diff options
Diffstat (limited to 'tests')
-rw-r--r-- | tests/validation/CL/MatMulKernel.cpp | 49 | ||||
-rw-r--r-- | tests/validation/CL/MatMulLowpNativeKernel.cpp | 92 | ||||
-rw-r--r-- | tests/validation/CL/MatMulNativeMMULKernel.cpp | 154 | ||||
-rw-r--r-- | tests/validation/fixtures/MatMulKernelFixture.h | 103 |
4 files changed, 279 insertions, 119 deletions
diff --git a/tests/validation/CL/MatMulKernel.cpp b/tests/validation/CL/MatMulKernel.cpp index ff872aaa0a..b47f8bc924 100644 --- a/tests/validation/CL/MatMulKernel.cpp +++ b/tests/validation/CL/MatMulKernel.cpp @@ -75,6 +75,9 @@ const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, template <typename T> using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>; +template <typename T> +using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeKernel>; + TEST_SUITE(CL) TEST_SUITE(MatMulKernel) TEST_SUITE(Validate) @@ -162,7 +165,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) for(auto &pair : supported_block_sizes) { TensorInfo output_info; - Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); + Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first); if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported) { @@ -222,7 +225,7 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL) }; TensorInfo output_info; - Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info); const bool expected = std::get<4>(tuple); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); @@ -233,22 +236,25 @@ TEST_CASE(ExportToCLImage, framework::DatasetMode::ALL) 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, bool>; + using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; const std::vector<ShapeConfigurationTuple> shape_configurations = { - { TensorShape(5U, 1U), TensorShape(3U, 5U), true }, - { TensorShape(10U, 12U), TensorShape(3U, 10U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension - { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension - { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true }, - { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting - { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension + { 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 }, // Unsupported bias broadcasting. + { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2D bias is unsupported. + { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(6U), false }, // bias first dimension != dst first dimension }; for(auto &tuple : shape_configurations) { - const bool expected = std::get<2>(tuple); + const bool expected = std::get<3>(tuple); for(bool adj_lhs : { @@ -262,6 +268,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) { TensorShape lhs_shape = std::get<0>(tuple); TensorShape rhs_shape = std::get<1>(tuple); + TensorShape bia_shape = std::get<2>(tuple); if(adj_lhs) { @@ -275,11 +282,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); + const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32); TensorInfo output_info; MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; - Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -322,7 +330,7 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); TensorInfo output_info(shape, 1, std::get<2>(tuple)); - Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -356,6 +364,19 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulKernelFixture<float>, framework::Datase // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } +FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) + +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), framework::dataset::make("TransposeA", { false })), framework::dataset::make("TransposeB", { false })), diff --git a/tests/validation/CL/MatMulLowpNativeKernel.cpp b/tests/validation/CL/MatMulLowpNativeKernel.cpp index fd7a4cb156..90eee4fb82 100644 --- a/tests/validation/CL/MatMulLowpNativeKernel.cpp +++ b/tests/validation/CL/MatMulLowpNativeKernel.cpp @@ -49,6 +49,9 @@ constexpr AbsoluteTolerance<float> tolerance_quant(1); /**< Tolerance value for template <typename T> using CLMatMulLowpNativeKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeKernel>; +template <typename T> +using CLMatMulLowpKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeKernel>; + /** M0 values to test --precommit*/ const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 }); @@ -103,7 +106,7 @@ TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL) for(auto &pair : supported_block_sizes) { TensorInfo output_info; - Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); + Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first); ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); } @@ -112,22 +115,24 @@ TEST_CASE(SupportedKernelConfigurations, framework::DatasetMode::ALL) 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, bool>; + using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; const std::vector<ShapeConfigurationTuple> shape_configurations = { - { TensorShape(5U, 1U), TensorShape(3U, 5U), true }, - { TensorShape(10U, 12U), TensorShape(3U, 10U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension - { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension - { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true }, - { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting - { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension + { 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 }; for(auto &tuple : shape_configurations) { - const bool expected = std::get<2>(tuple); + const bool expected = std::get<3>(tuple); for(bool adj_lhs : { @@ -141,6 +146,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) { TensorShape lhs_shape = std::get<0>(tuple); TensorShape rhs_shape = std::get<1>(tuple); + TensorShape bia_shape = std::get<2>(tuple); if(adj_lhs) { @@ -154,11 +160,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) 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, 1, false /* export_rhs_to_cl_image */ }; - Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -167,41 +174,44 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) { - using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>; + using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>; const std::vector<DataTypeConfigurationTuple> data_type_configurations = { - { DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types - { DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types - { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision - { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, true }, - { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, true }, - { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported - { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported - { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported - { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported - { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM8, false }, // no mixed data types - { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types - { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types - { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types - { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types - { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types - { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types - { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types - { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types + { 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(10U, 10U); + const TensorShape shape = TensorShape(10U, 10U); + const TensorShape bia_shape = TensorShape(10U); const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; for(auto &tuple : data_type_configurations) { - const bool expected = std::get<3>(tuple); + const bool expected = std::get<4>(tuple); const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); - TensorInfo output_info(shape, 1, std::get<2>(tuple)); + const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple)); + TensorInfo output_info(shape, 1, std::get<3>(tuple)); - Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -234,6 +244,18 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture<int8_t>, framew // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); } +FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulLowpKernelWithBiasFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { true, false })), + framework::dataset::make("TransposeB", { true, false })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), framework::dataset::make("TransposeA", { false })), diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp index b63af75169..70c80985db 100644 --- a/tests/validation/CL/MatMulNativeMMULKernel.cpp +++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp @@ -70,6 +70,9 @@ const auto k0_value = framework::dataset::make("K0", { 1 }); template <typename T> using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>; +template <typename T> +using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>; + TEST_SUITE(CL) TEST_SUITE(MatMulNativeMMULKernel) TEST_SUITE(Validate) @@ -117,7 +120,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) { MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16} { MatMulKernelInfo(true, true, 6, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16} { MatMulKernelInfo(true, true, 5, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16} - { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1 + { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1 { MatMulKernelInfo(true, true, 4, 8, 1), true }, { MatMulKernelInfo(true, true, 3, 3, 1), true }, { MatMulKernelInfo(true, true, 16, 4, 1), true }, @@ -132,7 +135,7 @@ TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL) for(auto &pair : supported_block_sizes) { TensorInfo output_info; - Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first); + Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first); ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS); } } @@ -148,28 +151,30 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())) { // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations - using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>; + using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; // lhs, rhs, bias, result const std::vector<ShapeConfigurationTuple> shape_configurations = { - { TensorShape(4U, 1U), TensorShape(3U, 4U), true }, - { TensorShape(12U, 12U), TensorShape(3U, 12U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 8U), true }, - { TensorShape(8U, 4U), TensorShape(2U, 4U), false }, // Mismatch in the K dimension - { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension - { TensorShape(5U, 7U), TensorShape(2U, 5U), false }, // K not a multiple of 4 (MMUL_K0) - { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), true }, - { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // No batch broadcasting - { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // Mismatch in batch dimension + { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(3U), true }, + { TensorShape(12U, 12U), TensorShape(3U, 12U), TensorShape(3U), true }, + { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true }, + { TensorShape(8U, 4U), TensorShape(2U, 4U), TensorShape(2U), false }, // Mismatch in the K dimension + { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension + { TensorShape(5U, 7U), TensorShape(2U, 5U), TensorShape(2U), false }, // K not a multiple of 4 (MMUL_K0) + { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 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(4U, 1U), TensorShape(3U, 4U), TensorShape(1U), false }, // Bias first dimensions != dst first dimension. + { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(5U, 6U), false }, // Bias is 2d which is invalid. }; for(auto &tuple : shape_configurations) { - const bool expected = std::get<2>(tuple); + const bool expected = std::get<3>(tuple); - for(bool adj_lhs : - { - false, true - }) + for(bool adj_lhs : + { + false, true + }) { for(bool adj_rhs : { @@ -178,6 +183,7 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) { TensorShape lhs_shape = std::get<0>(tuple); TensorShape rhs_shape = std::get<1>(tuple); + TensorShape bia_shape = std::get<2>(tuple); if(adj_lhs) { @@ -191,11 +197,12 @@ TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL) const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32); const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32); + const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32); TensorInfo output_info; MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ }; - Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -213,40 +220,44 @@ TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL) if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device())) { // Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations - using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>; + using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>; const std::vector<DataTypeConfigurationTuple> data_type_configurations = { - { DataType::F32, DataType::F32, DataType::F32, true }, - { DataType::F16, DataType::F16, DataType::F16, true }, - { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision - { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision - { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types - { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types - { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types - { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types - { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types - { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types - { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types - { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types - { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types - { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types - { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types - { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types - { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types - { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types + { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true }, + { DataType::F16, DataType::F16, DataType::F16, DataType::F16, true }, + { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true }, + { DataType::F32, DataType::F32, DataType::F16, DataType::F32, false }, // incorrect bias type + { DataType::F16, DataType::F32, DataType::F32, DataType::F32, false }, // no mixed precision + { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision + { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, false }, // no quantized types + { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, false }, // no quantized types + { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types + { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // no quantized types + { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // no quantized types + { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // no quantized 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 }; - const TensorShape shape = TensorShape(8U, 8U); + const TensorShape shape = TensorShape(8U, 8U); + const TensorShape bia_shape = TensorShape(8U); const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false }; for(auto &tuple : data_type_configurations) { - const bool expected = std::get<3>(tuple); + const bool expected = std::get<4>(tuple); const TensorInfo lhs_info(shape, 1, std::get<0>(tuple)); const TensorInfo rhs_info(shape, 1, std::get<1>(tuple)); - TensorInfo output_info(shape, 1, std::get<2>(tuple)); + const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple)); + TensorInfo output_info(shape, 1, std::get<3>(tuple)); - Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info); + Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info); ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); } } @@ -292,7 +303,23 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<float>, framewo validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } } -FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), +FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + m0_values_precommit), + n0_values_precommit), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + if(_device_supports_mmul) + { + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); + } +} +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), framework::dataset::make("TransposeA", { false })), framework::dataset::make("TransposeB", { false })), m0_values_nightly_lhs_nt), @@ -308,7 +335,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<floa } } -FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), +FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), framework::dataset::make("TransposeA", { false })), framework::dataset::make("TransposeB", { true })), m0_values_nightly_lhs_nt), @@ -323,14 +351,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<flo validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } } -FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), - framework::dataset::make("TransposeA", { true })), - framework::dataset::make("TransposeB", { false })), - m0_values_nightly_lhs_t), - n0_values_nightly_rhs_nt), - k0_value), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { false })), + m0_values_nightly_lhs_t), + n0_values_nightly_rhs_nt), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F32))) { // Validate output // Validate output @@ -395,7 +424,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<half>, framewor validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); } } -FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), framework::dataset::make("TransposeA", { false })), framework::dataset::make("TransposeB", { false })), m0_values_nightly_lhs_nt), @@ -410,7 +440,8 @@ FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); } } -FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), +FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), framework::dataset::make("TransposeA", { false })), framework::dataset::make("TransposeB", { true })), m0_values_nightly_lhs_nt), @@ -425,14 +456,15 @@ FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<hal validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); } } -FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), - framework::dataset::make("TransposeA", { true })), - framework::dataset::make("TransposeB", { false })), - m0_values_nightly_lhs_t), - n0_values_nightly_rhs_nt), - k0_value), - framework::dataset::make("ExportRhsToCLImage", { false })), - framework::dataset::make("DataType", DataType::F16))) +FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { false })), + m0_values_nightly_lhs_t), + n0_values_nightly_rhs_nt), + k0_value), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::F16))) { // Validate output // Validate output diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h index 59bcfe5b2d..88fdf8b291 100644 --- a/tests/validation/fixtures/MatMulKernelFixture.h +++ b/tests/validation/fixtures/MatMulKernelFixture.h @@ -36,7 +36,7 @@ #include "tests/validation/reference/GEMMLowp.h" #include "tests/validation/reference/Permute.h" #include "tests/validation/reference/ReshapeLayer.h" - +#include <cmath> #include <random> namespace arm_compute @@ -48,12 +48,16 @@ namespace validation using namespace arm_compute::opencl::kernels; template <typename T, typename KernelType, bool use_mmul = false> -class MatMulKernelValidationFixture : public framework::Fixture +class MatMulKernelGenericValidationFixture : public framework::Fixture { public: template <typename...> - void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type, + bool enable_bias) { + // Flag to create a bias + _enable_bias = enable_bias; + // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices. QuantizationInfo lhs_q_info; QuantizationInfo rhs_q_info; @@ -138,6 +142,16 @@ protected: } } + template <typename U> + void fill_bias_s32(U &&tensor, int i, const UniformQuantizationInfo &q_info) + { + // For quantized cases, fill the S32 bias according to the following to avoid saturation of test cases. + // The following code limits size of bias values to within expected range of output quantization. + const unsigned int bound = std::abs(q_info.scale * 256); // 256 is size of 8 bit datatype + std::uniform_int_distribution<int32_t> distribution(-(bound / 10), (bound / 10)); + library->fill(tensor, distribution, i); + } + template <typename U, typename D> void fill_constant(U &&tensor, D value) { @@ -156,12 +170,15 @@ protected: matmul_info.k0 = K0; matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image; + bool is_quantized = is_data_type_quantized(data_type); + // Create tensors - CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info); - CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info); - CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info); + CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info); + CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info); + CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info); + CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info); - matMul.configure(a.info(), b.info(), dst.info(), matmul_info); + matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, dst.info(), matmul_info); ARM_COMPUTE_ASSERT(a.info()->is_resizable()); ARM_COMPUTE_ASSERT(b.info()->is_resizable()); ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); @@ -184,6 +201,22 @@ protected: { ACL_SRC_1, &b }, { ACL_DST, &dst } }); + + if(_enable_bias) + { + // Allocate, fill and add bias to TensorPack obj + bias.allocator()->allocate(); + if(is_quantized) + { + fill_bias_s32(CLAccessor(bias), 2, dst_q_info.uniform()); + } + else + { + fill(CLAccessor(bias), 2); + } + tensors_pack.add_tensor(ACL_SRC_2, &bias); + } + matMul.run(tensors_pack); return dst; @@ -252,9 +285,21 @@ protected: template <typename U = T> typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c) { + // Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast + // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel + if(_enable_bias) + { + fill(c, 2); + const int n = c.shape().x(); + const int other_dims = c.shape().collapsed_from(1)[1]; + for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches + { + memcpy(c.data() + i * n, c.data(), n * sizeof(T)); + } + } // Setting beta to 0 will effectively disable C for the // computation of the reference: alpha * A * B + 0 * C - return reference::gemm<U>(a, b, c, 1.0f, 0.f); + return reference::gemm<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f); } template <typename U = T> @@ -276,19 +321,59 @@ protected: constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max(); SimpleTensor<int> bias{ c.shape(), DataType::S32 }; - fill_constant(bias, static_cast<int32_t>(0)); + if(_enable_bias) + { + // Identical to float implementation, fill and copy values of bias first dimension + fill_bias_s32(bias, 2, cq); + const int n = bias.shape().x(); + const int other_dims = bias.shape().collapsed_from(1)[1]; + const unsigned int dt_size = sizeof(int32_t); + for(int i = 1; i < other_dims; ++i) + { + memcpy(bias.data() + i * n, bias.data(), n * dt_size); + } + } + else + { + fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias + } const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias, gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound); + return final_result; } CLTensor _target{}; SimpleTensor<T> _reference{}; + bool _enable_bias{ false }; bool _device_supports_export_to_cl_image{ true }; bool _device_supports_mmul{ true }; }; +template <typename T, typename KernelType, bool use_mmul = false> +class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul> +{ +public: + template <typename...> + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type) + { + MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, + false /* enable bias */); + } +}; + +template <typename T, typename KernelType, bool use_mmul = false> +class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul> +{ +public: + template <typename...> + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type) + { + MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, + true /* enable bias */); + } +}; } // namespace validation } // namespace test } // namespace arm_compute |