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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40066 Builds on top of the previous PR to ensure that all remotely profiled events are prefixed with the key for the RPC that generated them. The key is generated by the result of `_build_rpc_profiling_key` in `rpc/internal.py` and prefixed onto the event name. In order to do this, we set the current-key when creating the RPC in Python, retrieve the currently-set key in C++ and save a GloballyUniqueId -> key mapping to an in-memory map. When we receive an RPC with profiling information, we expect to receive this ID back, and look up the corresponding profiling key in the map. The key is then added to all the remote events. Tested by adding tests to ensure the key is added to all the remote events. Also added a UT which tests in under the multi-threading scenario, to ensure that the mapping's correctness is maintained when several RPCs are in the process of being created at once. ghstack-source-id: 106316106 Test Plan: Unit test Differential Revision: D22040035 fbshipit-source-id: 9215feb06084b294edbfa6e03385e13c1d730c43
276 lines
9.2 KiB
C++
276 lines
9.2 KiB
C++
#include <torch/csrc/distributed/rpc/rref_impl.h>
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#include <ATen/record_function.h>
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#include <fmt/format.h>
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#include <torch/csrc/distributed/autograd/rpc_messages/rpc_with_autograd.h>
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#include <torch/csrc/distributed/autograd/utils.h>
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#include <torch/csrc/distributed/rpc/profiler/remote_profiler_manager.h>
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#include <torch/csrc/distributed/rpc/rref_context.h>
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#include <torch/csrc/distributed/rpc/rref_proto.h>
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#include <torch/csrc/distributed/rpc/utils.h>
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namespace {
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// If the type is subtype of named type, return its qualifiedname, otherwise
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// return its type str.
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std::string getTypeStr(const c10::TypePtr& type) {
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switch (type->kind()) {
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case c10::TypeKind::FunctionType:
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return type->cast<c10::FunctionType>()->name()->qualifiedName();
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case c10::TypeKind::TupleType:
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return type->cast<c10::TupleType>()->name()->qualifiedName();
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case c10::TypeKind::ClassType:
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return type->cast<c10::ClassType>()->name()->qualifiedName();
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case c10::TypeKind::InterfaceType:
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return type->cast<c10::InterfaceType>()->name()->qualifiedName();
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default:
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return type->annotation_str();
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}
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}
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} // namespace
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namespace torch {
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namespace distributed {
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namespace rpc {
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std::atomic<local_id_t> RRefContext::nextLocalId_{0};
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////////////////////////// RRefForkData /////////////////////////////////
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RRefForkData::RRefForkData(
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worker_id_t ownerId,
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const RRefId& rrefId,
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const ForkId& forkId,
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worker_id_t parent,
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std::string typeStr)
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: ownerId_(ownerId),
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rrefId_(rrefId),
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forkId_(forkId),
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parent_(parent),
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typeStr_(std::move(typeStr)) {}
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////////////////////////////// RRef /////////////////////////////////////
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RRef::RRef(worker_id_t ownerId, const RRefId& rrefId, TypePtr type)
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: RRefInterface(),
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ownerId_(ownerId),
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rrefId_(rrefId),
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type_(std::move(type)) {}
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RRefForkData RRef::fork() const {
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auto& ctx = RRefContext::getInstance();
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return RRefForkData(
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ownerId_,
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rrefId_,
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ctx.genGloballyUniqueId(),
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ctx.getWorkerId(),
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getTypeStr(type_));
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}
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void RRef::handleError(
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RPCErrorType errorType,
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const FutureMessage& futMessage) {
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static std::unordered_map<
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RPCErrorType,
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std::function<void(const FutureMessage& fm)>,
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std::hash<int>>
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errorHandlers = {
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{RPCErrorType::TIMEOUT,
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[this](const FutureMessage& /* unused */) { setTimedOut(); }},
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{RPCErrorType::INTENTIONAL_FAILURE,
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[this](const FutureMessage& /* unused */) { setTimedOut(); }},
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{RPCErrorType::UNKNOWN_ERROR, [](const FutureMessage& fm) {
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// Default error handler
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RRefContext::handleException(fm);
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}}};
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errorHandlers.find(errorType)->second(futMessage);
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}
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////////////////////////// UserRRef /////////////////////////////////////
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UserRRef::UserRRef(
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worker_id_t ownerId,
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const RRefId& rrefId,
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const ForkId& forkId,
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TypePtr type)
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: RRef(ownerId, rrefId, std::move(type)),
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forkId_(forkId),
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confirmedByOwner_(false) {
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// Do nothing,
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// (1) If this UserRRef is a fork of an existing RRef, RRefContext will send
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// a RREF_FORK_REQUEST message to the owner.
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// (2) If this the creator UserRRef, ScriptRemoteCall or PythonRemoteCall will
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// properly notify the owner.
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}
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void UserRRef::tryDel() {
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std::lock_guard<std::mutex> lockGuard(deletedOnOwnerMutex_);
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if (!deletedOnOwner_) {
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try {
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RRefContext::getInstance().delUser(ownerId_, rrefId_, forkId_);
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deletedOnOwner_ = true;
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} catch (const std::exception& ex) {
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LOG(ERROR) << "Error occurred when deleting" << *this << " : "
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<< ex.what();
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} catch (...) {
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LOG(ERROR) << "Error occurred when deleting" << *this << " : "
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<< "unknown error";
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}
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}
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}
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void UserRRef::release_resources() {
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tryDel();
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}
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const ForkId& UserRRef::forkId() const {
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return forkId_;
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}
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IValue UserRRef::toHere(const float timeoutSeconds) const {
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TORCH_CHECK(
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!getTimedOut(),
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"RRef creation via rpc.remote() timed out, and it "
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"is possible that the RRef on the owner node does not exist.");
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// see Note [Best-Effort Check on Deleted UserRRefs]
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TORCH_CHECK(
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!deletedOnOwner_,
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*this,
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" has been deleted. Cannot call to_here() on it after deletion.");
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auto toHereKey = std::string("");
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if (torch::autograd::profiler::profilerEnabled()) {
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toHereKey = fmt::format(
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"to_here#({})->({})",
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RpcAgent::getCurrentRpcAgent()->getWorkerInfo().name_,
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RpcAgent::getCurrentRpcAgent()->getWorkerInfo(ownerId_).name_);
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auto& remoteProfilerManager =
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torch::distributed::rpc::RemoteProfilerManager::getInstance();
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remoteProfilerManager.setCurrentKey(toHereKey);
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}
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RECORD_USER_SCOPE(toHereKey);
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TORCH_CHECK(
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!type_->is_module(),
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*this,
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" is an RRef to a ScriptModule. "
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"It can't be sent through RPC "
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"from owner, ",
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ownerWorkerInfo(),
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", to user, ",
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RpcAgent::getCurrentRpcAgent()->getWorkerInfo(),
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".");
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auto agent = RpcAgent::getCurrentRpcAgent();
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// ScriptRRefFetchCall message always carries autograd context id even if
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// the message itself does not contain any tensor, because the response would
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// potentially contain tensors.
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Message msgToSend;
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if (isPyObj()) {
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msgToSend = PythonRRefFetchCall(ownerId_, rrefId()).toMessage();
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} else {
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msgToSend = ScriptRRefFetchCall(ownerId_, rrefId()).toMessage();
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}
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auto futureResponse = autograd::sendMessageWithAutograd(
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*agent,
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agent->getWorkerInfo(ownerId_),
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std::move(msgToSend),
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true /* forceGradRecording */,
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timeoutSeconds);
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// TODO: we should ideally be able to interrupt this blocking wait if we check
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// getTimedOut() and it is true
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// (https://github.com/pytorch/pytorch/issues/39411).
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const Message& message = futureResponse->wait();
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MessageType msgType = message.type();
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auto response = deserializeResponse(message, msgType);
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TORCH_INTERNAL_ASSERT(
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msgType == MessageType::SCRIPT_RREF_FETCH_RET ||
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msgType == MessageType::PYTHON_RREF_FETCH_RET,
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"Message type should either be SCRIPT_RREF_FETCH_RET "
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"or PYTHON_RREF_FETCH_RET");
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RpcCommandBase& rpc = *response;
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auto& rrefFetchRet = static_cast<RRefFetchRet&>(rpc);
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if (isPyObj()) {
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// wrap python serialized vector of ivalues into tuple, this
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// made the C++ toHere interface to return single IValue
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return ivalue::Tuple::create(rrefFetchRet.values());
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} else {
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return rrefFetchRet.values().front();
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}
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}
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RRefForkData UserRRef::fork() const {
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// Note [Best-Effort Check on Deleted UserRRefs]
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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// This check does not guarantee correctness, as there could be another thread
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// trying to delete this UserRRef concurrently. Passing this check does not
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// mean this RRef will be alive throughout this function. This is just our
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// best-effort attempt to raise proper error messages. The behavior of using
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// deleted UserRRefs is undefined.
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//
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// The reason for not implementing strict checks are:
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// 1. This would need to acquire lock on deletedOnOwnerMutex_, which would
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// introduce unnecessary overhead for most normal use cases.
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// 2. This would introduce a lot of complexities to get the behavior correct.
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// Assume we acquired the lock here, and there is another thread X block
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// waiting in tryDel() on the lock. Exiting this fork function would
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// unblock thread X. However, while X proceeds with deleting this UserRRef,
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// the call site of fork() might have added the UserRRef to
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// pendingChildren_ map, but up to this point, nothing prevents X from
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// deleting this RRef even if it shouldn't do so due to the state change
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// in pendingChildren_. We might be able to get it right for now by locking
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// and checking pendingChildren_ in X, but the gain does not seem to
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// worth the complexity.
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TORCH_CHECK(
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!deletedOnOwner_,
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*this,
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" has been deleted. Cannot call fork an UserRRef after deletion.");
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return RRef::fork();
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}
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////////////////////////// OwnerRRef /////////////////////////////////////
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const IValue& OwnerRRef::getValue() const {
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TORCH_CHECK(
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!getTimedOut(),
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"RRef creation via rpc.remote() timed out, and it "
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"is possible that the RRef on the owner node does not exist.");
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future_->wait();
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if (future_->hasError()) {
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(void)future_->value(); // Throws the error.
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}
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return future_->constValue();
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}
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bool OwnerRRef::hasValue() const {
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return future_->completed();
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}
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std::shared_ptr<JitFuture> OwnerRRef::getFuture() {
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return future_;
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}
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void OwnerRRef::setValue(IValue&& value) {
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future_->markCompleted(value);
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}
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void OwnerRRef::setError(const std::string& error) {
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future_->setErrorIfNeeded(error);
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}
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std::ostream& operator<<(std::ostream& os, const RRef& rref) {
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if (rref.isOwner()) {
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return os << "OwnerRRef("
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<< "rref_id=" << rref.rrefId() << ")";
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} else {
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return os << "UserRRef("
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<< "rref_id=" << rref.rrefId()
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<< ", fork_id=" << static_cast<const UserRRef*>(&rref)->forkId()
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<< ")";
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}
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}
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} // namespace rpc
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} // namespace distributed
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} // namespace torch
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