Rename SoftArgMax operator to SoftMax
- Match naming in TensorFlow, PyTorch, and ONNX
- Avoid confusion with occasionally proposed SoftArgMax operator with different
semantics y[i] := exp(x[i]) * i / sum(exp(x[j]))
PiperOrigin-RevId: 291812880
diff --git a/bench/softmax.cc b/bench/softmax.cc
new file mode 100644
index 0000000..c0eb362
--- /dev/null
+++ b/bench/softmax.cc
@@ -0,0 +1,104 @@
+// Copyright (c) Facebook, Inc. and its affiliates.
+// All rights reserved.
+//
+// This source code is licensed under the BSD-style license found in the
+// LICENSE file in the root directory of this source tree.
+
+#include <algorithm>
+#include <cmath>
+#include <functional>
+#include <random>
+#include <vector>
+
+#include <xnnpack.h>
+
+#include <benchmark/benchmark.h>
+#include "bench/utils.h"
+
+
+static void 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));
+
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
+
+ std::vector<uint8_t> input(batch_size * channels);
+ std::vector<uint8_t> output(batch_size * channels);
+ std::generate(input.begin(), input.end(), std::ref(u8rng));
+ std::fill(output.begin(), output.end(), 0xA5);
+
+ 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_q8(
+ channels, channels /* input stride */, channels /* output stride */,
+ 1.0f /* input scale */,
+ 0 /* output zero point */, 1.0f / 256.0f /* output scale */,
+ 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_q8(
+ 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(uint8_t);
+ state.counters["bytes"] =
+ benchmark::Counter(uint64_t(state.iterations()) * bytes_per_iteration, benchmark::Counter::kIsRate);
+}
+
+static void CharacteristicArguments(benchmark::internal::Benchmark* b)
+{
+ b->ArgNames({"N", "C"});
+
+ // CIFAR-10
+ b->Args({1, 10});
+ // CIFAR-100 */
+ b->Args({1, 100});
+ // ImageNet-1K
+ b->Args({1, 1000});
+ // ImageNet-1K+1
+ b->Args({1, 1001});
+ // ImageNet-22K
+ b->Args({1, 21841});
+}
+
+BENCHMARK(softmax_q8)->Apply(CharacteristicArguments)->UseRealTime();
+
+#ifndef XNNPACK_BENCHMARK_NO_MAIN
+BENCHMARK_MAIN();
+#endif