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author | Ramy Elgammal <ramy.elgammal@arm.com> | 2023-03-09 21:15:37 +0000 |
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committer | Gunes Bayir <gunes.bayir@arm.com> | 2023-03-17 11:00:57 +0000 |
commit | 2b6ebfe4270b06b09e45f306e8384950aeca7e4e (patch) | |
tree | 44a65551aa32352a57090bf550b96cbf5b54222f /tests/validation/CL | |
parent | 40aab11ff88ed432d5028478e531f8d0fc404d4c (diff) | |
download | ComputeLibrary-2b6ebfe4270b06b09e45f306e8384950aeca7e4e.tar.gz |
Implement OpenCL MatMul for Lhs NT Rhs T/NT FP32/16
- Implement ClNativeMatMulKernel class
- Implement opencl kernel for LHS non-transposed and RHS non-transposed
- Implement opencl kernel for LHS non-transposed and RHS transposed
- Add test fixture and dataset for matmul
- Implement transpose_tensor() for reference implementation to transpose high dimensional tensors
Resolves: COMPMID-5944, COMPMID-5951
Co-authored-by: Gunes Bayir <gunes.bayir@arm.com>
Co-authored-by: Ramy Elgammal <ramy.elgammal@arm.com>
Change-Id: I1d5b8978f41be27baddb3153ade880472141573f
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9333
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/CL')
-rw-r--r-- | tests/validation/CL/BatchMatMul.cpp | 239 |
1 files changed, 239 insertions, 0 deletions
diff --git a/tests/validation/CL/BatchMatMul.cpp b/tests/validation/CL/BatchMatMul.cpp new file mode 100644 index 0000000000..fd84526000 --- /dev/null +++ b/tests/validation/CL/BatchMatMul.cpp @@ -0,0 +1,239 @@ +/* + * 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 |