[SPARK-12796] [SQL] Whole stage codegen

This is the initial work for whole stage codegen, it support Projection/Filter/Range, we will continue work on this to support more physical operators.

A micro benchmark show that a query with range, filter and projection could be 3X faster then before.

It's turned on by default. For a tree that have at least two chained plans, a WholeStageCodegen will be inserted into it, for example, the following plan
```
Limit 10
+- Project [(id#5L + 1) AS (id + 1)#6L]
   +- Filter ((id#5L & 1) = 1)
      +- Range 0, 1, 4, 10, [id#5L]
```
will be translated into
```
Limit 10
+- WholeStageCodegen
      +- Project [(id#1L + 1) AS (id + 1)#2L]
         +- Filter ((id#1L & 1) = 1)
            +- Range 0, 1, 4, 10, [id#1L]
```

Here is the call graph to generate Java source for A and B (A  support codegen, but B does not):

```
  *   WholeStageCodegen       Plan A               FakeInput        Plan B
  * =========================================================================
  *
  * -> execute()
  *     |
  *  doExecute() -------->   produce()
  *                             |
  *                          doProduce()  -------> produce()
  *                                                   |
  *                                                doProduce() ---> execute()
  *                                                   |
  *                                                consume()
  *                          doConsume()  ------------|
  *                             |
  *  doConsume()  <-----    consume()
```

A SparkPlan that support codegen need to implement doProduce() and doConsume():

```
def doProduce(ctx: CodegenContext): (RDD[InternalRow], String)
def doConsume(ctx: CodegenContext, child: SparkPlan, input: Seq[ExprCode]): String
```

Author: Davies Liu <davies@databricks.com>

Closes #10735 from davies/whole2.