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/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.optim
import com.intel.analytics.bigdl.{Module, _}
import com.intel.analytics.bigdl.dataset.{DataSet, DistributedDataSet, MiniBatch, PaddingParam, Sample, SampleToMiniBatch}
import com.intel.analytics.bigdl.nn.{Container, Module, Utils}
import com.intel.analytics.bigdl.parameters.{AllReduceParameter, ParameterProcessor}
import com.intel.analytics.bigdl.nn.{Container, Module, Utils}
import com.intel.analytics.bigdl.parameters.AllReduceParameter
import com.intel.analytics.bigdl.tensor.{FloatType, Tensor}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils._
import java.io.{File, FilenameFilter}
import java.text.SimpleDateFormat
import java.util.Calendar
import com.intel.analytics.bigdl.models.utils.{CachedModels, ModelBroadcast}
import com.intel.analytics.bigdl.nn.abstractnn.Activity
import com.intel.analytics.bigdl.nn.mkldnn.Phase.TrainingPhase
import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer, MklDnnModule}
import com.intel.analytics.bigdl.utils.intermediate.IRGraph
import org.apache.commons.lang.exception.ExceptionUtils
import com.intel.analytics.bigdl.visualization.{TrainSummary, ValidationSummary}
import org.apache.log4j.Logger
import org.apache.spark.network.netty.SparkTransportConf
import org.apache.spark.{SparkContext, TaskContext}
import org.apache.spark.rdd.{RDD, ZippedPartitionsWithLocalityRDD}
import scala.collection.mutable
import scala.collection.mutable.ArrayBuffer
import scala.concurrent.Future
import scala.reflect.ClassTag
import com.intel.analytics.bigdl.nn.{Container, Graph, Module, Utils}
import com.intel.analytics.bigdl.nn.mkldnn.{DnnGraph, MklDnnContainer, MklDnnLayer}
import com.intel.analytics.bigdl.utils.intermediate.{ConversionUtils, IRGraph}
object DistriOptimizer extends AbstractOptimizer {
import Optimizer._
val logger: Logger = Logger.getLogger(getClass)
/**
* Optimizer cache some metadata on each executor
*
* @param localModels cached models
* @param modelWeights weights of the cached models
* @param modelGradients gradients of the cached models
* @param localCriterions cached criterion
* @param localStates cached state
* @param moduleTimeList module running time
* @param localMethods cached validation methods
* @param optimMethods cached optim methods
* @param parameterSynchronizer cached parameter synchronizer
* @tparam T Tensor element type
*/
case class Cache[T](
localModels: Array[Module[T]],
modelWeights: Array[Tensor[T]],
modelGradients: Array[Tensor[T]],
localCriterions: Array[Criterion[T]],
localStates: Array[Table],
var moduleTimeList: Array[Long] = null,
localMethods: Array[Option[Array[ValidationMethod[T]]]],
var optimMethods: Map[String, OptimMethod[T]],
parameterSynchronizer: DistriParameterSynchronizer[T] = null
)
/**
* Train the model.
*
* @param dataset train dataset
* @param coresPerNode cores per node
* @param state state table
* @param endWhen trigger to stop training
* @param metrics metrics
* @param models cached models
* @param optimMethods optimization methods
* @param parameters [[AllReduceParameter]]
* @param validationTrigger validation trigger
* @param validationDataSet validation dataset
* @param validationMethods validation methods
* @param cacheTrigger cache trigger
* @param cachePath cache path
* @param trainSummary train summary
* @param validationSummary validation summary
* @param isOverWrite if overwrite the checkpoint
* @param parameterProcessers a list of ParameterProcessor used to process parameters
*/
private[optim] def optimize[T: ClassTag](
trainingModel: Module[T],
dataset: DistributedDataSet[MiniBatch[T]],
coresPerNode: Int,
state: Table,
endWhen: Trigger,
metrics: Metrics,
models: RDD[Cache[T]],
optimMethods: Map[String, OptimMethod[T]],
parameters: Map[String, AllReduceParameter[T]],
validationTrigger: Option[Trigger],
validationDataSet: Option[DataSet[MiniBatch[T]]],
validationMethods: Option[Array[ValidationMethod[T]]],
cacheTrigger: Option[Trigger],
cachePath: Option[String],
trainSummary: Option[TrainSummary],
validationSummary: Option[ValidationSummary],
isOverWrite: Boolean,
parameterProcessers: Array[ParameterProcessor]
)(implicit ev: TensorNumeric[T]): Unit = {
val sc = dataset.originRDD().sparkContext
val partitionNum = dataset.originRDD().partitions.length
var wallClockTime = 0L
var lastEpochTime = 0L
// driverState is needed to prevent serializing the whole optimizer
optimMethods.values.foreach{ optimMethod =>
if (!optimMethod.state.contains("epoch")) optimMethod.state.update("epoch", 1)
if (!optimMethod.state.contains("neval")) optimMethod.state.update("neval", 1)
if (!optimMethod.state.contains("Loss")) {
optimMethod.state.update("Loss", Float.PositiveInfinity)
}
if (!optimMethod.state.contains("score")) optimMethod.state.update("score", 0f)
if (!optimMethod.state.contains("recordsProcessedThisEpoch")) {
optimMethod.state.update("recordsProcessedThisEpoch", 0)
}
}
val _subModelNumber = Engine.getEngineType() match {
case MklBlas => coresPerNode
case MklDnn => 1
}
val driverState = T(
"epoch" -> optimMethods.values.head.state("epoch"),
"neval" -> optimMethods.values.head.state("neval"),
"Loss" -> optimMethods.values.head.state("Loss"),
"score" -> optimMethods.values.head.state("score"),
"parallelism" -> _subModelNumber
)
logger.info("Count dataset")
val countBefore = System.nanoTime()
val numSamples = dataset.data(train = false).map(_.size()).reduce(_ + _)
val countAfter = System.nanoTime()
logger.info(s"Count dataset complete. Time elapsed: ${(countAfter - countBefore) / 1e9}s")
if (numSamples != dataset.size()) {
logger.warn("If the dataset is built directly from RDD[Minibatch], the data in each " +
"minibatch is fixed, and a single minibatch is randomly selected in each partition. If " +
"the dataset is transformed from RDD[Sample], each minibatch will be constructed on the " +
"fly from random samples, which is better for convergence.")
}
logger.info(s"config $state")
var recordsProcessedThisEpoch = optimMethods.values.head.state[Int]("recordsProcessedThisEpoch")
if (recordsProcessedThisEpoch == 0) {
val shuffleBefore = System.nanoTime()
logger.info("Shuffle data")
dataset.shuffle()
val shuffleEnd = System.nanoTime()
logger.info(s"Shuffle data complete. Takes ${(shuffleEnd - shuffleBefore) / 1e9}s")
}
var tasks: ArrayBuffer[Future[_]] = new ArrayBuffer()
var threshold = Long.MaxValue
var timeout = Long.MaxValue
var iteration = 0
val dropPercentage = state.get[Double]("dropPercentage").get
val warmupIterationNum = state.get[Int]("warmupIterationNum").get
val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get
val maxDropPercentage = state.get[Double]("maxDropPercentage").get
val driverSubModelNum = partitionNum * _subModelNumber
var dropModelNumBatch = 0
var lossArray = new Array[Double](_subModelNumber)
var epochStart = System.nanoTime()
var dataRDD = dataset.data(train = true)
while (!endWhen(driverState)) {
val lossSum = sc.accumulator(0.0, "loss sum")
val recordsNum = sc.accumulator(0, "record number")
metrics.set("computing time for each node", mutable.ArrayBuffer[Double](), sc)
metrics.set("get weights for each node", mutable.ArrayBuffer[Double](), sc)
metrics.set("computing time average", 0.0, sc, partitionNum)
metrics.set("aggregate gradient time", 0.0, sc, partitionNum)
metrics.set("get weights average", 0.0, sc, partitionNum)
metrics.set("put gradient", 0.0, sc, Engine.nodeNumber())
metrics.set("aggregrateGradientParition average executor", 0.0, sc, Engine.nodeNumber())
metrics.set("compute weight average", 0.0, sc, Engine.nodeNumber())
metrics.set("send weights average", 0.0, sc, Engine.nodeNumber())
val driverMetrics = metrics
val start = System.nanoTime()
/*
Run the forwards/backwards pass using multiple threads in each partition, and track the
number of model updates that finished before the thread timeout mechanism.
*/
val numFinishedModelUpdates: Int = dataRDD
.zipPartitions(models, preservesPartitioning = true) { (data, modelIter) => {
val cached = modelIter.next()
val syWStart = System.nanoTime()
/*
Note: All models in `cached` share the same storage for weights, so we only need to
copy the weights from parameter server into the first model's weights.
*/
val weightsResults = parameters.values.map(p =>
p.getWeights(cached.modelWeights.head.narrow(1, p.paramOffset, p.size))
).toArray
val miniBatchBuffer = new Array[MiniBatch[T]](_subModelNumber)
val batch = data.next()
val stackSize = batch.size() / _subModelNumber
tasks += Engine.default.invoke(() => {
require((batch.size() >= _subModelNumber) &&
(batch.size() % _subModelNumber == 0), "total batch size: " +
s"${batch.size()} should be divided by total core number: ${_subModelNumber}")
if (batch.size() < _subModelNumber * 2) {
logger.warn("Warning: for better training speed, " +
"total batch size is recommended to be at least two times of core number" +
s"${_subModelNumber}, please tune your batch size accordingly")
}
var b = 0
while (b < _subModelNumber) {
miniBatchBuffer(b) = batch.slice(b * stackSize + 1, stackSize)
b += 1
}
})
Engine.default.sync(tasks)
weightsResults.foreach(_.waitResult())
val weightSyncTime = System.nanoTime() - syWStart
driverMetrics.add("get weights average", weightSyncTime)
driverMetrics.add("get weights for each node", weightSyncTime)
tasks.clear()
// ======================Start train models===================================
var time = System.nanoTime()
if (dropPercentage > 0.0 && iteration > warmupIterationNum +
computeThresholdbatchSize - 1) {
timeout = threshold - weightSyncTime
}
val pre = (iteration % computeThresholdbatchSize) * _subModelNumber
val trainingThreads = Engine.default.invokeAndWait2((0 until _subModelNumber).map(i =>
() => {
val trainStart = System.nanoTime()
val localModel = cached.localModels(i)
localModel.training()
val localCriterion = cached.localCriterions(i)
val input = miniBatchBuffer(i).getInput()
val target = miniBatchBuffer(i).getTarget()
if (Engine.getEngineType() == MklBlas || localModel.isInstanceOf[IRGraph[T]]) {
val output = localModel.forward(input)
lossArray(i) = ev.toType[Double](localCriterion.forward(output, target))
val errors = localCriterion.backward(output, target)
localModel.backward(input, errors)
} else {
Engine.dnnComputing.invokeAndWait2(Array(0).map(_ => () => {
val output = localModel.forward(input)
lossArray(i) = ev.toType[Double](localCriterion.forward(output, target))
val errors = localCriterion.backward(output, target)
localModel.backward(input, errors)
}))
}
cached.moduleTimeList(i + pre) = System.nanoTime() - trainStart + weightSyncTime
i
}
), timeout)
val computingTime = System.nanoTime() - time
driverMetrics.add("computing time average", computingTime)
driverMetrics.add("computing time for each node", computingTime)
val finishedThreads = trainingThreads.filter(!_.isCancelled).map(_.get())
recordsNum += finishedThreads.size * stackSize
var i = 0
while (i < finishedThreads.size) {
lossSum += lossArray(finishedThreads(i))
i += 1
}
if (finishedThreads.nonEmpty) {
val finishedGradients = finishedThreads.map(cached.modelGradients(_))
parameters.values.foreach { p =>
time = System.nanoTime()
val pOffset = p.paramOffset
val pLength = p.size
val taskSize = pLength / _subModelNumber
val extraTask = pLength % _subModelNumber
// Aggregate multi-model's gradient to the first model's gradient
val parallelNum = if (taskSize == 0) extraTask else _subModelNumber
if (parallelNum != 1) {
Engine.default.invokeAndWait((0 until parallelNum).map(tid => () => {
val offset = pOffset + tid * taskSize + math.min(tid, extraTask)
val length = taskSize + (if (tid < extraTask) 1 else 0)
var i = 1
while (i < finishedGradients.length) {
finishedGradients(0).narrow(1, offset, length)
.add(finishedGradients(i).narrow(1, offset, length))
i += 1
}
}))
driverMetrics.add("aggregate gradient time", System.nanoTime() - time)
}
val putG = System.nanoTime()
// Put first finished model's gradient who aggregated
// all other models' gradient to AllReduceParameter
p.putGradients(finishedGradients(0).narrow(1, pOffset, pLength))
driverMetrics.add("put gradient", System.nanoTime() - putG)
}
} else {
val putG = System.nanoTime()
// zero gradient in BlockManager when no thread finished.
cached.modelGradients(0).zero()
parameters.values.foreach{p =>
p.putGradients(cached.modelGradients(0).narrow(1, p.paramOffset, p.size))
}
driverMetrics.add("put gradient", System.nanoTime() - putG)
}
tasks ++= Engine.default.invoke {
(0 until _subModelNumber).map { i =>
() => {
cached.localModels(i).training()
cached.localModels(i).zeroGradParameters()
}
}
}
Iterator.single(finishedThreads.size)
}
}.reduce(_ + _)
dropModelNumBatch += (driverSubModelNum - numFinishedModelUpdates)
if (dropPercentage == 0.0 ||
numFinishedModelUpdates >= driverSubModelNum * (1.0 - maxDropPercentage)) {
// enough records were processed for this batch, so update the model
val value = lossSum.value / numFinishedModelUpdates
driverState("numFinishedModel") = numFinishedModelUpdates
// isGradientUpdated is flag to mark whether gradient is updated. May changed in the future.
driverState("isGradientUpdated") = false
// parameterProcesser like L2NormClippingProcessor may aggregate gradient,
// and change the value of isGradientUpdated in driverState.
parameters.foreach { p =>
parameterProcessers.foreach(_.collectGlobalData(models, p._2, metrics, driverState))
}
val isGradientUpdated = driverState[Boolean]("isGradientUpdated")
val stateBroadcast = sc.broadcast(driverState)
models.mapPartitions { modelIter =>
val modelCache = modelIter.next()
// if parameterProcesser has aggregated gradient, we can skip this aggregation.
if (!isGradientUpdated) {
val getG = System.nanoTime()
parameters.values.foreach(_.aggregateGradientPartition(numFinishedModelUpdates))
driverMetrics.add("aggregrateGradientParition average executor",
System.nanoTime() - getG)
}
parameters.foreach { p =>
parameterProcessers.foreach(_.processParameters(p._2, modelCache, driverState))
}
modelCache.optimMethods.foreach{ case (name, optimMethod) =>
var time = System.nanoTime()
optimMethod.state.update("epoch", driverState[Int]("epoch"))
optimMethod.state.update("neval", driverState[Int]("neval"))
optimMethod.state.update("Loss", driverState[Float]("Loss"))
if (validationMethods.isDefined) {
optimMethod.state.update("score", driverState[Float]("score"))
}
val p = parameters(name)
optimMethod.optimize(_ => (ev.fromType(value), p.gradientPartition),
p.weightPartition)
driverMetrics.add("compute weight average", System.nanoTime() - time)
time = System.nanoTime()
p.sendWeightPartition()
driverMetrics.add("send weights average", System.nanoTime() - time)
}
Iterator.empty
}.count()
stateBroadcast.destroy()
recordsProcessedThisEpoch += recordsNum.value
val end = System.nanoTime()
wallClockTime += end - start
driverState("isGradientUpdated") = true
driverState("Loss") = lossSum.value.toFloat / numFinishedModelUpdates
optimMethods.foreach{ v =>
v._2.updateHyperParameter()
}
// TODO: Support show learningrate for multiOptimMethod
driverState(s"LearningRate") = optimMethods.head._2.getLearningRate().toFloat
driverState("Throughput") = recordsNum.value.toFloat / ((end - start) / 1e9f)
val _header = header(driverState[Int]("epoch"), recordsProcessedThisEpoch, numSamples,
driverState[Int]("neval"), wallClockTime)
logger.info(s"${_header} Trained ${recordsNum.value} records in ${(end - start) / 1e9} " +
s"seconds. Throughput is ${driverState("Throughput")} records/second. Loss is ${
driverState("Loss")}. ${getHyperParameterLog(optimMethods)}")
logger.debug("\n" + metrics.summary())
logger.debug("Dropped modules: " + (driverSubModelNum - numFinishedModelUpdates))
lossArray = new Array[Double](_subModelNumber)
// compute threshold
iteration += 1
if (dropPercentage > 0.0 && iteration > warmupIterationNum &&
iteration % computeThresholdbatchSize == 0) {
val moduleTimeList = models.mapPartitions { iter =>
iter.next().moduleTimeList.iterator
}.collect()
val k = (dropPercentage * computeThresholdbatchSize * driverSubModelNum).toInt
if (k > dropModelNumBatch) {
threshold = Util.kthLargest(moduleTimeList, 0, moduleTimeList.length-1,
k - dropModelNumBatch)
} else {
threshold = (threshold * 1.01).toLong
}
logger.info("threshold: " + threshold)
// clear moduleTimeList in each node
models.mapPartitions { iter =>
val timeList = iter.next.moduleTimeList
var i = 0
while (i < timeList.length) {
timeList(i) = 0
i += 1
}
Iterator.empty
}.count()
dropModelNumBatch = 0
}
driverState("neval") = driverState[Int]("neval") + 1
if (recordsProcessedThisEpoch >= numSamples) {
// Epoch is finished
val epochEnd = System.nanoTime()
wallClockTime = lastEpochTime + epochEnd - epochStart
lastEpochTime = wallClockTime
epochStart = System.nanoTime()
logger.info(s"${_header} Epoch finished. Wall clock time is ${wallClockTime / 1e6} ms")
driverState("epoch") = driverState[Int]("epoch") + 1
dataset.shuffle()
dataRDD = dataset.data(train = true)
recordsProcessedThisEpoch = 0
}
optimMethods.map { case (moduleName, optimMethod) =>
optimMethod.state.update("recordsProcessedThisEpoch", recordsProcessedThisEpoch)
optimMethod.state.update("epoch", driverState[Int]("epoch"))
optimMethod.state.update("neval", driverState[Int]("neval"))
optimMethod.state.update("Loss", driverState[Float]("Loss"))
if (validationMethods.isDefined) {
optimMethod.state.update("score", driverState[Float]("score"))
}
}
validate(
validationTrigger,
validationDataSet,
validationMethods,
coresPerNode,
models,
driverState,
validationSummary,
_header,
parameters
)
trainSummary.foreach { summary =>
saveSummary(
summary,
models,
driverState,
parameters,
trainingModel
)
}
checkpoint(
cacheTrigger,
cachePath,
isOverWrite,
wallClockTime,
models,
driverState,
parameters,
optimMethods,
trainingModel
)
} else {
logger.info(s"Warning! Not enough training samples were successfully processed in this " +
s"iteration due to some slow tasks. The gradients computed in this iteration will be " +
s"discarded. Only $numFinishedModelUpdates/$driverSubModelNum threads successfully " +
s"completed training.")
}
}
}
/**
* Init engine and cache models, weights, gradients, criterions, state tables
* and validation methods on worker nodes.
*
* @param model train model
* @param dataset train dataset
* @param criterion loss function
* @param state state table
* @param nodeNumber node number
* @param coresPerNode cores per node
* @param checkSingleton if checkSingleton
* @param parameters all reduce parameter instance
* @param validationMethods validation methods
* @param optimMethod optimization method
* @param parameterProcessors a list of ParameterProcessor used to process parameters
* @return cached models
*/
private def initThreadModels[T: ClassTag](
model: Module[T],
dataset: DistributedDataSet[MiniBatch[T]],
criterion: Criterion[T],
state: Table,
nodeNumber: Int,
coresPerNode: Int,
checkSingleton: Boolean,
parameters: Map[String, AllReduceParameter[T]],
validationMethods: Option[Array[ValidationMethod[T]]],
optimMethod: Map[String, OptimMethod[T]],
parameterProcessors: ArrayBuffer[ParameterProcessor]
)(implicit ev: TensorNumeric[T]): (RDD[DistriOptimizer.Cache[T]], ModelBroadcast[T]) = {
val sc = dataset.originRDD().sparkContext
val broadcast = sc.broadcast((criterion, state, validationMethods, optimMethod))
// ensure model's parameter is compacted for getting a better performance when broadcasting
model.getParameters()
// As cloneModel is using Serialization to implement deep copy, and will throw OOMError
// when model's size is bigger than SerializationUtils' buffer size. So we can use
// ModelBroadcast to clone model here.
// Notes: All models returned by modelBroadcast.value() share the same weight&bias, while
// gradWeight&gradBias is unshared.
val modelBroadcast = ModelBroadcast[T]().broadcast(sc, ConversionUtils.convert(model))
val _subModelNumber = Engine.getEngineType match {
case MklBlas => coresPerNode
case MklDnn => 1
case _ => throw new IllegalArgumentException
}
require(dataset.originRDD().partitions.length == nodeNumber,
s"Passed in rdd partition number ${dataset.originRDD().partitions.length}" +
s" is not equal to configured node number ${nodeNumber}")
val computeThresholdbatchSize = state.get[Int]("computeThresholdbatchSize").get
val nExecutor = Engine.nodeNumber()
val executorCores = Engine.coreNumber()
val models = dataset.originRDD().mapPartitions(_ => {
val partitionId = TaskContext.getPartitionId
val (broadcastCriterion, broadcastState, broadcastMethod,
broadcastOptim) = broadcast.value
if (!Engine.checkSingleton()) {
if (checkSingleton) {
require(Engine.checkSingleton(), "Partitions of the training data are not evenly" +
"distributed across the executors in the Spark cluster; are there sufficient training" +
"data to be distributed? Set property \"bigdl.check.singleton\" to false to skip " +
"this check")
} else {
logger.warn("Partitions of the training data are not evenly" +
"distributed across the executors in the Spark cluster; are there sufficient training" +
"data to be distributed?")
}
}
Engine.setNodeAndCore(nExecutor, executorCores)
val cached = (0 until _subModelNumber).map { _ =>
val localModel = modelBroadcast.value(true)
if (Engine.getEngineType() == MklDnn && !localModel.isInstanceOf[IRGraph[T]]) {
Engine.dnnComputing.invokeAndWait2((0 until _subModelNumber).map(i =>
() => {
localModel match {
case container: MklDnnContainer => container.compile(TrainingPhase)
case graph: DnnGraph => graph.compile(TrainingPhase)
case _ =>
}
}))
}
setModelId(localModel, partitionId)
val localCriterion = broadcastCriterion.cloneCriterion()
val localState = broadcastState.clone()
val localMethod =
if (broadcastMethod.isDefined) Some(broadcastMethod.get.map(_.clone())) else None
val (weights, grads) = localModel.getParameters()
(localModel, weights, grads, localCriterion, localState, localMethod)
}.toArray
logger.info("model thread pool size is " + Engine.model.getPoolSize)
val weights = cached.head._2
parameters.foreach(v =>
v._2.init(weights.narrow(1, v._2.paramOffset, v._2.size))
)
Iterator.single(Cache(
cached.map(_._1), // models
cached.map(_._2), // weights
cached.map(_._3), // gradients
cached.map(_._4), // criterions
cached.map(_._5), // states
new Array[Long](_subModelNumber * computeThresholdbatchSize),
cached.map(_._6),
broadcastOptim.map(v => (v._1, v._2.clone()))
))
}).persist()
models.setName("Thread Model RDD")
logger.info("Cache thread models...")
models.count()
logger.info("Cache thread models... done")
(models, modelBroadcast)
}
private def setModelId[T: ClassTag](model: Module[T], partitionId: Int): Unit = {
model.setId(partitionId)
if (model.isInstanceOf[Container[_, _, T]]) {
model.asInstanceOf[Container[_, _, T]].modules.
foreach(sub => setModelId(sub, partitionId))
}
}
/**
* Fetch current model parameters to driver, and copy to trainingModel.
*
* @param models cached models
* @param parameters [[AllReduceParameter]]
* @param trainingModel the model is trained by optimizer
* @return trained model
*/
override protected def getModel[T: ClassTag](
models: RDD[Cache[T]],
parameters: Map[String, AllReduceParameter[T]],
trainingModel: Module[T])(implicit ev: TensorNumeric[T]): Module[T] = {
val partitionNum = models.partitions.length
val extraState = models.map(_.localModels.head.getExtraParameter()).first()
trainingModel.setExtraParameter(extraState)
// make sure gradient is as the same length as weight
val parameterArray = trainingModel.parameters()
(0 until parameterArray._2.length).foreach(i =>
parameterArray._2(i).resizeAs(parameterArray._1(i))
)
val (parameter, gradientParameter) = trainingModel.getParameters()
parameters.foreach { case (moduleName, p) =>
val currentModule = trainingModel(moduleName)
require(currentModule.isDefined, s"Couldn't find $moduleName in $trainingModel")
val (weights, gradients) = models.mapPartitions(iter => {
val cached = iter.next()
val curPartitionId = TaskContext.getPartitionId()
Iterator.single((Map(curPartitionId -> p.weightPartition),
Map(curPartitionId -> p.gradientPartition)))
}).reduce((a, b) => (a._1 ++ b._1, a._2 ++ b._2))
val taskSize = p.size / partitionNum
require(taskSize != 0, "parameter length should not less than partition number")
val extraSize = p.size % partitionNum
(0 until partitionNum).map(pid => {
val start = p.paramOffset + pid * taskSize + math.min(pid, extraSize)
val length = taskSize + (if (pid < extraSize) 1 else 0)
parameter.narrow(1, start, length).copy(weights(pid))
gradientParameter.narrow(1, start, length).copy(gradients(pid))
})
}
trainingModel
}
}
/**
* The optimizer run on a distributed cluster.
*
* @param _model train model
* @param _dataset train dataset
* @param _criterion loss function
*/
class DistriOptimizer[T: ClassTag] (
_model: Module[T],
_dataset: DistributedDataSet[MiniBatch[T]],
_criterion: Criterion[T]
)(implicit ev: TensorNumeric[T])
extends Optimizer[T, MiniBatch[T]](
_model, _dataset, _criterion) {
val metrics = new Metrics
private var models: RDD[DistriOptimizer.Cache[T]] = null
// this variable is used to check the models cloned when broadcast, if there're native resources,
// it will be deleted at the end of Optimizer.
private var modelBroadcast: ModelBroadcast[T] = null
/**
* Clean some internal states, so this or other optimizers can run optimize again
*
* This method will be called at the end of optimize. You need not call it if optimize succeed.
* If the optimize fails, you may call it before next optimize.
*/
def clearState() : Unit = {
DistriOptimizer.clearState(models)
}
// By default, optimMethod internal state for each worker will not be reserved and reuse.
private var reserveOptimMethod = false
private[bigdl] var previousOptim: RDD[Map[String, OptimMethod[T]]] = null
/**
* If you want to reserve optimMethod for each worker, and reuse those methods in
* next training task, you can call it.
*/
/**
* If you want to reserve optimMethod for each worker and reuse those methods in
* next training task, please set reserve = true
* Otherwise, if just using optimMethod you set in optimizer, please set reserve = false
* @param reserve whether to reserve optim method for each worker
* @return
*/
override def reserveOptim(reserve: Boolean): this.type = {
reserveOptimMethod = reserve
this
}
// replace optim methods with previous
private def resetOptimMethods[T: ClassTag](
models: RDD[DistriOptimizer.Cache[T]],
previousOptimMethods: RDD[Map[String, OptimMethod[T]]]):
RDD[DistriOptimizer.Cache[T]] = {
models.zipPartitions(previousOptimMethods) { (m1, m2) => {
val cache = m1.next()
cache.optimMethods = m2.next()
Iterator(cache)
}
}
}
private def endEpoch(): Unit = {
DistriOptimizer.endEpoch(optimMethods)
}
override def setTrainData(sampleRDD: RDD[Sample[T]],
batchSize: Int,
miniBatch: MiniBatch[T]): this.type = {
this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize, miniBatch)
// if current epoch is not finished, we will end the
// current epoch and start a new epoch when optimize is called
endEpoch()
this
}
override def setTrainData(sampleRDD: RDD[Sample[T]],
batchSize: Int,
featurePaddingParam: PaddingParam[T] = null,
labelPaddingParam: PaddingParam[T] = null) : this.type = {
val _featurePaddingParam = if (featurePaddingParam != null) Some(featurePaddingParam) else None
val _labelPaddingParam = if (labelPaddingParam != null) Some(labelPaddingParam) else None
this.dataset = DistriOptimizer.setTrainData(sampleRDD, batchSize,
featurePaddingParam, labelPaddingParam)
// if current epoch is not finished, we will end the
// current epoch and start a new epoch when optimize is called
endEpoch()
this
}
override def prepareInput(): Unit = {
if (!dataset.toDistributed().isCached) {
DistriOptimizer.logger.info("caching training rdd ...")
DistriOptimizer.prepareInput(this.dataset, this.validationDataSet)
}
}
override def optimize(): Module[T] = {
val distDataset = dataset.toDistributed()
val trainingModel = if (Engine.getEngineType() == MklDnn && !model.isInstanceOf[MklDnnModule]
&& !model.isInstanceOf[IRGraph[T]] && !model.isInstanceOf[Graph[T]]) {
model.toGraph().setName(model.getName())
} else model
optimMethods.values.foreach { optimMethod =>
optimMethod.clearHistory()
}
// To be compatible with the old usage that user define hyperparameters in a table.
if (optimMethods.size == 1) {
optimMethods.head._2.loadFromTable(state)
}
state("dropPercentage") = dropPercentage
state("warmupIterationNum") = warmupIterationNum
state("computeThresholdbatchSize") = computeThresholdbatchSize
state("maxDropPercentage") = maxDropPercentage
state("isLayerwiseScaled") = Utils.isLayerwiseScaled(_model)
val nodeNumber = Engine.nodeNumber()
val coresPerNode = Engine.coreNumber()
val partitionNum = distDataset.originRDD().partitions.length
val modelParameters = trainingModel.getParameters()
// subModuleName -> (storageOffset, length, AllReduceParameter)
val parameters = if (optimMethods.size != 1) {
val p = optimMethods.map{case (subModuleName, optimMethods) =>
val subModule = trainingModel(subModuleName)
require(subModule.isDefined, s"Optimizer couldn't find $subModuleName in $model")
val subModuleWeights = subModule.get.getParameters()._1
(subModuleName, subModuleWeights)
}
val sortedWeights = p.values.toArray.sortWith((a, b) => a.storageOffset() < b.storageOffset())
val compactWeights = Module.isCompact(sortedWeights)
require(modelParameters._1 == compactWeights,
s"DistriOptimizer: All subModules should have an OptimMethod.")
p.map{case (subModuleName, weights) =>
(subModuleName, AllReduceParameter.newParameter[T](
partitionNum, weights.nElement(), weights.storageOffset()))
}
} else if (optimMethods.contains(trainingModel.getName())) {
Map(trainingModel.getName() -> AllReduceParameter.newParameter[T](
partitionNum, modelParameters._1.nElement()))
} else {
throw new IllegalArgumentException(s"${trainingModel.getName()} doesn't " +
s"have corresponding OptimMethod")
}
prepareInput()
val modelsAndBroadcast = DistriOptimizer.initThreadModels(trainingModel, distDataset, criterion,
state, nodeNumber, coresPerNode, checkSingleton, parameters, validationMethods,
optimMethods, parameterProcessors)
models = if (reserveOptimMethod && previousOptim != null) {
// replace optimMethods with previous ones
resetOptimMethods(modelsAndBroadcast._1, previousOptim)
} else {
modelsAndBroadcast._1
}
modelBroadcast = modelsAndBroadcast._2
if (checkpointPath.isDefined) {
val file = checkpointPath.get + "/" +
new SimpleDateFormat("yyyyMMdd_HHmmss").format(Calendar.getInstance().getTime())
new File(file).mkdir()
checkpointPath = Some(file)
}
var retryNum = 0
val maxRetry = System.getProperty("bigdl.failure.retryTimes", "5").toInt
val retryTimeInterval = System.getProperty("bigdl.failure.retryTimeInterval", "120").toInt
var lastFailureTimestamp = System.nanoTime()
while (retryNum < maxRetry) {
try {
DistriOptimizer.optimize(
trainingModel,
distDataset,
coresPerNode,
state,
endWhen,
metrics,
models,
optimMethods,
parameters,
validationTrigger,
validationDataSet,
validationMethods,
checkpointTrigger,
checkpointPath,
trainSummary,
validationSummary,
isOverWrite,
parameterProcessors.toArray
)
retryNum = Int.MaxValue
} catch {
case e: IllegalArgumentException =>
throw e
case t: Throwable =>
DistriOptimizer.logger.error("Error: " + ExceptionUtils.getStackTrace(t))
if (checkpointPath.isDefined) {
/* To avoid retry number is used up by first few exceptions, we count time here.
* If exception exceeds maxRetry times in maxRetry*retryTimeInterval seconds,
* we will give up retry Or we will reset retryNum
*/
if (System.nanoTime() - lastFailureTimestamp < maxRetry * retryTimeInterval * 1e9) {
retryNum += 1
if (retryNum == maxRetry) {
throw t
}
} else {
retryNum = 1
}
DistriOptimizer.logger.info(s"Retrying $retryNum times")
lastFailureTimestamp = System.nanoTime()
val modelFile = getLatestFile(checkpointPath.get, "model")
clearState()
models.unpersist()
val newModel = if (modelFile != null) {
DistriOptimizer.logger.info("Model recover from last snapshot")
Module.load[T](modelFile)
} else {
DistriOptimizer.logger.info("Model recover from origin model")
trainingModel
}
optimMethods = optimMethods.map { case (moduleName, optimMethod) =>
val methodFile = getLatestFile(checkpointPath.get, s"optimMethod-$moduleName")
val newOptimMethod = if (methodFile != null) {
DistriOptimizer.logger.info(s"$moduleName's OptimMethod recover from last snapshot")
OptimMethod.load[T](methodFile)
} else {
DistriOptimizer.logger.info(s"$moduleName's OptimMethod recover from origin model")
optimMethod
}
newOptimMethod.clearHistory()
(moduleName, newOptimMethod)
}
val modelsAndBroadcast = DistriOptimizer.initThreadModels(newModel, distDataset,
criterion, state, nodeNumber, coresPerNode, checkSingleton, parameters,
validationMethods, optimMethods, parameterProcessors)
models = modelsAndBroadcast._1
modelBroadcast = modelsAndBroadcast._2
} else {
throw t
}
}
}
DistriOptimizer.getModel(models, parameters, trainingModel)
// Reset some internal states, so this or other optimizers can run optimize again
clearState()
// unpersist the model because the next time optimize is called, new `models` will be
// created
shutdown()
// reserve optimMethod internal state for each worker if need
if (reserveOptimMethod) {
previousOptim = models.map(m => m.optimMethods).cache()
previousOptim.count()
} else {
if (previousOptim != null) previousOptim.unpersist()
}
models.unpersist()
trainingModel
}
private def getLatestFile(path: String, fileName: String): String = {
val fl = new java.io.File(path)
val files = fl.listFiles(new FilenameFilter {
override def accept(dir: File, name: String): Boolean = {
name.startsWith(fileName)
}
})
var lastMod = Long.MinValue
var choice: String = null
files.map {file =>
if (file.lastModified() > lastMod) {
choice = file.getPath;
lastMod = file.lastModified();
}
}
return choice;
}
// this shutdown should not be called out of this scope.
private[optim] override def shutdown(): Unit = {
models.mapPartitions { iter =>
iter.foreach { arrayModels =>
arrayModels.localModels.foreach(_.release())
}
iter
}.count()
CachedModels.deleteKey(modelBroadcast.uuid)
}
}
