Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check

Author: jkbradley

core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala

@@ -200,6 +200,12 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging {
     stageData.shuffleReadBytes += shuffleReadDelta
     execSummary.shuffleRead += shuffleReadDelta
 
+    val inputBytesDelta =
+      (taskMetrics.inputMetrics.map(_.bytesRead).getOrElse(0L)
+      - oldMetrics.flatMap(_.inputMetrics).map(_.bytesRead).getOrElse(0L))
+    stageData.inputBytes += inputBytesDelta
+    execSummary.inputBytes += inputBytesDelta
+
     val diskSpillDelta =
       taskMetrics.diskBytesSpilled - oldMetrics.map(_.diskBytesSpilled).getOrElse(0L)
     stageData.diskBytesSpilled += diskSpillDelta

core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala

@@ -22,7 +22,7 @@ import org.scalatest.Matchers
 
 import org.apache.spark._
 import org.apache.spark.{LocalSparkContext, SparkConf, Success}
-import org.apache.spark.executor.{ShuffleWriteMetrics, ShuffleReadMetrics, TaskMetrics}
+import org.apache.spark.executor._
 import org.apache.spark.scheduler._
 import org.apache.spark.util.Utils
 
@@ -150,6 +150,9 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
       taskMetrics.executorRunTime = base + 4
       taskMetrics.diskBytesSpilled = base + 5
       taskMetrics.memoryBytesSpilled = base + 6
+      val inputMetrics = new InputMetrics(DataReadMethod.Hadoop)
+      taskMetrics.inputMetrics = Some(inputMetrics)
+      inputMetrics.bytesRead = base + 7
       taskMetrics
     }
 
@@ -182,6 +185,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     assert(stage1Data.diskBytesSpilled == 205)
     assert(stage0Data.memoryBytesSpilled == 112)
     assert(stage1Data.memoryBytesSpilled == 206)
+    assert(stage0Data.inputBytes == 114)
+    assert(stage1Data.inputBytes == 207)
     assert(stage0Data.taskData.get(1234L).get.taskMetrics.get.shuffleReadMetrics.get
       .totalBlocksFetched == 2)
     assert(stage0Data.taskData.get(1235L).get.taskMetrics.get.shuffleReadMetrics.get
@@ -208,6 +213,8 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc
     assert(stage1Data.diskBytesSpilled == 610)
     assert(stage0Data.memoryBytesSpilled == 412)
     assert(stage1Data.memoryBytesSpilled == 612)
+    assert(stage0Data.inputBytes == 414)
+    assert(stage1Data.inputBytes == 614)
     assert(stage0Data.taskData.get(1234L).get.taskMetrics.get.shuffleReadMetrics.get
       .totalBlocksFetched == 302)
     assert(stage1Data.taskData.get(1237L).get.taskMetrics.get.shuffleReadMetrics.get

dev/create-release/create-release.sh

@@ -117,12 +117,13 @@ make_binary_release() {
     spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
 }
 
-make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4"
-make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0"
+make_binary_release "hadoop1" "-Phive -Phive-thriftserver -Dhadoop.version=1.0.4" &
+make_binary_release "cdh4" "-Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" &
 make_binary_release "hadoop2" \
-  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Phive -Phive-thriftserver -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
 make_binary_release "hadoop2-without-hive" \
-  "-Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0"
+  "-Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 -Pyarn.version=2.2.0" &
+wait
 
 # Copy data
 echo "Copying release tarballs"

docs/mllib-feature-extraction.md

@@ -9,4 +9,65 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Feature Extraction
 
 ## Word2Vec 
 
-## TFIDF
+Word2Vec computes distributed vector representation of words. The main advantage of the distributed
+representations is that similar words are close in the vector space, which makes generalization to 
+novel patterns easier and model estimation more robust. Distributed vector representation is 
+showed to be useful in many natural language processing applications such as named entity 
+recognition, disambiguation, parsing, tagging and machine translation.
+
+### Model
+
+In our implementation of Word2Vec, we used skip-gram model. The training objective of skip-gram is
+to learn word vector representations that are good at predicting its context in the same sentence. 
+Mathematically, given a sequence of training words `$w_1, w_2, \dots, w_T$`, the objective of the
+skip-gram model is to maximize the average log-likelihood 
+`\[
+\frac{1}{T} \sum_{t = 1}^{T}\sum_{j=-k}^{j=k} \log p(w_{t+j} | w_t)
+\]`
+where $k$ is the size of the training window.  
+
+In the skip-gram model, every word $w$ is associated with two vectors $u_w$ and $v_w$ which are 
+vector representations of $w$ as word and context respectively. The probability of correctly 
+predicting word $w_i$ given word $w_j$ is determined by the softmax model, which is 
+`\[
+p(w_i | w_j ) = \frac{\exp(u_{w_i}^{\top}v_{w_j})}{\sum_{l=1}^{V} \exp(u_l^{\top}v_{w_j})}
+\]`
+where $V$ is the vocabulary size. 
+
+The skip-gram model with softmax is expensive because the cost of computing $\log p(w_i | w_j)$ 
+is proportional to $V$, which can be easily in order of millions. To speed up training of Word2Vec, 
+we used hierarchical softmax, which reduced the complexity of computing of $\log p(w_i | w_j)$ to
+$O(\log(V))$
+
+### Example 
+
+The example below demonstrates how to load a text file, parse it as an RDD of `Seq[String]`,
+construct a `Word2Vec` instance and then fit a `Word2VecModel` with the input data. Finally,
+we display the top 40 synonyms of the specified word. To run the example, first download
+the [text8](http://mattmahoney.net/dc/text8.zip) data and extract it to your preferred directory.
+Here we assume the extracted file is `text8` and in same directory as you run the spark shell.  
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark._
+import org.apache.spark.rdd._
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.feature.Word2Vec
+
+val input = sc.textFile("text8").map(line => line.split(" ").toSeq)
+
+val word2vec = new Word2Vec()
+
+val model = word2vec.fit(input)
+
+val synonyms = model.findSynonyms("china", 40)
+
+for((synonym, cosineSimilarity) <- synonyms) {
+  println(s"$synonym $cosineSimilarity")
+}
+{% endhighlight %}
+</div>
+</div>
+
+## TFIDF

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -34,6 +34,7 @@ import org.apache.spark.mllib.rdd.RDDFunctions._
 import org.apache.spark.rdd._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
+import org.apache.spark.util.collection.PrimitiveKeyOpenHashMap
 
 /**
  *  Entry in vocabulary 
@@ -287,11 +288,12 @@ class Word2Vec extends Serializable with Logging {
     var syn0Global =
       Array.fill[Float](vocabSize * vectorSize)((initRandom.nextFloat() - 0.5f) / vectorSize)
     var syn1Global = new Array[Float](vocabSize * vectorSize)
-
     var alpha = startingAlpha
     for (k <- 1 to numIterations) {
       val partial = newSentences.mapPartitionsWithIndex { case (idx, iter) =>
         val random = new XORShiftRandom(seed ^ ((idx + 1) << 16) ^ ((-k - 1) << 8))
+        val syn0Modify = new Array[Int](vocabSize)
+        val syn1Modify = new Array[Int](vocabSize)
         val model = iter.foldLeft((syn0Global, syn1Global, 0, 0)) {
           case ((syn0, syn1, lastWordCount, wordCount), sentence) =>
             var lwc = lastWordCount
@@ -321,7 +323,8 @@ class Word2Vec extends Serializable with Logging {
                     // Hierarchical softmax
                     var d = 0
                     while (d < bcVocab.value(word).codeLen) {
-                      val l2 = bcVocab.value(word).point(d) * vectorSize
+                      val inner = bcVocab.value(word).point(d)
+                      val l2 = inner * vectorSize
                       // Propagate hidden -> output
                       var f = blas.sdot(vectorSize, syn0, l1, 1, syn1, l2, 1)
                       if (f > -MAX_EXP && f < MAX_EXP) {
@@ -330,10 +333,12 @@ class Word2Vec extends Serializable with Logging {
                         val g = ((1 - bcVocab.value(word).code(d) - f) * alpha).toFloat
                         blas.saxpy(vectorSize, g, syn1, l2, 1, neu1e, 0, 1)
                         blas.saxpy(vectorSize, g, syn0, l1, 1, syn1, l2, 1)
+                        syn1Modify(inner) += 1
                       }
                       d += 1
                     }
                     blas.saxpy(vectorSize, 1.0f, neu1e, 0, 1, syn0, l1, 1)
+                    syn0Modify(lastWord) += 1
                   }
                 }
                 a += 1
@@ -342,21 +347,36 @@ class Word2Vec extends Serializable with Logging {
             }
             (syn0, syn1, lwc, wc)
         }
-        Iterator(model)
+        val syn0Local = model._1
+        val syn1Local = model._2
+        val synOut = new PrimitiveKeyOpenHashMap[Int, Array[Float]](vocabSize * 2)
+        var index = 0
+        while(index < vocabSize) {
+          if (syn0Modify(index) != 0) {
+            synOut.update(index, syn0Local.slice(index * vectorSize, (index + 1) * vectorSize))
+          }
+          if (syn1Modify(index) != 0) {
+            synOut.update(index + vocabSize,
+              syn1Local.slice(index * vectorSize, (index + 1) * vectorSize))
+          }
+          index += 1
+        }
+        Iterator(synOut)
       }
-      val (aggSyn0, aggSyn1, _, _) =
-        partial.treeReduce { case ((syn0_1, syn1_1, lwc_1, wc_1), (syn0_2, syn1_2, lwc_2, wc_2)) =>
-          val n = syn0_1.length
-          val weight1 = 1.0f * wc_1 / (wc_1 + wc_2)
-          val weight2 = 1.0f * wc_2 / (wc_1 + wc_2)
-          blas.sscal(n, weight1, syn0_1, 1)
-          blas.sscal(n, weight1, syn1_1, 1)
-          blas.saxpy(n, weight2, syn0_2, 1, syn0_1, 1)
-          blas.saxpy(n, weight2, syn1_2, 1, syn1_1, 1)
-          (syn0_1, syn1_1, lwc_1 + lwc_2, wc_1 + wc_2)
+      val synAgg = partial.flatMap(x => x).reduceByKey { case (v1, v2) =>
+          blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
+          v1
+      }.collect()
+      var i = 0
+      while (i < synAgg.length) {
+        val index = synAgg(i)._1
+        if (index < vocabSize) {
+          Array.copy(synAgg(i)._2, 0, syn0Global, index * vectorSize, vectorSize)
+        } else {
+          Array.copy(synAgg(i)._2, 0, syn1Global, (index - vocabSize) * vectorSize, vectorSize)
         }
-      syn0Global = aggSyn0
-      syn1Global = aggSyn1
+        i += 1
+      }
     }
     newSentences.unpersist()
 
@@ -414,15 +434,6 @@ class Word2VecModel private[mllib] (
     }
   }
 
-  /**
-   * Transforms an RDD to its vector representation
-   * @param dataset a an RDD of words 
-   * @return RDD of vector representation 
-   */
-  def transform(dataset: RDD[String]): RDD[Vector] = {
-    dataset.map(word => transform(word))
-  }
-  
   /**
    * Find synonyms of a word
    * @param word a word