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Diffstat (limited to 'tests/validation/CL/MatMulLowpNativeMMULKernel.cpp')
| -rw-r--r-- | tests/validation/CL/MatMulLowpNativeMMULKernel.cpp | 394 |
1 files changed, 394 insertions, 0 deletions
diff --git a/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp new file mode 100644 index 0000000000..ac46b67c9e --- /dev/null +++ b/tests/validation/CL/MatMulLowpNativeMMULKernel.cpp @@ -0,0 +1,394 @@ +/* + * 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/ClMatMulLowpNativeMMULKernel.h" + +#include "tests/datasets/MatMulLowpMMULDataset.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/MatMulKernelFixture.h" +#include "tests/validation/reference/Permute.h" + +#include <tuple> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +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, true /* use_mmul */>; + +template <typename T> +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 }); +const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 2, 4, 8 }); + +/** N0 values to test --nightly*/ +const auto n0_values_nightly = framework::dataset::make("N0", { 1, 3, 8, 16 }); + +TEST_SUITE(CL) +TEST_SUITE(MatMulLowpNativeMMULKernel) +TEST_SUITE(Validate) + +TEST_CASE(SupportedKernelConfigurations, 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) + { MatMulKernelInfo(false, false, 0, 1, 4), false }, // M0 should be > 0 + { MatMulKernelInfo(false, true, 3, 5, 4), false }, // N0 not in {1, 2, 3, 4, 8, 16} + { MatMulKernelInfo(false, false, 3, 6, 4), false }, // N0 not in {1, 2, 3, 4, 8, 16} + { MatMulKernelInfo(false, false, 3, 3, 8), false }, // K0 not in 4 + { MatMulKernelInfo(true, false, 5, 3, 4), false }, // M0 not in {1, 2, 3, 4, 8, 16} when Lhs is transposed + { MatMulKernelInfo(false, false, 9, 1, 4), true }, + { MatMulKernelInfo(false, true, 3, 16, 4), true }, + { MatMulKernelInfo(false, false, 7, 3, 4), true }, + { MatMulKernelInfo(true, false, 8, 3, 4), true }, + { MatMulKernelInfo(true, true, 4, 3, 4), true }, + { MatMulKernelInfo(false, false, 7, 3, 4, true), false }, // export to CLImage is unsupported for quantized types + }; + + // 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 TensorInfo lhs_info = TensorInfo(TensorShape(64U, 64U), 1, DataType::QASYMM8_SIGNED); + const TensorInfo rhs_info = TensorInfo(TensorShape(64U, 64U), 1, DataType::QASYMM8_SIGNED); + + for(auto &pair : supported_block_sizes) + { + TensorInfo output_info; + 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) == expected, framework::LogLevel::ERRORS); + } +} + +TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) +{ + // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations + using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; + const std::vector<ShapeConfigurationTuple> shape_configurations = + { + { 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) && arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())); + + for(bool adj_lhs : + { + false, true + }) + { + for(bool adj_rhs : + { + false, true + }) + { + TensorShape lhs_shape = std::get<0>(tuple); + TensorShape rhs_shape = std::get<1>(tuple); + TensorShape bia_shape = std::get<2>(tuple); + + if(adj_lhs) + { + permute(lhs_shape, PermutationVector(1U, 0U)); + } + + if(adj_rhs) + { + permute(rhs_shape, PermutationVector(1U, 0U)); + } + + const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::QASYMM8_SIGNED); + const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED); + const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::S32); + TensorInfo output_info; + + 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); + } + } + } +} + +TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) +{ + using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>; + const std::vector<DataTypeConfigurationTuple> data_type_configurations = + { + { DataType::F32, DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types + { DataType::F16, DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types + { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision + { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, true }, + { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, true }, + { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported + { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported + { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported + { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported + { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8, false }, // no mixed data types + { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false }, // no integral types + { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false }, // no integral types + { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false }, // no integral types + { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false }, // no integral types + { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false }, // no integral types + { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false }, // no integral types + { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false }, // no integral types + { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false }, // no integral types + { DataType::QASYMM8, DataType::QASYMM8, DataType::F32, DataType::QASYMM8, false } // Only S32 bias is supported + }; + + // 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, 4, false }; + for(auto &tuple : data_type_configurations) + { + 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)); + const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple)); + 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); + } +} + +TEST_SUITE_END() // Validate + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8_SIGNED) + +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULDataset(), + make("TransposeA", { false, true }), + make("TransposeB", { false, true }), + 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, true }), + make("TransposeB", { false, true }), + 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(RunLargeLhsNotTransposed, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false, true }), + m0_values_nightly_lhs_nt, + n0_values_nightly, + 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(RunLargeLhsTransposed, CLMatMulLowpNativeMMULKernelFixture<int8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { true }), + make("TransposeB", { false, true }), + m0_values_nightly_lhs_t, + n0_values_nightly, + 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, true }), + make("TransposeB", { false, true }), + 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) + +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeMMULKernelFixture<uint8_t>, + framework::DatasetMode::ALL, + combine(datasets::SmallMatMulLowpMMULDatasetSubset(), + make("TransposeA", { false, true }), + make("TransposeB", { false, true }), + 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, true }), + make("TransposeB", { false, true }), + 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(RunLargeLhsNotTransposed, CLMatMulLowpNativeMMULKernelFixture<uint8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { false }), + make("TransposeB", { false, true }), + m0_values_nightly_lhs_nt, + n0_values_nightly, + 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(RunLargeLhsTransposed, CLMatMulLowpNativeMMULKernelFixture<uint8_t>, + framework::DatasetMode::NIGHTLY, + combine(datasets::LargeMatMulLowpMMULDataset(), + make("TransposeA", { true }), + make("TransposeB", { false, true }), + m0_values_nightly_lhs_t, + n0_values_nightly, + 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 +TEST_SUITE_END() // MatMulLowpNativeMMULKernel +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute |