wbo4958 commented on code in PR #43494: URL: https://github.com/apache/spark/pull/43494#discussion_r1413397783
########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource + * @param assignedResource the assigned resource information + */ + def acquire(assignedResource: Map[String, Map[String, Long]]): 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 $address doesn't exist.")) + + val left = prevInternalTotalAmount - amount + if (left < 0) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(left)} " + + s"after acquiring $rName address $address should be >= 0") + } + internalResources(rName)(address) = left + } + } + } + + /** + * Release the assigned resources to the resource pool + * @param assignedResource resource to be released + */ + def release(assignedResource: Map[String, Map[String, Long]]): 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 $address is not assigned.")) + val total = prevInternalTotalAmount + amount + if (total > ONE_ENTIRE_RESOURCE) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(total)} " + + s"after releasing $rName address $address should be <= 1.0") + } + internalResources(rName)(address) = total + } + } + } + + /** + * Try to assign the addresses 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 optional resources amounts + */ + def assignResources(taskSetProf: ResourceProfile): Option[Map[String, Map[String, Long]]] = { + // only look at the resource other than cpus + val tsResources = taskSetProf.getCustomTaskResources() + if (tsResources.isEmpty) { + return Some(Map.empty) + } + + val allocatedAddresses = HashMap[String, Map[String, Long]]() + + // 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 + var taskAmount = taskReqs.amount + + internalResources.get(rName) match { + case Some(addressesAmountMap) => + val allocatedAddressesMap = HashMap[String, Long]() + + // always sort the addresses Review Comment: Hmm, this is to keep the same design as the original https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/resource/ResourceAllocator.scala#L53 ########## core/src/main/scala/org/apache/spark/resource/ResourceAllocator.scala: ########## @@ -29,59 +84,53 @@ private[spark] trait ResourceAllocator { protected def resourceName: String protected def resourceAddresses: Seq[String] - protected def slotsPerAddress: Int /** - * Map from an address to its availability, a value > 0 means the address is available, - * while value of 0 means the address is fully assigned. - * - * For task resources ([[org.apache.spark.scheduler.ExecutorResourceInfo]]), this value - * can be a multiple, such that each address can be allocated up to [[slotsPerAddress]] - * times. + * Map from an address to its availability default to 1.0 (we multiply ONE_ENTIRE_RESOURCE + * to avoid precision error), a value > 0 means the address is available, while value of + * 0 means the address is fully assigned. */ private lazy val addressAvailabilityMap = { - mutable.HashMap(resourceAddresses.map(_ -> slotsPerAddress): _*) + mutable.HashMap(resourceAddresses.map(address => address -> ONE_ENTIRE_RESOURCE): _*) } /** - * Sequence of currently available resource addresses. - * - * With [[slotsPerAddress]] greater than 1, [[availableAddrs]] can contain duplicate addresses - * e.g. with [[slotsPerAddress]] == 2, availableAddrs for addresses 0 and 1 can look like - * Seq("0", "0", "1"), where address 0 has two assignments available, and 1 has one. + * Get the amounts of resources that have been multiplied by ONE_ENTIRE_RESOURCE. + * @return the resources amounts + */ + def resourcesAmounts: Map[String, Long] = addressAvailabilityMap.toMap + + /** + * Sequence of currently available resource addresses which are not fully assigned. */ def availableAddrs: Seq[String] = addressAvailabilityMap - .flatMap { case (addr, available) => - (0 until available).map(_ => addr) - }.toSeq.sorted + .filter(addresses => addresses._2 > 0).keys.toSeq.sorted /** * Sequence of currently assigned resource addresses. - * - * With [[slotsPerAddress]] greater than 1, [[assignedAddrs]] can contain duplicate addresses - * e.g. with [[slotsPerAddress]] == 2, assignedAddrs for addresses 0 and 1 can look like - * Seq("0", "1", "1"), where address 0 was assigned once, and 1 was assigned twice. */ private[spark] def assignedAddrs: Seq[String] = addressAvailabilityMap - .flatMap { case (addr, available) => - (0 until slotsPerAddress - available).map(_ => addr) - }.toSeq.sorted + .filter(addresses => addresses._2 < ONE_ENTIRE_RESOURCE).keys.toSeq.sorted /** * Acquire a sequence of resource addresses (to a launched task), these addresses must be * available. When the task finishes, it will return the acquired resource addresses. * Throw an Exception if an address is not available or doesn't exist. */ - def acquire(addrs: Seq[String]): Unit = { - addrs.foreach { address => - val isAvailable = addressAvailabilityMap.getOrElse(address, + def acquire(addressesAmounts: Map[String, Long]): Unit = { + addressesAmounts.foreach { case (address, amount) => + val prevAmount = addressAvailabilityMap.getOrElse(address, throw new SparkException(s"Try to acquire an address that doesn't exist. $resourceName " + - s"address $address doesn't exist.")) - if (isAvailable > 0) { - addressAvailabilityMap(address) -= 1 + s"address $address doesn't exist.")) + + val left = addressAvailabilityMap(address) - amount Review Comment: Done ########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource Review Comment: Yeah, you're correct. Removed the "update" ########## core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala: ########## @@ -446,11 +441,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Do resources allocation here. The allocated resources will get released after the task // finishes. executorData.freeCores -= task.cpus - task.resources.foreach { case (rName, rInfo) => - assert(executorData.resourcesInfo.contains(rName)) - executorData.resourcesInfo(rName).acquire(rInfo.addresses.toImmutableArraySeq) + task.resources.foreach { case (rName, addressAmounts) => + addressAmounts.foreach { case (address, amount) => Review Comment: Done. Thx ########## core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala: ########## @@ -172,9 +172,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp executorDataMap.get(executorId) match { case Some(executorInfo) => executorInfo.freeCores += taskCpus - resources.foreach { case (k, v) => - executorInfo.resourcesInfo.get(k).foreach { r => - r.release(v.addresses.toImmutableArraySeq) + resources.foreach { case (rName, addressAmount) => + addressAmount.foreach { case (address, amount) => Review Comment: Wow, good catch. Done. Thx ########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource + * @param assignedResource the assigned resource information + */ + def acquire(assignedResource: Map[String, Map[String, Long]]): 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 $address doesn't exist.")) + + val left = prevInternalTotalAmount - amount + if (left < 0) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(left)} " + + s"after acquiring $rName address $address should be >= 0") + } + internalResources(rName)(address) = left + } + } + } + + /** + * Release the assigned resources to the resource pool + * @param assignedResource resource to be released + */ + def release(assignedResource: Map[String, Map[String, Long]]): 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 $address is not assigned.")) + val total = prevInternalTotalAmount + amount + if (total > ONE_ENTIRE_RESOURCE) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(total)} " + + s"after releasing $rName address $address should be <= 1.0") + } + internalResources(rName)(address) = total + } + } + } + + /** + * Try to assign the addresses 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 optional resources amounts + */ + def assignResources(taskSetProf: ResourceProfile): Option[Map[String, Map[String, Long]]] = { + // only look at the resource other than cpus + val tsResources = taskSetProf.getCustomTaskResources() + if (tsResources.isEmpty) { + return Some(Map.empty) + } + + val allocatedAddresses = HashMap[String, Map[String, Long]]() + + // 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 + var taskAmount = taskReqs.amount + + internalResources.get(rName) match { + case Some(addressesAmountMap) => + val allocatedAddressesMap = HashMap[String, Long]() + + // always sort the addresses + val addresses = addressesAmountMap.keys.toSeq.sorted + + // task.amount is a whole number + if (taskAmount >= 1.0) { + for (address <- addresses if taskAmount > 0) { + // The address is still a whole resource + if (ResourceAmountUtils.isOneEntireResource(addressesAmountMap(address))) { + taskAmount -= 1.0 + // Assign the full resource of the address + allocatedAddressesMap(address) = ONE_ENTIRE_RESOURCE + } + } + } else if (taskAmount > 0.0) { // 0 < task.amount < 1.0 + val internalTaskAmount = ResourceAmountUtils.toInternalResource(taskAmount) + for (address <- addresses if taskAmount > 0) { + if (addressesAmountMap(address) >= internalTaskAmount) { + // Assign the part of the address. + allocatedAddressesMap(address) = internalTaskAmount + taskAmount = 0 + } + } + } + + if (taskAmount == 0 && allocatedAddressesMap.size > 0) { + allocatedAddresses.put(rName, allocatedAddressesMap.toMap) + } else return None Review Comment: Done ########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource + * @param assignedResource the assigned resource information + */ + def acquire(assignedResource: Map[String, Map[String, Long]]): 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 $address doesn't exist.")) + + val left = prevInternalTotalAmount - amount + if (left < 0) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(left)} " + + s"after acquiring $rName address $address should be >= 0") + } + internalResources(rName)(address) = left + } + } + } + + /** + * Release the assigned resources to the resource pool + * @param assignedResource resource to be released + */ + def release(assignedResource: Map[String, Map[String, Long]]): 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 $address is not assigned.")) + val total = prevInternalTotalAmount + amount + if (total > ONE_ENTIRE_RESOURCE) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(total)} " + + s"after releasing $rName address $address should be <= 1.0") + } + internalResources(rName)(address) = total + } + } + } + + /** + * Try to assign the addresses 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 optional resources amounts + */ + def assignResources(taskSetProf: ResourceProfile): Option[Map[String, Map[String, Long]]] = { + // only look at the resource other than cpus + val tsResources = taskSetProf.getCustomTaskResources() + if (tsResources.isEmpty) { + return Some(Map.empty) + } + + val allocatedAddresses = HashMap[String, Map[String, Long]]() + + // 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 + var taskAmount = taskReqs.amount + + internalResources.get(rName) match { + case Some(addressesAmountMap) => + val allocatedAddressesMap = HashMap[String, Long]() + + // always sort the addresses Review Comment: Updated the comment to the function description ########## core/src/main/scala/org/apache/spark/resource/ResourceAllocator.scala: ########## @@ -91,16 +140,21 @@ private[spark] trait ResourceAllocator { * addresses are released when a task has finished. * Throw an Exception if an address is not assigned or doesn't exist. */ - def release(addrs: Seq[String]): Unit = { - addrs.foreach { address => - val isAvailable = addressAvailabilityMap.getOrElse(address, + def release (addressesAmounts: Map[String, Long]): Unit = { Review Comment: Thx, Done. ########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource + * @param assignedResource the assigned resource information + */ + def acquire(assignedResource: Map[String, Map[String, Long]]): 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 $address doesn't exist.")) + + val left = prevInternalTotalAmount - amount + if (left < 0) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(left)} " + + s"after acquiring $rName address $address should be >= 0") + } + internalResources(rName)(address) = left + } + } + } + + /** + * Release the assigned resources to the resource pool + * @param assignedResource resource to be released + */ + def release(assignedResource: Map[String, Map[String, Long]]): 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 $address is not assigned.")) + val total = prevInternalTotalAmount + amount + if (total > ONE_ENTIRE_RESOURCE) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(total)} " + + s"after releasing $rName address $address should be <= 1.0") + } + internalResources(rName)(address) = total + } + } + } + + /** + * Try to assign the addresses 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 optional resources amounts + */ + def assignResources(taskSetProf: ResourceProfile): Option[Map[String, Map[String, Long]]] = { + // only look at the resource other than cpus + val tsResources = taskSetProf.getCustomTaskResources() + if (tsResources.isEmpty) { + return Some(Map.empty) + } + + val allocatedAddresses = HashMap[String, Map[String, Long]]() + + // 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 + var taskAmount = taskReqs.amount + + internalResources.get(rName) match { + case Some(addressesAmountMap) => + val allocatedAddressesMap = HashMap[String, Long]() + + // always sort the addresses + val addresses = addressesAmountMap.keys.toSeq.sorted + + // task.amount is a whole number + if (taskAmount >= 1.0) { + for (address <- addresses if taskAmount > 0) { + // The address is still a whole resource + if (ResourceAmountUtils.isOneEntireResource(addressesAmountMap(address))) { + taskAmount -= 1.0 + // Assign the full resource of the address + allocatedAddressesMap(address) = ONE_ENTIRE_RESOURCE + } + } + } else if (taskAmount > 0.0) { // 0 < task.amount < 1.0 + val internalTaskAmount = ResourceAmountUtils.toInternalResource(taskAmount) + for (address <- addresses if taskAmount > 0) { + if (addressesAmountMap(address) >= internalTaskAmount) { Review Comment: yeah. you're correct. there will be "wasting" in some cases. Anyways, I've updated the comment to the function description and added a TODO for it. ########## core/src/main/scala/org/apache/spark/scheduler/ExecutorResourcesAmounts.scala: ########## @@ -0,0 +1,202 @@ +/* + * 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.{ResourceAmountUtils, ResourceProfile} +import org.apache.spark.resource.ResourceAmountUtils.ONE_ENTIRE_RESOURCE + +/** + * 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" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.2), + * "1" -> ResourceAmountUtils.toInternalResource(1.0)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.3), + * "b" -> ResourceAmountUtils.toInternalResource(0.9)) + * ) + */ +private[spark] class ExecutorResourcesAmounts( + private val resources: Map[String, Map[String, Long]]) extends Serializable { + + /** + * convert the resources to be mutable HashMap + */ + private val internalResources: Map[String, HashMap[String, Long]] = { + resources.map { case (rName, addressAmounts) => + rName -> HashMap(addressAmounts.toSeq: _*) + } + } + + /** + * The total address count of each resource. Eg, + * Map("gpu" -> Map("0" -> ResourceAmountUtils.toInternalResource(0.5), + * "1" -> ResourceAmountUtils.toInternalResource(0.5), + * "2" -> ResourceAmountUtils.toInternalResource(0.5)), + * "fpga" -> Map("a" -> ResourceAmountUtils.toInternalResource(0.5), + * "b" -> ResourceAmountUtils.toInternalResource(0.5))) + * the resourceAmount will be Map("gpu" -> 3, "fpga" -> 2) + */ + lazy val resourceAddressAmount: 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 -> ResourceAmountUtils.toFractionalResource(amount) + }.toMap + } + } + + /** + * Acquire the resource and update the resource + * @param assignedResource the assigned resource information + */ + def acquire(assignedResource: Map[String, Map[String, Long]]): 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 $address doesn't exist.")) + + val left = prevInternalTotalAmount - amount + if (left < 0) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(left)} " + + s"after acquiring $rName address $address should be >= 0") + } + internalResources(rName)(address) = left + } + } + } + + /** + * Release the assigned resources to the resource pool + * @param assignedResource resource to be released + */ + def release(assignedResource: Map[String, Map[String, Long]]): 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 $address is not assigned.")) + val total = prevInternalTotalAmount + amount + if (total > ONE_ENTIRE_RESOURCE) { + throw new SparkException(s"The total amount " + + s"${ResourceAmountUtils.toFractionalResource(total)} " + + s"after releasing $rName address $address should be <= 1.0") + } + internalResources(rName)(address) = total + } + } + } + + /** + * Try to assign the addresses 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 optional resources amounts Review Comment: Done. -- This is an automated message from the Apache Git Service. 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