diff options
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/MatMulNativeMMULKernel.cpp | 348 | ||||
-rw-r--r-- | tests/validation/fixtures/MatMulKernelFixture.h | 21 |
2 files changed, 365 insertions, 4 deletions
diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp new file mode 100644 index 0000000000..b33a4fae89 --- /dev/null +++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp @@ -0,0 +1,348 @@ +/* + * 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/ClMatMulNativeMMULKernel.h" +#include "tests/datasets/LargeMatMulMMULDataset.h" +#include "tests/datasets/SmallMatMulMMULDataset.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 }); + +/** M0 values to test --nightly*/ +const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 }); + +/** N0 values to test --nightly*/ +const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 }); + +/** K0 value -- Fixed to 1 */ +const auto k0_value = framework::dataset::make("K0", { 1 }); + +template <typename T> +using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>; + +TEST_SUITE(CL) +TEST_SUITE(MatMulNativeMMULKernel) +TEST_SUITE(Validate) + +TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) +{ + if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())) + { + 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, 4), false }, // K0 not 1 + { MatMulKernelInfo(false, false, 9, 1, 2), true }, + { MatMulKernelInfo(false, false, 3, 16, 3), true }, + { MatMulKernelInfo(false, false, 7, 3, 4), true }, + + // Lhs not-transposed, Rhs transposed + // TODO: COMPMID-6195 + + // Lhs transposed, Rhs-not-transposed + // TODO: COMPMID-6196 + + // Lhs transposed, Rhs-transposed + // TODO: COMPMID-6197 + }; + + // 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); + + for(auto &pair : supported_block_sizes) + { + TensorInfo output_info; + Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); + } + } + else + { + ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} + +TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) +{ + if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())) + { + // 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(4U, 1U), TensorShape(3U, 4U), true }, + { TensorShape(12U, 12U), TensorShape(3U, 12U), true }, + { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, + { TensorShape(8U, 4U), TensorShape(2U, 4U), false }, // Mismatch in the K dimension + { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension + { TensorShape(5U, 7U), TensorShape(2U, 5U), false }, // K not a multiple of 4 (MMUL_K0) + { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 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 // TODO: COMPMID-6195, COMPMID-6196, COMPMID-6197 + }) + { + for(bool adj_rhs : + { + false // TODO: COMPMID-6195, COMPMID-6196, COMPMID-6197 + }) + { + 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 = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); + } + } + } + } + else + { + ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} + +TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) +{ + if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())) + { + // 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(8U, 8U); + 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 = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); + } + } + else + { + ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + } +} + +TEST_SUITE_END() // Validate + +TEST_SUITE(Float) +TEST_SUITE(FP32) +TEST_SUITE(Buffer) +FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + m0_values_precommit), + n0_values_precommit), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); + } +} +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + m0_values_precommit), + n0_values_precommit), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); + } +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_nt), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + if(_device_supports_mmul) + { + 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, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + framework::dataset::make("M0", { 2 })), + framework::dataset::make("N0", { 2 })), + framework::dataset::make("K0", { 1 })), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); + } +} +TEST_SUITE_END() // Buffer + +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +TEST_SUITE(Buffer) +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + m0_values_precommit), + n0_values_precommit), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); + } +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { false })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_nt), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); + } +} +TEST_SUITE_END() // Buffer + +TEST_SUITE_END() // FP16 +TEST_SUITE_END() // Float +TEST_SUITE_END() // MatMulNativeMMULKernel +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h index 7d0b1a40a9..59bcfe5b2d 100644 --- a/tests/validation/fixtures/MatMulKernelFixture.h +++ b/tests/validation/fixtures/MatMulKernelFixture.h @@ -47,7 +47,7 @@ namespace validation { using namespace arm_compute::opencl::kernels; -template <typename T, typename KernelType> +template <typename T, typename KernelType, bool use_mmul = false> class MatMulKernelValidationFixture : public framework::Fixture { public: @@ -94,13 +94,25 @@ public: permute(shape_b, PermutationVector(1U, 0U)); } + // Skip configurations unsupported by the device. _device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); + if(!_device_supports_export_to_cl_image && export_rhs_to_cl_image) + { + ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs. + } - if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image) + _device_supports_mmul = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()); + if(!_device_supports_mmul && use_mmul) { - _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info); - _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info); + ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped"); + framework::ARM_COMPUTE_PRINT_INFO(); + return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs. } + + _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info); + _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info); } protected: @@ -274,6 +286,7 @@ protected: CLTensor _target{}; SimpleTensor<T> _reference{}; bool _device_supports_export_to_cl_image{ true }; + bool _device_supports_mmul{ true }; }; } // namespace validation |