| /* |
| * 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 |