diff options
Diffstat (limited to 'tests/validation')
-rw-r--r-- | tests/validation/CL/MatMul.cpp | 182 | ||||
-rw-r--r-- | tests/validation/fixtures/MatMulFixture.h | 60 |
2 files changed, 167 insertions, 75 deletions
diff --git a/tests/validation/CL/MatMul.cpp b/tests/validation/CL/MatMul.cpp index 6364b16200..5a262a8e78 100644 --- a/tests/validation/CL/MatMul.cpp +++ b/tests/validation/CL/MatMul.cpp @@ -26,6 +26,7 @@ #include "arm_compute/runtime/CL/functions/CLMatMul.h" #include "tests/CL/CLAccessor.h" +#include "tests/datasets/ActivationFunctionsDataset.h" #include "tests/framework/DatasetModes.h" #include "tests/framework/Macros.h" #include "tests/framework/TestCase.h" @@ -44,11 +45,13 @@ namespace validation { namespace { -RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for fp32 data type */ -constexpr float abs_tolerance_f32(0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp32 data type 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 type 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 fp16 data type */ -constexpr AbsoluteTolerance<uint8_t> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ +RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for fp32 data type */ +constexpr float abs_tolerance_f32( + 0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp32 data type 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 type 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 fp16 data type */ +constexpr AbsoluteTolerance<uint8_t> tolerance_quant(1); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ } // namespace template <typename T> @@ -57,25 +60,71 @@ using CLMatMulFixture = MatMulValidationFixture<CLTensor, CLAccessor, CLMatMul, template <typename T> using CLQuantizedMatMulFixture = QuantizedMatMulValidationFixture<CLTensor, CLAccessor, CLMatMul, GpuMatMulSettings, T>; +template <typename T> +using CLMatMulActivationFixture = MatMulValidationWithActivationFixture<CLTensor, CLAccessor, CLMatMul, GpuMatMulSettings, T>; + +template <typename T> +using CLMatMulActivationAlphaBetaFixture = MatMulValidationWithActivationAlphaBetaFixture<CLTensor, CLAccessor, CLMatMul, GpuMatMulSettings, T>; + +template <typename T> +using CLQuantizedMatMulActivationFixture = QuantizedMatMulValidationWithActivationFixture<CLTensor, CLAccessor, CLMatMul, GpuMatMulSettings, T>; + +/* The main act functions matmul is expected to support */ +const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +{ + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH) +}); + +const auto ActivationFunctionsQuantizedDataset = concat(concat(concat( + framework::dataset::make("ActivationInfo", ActivationLayerInfo()), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f))), + framework::dataset::make("ActivationInfo", ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.75f, 0.25f))); + +/* Larger activation functions dataset, used during some nightly tests. */ +const auto AllActivationsDataset = combine(datasets::ActivationFunctions(), framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); + +const auto AllQuantizedActivationsDataset = combine(concat(datasets::ActivationFunctionsQuantized(), + framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::HARD_SWISH, + ActivationLayerInfo::ActivationFunction::LEAKY_RELU + })), + framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); + TEST_SUITE(CL) TEST_SUITE(MatMul) TEST_SUITE(Float) TEST_SUITE(FP32) -FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallMatMulDataset(), +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulActivationFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::F32)), + ActivationFunctionsDataset)) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} + +FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulActivationFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeMatMulDataset(), framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::F32))) + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::F32)), + ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::F32))) +FIXTURE_DATA_TEST_CASE(RunAllActivations, CLMatMulActivationAlphaBetaFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallerMatMulDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { true })), + framework::dataset::make("DataType", DataType::F32)), + AllActivationsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); @@ -85,19 +134,21 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) -FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::F16))) +FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulActivationFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::F16)), + ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); } -FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::F16))) +FIXTURE_DATA_TEST_CASE(RunLarge, CLMatMulActivationFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::F16)), + ActivationFunctionsDataset)) { // Validate output validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); @@ -110,32 +161,30 @@ TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) FIXTURE_DATA_TEST_CASE(RunSmall, CLQuantizedMatMulFixture<uint8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine( - datasets::SmallMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), - framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), - framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 50, 1) })), - framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 30, -1) })), - framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })) -) + datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + ActivationFunctionsQuantizedDataset), + framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), + framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 50, 1) })), + framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 30, -1) })), + framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); } FIXTURE_DATA_TEST_CASE(RunLarge, CLQuantizedMatMulFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine( - datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::QASYMM8)), - framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), - framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), - framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })), - framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })), - framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })) -) + datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + ActivationFunctionsQuantizedDataset), + framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), + framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })), + framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })), + framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); @@ -146,32 +195,45 @@ TEST_SUITE_END() // QASYMM8 TEST_SUITE(QASYMM8_SIGNED) FIXTURE_DATA_TEST_CASE(RunSmall, CLQuantizedMatMulFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine( - datasets::SmallMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), - framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), - framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 50, 1) })), - framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 30, -1) })), - framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })) -) + datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + ActivationFunctionsQuantizedDataset), + framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), + framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 50, 1) })), + framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 30, -1) })), + framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); } FIXTURE_DATA_TEST_CASE(RunLarge, CLQuantizedMatMulFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine( - datasets::LargeMatMulDataset(), - framework::dataset::make("TransposeA", { false, true })), - framework::dataset::make("TransposeB", { false, true })), - framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), - framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), - framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), - framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })), - framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })), - framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) })) -) + datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { false, true })), + framework::dataset::make("TransposeB", { false, true })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + ActivationFunctionsQuantizedDataset), + framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), + framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })), + framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })), + framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} + +FIXTURE_DATA_TEST_CASE(RunAllActivations, CLQuantizedMatMulActivationFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(combine( + datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { false })), + framework::dataset::make("TransposeB", { true })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + AllQuantizedActivationsDataset), + framework::dataset::make("NumberOfExtraRuns", { 0, 1 })), + framework::dataset::make("LhsQInfo", { QuantizationInfo(1.f / 100, 1) })), + framework::dataset::make("RhsQInfo", { QuantizationInfo(1.f / 200, -1) })), + framework::dataset::make("DstQInfo", { QuantizationInfo(1.f, 2) }))) { // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); diff --git a/tests/validation/fixtures/MatMulFixture.h b/tests/validation/fixtures/MatMulFixture.h index 2f94c1f9d2..3e4cac5e34 100644 --- a/tests/validation/fixtures/MatMulFixture.h +++ b/tests/validation/fixtures/MatMulFixture.h @@ -112,14 +112,14 @@ protected: // Configure MatMulInfo class MatMulInfo mm_info; - mm_info.adj_lhs(transpose_a).adj_rhs(transpose_b).fused_activation(act_info); + mm_info.adj_lhs(transpose_a).adj_rhs(transpose_b); // Ensure values are dynamic a.info()->set_are_values_constant(false); b.info()->set_are_values_constant(false); // Configure operator - matmul.configure(&a, &b, &dst, mm_info, settings); + matmul.configure(&a, &b, &dst, mm_info, settings, act_info); // Assertions ARM_COMPUTE_ASSERT(a.info()->is_resizable()); @@ -162,8 +162,8 @@ protected: } template <typename TT> - typename std::enable_if<!std::is_integral<TT>::value, SimpleTensor<TT>>::type - compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo) + typename std::enable_if < !std::is_integral<TT>::value, SimpleTensor<TT >>::type + compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo) { ARM_COMPUTE_UNUSED(o_qinfo); @@ -172,7 +172,7 @@ protected: template <typename TT> typename std::enable_if<std::is_integral<TT>::value, SimpleTensor<TT>>::type - compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo) + compute_reference_gemm(const SimpleTensor<TT> &a, const SimpleTensor<TT> &b, const SimpleTensor<TT> &c, float alpha, float beta, const QuantizationInfo &o_qinfo) { ARM_COMPUTE_UNUSED(alpha, beta); @@ -183,18 +183,18 @@ protected: const auto multiplier = aq.scale * bq.scale / oq.scale; int32_t output_multiplier = 0; - int32_t output_shift = 0; + int32_t output_shift = 0; quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); std::vector<int32_t> output_multipliers{ output_multiplier }; std::vector<int32_t> output_shifts{ output_shift }; //The lhs and rhs offsets are negated here to keep the reference aligned with the function implementation where the lhs and rhs offsets are also negated. const auto tmp = reference::gemmlowp_matrix_multiply_core<int32_t>( - a, b, c.shape(), -aq.offset, -bq.offset); + a, b, c.shape(), -aq.offset, -bq.offset); auto output = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TT>( - tmp, output_multipliers, output_shifts, oq.offset, - std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()); + tmp, output_multipliers, output_shifts, oq.offset, + std::numeric_limits<int32_t>::lowest(), std::numeric_limits<int32_t>::max()); output.quantization_info(o_qinfo); return output; @@ -280,6 +280,30 @@ public: }; template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulValidationWithDynamicTensorsFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + template <typename...> + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class QuantizedMatMulValidationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + template <typename...> + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs, + QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(), + a_qinfo, b_qinfo, o_qinfo); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> class MatMulValidationWithActivationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> { public: @@ -291,24 +315,30 @@ public: }; template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> -class MatMulValidationWithDynamicTensorsFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +class MatMulValidationWithActivationAlphaBetaFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> { public: template <typename...> - void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo::ActivationFunction function, + float alpha_beta) { - MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings()); + ActivationLayerInfo act_info(function, alpha_beta, alpha_beta); + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings()); } }; template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> -class QuantizedMatMulValidationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +class QuantizedMatMulValidationWithActivationFixture : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> { public: template <typename...> - void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo act_info, int num_extra_runs, QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo) + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool transpose_a, bool transpose_b, DataType data_type, ActivationLayerInfo::ActivationFunction function, + float alpha_beta, int num_extra_runs, + QuantizationInfo a_qinfo, QuantizationInfo b_qinfo, QuantizationInfo o_qinfo) { - MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(), a_qinfo, b_qinfo, o_qinfo); + ActivationLayerInfo act_info(function, alpha_beta, alpha_beta); + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(), + a_qinfo, b_qinfo, o_qinfo); } }; |