status = GpuMatMul::validate_op(sketch, &lhs_info, &rhs_info, matmul_attr, matmul_settings); + Status status = GpuMatMul::validate_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -250,59 +242,75 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) TEST_SUITE_END() // Validate template <typename T> -using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor,GpuMatMul, T>; +using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor, GpuMatMul, T>; TEST_SUITE(Float) TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunTiny, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE( + RunTiny, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F32))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE( + RunSmall, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F32))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - m0_values_nightly_lhs_nt), - n0_values_nightly_rhs_t), - k0_values_nightly_rhs_t), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE( + RunLargeRhsTransposed, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_t), + k0_values_nightly_rhs_t), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F32))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } // Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0 -FIXTURE_DATA_TEST_CASE(RunHighDimensional, DynamicFusionGpuMatmulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - framework::dataset::make("M0", { 2 })), - framework::dataset::make("N0", { 2 })), - framework::dataset::make("K0", { 2 })), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE( + RunHighDimensional, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + framework::dataset::make("M0", {2})), + framework::dataset::make("N0", {2})), + framework::dataset::make("K0", {2})), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F32))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); @@ -311,28 +319,35 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, DynamicFusionGpuMatmulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F16))) +FIXTURE_DATA_TEST_CASE( + RunSmall, + DynamicFusionGpuMatmulFixture<half>, + framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F16))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); } - -FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, DynamicFusionGpuMatmulFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false })), - framework::dataset::make("TransposeB", { true })), - m0_values_nightly_lhs_nt), - n0_values_nightly_rhs_t), - k0_values_nightly_rhs_t), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F16))) +FIXTURE_DATA_TEST_CASE( + RunLargeRhsTransposed, + DynamicFusionGpuMatmulFixture<half>, + framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", {false})), + framework::dataset::make("TransposeB", {true})), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_t), + k0_values_nightly_rhs_t), + framework::dataset::make("ExportRhsToCLImage", {false})), + framework::dataset::make("DataType", DataType::F16))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); 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