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[Profiler] Optimize reportMemoryUsage (#71538)

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Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71538

`reportMemoryUsage` is kind of awful. It does a bunch of string writes and such that makes it VERY expensive. Just moving that work off the hot path reduces the overhead for `profile_memory` from ~6.5 us to ~1.2 us. (85% reduction in the kineto contribution to profiling overhead.)

Test Plan: Ran ubenchmark with `--op empty --stressTestKineto --kinetoProfileMemory`

Reviewed By: swolchok

Differential Revision: D32730167

fbshipit-source-id: fe18e8fa3881967cad8fa1c26c71c805e9b034e5
This commit is contained in:
Taylor Robie 2022-02-20 14:32:29 -08:00 committed by Facebook GitHub Bot
parent a1d18e3e6a
commit 0d394cb252
1 changed files with 46 additions and 23 deletions
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63
torch/csrc/autograd/profiler_kineto.cpp
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@ -155,6 +155,19 @@ struct OpEventData {
torch::profiler::impl::CUDAEventStub cuda_event_end_ = nullptr; torch::profiler::impl::CUDAEventStub cuda_event_end_ = nullptr;
}; };
struct MemoryEventData {
int64_t start_time;
void* ptr;
int64_t alloc_size;
int64_t total_allocated;
int64_t total_reserved;
uint64_t threadID;
torch::profiler::impl::kineto::DeviceAndResource kineto_info;
c10::DeviceType device_type;
c10::DeviceIndex device_index;
};
static_assert(std::is_pod<MemoryEventData>::value, "Non-POD member of MemoryEventData.");
// Assumption: Total threads number will not exceed 2^16-1, and total ops will // Assumption: Total threads number will not exceed 2^16-1, and total ops will
// not exceed 2^48 -1. // not exceed 2^48 -1.
static inline uint64_t getForwardThreadKey(uint64_t tid, uint64_t seqNr) { static inline uint64_t getForwardThreadKey(uint64_t tid, uint64_t seqNr) {
@ -204,29 +217,16 @@ struct KinetoThreadLocalState : public ProfilerThreadLocalStateBase {
int64_t total_reserved, int64_t total_reserved,
c10::Device device) override { c10::Device device) override {
if (config_.profile_memory && config_.state != ProfilerState::Disabled) { if (config_.profile_memory && config_.state != ProfilerState::Disabled) {
std::lock_guard<std::mutex> guard(state_mutex_); memory_events_.push_back(
auto start_time = getTimeUs(); {getTimeUs(),
if (cpu_trace_) {
torch::profiler::impl::kineto::recordThreadInfo();
cpu_trace_.addMemoryUsageActivity(
kMemoryEventName,
torch::profiler::impl::kineto::kineto_ids(),
start_time,
device,
ptr, ptr,
alloc_size, alloc_size,
total_allocated, total_allocated,
total_reserved); total_reserved,
} at::RecordFunction::currentThreadId(),
torch::profiler::impl::kineto::kineto_ids(),
kineto_events_.emplace_back(); device.type(),
auto& evt = kineto_events_.back(); device.index()});
evt.name(kMemoryEventName)
.startUs(start_time)
.deviceIndex(device.index())
.deviceType(device.type())
.nBytes(alloc_size)
.startThreadId(at::RecordFunction::currentThreadId());
} }
} }
@ -264,6 +264,28 @@ struct KinetoThreadLocalState : public ProfilerThreadLocalStateBase {
void materializeOpEvents() { void materializeOpEvents() {
std::lock_guard<std::mutex> guard(state_mutex_); std::lock_guard<std::mutex> guard(state_mutex_);
for (const auto& e : memory_events_) {
cpu_trace_.addMemoryUsageActivity(
kMemoryEventName,
e.kineto_info,
e.start_time,
c10::Device(e.device_type, e.device_index),
e.ptr,
e.alloc_size,
e.total_allocated,
e.total_reserved);
kineto_events_.emplace_back();
auto& evt = kineto_events_.back();
evt.name(kMemoryEventName)
.startUs(e.start_time)
.deviceIndex(e.device_index)
.deviceType(e.device_type)
.nBytes(e.alloc_size)
.startThreadId(e.threadID);
}
for (const auto& e : op_events_) { for (const auto& e : op_events_) {
if (e.end_us_ < e.start_us_) { if (e.end_us_ < e.start_us_) {
// We initialize end_us_ to the smallest int64_t, so this means that // We initialize end_us_ to the smallest int64_t, so this means that
@ -585,6 +607,7 @@ struct KinetoThreadLocalState : public ProfilerThreadLocalStateBase {
uint64_t start_time_; uint64_t start_time_;
std::set<torch::profiler::impl::ActivityType> activities_; std::set<torch::profiler::impl::ActivityType> activities_;
std::deque<OpEventData> op_events_; std::deque<OpEventData> op_events_;
std::deque<MemoryEventData> memory_events_;
torch::profiler::impl::kineto::TraceWrapper cpu_trace_; torch::profiler::impl::kineto::TraceWrapper cpu_trace_;
std::vector<KinetoEvent> kineto_events_; std::vector<KinetoEvent> kineto_events_;
// Optional, if event post-processing is enabled. // Optional, if event post-processing is enabled.