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Diffstat (limited to 'tests/benchmark/NEON/ConvolutionLayer.cpp')
| -rw-r--r-- | tests/benchmark/NEON/ConvolutionLayer.cpp | 303 |
1 files changed, 303 insertions, 0 deletions
diff --git a/tests/benchmark/NEON/ConvolutionLayer.cpp b/tests/benchmark/NEON/ConvolutionLayer.cpp new file mode 100644 index 0000000000..0cfff8494b --- /dev/null +++ b/tests/benchmark/NEON/ConvolutionLayer.cpp @@ -0,0 +1,303 @@ +/* + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "Globals.h" +#include "NEON/Helper.h" +#include "NEON/NEAccessor.h" +#include "TensorLibrary.h" +#include "benchmark/Datasets.h" +#include "benchmark/Profiler.h" +#include "benchmark/WallClockTimer.h" + +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" +#include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/TensorAllocator.h" + +#include "benchmark/benchmark_api.h" + +using namespace arm_compute; +using namespace arm_compute::test; +using namespace arm_compute::test::benchmark; +using namespace arm_compute::test::neon; + +#include "benchmark/common/ConvolutionLayer.h" + +namespace +{ +using ConvolutionLayerAlexNetF32 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>; +using ConvolutionLayerAlexNetQS8 = ConvolutionLayer<AlexNetConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer, DataType::QS8>; +using ConvolutionLayerLeNet5 = ConvolutionLayer<LeNet5ConvolutionLayerDataset, Tensor, NEAccessor, NEConvolutionLayer>; +using ConvolutionLayerGoogLeNet1 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset1, Tensor, NEAccessor, NEConvolutionLayer>; +using ConvolutionLayerGoogLeNet2 = ConvolutionLayer<GoogLeNetConvolutionLayerDataset2, Tensor, NEAccessor, NEConvolutionLayer>; +} // namespace + +// F32 +BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetF32, neon_alexnet) +(::benchmark::State &state) +{ + while(state.KeepRunning()) + { + // Run function + profiler.start(); + conv_layer->run(); + profiler.stop(); + } +} + +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetF32, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>); + +// QS8 +BENCHMARK_DEFINE_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +(::benchmark::State &state) +{ + while(state.KeepRunning()) + { + // Run function + profiler.start(); + conv_layer->run(); + profiler.stop(); + } +} + +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 0, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 1, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 2, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 3, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerAlexNetQS8, neon_alexnet) +->Threads(1) +->Apply(DataSetArgBatched<AlexNetConvolutionLayerDataset, 4, 1, 4, 8>); + +BENCHMARK_DEFINE_F(ConvolutionLayerLeNet5, neon_lenet5) +(::benchmark::State &state) +{ + while(state.KeepRunning()) + { + // Run function + profiler.start(); + conv_layer->run(); + profiler.stop(); + } +} + +BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5) +->Threads(1) +->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 0, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerLeNet5, neon_lenet5) +->Threads(1) +->Apply(DataSetArgBatched<LeNet5ConvolutionLayerDataset, 1, 1, 4, 8>); + +BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +(::benchmark::State &state) +{ + while(state.KeepRunning()) + { + // Run function + profiler.start(); + conv_layer->run(); + profiler.stop(); + } +} + +BENCHMARK_DEFINE_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +(::benchmark::State &state) +{ + while(state.KeepRunning()) + { + // Run function + profiler.start(); + conv_layer->run(); + profiler.stop(); + } +} + +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 0, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 1, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 2, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 3, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 4, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 5, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 6, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 7, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 8, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 9, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 10, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 11, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 12, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 13, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 14, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 15, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 16, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 17, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 18, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 19, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 20, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 21, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 22, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 23, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 24, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 25, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 26, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 27, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 28, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 29, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 30, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet1, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset1, 31, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 0, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 1, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 2, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 3, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 4, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 5, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 6, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 7, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 8, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 9, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 10, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 11, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 12, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 13, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 14, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 15, 1, 4, 8>); +BENCHMARK_REGISTER_F(ConvolutionLayerGoogLeNet2, neon_googlenet) +->Threads(1) +->Apply(DataSetArgBatched<GoogLeNetConvolutionLayerDataset2, 16, 1, 4, 8>); |