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#pragma once
#include "common/options.h"
#include "training/training_state.h"
#include "training/validator.h"
#include "training/communicator.h"
#include "layers/loss.h"
namespace marian {
class Scheduler : public TrainingObserver {
private:
Ptr<Options> options_;
Ptr<TrainingState> state_;
std::vector<Ptr<ValidatorBase>> validators_;
bool first_{true};
timer::Timer timer_, heartBeatTimer_;
// determine scheduled LR decay factor (--lr-decay-inv-sqrt option)
float getScheduledLRDecayFactor(const TrainingState& state) const {
auto args = options_->get<std::vector<std::string>>("lr-decay-inv-sqrt");
ABORT_IF(args.empty() || args.size() > 2, "--lr-decay-inv-sqrt argument must be one or two numbers with units");
auto decayGoogle = SchedulingParameter::parse(args[0]);
size_t progress = state.getProgressIn(decayGoogle.unit);
size_t start = decayGoogle.n;
if (args.size() > 1) {
auto decayStart = SchedulingParameter::parse(args[1]);
ABORT_IF(decayStart && decayStart.unit != decayGoogle.unit, "both --lr-decay-inv-sqrt arguments must have the same unit");
start = decayStart.n;
}
if (decayGoogle && progress > start) {
progress = progress - start + decayGoogle.n; // shift so that we get 1 at progress==start
return (float)(std::sqrt((double)decayGoogle.n / (double)progress));
}
else
return 1.f;
}
// update current learning rate in state.eta
// This considers
// - base LR (--learn-rate)
// - LR warm-up (--lr-warmup, --lr=warmup-start-rate)
// - scheduled LR decay (--lr-decay-inv-sqrt)
// - state-based LR decay (--lr-decay, --lr-decay-strategy)
void updateLearningRate(TrainingState& state) const {
float baselr = options_->get<float>("learn-rate");
// warm-up factor
float warmupFactor = 1.f;
auto warmupParam = SchedulingParameter::parse(options_->get<std::string>("lr-warmup"));
if(warmupParam) {
ABORT_IF(state.warmupStart && state.warmupStart.unit != warmupParam.unit, "lr-warmup and warmup-start must have the same unit");
auto bno = state.getProgressIn(warmupParam.unit) - state.warmupStart.n;
warmupFactor = std::min(1.f, (float)bno / (float)warmupParam.n);
}
float lrStart = options_->get<float>("lr-warmup-start-rate");
baselr = lrStart + (baselr - lrStart) * warmupFactor; // linear interpolation between lr-warmup-start-rate to learn-rate
// schedule-based decay factor (--lr-decay-inv-sqrt)
float scheduledDecayFactor = getScheduledLRDecayFactor(state);
baselr = baselr * scheduledDecayFactor;
// factor in state-based decay and set final LR as state.eta
state.updateEta(baselr);
}
std::string formatLoss(std::string lossType,
bool dispLabelCounts,
size_t batchLabels,
Ptr<TrainingState> state) {
std::stringstream ss;
ss << "Cost ";
ss << std::setprecision(8) << std::fixed;
// @TODO: put a single loss formatting function into loss.h and reuse here to avoid code duplication
// @TODO: use dispLabelCounts with any display type?
// @TODO: bugbug cost-type ce-mean-words with multi-loss-type mean divides too much in display
if(lossType == "ce-mean-words") {
ss << state->costSum / state->costCount;
} else if(lossType == "ce-sum" && dispLabelCounts) {
ss << state->costSum / state->costCount
<< " * " << utils::withCommas(state->costCount);
if(batchLabels > 0)
ss << " @ " << utils::withCommas(batchLabels);
ss << " after " << utils::withCommas(state->labelsTotal);
} else if(lossType == "ce-sum" && !dispLabelCounts) {
ss << state->costSum / state->updatesDisp; // average over batches
} else if(lossType == "perplexity") {
ss << std::exp(state->costSum / state->costCount);
} else if(lossType == "cross-entropy" || lossType == "ce-mean") { // backwards-compat, @TODO: get rid of this?
ss << state->costSum / state->samplesDisp;
} else {
ABORT("Unknown loss type {}", lossType);
}
return ss.str();
}
public:
// test if any parameters specify dynamic MB scaling
bool isDynamicMBSizeScaling() const {
auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
auto mbTracking = options_->get<bool>("mini-batch-track-lr");
return mbWarmup || mbTracking;
}
// determine dynamic MB scaling factor
double getDynamicMBSizeMultiplier() const {
double ratio = 1.0;
auto mbWarmup = SchedulingParameter::parse(options_->get<std::string>("mini-batch-warmup"));
if (mbWarmup) {
// mini-batch-warmup
LOG_ONCE(info, "[scheduler] Mini-batch size warmup {}", std::string(mbWarmup));
// This ramps up MB size at start, relative to progress within warm-up period.
size_t progress = state_->getProgressIn(mbWarmup.unit); // number of updates/labels processed
auto progressRatio = (double)progress / (double)mbWarmup.n; // where are we relatively within target warm-up period
// if unit is labels, then account for the fact that our increment itself is not constant
if (mbWarmup.unit == SchedulingUnit::trgLabels)
progressRatio = std::sqrt(progressRatio);
if (progressRatio < 1)
ratio *= progressRatio;
}
// dynamic MB-size tracking with learning rate
// As LR goes down, MB gets ramped up by the same ratio, which has been found to be safe.
auto mbTracking = options_->get<bool>("mini-batch-track-lr");
if (mbTracking) {
auto lrFactor = getScheduledLRDecayFactor(*state_) * state_->factor; // (don't include lr-warmup)
if (lrFactor != 1)
LOG_ONCE(info, "[scheduler] Dynamic mini-batch size adjustment enabled and kicking in");
ratio /= lrFactor;
}
return ratio;
}
Scheduler(Ptr<Options> options, Ptr<TrainingState> state)
: options_(options), state_(state) {
ABORT_IF(state_->factor != 1, "state.factor unexpectedly not 1 at this point??");
updateLearningRate(*state);
}
bool keepGoing() {
// stop if it reached the maximum number of epochs
size_t stopAfterEpochs = options_->get<size_t>("after-epochs");
if(stopAfterEpochs > 0 && state_->epochs > stopAfterEpochs)
return false;
// stop if it reached the maximum number of batch updates
size_t stopAfterBatches = options_->get<size_t>("after-batches");
if(stopAfterBatches > 0 && state_->batches >= stopAfterBatches)
return false;
// stop if the first validator did not improve for a given number of checks
size_t stopAfterStalled = options_->get<size_t>("early-stopping");
if(stopAfterStalled > 0 && !validators_.empty()
&& stalled() >= stopAfterStalled)
return false;
return true;
}
void increaseEpoch() {
LOG(info, "Seen {} samples", state_->samplesEpoch);
state_->newEpoch();
LOG(info, "Starting epoch {}", state_->epochs);
}
void started() { LOG(info, "Training started"); }
void finished() { LOG(info, "Training finished"); }
void addValidator(Ptr<ValidatorBase> validator) {
validators_.push_back(validator);
registerTrainingObserver(validators_.back());
if(!state_->loaded) {
state_->validators[validator->type()]["last-best"]
= validator->initScore();
state_->validators[validator->type()]["stalled"] = 0;
}
if(validators_.size() == 1)
state_->validator = validator->type();
}
bool validating() {
return (!validators_.empty()
&& state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq"))
&& keepGoing());
}
bool saving() {
return state_->enteredNewPeriodOf(options_->get<std::string>("save-freq"));
}
void validate(const std::vector<Ptr<ExpressionGraph>>& graphs,
bool final = false) {
// Do not validate if already validated (for instance, after the model is
// loaded) or if validation is scheduled for another update
if(state_->validated
|| (!state_->enteredNewPeriodOf(options_->get<std::string>("valid-freq"))
&& !final))
return;
bool firstValidator = true;
for(auto validator : validators_) {
if(!validator)
continue;
size_t stalledPrev = validator->stalled();
float value = validator->validate(graphs);
if(validator->stalled() > 0) {
LOG_VALID(info,
"Ep. {} : Up. {} : {} : {} : stalled {} times (last best: {})",
state_->epochs,
state_->batches,
validator->type(),
value,
validator->stalled(), validator->lastBest());
} else {
LOG_VALID(info,
"Ep. {} : Up. {} : {} : {} : new best",
state_->epochs,
state_->batches,
validator->type(),
value);
if(firstValidator)
state_->validBest = value;
}
state_->validators[validator->type()]["last-best"]
= validator->lastBest();
state_->validators[validator->type()]["stalled"] = validator->stalled();
// notify training observers if the first validator did not improve
if(firstValidator && validator->stalled() > stalledPrev)
state_->newStalled(validator->stalled());
firstValidator = false;
}
state_->validated = true;
}
size_t stalled() {
if(!validators_.empty())
if(validators_[0])
return validators_[0]->stalled();
return 0;
}
void update(StaticLoss rationalLoss, Ptr<data::Batch> batch) {
update(rationalLoss, /*numReadBatches=*/1, /*batchSize=*/batch->size(), /*batchLabels=*/batch->wordsTrg());
}
// @TODO: go back to function which takes batch as an argument? The current arguments make it hard to choose
// which subbatch should be used for speed display. For sequence-classifiers it's more interesting to see the
// source-words consumed rather than the labels.
void update(StaticLoss rationalLoss,
size_t numReadBatches, // number of batches read by the reader (for seeking in case of restart)
size_t batchSize, // total number of sentences in batch
size_t batchLabels, // total number of target words in batch
Ptr<IMPIWrapper> mpi = nullptr) {
state_->rememberPreviousProgress(); // note: epoch increases happen at the wrong place, hence -freq parameters do not support epoch units
state_->validated = false;
// Since batchLabels is counted across all MPI processes, we also should temporarily
// extrapolate cost across MPI processes, to have numbers in the right range.
// When doing the actual log, we then aggregate across MPI processes to get the accurate number.
if (mpi)
rationalLoss.loss *= mpi->numMPIProcesses();
state_->costSum += rationalLoss.loss; // aggregate sum cost since last display
state_->costCount += rationalLoss.count; // cost gets normalized w.r.t. this in display
state_->updatesDisp += 1;
state_->samplesDisp += batchSize;
state_->wordsDisp += batchLabels; //@TODO: this is wrong // words at given input processed since last display, for speed display
state_->samplesEpoch += batchSize; // sentences processed in this epoch
state_->labelsTotal += rationalLoss.count; // total labels processed
state_->newUpdate(numReadBatches);
// reconstruct sum cost, for displaying epoch-level averages instead of minibatch-level
auto lossType = options_->get<std::string>("cost-type");
auto dispLabelCounts = options_->get<bool>("disp-label-counts"); // if true then show as "cost per label * number of labels"
if(state_->enteredNewPeriodOf(options_->get<std::string>("disp-freq")) ||
state_->batches <= options_->get<size_t>("disp-first")) {
// if MPI then aggregate precise cost across workers
if (mpi) {
state_->costSum /= mpi->numMPIProcesses(); // undo the extra scaling
mpi->allReduce(&state_->costSum, &state_->costSum, 1, MPI_FLOAT, MPI_SUM);
}
if (mpi && mpi->myMPIRank() != 0) {
// skip the report on alternate worker processes
} else if(options_->get<bool>("lr-report")) {
LOG(info,
"Ep. {} : Up. {} : Sen. {} : {} : Time {:.2f}s : {:.2f} words/s : L.r. {:.4e}",
state_->epochs,
state_->batches,
utils::withCommas(state_->samplesEpoch),
formatLoss(lossType, dispLabelCounts, batchLabels, state_),
timer_.elapsed(),
state_->wordsDisp / timer_.elapsed(),
state_->eta);
} else {
LOG(info,
"Ep. {} : Up. {} : Sen. {} : {} : Time {:.2f}s : {:.2f} words/s",
state_->epochs,
state_->batches,
utils::withCommas(state_->samplesEpoch),
formatLoss(lossType, dispLabelCounts, 0, state_), // ignore batchLabels
timer_.elapsed(),
state_->wordsDisp / timer_.elapsed());
}
timer_.start();
state_->costSum = 0;
state_->costCount = 0;
state_->updatesDisp = 0;
state_->samplesDisp = 0;
state_->wordsDisp = 0;
}
// progress heartbeat for MS-internal Philly compute cluster
// This environment variable exists when running on the cluster.
using namespace std::chrono;
if((!mpi || mpi->myMPIRank() == 0) && getenv("PHILLY_JOB_ID")
&& heartBeatTimer_.elapsed<std::chrono::minutes>() >= 10) {
printf("PROGRESS: %.2f%%\nEVALERR: %.7f%%\n",
(double)state_->epochs,
state_->costSum / state_->costCount / (mpi ? mpi->numMPIProcesses() : 1));
fflush(stdout);
std::cout << "MBSIZE: " << batchLabels << " after " << state_->batches << " updates = " << state_->labelsTotal << " labels" << std::endl << std::flush;
heartBeatTimer_.start();
}
}
void load(const std::string& name) {
std::string nameYaml = name + ".progress.yml";
if(filesystem::exists(nameYaml))
state_->load(nameYaml);
if(options_->get<bool>("no-restore-corpus")) {
state_->samplesEpoch = 0;
state_->costSum = 0;
state_->costCount = 0;
state_->updatesDisp = 0;
state_->samplesDisp = 0;
state_->wordsDisp = 0;
}
state_->newLoad();
}
void save(const std::string& name) {
// Save config options
YAML::Node yaml = options_->getYaml();
std::ofstream fout(name + ".yml");
fout << yaml;
// Save training progress
state_->save(name + ".progress.yml");
}
size_t numberOfBatches() { return state_->batches; }
void registerTrainingObserver(Ptr<TrainingObserver> observer) {
state_->registerObserver(observer);
}
void actAfterEpoch(TrainingState& state) override {
float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
updateLearningRate(state);
if(factor > 0.0) {
bool decay = false;
auto strategy = options_->get<std::string>("lr-decay-strategy");
state.reset = false;
if(strategy == "epoch" || strategy == "epoch+batches"
|| strategy == "epoch+stalled") {
size_t startEpoch
= options_->get<std::vector<size_t>>("lr-decay-start").front();
if(startEpoch && state.epochs >= startEpoch)
decay = true;
}
if(strategy == "epoch+batches") {
size_t startBatches
= options_->get<std::vector<size_t>>("lr-decay-start")[1];
if(startBatches && state.batches >= startBatches)
decay = true;
}
if(strategy == "epoch+stalled") {
size_t startStalled
= options_->get<std::vector<size_t>>("lr-decay-start")[1];
if(startStalled && state.maxStalled >= startStalled)
decay = true;
}
if(decay) {
state.factor *= factor;
updateLearningRate(state);
LOG(info,
"Decaying learning rate to {} in epoch {}",
state.eta,
state.epochs);
state.reset = options_->get<bool>("lr-decay-reset-optimizer");
if(state.reset)
LOG(info, "Resetting optimizer statistics");
if(options_->get<bool>("lr-decay-repeat-warmup")) {
LOG(info, "Restarting learning rate warmup");
state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
}
}
}
}
void actAfterBatches(TrainingState& state) override {
float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
state.reset = false;
updateLearningRate(state);
if(factor > 0.0) {
if(options_->get<std::string>("lr-decay-strategy") == "batches") {
size_t start = options_->get<std::vector<size_t>>("lr-decay-start").front();
size_t freq = options_->get<size_t>("lr-decay-freq"); // note: unlike e.g. disp-freq, this is always in batches
if(start > 0 && freq > 0 && state.batches >= start
&& ((state.batches - start) % freq == 0)) {
state.factor *= factor;
updateLearningRate(state);
LOG(info,
"Decaying learning rate to {} after {} batches",
state.eta,
state.batches);
state.reset = options_->get<bool>("lr-decay-reset-optimizer");
if(state.reset)
LOG(info, "Resetting optimizer statistics");
if(options_->get<bool>("lr-decay-repeat-warmup")) {
LOG(info, "Restarting learning rate warmup");
state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
}
}
}
}
if(first_ && options_->get<bool>("lr-warmup-at-reload")) {
LOG(info, "Restarting learning rate warmup");
state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
}
if(options_->get<bool>("lr-warmup-cycle")) {
if(state_->enteredNewPeriodOf(options_->get<std::string>("lr-warmup")))
state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
}
first_ = false;
}
void actAfterStalled(TrainingState& state) override {
float factor = (float)options_->get<double>("lr-decay"); // @TODO: <float>?
state.reset = false;
updateLearningRate(state);
if(factor > 0.0) {
if(options_->get<std::string>("lr-decay-strategy") == "stalled") {
size_t startStalled
= options_->get<std::vector<size_t>>("lr-decay-start").front();
if(startStalled && state.stalled && state.stalled % startStalled == 0) {
state.factor *= factor;
updateLearningRate(state);
LOG(info,
"Decaying learning rate to {} after having stalled {} time(s)",
state.eta,
state.stalled);
state.reset = options_->get<bool>("lr-decay-reset-optimizer");
if(state.reset)
LOG(info, "Resetting optimizer statistics");
if(options_->get<bool>("lr-decay-repeat-warmup")) {
LOG(info, "Restarting learning rate warmup");
state.warmupStart.n = state.getProgressIn(SchedulingParameter::parse(options_->get<std::string>("lr-warmup")).unit);
}
}
}
}
}
};
} // namespace marian
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