F32 Softmax operator benchmark
PiperOrigin-RevId: 291990394
diff --git a/bench/softmax.cc b/bench/softmax.cc
index c0eb362..7f261bb 100644
--- a/bench/softmax.cc
+++ b/bench/softmax.cc
@@ -14,9 +14,17 @@
#include <benchmark/benchmark.h>
#include "bench/utils.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
-static void softmax_q8(benchmark::State& state) {
+static void xnnpack_softmax_q8(benchmark::State& state) {
const size_t batch_size = static_cast<size_t>(state.range(0));
const size_t channels = static_cast<size_t>(state.range(1));
@@ -81,6 +89,185 @@
benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
}
+static void xnnpack_softmax_f32(benchmark::State& state) {
+ const size_t batch_size = static_cast<size_t>(state.range(0));
+ const size_t channels = static_cast<size_t>(state.range(1));
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(-100.0f, 100.0f), rng);
+
+ std::vector<float> input(batch_size * channels + XNN_EXTRA_BYTES / sizeof(float));
+ std::vector<float> output(batch_size * channels);
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ 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 softmax_op = nullptr;
+ status = xnn_create_softmax_nc_f32(
+ channels, channels /* input stride */, channels /* output stride */,
+ 0 /* flags */, &softmax_op);
+ if (status != xnn_status_success || softmax_op == nullptr) {
+ state.SkipWithError("failed to create SoftMax operator");
+ return;
+ }
+
+ status = xnn_setup_softmax_nc_f32(
+ softmax_op,
+ batch_size,
+ input.data(), output.data(),
+ nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to setup SoftMax operator");
+ return;
+ }
+
+ for (auto _ : state) {
+ status = xnn_run_operator(softmax_op, nullptr /* thread pool */);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to run SoftMax operator");
+ return;
+ }
+ }
+
+ status = xnn_delete_operator(softmax_op);
+ if (status != xnn_status_success) {
+ state.SkipWithError("failed to delete SoftMax operator");
+ return;
+ }
+
+ state.counters["Freq"] = benchmark::utils::GetCurrentCpuFrequency();
+
+ const size_t elements_per_iteration = batch_size * channels;
+ state.counters["elements"] =
+ benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
+
+ const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
+ state.counters["bytes"] =
+ benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
+}
+
+#ifdef BENCHMARK_TENSORFLOW_LITE
+static void tflite_softmax_f32(benchmark::State& state) {
+ const size_t batch_size = state.range(0);
+ const size_t channels = state.range(1);
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(-100.0f, 100.0f), rng);
+
+ flatbuffers::FlatBufferBuilder builder;
+ flatbuffers::Offset<tflite::OperatorCode> operator_code =
+ tflite::CreateOperatorCode(builder, tflite::BuiltinOperator_SOFTMAX);
+
+ flatbuffers::Offset<tflite::SoftmaxOptions> softmax_options =
+ tflite::CreateSoftmaxOptions(builder, 1.0f /* beta */);
+
+ 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>(1 /* height */),
+ static_cast<int32_t>(1 /* width */),
+ static_cast<int32_t>(channels)
+ };
+ const int32_t output_shape[4] = {
+ static_cast<int32_t>(batch_size),
+ static_cast<int32_t>(1 /* height */),
+ static_cast<int32_t>(1 /* 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 = tflite::CreateOperator(
+ builder,
+ 0 /* opcode_index */,
+ builder.CreateVector<int32_t>(op_inputs, 1),
+ builder.CreateVector<int32_t>(op_outputs, 1),
+ tflite::BuiltinOptions_SoftmaxOptions, softmax_options.Union());
+
+ const int32_t graph_inputs[1] = { 0 };
+ const int32_t graph_outputs[1] = { 1 };
+ flatbuffers::Offset<tflite::SubGraph> subgraph = tflite::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<flatbuffers::String> description = builder.CreateString("Softmax model");
+
+ flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder,
+ TFLITE_SCHEMA_VERSION,
+ builder.CreateVector(&operator_code, 1),
+ builder.CreateVector(&subgraph, 1),
+ description,
+ 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 * 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();
+
+ const size_t elements_per_iteration = batch_size * channels;
+ state.counters["elements"] =
+ benchmark::Counter(uint64_t(state.iterations()) * elements_per_iteration, benchmark::Counter::kIsRate);
+
+ const size_t bytes_per_iteration = 2 * elements_per_iteration * sizeof(float);
+ state.counters["bytes"] =
+ benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
+
+ interpreter.reset();
+}
+#endif // BENCHMARK_TENSORFLOW_LITE
+
static void CharacteristicArguments(benchmark::internal::Benchmark* b)
{
b->ArgNames({"N", "C"});
@@ -97,7 +284,11 @@
b->Args({1, 21841});
}
-BENCHMARK(softmax_q8)->Apply(CharacteristicArguments)->UseRealTime();
+BENCHMARK(xnnpack_softmax_q8)->Apply(CharacteristicArguments)->UseRealTime();
+BENCHMARK(xnnpack_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
+#ifdef BENCHMARK_TENSORFLOW_LITE
+BENCHMARK(tflite_softmax_f32)->Apply(CharacteristicArguments)->UseRealTime();
+#endif // BENCHMARK_TENSORFLOW_LITE
#ifndef XNNPACK_BENCHMARK_NO_MAIN
BENCHMARK_MAIN();