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author | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-13 11:59:34 +0100 |
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committer | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-18 13:51:15 +0000 |
commit | a116cd3676796412cd4d9318a6cc1c1eef4c093c (patch) | |
tree | 21788d6776e7a0808d0f6d6c1bef452cfb2c7f27 /tests/validation | |
parent | 40a9d3ea62d7dfed3fb42b5bc5c2ee5272fd89bf (diff) | |
download | ComputeLibrary-a116cd3676796412cd4d9318a6cc1c1eef4c093c.tar.gz |
Implement Quantized MatMul kernel using MMUL extension
Resolves: COMPMID-6475
Change-Id: Ic867cdfff5d4391cb749a04bf7cc35cda63d3b71
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10311
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')
-rw-r--r-- | tests/validation/CL/MatMulLowpNativeMMULKernel.cpp | 188 |
1 files changed, 164 insertions, 24 deletions
diff --git a/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp index 10d893e5c4..a361a5af16 100644 --- a/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp +++ b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp @@ -26,8 +26,7 @@ #include "src/gpu/cl/kernels/ClMatMulLowpNativeMMULKernel.h" -#include "tests/datasets/LargeMatMulDataset.h" -#include "tests/datasets/SmallMatMulDataset.h" +#include "tests/datasets/MatMulLowpMMULDataset.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" #include "tests/validation/Validation.h" @@ -44,14 +43,27 @@ namespace validation { namespace { -// TODO: enable -// constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ +constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ } +using framework::dataset::make; + template <typename T> -using CLMatMulLowpNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeMMULKernel>; +using CLMatMulLowpNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeMMULKernel, true /* use_mmul */>; template <typename T> -using CLMatMulLowpKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeMMULKernel>; +using CLMatMulLowpNativeMMULKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeMMULKernel, true /* use_mmul */>; + +/** 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", { 2, 4, 5, 8 }); + +/** N0 values to test --nightly*/ +const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 3, 8, 16 }); TEST_SUITE(CL) TEST_SUITE(MatMulLowpNativeMMULKernel) @@ -77,9 +89,10 @@ TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL) for(auto &pair : supported_block_sizes) { TensorInfo output_info; - Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first); + Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first); + const bool expected = (pair.second && arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())); - ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -89,21 +102,22 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; const std::vector<ShapeConfigurationTuple> shape_configurations = { - { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true }, - { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension - { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension - { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true }, - { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting - { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension - { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false }, // invalid broadcast of bias - { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2d bias is invalid + { TensorShape(32U, 1U), TensorShape(3U, 32U), TensorShape(3U), true }, + { TensorShape(16U, 12U), TensorShape(3U, 16U), TensorShape(3U), true }, + { TensorShape(64U, 4U), TensorShape(2U, 64U), TensorShape(2U), true }, + { TensorShape(16U, 4U), TensorShape(2U, 32U), TensorShape(2U), false }, // Mismatch in the K dimension + { TensorShape(16U, 0U), TensorShape(2U, 16U), TensorShape(2U), false }, // Invalid dimension + { TensorShape(32U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 32U, 3U, 4U, 5U, 6U), TensorShape(2U), true }, + { TensorShape(32U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 32U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting + { TensorShape(32U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 32U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension + { TensorShape(32U, 1U), TensorShape(3U, 32U), TensorShape(1U), false }, // invalid broadcast of bias + { TensorShape(32U, 1U), TensorShape(3U, 32U), TensorShape(3U, 3U), false }, // 2d bias is invalid + { TensorShape(12U, 12U), TensorShape(3U, 12U), TensorShape(3U), false }, // K must be multiple of 16 }; for(auto &tuple : shape_configurations) { - const bool expected = std::get<3>(tuple); + const bool expected = (std::get<3>(tuple) && arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())); for(bool adj_lhs : { @@ -134,7 +148,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::S32); TensorInfo output_info; - MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; + MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 4, false /* export_rhs_to_cl_image */ }; Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); @@ -172,10 +186,10 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) // It's enough to test a single shape and block size configuration while checking data types const TensorShape shape = TensorShape(48U, 48U); const TensorShape bia_shape = TensorShape(48U); - const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; + const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 4, false }; for(auto &tuple : data_type_configurations) { - const bool expected = std::get<4>(tuple); + const bool expected = (std::get<4>(tuple) && arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())); const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); @@ -183,6 +197,7 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) TensorInfo output_info(shape, 1, std::get<3>(tuple)); Status status = ClMatMulLowpNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -192,12 +207,137 @@ TEST_SUITE_END() // Validate TEST_SUITE(Quantized) TEST_SUITE(QASYMM8_SIGNED) -// TODO: tests +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_precommit, + n0_values_precommit, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8_SIGNED))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} + +FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulLowpNativeMMULKernelWithBiasFixture<int8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULWithBiasDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_precommit, + n0_values_precommit, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8_SIGNED))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} + +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_nightly_lhs_nt, + n0_values_nightly_rhs_nt, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8_SIGNED))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} + +// Running High Dimensional test is enough for qasymm8_signed, 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, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::ALL, + combine(datasets::HighDimensionalMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + make("M0", { 2 }), + make("N0", { 2 }), + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8_SIGNED))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} TEST_SUITE_END() // QASYMM8_SIGNED + TEST_SUITE(QASYMM8) -// TODO: tests +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeMMULKernelFixture<uint8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULDatasetSubset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_precommit, + n0_values_precommit, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} + +FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulLowpNativeMMULKernelWithBiasFixture<uint8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULWithBiasDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_precommit, + n0_values_precommit, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} + +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeMMULKernelFixture<uint8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false }), + m0_values_nightly_lhs_nt, + n0_values_nightly_rhs_nt, + make("K0", { 4 }), + make("ExportRhsToCLImage", { false }), + make("DataType", DataType::QASYMM8))) +{ + if(_device_supports_mmul) + { + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); + } +} TEST_SUITE_END() // QASYMM8 TEST_SUITE_END() // Quantized |