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#include <ATen/core/ivalue.h>
#include <torch/csrc/utils/init.h>
#include <torch/csrc/utils/throughput_benchmark.h>
#include <pybind11/functional.h>
#include <torch/csrc/utils/pybind.h>
namespace torch::throughput_benchmark {
void initThroughputBenchmarkBindings(PyObject* module) {
auto m = py::handle(module).cast<py::module>();
using namespace torch::throughput_benchmark;
py::class_<BenchmarkConfig>(m, "BenchmarkConfig")
.def(py::init<>())
.def_readwrite(
"num_calling_threads", &BenchmarkConfig::num_calling_threads)
.def_readwrite("num_worker_threads", &BenchmarkConfig::num_worker_threads)
.def_readwrite("num_warmup_iters", &BenchmarkConfig::num_warmup_iters)
.def_readwrite("num_iters", &BenchmarkConfig::num_iters)
.def_readwrite(
"profiler_output_path", &BenchmarkConfig::profiler_output_path);
py::class_<BenchmarkExecutionStats>(m, "BenchmarkExecutionStats")
.def_readonly("latency_avg_ms", &BenchmarkExecutionStats::latency_avg_ms)
.def_readonly("num_iters", &BenchmarkExecutionStats::num_iters);
py::class_<ThroughputBenchmark>(m, "ThroughputBenchmark", py::dynamic_attr())
.def(py::init<jit::Module>())
.def(py::init<py::object>())
.def(
"add_input",
[](ThroughputBenchmark& self, py::args args, py::kwargs kwargs) {
self.addInput(std::move(args), std::move(kwargs));
})
.def(
"run_once",
[](ThroughputBenchmark& self,
const py::args& args,
const py::kwargs& kwargs) {
// Depending on this being ScriptModule of nn.Module we will release
// the GIL or not further down in the stack
return self.runOnce(args, kwargs);
})
.def(
"benchmark",
[](ThroughputBenchmark& self, const BenchmarkConfig& config) {
// The benchmark always runs without the GIL. GIL will be used where
// needed. This will happen only in the nn.Module mode when
// manipulating inputs and running actual inference
pybind11::gil_scoped_release no_gil_guard;
return self.benchmark(config);
});
}
} // namespace torch::throughput_benchmark
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