pytorch/torch/csrc/distributed/rpc/rref_impl.cpp
Luca Wehrstedt 0422e67336 Use Devices instead of DeviceIndexes in TensorPipe agent (#57294)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57294

With the advent of CPUs in the device maps, and to be more generic (e.g., to support AMD GPUs), and to avoid conversions when passing to Future and RRef and such, it's easier to use Devices instead of DeviceIndices. This started by just migrating the TensorPipe agent but the RPC layer is quite intertwined so I had to migrate a lot of stuff.
ghstack-source-id: 127916562

Test Plan: CI

Reviewed By: mrshenli

Differential Revision: D28092733

fbshipit-source-id: 024dcb3648c5898ab13e770413c43958f04f1a8a
2021-05-01 16:12:55 -07:00

337 lines
11 KiB
C++

#include <torch/csrc/distributed/rpc/rref_impl.h>
#include <ATen/record_function.h>
#include <c10/core/impl/DeviceGuardImplInterface.h>
#include <fmt/format.h>
#include <torch/csrc/distributed/autograd/rpc_messages/rpc_with_autograd.h>
#include <torch/csrc/distributed/autograd/utils.h>
#include <torch/csrc/distributed/rpc/profiler/remote_profiler_manager.h>
#include <torch/csrc/distributed/rpc/rref_context.h>
#include <torch/csrc/distributed/rpc/rref_proto.h>
#include <torch/csrc/distributed/rpc/utils.h>
namespace {
// If the type is subtype of named type, return its qualifiedname, otherwise
// return its type str.
std::string getTypeStr(const c10::TypePtr& type) {
switch (type->kind()) {
case c10::TypeKind::FunctionType:
return type->castRaw<c10::FunctionType>()->name()->qualifiedName();
case c10::TypeKind::TupleType:
return type->castRaw<c10::TupleType>()->name()->qualifiedName();
case c10::TypeKind::ClassType:
return type->castRaw<c10::ClassType>()->name()->qualifiedName();
case c10::TypeKind::InterfaceType:
return type->castRaw<c10::InterfaceType>()->name()->qualifiedName();
default:
return type->annotation_str();
}
}
void blockCurrentStreams(const std::vector<c10::Event>& events) {
for (const c10::Event& event : events) {
c10::Device device{event.device_type(), event.device_index()};
c10::Stream stream =
c10::impl::getDeviceGuardImpl(device.type())->getStream(device);
event.block(stream);
}
}
} // namespace
namespace torch {
namespace distributed {
namespace rpc {
std::atomic<local_id_t> RRefContext::nextLocalId_{0};
////////////////////////// RRefForkData /////////////////////////////////
RRefForkData::RRefForkData(
worker_id_t ownerId,
const RRefId& rrefId,
const ForkId& forkId,
worker_id_t parent,
std::string typeStr)
: ownerId_(ownerId),
rrefId_(rrefId),
forkId_(forkId),
parent_(parent),
typeStr_(std::move(typeStr)) {}
////////////////////////////// RRef /////////////////////////////////////
RRef::RRef(worker_id_t ownerId, const RRefId& rrefId, TypePtr type)
: RRefInterface(),
ownerId_(ownerId),
rrefId_(rrefId),
type_(std::move(type)) {}
RRefForkData RRef::fork() const {
auto& ctx = RRefContext::getInstance();
return RRefForkData(
ownerId_,
rrefId_,
ctx.genGloballyUniqueId(),
ctx.getWorkerId(),
getTypeStr(type_));
}
void RRef::handleError(RPCErrorType errorType, const JitFuture& jitFuture) {
static std::unordered_map<
RPCErrorType,
std::function<void(const JitFuture& jitFuture)>,
std::hash<int>>
errorHandlers = {
{RPCErrorType::TIMEOUT,
[this](const JitFuture& /* unused */) { setTimedOut(); }},
{RPCErrorType::INTENTIONAL_FAILURE,
[this](const JitFuture& /* unused */) { setTimedOut(); }},
{RPCErrorType::UNKNOWN_ERROR, [](const JitFuture& jitFuture) {
// Default error handler
RRefContext::handleException(jitFuture);
}}};
errorHandlers.find(errorType)->second(jitFuture);
}
////////////////////////// UserRRef /////////////////////////////////////
UserRRef::UserRRef(
worker_id_t ownerId,
const RRefId& rrefId,
const ForkId& forkId,
TypePtr type)
: RRef(ownerId, rrefId, std::move(type)),
forkId_(forkId),
confirmedByOwner_(false) {
// Do nothing,
// (1) If this UserRRef is a fork of an existing RRef, RRefContext will send
// a RREF_FORK_REQUEST message to the owner.
// (2) If this the creator UserRRef, ScriptRemoteCall or PythonRemoteCall will
// properly notify the owner.
}
void UserRRef::tryDel() {
std::lock_guard<std::mutex> lockGuard(deletedOnOwnerMutex_);
if (!deletedOnOwner_) {
try {
RRefContext::getInstance().delUser(ownerId_, rrefId_, forkId_);
deletedOnOwner_ = true;
} catch (const std::exception& ex) {
LOG(ERROR) << "Error occurred when deleting" << *this << " : "
<< ex.what();
} catch (...) {
LOG(ERROR) << "Error occurred when deleting" << *this << " : "
<< "unknown error";
}
}
}
void UserRRef::release_resources() {
tryDel();
}
const ForkId& UserRRef::forkId() const {
return forkId_;
}
IValue UserRRef::toHere(const float timeoutSeconds) const {
TORCH_CHECK(
!getTimedOut(),
"RRef creation via rpc.remote() timed out, and it "
"is possible that the RRef on the owner node does not exist.");
// see Note [Best-Effort Check on Deleted UserRRefs]
TORCH_CHECK(
!deletedOnOwner_,
*this,
" has been deleted. Cannot call to_here() on it after deletion.");
auto toHereKey = std::string("");
if (torch::autograd::profiler::profilerEnabled()) {
toHereKey = fmt::format(
"to_here#({})->({})",
RpcAgent::getCurrentRpcAgent()->getWorkerInfo().name_,
RpcAgent::getCurrentRpcAgent()->getWorkerInfo(ownerId_).name_);
}
RECORD_USER_SCOPE(toHereKey);
TORCH_CHECK(
!type_->is_module(),
*this,
" is an RRef to a ScriptModule. "
"It can't be sent through RPC "
"from owner, ",
ownerWorkerInfo(),
", to user, ",
RpcAgent::getCurrentRpcAgent()->getWorkerInfo(),
".");
auto agent = RpcAgent::getCurrentRpcAgent();
// ScriptRRefFetchCall message always carries autograd context id even if
// the message itself does not contain any tensor, because the response would
// potentially contain tensors.
Message msgToSend;
if (isPyObj()) {
msgToSend = PythonRRefFetchCall(ownerId_, rrefId()).toMessage();
} else {
msgToSend = ScriptRRefFetchCall(ownerId_, rrefId()).toMessage();
}
// toHere is profiled as a blocking call, and does not execute operations on
// the remote node. Hence, don't wrap it with a profiling message since we
// don't need the profiler to be enabled remotely.
auto jitFuture = autograd::sendMessageWithAutograd(
*agent,
agent->getWorkerInfo(ownerId_),
std::move(msgToSend),
true /* forceGradRecording */,
timeoutSeconds,
true /* forceDisableProfiling */);
// TODO: we should ideally be able to interrupt this blocking wait if we check
// getTimedOut() and it is true
// (https://github.com/pytorch/pytorch/issues/39411).
jitFuture->waitAndThrow();
auto messagePtr = jitFuture->constValue().toCustomClass<Message>();
MessageType msgType = messagePtr->type();
auto response = deserializeResponse(*messagePtr, msgType);
TORCH_INTERNAL_ASSERT(
msgType == MessageType::SCRIPT_RREF_FETCH_RET ||
msgType == MessageType::PYTHON_RREF_FETCH_RET,
"Message type should either be SCRIPT_RREF_FETCH_RET "
"or PYTHON_RREF_FETCH_RET");
RpcCommandBase& rpc = *response;
auto& rrefFetchRet = static_cast<RRefFetchRet&>(rpc);
if (isPyObj()) {
// wrap python serialized vector of ivalues into tuple, this
// made the C++ toHere interface to return single IValue
return ivalue::Tuple::create(rrefFetchRet.values());
} else {
return rrefFetchRet.values().front();
}
}
RRefForkData UserRRef::fork() const {
// Note [Best-Effort Check on Deleted UserRRefs]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// This check does not guarantee correctness, as there could be another thread
// trying to delete this UserRRef concurrently. Passing this check does not
// mean this RRef will be alive throughout this function. This is just our
// best-effort attempt to raise proper error messages. The behavior of using
// deleted UserRRefs is undefined.
//
// The reason for not implementing strict checks are:
// 1. This would need to acquire lock on deletedOnOwnerMutex_, which would
// introduce unnecessary overhead for most normal use cases.
// 2. This would introduce a lot of complexities to get the behavior correct.
// Assume we acquired the lock here, and there is another thread X block
// waiting in tryDel() on the lock. Exiting this fork function would
// unblock thread X. However, while X proceeds with deleting this UserRRef,
// the call site of fork() might have added the UserRRef to
// pendingChildren_ map, but up to this point, nothing prevents X from
// deleting this RRef even if it shouldn't do so due to the state change
// in pendingChildren_. We might be able to get it right for now by locking
// and checking pendingChildren_ in X, but the gain does not seem to
// worth the complexity.
TORCH_CHECK(
!deletedOnOwner_,
*this,
" has been deleted. Cannot call fork an UserRRef after deletion.");
return RRef::fork();
}
////////////////////////// OwnerRRef /////////////////////////////////////
OwnerRRef::OwnerRRef(
worker_id_t ownerId,
const RRefId& rrefId,
TypePtr type,
std::vector<c10::DeviceIndex> devices)
: OwnerRRef(ownerId, rrefId, type, /* value */ {}, std::move(devices)) {}
OwnerRRef::OwnerRRef(
worker_id_t ownerId,
const RRefId& rrefId,
TypePtr type,
c10::optional<IValue> value,
std::vector<c10::DeviceIndex> devices)
: RRef(ownerId, rrefId, type) {
std::vector<c10::Device> fullDevices;
fullDevices.reserve(devices.size());
for (const c10::DeviceIndex& idx : devices) {
fullDevices.emplace_back(c10::kCUDA, idx);
}
future_ = std::make_shared<JitFuture>(
at::AnyClassType::get(), std::move(fullDevices));
if (value.has_value()) {
future_->markCompleted(value.value());
}
}
const IValue& OwnerRRef::getValue() const {
TORCH_CHECK(
!getTimedOut(),
"RRef creation via rpc.remote() timed out, and it "
"is possible that the RRef on the owner node does not exist.");
future_->wait();
if (future_->hasError()) {
(void)future_->value(); // Throws the error.
}
// Before accessing the value in this RRef, current CUDA streams must wait
// for pending CUDA operations that create the value.
blockCurrentStreams(events_);
return future_->constValue();
}
bool OwnerRRef::hasValue() const {
return future_->completed();
}
std::shared_ptr<JitFuture> OwnerRRef::getFuture() {
return future_;
}
void OwnerRRef::setValue(IValue&& value) {
future_->markCompleted(value);
}
void OwnerRRef::setError(std::exception_ptr eptr) {
future_->setErrorIfNeeded(std::move(eptr));
}
void OwnerRRef::recordAllStreams(
const std::shared_ptr<LazyStreamContext>& ctx) {
if (ctx) {
for (auto stream : ctx->getReservedStreams()) {
c10::Event event{ctx->deviceType()};
event.record(stream);
events_.push_back(std::move(event));
}
}
}
void OwnerRRef::blockAllStreams(std::shared_ptr<LazyStreamContext>& ctx) {
if (ctx) {
for (c10::Event& event : events_) {
event.block(ctx->getStream(event.device()));
}
}
}
std::ostream& operator<<(std::ostream& os, const RRef& rref) {
if (rref.isOwner()) {
return os << "OwnerRRef("
<< "rref_id=" << rref.rrefId() << ")";
} else {
return os << "UserRRef("
<< "rref_id=" << rref.rrefId()
<< ", fork_id=" << static_cast<const UserRRef*>(&rref)->forkId()
<< ")";
}
}
} // namespace rpc
} // namespace distributed
} // namespace torch