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fix(scheduler): count shared DRA GPU device once per node #1931
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kai-scheduler:main
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TensorRaya:fix/dra-shared-claim-double-count
Aug 6, 2026
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,6 @@ | ||
| kind: Fixed | ||
| body: Count a GPU shared by multiple pods through one DRA ResourceClaim once per node, preventing negative idle GPUs. | ||
| time: 2026-07-17T23:34:28.999387137Z | ||
| custom: | ||
| Author: TensorRaya | ||
| Issue: "1930" |
260 changes: 260 additions & 0 deletions
260
pkg/scheduler/actions/integration_tests/allocate/allocate_shared_dra_test.go
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,260 @@ | ||
| // Copyright 2026 NVIDIA CORPORATION | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| package allocate | ||
|
|
||
| import ( | ||
| "testing" | ||
| "time" | ||
|
|
||
| resourceapi "k8s.io/api/resource/v1" | ||
|
|
||
| commonconstants "github.com/kai-scheduler/KAI-scheduler/pkg/common/constants" | ||
| featuregates "github.com/kai-scheduler/KAI-scheduler/pkg/common/feature_gates" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/actions/integration_tests/integration_tests_utils" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/api/pod_status" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/constants" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/test_utils" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/test_utils/dra_fake" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/test_utils/jobs_fake" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/test_utils/nodes_fake" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/test_utils/tasks_fake" | ||
| ) | ||
|
|
||
| func TestSharedDRADeviceDoesNotBlockCPUOnlyPod(t *testing.T) { | ||
| featuregates.SetDynamicResourcesEnabledForTest(true) | ||
| t.Cleanup(func() { | ||
| featuregates.SetDynamicResourcesEnabledForTest(false) | ||
| }) | ||
|
|
||
| integration_tests_utils.RunTests(t, []integration_tests_utils.TestTopologyMetadata{ | ||
| { | ||
| Name: "shared DRA device does not block CPU-only pod", | ||
| TestTopologyBasic: test_utils.TestTopologyBasic{ | ||
| Name: "shared DRA device does not block CPU-only pod", | ||
| Jobs: []*jobs_fake.TestJobBasic{ | ||
| { | ||
| Name: "shared_dra_job0", | ||
| Namespace: "test", | ||
| Priority: constants.PriorityTrainNumber, | ||
| QueueName: "queue1", | ||
| Tasks: []*tasks_fake.TestTaskBasic{ | ||
| { | ||
| NodeName: "node0", | ||
| State: pod_status.Running, | ||
| ResourceClaimNames: []string{"shared-claim"}, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| Name: "shared_dra_job1", | ||
| Namespace: "test", | ||
| Priority: constants.PriorityTrainNumber, | ||
| QueueName: "queue1", | ||
| Tasks: []*tasks_fake.TestTaskBasic{ | ||
| { | ||
| NodeName: "node0", | ||
| State: pod_status.Running, | ||
| ResourceClaimNames: []string{"shared-claim"}, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| Name: "cpu_only_job", | ||
| Namespace: "test", | ||
| Priority: constants.PriorityTrainNumber, | ||
| QueueName: "queue1", | ||
| Tasks: []*tasks_fake.TestTaskBasic{ | ||
| { | ||
| State: pod_status.Pending, | ||
| NodeAffinityNames: []string{"node0"}, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| TestDRAObjects: dra_fake.TestDRAObjects{ | ||
| DeviceClasses: []string{"nvidia.com/gpu"}, | ||
| ResourceSlices: []*dra_fake.TestResourceSlice{ | ||
| { | ||
| Name: "node0-gpu", | ||
| DeviceClassName: "nvidia.com/gpu", | ||
| NodeName: "node0", | ||
| Count: 1, | ||
| }, | ||
| }, | ||
| ResourceClaims: []*dra_fake.TestResourceClaim{ | ||
| { | ||
| Name: "shared-claim", | ||
| Namespace: "test", | ||
| DeviceClassName: "nvidia.com/gpu", | ||
| Count: 1, | ||
| Labels: map[string]string{ | ||
| commonconstants.DefaultQueueLabel: "queue1", | ||
| }, | ||
| ClaimStatus: &resourceapi.ResourceClaimStatus{ | ||
| Allocation: &resourceapi.AllocationResult{ | ||
| Devices: resourceapi.DeviceAllocationResult{ | ||
| Results: []resourceapi.DeviceRequestAllocationResult{ | ||
| { | ||
| Request: "request", | ||
| Driver: "nvidia.com/gpu", | ||
| Pool: "node0", | ||
| Device: "0", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| ReservedFor: []resourceapi.ResourceClaimConsumerReference{ | ||
| {Resource: "pods", Name: "shared_dra_job0-0", UID: "shared_dra_job0-0"}, | ||
| {Resource: "pods", Name: "shared_dra_job1-0", UID: "shared_dra_job1-0"}, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| Nodes: map[string]nodes_fake.TestNodeBasic{ | ||
| "node0": {}, | ||
| }, | ||
| Queues: []test_utils.TestQueueBasic{ | ||
| { | ||
| Name: "queue1", | ||
| DeservedGPUs: 1, | ||
| }, | ||
| }, | ||
| JobExpectedResults: map[string]test_utils.TestExpectedResultBasic{ | ||
| "shared_dra_job0": { | ||
| NodeName: "node0", | ||
| Status: pod_status.Running, | ||
| }, | ||
| "shared_dra_job1": { | ||
| NodeName: "node0", | ||
| Status: pod_status.Running, | ||
| }, | ||
| "cpu_only_job": { | ||
| NodeName: "node0", | ||
| Status: pod_status.Running, | ||
| }, | ||
| }, | ||
| Mocks: &test_utils.TestMock{ | ||
| CacheRequirements: &test_utils.CacheMocking{ | ||
| NumberOfCacheBinds: 1, | ||
| }, | ||
| }, | ||
| }, | ||
| RoundsUntilMatch: 1, | ||
| RoundsAfterMatch: 1, | ||
| SchedulingDuration: time.Millisecond, | ||
| }, | ||
| }) | ||
| } | ||
|
|
||
| func TestPendingPodCanUseSharedDRADevice(t *testing.T) { | ||
| featuregates.SetDynamicResourcesEnabledForTest(true) | ||
| t.Cleanup(func() { | ||
| featuregates.SetDynamicResourcesEnabledForTest(false) | ||
| }) | ||
|
|
||
| integration_tests_utils.RunTests(t, []integration_tests_utils.TestTopologyMetadata{ | ||
| { | ||
| Name: "pending pod can use shared DRA device", | ||
| TestTopologyBasic: test_utils.TestTopologyBasic{ | ||
| Name: "pending pod can use shared DRA device", | ||
| Jobs: []*jobs_fake.TestJobBasic{ | ||
| { | ||
| Name: "running_shared_dra_job", | ||
| Namespace: "test", | ||
| Priority: constants.PriorityTrainNumber, | ||
| QueueName: "queue1", | ||
| Tasks: []*tasks_fake.TestTaskBasic{ | ||
| { | ||
| NodeName: "node0", | ||
| State: pod_status.Running, | ||
| ResourceClaimNames: []string{"shared-claim"}, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| Name: "pending_shared_dra_job", | ||
| Namespace: "test", | ||
| Priority: constants.PriorityTrainNumber, | ||
| QueueName: "queue1", | ||
| Tasks: []*tasks_fake.TestTaskBasic{ | ||
| { | ||
| State: pod_status.Pending, | ||
| NodeAffinityNames: []string{"node0"}, | ||
| ResourceClaimNames: []string{"shared-claim"}, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| TestDRAObjects: dra_fake.TestDRAObjects{ | ||
| DeviceClasses: []string{"nvidia.com/gpu"}, | ||
| ResourceSlices: []*dra_fake.TestResourceSlice{ | ||
| { | ||
| Name: "node0-gpu", | ||
| DeviceClassName: "nvidia.com/gpu", | ||
| NodeName: "node0", | ||
| Count: 1, | ||
| }, | ||
| }, | ||
| ResourceClaims: []*dra_fake.TestResourceClaim{ | ||
| { | ||
| Name: "shared-claim", | ||
| Namespace: "test", | ||
| DeviceClassName: "nvidia.com/gpu", | ||
| Count: 1, | ||
| Labels: map[string]string{ | ||
| commonconstants.DefaultQueueLabel: "queue1", | ||
| }, | ||
| ClaimStatus: &resourceapi.ResourceClaimStatus{ | ||
| Allocation: &resourceapi.AllocationResult{ | ||
| Devices: resourceapi.DeviceAllocationResult{ | ||
| Results: []resourceapi.DeviceRequestAllocationResult{ | ||
| { | ||
| Request: "request", | ||
| Driver: "nvidia.com/gpu", | ||
| Pool: "node0", | ||
| Device: "0", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| ReservedFor: []resourceapi.ResourceClaimConsumerReference{ | ||
| {Resource: "pods", Name: "running_shared_dra_job-0", UID: "running_shared_dra_job-0"}, | ||
| {Resource: "pods", Name: "pending_shared_dra_job-0", UID: "pending_shared_dra_job-0"}, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| Nodes: map[string]nodes_fake.TestNodeBasic{ | ||
| "node0": {}, | ||
| }, | ||
| Queues: []test_utils.TestQueueBasic{ | ||
| { | ||
| Name: "queue1", | ||
| DeservedGPUs: 1, | ||
| }, | ||
| }, | ||
| JobExpectedResults: map[string]test_utils.TestExpectedResultBasic{ | ||
| "running_shared_dra_job": { | ||
| NodeName: "node0", | ||
| Status: pod_status.Running, | ||
| }, | ||
| "pending_shared_dra_job": { | ||
| NodeName: "node0", | ||
| Status: pod_status.Running, | ||
| }, | ||
| }, | ||
| Mocks: &test_utils.TestMock{ | ||
| CacheRequirements: &test_utils.CacheMocking{ | ||
| NumberOfCacheBinds: 1, | ||
| }, | ||
| }, | ||
| }, | ||
| RoundsUntilMatch: 1, | ||
| RoundsAfterMatch: 1, | ||
| SchedulingDuration: time.Millisecond, | ||
| }, | ||
| }) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| // Copyright 2025 NVIDIA CORPORATION | ||
| // SPDX-License-Identifier: Apache-2.0 | ||
|
|
||
| package node_info | ||
|
|
||
| import ( | ||
| resourceapi "k8s.io/api/resource/v1" | ||
|
|
||
| "github.com/kai-scheduler/KAI-scheduler/pkg/common/resources" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/api/pod_info" | ||
| "github.com/kai-scheduler/KAI-scheduler/pkg/scheduler/api/resource_info" | ||
| ) | ||
|
|
||
| // draDeviceKey uniquely identifies a physical DRA device on the node. | ||
| func draDeviceKey(result resourceapi.DeviceRequestAllocationResult) string { | ||
| return result.Driver + "/" + result.Pool + "/" + result.Device | ||
| } | ||
|
|
||
| // allocatedGPUDeviceKeys returns the keys of all GPU devices allocated to the | ||
| // task via DRA ResourceClaims. Non-GPU devices are ignored: they are not part | ||
| // of the GPU accounting that this dedup protects. | ||
| func (ni *NodeInfo) allocatedGPUDeviceKeys(task *pod_info.PodInfo) []string { | ||
| var keys []string | ||
| for _, claimAllocation := range task.ResourceClaimInfo { | ||
| if claimAllocation == nil || claimAllocation.Allocation == nil { | ||
| continue | ||
| } | ||
| for _, result := range claimAllocation.Allocation.Devices.Results { | ||
| if !resources.IsGPUDeviceClass(result.Driver) { | ||
| continue | ||
| } | ||
| keys = append(keys, draDeviceKey(result)) | ||
| } | ||
| } | ||
| return keys | ||
| } | ||
|
|
||
| // sharedDRAGpuDiscount returns the number of GPU devices the task requests via | ||
| // DRA claims that are already counted on this node for other pods. A task that | ||
| // shares an allocated device with a running pod does not need additional GPU | ||
| // capacity for that device. | ||
| func (ni *NodeInfo) sharedDRAGpuDiscount(task *pod_info.PodInfo) float64 { | ||
| discount := 0.0 | ||
| for _, key := range ni.allocatedGPUDeviceKeys(task) { | ||
| if ni.DRASharedDeviceRefCount[key] > 0 { | ||
| discount++ | ||
| } | ||
| } | ||
| return discount | ||
| } | ||
|
|
||
| // dedupSharedDRAGpus removes from resourcesToTrack the GPU count that would | ||
| // double-count physical DRA devices already referenced by other pods on the | ||
| // node. It also updates the node's per-device reference count. It must be | ||
| // called once per addTaskResources, before the vector is added to UsedVector. | ||
| func (ni *NodeInfo) dedupSharedDRAGpus(task *pod_info.PodInfo, resourcesToTrack resource_info.ResourceVector) { | ||
| current := resourcesToTrack.Get(resource_info.GPUIndex) | ||
| if current <= 0 { | ||
| // The task contributes no GPUs to the used vector (e.g. a resource | ||
| // reservation task whose GPU index was zeroed). Tracking its devices | ||
| // would both risk a negative deduction below and mask the reference | ||
| // count of the real consuming pods, so leave the accounting untouched. | ||
| return | ||
| } | ||
|
|
||
| alreadyCounted := 0.0 | ||
| for _, key := range ni.allocatedGPUDeviceKeys(task) { | ||
| if ni.DRASharedDeviceRefCount[key] > 0 { | ||
| // Another pod on this node already contributed this physical | ||
| // device to the used vector: do not count it again. | ||
| alreadyCounted++ | ||
| } | ||
| ni.DRASharedDeviceRefCount[key]++ | ||
| } | ||
|
|
||
| if alreadyCounted > current { | ||
| // Never deduct more than the task's own GPU contribution. | ||
| alreadyCounted = current | ||
| } | ||
| if alreadyCounted > 0 { | ||
| resourcesToTrack.Set(resource_info.GPUIndex, current-alreadyCounted) | ||
| } | ||
| } | ||
|
|
||
| // releaseSharedDRAGpus is the inverse of dedupSharedDRAGpus: it decrements the | ||
| // per-device reference count and adds back the GPU count for devices that | ||
| // remain referenced by other pods (and were therefore never subtracted on this | ||
| // task's removal path). It must be called once per removeTaskResources. | ||
| func (ni *NodeInfo) releaseSharedDRAGpus(task *pod_info.PodInfo, resourcesToTrack resource_info.ResourceVector) { | ||
| current := resourcesToTrack.Get(resource_info.GPUIndex) | ||
| if current <= 0 { | ||
| // Mirror of dedupSharedDRAGpus: a task that contributed no GPUs never | ||
| // incremented the reference count, so it must not decrement it here. | ||
| return | ||
| } | ||
|
|
||
| stillShared := 0.0 | ||
| for _, key := range ni.allocatedGPUDeviceKeys(task) { | ||
| if ni.DRASharedDeviceRefCount[key] > 1 { | ||
| // The device stays referenced by another pod after this removal: | ||
| // it must remain in the used vector, so this task's removal must | ||
| // not subtract it. | ||
| stillShared++ | ||
| } | ||
| if ni.DRASharedDeviceRefCount[key] > 0 { | ||
| ni.DRASharedDeviceRefCount[key]-- | ||
| } | ||
| if ni.DRASharedDeviceRefCount[key] == 0 { | ||
| delete(ni.DRASharedDeviceRefCount, key) | ||
| } | ||
| } | ||
|
|
||
| if stillShared > current { | ||
| // Never add back more than the task's own GPU contribution. | ||
| stillShared = current | ||
| } | ||
| if stillShared > 0 { | ||
| resourcesToTrack.Set(resource_info.GPUIndex, current-stillShared) | ||
| } | ||
| } | ||
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