Update Average Pooling operator benchmark
- Benchmark F32 operator
- Benchmark TFLite implementation
- Benchmark on the final global average pooling of ImageNet classifiers
PiperOrigin-RevId: 300295930
diff --git a/bench/average-pooling.cc b/bench/average-pooling.cc
index a757890..b1bdc7e 100644
--- a/bench/average-pooling.cc
+++ b/bench/average-pooling.cc
@@ -16,10 +16,18 @@
#include <xnnpack.h>
#include <benchmark/benchmark.h>
+#ifdef BENCHMARK_TENSORFLOW_LITE
+#include "flatbuffers/include/flatbuffers/flatbuffers.h"
+#include "tensorflow/lite/interpreter.h"
+#include "tensorflow/lite/kernels/register.h"
+#include "tensorflow/lite/model.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/version.h"
+#endif // BENCHMARK_TENSORFLOW_LITE
#include "bench/utils.h"
-static void average_pooling_q8(benchmark::State& state, const char* net) {
+static void xnnpack_average_pooling_q8(benchmark::State& state, const char* net) {
const size_t batch_size = state.range(0);
const size_t input_height = state.range(1);
const size_t input_width = state.range(2);
@@ -94,6 +102,224 @@
benchmark::Counter::kIsRate);
}
+static void xnnpack_average_pooling_f32(benchmark::State& state, const char* net) {
+ const size_t batch_size = state.range(0);
+ const size_t input_height = state.range(1);
+ const size_t input_width = state.range(2);
+ const size_t pooling_size = state.range(3);
+ const size_t padding_size = state.range(4);
+ const size_t stride = state.range(5);
+ const size_t channels = state.range(6);
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
+
+ const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
+ const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
+
+ std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::vector<float> output(batch_size * output_height * output_width * channels);
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ xnn_status status = xnn_initialize(nullptr /* allocator */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to initialize XNNPACK");
+ return;
+ }
+
+ xnn_operator_t pooling_op = nullptr;
+ status = xnn_create_average_pooling2d_nhwc_f32(
+ padding_size, padding_size, padding_size, padding_size,
+ pooling_size, pooling_size,
+ stride, stride,
+ channels, channels /* input pixel stride */, channels /* output pixel stride */,
+ -std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity(),
+ 0 /* flags */, &pooling_op);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to create Average Pooling operator");
+ return;
+ }
+
+ status = xnn_setup_average_pooling2d_nhwc_f32(
+ pooling_op,
+ batch_size, input_height, input_width,
+ input.data(), output.data(),
+ nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to setup Average Pooling operator");
+ return;
+ }
+
+ for (auto _ : state) {
+ status = xnn_run_operator(pooling_op, nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to run Average Pooling operator");
+ return;
+ }
+ }
+
+ status = xnn_delete_operator(pooling_op);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to delete Average Pooling operator");
+ return;
+ }
+ pooling_op = nullptr;
+
+ state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
+
+ state.counters["bytes"] = benchmark::Counter(
+ uint64_t(state.iterations()) *
+ batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
+ benchmark::Counter::kIsRate);
+}
+
+#ifdef BENCHMARK_TENSORFLOW_LITE
+void tflite_average_pooling_f32(benchmark::State& state, const char* net) {
+ const size_t batch_size = state.range(0);
+ const size_t input_height = state.range(1);
+ const size_t input_width = state.range(2);
+ const size_t pooling_size = state.range(3);
+ const size_t padding_size = state.range(4);
+ const size_t stride = state.range(5);
+ const size_t channels = state.range(6);
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
+
+ tflite::Padding padding = tflite::Padding_VALID;
+ if (2 * padding_size == (pooling_size - 1)) {
+ padding = tflite::Padding_SAME;
+ } else if (padding_size == 0) {
+ padding = tflite::Padding_VALID;
+ } else {
+ state.SkipWithError("unsupported padding");
+ return;
+ }
+
+ const size_t output_height = (2 * padding_size + input_height - pooling_size) / stride + 1;
+ const size_t output_width = (2 * padding_size + input_width - pooling_size) / stride + 1;
+
+ std::vector<float> input(batch_size * input_height * input_width * channels + XNN_EXTRA_BYTES / sizeof(float));
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::vector<float> output(batch_size * output_height * output_width * channels);
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ flatbuffers::FlatBufferBuilder builder;
+ flatbuffers::Offset<tflite::OperatorCode> operator_code =
+ CreateOperatorCode(builder, tflite::BuiltinOperator_AVERAGE_POOL_2D);
+
+ flatbuffers::Offset<tflite::Pool2DOptions> pool2d_options = CreatePool2DOptions(
+ builder, padding,
+ stride /* stride_w */, stride /* stride_h */,
+ pooling_size /* filter_width */, pooling_size /* filter_height */,
+ tflite::ActivationFunctionType_NONE);
+
+ flatbuffers::Offset<tflite::Buffer> buffers[1] = {
+ tflite::CreateBuffer(builder, builder.CreateVector({})),
+ };
+
+ const int32_t input_shape[4] = {
+ static_cast<int32_t>(batch_size),
+ static_cast<int32_t>(input_height),
+ static_cast<int32_t>(input_width),
+ static_cast<int32_t>(channels)
+ };
+ const int32_t output_shape[4] = {
+ static_cast<int32_t>(batch_size),
+ static_cast<int32_t>(output_height),
+ static_cast<int32_t>(output_width),
+ static_cast<int32_t>(channels)
+ };
+
+ flatbuffers::Offset<tflite::Tensor> tensors[2] = {
+ tflite::CreateTensor(builder,
+ builder.CreateVector<int32_t>(input_shape, 4),
+ tflite::TensorType_FLOAT32),
+ tflite::CreateTensor(builder,
+ builder.CreateVector<int32_t>(output_shape, 4),
+ tflite::TensorType_FLOAT32),
+ };
+
+ const int32_t op_inputs[1] = { 0 };
+ const int32_t op_outputs[1] = { 1 };
+ flatbuffers::Offset<tflite::Operator> op = CreateOperator(
+ builder,
+ 0 /* opcode_index */,
+ builder.CreateVector<int32_t>(op_inputs, 1),
+ builder.CreateVector<int32_t>(op_outputs, 1),
+ tflite::BuiltinOptions_Pool2DOptions,
+ pool2d_options.Union());
+
+ const int32_t graph_inputs[1] = { 0 };
+ const int32_t graph_outputs[1] = { 1 };
+ flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph(
+ builder,
+ builder.CreateVector(tensors, 2),
+ builder.CreateVector<int32_t>(graph_inputs, 1),
+ builder.CreateVector<int32_t>(graph_outputs, 1),
+ builder.CreateVector(&op, 1));
+
+ flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
+ TFLITE_SCHEMA_VERSION,
+ builder.CreateVector(&operator_code, 1),
+ builder.CreateVector(&subgraph, 1),
+ builder.CreateString("AVERAGE_POOL_2D model"),
+ builder.CreateVector(buffers, 1));
+
+ builder.Finish(model_buffer);
+
+ const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer());
+ tflite::ops::builtin::BuiltinOpResolver resolver;
+ tflite::InterpreterBuilder interpreterBuilder(model, resolver);
+ std::unique_ptr<tflite::Interpreter> interpreter;
+ if (interpreterBuilder(&interpreter) != kTfLiteOk) {
+ state.SkipWithError("failed to create TFLite interpreter");
+ return;
+ }
+ if (interpreter == nullptr) {
+ state.SkipWithError("TFLite interpreter is null");
+ return;
+ }
+ interpreter->SetNumThreads(1);
+
+ if (interpreter->AllocateTensors() != kTfLiteOk) {
+ state.SkipWithError("failed to allocate tensors");
+ return;
+ }
+
+ std::generate(
+ interpreter->typed_tensor<float>(0),
+ interpreter->typed_tensor<float>(0) + batch_size * input_height * input_width * channels,
+ std::ref(f32rng));
+
+ for (auto _ : state) {
+ if (interpreter->Invoke() != kTfLiteOk) {
+ state.SkipWithError("failed to invoke TFLite interpreter");
+ return;
+ }
+ }
+
+ state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
+
+ state.counters["bytes"] = benchmark::Counter(
+ uint64_t(state.iterations()) *
+ batch_size * (input_height * input_width + output_height * output_width) * channels * sizeof(float),
+ benchmark::Counter::kIsRate);
+}
+#endif // BENCHMARK_TENSORFLOW_LITE
+
+// Final global average pooling in ImageNet classification models.
+static void ImageNet(benchmark::internal::Benchmark* b) {
+ b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
+
+ /* N H W K P S C */
+ b->Args({1, 13, 13, 13, 0, 1, 1000});
+ b->Args({1, 7, 7, 7, 0, 1, 1000});
+}
+
// ShuffleNet v1 with 1 group.
static void ShuffleNetV1G1(benchmark::internal::Benchmark* b) {
b->ArgNames({"N", "H", "W", "K", "P", "S", "C"});
@@ -149,11 +375,28 @@
b->Args({1, 7, 7, 3, 1, 2, 1536});
}
-BENCHMARK_CAPTURE(average_pooling_q8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
-BENCHMARK_CAPTURE(average_pooling_q8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
-BENCHMARK_CAPTURE(average_pooling_q8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
-BENCHMARK_CAPTURE(average_pooling_q8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
-BENCHMARK_CAPTURE(average_pooling_q8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
+
+#ifdef BENCHMARK_TENSORFLOW_LITE
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
+BENCHMARK_CAPTURE(tflite_average_pooling_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
+#endif // BENCHMARK_TENSORFLOW_LITE
+
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, imagenet, "ImageNet")->Apply(ImageNet)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime();
+BENCHMARK_CAPTURE(xnnpack_average_pooling_q8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime();
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();