Switch back to native blas calls

Author: MechCoder

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

@@ -434,13 +434,11 @@ class Word2VecModel private[mllib] (
   // Maintain a ordered list of words based on the index in the initial model.
   private val wordList: Array[String] = model.keys.toArray
   private val wordIndex: Map[String, Int] = wordList.zip(0 until model.size).toMap
+  private val numDim = model.head._2.size
+  private val numWords = wordIndex.size
 
-  private val (wordVectors: DenseMatrix, wordVecNorms: Array[Double]) = {
-    val numDim = model.head._2.size
-    val numWords = wordIndex.size
-    val flatVec = model.toSeq.flatMap { case(w, v) =>
-      v.map(_.toDouble)}.toArray
-    val wordVectors = new DenseMatrix(numWords, numDim, flatVec, isTransposed=true)
+  private val (wordVectors: Array[Float], wordVecNorms: Array[Double]) = {
+    val wordVectors = model.toSeq.flatMap { case (w, v) => v }.toArray
     val wordVecNorms = new Array[Double](numWords)
     var i = 0
     while (i < numWords) {
@@ -500,9 +498,13 @@ class Word2VecModel private[mllib] (
   def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = {
     require(num > 0, "Number of similar words should > 0")
 
-    val numWords = wordVectors.numRows
-    val cosineVec = Vectors.zeros(numWords).asInstanceOf[DenseVector]
-    BLAS.gemv(1.0, wordVectors, new DenseVector(vector.toArray), 0.0, cosineVec)
+    val fVector = vector.toArray.map(_.toFloat)
+    val cosineVec = Array.fill[Float](numWords)(0)
+    val alpha: Float = 1
+    val beta: Float = 1
+
+    blas.sgemv(
+      "T", numDim, numWords, alpha, wordVectors, numDim, fVector, 1, beta, cosineVec, 1)
 
     // Need not divide with the norm of the given vector since it is constant.
     val updatedCosines = new Array[Double](numWords)
@@ -523,11 +525,9 @@ class Word2VecModel private[mllib] (
    * Returns a map of words to their vector representations.
    */
   def getVectors: Map[String, Array[Float]] = {
-    val numDim = wordVectors.numCols
     wordIndex.map { case (word, ind) =>
-      val startInd = numDim * ind
-      val endInd = startInd + numDim
-      (word, wordVectors.values.slice(startInd, endInd).map(_.toFloat)) }
+      (word, wordVectors.slice(numDim * ind, numDim * ind + numDim))
+    }
   }
 }