| /* |
| * 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 |