Extend range of supported decimal divide operations

integration_tests/src/main/python/arithmetic_ops_test.py

@@ -151,13 +151,15 @@ def test_division_fallback_on_decimal(data_gen):
                 f.col('a') / f.col('b')),
             'Divide')
 
-@pytest.mark.parametrize('lhs', [DecimalGen(5, 3), DecimalGen(4, 2), DecimalGen(1, -2)], ids=idfn)
-@pytest.mark.parametrize('rhs', [DecimalGen(4, 1)], ids=idfn)
+@approximate_float # we should get the perfectly correct answer for floats except when casting a deciml to a float in some corner cases.
+@pytest.mark.parametrize('rhs', [byte_gen, short_gen, int_gen, long_gen, float_gen, DecimalGen(4, 1), DecimalGen(5, 0), DecimalGen(5, 1), DecimalGen(10, 5)], ids=idfn)
+@pytest.mark.parametrize('lhs', [byte_gen, short_gen, int_gen, long_gen, float_gen, DecimalGen(5, 3), DecimalGen(4, 2), DecimalGen(1, -2), DecimalGen(16, 1)], ids=idfn)
 def test_division_mixed(lhs, rhs):
     assert_gpu_and_cpu_are_equal_collect(
             lambda spark : two_col_df(spark, lhs, rhs).select(
                 f.col('a') / f.col('b')),
-            conf=allow_negative_scale_of_decimal_conf)
+            conf=copy_and_update(allow_negative_scale_of_decimal_conf,
+                {'spark.rapids.sql.castDecimalToFloat.enabled': 'true'}))
 
 @pytest.mark.parametrize('data_gen', integral_gens +  [decimal_gen_default, decimal_gen_scale_precision,
         decimal_gen_same_scale_precision, decimal_gen_64bit, decimal_gen_18_3, decimal_gen_30_2,

sql-plugin/src/main/scala/com/nvidia/spark/rapids/DecimalUtil.scala

@@ -213,4 +213,16 @@ object DecimalUtil extends Arm {
       case t => t.defaultSize
     }
   }
+
+
+  /**
+   * Get the number of decimal places needed to hold the integral type held by this column
+   */
+  def getPrecisionForIntegralType(input: DType): Int = input match {
+    case DType.INT8 =>  3 // -128 to 127
+    case DType.INT16 => 5 // -32768 to 32767
+    case DType.INT32 => 10 // -2147483648 to 2147483647
+    case DType.INT64 => 19 // -9223372036854775808 to 9223372036854775807
+    case t => throw new IllegalArgumentException(s"Unsupported type $t")
+  }
 }

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuCast.scala

@@ -1116,22 +1116,11 @@ object GpuCast extends Arm {
     }
   }
 
-  /**
-   * Get the number of decimal places needed to hold the integral type held by this column
-   */
-  private def getPrecisionForIntegralInput(input: ColumnView): Int = input.getType match {
-    case DType.INT8 =>  3 // -128 to 127
-    case DType.INT16 => 5 // -32768 to 32767
-    case DType.INT32 => 10 // -2147483648 to 2147483647
-    case DType.INT64 => 19 // -9223372036854775808 to 9223372036854775807
-    case t => throw new IllegalArgumentException(s"Unsupported type $t")
-  }
-
   private def castIntegralsToDecimal(
       input: ColumnView,
       dt: DecimalType,
       ansiMode: Boolean): ColumnVector = {
-    val prec = getPrecisionForIntegralInput(input)
+    val prec = DecimalUtil.getPrecisionForIntegralType(input.getType)
     // Cast input to decimal
     val inputDecimalType = new DecimalType(prec, 0)
     withResource(input.castTo(DecimalUtil.createCudfDecimal(prec, 0))) { castedInput =>

sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala

@@ -912,9 +912,57 @@ object GpuOverrides extends Logging {
       "CheckOverflow after arithmetic operations between DecimalType data",
       ExprChecks.unaryProjectInputMatchesOutput(TypeSig.DECIMAL_128_FULL,
         TypeSig.DECIMAL_128_FULL),
-      (a, conf, p, r) => new UnaryExprMeta[CheckOverflow](a, conf, p, r) {
-        override def convertToGpu(child: Expression): GpuExpression =
-          GpuCheckOverflow(child, wrapped.dataType, wrapped.nullOnOverflow)
+      (a, conf, p, r) => new ExprMeta[CheckOverflow](a, conf, p, r) {
+        private[this] def extractOrigParam(expr: BaseExprMeta[_]): BaseExprMeta[_] =
+          expr.wrapped match {
+            case PromotePrecision(_: Cast) =>
+              // Strip out the promote precision and the cast so we get as close to the original
+              // values as we can.
+              val castExpr = expr.childExprs.head
+              castExpr.childExprs.head
+            case _ => expr
+          }
+        private[this] lazy val binExpr = childExprs.head
+        private[this] lazy val lhs = extractOrigParam(binExpr.childExprs.head)
+        private[this] lazy val rhs = extractOrigParam(binExpr.childExprs(1))
+
+        override def tagExprForGpu(): Unit = {
+          a.child match {
+            case _: Divide =>
+              // Division of Decimal types is a little odd. Spark will cast the inputs
+              // to a common wider value where the scale is the max of the two input scales, and
+              // the precision is max of the two input non-scale portions + the new scale. Then it
+              // will do the divide as a BigDecimal value but lie about the return type. Then here
+              // in CheckOverflow it will reset the scale and check the precision so that they know
+              // it fits in final desired result.
+              // Here we try to strip out the extra casts, etc to get to as close to the original
+              // query as possible. This lets us then calculate what CUDF needs to get the correct
+              // answer, which in some cases is a lot smaller. Our GpuDecimalDivide handles the
+              // overflow checking/etc.
+              val l = GpuDecimalDivide.asDecimalType(lhs.wrapped.asInstanceOf[Expression].dataType)
+              val r = GpuDecimalDivide.asDecimalType(rhs.wrapped.asInstanceOf[Expression].dataType)
+              val intermediatePrecision =
+                GpuDecimalDivide.nonRoundedIntermediateArgPrecision(l, r, a.dataType)
+
+              if (intermediatePrecision > DType.DECIMAL128_MAX_PRECISION) {
+                binExpr.willNotWorkOnGpu(s"The intermediate precision of $intermediatePrecision " +
+                    s"that is required to guarnatee no overflow issues for this divide is too " +
+                    s"large to be supported on the GPU")
+              }
+            case _ => // NOOP
+          }
+        }
+
+        override def convertToGpu(): GpuExpression = {
+          a.child match {
+            case _: Divide =>
+              // Get as close to the original divide as possible
+              GpuDecimalDivide(lhs.convertToGpu(), rhs.convertToGpu(), wrapped.dataType)
+            case _ =>
+              GpuCheckOverflow(childExprs.head.convertToGpu(),
+                wrapped.dataType, wrapped.nullOnOverflow)
+          }
+        }
       }),
     expr[ToDegrees](
       "Converts radians to degrees",
@@ -2002,46 +2050,15 @@ object GpuOverrides extends Logging {
         ("rhs", TypeSig.DOUBLE + TypeSig.DECIMAL_128_FULL,
             TypeSig.DOUBLE + TypeSig.DECIMAL_128_FULL)),
       (a, conf, p, r) => new BinaryExprMeta[Divide](a, conf, p, r) {
-        override def tagExprForGpu(): Unit = {
-          // Division of Decimal types is a little odd. Spark will cast the inputs
-          // to a common wider value where scale is max of the two input scales, and precision is
-          // max of the two input non-scale portions + the new scale. Then it will do the divide,
-          // which the rules for it are a little complex, but lie about it
-          // in the return type of the Divide operator. Then in CheckOverflow it will reset the
-          // scale and check the precision so that they know it fits in final desired result.
-          // We would like to avoid all of this if possible because having a temporary intermediate
-          // value that can have a scale quite a bit larger than the final result reduces the
-          // maximum precision that we could support, as we don't have unlimited precision. But
-          // sadly because of how the logical plan is compiled down to the physical plan we have
-          // lost what the original types were and cannot recover it. As such for now we are going
-          // to do what Spark does, but we have to recompute/recheck the temporary precision to be
-          // sure it will fit on the GPU. In addition to this we have it a little harder because
-          // the decimal divide itself will do rounding on the result before it is returned,
-          // effectively calculating an extra digit of precision. Because cudf does not support this
-          // right now we actually increase the scale (and corresponding precision) to get an extra
-          // decimal place so we can round it in GpuCheckOverflow
-          val Seq(leftDataType, rightDataType) = childExprs.flatMap(_.typeMeta.dataType)
-          (leftDataType, rightDataType) match {
-            case (l: DecimalType, r: DecimalType) =>
-              val outputScale = GpuDivideUtil.outputDecimalScale(l, r)
-              val outputPrecision = GpuDivideUtil.outputDecimalPrecision(l, r, outputScale)
-              if (outputPrecision > DType.DECIMAL128_MAX_PRECISION) {
-                willNotWorkOnGpu("The final output precision of the divide is too " +
-                    s"large to be supported on the GPU $outputPrecision")
-              }
-              val intermediatePrecision =
-                GpuDivideUtil.intermediateDecimalPrecision(l, r, outputScale)
-
-              if (intermediatePrecision > DType.DECIMAL128_MAX_PRECISION) {
-                willNotWorkOnGpu("The intermediate output precision of the divide is too " +
-                  s"large to be supported on the GPU $intermediatePrecision")
-              }
-            case _ => // NOOP
-          }
-        }
-
+        // Division of Decimal types is a little odd. To work around some issues with
+        // what Spark does the tagging/checks are in CheckOverflow instead of here.
         override def convertToGpu(lhs: Expression, rhs: Expression): GpuExpression =
-          GpuDivide(lhs, rhs)
+          a.dataType match {
+            case _: DecimalType =>
+              throw new IllegalStateException("Decimal Divide should be converted in CheckOverflow")
+            case _ =>
+              GpuDivide(lhs, rhs)
+          }
       }),
     expr[IntegralDivide](
       "Division with a integer result",