| /* |
| * 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/ClNativeMatMulKernel.h" |
| #include "tests/datasets/LargeBatchMatMulDataset.h" |
| #include "tests/datasets/SmallBatchMatMulDataset.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/BatchMatMulFixture.h" |
| |
| 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 }); // To be enabled |
| |
| /** 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_lhs_nt_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 }); // To be enabled |
| |
| template <typename T> |
| using CLBatchMatMulFixture = BatchMatMulValidationFixture<T>; |
| |
| TEST_SUITE(CL) |
| TEST_SUITE(BatchMatMul) |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( |
| framework::dataset::make("LhsInfo", |
| { |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::S32), // Unsupported data type |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), |
| }), |
| framework::dataset::make("RhsInfo", |
| { |
| TensorInfo(TensorShape(8U, 27U), 1, DataType::S32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), |
| })), |
| framework::dataset::make("OutputInfo", |
| { |
| TensorInfo(TensorShape(8U, 13U), 1, DataType::S32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), |
| })), |
| framework::dataset::make("MatMulInfo", |
| { |
| MatMulKernelInfo(false, false, 2, 2, 2, false), MatMulKernelInfo(false, false, 2, 2, 2, false), MatMulKernelInfo(false, false, 9, 2, 2, false), MatMulKernelInfo(false, false, 0, 2, 2, false), // M0 cannot be < 1 |
| MatMulKernelInfo(false, true, 4, 5, 2, false), // For LHS NT RHS NT: N0 cannot be 5 |
| MatMulKernelInfo(false, true, 4, 6, 2, false), // For LHS NT RHS NT: N0 cannot be 6 |
| MatMulKernelInfo(false, true, 4, 9, 2, false), // For LHS NT RHS NT: N0 cannot be 9 |
| MatMulKernelInfo(false, true, 4, 10, 2, false), // For LHS NT RHS NT: N0 cannot be 10 |
| MatMulKernelInfo(false, true, 4, 11, 2, false), // For LHS NT RHS NT: N0 cannot be 11 |
| MatMulKernelInfo(false, true, 4, 17, 2, false), // For LHS NT RHS NT: N0 cannot be 17 |
| })), |
| framework::dataset::make("Expected", { false, true, true, false, false, false, false, false, false, false })), |
| lhs_info, rhs_info, output_info, matmul_info, expected) |
| { |
| bool is_valid = bool(ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_info)); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| TEST_SUITE(Float) |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmallNoTranspose, CLBatchMatMulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { false })), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLBatchMatMulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { true })), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLBatchMatMulFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { false })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_nt_rhs_nt), |
| 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(RunHighDimNoTranspose, CLBatchMatMulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::HighDimensionalBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { false })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 2 })), |
| framework::dataset::make("K0", { 2 })), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLBatchMatMulFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { true })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_t), |
| k0_values_nightly_lhs_nt_rhs_t), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunHighDimRhsTransposed, CLBatchMatMulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::HighDimensionalBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { true })), |
| framework::dataset::make("M0", { 2 })), |
| framework::dataset::make("N0", { 2 })), |
| framework::dataset::make("K0", { 2 })), |
| 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(RunSmallNoTranspose, CLBatchMatMulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { false })), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLBatchMatMulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { true })), |
| m0_values_precommit), |
| n0_values_precommit), |
| k0_values_precommit), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLBatchMatMulFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { false })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_nt), |
| k0_values_nightly_lhs_nt_rhs_nt), |
| framework::dataset::make("DataType", DataType::F16))) |
| { |
| // Validate output |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLBatchMatMulFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), |
| framework::dataset::make("pretransose_A", { false })), |
| framework::dataset::make("pretransose_B", { true })), |
| m0_values_nightly_lhs_nt), |
| n0_values_nightly_rhs_t), |
| k0_values_nightly_lhs_nt_rhs_t), |
| 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() // BatchMatMul |
| TEST_SUITE_END() // CL |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |