wbo4958 commented on code in PR #43494:
URL: https://github.com/apache/spark/pull/43494#discussion_r1378502033
##########
core/src/main/scala/org/apache/spark/resource/ResourceUtils.scala:
##########
@@ -170,16 +170,16 @@ private[spark] object ResourceUtils extends Logging {
// integer amount and the number of slots per address. For instance, if the
amount is 0.5,
// the we get (1, 2) back out. This indicates that for each 1 address, it
has 2 slots per
// address, which allows you to put 2 tasks on that address. Note if amount
is greater
- // than 1, then the number of slots per address has to be 1. This would
indicate that a
+ // than 1, then the number of parts per address has to be 1. This would
indicate that a
// would have multiple addresses assigned per task. This can be used for
calculating
// the number of tasks per executor -> (executorAmount * numParts) /
(integer amount).
// Returns tuple of (integer amount, numParts)
def calculateAmountAndPartsForFraction(doubleAmount: Double): (Int, Int) = {
- val parts = if (doubleAmount <= 0.5) {
+ val parts = if (doubleAmount <= 1.0) {
Review Comment:
Yeah, I finally understand your concern. Let me try to figure out a way not
to change this part with dynamic allocation on.
##########
core/src/main/scala/org/apache/spark/resource/TaskResourceRequest.scala:
##########
@@ -37,8 +37,8 @@ import org.apache.spark.annotation.{Evolving, Since}
class TaskResourceRequest(val resourceName: String, val amount: Double)
extends Serializable {
- assert(amount <= 0.5 || amount % 1 == 0,
- s"The resource amount ${amount} must be either <= 0.5, or a whole number.")
+ assert(amount <= 1.0 || amount % 1 == 0,
Review Comment:
Good suggestion, seems doable.
##########
core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala:
##########
@@ -0,0 +1,212 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import scala.collection.mutable.HashMap
+
+import org.apache.spark.SparkException
+import org.apache.spark.resource.{ResourceInformation, ResourceProfile}
+import org.apache.spark.resource.ResourceAmountUtils.RESOURCE_TOTAL_AMOUNT
+
+/**
+ * Class to hold information about a series of resources belonging to an
executor.
+ * A resource could be a GPU, FPGA, etc. And it is used as a temporary
+ * class to calculate the resources amounts when offering resources to
+ * the tasks in the [[TaskSchedulerImpl]]
+ *
+ * One example is GPUs, where the addresses would be the indices of the GPUs
+ *
+ * @param resources The executor available resources and amount. eg,
+ * Map("gpu" -> mutable.Map("0" -> 0.2, "1" -> 1.0),
+ * "fpga" -> mutable.Map("a" -> 0.3, "b" -> 0.9)
+ * )
+ */
+private[spark] class ExecutorResourcesAmounts(
+ private val resources: Map[String, Map[String, Double]]) extends
Serializable {
+
+ /**
+ * Multiply the RESOURCE_TOTAL_AMOUNT to avoid using double directly.
+ * and convert the addressesAmounts to be mutable.HashMap
+ */
+ private val internalResources: Map[String, HashMap[String, Long]] = {
+ resources.map { case (rName, addressAmounts) =>
+ rName -> HashMap(addressAmounts.map { case (address, amount) =>
+ address -> (amount * RESOURCE_TOTAL_AMOUNT).toLong
+ }.toSeq: _*)
+ }
+ }
+
+ /**
+ * The total address count of each resource. Eg,
+ * Map("gpu" -> Map("0" -> 0.5, "1" -> 0.5, "2" -> 0.5),
+ * "fpga" -> Map("a" -> 0.5, "b" -> 0.5))
+ * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2)
+ */
+ lazy val resourceAmount: Map[String, Int] = internalResources.map { case
(rName, addressMap) =>
+ rName -> addressMap.size
+ }
+
+ /**
+ * For testing purpose. convert internal resources back to the "fraction"
resources.
+ */
+ private[spark] def availableResources: Map[String, Map[String, Double]] = {
+ internalResources.map { case (rName, addressMap) =>
+ rName -> addressMap.map { case (address, amount) =>
+ address -> amount.toDouble / RESOURCE_TOTAL_AMOUNT
+ }.toMap
+ }
+ }
+
+ /**
+ * Acquire the resource and update the resource
+ * @param assignedResource the assigned resource information
+ */
+ def acquire(assignedResource: Map[String, Map[String, Double]]): Unit = {
+ assignedResource.foreach { case (rName, taskResAmounts) =>
+ val availableResourceAmounts = internalResources.getOrElse(rName,
+ throw new SparkException(s"Try to acquire an address from $rName that
doesn't exist"))
+ taskResAmounts.foreach { case (address, amount) =>
+ val prevInternalTotalAmount =
availableResourceAmounts.getOrElse(address,
+ throw new SparkException(s"Try to acquire an address that doesn't
exist. $rName " +
+ s"address $address doesn't exist."))
+
+ val internalTaskAmount = (amount * RESOURCE_TOTAL_AMOUNT).toLong
+ val internalLeft = prevInternalTotalAmount - internalTaskAmount
+ val realLeft = internalLeft.toDouble / RESOURCE_TOTAL_AMOUNT
+ if (realLeft < 0) {
+ throw new SparkException(s"The total amount ${realLeft} " +
+ s"after acquiring $rName address $address should be >= 0")
+ }
+ internalResources(rName)(address) = internalLeft
+ }
+ }
+ }
+
+ /**
+ * Release the assigned resources to the resource pool
+ * @param assignedResource resource to be released
+ */
+ def release(assignedResource: Map[String, Map[String, Double]]): Unit = {
+ assignedResource.foreach { case (rName, taskResAmounts) =>
+ val availableResourceAmounts = internalResources.getOrElse(rName,
+ throw new SparkException(s"Try to release an address from $rName that
doesn't exist"))
+ taskResAmounts.foreach { case (address, amount) =>
+ val prevInternalTotalAmount =
availableResourceAmounts.getOrElse(address,
+ throw new SparkException(s"Try to release an address that is not
assigned. $rName " +
+ s"address $address is not assigned."))
+ val internalTaskAmount = (amount * RESOURCE_TOTAL_AMOUNT).toLong
+ val internalTotal = prevInternalTotalAmount + internalTaskAmount
+ if (internalTotal > RESOURCE_TOTAL_AMOUNT) {
+ throw new SparkException(s"The total amount " +
+ s"${internalTotal.toDouble / RESOURCE_TOTAL_AMOUNT} " +
+ s"after releasing $rName address $address should be <= 1.0")
+ }
+ internalResources(rName)(address) = internalTotal
+ }
+ }
+ }
+
+ /**
+ * Try to assign the address according to the task requirement.
+ * Please note that this function will not update the values.
+ *
+ * @param taskSetProf assign resources based on which resource profile
+ * @return the resource
+ */
+ def assignResources(taskSetProf: ResourceProfile):
+ Option[(Map[String, ResourceInformation], Map[String, Map[String,
Double]])] = {
+
+ // only look at the resource other than cpus
+ val tsResources = taskSetProf.getCustomTaskResources()
+ if (tsResources.isEmpty) {
+ return Some(Map.empty, Map.empty)
+ }
+
+ val localTaskReqAssign = HashMap[String, ResourceInformation]()
+ val allocatedAddresses = HashMap[String, Map[String, Double]]()
+
+ // we go through all resources here so that we can make sure they match
and also get what the
+ // assignments are for the next task
+ for ((rName, taskReqs) <- tsResources) {
+ // TaskResourceRequest checks the task amount should be in (0, 1] or a
whole number
+ val taskAmount = taskReqs.amount
+
+ internalResources.get(rName) match {
+ case Some(addressesAmountMap) =>
+
+ var internalTaskAmount = (taskAmount * RESOURCE_TOTAL_AMOUNT).toLong
Review Comment:
yes, you're right. Thx for catching. I will fix it.
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