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add multi thread sample to minibatch (#1589)
* add mt sample to minibatch * revert log level * delete some debug code * add comments * fix style check * update pom
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139
zoo/src/main/scala/com/intel/analytics/zoo/feature/common/MTSampleToMiniBatch.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,139 @@ | ||
| /* | ||
| * Copyright 2018 Analytics Zoo 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.zoo.feature.common | ||
|
|
||
| import com.intel.analytics.bigdl.dataset._ | ||
| import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric | ||
| import com.intel.analytics.zoo.pipeline.api.keras.layers.utils.EngineRef | ||
|
|
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| import scala.reflect.ClassTag | ||
|
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| /** | ||
| * Convert a sequence of [[Sample]] to a sequence of [[MiniBatch]] | ||
| * through function toMiniBatch using multi thread. | ||
| */ | ||
| class MTSampleToMiniBatch[A: ClassTag, T: ClassTag] ( | ||
| totalBatch: Int, | ||
| transformer: Transformer[A, Sample[T]], | ||
| miniBatch: Option[MiniBatch[T]] = None, | ||
| featurePaddingParam: Option[PaddingParam[T]] = None, | ||
| labelPaddingParam: Option[PaddingParam[T]] = None, | ||
| partitionNum: Option[Int] = None) | ||
| (implicit ev: TensorNumeric[T]) extends Transformer[A, MiniBatch[T]] { | ||
|
|
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| private val batchPerPartition = Utils.getBatchSize(totalBatch, partitionNum) | ||
| var miniBatchBuffer = miniBatch.orNull | ||
| private val batchSize = batchPerPartition | ||
| private val sampleData = new Array[Sample[T]](batchSize) | ||
|
|
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| private val parallelism = EngineRef.getCoreNumber() | ||
|
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| private val transformers = (0 until parallelism).map( | ||
| _ => transformer.cloneTransformer() | ||
| ).toArray | ||
|
|
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| private val rawDataCache = new Array[Iterator[A]](batchSize) | ||
|
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| override def apply(prev: Iterator[A]): Iterator[MiniBatch[T]] = { | ||
| new Iterator[MiniBatch[T]] { | ||
|
|
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| override def hasNext: Boolean = prev.hasNext | ||
|
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| override def next(): MiniBatch[T] = { | ||
| if (prev.hasNext) { | ||
| // prefetch | ||
| var count = 0 | ||
| while (count < batchSize && prev.hasNext) { | ||
| val raw = prev.next() | ||
| rawDataCache(count) = Iterator.single(raw) | ||
| count += 1 | ||
| } | ||
|
|
||
| // multi thread processing | ||
| (0 until parallelism).toParArray.foreach{tid => | ||
| var j = tid | ||
| while (j < count) { | ||
| sampleData(j) = transformers(tid).apply(rawDataCache(j)).next() | ||
| j += parallelism | ||
| } | ||
| } | ||
|
|
||
| if (null == miniBatchBuffer) { | ||
| val firstSample = sampleData(0) | ||
| miniBatchBuffer = if (firstSample.isInstanceOf[TensorSample[T]]) { | ||
| SparseMiniBatch(firstSample.numFeature(), firstSample.numLabel()) | ||
| } else { | ||
| MiniBatch(firstSample.numFeature(), firstSample.numLabel(), | ||
| featurePaddingParam, labelPaddingParam) | ||
| } | ||
| } | ||
|
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| if (count < batchSize) { | ||
| miniBatchBuffer.set(sampleData.slice(0, count)) | ||
| } else { | ||
| miniBatchBuffer.set(sampleData) | ||
| } | ||
| } else { | ||
| null | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } | ||
|
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| object MTSampleToMiniBatch { | ||
| /** | ||
| * Apply an MTSampleToMiniBatch transformer. | ||
| * | ||
| * @param batchSize total batch size | ||
| * @param transformer transformer who rawData to Sample | ||
| * @param featurePaddingParam feature padding strategy, see | ||
| * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. | ||
| * @param labelPaddingParam label padding strategy, see | ||
| * [[com.intel.analytics.bigdl.dataset.PaddingParam]] for details. | ||
| * @return | ||
| */ | ||
| def apply[A: ClassTag, T: ClassTag]( | ||
| batchSize : Int, | ||
| transformer: Transformer[A, Sample[T]], | ||
| featurePaddingParam: Option[PaddingParam[T]] = None, | ||
| labelPaddingParam: Option[PaddingParam[T]] = None, | ||
| partitionNum: Option[Int] = None | ||
| )(implicit ev: TensorNumeric[T]): MTSampleToMiniBatch[A, T] = { | ||
| new MTSampleToMiniBatch[A, T](batchSize, | ||
| transformer, | ||
| None, featurePaddingParam, labelPaddingParam, partitionNum) | ||
| } | ||
|
|
||
| /** | ||
| * Apply an MTSampleToMiniBatch transformer with UDF MiniBatch. | ||
| * | ||
| * @param batchSize total batch size | ||
| * @param miniBatch An User-Defined MiniBatch to construct a mini batch. | ||
| * @param transformer transformer who rawData to Sample | ||
| * @return | ||
| */ | ||
| def apply[A: ClassTag, T: ClassTag]( | ||
| miniBatch: MiniBatch[T], | ||
| batchSize : Int, | ||
| transformer: Transformer[A, Sample[T]], | ||
| partitionNum: Option[Int]) | ||
| (implicit ev: TensorNumeric[T]): MTSampleToMiniBatch[A, T] = { | ||
| new MTSampleToMiniBatch[A, T](batchSize, | ||
| transformer, | ||
| Some(miniBatch), partitionNum = partitionNum) | ||
| } | ||
| } |