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#include <benchmark/benchmark.h>
#include <torch/csrc/jit/runtime/static/impl.h>
#include "deep_wide_pt.h"
const int embedding_size = 32;
const int num_features = 50;
using namespace torch;
static void BM_deep_wide_base(benchmark::State& state) {
std::shared_ptr<DeepAndWide> net =
std::make_shared<DeepAndWide>(num_features);
const int batch_size = state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
// warmup
net->forward(ad_emb_packed, user_emb, wide);
for (auto _ : state) {
net->forward(ad_emb_packed, user_emb, wide);
}
}
static void BM_deep_wide_fast(benchmark::State& state) {
std::shared_ptr<DeepAndWideFast> net =
std::make_shared<DeepAndWideFast>(num_features);
const int batch_size = state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
// warmup
net->forward(ad_emb_packed, user_emb, wide);
for (auto _ : state) {
net->forward(ad_emb_packed, user_emb, wide);
}
}
static void BM_deep_wide_jit_graph_executor(benchmark::State& state) {
auto mod = getDeepAndWideSciptModel();
const int batch_size = state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
std::vector<IValue> inputs({ad_emb_packed, user_emb, wide});
TORCH_CHECK_EQ(setenv("TORCH_JIT_DISABLE_NEW_EXECUTOR", "1", 1), 0);
mod.forward(inputs);
for (auto _ : state) {
mod.forward(inputs);
}
}
static void BM_deep_wide_jit_profiling_executor(benchmark::State& state) {
auto mod = getDeepAndWideSciptModel();
const int batch_size = state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
std::vector<IValue> inputs({ad_emb_packed, user_emb, wide});
TORCH_CHECK_EQ(unsetenv("TORCH_JIT_DISABLE_NEW_EXECUTOR"), 0);
mod.forward(inputs);
for (auto _ : state) {
mod.forward(inputs);
}
}
static void BM_deep_wide_static(benchmark::State& state) {
auto mod = getDeepAndWideSciptModel();
torch::jit::StaticModule smod(mod);
const int batch_size = state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
std::vector<c10::IValue> inputs({ad_emb_packed, user_emb, wide});
smod(inputs, {});
for (auto _ : state) {
smod(inputs, {});
}
}
std::shared_ptr<torch::jit::StaticModule> getStaticModule() {
static auto smod =
std::make_shared<torch::jit::StaticModule>(getDeepAndWideSciptModel());
return smod;
}
static void BM_deep_wide_static_threaded(benchmark::State& state) {
auto sm = getStaticModule();
torch::jit::StaticRuntime sr(*sm);
const int batch_size = 1; // state.range(0);
auto ad_emb_packed = torch::randn({batch_size, 1, embedding_size});
auto user_emb = torch::randn({batch_size, 1, embedding_size});
auto wide = torch::randn({batch_size, num_features});
std::vector<c10::IValue> inputs({ad_emb_packed, user_emb, wide});
sr(inputs, {});
for (auto _ : state) {
sr(inputs, {});
}
}
static void BM_leaky_relu_const(benchmark::State& state) {
auto mod = getLeakyReLUConstScriptModel();
torch::jit::StaticModule smod(mod);
const int batch_size = state.range(0);
auto data = torch::randn({batch_size, num_features});
std::vector<c10::IValue> inputs({data});
smod(inputs, {});
for (auto _ : state) {
smod(inputs, {});
}
}
static void BM_leaky_relu(benchmark::State& state) {
auto mod = getLeakyReLUScriptModel();
torch::jit::StaticModule smod(mod);
const int batch_size = state.range(0);
auto neg_slope = torch::randn(1);
auto data = torch::randn({batch_size, num_features});
std::vector<c10::IValue> inputs({data, neg_slope[0]});
smod(inputs, {});
for (auto _ : state) {
smod(inputs, {});
}
}
BENCHMARK(BM_leaky_relu)->RangeMultiplier(8)->Ranges({{1, 20}});
BENCHMARK(BM_leaky_relu_const)->RangeMultiplier(8)->Ranges({{1, 20}});
static void BM_signed_log1p(benchmark::State& state) {
auto mod = getSignedLog1pModel();
torch::jit::StaticModule smod(mod);
const int num_elements = state.range(0);
auto data = torch::randn({num_elements});
std::vector<c10::IValue> inputs({data});
smod(inputs, {});
for (auto _ : state) {
smod(inputs, {});
}
}
BENCHMARK(BM_signed_log1p)->RangeMultiplier(8)->Ranges({{16, 65536}});
static void BM_long_static_memory_optimization(benchmark::State& state) {
auto mod = getLongScriptModel();
torch::jit::StaticModuleOptions opts;
opts.optimize_memory = state.range(1);
torch::jit::StaticModule smod(mod, false, opts);
const auto N = state.range(0);
auto a = torch::randn({N, N});
auto b = torch::randn({N, N});
auto c = torch::randn({N, N});
std::vector<c10::IValue> inputs({a, b, c});
smod(inputs, {});
for (auto _ : state) {
smod(inputs, {});
}
}
BENCHMARK(BM_deep_wide_base)->RangeMultiplier(8)->Ranges({{1, 20}});
BENCHMARK(BM_deep_wide_fast)->RangeMultiplier(8)->Ranges({{1, 20}});
BENCHMARK(BM_deep_wide_jit_graph_executor)
->RangeMultiplier(8)
->Ranges({{1, 20}});
BENCHMARK(BM_deep_wide_jit_profiling_executor)
->RangeMultiplier(8)
->Ranges({{1, 20}});
BENCHMARK(BM_deep_wide_static)->RangeMultiplier(8)->Ranges({{1, 20}});
BENCHMARK(BM_deep_wide_static_threaded)->Threads(8);
BENCHMARK(BM_long_static_memory_optimization)
->Args({2 << 0, 0})
->Args({2 << 2, 0})
->Args({2 << 4, 0})
->Args({2 << 8, 0})
->Args({2 << 0, 1})
->Args({2 << 2, 1})
->Args({2 << 4, 1})
->Args({2 << 8, 1});
int main(int argc, char** argv) {
c10::ParseCommandLineFlags(&argc, &argv);
::benchmark::Initialize(&argc, argv);
::benchmark::RunSpecifiedBenchmarks();
}
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