diff options
Diffstat (limited to 'tests/validation/CL')
| -rw-r--r-- | tests/validation/CL/ConvolutionLayer.cpp | 291 | ||||
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyNative.cpp | 244 | ||||
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp | 421 | ||||
| -rw-r--r-- | tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp | 294 |
4 files changed, 23 insertions, 1227 deletions
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp index bced540d2a..986d76708d 100644 --- a/tests/validation/CL/ConvolutionLayer.cpp +++ b/tests/validation/CL/ConvolutionLayer.cpp @@ -22,7 +22,6 @@ * SOFTWARE. */ #include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/PostOps.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" @@ -69,53 +68,30 @@ constexpr float tolerance_num = 0.07f; /**< T const auto CNNDataTypes = framework::dataset::make("DataType", { DataType::F16, - DataType::F32, - DataType::QASYMM8, - DataType::QASYMM8_SIGNED, + DataType::F32, + DataType::QASYMM8, + DataType::QASYMM8_SIGNED, }); /** Grouped CNN data types */ const auto GroupedCNNDataTypes = framework::dataset::make("DataType", { DataType::F16, - DataType::F32 + DataType::F32 }); -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +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, 0.5f) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) }); const auto ActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) }); - -bool is_post_op_list_valid_in_gemmconv(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &output_shape, DataType data_type, DataLayout data_layout, - const PadStrideInfo &conv_info, const experimental::PostOpList<ITensorInfo *> &post_ops) -{ - const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); - - const auto dilation = Size2D(1U, 1U); - const unsigned int num_groups = 1U; - - TensorInfo input_info(input_shape, 1, data_type, data_layout); - TensorInfo weights_info(weights_shape, 1, data_type, data_layout); - - TensorInfo output_info(output_shape, 1, data_type, data_layout); - - WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels)); - - const auto status = CLGEMMConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true), - &weights_info.clone()->set_is_resizable(true), nullptr, &output_info.clone()->set_is_resizable(true), - conv_info, w_info, dilation, ActivationLayerInfo(), num_groups, post_ops); - return bool(status); -} } // namespace TEST_SUITE(CL) @@ -207,72 +183,6 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z enable_fast_math); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } - -DATA_TEST_CASE(ValidatePostOpSupportInConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(2U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC), // Select GEMM - TensorInfo(TensorShape(17U, 31U, 32U), 1, DataType::F32, DataLayout::NCHW), // Select WINOGRAD - TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW), // Select Direct - TensorInfo(TensorShape(27U, 27U, 48U), 1, DataType::F32, DataLayout::NCHW), // Select FFT - }), - framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(2U, 1U, 1U, 19U), 1, DataType::F32, DataLayout::NHWC), - TensorInfo(TensorShape(5U, 5U, 32U, 19U), 1, DataType::F32, DataLayout::NCHW), - TensorInfo(TensorShape(5U, 5U, 48U, 128U), 1, DataType::F32, DataLayout::NCHW), - TensorInfo(TensorShape(11U, 11U, 48U, 24), 1, DataType::F32, DataLayout::NCHW), - })), - framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(19U, 17U, 31U), 1, DataType::F32, DataLayout::NHWC), - TensorInfo(TensorShape(17U, 31U, 19U), 1, DataType::F32, DataLayout::NCHW), - TensorInfo(TensorShape(27U, 27U, 128U), 1, DataType::F32, DataLayout::NCHW), - TensorInfo(TensorShape(27U, 27U, 24U), 1, DataType::F32, DataLayout::NCHW), - })), - framework::dataset::make("ConvInfo", { PadStrideInfo(1U, 1U, 0U, 0U), - PadStrideInfo(1U, 1U, 2U, 2U), - PadStrideInfo(1U, 1U, 2U, 2U), - PadStrideInfo(1U, 1U, 5U, 5U), - })), - framework::dataset::make("EnableFastMath", { false, true, false, false})), - framework::dataset::make("ExpectedMethod",{ ConvolutionMethod::GEMM, - ConvolutionMethod::WINOGRAD, - ConvolutionMethod::DIRECT, - ConvolutionMethod::FFT, - })), - framework::dataset::make("PostOpSupported",{ true, false, false, false - })), - input_info, weights_info, output_info, conv_info, enable_fast_math, expected_method, post_op_supported) -{ - const int idx_width = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::WIDTH); - const int idx_height = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::HEIGHT); - const int idx_kernels = get_data_layout_dimension_index(input_info.data_layout(), DataLayoutDimension::BATCHES); - - const auto dilation = Size2D(1U, 1U); - const unsigned int num_groups = 1U; - - WeightsInfo w_info(false, weights_info.dimension(idx_width), weights_info.dimension(idx_height), weights_info.dimension(idx_kernels)); - - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<ITensorInfo*>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - - ConvolutionMethod actual_method = CLConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true), - &weights_info.clone()->set_is_resizable(true), - &output_info.clone()->set_is_resizable(true), conv_info, - WeightsInfo(), - ActivationLayerInfo(), - GPUTarget::BIFROST, - dilation, - enable_fast_math); - ARM_COMPUTE_EXPECT(actual_method == expected_method, framework::LogLevel::ERRORS); - const auto is_valid = CLConvolutionLayer::validate(&input_info.clone()->set_is_resizable(true), - &weights_info.clone()->set_is_resizable(true), - nullptr, - &output_info.clone()->set_is_resizable(true), - conv_info, - w_info, - dilation, - ActivationLayerInfo(), - enable_fast_math, - num_groups, - post_ops); - ARM_COMPUTE_EXPECT( bool(is_valid) == post_op_supported, framework::LogLevel::ERRORS); -} // clang-format on // *INDENT-ON* TEST_SUITE_END() // ConvolutionLayer @@ -285,167 +195,11 @@ using CLGEMMConvolutionLayerMixedDataLayoutFixture = ConvolutionValidationFixtur template <typename T> using CLConvolutionValidationWithPaddingFixture = ConvolutionValidationWithPaddingFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T>; -TEST_SUITE(ValidateFusedPostOpsConfigs) -TEST_SUITE(Invalid) -TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - const TensorShape post_op_arg0_shape(output_shape); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - auto post_op_arg1_info = post_op_arg_info.clone(); - - // Unsupported sequence of post ops - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - post_op_arg1_info.get(), - 0, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OnlyNHWCIsSupported, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NCHW; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(14U, 12U, 16U, 2U); - const auto weights_shape = TensorShape(1U, 1U, 16U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - const TensorShape post_op_arg0_shape(output_shape); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OnlyFloatingTypeIsSupported, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::QASYMM8; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - const TensorShape post_op_arg0_shape(output_shape); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedKernelSize, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 3U, 3U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - const TensorShape post_op_arg0_shape(output_shape); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OnlyConv1x1Stride1IsSupported_UnsupportedStride, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(3, 3, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - const TensorShape post_op_arg0_shape(output_shape); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Invalid -TEST_SUITE(Valid) -TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(SupportedPostOps, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const auto data_layout = DataLayout::NHWC; - const auto conv_info = PadStrideInfo(1, 1, 0, 0); - const auto input_shape = TensorShape(16U, 14U, 12U, 2U); - const auto weights_shape = TensorShape(16U, 1U, 1U, 24U); - - const auto output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_shape, data_layout, weights_shape, conv_info); - - TensorShape post_op_arg0_shape(output_shape); - post_op_arg0_shape[1] = 1; // Broadcast in "Y" (second) dimension - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - - experimental::PostOpList<ITensorInfo *> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo *>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid_in_gemmconv(input_shape, weights_shape, output_shape, data_type, data_layout, conv_info, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Valid -TEST_SUITE_END() // ValidateFusedPostOps TEST_SUITE(Float) TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), - framework::dataset::make("DataType", - DataType::F16)), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), ActivationFunctionsSmallDataset)) { // Validate output @@ -456,10 +210,7 @@ TEST_SUITE_END() // FP16 TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), - framework::dataset::make("DataType", - DataType::F32)), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), ActivationFunctionsSmallDataset)) { // Validate output @@ -503,16 +254,16 @@ using CLGEMMConvolutionLayerQuantizedMixedDataLayoutFixture = ConvolutionValidat template <typename T> using CLGEMMConvolutionLayerQuantizedPerChannelFixture = ConvolutionValidationQuantizedPerChannelFixture<CLTensor, CLAccessor, CLGEMMConvolutionLayer, T, int8_t>; -const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) }); const auto QuantizedActivationFunctionsSmallDataset = framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), - ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f) }); TEST_SUITE(Quantized) @@ -520,8 +271,8 @@ TEST_SUITE(Quantized) const auto QuantizationData = framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10), - QuantizationInfo(0.3f, 3), - QuantizationInfo(1.1f, 10), + QuantizationInfo(0.3f, 3), + QuantizationInfo(1.1f, 10), }); TEST_SUITE(QASYMM8) @@ -637,9 +388,7 @@ TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("DataLayout", { DataLayout::NCHW })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW })), ActivationFunctionsSmallDataset)) { // Validate output @@ -661,9 +410,7 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMGroupedConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallGroupedConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), - framework::dataset::make("DataType", DataType::F16)), - framework::dataset::make("DataLayout", { DataLayout::NCHW })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NCHW })), ActivationFunctionsSmallDataset)) { // Validate output diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp index 7f63a03371..0ddf43766f 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2022 Arm Limited. + * Copyright (c) 2019-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -53,11 +53,6 @@ using CLGEMMMatrixMultiplyNative = CLSynthetizeOperator<ClGemmMatrixMultiplyNati template <typename T> using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>; -// Fixture for CLGEMMMatrixMultiplyNative with post ops -template <typename T> -using CLGEMMMatrixMultiplyNativeWithPostOpsFixture = - GEMMMatrixMultiplyNativeWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>; - // Fixture for CLGEMMMatrixMultiplyNative3D template <typename T> using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>; @@ -146,105 +141,6 @@ const auto boundary_handling_cases = combine(combine(combine(combine(combine(com broadcast_bias_values), framework::dataset::make("Activation", ActivationLayerInfo())); -/** Post Ops */ -using PostOpArgBroadcast = CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>::PostOpArgBroadcast; -experimental::PostOpList<PostOpArgBroadcast> post_ops_1() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_2() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_3() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - // post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, false, false), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - return post_ops; -} -// To test that the output of the main op is the first parameter in prelu post op -experimental::PostOpList<PostOpArgBroadcast> post_ops_4() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param -experimental::PostOpList<PostOpArgBroadcast> post_ops_5() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -/** Different Post Op Lists */ -const auto post_op_lists = framework::dataset::make("post_op_lists", { - post_ops_1(), - post_ops_2(), - post_ops_3(), - post_ops_4(), - post_ops_5() - } ); - -bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops) -{ - const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true); - const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false); - - // Create TensorInfo for post op arguments - TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type); - TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type); - TensorInfo input2_info(TensorShape(n), 1, data_type); - TensorInfo output_info(TensorShape(n, m, batch), 1, data_type); - - GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */, - false /**< reinterpret the input as 3D */, - true /**< Flag used to broadcast the bias addition */, - false /**< wider accumm */, - false /**< has pad y */, - ActivationLayerInfo::ActivationFunction::IDENTITY, - 1 /**< Multiplication factor for the width of the 1xW transposed block */, - 1 /**< Multiplication factor for the height of the 4x4 interleaved block */, - lhs_info, - rhs_info, - 0 /**< Offset to be added to each element of the matrix A */, - 0 /**< Offset to be added to each element of the matrix B */, - post_ops); - return bool(ClGemmMatrixMultiplyNativeKernel::validate(&input0_info.clone()->set_is_resizable(true), - &input1_info.clone()->set_is_resizable(true), - &input2_info.clone()->set_is_resizable(true), - &output_info.clone()->set_is_resizable(true),1.f,1.f, - lhs_info, - rhs_info, - gemm_info)); -} - /** Configuration test */ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, bool broadcast_bias, DataType data_type, const ActivationLayerInfo &act_info) { @@ -295,119 +191,6 @@ void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned TEST_SUITE(CL) TEST_SUITE(GEMMMatrixMultiplyNative) -TEST_SUITE(ValidateFusedPostOpsConfigs) -TEST_SUITE(Invalid) -TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 17; - const unsigned int n = 1; - const unsigned int k = 13; - const unsigned int batch = 2; - TensorShape post_op_arg0_shape(n, m, batch); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - auto post_op_arg1_info = post_op_arg_info.clone(); - - // Unsupported sequence of post ops - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - post_op_arg1_info.get(), - 0, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OutputWidened, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors "widen" the output tensor - const auto data_type = DataType::F32; - const unsigned int m = 1; - const unsigned int n = 18; - const unsigned int k = 13; - const unsigned int batch = 2; - TensorShape post_op_arg_shape(n, m + 1, batch); // output's Y dimension (m) is "widened", which is not allowed - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors broadcast in the first dimension (X) only - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, m, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Invalid -TEST_SUITE(Valid) -TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - experimental::PostOpList<ITensorInfo*> post_ops{}; - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(n, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, 1); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Valid -TEST_SUITE_END() // ValidateFusedPostOps TEST_SUITE(Float) TEST_SUITE(FP32) DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(combine( @@ -541,31 +324,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<float>, f validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeWithPostOpsFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - framework::dataset::make("M0", { 4 })), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("DataType", DataType::F32)), - framework::dataset::make("alpha", {1.0f} )), - framework::dataset::make("beta", {1.0f} )), - framework::dataset::make("broadcast_bias", { false, true } )), - framework::dataset::make("Activation", { ActivationLayerInfo() })), - post_op_lists) - ) -{ - // Validate output - validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); -} - -TEST_SUITE_END() // FusedPostOps - TEST_SUITE_END() // FP32 TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMulipltyNative diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp index cdd89670fa..b06e4bf213 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyReshaped.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2022 Arm Limited. + * Copyright (c) 2018-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,7 +23,6 @@ */ #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/PostOps.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" @@ -62,21 +61,11 @@ using CLGEMMMatrixMultiplyReshaped = CLSynthetizeOperator<ClGemmMatrixMultiplyRe template <typename T> using CLGEMMMatrixMultiplyReshapedFixture = GEMMMatrixMultiplyReshapedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>; -// Fixture for CLGEMMMatrixMultiplyReshaped with post ops -template <typename T> -using CLGEMMMatrixMultiplyReshapedWithPostOpsFixture = - GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>; - // Fixture for CLGEMMMatrixMultiplyReshaped mixed precision template <typename T> using CLGEMMMatrixMultiplyReshapedMixedPrecisionFixture = GEMMMatrixMultiplyReshapedValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped, true>; -// Fixture for CLGEMMMatrixMultiplyReshaped mixed precision with post ops -template <typename T> -using CLGEMMMatrixMultiplyReshapedMixedPrecisionWithPostOpsFixture = - GEMMMatrixMultiplyReshapedWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped, true>; - // Fixture for CLGEMMMatrixMultiplyReshaped3D template <typename T> using CLGEMMMatrixMultiplyReshaped3DFixture = GEMMMatrixMultiplyReshaped3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeLHSMatrix, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshaped>; @@ -184,108 +173,6 @@ const auto broadcast_bias_values = framework::dataset::make("broadcast_bias", { /** LHS transposed values */ const auto lhs_transpose_values = framework::dataset::make("lhs_transpose", { false, true } ); -/** Post Ops */ -using PostOpArgBroadcast = CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>::PostOpArgBroadcast; -experimental::PostOpList<PostOpArgBroadcast> post_ops_1() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_2() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_3() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, false, true), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - return post_ops; -} -// To test that the output of the main op is the first parameter in prelu post op -experimental::PostOpList<PostOpArgBroadcast> post_ops_4() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param -experimental::PostOpList<PostOpArgBroadcast> post_ops_5() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -/** Different Post Op Lists */ -const auto post_op_lists = framework::dataset::make("post_op_lists", { - post_ops_1(), - post_ops_2(), - post_ops_3(), - post_ops_4(), - post_ops_5() - } ); - -bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops) -{ - const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true); - const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false); - - // Create TensorInfo for post op arguments - TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type); - TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type); - TensorInfo input2_info(TensorShape(n), 1, data_type); - TensorInfo output_info(TensorShape(n, m, batch), 1, data_type); - - const TensorInfo reshaped_input0_info = input0_info.clone()->set_tensor_shape(misc::shape_calculator::compute_lhs_reshaped_shape(input0_info, lhs_info)); - const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info)); - - GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */, - false /**< reinterpret the input as 3D */, - true /**< Flag used to broadcast the bias addition */, - false /**< wider accumm */, - false /**< has pad y */, - ActivationLayerInfo::ActivationFunction::IDENTITY, - 1 /**< Multiplication factor for the width of the 1xW transposed block */, - 1 /**< Multiplication factor for the height of the 4x4 interleaved block */, - lhs_info, - rhs_info, - 0 /**< Offset to be added to each element of the matrix A */, - 0 /**< Offset to be added to each element of the matrix B */, - post_ops); - return bool(ClGemmMatrixMultiplyReshapedKernel::validate(&reshaped_input0_info.clone()->set_is_resizable(true), - &reshaped_input1_info.clone()->set_is_resizable(true), - &input2_info.clone()->set_is_resizable(true), - &output_info.clone()->set_is_resizable(true),1.f,1.f, - lhs_info, - rhs_info, - gemm_info)); -} - } // namespace TEST_SUITE(CL) @@ -450,119 +337,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zi rhs_info, gemm_info)) == expected, framework::LogLevel::ERRORS); } -TEST_SUITE(ValidateFusedPostOpsConfigs) -TEST_SUITE(Invalid) -TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 17; - const unsigned int n = 1; - const unsigned int k = 13; - const unsigned int batch = 2; - TensorShape post_op_arg0_shape(n, m, batch); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - auto post_op_arg1_info = post_op_arg_info.clone(); - - // Unsupported sequence of post ops - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - post_op_arg1_info.get(), - 0, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OutputWidened, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors "widen" the output tensor - const auto data_type = DataType::F32; - const unsigned int m = 17; - const unsigned int n = 1; - const unsigned int k = 13; - const unsigned int batch = 2; - TensorShape post_op_arg_shape(n + 4, m, batch); // output's X dimension (n) is "widened", which is not allowed - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors broadcast in the first dimension (X) only - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, m, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Invalid -TEST_SUITE(Valid) -TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - experimental::PostOpList<ITensorInfo*> post_ops{}; - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(n, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, 1); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Valid -TEST_SUITE_END() // ValidateFusedPostOps + TEST_SUITE(Float) TEST_SUITE(FP32) @@ -697,44 +472,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::ARM_COMPUTE_PRINT_INFO(); } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - v0_values_precommit), - h0_values_precommit), - framework::dataset::make("interleave_lhs", { false })), - framework::dataset::make("interleave_rhs", { false })), - framework::dataset::make("export_to_cl_image_rhs", false)), - framework::dataset::make("DataType", DataType::F32)), - a_values_precommit), - beta_values_precommit), - framework::dataset::make("broadcast_bias", { true } )), - lhs_transpose_values), - act_values), - post_op_lists) - ) -{ - // Validate output - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps TEST_SUITE(ExportToCLImage) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( @@ -1002,44 +739,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<float>, framework::ARM_COMPUTE_PRINT_INFO(); } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - v0_values_precommit), - h0_values_precommit), - framework::dataset::make("interleave_lhs", { false })), - framework::dataset::make("interleave_rhs", { false })), - framework::dataset::make("export_to_cl_image_rhs", true)), - framework::dataset::make("DataType", DataType::F32)), - a_values_precommit), - beta_values_precommit), - framework::dataset::make("broadcast_bias", { true } )), - lhs_transpose_values), - act_values), - post_op_lists) - ) -{ - // Validate output only if validate() is successful - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps TEST_SUITE_END() // ExportToCLImage TEST_SUITE_END() // FP32 @@ -1178,45 +877,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>, } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - v0_values_precommit), - h0_values_precommit), - framework::dataset::make("interleave_lhs", { false })), - framework::dataset::make("interleave_rhs", { false })), - framework::dataset::make("export_to_cl_image_rhs", false)), - framework::dataset::make("DataType", DataType::F16)), - a_values_precommit), - beta_values_precommit), - framework::dataset::make("broadcast_bias", { true } )), - lhs_transpose_values), - act_values), - post_op_lists) - ) -{ - // Validate output - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps - TEST_SUITE(ExportToCLImage) DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("Input0Info", { TensorInfo(TensorShape(256U, 16U, 2U), 1, DataType::F16), // OK or incorrect if cl_khr_image2d_from_buffer not supported @@ -1483,44 +1143,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DFixture<half>, framework::ARM_COMPUTE_PRINT_INFO(); } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedWithPostOpsFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - v0_values_precommit), - h0_values_precommit), - framework::dataset::make("interleave_lhs", { false })), - framework::dataset::make("interleave_rhs", { false })), - framework::dataset::make("export_to_cl_image_rhs", true)), - framework::dataset::make("DataType", DataType::F16)), - a_values_precommit), - beta_values_precommit), - framework::dataset::make("broadcast_bias", { true } )), - lhs_transpose_values), - act_values), - post_op_lists) - ) -{ - // Validate output only if validate() is successful - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps TEST_SUITE_END() // ExportToCLImage TEST_SUITE_END() // FP16 @@ -1659,45 +1281,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyReshaped3DMixedPrecisionF } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyReshapedMixedPrecisionWithPostOpsFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - v0_values_precommit), - h0_values_precommit), - framework::dataset::make("interleave_lhs", { false })), - framework::dataset::make("interleave_rhs", { false })), - framework::dataset::make("export_to_cl_image_rhs", { true, false })), - framework::dataset::make("DataType", DataType::F16)), - a_values_precommit), - beta_values_precommit), - framework::dataset::make("broadcast_bias", { true } )), - lhs_transpose_values), - act_values), - post_op_lists) - ) -{ - // Validate output - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f16_mixed_precision, 0.f, abs_tolerance_f16_mixed_precision); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps - TEST_SUITE_END() // MixedPrecision TEST_SUITE_END() // Float TEST_SUITE_END() // GEMMMatrixMultiplyReshaped diff --git a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp index 53038c8177..dafc8dc5ec 100644 --- a/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp +++ b/tests/validation/CL/GEMMMatrixMultiplyReshapedOnlyRHS.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019-2022 Arm Limited. + * Copyright (c) 2019-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,7 +23,6 @@ */ #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Types.h" -#include "arm_compute/core/experimental/PostOps.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/CL/CLTensorAllocator.h" @@ -62,11 +61,6 @@ using CLGEMMMatrixMultiplyReshapedOnlyRHSFixture = GEMMMatrixMultiplyReshapedOnl template <typename T> using CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixture = GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>; -// Fixture for CLGEMMMatrixMultiplyReshapedOnlyRHS with post ops -template <typename T> -using CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture = - GEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsValidationFixture<CLTensor, CLAccessor, T, CLGEMMReshapeRHSMatrix, CLGEMMMatrixMultiplyReshapedOnlyRHS>; - namespace { // *INDENT-OFF* @@ -164,106 +158,6 @@ const auto boundary_handling_cases = combine(combine(combine(combine(combine(com broadcast_bias_values), framework::dataset::make("Activation", ActivationLayerInfo())); -/** Post Ops */ -using PostOpArgBroadcast = CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>::PostOpArgBroadcast; -experimental::PostOpList<PostOpArgBroadcast> post_ops_1() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(true, true, false), // If broadcast in dims 0, 1 and 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_2() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, true, true), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -experimental::PostOpList<PostOpArgBroadcast> post_ops_3() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<PostOpArgBroadcast>>( - std::make_tuple(false, false, true), // If broadcast in dims 0, 1 and 2 - 1, - ConvertPolicy::SATURATE); - return post_ops; -} -// To test that the output of the main op is the first parameter in prelu post op -experimental::PostOpList<PostOpArgBroadcast> post_ops_4() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, true), // If true, broadcast in corresponding dim: 0, 1 or 2 - 0, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -// To test that the output of the main op is the second parameter in prelu post op i.e. it is the alpha_param -experimental::PostOpList<PostOpArgBroadcast> post_ops_5() -{ - experimental::PostOpList<PostOpArgBroadcast> post_ops{}; - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::LINEAR, 0.5F, 0.0F}); - post_ops.push_back_op<experimental::PostOpEltwisePRelu<PostOpArgBroadcast>>( - std::make_tuple(false, false, false), // If true, broadcast in corresponding dim: 0, 1 or 2 - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpAct<PostOpArgBroadcast>>(ActivationLayerInfo{ActivationLayerInfo::ActivationFunction::RELU, 2.1F, 1.3F}); - return post_ops; -} -/** Different Post Op Lists */ -const auto post_op_lists = framework::dataset::make("post_op_lists", { - post_ops_1(), - post_ops_2(), - post_ops_3(), - post_ops_4(), - post_ops_5() - } ); - - bool is_post_op_list_valid(unsigned int m, unsigned int n, unsigned int k, unsigned int batch, DataType data_type, const experimental::PostOpList<ITensorInfo*>& post_ops) -{ - const auto lhs_info = GEMMLHSMatrixInfo(4,4,1,false,true); - const auto rhs_info = GEMMRHSMatrixInfo(4,4,1,true,true,false); - - // Create TensorInfo for post op arguments - TensorInfo input0_info(TensorShape(k, m, batch), 1, data_type); - TensorInfo input1_info(TensorShape(n, k, batch), 1, data_type); - TensorInfo input2_info(TensorShape(n), 1, data_type); - TensorInfo output_info(TensorShape(n, m, batch), 1, data_type); - - const TensorInfo reshaped_input1_info = input1_info.clone()->set_tensor_shape(misc::shape_calculator::compute_rhs_reshaped_shape(input1_info, rhs_info)); - - GEMMKernelInfo gemm_info(m, n, k, 0 /**< Depth of the output tensor in case is reinterpreted as 3D */, - false /**< reinterpret the input as 3D */, - true /**< Flag used to broadcast the bias addition */, - false /**< wider accumm */, - false /**< has pad y */, - ActivationLayerInfo::ActivationFunction::IDENTITY, - 1 /**< Multiplication factor for the width of the 1xW transposed block */, - 1 /**< Multiplication factor for the height of the 4x4 interleaved block */, - lhs_info, - rhs_info, - 0 /**< Offset to be added to each element of the matrix A */, - 0 /**< Offset to be added to each element of the matrix B */, - post_ops); - return bool(ClGemmMatrixMultiplyReshapedOnlyRhsKernel::validate(&input0_info.clone()->set_is_resizable(true), - &reshaped_input1_info.clone()->set_is_resizable(true), - &input2_info.clone()->set_is_resizable(true), - &output_info.clone()->set_is_resizable(true),1.f,1.f, - lhs_info, - rhs_info, - gemm_info)); -} /** Configuration test */ bool validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, unsigned int h0_value, @@ -370,119 +264,6 @@ b_value, m0_value, n0_value, k0_value, broadcast_bias, input_as_3d, depth_output ARM_COMPUTE_EXPECT(status == expected_value, framework::LogLevel::ERRORS); } -TEST_SUITE(ValidateFusedPostOpsConfigs) -TEST_SUITE(Invalid) -TEST_CASE(UnsupportedPostOpSequence, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 17; - const unsigned int n = 1; - const unsigned int k = 13; - const unsigned int batch = 2; - TensorShape post_op_arg0_shape(n, m, batch); - TensorInfo post_op_arg_info(post_op_arg0_shape, 1, data_type); - auto post_op_arg1_info = post_op_arg_info.clone(); - - // Unsupported sequence of post ops - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - &post_op_arg_info, - 1, - ConvertPolicy::SATURATE); - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( - post_op_arg1_info.get(), - 0, - ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(OutputWidened, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors "widen" the output tensor - const auto data_type = DataType::F32; - const unsigned int m = 17; - const unsigned int n = 1; - const unsigned int k = 1; - const unsigned int batch = 1; - TensorShape post_op_arg_shape(n, m, batch + 4); // output's batch dimension is "widened", which is not allowed - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInXDimOnly, framework::DatasetMode::ALL) -{ - // Invalid broadcast: post op tensors broadcast in the first dimension (X) only - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, m, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == false, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Invalid -TEST_SUITE(Valid) -TEST_CASE(EmptyPostOpList, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - experimental::PostOpList<ITensorInfo*> post_ops{}; - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInYDimOnly, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(n, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInBothXandYDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, batch); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_CASE(BroadcastInAllDims, framework::DatasetMode::ALL) -{ - const auto data_type = DataType::F32; - const unsigned int m = 22; - const unsigned int n = 16; - const unsigned int k = 15; - const unsigned int batch = 3; - TensorShape post_op_arg_shape(1, 1, 1); - TensorInfo post_op_arg_info(post_op_arg_shape, 1, data_type); - experimental::PostOpList<ITensorInfo*> post_ops{}; - post_ops.push_back_op<experimental::PostOpEltwiseAdd<ITensorInfo*>>( &post_op_arg_info, 0, ConvertPolicy::SATURATE); - - ARM_COMPUTE_EXPECT(is_post_op_list_valid(m, n, k, batch, data_type, post_ops) == true, framework::LogLevel::ERRORS); -} -TEST_SUITE_END() // Valid -TEST_SUITE_END() // ValidateFusedPostOps TEST_SUITE(Float) TEST_SUITE(FP32) @@ -684,43 +465,6 @@ FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixtur } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<float>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("H0", {1})), - framework::dataset::make("interleave_rhs", { true })), - t_values_rhs), - framework::dataset::make("export_to_cl_image_rhs", {false, true})), - framework::dataset::make("DataType", DataType::F32)), - a_values), - beta_values), - framework::dataset::make("broadcast_bias", { false } )), - act_values), - post_op_lists) - ) -{ - // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps - TEST_SUITE_END() // FP32 TEST_SUITE(FP16) @@ -849,42 +593,6 @@ FIXTURE_DATA_TEST_CASE(RunNightly3D, CLGEMMMatrixMultiplyReshapedOnlyRHS3DFixtur framework::ARM_COMPUTE_PRINT_INFO(); } } -TEST_SUITE(FusedPostOps) - -FIXTURE_DATA_TEST_CASE(RunPrecommit, CLGEMMMatrixMultiplyReshapedOnlyRHSWithPostOpsFixture<half>, framework::DatasetMode::ALL, - combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine(combine( - m_values, - n_values), - k_values), - b_values), - m0_values_precommit), - n0_values_precommit), - k0_values_precommit), - framework::dataset::make("H0", {1})), - framework::dataset::make("interleave_rhs", { true })), - t_values_rhs), - framework::dataset::make("export_to_cl_image_rhs", true)), - framework::dataset::make("DataType", DataType::F16)), - a_values), - beta_values), - framework::dataset::make("broadcast_bias", { false } )), - act_values), - post_op_lists) - ) -{ - // Validate output only if the target platform supports the OpenCL cl_khr_image2d_from_buffer extension - if(validate_result) - { - validate(CLAccessor(_target), _reference, rel_tolerance_f16, 0.f, abs_tolerance_f16); - } - else - { - ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped"); - framework::ARM_COMPUTE_PRINT_INFO(); - } -} - -TEST_SUITE_END() // FusedPostOps TEST_SUITE_END() // FP16 |