| /* |
| * Copyright (c) 2023 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/runtime/CL/CLTensor.h" |
| #include "src/gpu/cl/kernels/ClMatMulNativeKernel.h" |
| #include "tests/datasets/LargeMatMulDataset.h" |
| #include "tests/datasets/SmallMatMulDataset.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/MatMulKernelFixture.h" |
| #include "tests/validation/reference/Permute.h" |
| |
| #include <tuple> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| 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( |
| 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(0.01)); /**< 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 }); |
| |
| /** N0 values to test --precommit*/ |
| const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 }); |
| |
| /** K0 values to test --precommit*/ |
| const auto k0_values_precommit = framework::dataset::make("K0", { 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 }); |
| |
| /** 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 }); |
| |
| /** 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 }); |
| |
| template <typename T> |
| using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>; |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(MatMulKernel) |
| TEST_SUITE(Validate) |
| |
| TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) |
| { |
| using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>; |
| |
| 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-not-transposed |
| { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0 |
| { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(false, false, 3, 3, 17), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(false, false, 3, 3, 7), false }, // K0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(false, false, 9, 1, 2), true }, |
| { MatMulKernelInfo(false, false, 3, 16, 3), true }, |
| { MatMulKernelInfo(false, false, 7, 3, 4), true }, |
| { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, false, 7, 1, 4, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, false, 7, 12, 4, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, false, 7, 4, 4, true), true }, |
| { MatMulKernelInfo(false, false, 7, 8, 4, true), true }, |
| { MatMulKernelInfo(false, false, 7, 16, 4, true), true }, |
| |
| // 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, 2, 4, 5, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, true, 2, 4, 9, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, true, 2, 4, 3, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(false, true, 2, 4, 4, true), true }, |
| { MatMulKernelInfo(false, true, 2, 4, 8, true), true }, |
| { MatMulKernelInfo(false, true, 2, 8, 16, true), true }, |
| |
| // Lhs transposed, Rhs-not-transposed |
| { MatMulKernelInfo(true, false, 1, 1, 0), false }, // K0 should be > 0 |
| { MatMulKernelInfo(true, false, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, false, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, false, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, false, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, false, 4, 1, 22), true }, |
| { MatMulKernelInfo(true, false, 3, 3, 3), true }, |
| { MatMulKernelInfo(true, false, 2, 4, 8), true }, |
| { MatMulKernelInfo(true, false, 2, 3, 8, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, false, 2, 7, 8, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, false, 2, 5, 8, true), false }, // N0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, false, 2, 4, 8, true), true }, |
| { MatMulKernelInfo(true, false, 2, 8, 8, true), true }, |
| { MatMulKernelInfo(true, false, 2, 16, 8, true), true }, |
| |
| // Lhs transposed, Rhs-transposed |
| { MatMulKernelInfo(true, true, 2, 1, 5), false }, // K0 should in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 1, 8, 7), false }, // K0 should in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 3, 11, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 6, 3, 12), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 5, 3, 6), false }, // M0 not in {1, 2, 3, 4, 8, 16} |
| { MatMulKernelInfo(true, true, 4, 8, 16), true }, |
| { MatMulKernelInfo(true, true, 3, 3, 4), true }, |
| { MatMulKernelInfo(true, true, 16, 4, 8), true }, |
| { MatMulKernelInfo(true, true, 2, 2, 1, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, true, 2, 2, 5, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, true, 2, 4, 7, true), false }, // K0 not in {4, 8, 16} |
| { MatMulKernelInfo(true, true, 2, 4, 4, true), true }, |
| { MatMulKernelInfo(true, true, 2, 8, 8, true), true }, |
| { MatMulKernelInfo(true, true, 2, 8, 16, true), true }, |
| }; |
| |
| // 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 = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32); |
| const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32); |
| |
| const bool export_to_cl_image_supported = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); |
| for(auto &pair : supported_block_sizes) |
| { |
| TensorInfo output_info; |
| Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); |
| |
| if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported) |
| { |
| ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); |
| } |
| } |
| } |
| |
| TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL) |
| { |
| // We skip this test if the hardware does not support exporting to CL Image |
| if(image2d_from_buffer_supported(CLKernelLibrary::get().get_device())) |
| { |
| constexpr size_t pixel_size = 4; |
| const size_t max_image_w = pixel_size * CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_WIDTH>(); |
| const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_IMAGE2D_MAX_HEIGHT>(); |
| |
| using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool, bool, bool>; |
| const std::vector<ShapeConfigurationTuple> shape_configurations = |
| { |
| // lhs_shape, rhs_shape, adj_lhs, adj_rhs, expected |
| // Lhs t/Nt, Rhs Nt |
| // Transposition of Lhs doesn't add any value to the tests, therefore always assumed false below |
| { TensorShape(5U, 1U), TensorShape(3U, 5U), false, false, false }, // N should be multiple of 4 |
| { TensorShape(5U, 1U), TensorShape(14U, 5U), false, false, false }, // N should be multiple of 4 |
| { TensorShape(5U, 1U), TensorShape(12U, 5U), false, false, true }, |
| { TensorShape(5U, 1U), TensorShape(8U, 5U), false, false, true }, |
| { TensorShape(5U, 1U), TensorShape(4U, 5U), false, false, true }, |
| { TensorShape(max_image_h + 1, 1U), TensorShape(4U, max_image_h + 1), false, false, false }, // Cannot fit into CL Image memory's height |
| { TensorShape(5U, 1U), TensorShape(max_image_w + 1, 5U), false, false, false }, // Cannot fit into CL Image memory's width |
| { TensorShape(max_image_h, 1U), TensorShape(4U, max_image_h), false, false, true }, // Barely fits into CL Image memory's height |
| { TensorShape(5U, 1U), TensorShape(max_image_w, 5U), false, false, true }, // Barely fits into CL Image memory's width |
| |
| // Lhs Nt/T , Rhs T |
| { TensorShape(5U, 1U), TensorShape(5U, 3U), false, true, false }, // K should be multiple of 4 |
| { TensorShape(5U, 1U), TensorShape(5U, 14U), false, true, false }, // K should be multiple of 4 |
| { TensorShape(4U, 1U), TensorShape(4U, 10U), false, true, true }, |
| { TensorShape(8U, 1U), TensorShape(8U, 9U), false, true, true }, |
| { TensorShape(12U, 1U), TensorShape(12U, 6U), false, true, true }, |
| }; |
| |
| for(auto &tuple : shape_configurations) |
| { |
| TensorShape lhs_shape = std::get<0>(tuple); |
| TensorShape rhs_shape = std::get<1>(tuple); |
| |
| const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); |
| const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); |
| |
| const bool adj_lhs = std::get<2>(tuple); |
| const bool adj_rhs = std::get<3>(tuple); |
| |
| // We choose M0, N0, K0 equal to 4 so that they're always valid for CLImage in any combination |
| const MatMulKernelInfo matmul_kernel_info |
| { |
| adj_lhs, adj_rhs, 4, 4, 4, true /* export_rhs_to_cl_image */ |
| }; |
| |
| TensorInfo output_info; |
| Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| |
| const bool expected = std::get<4>(tuple); |
| ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| } |
| } |
| } |
| |
| TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) |
| { |
| // 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 |
| }; |
| |
| for(auto &tuple : shape_configurations) |
| { |
| const bool expected = std::get<2>(tuple); |
| |
| for(bool adj_lhs : |
| { |
| false, true |
| }) |
| { |
| for(bool adj_rhs : |
| { |
| false, true |
| }) |
| { |
| TensorShape lhs_shape = std::get<0>(tuple); |
| TensorShape rhs_shape = std::get<1>(tuple); |
| |
| if(adj_lhs) |
| { |
| permute(lhs_shape, PermutationVector(1U, 0U)); |
| } |
| |
| if(adj_rhs) |
| { |
| permute(rhs_shape, PermutationVector(1U, 0U)); |
| } |
| |
| const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); |
| const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); |
| TensorInfo output_info; |
| |
| MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; |
| |
| Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| 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 TensorShape shape = TensorShape(10U, 10U); |
| const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; |
| for(auto &tuple : data_type_configurations) |
| { |
| const bool expected = std::get<3>(tuple); |
| |
| const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); |
| const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); |
| TensorInfo output_info(shape, 1, std::get<2>(tuple)); |
| |
| Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); |
| ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); |
| } |
| } |
| |
| TEST_SUITE_END() // Validate |
| |
| TEST_SUITE(Float) |
| TEST_SUITE(FP32) |
| TEST_SUITE(Buffer) |
| FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), |
| framework::dataset::make("TransposeA", { false, true })), |
| framework::dataset::make("TransposeB", { false, 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, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| framework::dataset::make("TransposeA", { false, true })), |
| framework::dataset::make("TransposeB", { false, 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(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { false })), |
| framework::dataset::make("TransposeB", { false })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_nt_rhs_nt), |
| 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, CLMatMulKernelFixture<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); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { true })), |
| framework::dataset::make("TransposeB", { false })), |
| m0_values_nightly_lhs_t), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_t_rhs_nt), |
| 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(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { true })), |
| framework::dataset::make("TransposeB", { true })), |
| m0_values_nightly_lhs_t), |
| 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 |
| // It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels |
| FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), |
| framework::dataset::make("TransposeA", { false, true })), |
| framework::dataset::make("TransposeB", { false, 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); |
| } |
| TEST_SUITE_END() // Buffer |
| |
| TEST_SUITE(ExportRhsToCLImage) |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { false })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 4, 8, 16 })), |
| framework::dataset::make("K0", { 2, 4 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { false })), |
| framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| framework::dataset::make("N0", { 4, 8, 16 })), |
| framework::dataset::make("K0", { 1, 2, 3, 4 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { true })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 2, 4 })), |
| framework::dataset::make("K0", { 4, 8, 16 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { true })), |
| framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| framework::dataset::make("N0", { 1, 2, 3, 4 })), |
| framework::dataset::make("K0", { 4, 8, 16 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| } |
| TEST_SUITE_END() // ExportRhsToCLImage |
| TEST_SUITE_END() // FP32 |
| |
| TEST_SUITE(FP16) |
| TEST_SUITE(Buffer) |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), |
| framework::dataset::make("TransposeA", { false, true })), |
| framework::dataset::make("TransposeB", { false, 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(RunLargeNoTranspose, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { false })), |
| framework::dataset::make("TransposeB", { false })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_nt_rhs_nt), |
| 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, CLMatMulKernelFixture<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); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { true })), |
| framework::dataset::make("TransposeB", { false })), |
| m0_values_nightly_lhs_t), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_t_rhs_nt), |
| 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(RunLargeLhsTransposedRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), |
| framework::dataset::make("TransposeA", { true })), |
| framework::dataset::make("TransposeB", { true })), |
| m0_values_nightly_lhs_t), |
| 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); |
| } |
| TEST_SUITE_END() // Buffer |
| |
| TEST_SUITE(ExportRhsToCLImage) |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsNT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { false })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 4, 8, 16 })), |
| framework::dataset::make("K0", { 2, 4 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsNotTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsNT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { false })), |
| framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| framework::dataset::make("N0", { 4, 8, 16 })), |
| framework::dataset::make("K0", { 1, 2, 3, 4 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::ALL, |
| combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDatasetRhsExportToCLImageRhsT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { true })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 2, 4 })), |
| framework::dataset::make("K0", { 4, 8, 16 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLMatMulKernelFixture<half>, framework::DatasetMode::NIGHTLY, |
| combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDatasetRhsExportToCLImageRhsT(), |
| framework::dataset::make("TransposeA", { true, false })), |
| framework::dataset::make("TransposeB", { true })), |
| framework::dataset::make("M0", { 2 })), // Choices of M0 does not matter much because it's related to Lhs tensor |
| framework::dataset::make("N0", { 1, 2, 3, 4 })), |
| framework::dataset::make("K0", { 4, 8, 16 })), |
| framework::dataset::make("ExportRhsToCLImage", { true })), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| if(_device_supports_export_to_cl_image) |
| { |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| } |
| TEST_SUITE_END() // ExportRhsToCLImage |
| TEST_SUITE_END() // FP16 |
| TEST_SUITE_END() // Float |
| TEST_SUITE_END() // MatMulKernel |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |