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#include <torch/csrc/python_headers.h>
#include <torch/csrc/distributed/rpc/request_callback_impl.h>
#include <torch/csrc/distributed/rpc/rpc_agent.h>
#include <torch/csrc/distributed/rpc/tensorpipe_agent.h>
#include <torch/csrc/distributed/rpc/testing/faulty_tensorpipe_agent.h>
#include <torch/csrc/utils/pybind.h>
#include <pybind11/chrono.h>
namespace torch {
namespace distributed {
namespace rpc {
namespace testing {
namespace {
template <typename T>
using shared_ptr_class_ = py::class_<T, std::shared_ptr<T>>;
PyObject* faulty_agent_init(PyObject* _unused, PyObject* noargs) {
// Add the FaultyTensorPipeAgent and its backend options object
// to the python module torch._C._distributed_rpc_testing
auto torch_C_module = THPObjectPtr(PyImport_ImportModule("torch._C"));
if (!torch_C_module) {
throw python_error();
}
auto torch_C_m = py::handle(torch_C_module).cast<py::module>();
auto m = torch_C_m.def_submodule(
"_distributed_rpc_testing", "distributed rpc testing bindings");
auto module = py::handle(m).cast<py::module>();
// Import the rpc_module so we can subclass TensorPipeAgent
py::module rpc_module = py::module::import("torch.distributed.rpc");
#ifdef USE_TENSORPIPE
shared_ptr_class_<FaultyTensorPipeRpcBackendOptions>(
module,
"FaultyTensorPipeRpcBackendOptions",
rpc_module.attr("_TensorPipeRpcBackendOptionsBase"))
.def(
py::init<
int,
float,
std::string,
std::vector<std::string>,
std::unordered_map<std::string, float>,
int>(),
py::arg("num_worker_threads"),
py::arg("rpc_timeout"),
py::arg("init_method"),
py::arg("messages_to_fail"),
py::arg("messages_to_delay"),
py::arg("num_fail_sends"))
.def_readwrite(
"num_worker_threads", &TensorPipeRpcBackendOptions::numWorkerThreads)
.def_readwrite(
"messages_to_fail",
&FaultyTensorPipeRpcBackendOptions::messagesToFail)
.def_readwrite(
"messages_to_delay",
&FaultyTensorPipeRpcBackendOptions::messagesToDelay)
.def_readwrite(
"num_fail_sends", &FaultyTensorPipeRpcBackendOptions::numFailSends);
shared_ptr_class_<FaultyTensorPipeAgent>(
module, "FaultyTensorPipeAgent", rpc_module.attr("TensorPipeAgent"))
.def(
py::init(
[](const c10::intrusive_ptr<::c10d::Store> store,
std::string name,
worker_id_t rank,
int world_size,
FaultyTensorPipeRpcBackendOptions opts,
std::unordered_map<std::string, DeviceMap> reverse_device_maps,
std::vector<c10::Device> devices) {
return std::shared_ptr<FaultyTensorPipeAgent>(
new FaultyTensorPipeAgent(
store,
std::move(name),
rank,
world_size,
opts,
reverse_device_maps,
devices,
std::make_unique<RequestCallbackImpl>()),
impl::destroy_without_gil<FaultyTensorPipeAgent>);
}),
py::arg("store"),
py::arg("name"),
py::arg("rank"),
py::arg("world_size"),
py::arg("opts"),
py::arg("reverse_device_maps"),
py::arg("devices"))
.def(
"join",
&TensorPipeAgent::join,
py::call_guard<py::gil_scoped_release>(),
py::arg("shutdown") = false,
py::arg("timeout") = 0)
.def(
"shutdown",
&TensorPipeAgent::shutdown,
py::call_guard<py::gil_scoped_release>())
.def(
"get_worker_info",
(const WorkerInfo& (TensorPipeAgent::*)(void) const) &
RpcAgent::getWorkerInfo,
py::call_guard<py::gil_scoped_release>())
.def(
"get_worker_info",
(const WorkerInfo& (TensorPipeAgent::*)(const std::string&) const) &
TensorPipeAgent::getWorkerInfo,
py::call_guard<py::gil_scoped_release>())
.def(
"get_worker_info",
(const WorkerInfo& (TensorPipeAgent::*)(worker_id_t id) const) &
TensorPipeAgent::getWorkerInfo,
py::call_guard<py::gil_scoped_release>())
.def(
"get_worker_infos",
(std::vector<WorkerInfo>(TensorPipeAgent::*)() const) &
TensorPipeAgent::getWorkerInfos,
py::call_guard<py::gil_scoped_release>());
#endif // USE_TENSORPIPE
Py_RETURN_TRUE;
}
} // namespace
static PyMethodDef methods[] = { // NOLINT
{"_faulty_agent_init", faulty_agent_init, METH_NOARGS, nullptr},
{nullptr, nullptr, 0, nullptr}};
PyMethodDef* python_functions() {
return methods;
}
} // namespace testing
} // namespace rpc
} // namespace distributed
} // namespace torch
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