Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 1 | // Copyright 2020 Google LLC |
| 2 | // |
| 3 | // This source code is licensed under the BSD-style license found in the |
| 4 | // LICENSE file in the root directory of this source tree. |
| 5 | |
| 6 | #include <algorithm> |
| 7 | #include <array> |
| 8 | #include <cmath> |
| 9 | #include <functional> |
| 10 | #include <limits> |
| 11 | #include <random> |
| 12 | #include <vector> |
| 13 | |
Frank Barchard | fc2844e | 2020-09-15 13:06:28 -0700 | [diff] [blame] | 14 | #include <fp16.h> |
| 15 | |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 16 | #include <xnnpack.h> |
| 17 | |
| 18 | #include <benchmark/benchmark.h> |
| 19 | #include "bench/utils.h" |
| 20 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 21 | #include "flatbuffers/include/flatbuffers/flatbuffers.h" |
| 22 | #include "tensorflow/lite/interpreter.h" |
| 23 | #include "tensorflow/lite/kernels/register.h" |
| 24 | #include "tensorflow/lite/model.h" |
| 25 | #include "tensorflow/lite/schema/schema_generated.h" |
| 26 | #include "tensorflow/lite/version.h" |
| 27 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 28 | |
| 29 | |
| 30 | static void xnnpack_hardswish_f32(benchmark::State& state) { |
| 31 | const size_t batch_size = state.range(0); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 32 | |
| 33 | std::random_device random_device; |
| 34 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 35 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-5.0f, 5.0f), std::ref(rng)); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 36 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 37 | std::vector<float> input(batch_size + XNN_EXTRA_BYTES / sizeof(float)); |
| 38 | std::vector<float> output(batch_size); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 39 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 40 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 41 | |
| 42 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 43 | if (status != xnn_status_success) { |
| 44 | state.SkipWithError("failed to initialize XNNPACK"); |
| 45 | return; |
| 46 | } |
| 47 | |
| 48 | xnn_operator_t hardswish_op = nullptr; |
| 49 | status = xnn_create_hardswish_nc_f32( |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 50 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 51 | 0 /* flags */, &hardswish_op); |
| 52 | if (status != xnn_status_success || hardswish_op == nullptr) { |
Marat Dukhan | 6804bbd | 2020-06-30 19:26:11 -0700 | [diff] [blame] | 53 | state.SkipWithError("failed to create HardSwish operator"); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 54 | return; |
| 55 | } |
| 56 | |
| 57 | status = xnn_setup_hardswish_nc_f32( |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 58 | hardswish_op, batch_size, |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 59 | input.data(), output.data(), |
| 60 | nullptr /* thread pool */); |
| 61 | if (status != xnn_status_success) { |
Marat Dukhan | 6804bbd | 2020-06-30 19:26:11 -0700 | [diff] [blame] | 62 | state.SkipWithError("failed to setup HardSwish operator"); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 63 | return; |
| 64 | } |
| 65 | |
| 66 | for (auto _ : state) { |
| 67 | status = xnn_run_operator(hardswish_op, nullptr /* thread pool */); |
| 68 | if (status != xnn_status_success) { |
Marat Dukhan | 6804bbd | 2020-06-30 19:26:11 -0700 | [diff] [blame] | 69 | state.SkipWithError("failed to run HardSwish operator"); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 70 | return; |
| 71 | } |
| 72 | } |
| 73 | |
| 74 | status = xnn_delete_operator(hardswish_op); |
| 75 | if (status != xnn_status_success) { |
Marat Dukhan | 6804bbd | 2020-06-30 19:26:11 -0700 | [diff] [blame] | 76 | state.SkipWithError("failed to delete HardSwish operator"); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 77 | return; |
| 78 | } |
| 79 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 80 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 81 | if (cpu_frequency != 0) { |
| 82 | state.counters["cpufreq"] = cpu_frequency; |
| 83 | } |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 84 | |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 85 | state.counters["elements"] = |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 86 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 87 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 88 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 89 | state.counters["bytes"] = |
| 90 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 91 | } |
| 92 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 93 | #ifndef XNN_NO_F16_OPERATORS |
| 94 | static void xnnpack_hardswish_f16(benchmark::State& state) { |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 95 | const size_t batch_size = state.range(0); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 96 | |
| 97 | std::random_device random_device; |
| 98 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | a11ca34 | 2020-06-25 23:45:07 -0700 | [diff] [blame] | 99 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng)); |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 100 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 101 | |
| 102 | std::vector<uint16_t> input(batch_size + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| 103 | std::vector<uint16_t> output(batch_size); |
| 104 | std::generate(input.begin(), input.end(), std::ref(f16rng)); |
| 105 | std::fill(output.begin(), output.end(), std::nanf("")); |
| 106 | |
| 107 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 108 | if (status != xnn_status_success) { |
| 109 | state.SkipWithError("failed to initialize XNNPACK"); |
| 110 | return; |
| 111 | } |
| 112 | |
| 113 | xnn_operator_t hardswish_op = nullptr; |
| 114 | status = xnn_create_hardswish_nc_f16( |
| 115 | 1 /* channels */, 1 /* input stride */, 1 /* output stride */, |
| 116 | 0 /* flags */, &hardswish_op); |
| 117 | if (status != xnn_status_success || hardswish_op == nullptr) { |
| 118 | state.SkipWithError("failed to create HardSwish operator"); |
| 119 | return; |
| 120 | } |
| 121 | |
| 122 | status = xnn_setup_hardswish_nc_f16( |
| 123 | hardswish_op, batch_size, |
| 124 | input.data(), output.data(), |
| 125 | nullptr /* thread pool */); |
| 126 | if (status != xnn_status_success) { |
| 127 | state.SkipWithError("failed to setup HardSwish operator"); |
| 128 | return; |
| 129 | } |
| 130 | |
| 131 | for (auto _ : state) { |
| 132 | status = xnn_run_operator(hardswish_op, nullptr /* thread pool */); |
| 133 | if (status != xnn_status_success) { |
| 134 | state.SkipWithError("failed to run HardSwish operator"); |
| 135 | return; |
| 136 | } |
| 137 | } |
| 138 | |
| 139 | status = xnn_delete_operator(hardswish_op); |
| 140 | if (status != xnn_status_success) { |
| 141 | state.SkipWithError("failed to delete HardSwish operator"); |
| 142 | return; |
| 143 | } |
| 144 | |
| 145 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 146 | if (cpu_frequency != 0) { |
| 147 | state.counters["cpufreq"] = cpu_frequency; |
| 148 | } |
| 149 | |
| 150 | state.counters["elements"] = |
| 151 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
| 152 | |
| 153 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(uint16_t); |
| 154 | state.counters["bytes"] = |
| 155 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 156 | } |
| 157 | #endif // XNN_NO_F16_OPERATORS |
| 158 | |
| 159 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 160 | static void tflite_hardswish_f32(benchmark::State& state) { |
| 161 | const size_t batch_size = state.range(0); |
| 162 | |
| 163 | std::random_device random_device; |
| 164 | auto rng = std::mt19937(random_device()); |
| 165 | auto f32rng = std::bind(std::uniform_real_distribution<float>(-5.0f, 5.0f), std::ref(rng)); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 166 | |
| 167 | flatbuffers::FlatBufferBuilder builder; |
| 168 | const flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 169 | CreateOperatorCode(builder, tflite::BuiltinOperator_HARD_SWISH); |
| 170 | |
| 171 | const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{ |
| 172 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 173 | }}; |
| 174 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 175 | const std::array<int32_t, 1> shape{{ |
| 176 | static_cast<int32_t>(batch_size) |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 177 | }}; |
| 178 | |
| 179 | const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{ |
| 180 | tflite::CreateTensor(builder, |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 181 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 182 | tflite::TensorType_FLOAT32), |
| 183 | tflite::CreateTensor(builder, |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 184 | builder.CreateVector<int32_t>(shape.data(), shape.size()), |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 185 | tflite::TensorType_FLOAT32), |
| 186 | }}; |
| 187 | |
| 188 | const std::array<int32_t, 1> op_inputs{{ 0 }}; |
| 189 | const std::array<int32_t, 1> op_outputs{{ 1 }}; |
| 190 | flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator( |
| 191 | builder, |
| 192 | 0 /* opcode_index */, |
| 193 | builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()), |
| 194 | builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size())); |
| 195 | |
| 196 | const std::array<int32_t, 1> graph_inputs{{ 0 }}; |
| 197 | const std::array<int32_t, 1> graph_outputs{{ 1 }}; |
| 198 | const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph( |
| 199 | builder, |
| 200 | builder.CreateVector(tensors.data(), tensors.size()), |
| 201 | builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()), |
| 202 | builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()), |
| 203 | builder.CreateVector(&op, 1)); |
| 204 | |
| 205 | const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 206 | TFLITE_SCHEMA_VERSION, |
| 207 | builder.CreateVector(&operator_code, 1), |
| 208 | builder.CreateVector(&subgraph, 1), |
| 209 | builder.CreateString("HardSwish model"), |
| 210 | builder.CreateVector(buffers.data(), buffers.size())); |
| 211 | |
| 212 | builder.Finish(model_buffer); |
| 213 | |
| 214 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
Chao Mei | f9fdaa7 | 2021-05-18 23:04:34 -0700 | [diff] [blame] | 215 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 216 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 217 | std::unique_ptr<tflite::Interpreter> interpreter; |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 218 | if (interpreterBuilder(&interpreter) != kTfLiteOk || interpreter == nullptr) { |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 219 | state.SkipWithError("failed to create TFLite interpreter"); |
| 220 | return; |
| 221 | } |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 222 | interpreter->SetNumThreads(1); |
| 223 | |
| 224 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 225 | state.SkipWithError("failed to allocate tensors"); |
| 226 | return; |
| 227 | } |
| 228 | |
| 229 | std::generate( |
| 230 | interpreter->typed_tensor<float>(0), |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 231 | interpreter->typed_tensor<float>(0) + batch_size, |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 232 | std::ref(f32rng)); |
| 233 | |
| 234 | for (auto _ : state) { |
| 235 | if (interpreter->Invoke() != kTfLiteOk) { |
| 236 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 237 | return; |
| 238 | } |
| 239 | } |
| 240 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 241 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 242 | if (cpu_frequency != 0) { |
| 243 | state.counters["cpufreq"] = cpu_frequency; |
| 244 | } |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 245 | |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 246 | state.counters["elements"] = |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 247 | benchmark::Counter(uint64_t(state.iterations()) * batch_size, benchmark::Counter::kIsRate); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 248 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 249 | const size_t bytes_per_iteration = 2 * batch_size * sizeof(float); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 250 | state.counters["bytes"] = |
| 251 | benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate); |
| 252 | |
| 253 | interpreter.reset(); |
| 254 | } |
| 255 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 256 | |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 257 | BENCHMARK(xnnpack_hardswish_f32) |
| 258 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
| 259 | ->UseRealTime(); |
Frank Barchard | fc2844e | 2020-09-15 13:06:28 -0700 | [diff] [blame] | 260 | #ifndef XNN_NO_F16_OPERATORS |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 261 | BENCHMARK(xnnpack_hardswish_f16) |
| 262 | ->Apply(benchmark::utils::UnaryElementwiseParameters<uint16_t, uint16_t>) |
| 263 | ->UseRealTime(); |
Frank Barchard | fc2844e | 2020-09-15 13:06:28 -0700 | [diff] [blame] | 264 | #endif // XNN_NO_F16_OPERATORS |
| 265 | |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 266 | #ifdef BENCHMARK_TENSORFLOW_LITE |
Marat Dukhan | a0129e9 | 2021-12-30 15:59:28 -0800 | [diff] [blame] | 267 | BENCHMARK(tflite_hardswish_f32) |
| 268 | ->Apply(benchmark::utils::UnaryElementwiseParameters<float, float>) |
| 269 | ->UseRealTime(); |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 270 | #endif // BENCHMARK_TENSORFLOW_LITE |
Marat Dukhan | ad35260 | 2020-06-25 21:50:54 -0700 | [diff] [blame] | 271 | |
| 272 | #ifndef XNNPACK_BENCHMARK_NO_MAIN |
| 273 | BENCHMARK_MAIN(); |
| 274 | #endif |