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Learner.scala
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package BIDMach
import BIDMat.{BMat,Mat,SBMat,CMat,DMat,FMat,FFilter,IMat,HMat,GDMat,GFilter,GLMat,GMat,GIMat,GSDMat,GSMat,LMat,SMat,SDMat,TMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Plotting._
import BIDMat.about
import BIDMat.MatIOtrait
import BIDMach.models._
import BIDMach.updaters._
import BIDMach.datasources._
import BIDMach.datasinks._
import BIDMach.mixins._
import BIDMach.viz._
import scala.collection.immutable.List
import scala.collection.mutable.ListBuffer
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.Future;
import java.io.ByteArrayOutputStream;
import java.io.PrintStream;
import java.util.logging.Level;
//import scala.concurrent.Future
//import scala.concurrent.ExecutionContext.Implicits.global
/**
* Basic sequential Learner class with a single datasource
*/
@SerialVersionUID(100L)
class Learner(
val datasource:DataSource,
val model:Model,
val mixins:Array[Mixin],
val updater:Updater,
val datasink:DataSink,
val opts:Learner.Opts = new Learner.Options) extends Serializable {
var myLogger = Mat.consoleLogger;
var fut:Future[_] = null;
var results:FMat = null
val dopts:DataSource.Opts = if (datasource != null) datasource.opts else null
val mopts:Model.Opts = model.opts
val ropts:Mixin.Opts = if (mixins != null) mixins(0).opts else null
val uopts:Updater.Opts = if (updater != null) updater.opts else null
var useGPU = false
var reslist:ListBuffer[FMat] = null;
var samplist:ListBuffer[Float] = null;
var viz:ListBuffer[Visualization] = null;
var nextCheckPoint = 0;
@volatile var done = false;
@volatile var paused = false;
@volatile var pauseAt = -1L;
@volatile var ipass = 0;
@volatile var istep = 0;
var here = 0L;
var lasti = 0;
var bytes = 0L;
var nsamps = 0L;
var cacheState = false;
var cacheGPUstate = false;
var debugMemState = false;
var debugCPUmemState = false;
def init = {
cacheState = Mat.useCache;
Mat.useCache = opts.useCache;
cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useCache;
datasource.init;
model.bind(datasource);
if (datasink.asInstanceOf[AnyRef] != null) {
datasink.init;
model.bind(datasink);
}
model.init;
if (model.opts.logDataSink.asInstanceOf[AnyRef] != null) model.opts.logDataSink.init
if (mixins != null) mixins map (_ init(model))
if (updater != null) updater.init(model)
Mat.useCache = cacheState;
Mat.useGPUcache = cacheGPUstate;
useGPU = model.useGPU;
nextCheckPoint = opts.nextCheckPoint;
}
def launch(fn:()=>Unit) = {
val nthreads = opts match {
case mopts:FileSource.Opts => mopts.lookahead + 4;
case _ => 4;
}
val tmp = myLogger;
myLogger = Mat.getFileLogger(opts.logfile);
Mat.logger = myLogger;
val executor = Executors.newFixedThreadPool(nthreads);
val runner = new Runnable{
def run() = {
try {
fn();
} catch {
case e:Throwable => myLogger.severe("Learner thread failed: %s" format Learner.printStackTrace(e));
}
myLogger = tmp;
}
}
fut = executor.submit(runner);
fut;
}
def launchTrain = {
println("\nRunning training in the background.\nLogging to file %s in the current directory." format opts.logfile);
launch(()=>this.train);
}
def train(doInit:Boolean) = {
retrain(doInit)
}
def train:Unit = retrain(true);
def retrain(doInit:Boolean) = {
flip
cacheState = Mat.useCache;
Mat.useCache = opts.useCache;
cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useCache;
debugMemState = Mat.debugMem;
debugCPUmemState = Mat.debugCPUmem;
if (updater != null) updater.clear;
reslist = new ListBuffer[FMat];
samplist = new ListBuffer[Float];
firstPass(null, doInit);
updateM(ipass-1)
while (ipass < opts.npasses && ! done) {
nextPass(null)
updateM(ipass-1)
}
wrapUp(ipass);
}
def retrain:Unit = retrain(true)
def firstPass(iter:Iterator[(AnyRef, MatIOtrait)], doInit:Boolean = true):Unit = {
if (doInit) {
init
}
done = false;
ipass = 0;
here = 0L;
lasti = 0;
bytes = 0L;
nsamps = 0L;
if (updater != null) updater.clear;
cacheState = Mat.useCache;
Mat.useCache = opts.useCache;
cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useGPUcache;
reslist = new ListBuffer[FMat];
samplist = new ListBuffer[Float];
flip;
nextPass(iter);
}
def nextPass(iter:Iterator[(AnyRef, MatIOtrait)]): Unit = {
if (opts.debugMem && ipass > 0) {
Mat.debugMem = true;
}
if (opts.debugCPUmem && ipass > 0) {
Mat.debugCPUmem = true;
}
var lastp = 0f
if (iter != null) {
datasource.asInstanceOf[IteratorSource].opts.iter = iter;
}
datasource.reset
istep = 0;
myLogger.info("pass=%2d" format ipass)
while (!done && datasource.hasNext) {
val mats = datasource.next;
nsamps += mats(0).ncols;
here += datasource.opts.batchSize
bytes += mats.map(Learner.numBytes _).reduce(_+_);
val dsp = datasource.progress;
val gprogress = (ipass + dsp)/opts.npasses;
if ((istep - 1) % opts.evalStep == 0 || (istep > 0 && (! datasource.hasNext))) {
if (opts.updateAll) {
model.dobatchg(mats, ipass, here);
if (mixins != null) mixins map (_ compute(mats, here));
while (paused || (pauseAt > 0 && pauseAt <= istep)) Thread.sleep(1000);
if (updater != null) updater.update(ipass, here, gprogress);
}
val tmpscores = model.evalbatchg(mats, ipass, here);
val scores = if (tmpscores.ncols > 1) mean(tmpscores, 2) else tmpscores;
if (datasink != null) datasink.put;
reslist.append(scores.newcopy)
samplist.append(here)
} else {
if (model.opts.naturalLambda > 0) {
model.evalbatchg(mats, ipass, here);
if (updater != null) updater.preupdate(ipass, here, gprogress);
}
for (inatural <- 0 until opts.nNatural) {
model.dobatchg(mats, ipass, here);
if (mixins != null) mixins map (_ compute(mats, here));
while (paused || (pauseAt > 0 && pauseAt <= istep)) Thread.sleep(1000);
if (updater != null) updater.update(ipass, here, gprogress);
}
}
if (viz.asInstanceOf[AnyRef] != null) {
viz.foreach(_.update(model,mats,ipass,here))
}
istep += 1
if (dsp > lastp + opts.pstep && reslist.length > lasti) {
val gf = gflop
lastp = dsp - (dsp % opts.pstep)
myLogger.info(("%5.2f%%, score=%6.5f, secs=%3.1f, samps/s=%4.1f, gf=%4.1f, MB/s=%3.2f" format (
100f*lastp,
Learner.scoreSummary(reslist, lasti, reslist.length, opts.cumScore),
gf._2,
nsamps/gf._2,
gf._1,
bytes/gf._2*1e-6)) + (if (useGPU) {", GPUmem=%3.6f" format GPUmem._1} else ""));
lasti = reslist.length;
}
if (opts.checkPointFile != null && ipass + dsp > opts.checkPointInterval * (1 + nextCheckPoint - opts.nextCheckPoint)) {
model.save(opts.checkPointFile format nextCheckPoint);
val oldCheckPoint = nextCheckPoint - opts.keepCheckPoints;
nextCheckPoint += 1;
if (opts.keepCheckPoints > 0 && oldCheckPoint >= 0) {
model.delete(opts.checkPointFile format oldCheckPoint);
}
}
}
ipass += 1
}
def updateM(ipass: Int): Unit = {
if (updater != null) updater.updateM(ipass)
}
def wrapUp(ipass:Int) {
if (opts.checkPointFile != null) model.save(opts.checkPointFile format nextCheckPoint)
model.wrapUp(ipass);
val gf = gflop;
Mat.useCache = cacheState;
Mat.useGPUcache = cacheGPUstate;
Mat.debugMem = debugMemState;
Mat.debugCPUmem = debugCPUmemState;
myLogger.info("Time=%5.4f secs,\nTTAAGG,%4.2f" format (gf._2, gf._1));
if (opts.autoReset && useGPU) {
Learner.toCPU(modelmats);
model.clear;
resetGPUs;
Mat.clearCaches;
}
datasource.close;
if (datasink != null) datasink.close;
if (model.opts.logDataSink.asInstanceOf[AnyRef] != null) model.opts.logDataSink.close
results = Learner.scores2FMat(reslist) on row(samplist.toList);
done = true;
}
def pause = {
paused = true;
Thread.sleep(500);
}
def unpause = {
paused = false;
}
def stop = {
done = true;
}
def getResults = {
val top = Learner.scores2FMat(reslist);
val bottom = row(samplist.toList);
val n = math.min(top.ncols, bottom.ncols);
results = top(?,0->n) on bottom(?,0->n);
results;
}
def smoothResults(n:Int=10):FMat = {
getResults;
val b = zeros(n, results.ncols/n);
b(?) = results(0,0->b.length);
mean(b);
}
def plotResults(n:Int = 10) = {
getResults;
val b = zeros(n, results.ncols/n);
b(?) = results(0,0->b.length);
plot(mean(b))
}
def addViz(v: Visualization) = {
if (viz.asInstanceOf[AnyRef] == null) viz = new ListBuffer[Visualization]()
viz+=v
v
}
def predict() = {
datasource.init;
model.bind(datasource);
if (datasink.asInstanceOf[AnyRef] != null) {
datasink.init;
model.bind(datasink);
}
val rstate = model.refresh;
model.refresh = false
model.init
val results = repredict
model.refresh = rstate
results
}
def repredict() = {
flip
useGPU = model.useGPU
cacheState = Mat.useCache
Mat.useCache = opts.useCache
cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useCache;
here = 0L;
lasti = 0;
bytes = 0L;
nsamps = 0;
var lastp = 0f;
val reslist = new ListBuffer[FMat]
val samplist = new ListBuffer[Float]
myLogger.info("Predicting")
datasource.reset
while (datasource.hasNext) {
val mats = datasource.next
here += datasource.opts.batchSize
bytes += mats.map(Learner.numBytes _).reduce(_+_);
nsamps += mats(0).ncols;
val tmpscores = model.evalbatchg(mats, 0, here);
val scores = if (tmpscores.ncols > 1) mean(tmpscores,2) else tmpscores;
if (datasink != null) datasink.put
reslist.append(scores.newcopy);
samplist.append(here);
val dsp = datasource.progress;
if (dsp > lastp + opts.pstep && reslist.length > lasti) {
val gf = gflop
lastp = dsp - (dsp % opts.pstep);
myLogger.info(("%5.2f%%, score=%6.5f, secs=%3.1f, samps/s=%4.1f, gf=%4.1f, MB/s=%3.2f" format (
100f*lastp,
Learner.scoreSummary(reslist, lasti, reslist.length, opts.cumScore),
gf._2,
nsamps/gf._2,
gf._1,
bytes/gf._2*1e-6)) + (if (useGPU) {", GPUmem=%3.6f" format GPUmem._1} else ""));
lasti = reslist.length;
}
}
val gf = gflop;
Mat.useCache = cacheState;
Mat.useGPUcache = cacheGPUstate;
myLogger.info("Time=%5.4f secs, gflops=%4.2f" format (gf._2, gf._1));
if (opts.autoReset && useGPU) {
Learner.toCPU(modelmats)
model.clear
resetGPUs
Mat.clearCaches
}
datasource.close;
if (datasink != null) datasink.close;
results = Learner.scores2FMat(reslist) on row(samplist.toList)
results
}
/**
* Load a specified checkPoint number, or the last one if no arg given.
*/
def loadCheckPoint(checkPointNumber:Int= -1) = {
var lasti = -1;
var foundany = false;
var foundall = false;
val limit = 100000;
while (!foundall && lasti < limit) {
lasti += 1;
val f = new java.io.File(opts.checkPointFile format lasti);
if (f.exists()) {
foundany = true;
} else {
if (foundany) {
foundall = true;
}
}
}
lasti -= 1;
val toload = if (checkPointNumber >= 0) {
checkPointNumber
} else {
lasti;
}
if (foundany) {
model.load(opts.checkPointFile format toload);
opts.nextCheckPoint = lasti+1;
} else {
myLogger.warning("loadCheckPoint: didnt find any checkpoint files");
}
}
def datamats = datasource.asInstanceOf[MatSource].mats;
def modelmats = model.modelmats;
def datamat = datasource.asInstanceOf[MatSource].mats(0);
def modelmat = model.modelmats(0);
def preds = datasink.asInstanceOf[MatSink].mats
}
/**
* Parallel Learner with a single datasource.
*/
class ParLearner(
val datasource:DataSource,
val mkModelFn:(Int)=>Model,
val mkMixinsFn:(Int)=>Array[Mixin],
val mkUpdaterFn:(Int)=>Updater,
val datasink:DataSink,
val opts:ParLearner.Opts = new ParLearner.Options) extends Serializable {
var models:Array[Model] = null;
var mixins:Array[Array[Mixin]] = null;
var updaters:Array[Updater] = null;
var myLogger = Mat.consoleLogger;
var fut:Future[_] = null;
var workers:Array[Future[_]] = null;
var executor:ExecutorService = null;
var um:Array[Mat] = null;
var mm:Array[Mat] = null;
var results:FMat = null;
var cmats:Array[Array[Mat]] = null;
var useGPU = false;
var reslist:ListBuffer[FMat] = null;
var samplist:ListBuffer[Float] = null;
var nextCheckPoint = 0;
@volatile var done = false;
@volatile var paused = false;
@volatile var ipass = 0;
@volatile var istep = 0;
@volatile var here = 0L;
@volatile var lasti = 0;
@volatile var bytes = 0L;
@volatile var nsamps = 0L;
@volatile var dones:IMat = null;
var cacheState = false;
var cacheGPUstate = false;
var debugMemState = false;
var debugCPUmemState = false;
var nthreads = 1;
def init = {
nthreads = math.min(opts.nthreads, Mat.hasCUDA);
cacheState = Mat.useCache;
Mat.useCache = opts.useCache;
cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useCache;
datasource.init;
val thisGPU = getGPU;
cmats = new Array[Array[Mat]](nthreads);
val mats = datasource.next;
models = new Array[Model](nthreads)
if (mkMixinsFn.asInstanceOf[AnyRef] != null) mixins = new Array[Array[Mixin]](nthreads)
if (mkUpdaterFn.asInstanceOf[AnyRef] != null) updaters = new Array[Updater](nthreads);
for (i <- 0 until nthreads) {
cmats(i) = new Array[Mat](datasource.omats.length);
if (i < Mat.hasCUDA) setGPU(i);
models(i) = mkModelFn(i);
for (j <- 0 until mats.length) {
cmats(i)(j) = safeCopy(mats(j), i);
}
models(i).bind(datasource);
models(i).mats = cmats(i);
models(i).init;
if (mixins.asInstanceOf[AnyRef] != null) {
mixins(i) = mkMixinsFn(i);
mixins(i) map (_ init(models(i)));
}
if (updaters.asInstanceOf[AnyRef] != null) {
updaters(i) = mkUpdaterFn(i);
updaters(i).init(models(i));
}
}
useGPU = models(0).opts.useGPU;
datasource.reset;
Mat.useCache = cacheState;
Mat.useGPUcache = cacheGPUstate;
useGPU = models(0).useGPU;
if (executor.asInstanceOf[AnyRef] == null) executor = Executors.newFixedThreadPool(nthreads+4);
if (workers.asInstanceOf[AnyRef] == null) workers = new Array[Future[_] ](nthreads)
setGPU(thisGPU)
val mml = models(0).modelmats.length
um = new Array[Mat](mml)
mm = new Array[Mat](mml)
for (i <- 0 until mml) {
val mm0 = models(0).modelmats(i)
mm(i) = zeros(mm0.dims)
um(i) = zeros(mm0.dims)
}
dones = iones(1, nthreads)
ParLearner.syncmodels(models, mm, um, 0, useGPU)
}
def launch(fn:()=>Unit) = {
val ntotalthreads = opts match {
case mopts:FileSource.Opts => mopts.lookahead + nthreads + 2;
case _ => nthreads + 2;
}
val tmp = myLogger;
myLogger = Mat.getFileLogger(opts.logfile);
if (executor.asInstanceOf[AnyRef] == null) executor = Executors.newFixedThreadPool(ntotalthreads);
val runner = new Runnable{
def run() = {
try {
fn();
} catch {
case e:Throwable => myLogger.severe("Learner thread failed: %s" format Learner.printStackTrace(e));
}
myLogger = tmp;
}
}
fut = executor.submit(runner);
fut;
}
def launchTrain = {
println("\nRunning training in the background.\nLogging to file %s in the current directory." format opts.logfile);
launch(()=>this.train);
}
def train = {
init
retrain
}
def retrain = {
BIDMat.MatFunctions.flip;
val mm0 = models(0).modelmats(0)
var cacheState = Mat.useCache;
var cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useGPUcache;
Mat.useCache = opts.useCache
val thisGPU = if (useGPU) getGPU else 0
if (useGPU) {
for (i <- 0 until nthreads) {
// if (i != thisGPU) connect(i)
}
}
ipass = 0
istep = 0
here = 0L
lasti = 0
bytes = 0L
reslist = new ListBuffer[FMat]
samplist = new ListBuffer[Float]
for (i <- 0 until nthreads) {
if (useGPU && i < Mat.hasCUDA) setGPU(i)
if (updaters != null && updaters(i) != null) updaters(i).clear
}
setGPU(thisGPU)
var lastp = 0f
var progress = 0f;
var gprogress = 0f;
class LearnThread(val ithread:Int) extends Runnable {
def run() = {
if (useGPU && ithread < Mat.hasCUDA) setGPU(ithread)
while (!done) {
while (dones(ithread) == 1) Thread.sleep(1)
try {
if ((istep/nthreads) % opts.evalStep == 0 || !datasource.hasNext ) {
if (opts.updateAll) {
models(ithread).dobatchg(cmats(ithread), ipass, here)
if (mixins != null && mixins(ithread) != null) mixins(ithread) map (_ compute(cmats(ithread), here));
while (paused || (opts.pauseAt > 0 && opts.pauseAt <= istep)) Thread.sleep(1000);
if (updaters != null && updaters(ithread) != null) updaters(ithread).update(ipass, here, gprogress);
}
val tmpscores = models(ithread).evalbatchg(cmats(ithread), ipass, here);
val scores = if (tmpscores.ncols > 1) mean(tmpscores, 2) else tmpscores;
reslist.synchronized { reslist.append(scores.newcopy) }
samplist.synchronized { samplist.append(here) }
} else {
models(ithread).dobatchg(cmats(ithread), ipass, here)
if (mixins != null && mixins(ithread) != null) mixins(ithread) map (_ compute(cmats(ithread), here));
while (paused || (opts.pauseAt > 0 && opts.pauseAt <= istep)) Thread.sleep(1000);
if (updaters != null && updaters(ithread) != null) updaters(ithread).update(ipass, here, gprogress);
}
} catch {
case e:Throwable => {
myLogger.severe("Caught exception in thread %d %s\n" format (ithread, e.toString));
val se = e.getStackTrace();
for (i <- 0 until 8) {
myLogger.severe("thread %d, %s" format (ithread, se(i).toString));
}
myLogger.severe("Restarted: Keep on truckin...")
}
}
dones(ithread) = 1
}
}
}
for (i <- 0 until nthreads) {
workers(i) = executor.submit(new LearnThread(i));
}
while (ipass < opts.npasses) {
datasource.reset
istep = 0
lastp = 0f
myLogger.info("pass=%2d" format ipass)
while (datasource.hasNext) {
for (ithread <- 0 until nthreads) {
if (datasource.hasNext) {
val mats = datasource.next;
nsamps += mats(0).ncols;
progress = datasource.progress
gprogress = (ipass + progress)/opts.npasses
for (j <- 0 until mats.length) {
cmats(ithread)(j) = safeCopy(mats(j), ithread)
}
if (ithread == 0) here += datasource.opts.batchSize
dones(ithread) = 0;
bytes += mats.map(Learner.numBytes _).reduce(_+_);
}
}
while (mini(dones).v == 0) Thread.sleep(1)
Thread.sleep(opts.coolit)
istep += nthreads
if ((istep/nthreads) % opts.syncStep == 0) ParLearner.syncmodels(models, mm, um, istep/opts.syncStep, useGPU, opts.elastic_weight)
if (datasource.progress > lastp + opts.pstep) {
while (datasource.progress > lastp + opts.pstep) lastp += opts.pstep
val gf = BIDMat.MatFunctions.gflop;
if (reslist.length > lasti) {
val perfStr = ("%5.2f%%, score=%6.5f, secs=%3.1f, samps/s=%4.1f, gf=%4.1f, MB/s=%4.1f" format (
100f*lastp,
reslist.synchronized {
Learner.scoreSummary(reslist, lasti, reslist.length, opts.cumScore);
},
gf._2,
nsamps/gf._2,
gf._1,
bytes/gf._2*1e-6));
val gpuStr = if (useGPU) {
(0 until math.min(nthreads, Mat.hasCUDA)).map((i)=>{
setGPU(i);
if (i==0) (", GPUmem=%3.2f" format GPUmem._1) else (", %3.2f" format GPUmem._1)
}).reduce(_+_);
} else "";
myLogger.info(perfStr + gpuStr);
if (useGPU) setGPU(thisGPU);
}
lasti = reslist.length
}
}
for (i <- 0 until nthreads) {
if (useGPU && i < Mat.hasCUDA) setGPU(i);
if (updaters != null && updaters(i) != null) updaters(i).updateM(ipass)
}
setGPU(thisGPU)
ParLearner.syncmodelsPass(models, mm, um, ipass)
ipass += 1
if (opts.resFile != null) {
saveAs(opts.resFile, Learner.scores2FMat(reslist) on row(samplist.toList), "results")
}
}
done = true;
datasource.close
val gf = gflop
Mat.useCache = cacheState
if (useGPU) {
for (i <- 0 until nthreads) {
// if (i != thisGPU) disconnect(i);
}
}
if (opts.autoReset && useGPU) {
for (i <- 0 until nthreads) {
setGPU(i)
Learner.toCPU(models(i).modelmats)
}
resetGPUs
}
val perfStr = ("%5.2f%%, score=%6.5f, secs=%3.1f, samps/s=%4.1f, gf=%4.1f, MB/s=%4.1f" format (
100f*lastp,
reslist.synchronized {
Learner.scoreSummary(reslist, lasti, reslist.length, opts.cumScore)
},
gf._2,
nsamps/gf._2,
gf._1,
bytes/gf._2*1e-6));
val gpuStr = if (useGPU) {
(0 until math.min(nthreads, Mat.hasCUDA)).map((i)=>{
setGPU(i);
if (i==0) (", GPUmem=%3.2f" format GPUmem._1) else (", %3.2f" format GPUmem._1)
}).reduce(_+_);
} else "";
setGPU(thisGPU);
myLogger.info(perfStr + gpuStr);
results = Learner.scores2FMat(reslist) on row(samplist.toList)
}
def pause = {
paused = true;
Thread.sleep(500);
}
def unpause = {
paused = false;
}
def stop = {
done = true;
}
def safeCopy(m:Mat, ithread:Int):Mat = {
m match {
case ss:SMat => {
val out = SMat.newOrCheckSMat(ss.nrows, ss.ncols, ss.nnz, null, m.GUID, ithread, "safeCopy".##, true)
ss.copyTo(out)
}
case ss:SDMat => {
val out = SDMat.newOrCheckSDMat(ss.nrows, ss.ncols, ss.nnz, null, m.GUID, ithread, "safeCopy".##)
ss.copyTo(out)
}
case ss:FMat => {
val out = FMat.newOrCheckFMat(ss.dims, null, m.GUID, ithread, "safeCopy".##, true)
ss.copyTo(out)
}
case ss:DMat => {
val out = DMat.newOrCheckDMat(ss.dims, null, m.GUID, ithread, "safeCopy".##, true)
ss.copyTo(out)
}
case ss:BMat => {
val out = BMat.newOrCheckBMat(ss.dims, null, m.GUID, ithread, "safeCopy".##, true)
ss.copyTo(out)
}
case ss:IMat => {
val out = IMat.newOrCheckIMat(ss.nrows, ss.ncols, null, m.GUID, ithread, "safeCopy".##, true)
ss.copyTo(out)
}
}
}
def restart(ithread:Int) = {
if (useGPU) {
resetGPU
Mat.trimCaches(ithread)
}
models(ithread).bind(datasource)
models(ithread).init
models(ithread).modelmats(0) <-- mm(0)
updaters(ithread).init(models(ithread))
}
def datamats = datasource.asInstanceOf[MatSource].mats
def modelmats = models(0).modelmats
def datamat = datasource.asInstanceOf[MatSource].mats(0)
def modelmat = models(0).modelmats(0)
}
/**
* Parallel Learner with simplified Function arguments
*/
class ParLearnerF(
datasource:DataSource,
mkModelFn:()=>Model,
mkMixinsFn:()=>Array[Mixin],
mkUpdaterFn:()=>Updater,
datasink:DataSink,
opts:ParLearner.Opts = new ParLearner.Options) extends
ParLearner(datasource, (i:Int)=>mkModelFn(), (i:Int)=>mkMixinsFn(), (i:Int)=>mkUpdaterFn(), datasink, opts) {}
/**
* Parallel Learner class with multiple datasources, models, mixins, and updaters.
* i.e. several independent Learners whose models are synchronized periodically.
*/
class ParLearnerx(
val datasources:Array[DataSource],
val models:Array[Model],
val mixins:Array[Array[Mixin]],
val updaters:Array[Updater],
val datasinks:Array[DataSink],
val opts:ParLearner.Options = new ParLearner.Options) extends Serializable {
var um:Array[Mat] = null
var mm:Array[Mat] = null
var results:FMat = null
var useGPU = false
def init = {
val thisGPU = if (Mat.hasCUDA > 0) getGPU else 0
for (i <- 0 until opts.nthreads) {
if (i < Mat.hasCUDA) setGPU(i)
datasources(i).init
models(i).bind(datasources(i))
models(i).init
if (mixins != null) mixins(i) map(_ init(models(i)))
updaters(i).init(models(i))
}
useGPU = models(0).useGPU
if (Mat.hasCUDA > 0) setGPU(thisGPU)
val mml = models(0).modelmats.length
um = new Array[Mat](mml)
mm = new Array[Mat](mml)
for (i <- 0 until mml) {
val mm0 = models(0).modelmats(i)
mm(i) = zeros(mm0.nrows, mm0.ncols)
um(i) = zeros(mm0.nrows, mm0.ncols)
}
ParLearner.initmodels(models, mm, um, 0, useGPU)
}
def train = {
init
retrain
}
def retrain() = {
flip
var cacheState = Mat.useCache;
Mat.useCache = opts.useCache;
var cacheGPUstate = Mat.useGPUcache;
Mat.useGPUcache = opts.useCache;
val thisGPU = if (useGPU) getGPU else 0
if (useGPU) {
for (i <- 0 until opts.nthreads) {
if (i != thisGPU) connect(i)
}
}
@volatile var done = izeros(opts.nthreads, 1)
var ipass = 0
var istep0 = 0L
var ilast0 = 0L
var bytes = 0L
val reslist = new ListBuffer[FMat]
val samplist = new ListBuffer[Float]
var lastp = 0f
var lasti = 0
var gprogress = 0f
done.clear
(0 until opts.nthreads).par.foreach((ithread:Int) => {
if (useGPU && ithread < Mat.hasCUDA) setGPU(ithread)
var here = 0L
updaters(ithread).clear
while (done(ithread) < opts.npasses) {
var istep = 0
while (datasources(ithread).hasNext) {
val mats = datasources(ithread).next
here += datasources(ithread).opts.batchSize
bytes += mats.map(Learner.numBytes _).reduce(_+_);
gprogress = (dsProgress + ipass)/opts.npasses
models(0).synchronized {
istep += 1
istep0 += 1
}
try {
if (istep % opts.evalStep == 0) {
val scores = models(ithread).synchronized {models(ithread).evalbatchg(mats, ipass, here)}
reslist.synchronized { reslist.append(scores) }
samplist.synchronized { samplist.append(here) }
} else {
models(ithread).synchronized {
models(ithread).dobatchg(mats, ipass, here)
if (mixins != null && mixins(ithread) != null) mixins(ithread) map (_ compute(mats, here))
updaters(ithread).update(ipass, here, gprogress)
}
}
} catch {
case e:Exception => {
print("Caught exception in thread %d %s\nTrying restart..." format (ithread, e.toString))
restart(ithread)
println("Keep on truckin...")
}
}
if (useGPU) Thread.sleep(opts.coolit)
if (datasources(ithread).opts.putBack >= 0) datasources(ithread).putBack(mats, datasources(ithread).opts.putBack)
// if (istep % (opts.syncStep/opts.nthreads) == 0) syncmodel(models, ithread)
}
models(ithread).synchronized { updaters(ithread).updateM(ipass) }
done(ithread) += 1
while (done(ithread) > ipass) Thread.sleep(1)
}
});
println("pass=%2d" format ipass)
while (ipass < opts.npasses) {
while (mini(done).v == ipass) {
if (istep0 >= ilast0 + opts.syncStep) {
ParLearner.syncmodels(models, mm, um, istep0/opts.syncStep, useGPU)
ilast0 += opts.syncStep
}
if (dsProgress > lastp + opts.pstep) {
while (dsProgress > lastp + opts.pstep) lastp += opts.pstep
val gf = gflop
if (reslist.length > lasti) {
print("%5.2f%%, ll=%6.5f, gf=%5.3f, secs=%3.1f, GB=%4.2f, MB/s=%5.2f" format (
100f*lastp,
reslist.synchronized {
Learner.scoreSummary(reslist, lasti, reslist.length)
},
gf._1,
gf._2,
bytes*1e-9,
bytes/gf._2*1e-6))
if (useGPU) {
for (i <- 0 until math.min(opts.nthreads, Mat.hasCUDA)) {
setGPU(i)
if (i==0) print(", GPUmem=%3.2f" format GPUmem._1) else print(", %3.2f" format GPUmem._1)
}
setGPU(thisGPU)
}
println
}
lasti = reslist.length
} else {
Thread.sleep(1)
}
}
lastp = 0f
if (ipass < opts.npasses) {
for (i <- 0 until opts.nthreads) datasources(i).reset
println("pass=%2d" format ipass+1)
}
if (opts.resFile != null) {
saveAs(opts.resFile, Learner.scores2FMat(reslist) on row(samplist.toList), "results")
}
ipass += 1
}
val gf = gflop;
Mat.useCache = cacheState;
Mat.useGPUcache = cacheGPUstate;
println("Time=%5.4f secs, gflops=%4.2f, MB/s=%5.2f, GB=%5.2f" format (gf._2, gf._1, bytes/gf._2*1e-6, bytes*1e-9))
if (opts.autoReset && useGPU) {
Learner.toCPU(modelmats)
resetGPUs
}
for (ithread <- 0 until opts.nthreads) datasources(ithread).close
results = Learner.scores2FMat(reslist) on row(samplist.toList)
}
def syncmodel(models:Array[Model], ithread:Int) = {
mm.synchronized {
for (i <- 0 until models(ithread).modelmats.length) {
um(i) <-- models(ithread).modelmats(i)
um(i) ~ um(i) *@ (1f/opts.nthreads)
mm(i) ~ mm(i) *@ (1 - 1f/opts.nthreads)
mm(i) ~ mm(i) + um(i)
models(ithread).modelmats(i) <-- mm(i)
}
}
}
def restart(ithread:Int) = {
if (useGPU) {
resetGPU
Mat.trimCache2(ithread)
}
models(ithread).bind(datasources(ithread))
models(ithread).init
for (i <- 0 until models(ithread).modelmats.length) {
models(ithread).modelmats(i) <-- mm(i)
}
updaters(ithread).init(models(ithread))
}
def dsProgress:Float = {
var sum = 0f
for (i <- 0 until datasources.length) {
sum += datasources(i).progress
}
sum / datasources.length
}
def modelmats = models(0).modelmats
def modelmat = models(0).modelmats(0)
}
/**
* Parallel multi-datasource Learner that takes function arguments.
* This allows classes to be initialized later, when the learner is setup.
*/
class ParLearnerxF(
dopts:DataSource.Opts,
ddfun:(DataSource.Opts, Int)=>DataSource,
mopts:Model.Opts,