/* * 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 namespace arm_compute { namespace test { namespace validation { namespace { RelativeTolerance 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 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 using CLMatMulKernelFixture = MatMulKernelValidationFixture; template using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation; TEST_SUITE(CL) TEST_SUITE(MatMulKernel) TEST_SUITE(Validate) TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) { using MatMulConfigurationPair = std::pair; const std::vector 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, nullptr, &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(); const size_t max_image_h = CLKernelLibrary::get().get_device().getInfo(); using ShapeConfigurationTuple = std::tuple; const std::vector 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, nullptr, &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; const std::vector shape_configurations = { { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true }, { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true }, { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true }, { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true }, { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false }, // Unsupported bias broadcasting. { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2D bias is unsupported. { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(6U), false }, // bias first dimension != dst first dimension }; for(auto &tuple : shape_configurations) { const bool expected = std::get<3>(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); TensorShape bia_shape = std::get<2>(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); const TensorInfo bia_info = TensorInfo(bia_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, &bia_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; const std::vector 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, nullptr, &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, 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, 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(RunWithBias, CLMatMulKernelBiasFixture, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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