撰文|郑建华、赵露阳
1
Op在虚拟机里的履行
1.1 PhysicalRun和InstructionsBuilder
上一篇文章《OneFlow源码解析:Op、Kernel与解说器》 中说到:
PhysicalRun承受一个lambda函数作为参数,这儿即InstructionsBuilder->Call办法,该办法承受kernel、input/output的eager blob object、kernel履行的上下文作为参数。Call办法实践会完结OpCall指令的构建,并终究将其派发至vm指令列表中,等候VM实践调度履行。
这个PhysicalRun函数里包裹着一个lambda函数:
JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe<void> {
return builder->Call(xxx);
}));
其间,lambda函数承受一个InstructionsBuilder指针(builder),并调用builder->Call办法,用于实践完结Op指令在VM中的构建。而PhysicalRun(github.com/Oneflow-Inc… )在oneflow/core/framework/instructions_builder.h
中界说,其承受lambda函数作为模版参数(CallbackT):
// Make VM instructions with instruction builder and run instructions with physical/local view.
template<typename CallbackT>
Maybe<void> PhysicalRun(const CallbackT& Build) {
vm::InstructionList instruction_list;
InstructionsBuilder instructions_builder(&instruction_list);
JUST(Build(&instructions_builder));
JUST(vm::Run(instructions_builder.mut_instruction_list()));
return Maybe<void>::Ok();
}
可见,PhysicalRun函数中,首要初始化一个InstructionsBuilder,然后将InstructionsBuilder指针作为参数传给lambda函数,完结实践指令的构建;最后经过vm::Run()办法将该指令发送至VM,等候VM实践调度和履行。Run办法如下:
Maybe<void> Run(vm::InstructionList* instruction_list) {
auto* virtual_machine = JUST(SingletonMaybe<VirtualMachine>());
JUST(virtual_machine->Receive(instruction_list));
return Maybe<void>::Ok();
}
能够看见,Run()办法获取了全局单例的VM目标指针,然后经过vm的Receive()办法,将该条指令发送给虚拟机(所以这儿Run其实有点歧义,更恰当的意思,其实是指令发送或传送)。
这个VirtualMachine->Receive办法很重要,会在后边的第2.章节中详细打开。
1.2 InstructionsBuilder
上面PhysicalRun函数中的InstructionsBuilder,类似一个指令构建的helper,InstructionsBuilder的系列办法合作指令战略(InstructionPolicy),能够协助构建不同类型的vm指令。
从InstructionsBuilder(github.com/Oneflow-Inc… )的界说中,咱们能够看到指令的构建办法,其间常用办法如下:
// 用于lazy mode(nn.Graph)
// Build VM execution instructions with NNGraph's inputs/outputs/parameters for NNGraph execution.
Maybe<void> LaunchLazyJob(const vm::EagerBlobObjectListPtr& inputs,
const vm::EagerBlobObjectListPtr& outputs,
const vm::EagerBlobObjectListPtr& parameters,
const std::shared_ptr<NNGraphIf>& nn_graph);
// 用于全局同步,同步等候一切指令调用完结
Maybe<void> GlobalSync();
// 用于Tensor内存开释(归还allocator)
Maybe<void> ReleaseTensor(const std::shared_ptr<vm::EagerBlobObject>& eager_blob_object);
// 操作Tensor实践内存(blob)
template<typename T>
Maybe<void> AccessBlobByCallback(
const T tensor,
const std::function<void(ep::Stream*, const std::shared_ptr<vm::EagerBlobObject>&)>& callback,
const std::string& modifier);
// 最常用的指令构建办法,用于结构op履行所需的OpCall指令
Maybe<void> Call(const std::shared_ptr<one::StatefulOpKernel>& opkernel,
vm::EagerBlobObjectList&& input_eager_blob_objects,
vm::EagerBlobObjectList&& output_eager_blob_objects,
const one::OpExprInterpContext& ctx, Symbol<Stream> stream);
1.3 InstructionPolicy
InstructionPolicy(github.com/Oneflow-Inc… )——指令战略,一般用于合作InstructionsBuilder
实践构建出不同的vm指令。InstructionPolicy
的子类完成如下:
这些子类的InstructionPolicy可近似以为是指令类型。如,用于Op履行的OpCallInstructionPolicy
、用于Tensor内存开释的
ReleaseTensorInstructionPolicy
、用于屏障堵塞的BarrierInstructionPolicy
等。
以Op履行为例:
JUST(PhysicalRun([&](InstructionsBuilder* builder) -> Maybe<void> {
return builder->Call(xxx);
}));
实践上是经过InstructionsBuilder的Call办法(github.com/Oneflow-Inc… ),合作OpCall的指令战略(OpCallInstructionPolicy),结构了OpCall指令:
Maybe<void> InstructionsBuilder::Call(
const std::shared_ptr<one::StatefulOpKernel>& opkernel,
vm::EagerBlobObjectList&& input_eager_blob_objects,
vm::EagerBlobObjectList&& output_eager_blob_objects,
const std::shared_ptr<const one::GlobalTensorInferResult>& global_tensor_infer_result,
const one::OpExprInterpContext& ctx, Symbol<Stream> stream) {
...
...
// 获取当前vm stream
auto* vm_stream = JUST(Singleton<VirtualMachine>::Get()->GetVmStream(stream));
// 经过OpCallInstructionPolicy初始化OpCall指令
auto instruction = intrusive::make_shared<vm::Instruction>(
vm_stream, std::make_shared<vm::OpCallInstructionPolicy>(
vm_stream, opkernel, std::move(input_eager_blob_objects),
std::move(output_eager_blob_objects), global_tensor_infer_result, ctx,
*one::CurrentDevVmDepObjectConsumeMode()));
// 指令入列表
instruction_list_->EmplaceBack(std::move(instruction));
return Maybe<void>::Ok();
}
并将构建好的指令塞入指令列表,待后续VM调度并实践履行。
2
虚拟机的运转原理
2.1 VM初始化
OneFlow环境初始化时,会触发VirtualMachineScope(github.com/Oneflow-Inc… )的初始化:
VirtualMachineScope::VirtualMachineScope(const Resource& resource) {
Singleton<VirtualMachine>::New();
}
进而触发VM目标——VirtualMachine(github.com/Oneflow-Inc… )的初始化。VM作为一个Singleton目标,全局唯一。
VirtualMachine::VirtualMachine() : disable_vm_threads_(false), scheduler_stopped_(false) {
// Class VirtualMachineEngine only cares the basic logical of vm, while class VirtualMachine
// manages threads and condition variables.
// In order to notify threads in VirtualMachineEngine, a notify callback lambda should be take as
// an argument for VirtualMachineEngine's constructor.
engine_ = intrusive::make_shared<vm::VirtualMachineEngine>();
OF_PROFILER_NAME_THIS_HOST_THREAD("_Main");
std::function<void()> SchedulerInitializer;
GetSchedulerThreadInitializer(&SchedulerInitializer);
schedule_thread_ = std::thread(&VirtualMachine::ScheduleLoop, this, SchedulerInitializer);
transport_local_dep_object_.Reset();
}
VM初始化中最重要的内容,便是:
1.初始化了一个VM的履行引擎——VirtualMachineEngine
2.经过VirtualMachine::ScheduleLoop启动了VM的调度线程
VirtualMachine::ScheduleLoop
VM目标只担任条件变量和线程管理;而首要事务逻辑处理(包含指令构建、调度、派发和履行等),则由VirtualMachineEngine(github.com/Oneflow-Inc… )目标担任;VM初始化时还开辟了单独的schedule线程用于VM引擎处理调度逻辑,在VirtualMachine::ScheduleLoop(github.com/Oneflow-Inc… )中:
void VirtualMachine::ScheduleLoop(const std::function<void()>& Initializer) {
SyncVmModeGuard guard(SyncVmMode::kEnable);
Initializer();
MultiThreadScheduleCtx schedule_ctx{};
while (pending_notifier_.WaitAndClearNotifiedCnt() == kNotifierStatusSuccess) {
OF_PROFILER_RANGE_GUARD("VirtualMachine::ScheduleLoop");
auto start = std::chrono::steady_clock::now();
static constexpr int kWorkingMicroseconds = 1000;
// Every time this thread wakes up, engine_ is scheduled for about `kWorkingMicroseconds`.
// The cost of os thread switching is about 5-10 microseconds. Doing more scheduling in
// a single waiting up can reach higher performance.
do {
do {
const size_t total_inserted = engine_->total_inserted_instruction_cnt();
const size_t total_erased = engine_->total_erased_instruction_cnt();
engine_->Schedule(schedule_ctx);
if (ThreadLocalEnvBool<ONEFLOW_VM_ENABLE_SCHEDULE_YIELD>()
&& total_inserted == engine_->total_inserted_instruction_cnt()
&& total_erased == engine_->total_erased_instruction_cnt()) { // nothing handled.
std::this_thread::yield();
}
} while (!engine_->SchedulerThreadUnsafeEmpty());
} while (MicrosecondsFrom(start) < kWorkingMicroseconds);
}
ScheduleUntilVMEmpty(engine_.Mutable(), schedule_ctx);
CHECK_JUST(ForEachThreadCtx(engine_.Mutable(), [&](vm::ThreadCtx* thread_ctx) -> Maybe<void> {
thread_ctx->mut_notifier()->Close();
return Maybe<void>::Ok();
}));
{
std::unique_lock<std::mutex> lock(worker_threads_mutex_);
for (const auto& worker_thread : worker_threads_) { worker_thread->join(); }
}
scheduler_stopped_ = true;
}
ScheduleLoop
是一个近似于busy loop的while循环,pending_notifier_是VM内部保护的成员,实践上是ScheduleLoop
线程的告诉/唤醒者,其界说坐落oneflow/oneflow/core/common/notifier.h
:
class Notifier final {
public:
OF_DISALLOW_COPY_AND_MOVE(Notifier);
Notifier() : notified_cnt_(0), is_closed_(false) {}
~Notifier() = default;
NotifierStatus Notify();
NotifierStatus WaitAndClearNotifiedCnt();
void Close();
private:
size_t notified_cnt_;
std::mutex mutex_;
bool is_closed_;
std::condition_variable cond_;
};
其首要保护了互斥锁mutex_、线程是否封闭的flag is_closed_、条件变量cond_。忽略线程唤醒、超时相关逻辑,ScheduleLoop中最重要的工作是engine_->Schedule(schedule_ctx)
;
while (pending_notifier_.WaitAndClearNotifiedCnt() == kNotifierStatusSuccess) {
auto start = std::chrono::steady_clock::now();
...
do {
do {
...
engine_->Schedule(schedule_ctx);
...
} while (!engine_->SchedulerThreadUnsafeEmpty());
} while (MicrosecondsFrom(start) < kWorkingMicroseconds);
}
当VM保护的指令队列不为空时,便不断唤醒VM引擎履行指令调度逻辑——engine->Schedule()
2.2 VM指令调度
void VirtualMachineEngine::Schedule(const ScheduleCtx& schedule_ctx) {
// Release finished instructions and try to schedule out instructions in DAG onto ready list.
if (unlikely(mut_active_stream_list()->size())) { ReleaseFinishedInstructions(schedule_ctx); }
// Try run the first barrier instruction.
if (unlikely(mut_barrier_instruction_list()->size())) { TryRunBarrierInstruction(schedule_ctx); }
// Handle pending instructions, and try schedule them to ready list.
// Use thread_unsafe_size to avoid acquiring mutex lock.
// The inconsistency between pending_instruction_list.list_head_.list_head_.container_ and
// pending_instruction_list.list_head_.list_head_.size_ is not a fatal error because
// VirtualMachineEngine::Schedule is always in a buzy loop. All instructions will get handled
// eventually.
// VirtualMachineEngine::Receive may be less effiencient if the thread safe version
// `pending_instruction_list().size()` used here, because VirtualMachineEngine::Schedule is more
// likely to get the mutex lock.
if (unlikely(local_pending_instruction_list().size())) {
HandleLocalPending();
} else if (unlikely(pending_instruction_list().thread_unsafe_size())) {
// MoveTo is under a lock.
mut_pending_instruction_list()->MoveTo(mut_local_pending_instruction_list());
if (local_pending_instruction_list().size()) { HandleLocalPending(); }
}
// dispatch ready instructions and try to schedule out instructions in DAG onto ready list.
if (unlikely(mut_ready_instruction_list()->size())) {
DispatchAndPrescheduleInstructions(schedule_ctx);
}
// handle scheduler probes
if (unlikely(local_probe_list_.size())) {
HandleLocalProbe();
} else if (unlikely(probe_list_.thread_unsafe_size())) {
probe_list_.MoveTo(&local_probe_list_);
if (local_probe_list_.size()) { HandleLocalProbe(); }
}
}
VM引擎保护了一系列指令列表的成员:
InstructionMutexedList pending_instruction_list_;
// local_pending_instruction_list_ should be consider as the cache of pending_instruction_list_.
InstructionList local_pending_instruction_list_;
ReadyInstructionList ready_instruction_list_;
LivelyInstructionList lively_instruction_list_;
BarrierInstructionList barrier_instruction_list_;
- pending相关的instruction_list是悬挂/待处理的指令列表;
- lively相关的instruction_list是活泼的正在履行中的指令列表;
- ready相关的instruction_list则是已完结准备工作(指令融合、指令DAG构建等)待履行的指令列表;
VM引擎Schedule时,会对指令队列做相应处理,包含:
- 将已完结准备工作的指令放入ready_instruction_list_中保护;
- 测验运转barrier指令列表(barrier_instruction_list_)中的第一条指令;
- 如果本地pending指令列表(local_pending_instruction_list_)非空,则经过
HandleLocalPending
办法处理这些悬挂指令(指令融合、指令履行DAG图构建、插入ready列表) - 如果ready指令列表非空,则经过
DispatchAndPrescheduleInstructions
办法进行指令派发和预调度处理。
这儿重点介绍指令派发相关的
DispatchAndPrescheduleInstructions
办法,其间
DispatchAndPrescheduleInstructions
中最首要的是便是DispatchInstruction
指令派发办法,这儿的指令派发能够以为实践上便是指令履行。
2.3 VM指令派发
VirtualMachineEngine::DispatchInstruction(github.com/Oneflow-Inc… )办法是vm引擎中的中心,其实践完结了指令的派发和实践履行,代码如下:
template<void (VirtualMachineEngine::*OOMHandler)(vm::Stream*, const ScheduleCtx&)>
void VirtualMachineEngine::DispatchInstruction(Instruction* instruction,
const ScheduleCtx& schedule_ctx) {
auto* stream = instruction->mut_stream();
// Prepare
{
// 指令的Prepare
const auto& ret = TRY(instruction->Prepare());
if (unlikely(!ret.IsOk())) {
// 处理指令Prepare进程中的OOM的逻辑
if (ret.error()->has_out_of_memory_error()) {
// 让allocator开释不必要的cacahe,再从头履行指令的Prepare
(this->*OOMHandler)(stream, schedule_ctx);
...
}
}
}
// 将当前指令放入running_instruction_list
stream->mut_running_instruction_list()->PushBack(instruction);
if (stream->active_stream_hook().empty()) { mut_active_stream_list()->PushBack(stream); }
// Compute
if (OnSchedulerThread(*stream)) {
// StreamPolicy的Run办法触发指令的实践履行——Compute
stream->stream_policy().Run(instruction);
} else {
stream->mut_thread_ctx()->mut_worker_pending_instruction_list()->PushBack(instruction);
schedule_ctx.OnWorkerLoadPending(stream->mut_thread_ctx());
}
}
DispatchInstruction的中心首要有2块:
- 履行指令的Prepare
- 履行指令的Compute
Prepare担任一些指令履行前的准备;Compute则是实践的指令履行,指令履行并不是直接经过instruction->Run而是在StreamPolicy的Run办法中完结的,这儿又触及到一个StreamPolicy目标。
StreamPolicy::Run
StreamPolicy(github.com/Oneflow-Inc… )是个虚基类:
class StreamPolicy {
public:
virtual ~StreamPolicy() = default;
virtual ep::Stream* stream() = 0;
virtual vm::Allocator* mut_allocator() = 0;
virtual DeviceType device_type() const = 0;
virtual void InitInstructionStatus(const Stream& stream,
InstructionStatusBuffer* status_buffer) const = 0;
virtual void DeleteInstructionStatus(const Stream& stream,
InstructionStatusBuffer* status_buffer) const = 0;
virtual bool QueryInstructionStatusDone(const Stream& stream,
const InstructionStatusBuffer& status_buffer) const = 0;
virtual void Run(Instruction* instruction) const = 0;
virtual bool OnSchedulerThread(StreamType stream_type) const;
virtual bool SupportingTransportInstructions() const = 0;
protected:
StreamPolicy() = default;
};
- stream()办法回来ep::Stream指针,指向的是针对不同渠道的ep::stream目标。
- mut_allocator()办法回来一个vm的Allocator指针,用于内存分配/开释。 InitInstructionStatus/QueryInstructionStatusDone/DeleteInstructionStatus用于创立/查询/销毁指令履行状态
- Run办法则是中心,界说了该Stream具体运转时的逻辑。
这儿的ep在oneflow中是execution provider的缩写,ep从本质上来讲便是一个针对不同硬件渠道的executor笼统。
StreamPolicy相关的承继和子类如下:
看一下EpStreamPolicyBase的Run办法(github.com/Oneflow-Inc… ):
void EpStreamPolicyBase::Run(Instruction* instruction) const {
...
auto* stream = instruction->mut_stream();
EpStreamPolicyBase* ep_stream_policy_base =
dynamic_cast<EpStreamPolicyBase*>(stream->mut_stream_policy());
...
auto* ep_device = ep_stream_policy_base->GetOrCreateEpDevice();
ep_device->SetAsActiveDevice();
instruction->Compute();
...
}
首要获取了该stream对应的ep device,然后履行了instruction的Compute办法,即指令的实践履行。
2.4 VM履行履行
以OpCall指令为例,看一下op指令的Compute(github.com/Oneflow-Inc… ):
void OpCallInstructionPolicy::Compute(vm::Instruction* instruction) {
OpCallInstructionUtil::Compute(this, instruction);
}
OpCallInstructionPolicy办法调用了OpCallInstructionUtil的Compute办法:
上面咱们能够看到,在指令Prepare时,做了output tensor内存分配;而指令Compute中最重要的办法是:
- TryInitOpKernelStateAndCache——初始化一些kernel核算需要的状态或缓存
- OpKernelCompute——履行该op对应的kernel,kernel内首要是实践的op核算逻辑
user kernel一致坐落:oneflow/user/kernels目录下,.cpp一般对应cpu kernel逻辑;.cu为cuda kernel逻辑。到这儿,就会触发user_kernel的Compute办法,不同op的kernel核算逻辑不同,以rele op为例,实践Compute进程可参考文章《算子在深度学习结构中的履行及interpreter》的第5末节。
2.5 VM指令发送
这儿的VM指令发送,指的是VM外部的指令发送进程(不是VM内部的指令派发)。上面2.1~2.3末节介绍了VM以及VM引擎的初始化、VM内部指令的调度、派发和实践履行的进程,那么这些指令是如何发送到VM的呢?答案是:在1.1末节中说到的PhysicalRun
PhysicalRun
终究会触发VirtualMachine->Receive
办法,并经过VirtualMachineEngine的Receive办法完结外部指令 -> VM内部的发送。
VirtualMachineEngine的Receive办法(github.com/Oneflow-Inc… )首要将该指令经过MoveFrom办法push back到指令悬挂列表(pending_instruction_list_)的结尾,从而完结指令的发送。
// Returns true if old scheduler_pending_instruction_list is empty
Maybe<bool> VirtualMachineEngine::Receive(InstructionList* compute_instruction_list) {
OF_PROFILER_RANGE_GUARD("vm:Receive");
#ifdef OF_ENABLE_PROFILER
INTRUSIVE_UNSAFE_FOR_EACH_PTR(compute_instruction, compute_instruction_list) {
OF_PROFILER_RANGE_GUARD(compute_instruction->DebugName());
}
#endif
bool old_list_empty = mut_pending_instruction_list()->MoveFrom(compute_instruction_list);
return old_list_empty;
}
3
小结
至此,Op履行相关的流程算是大体串了一遍。一句flow.relu()
后边会触及这么多内容。但这儿其实也只重视了主干逻辑,忽略了中间大量的细节。
流程的梳理仅仅第一步,还需要从中归纳总结一些概念和概念之间的联系,再结合公开资料反推印证规划理念的落地完成。
不过目前对代码和规划的了解还很浅薄,下面的内容纯属大胆猜测。
3.1 UserOpExpr
UserOpExpr表明UserOp履行时所需的上下文,其实UserOp仅仅Op中的一种。下图展现了不同Op的承继联系。能够看到tensor从local/global之间的转化等也都触及不同的OpExpr。
3.2 Op履行的宏观头绪
从上面的类联系图出发,以中心类为节点,也能看出Op履行流程的宏观头绪。整个流程大体在下面这些角色之间流通:
- ReluFunctor
- UserOpExpr
- Interpreter
- PhysicalRun
- VirtualMachine->Receive
- VirtualMachine->ScheduleLoop …
3.3 虚拟机运转和调度总结
VM -> ScheduleLoop
VM引擎Schedule
处理悬挂指令(HandleLocalPending)
指令派发(DispatchInstruction)
准备(instruction->Prepare)
履行(StreamPolicy.Run -> instruction->Compute)
指令预调度
VM -> Receive
VM引擎 -> Receive
指令入悬挂列表
一般,咱们习气在动态图形式下训练深度学习网络,运用Python建立网络,并经过各种op进行前向、反向、loss核算、调试debug等进程,这些Python代码能够看作是动态的op的履行序列。
OneFlow虚拟机将op履行序列笼统成了各种VM指令序列。 OneFlow的虚拟机会对这些op履行序列进行动态翻译并生成VM指令序列,经过PhysicalRun结构结束后,动态地将指令发送至VM的悬挂列表中保护。这些指令或在时间上存在先后顺序,或在数据上存在依靠联系,所以悬挂列表中的指令后续会被虚拟机进行一些指令融合、指令连边、动态构建指令DAG图的进程,然后移入就绪列表中保护,等候虚拟机调度并实践履行。虚拟机担任保护若干个指令队列,以及指令在这些队列之间的状态转化。
OneFlow虚拟机还一致了动态图形式(Eager Mode)和静态图形式(Lazy Mode)。 静态图形式下,经过nn.Graph编译出深度学习网络的Job,这个Job同样被虚拟机笼统成了VM指令并承受虚拟机的调度和履行。大胆猜测一下,这也为日后动静转化、更极致的性能优化埋下了伏笔。
参考资料
-
OneFlow学习笔记:从OpExprInterpreter到OpKernel
-
动态调度的“诅咒”| 原有深度学习结构的缺点③
-
算子在深度学习结构中的履行及interpreter
-
OneFlow源码:(github.com/Oneflow-Inc…
欢迎下载体验 OneFlow v0.8.0 最新版本: github.com/Oneflow-Inc…