[SPARK-23177][SQL][PYSPARK] Extract zero-parameter UDFs from aggregate

## What changes were proposed in this pull request?

We extract Python UDFs in logical aggregate which depends on aggregate expression or grouping key in ExtractPythonUDFFromAggregate rule. But Python UDFs which don't depend on above expressions should also be extracted to avoid the issue reported in the JIRA.

A small code snippet to reproduce that issue looks like:
```python
import pyspark.sql.functions as f

df = spark.createDataFrame([(1,2), (3,4)])
f_udf = f.udf(lambda: str("const_str"))
df2 = df.distinct().withColumn("a", f_udf())
df2.show()
```

Error exception is raised as:
```
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#50
        at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
        at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:91)
        at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:90)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
        at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)
        at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
        at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
        at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)
        at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
        at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:90)
        at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:514)
        at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$38.apply(HashAggregateExec.scala:513)
```

This exception raises because `HashAggregateExec` tries to bind the aliased Python UDF expression (e.g., `pythonUDF0#50 AS a#44`) to grouping key.

## How was this patch tested?

Added test.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #20360 from viirya/SPARK-23177.

python/pyspark/sql/tests.py

@@ -1106,6 +1106,14 @@ def myudf(x):
         rows = [r[0] for r in df.selectExpr("udf(id)").take(2)]
         self.assertEqual(rows, [None, PythonOnlyPoint(1, 1)])
 
+    def test_nonparam_udf_with_aggregate(self):
+        import pyspark.sql.functions as f
+
+        df = self.spark.createDataFrame([(1, 2), (1, 2)])
+        f_udf = f.udf(lambda: "const_str")
+        rows = df.distinct().withColumn("a", f_udf()).collect()
+        self.assertEqual(rows, [Row(_1=1, _2=2, a=u'const_str')])
+
     def test_infer_schema_with_udt(self):
         from pyspark.sql.tests import ExamplePoint, ExamplePointUDT
         row = Row(label=1.0, point=ExamplePoint(1.0, 2.0))

sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala

@@ -30,7 +30,7 @@ import org.apache.spark.sql.execution.{FilterExec, ProjectExec, SparkPlan}
 
 /**
  * Extracts all the Python UDFs in logical aggregate, which depends on aggregate expression or
- * grouping key, evaluate them after aggregate.
+ * grouping key, or doesn't depend on any above expressions, evaluate them after aggregate.
  */
 object ExtractPythonUDFFromAggregate extends Rule[LogicalPlan] {
 
@@ -45,7 +45,8 @@ object ExtractPythonUDFFromAggregate extends Rule[LogicalPlan] {
 
   private def hasPythonUdfOverAggregate(expr: Expression, agg: Aggregate): Boolean = {
     expr.find {
-      e => PythonUDF.isScalarPythonUDF(e) && e.find(belongAggregate(_, agg)).isDefined
+      e => PythonUDF.isScalarPythonUDF(e) &&
+        (e.references.isEmpty || e.find(belongAggregate(_, agg)).isDefined)
     }.isDefined
   }