[SPARK-19110][ML][MLLIB] DistributedLDAModel returns different logPrior for original and loaded model

## What changes were proposed in this pull request?

While adding DistributedLDAModel training summary for SparkR, I found that the logPrior for original and loaded model is different.
For example, in the test("read/write DistributedLDAModel"), I add the test:
val logPrior = model.asInstanceOf[DistributedLDAModel].logPrior
val logPrior2 = model2.asInstanceOf[DistributedLDAModel].logPrior
assert(logPrior === logPrior2)
The test fails:
-4.394180878889078 did not equal -4.294290536919573

The reason is that `graph.vertices.aggregate(0.0)(seqOp, _ + _)` only returns the value of a single vertex instead of the aggregation of all vertices. Therefore, when the loaded model does the aggregation in a different order, it returns different `logPrior`.

Please refer to #16464 for details.
## How was this patch tested?
Add a new unit test for testing logPrior.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #16491 from wangmiao1981/ldabug.

2 files changed, 10 insertions, 2 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 933a5f1d52..ae33698209 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -745,12 +745,12 @@ class DistributedLDAModel private[clustering] (
           val N_wk = vertex._2
           val smoothed_N_wk: TopicCounts = N_wk + (eta - 1.0)
           val phi_wk: TopicCounts = smoothed_N_wk :/ smoothed_N_k
-          (eta - 1.0) * sum(phi_wk.map(math.log))
+          sumPrior + (eta - 1.0) * sum(phi_wk.map(math.log))
         } else {
           val N_kj = vertex._2
           val smoothed_N_kj: TopicCounts = N_kj + (alpha - 1.0)
           val theta_kj: TopicCounts = normalize(smoothed_N_kj, 1.0)
-          (alpha - 1.0) * sum(theta_kj.map(math.log))
+          sumPrior + (alpha - 1.0) * sum(theta_kj.map(math.log))
         }
     }
     graph.vertices.aggregate(0.0)(seqOp, _ + _)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
index 3f39deddf2..9aa11fbdbe 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
@@ -260,6 +260,14 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext with DefaultRead
         Vectors.dense(model2.topicsMatrix.toArray) absTol 1e-6)
       assert(Vectors.dense(model.getDocConcentration) ~==
         Vectors.dense(model2.getDocConcentration) absTol 1e-6)
+      val logPrior = model.asInstanceOf[DistributedLDAModel].logPrior
+      val logPrior2 = model2.asInstanceOf[DistributedLDAModel].logPrior
+      val trainingLogLikelihood =
+        model.asInstanceOf[DistributedLDAModel].trainingLogLikelihood
+      val trainingLogLikelihood2 =
+        model2.asInstanceOf[DistributedLDAModel].trainingLogLikelihood
+      assert(logPrior ~== logPrior2 absTol 1e-6)
+      assert(trainingLogLikelihood ~== trainingLogLikelihood2 absTol 1e-6)
     }
     val lda = new LDA()
     testEstimatorAndModelReadWrite(lda, dataset,