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Diffstat (limited to 'tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp')
| -rw-r--r-- | tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp | 335 |
1 files changed, 335 insertions, 0 deletions
diff --git a/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp new file mode 100644 index 0000000000..82d66ca6ce --- /dev/null +++ b/tests/validation/dynamic_fusion/gpu/cl/MatMul.cpp @@ -0,0 +1,335 @@ +/* + * Copyright (c) 2023-2024 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 "tests/AssetsLibrary.h" +#include "tests/CL/CLAccessor.h" +#include "tests/datasets/LargeMatMulDataset.h" +#include "tests/datasets/MatMulDataset.h" +#include "tests/datasets/SmallMatMulDataset.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> + +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( + 0.02)); /**< 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_lhs_nt_precommit = framework::dataset::make("M0", {1, 2, 3}); + +/** N0 values to test - precommit */ +const auto n0_values_rhs_t_precommit = framework::dataset::make("N0", {1, 2, 4}); + +/** K0 values to test - precommit */ +const auto k0_values_rhs_t_precommit = framework::dataset::make("K0", {1, 2, 4}); + +/** M0 values to test - nightly */ +const auto m0_values_lhs_nt_nightly = framework::dataset::make("M0", {1, 2, 3, 4}); + +/** N0 values to test - nightly */ +const auto n0_values_rhs_t_nightly = framework::dataset::make("N0", {1, 2, 3, 4, 8}); + +/** K0 values to test - nightly */ +const auto k0_values_rhs_t_nightly = framework::dataset::make("K0", {1, 2, 3, 4, 8}); + +class DFMatMulDataset final : public datasets::MatMulDataset +{ +public: + DFMatMulDataset() + { + // LHS = [K, M], RHS = [N, K], DST = [N, M] + add_config(TensorShape(1U, 1U), TensorShape(1U, 1U), TensorShape(1U, 1U)); + add_config(TensorShape(1U, 2U), TensorShape(2U, 1U), TensorShape(2U, 2U)); + add_config(TensorShape(9U, 6U), TensorShape(5U, 9U), TensorShape(5U, 6U)); + add_config(TensorShape(32U, 37U), TensorShape(17U, 32U), TensorShape(17U, 37U)); + } +}; + +TEST_SUITE(CL) +TEST_SUITE(DYNAMIC_FUSION) + +TEST_SUITE(MatMul) + +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 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}, + + }; + + // Create a new workload sketch + auto cl_compile_ctx = CLKernelLibrary::get().get_compile_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 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) + { + MatMulAttributes matmul_attr{}; + matmul_attr.adj_lhs(pair.first.adj_lhs); + matmul_attr.adj_rhs(pair.first.adj_rhs); + + 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); + ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); + } +} + +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}; + + // 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}) + { + for (bool adj_rhs : {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 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{}; + matmul_attr.adj_lhs(adj_lhs); + matmul_attr.adj_rhs(adj_rhs); + + 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); + 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 + }; + // Create a sketch + auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context(); + auto context = GpuWorkloadContext{&cl_compile_ctx}; + GpuWorkloadSketch sketch{&context}; + + const TensorShape shape = TensorShape(10U, 10U); + MatMulAttributes matmul_attr{}; + matmul_attr.adj_lhs(false); + matmul_attr.adj_rhs(false); + GpuMatMulSettings matmul_settings{}; + matmul_settings.m0(1); + matmul_settings.n0(1); + matmul_settings.k0(1); + + for (auto &tuple : data_type_configurations) + { + const bool expected = std::get<3>(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); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); + } +} + +TEST_SUITE_END() // Validate + +template <typename T> +using DynamicFusionGpuMatmulFixture = DynamicFusionGpuMatMulValidationFixture<CLTensor, CLAccessor, GpuMatMul, T>; + +TEST_SUITE(Float) +TEST_SUITE(FP32) + +FIXTURE_DATA_TEST_CASE(RunPrecommit, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::ALL, + combine(DFMatMulDataset(), + framework::dataset::make("TransposeA", {false}), + framework::dataset::make("TransposeB", {true}), + m0_values_lhs_nt_precommit, + n0_values_rhs_t_precommit, + k0_values_rhs_t_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(RunNightly, + DynamicFusionGpuMatmulFixture<float>, + framework::DatasetMode::NIGHTLY, + combine(DFMatMulDataset(), + framework::dataset::make("TransposeA", {false}), + framework::dataset::make("TransposeB", {true}), + m0_values_lhs_nt_nightly, + n0_values_rhs_t_nightly, + k0_values_rhs_t_nightly, + 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() // FP32 + +TEST_SUITE(FP16) + +FIXTURE_DATA_TEST_CASE(RunPrecommit, + DynamicFusionGpuMatmulFixture<half>, + framework::DatasetMode::ALL, + combine(DFMatMulDataset(), + framework::dataset::make("TransposeA", {false}), + framework::dataset::make("TransposeB", {true}), + m0_values_lhs_nt_precommit, + n0_values_rhs_t_precommit, + k0_values_rhs_t_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(RunNightly, + DynamicFusionGpuMatmulFixture<half>, + framework::DatasetMode::NIGHTLY, + combine(DFMatMulDataset(), + framework::dataset::make("TransposeA", {false}), + framework::dataset::make("TransposeB", {true}), + m0_values_lhs_nt_nightly, + n0_values_rhs_t_nightly, + k0_values_rhs_t_nightly, + 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() // FP16 + +TEST_SUITE_END() // Float +TEST_SUITE_END() // MatMul +TEST_SUITE_END() // DYNAMIC_FUSION +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute |