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
14 #include <xnnpack.h>
15
16 #include <benchmark/benchmark.h>
17 #include "bench/utils.h"
18 #ifdef BENCHMARK_TENSORFLOW_LITE
19 #include "flatbuffers/include/flatbuffers/flatbuffers.h"
20 #include "tensorflow/lite/interpreter.h"
21 #include "tensorflow/lite/kernels/register.h"
22 #include "tensorflow/lite/model.h"
23 #include "tensorflow/lite/schema/schema_generated.h"
24 #include "tensorflow/lite/version.h"
25 #endif // BENCHMARK_TENSORFLOW_LITE
26
27
xnnpack_bankers_rounding_f32(benchmark::State & state)28 static void xnnpack_bankers_rounding_f32(benchmark::State& state) {
29 const size_t batch_size = state.range(0);
30 const size_t channels = state.range(1);
31
32 std::random_device random_device;
33 auto rng = std::mt19937(random_device());
34 auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng));
35
36 std::vector<float> input(batch_size * channels);
37 std::vector<float> output(batch_size * channels);
38 std::generate(input.begin(), input.end(), std::ref(f32rng));
39 std::fill(output.begin(), output.end(), std::nanf(""));
40
41 xnn_status status = xnn_initialize(nullptr /* allocator */);
42 if (status != xnn_status_success) {
43 state.SkipWithError("failed to initialize XNNPACK");
44 return;
45 }
46
47 xnn_operator_t bankers_rounding_op = nullptr;
48 status = xnn_create_bankers_rounding_nc_f32(
49 channels, channels /* input stride */, channels /* output stride */,
50 0 /* flags */, &bankers_rounding_op);
51 if (status != xnn_status_success || bankers_rounding_op == nullptr) {
52 state.SkipWithError("failed to create Bankers' Rounding operator");
53 return;
54 }
55
56 status = xnn_setup_bankers_rounding_nc_f32(
57 bankers_rounding_op,
58 batch_size,
59 input.data(), output.data(),
60 nullptr /* thread pool */);
61 if (status != xnn_status_success) {
62 state.SkipWithError("failed to setup Bankers' Rounding operator");
63 return;
64 }
65
66 for (auto _ : state) {
67 status = xnn_run_operator(bankers_rounding_op, nullptr /* thread pool */);
68 if (status != xnn_status_success) {
69 state.SkipWithError("failed to run Bankers' Rounding operator");
70 return;
71 }
72 }
73
74 status = xnn_delete_operator(bankers_rounding_op);
75 if (status != xnn_status_success) {
76 state.SkipWithError("failed to delete Bankers' Rounding operator");
77 return;
78 }
79
80 const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
81 if (cpu_frequency != 0) {
82 state.counters["cpufreq"] = cpu_frequency;
83 }
84
85 const size_t elements_per_iteration = batch_size * channels;
86 state.counters["elements"] =
87 benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
88
89 const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
90 state.counters["bytes"] =
91 benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
92 }
93
94 #ifdef BENCHMARK_TENSORFLOW_LITE
tflite_bankers_rounding_f32(benchmark::State & state)95 static void tflite_bankers_rounding_f32(benchmark::State& state) {
96 const size_t batch_size = state.range(0);
97 const size_t channels = state.range(1);
98
99 std::random_device random_device;
100 auto rng = std::mt19937(random_device());
101 auto f32rng = std::bind(std::uniform_real_distribution<float>(-10.0f, 10.0f), std::ref(rng));
102
103 flatbuffers::FlatBufferBuilder builder;
104 const flatbuffers::Offset<tflite::OperatorCode> operator_code =
105 CreateOperatorCode(builder, tflite::BuiltinOperator_ROUND);
106
107 const std::array<flatbuffers::Offset<tflite::Buffer>, 1> buffers{{
108 tflite::CreateBuffer(builder, builder.CreateVector({})),
109 }};
110
111 const std::array<int32_t, 4> input_shape{{
112 static_cast<int32_t>(batch_size),
113 static_cast<int32_t>(1 /* height */),
114 static_cast<int32_t>(1 /* width */),
115 static_cast<int32_t>(channels)
116 }};
117 const std::array<int32_t, 4> output_shape{{
118 static_cast<int32_t>(batch_size),
119 static_cast<int32_t>(1 /* height */),
120 static_cast<int32_t>(1 /* width */),
121 static_cast<int32_t>(channels)
122 }};
123
124 const std::array<flatbuffers::Offset<tflite::Tensor>, 2> tensors{{
125 tflite::CreateTensor(builder,
126 builder.CreateVector<int32_t>(input_shape.data(), input_shape.size()),
127 tflite::TensorType_FLOAT32),
128 tflite::CreateTensor(builder,
129 builder.CreateVector<int32_t>(output_shape.data(), output_shape.size()),
130 tflite::TensorType_FLOAT32),
131 }};
132
133 const std::array<int32_t, 1> op_inputs{{ 0 }};
134 const std::array<int32_t, 1> op_outputs{{ 1 }};
135 flatbuffers::Offset<tflite::Operator> op = tflite::CreateOperator(
136 builder,
137 0 /* opcode_index */,
138 builder.CreateVector<int32_t>(op_inputs.data(), op_inputs.size()),
139 builder.CreateVector<int32_t>(op_outputs.data(), op_outputs.size()));
140
141 const std::array<int32_t, 1> graph_inputs{{ 0 }};
142 const std::array<int32_t, 1> graph_outputs{{ 1 }};
143 const flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::CreateSubGraph(
144 builder,
145 builder.CreateVector(tensors.data(), tensors.size()),
146 builder.CreateVector<int32_t>(graph_inputs.data(), graph_inputs.size()),
147 builder.CreateVector<int32_t>(graph_outputs.data(), graph_outputs.size()),
148 builder.CreateVector(&op, 1));
149
150 const flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
151 TFLITE_SCHEMA_VERSION,
152 builder.CreateVector(&operator_code, 1),
153 builder.CreateVector(&subgraph, 1),
154 builder.CreateString("Round model"),
155 builder.CreateVector(buffers.data(), buffers.size()));
156
157 builder.Finish(model_buffer);
158
159 const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
160 tflite::ops::builtin::BuiltinOpResolver resolver;
161 tflite::InterpreterBuilder interpreterBuilder(model, resolver);
162 std::unique_ptr<tflite::Interpreter> interpreter;
163 if (interpreterBuilder(&interpreter) != kTfLiteOk) {
164 state.SkipWithError("failed to create TFLite interpreter");
165 return;
166 }
167 if (interpreter == nullptr) {
168 state.SkipWithError("TFLite interpreter is null");
169 return;
170 }
171 interpreter->SetNumThreads(1);
172
173 if (interpreter->AllocateTensors() != kTfLiteOk) {
174 state.SkipWithError("failed to allocate tensors");
175 return;
176 }
177
178 std::generate(
179 interpreter->typed_tensor<float>(0),
180 interpreter->typed_tensor<float>(0) + batch_size * channels,
181 std::ref(f32rng));
182
183 for (auto _ : state) {
184 if (interpreter->Invoke() != kTfLiteOk) {
185 state.SkipWithError("failed to invoke TFLite interpreter");
186 return;
187 }
188 }
189
190 const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency();
191 if (cpu_frequency != 0) {
192 state.counters["cpufreq"] = cpu_frequency;
193 }
194
195 const size_t elements_per_iteration = batch_size * channels;
196 state.counters["elements"] =
197 benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
198
199 const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
200 state.counters["bytes"] =
201 benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
202
203 interpreter.reset();
204 }
205 #endif // BENCHMARK_TENSORFLOW_LITE
206
CharacteristicArguments(benchmark::internal::Benchmark * b)207 static void CharacteristicArguments(benchmark::internal::Benchmark* b)
208 {
209 b->ArgNames({"N", "C"});
210
211 int32_t c = 16;
212 for (int32_t n = 224; n >= 7; n /= 2) {
213 b->Args({n * n, c});
214 c *= 2;
215 }
216 }
217
218 BENCHMARK(xnnpack_bankers_rounding_f32)->Apply(CharacteristicArguments)->UseRealTime();
219
220 #ifdef BENCHMARK_TENSORFLOW_LITE
221 BENCHMARK(tflite_bankers_rounding_f32)->Apply(CharacteristicArguments)->UseRealTime();
222 #endif // BENCHMARK_TENSORFLOW_LITE
223
224 #ifndef XNNPACK_BENCHMARK_NO_MAIN
225 BENCHMARK_MAIN();
226 #endif
227