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#include <torch/csrc/utils/throughput_benchmark.h>
#include <pybind11/pybind11.h>
#include <torch/csrc/jit/python/pybind_utils.h>
#include <torch/csrc/utils/pybind.h>
namespace torch {
namespace throughput_benchmark {
std::ostream& operator<<(
std::ostream& os,
const BenchmarkExecutionStats& value) {
return os << "Average latency / iter (ms): " << value.latency_avg_ms
<< "\n Total number of iters: " << value.num_iters;
}
void ThroughputBenchmark::addInput(py::args args, py::kwargs kwargs) {
CHECK(script_module_.initialized() ^ module_.initialized());
if (script_module_.initialized()) {
script_module_.addInput(std::move(args), std::move(kwargs));
} else {
CHECK(module_.initialized());
module_.addInput(std::move(args), std::move(kwargs));
}
}
py::object ThroughputBenchmark::runOnce(py::args&& args, py::kwargs&& kwargs) {
CHECK(script_module_.initialized() ^ module_.initialized());
if (script_module_.initialized()) {
c10::IValue result;
{
pybind11::gil_scoped_release no_gil_guard;
result = script_module_.runOnce(std::move(args), std::move(kwargs));
}
return jit::toPyObject(std::move(result));
} else {
CHECK(module_.initialized());
return module_.runOnce(std::move(args), std::move(kwargs));
}
}
ThroughputBenchmark::ThroughputBenchmark(jit::Module script_module)
: script_module_(script_module) {}
ThroughputBenchmark::ThroughputBenchmark(py::object module)
: module_(std::move(module)) {}
BenchmarkExecutionStats ThroughputBenchmark::benchmark(
const BenchmarkConfig& config) const {
CHECK(script_module_.initialized() ^ module_.initialized());
// Main benchmark thread doesn't hold the GIL after scheduling worker threads
// But for now we don't release it as we will be implicitly manipulating with
// py::object ref. counts in the case of nn.Module benchmarking.
if (script_module_.initialized()) {
return script_module_.benchmark(config);
} else {
CHECK(module_.initialized());
TORCH_WARN(
"Starting benchmark on an nn.Module. This can be slow due "
"to Python GIL.For proper inference simulation you might want to switch to "
"a ScriptModule instead");
return module_.benchmark(config);
}
}
namespace detail {
template <>
void ScriptModuleBenchmark::runOnce(ScriptModuleInput&& input) const {
CHECK(initialized_);
// TODO: provide guarantees that compiler won't optimize this out
model_.get_method("forward").function()(std::move(input));
}
template <>
ScriptModuleOutput ScriptModuleBenchmark::runOnce(
py::args&& args,
py::kwargs&& kwargs) const {
CHECK(initialized_);
auto& function = model_.get_method("forward").function();
ScriptModuleInput stack = jit::createStackForSchema(
function.getSchema(),
std::move(args),
// NOLINTNEXTLINE(performance-move-const-arg)
std::move(kwargs),
model_._ivalue());
return function(std::move(stack));
}
template <>
void ModuleBenchmark::runOnce(ModuleInput&& input) const {
CHECK(initialized_);
pybind11::gil_scoped_acquire gil_guard;
model_(*input.args, **input.kwargs);
}
template <>
ModuleOutput ModuleBenchmark::runOnce(py::args&& args, py::kwargs&& kwargs)
const {
CHECK(initialized_);
pybind11::gil_scoped_acquire gil_guard;
return model_(*args, **kwargs);
}
template <>
void ScriptModuleBenchmark::addInput(py::args&& args, py::kwargs&& kwargs) {
jit::Stack stack = jit::createStackForSchema(
model_.get_method("forward").function().getSchema(),
std::move(args),
// NOLINTNEXTLINE(performance-move-const-arg)
std::move(kwargs),
model_._ivalue());
inputs_.emplace_back(std::move(stack));
}
template <>
void ScriptModuleBenchmark::addInput(ScriptModuleInput&& input) {
input.insert(input.begin(), model_._ivalue());
inputs_.emplace_back(std::move(input));
}
template <>
void ModuleBenchmark::addInput(py::args&& args, py::kwargs&& kwargs) {
inputs_.emplace_back(std::move(args), std::move(kwargs));
}
template <>
ModuleInput cloneInput<ModuleInput>(const ModuleInput& input) {
pybind11::gil_scoped_acquire gil_guard;
py::args args = input.args;
py::kwargs kwargs = input.kwargs;
return {std::move(args), std::move(kwargs)};
}
template <>
ScriptModuleInput cloneInput<ScriptModuleInput>(
const ScriptModuleInput& input) {
return input;
}
} // namespace detail
} // namespace throughput_benchmark
} // namespace torch
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