Improved support for Iulvatar GPUs (#1399)

Signed-off-by: 魏强 <[email protected]>

Author: qiangwei1983

charts/hami/README.md

@@ -43,13 +43,6 @@ This document provides detailed descriptions of all configurable values paramete
 | `dcuResourceMem` | DCU memory resource name | `"hygon.com/dcumem"` |
 | `dcuResourceCores` | DCU core resource name | `"hygon.com/dcucores"` |
 
-### Iluvatar GPU Resources
-| Parameter | Description | Default Value |
-|-----------|-------------|---------------|
-| `iluvatarResourceName` | GPU resource name | `"iluvatar.ai/vgpu"` |
-| `iluvatarResourceMem` | GPU memory resource name | `"iluvatar.ai/vcuda-memory"` |
-| `iluvatarResourceCore` | GPU core resource name | `"iluvatar.ai/vcuda-core"` |
-
 ### Metax GPU Resources
 | Parameter | Description | Default Value |
 |-----------|-------------|---------------|
@@ -231,3 +224,9 @@ This document provides detailed descriptions of all configurable values paramete
 | `devices.ascend.nodeSelector` | Node selector | `{"ascend": "on"}` |
 | `devices.ascend.tolerations` | Tolerations | `[]` |
 | `devices.ascend.customresources` | Custom resources | `["huawei.com/Ascend910A", "huawei.com/Ascend910A-memory", ...]` |
+
+### Iluvatar
+| Parameter | Description | Default Value |
+|-----------|-------------|---------------|
+| `devices.iluvatar.enabled` | Whether to enable | `false` |
+| `devices.iluvatar.customresources` | Custom resources | `["iluvatar.ai/BI-V150-vgpu", "iluvatar.ai/BI-V150.vMem","iluvatar.ai/BI-V150.vCore", ...]` |

charts/hami/templates/scheduler/configmap.yaml

@@ -63,6 +63,14 @@ data:
                       "ignoredByScheduler": true
                     },
                     {{- end }}
+                    {{- if .Values.devices.iluvatar.enabled }}
+                    {{- range .Values.devices.iluvatar.customresources }}
+                    {
+                      "name": "{{ . }}",
+                      "ignoredByScheduler": true
+                    },
+                    {{- end }}
+                    {{- end }}
                     {
                         "name": "{{ .Values.resourceName }}",
                         "ignoredByScheduler": true
@@ -99,10 +107,6 @@ data:
                         "name": "{{ .Values.dcuResourceCores }}",
                         "ignoredByScheduler": true
                     },
-                    {
-                        "name": "{{ .Values.iluvatarResourceName }}",
-                        "ignoredByScheduler": true
-                    },
                     {
                         "name": "metax-tech.com/gpu",
                         "ignoredByScheduler": true

charts/hami/templates/scheduler/configmapnew.yaml

@@ -48,8 +48,6 @@ data:
         ignoredByScheduler: true
       - name: {{ .Values.dcuResourceCores }}
         ignoredByScheduler: true
-      - name: {{ .Values.iluvatarResourceName }}
-        ignoredByScheduler: true
       - name: "metax-tech.com/gpu"
         ignoredByScheduler: true
       - name: {{ .Values.metaxResourceName }}
@@ -86,4 +84,10 @@ data:
       - name: {{ . }}
         ignoredByScheduler: true
       {{- end }}
+      {{- if .Values.devices.iluvatar.enabled }}
+      {{- range .Values.devices.iluvatar.customresources }}
+      - name: {{ . }}
+        ignoredByScheduler: true
+      {{- end }}
+      {{- end }}
 {{- end }}

charts/hami/templates/scheduler/deployment.yaml

@@ -108,6 +108,9 @@ spec:
             {{- if .Values.devices.ascend.enabled }}
             - --enable-ascend=true
             {{- end }}
+            {{- if .Values.devices.iluvatar.enabled }}
+            - --enable-iluvatar=true
+            {{- end }}
             {{- if .Values.scheduler.nodeLabelSelector }}
             - --node-label-selector={{- $first := true -}}
               {{- range $key, $value := .Values.scheduler.nodeLabelSelector -}}

charts/hami/templates/scheduler/device-configmap.yaml

@@ -106,10 +106,27 @@ data:
       resourceCountName: "mthreads.com/vgpu"
       resourceMemoryName: "mthreads.com/sgpu-memory"
       resourceCoreName: "mthreads.com/sgpu-core"
-    iluvatar:
-      resourceCountName: {{ .Values.iluvatarResourceName }}
-      resourceMemoryName: {{ .Values.iluvatarResourceMem }}
-      resourceCoreName: {{ .Values.iluvatarResourceCore }}
+    iluvatars:
+    - chipName: MR-V100
+      commonWord: MR-V100
+      resourceCountName: iluvatar.ai/MR-V100-vgpu
+      resourceMemoryName: iluvatar.ai/MR-V100.vMem
+      resourceCoreName: iluvatar.ai/MR-V100.vCore
+    - chipName: MR-V50
+      commonWord: MR-V50
+      resourceCountName: iluvatar.ai/MR-V50-vgpu
+      resourceMemoryName: iluvatar.ai/MR-V50.vMem
+      resourceCoreName: iluvatar.ai/MR-V50.vCore
+    - chipName: BI-V150
+      commonWord: BI-V150
+      resourceCountName: iluvatar.ai/BI-V150-vgpu
+      resourceMemoryName: iluvatar.ai/BI-V150.vMem
+      resourceCoreName: iluvatar.ai/BI-V150.vCore
+    - chipName: BI-V100
+      commonWord: BI-V100
+      resourceCountName: iluvatar.ai/BI-V100-vgpu
+      resourceMemoryName: iluvatar.ai/BI-V100.vMem
+      resourceCoreName: iluvatar.ai/BI-V100.vCore
     kunlun:
       resourceCountName: {{ .Values.kunlunResourceName }}
       resourceVCountName: {{ .Values.kunlunResourceVCountName }}

charts/hami/values.yaml

@@ -38,11 +38,6 @@ dcuResourceName: "hygon.com/dcunum"
 dcuResourceMem: "hygon.com/dcumem"
 dcuResourceCores: "hygon.com/dcucores"
 
-#Iluvatar GPU Parameters
-iluvatarResourceName: "iluvatar.ai/vgpu"
-iluvatarResourceMem: "iluvatar.ai/vcuda-memory"
-iluvatarResourceCore: "iluvatar.ai/vcuda-core"
-
 #Metax sGPU Parameters
 metaxResourceName: "metax-tech.com/sgpu"
 metaxResourceCore: "metax-tech.com/vcore"
@@ -389,4 +384,20 @@ devices:
       - huawei.com/Ascend910B4-1-memory
       - huawei.com/Ascend310P
       - huawei.com/Ascend310P-memory
+  iluvatar:
+    enabled: false
+    customresources:
+      - iluvatar.ai/BI-V100-vgpu
+      - iluvatar.ai/BI-V100.vCore
+      - iluvatar.ai/BI-V100.vMem
+      - iluvatar.ai/BI-V150-vgpu
+      - iluvatar.ai/BI-V150.vCore
+      - iluvatar.ai/BI-V150.vMem
+      - iluvatar.ai/MR-V100-vgpu
+      - iluvatar.ai/MR-V100.vCore
+      - iluvatar.ai/MR-V100.vMem
+      - iluvatar.ai/MR-V50-vgpu
+      - iluvatar.ai/MR-V50.vCore
+      - iluvatar.ai/MR-V50.vMem
+
 

docs/iluvatar-gpu-support.md

@@ -23,52 +23,67 @@
 
 > **NOTICE:** *Install only gpu-manager, don't install gpu-admission package.*
 
-* Identify the resource name about core and memory usage(i.e 'iluvatar.ai/vcuda-core', 'iluvatar.ai/vcuda-memory')
-
-* set the 'iluvatarResourceMem' and 'iluvatarResourceCore' parameters when install hami
-
+* set the devices.iluvatar.enabled=true when install hami
 ```
-helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set iluvatarResourceMem=iluvatar.ai/vcuda-memory --set iluvatarResourceCore=iluvatar.ai/vcuda-core -n kube-system
+helm install hami hami-charts/hami --set scheduler.kubeScheduler.imageTag={your kubernetes version} --set devices.iluvatar.enabled=true
 ```
 
-> **NOTE:** The default resource names are:
-> - `iluvatar.ai/vgpu` for GPU count
-> - `iluvatar.ai/vcuda-memory` for memory allocation
-> - `iluvatar.ai/vcuda-core` for core allocation
->
-> You can customize these names using the parameters above.
+**Note:** The currently supported GPU models and resource names are defined in (https://github.com/Project-HAMi/HAMi/blob/master/charts/hami/templates/scheduler/device-configmap.yaml):
+```yaml
+    iluvatars:
+    - chipName: MR-V100
+      commonWord: MR-V100
+      resourceCountName: iluvatar.ai/MR-V100-vgpu
+      resourceMemoryName: iluvatar.ai/MR-V100.vMem
+      resourceCoreName: iluvatar.ai/MR-V100.vCore
+    - chipName: MR-V50
+      commonWord: MR-V50
+      resourceCountName: iluvatar.ai/MR-V50-vgpu
+      resourceMemoryName: iluvatar.ai/MR-V50.vMem
+      resourceCoreName: iluvatar.ai/MR-V50.vCore
+    - chipName: BI-V150
+      commonWord: BI-V150
+      resourceCountName: iluvatar.ai/BI-V150-vgpu
+      resourceMemoryName: iluvatar.ai/BI-V150.vMem
+      resourceCoreName: iluvatar.ai/BI-V150.vCore
+    - chipName: BI-V100
+      commonWord: BI-V100
+      resourceCountName: iluvatar.ai/BI-V100-vgpu
+      resourceMemoryName: iluvatar.ai/BI-V100.vMem
+      resourceCoreName: iluvatar.ai/BI-V100.vCore
+```
 
 ## Device Granularity
 
 HAMi divides each Iluvatar GPU into 100 units for resource allocation. When you request a portion of a GPU, you're actually requesting a certain number of these units.
 
 ### Memory Allocation
 
-- Each unit of `iluvatar.ai/vcuda-memory` represents 256MB of device memory
+- Each unit of `iluvatar.ai/<card-type>.vMem` represents 256MB of device memory
 - If you don't specify a memory request, the system will default to using 100% of the available memory
 - Memory allocation is enforced with hard limits to ensure tasks don't exceed their allocated memory
 
 ### Core Allocation
 
-- Each unit of `iluvatar.ai/vcuda-core` represents 1% of the available compute cores
+- Each unit of `iluvatar.ai/<card-type>.vCore` represents 1% of the available compute cores
 - Core allocation is enforced with hard limits to ensure tasks don't exceed their allocated cores
 - When requesting multiple GPUs, the system will automatically set the core resources based on the number of GPUs requested
 
 ## Running Iluvatar jobs
 
 Iluvatar GPUs can now be requested by a container
-using the `iluvatar.ai/vgpu`, `iluvatar.ai/vcuda-memory` and `iluvatar.ai/vcuda-core`  resource type:
+using the `iluvatar.ai/BI-V150-vgpu`, `iluvatar.ai/BI-V150.vMem` and `iluvatar.ai/BI-V150.vCore`  resource type:
 
 ```yaml
 apiVersion: v1
 kind: Pod
 metadata:
-  name: poddemo
+  name: BI-V150-poddemo
 spec:
   restartPolicy: Never
   containers:
-  - name: poddemo
-    image: harbor.4pd.io/vgpu/corex_transformers@sha256:36a01ec452e6ee63c7aa08bfa1fa16d469ad19cc1e6000cf120ada83e4ceec1e
+  - name: BI-V150-poddemo
+    image: registry.iluvatar.com.cn:10443/saas/mr-bi150-4.3.0-x86-ubuntu22.04-py3.10-base-base:v1.0
     command:
     - bash
     args:
@@ -82,13 +97,13 @@ spec:
       sleep 360000
     resources:
       requests:
-        iluvatar.ai/vgpu: 1
-        iluvatar.ai/vcuda-core: 50
-        iluvatar.ai/vcuda-memory: 64
+        iluvatar.ai/BI-V150-vgpu: 1
+        iluvatar.ai/BI-V150.vCore: 50
+        iluvatar.ai/BI-V150.vMem: 64
       limits:
-        iluvatar.ai/vgpu: 1
-        iluvatar.ai/vcuda-core: 50
-        iluvatar.ai/vcuda-memory: 64
+        iluvatar.ai/BI-V150-vgpu: 1
+        iluvatar.ai/BI-V150.vCore: 50
+        iluvatar.ai/BI-V150.vMem: 64
 ```
 
 > **NOTICE1:** *Each unit of vcuda-memory indicates 256M device memory*
@@ -106,15 +121,13 @@ metadata:
   name: poddemo
   annotations:
     # Use specific GPU devices (comma-separated list)
-    iluvatar.ai/use-gpuuuid: "node1-iluvatar-0,node1-iluvatar-1"
+    hami.io/use-<card-type>-uuid: "device-uuid-1,device-uuid-2"
     # Or exclude specific GPU devices (comma-separated list)
-    iluvatar.ai/nouse-gpuuuid: "node1-iluvatar-2,node1-iluvatar-3"
+    hami.io/no-use-<card-type>-uuid: "device-uuid-1,device-uuid-2"
 spec:
   # ... rest of pod spec
 ```
 
-> **NOTE:** The device ID format is `{node-name}-iluvatar-{index}`. You can find the available device IDs in the node status.
-
 ### Finding Device UUIDs
 
 You can find the UUIDs of Iluvatar GPUs on a node using the following command:
@@ -126,7 +139,7 @@ kubectl get pod <pod-name> -o yaml | grep -A 10 "hami.io/<card-type>-devices-all
 Or by examining the node annotations:
 
 ```bash
-kubectl get node <node-name> -o yaml | grep -A 10 "hami.io/node-register-<card-type>"
+kubectl get node <node-name> -o yaml | grep -A 10 "hami.io/node-<card-type>-register"
 ```
 
 Look for annotations containing device information in the node status.
@@ -144,6 +157,6 @@ Look for annotations containing device information in the node status.
 
 2. Virtualization takes effect only for containers that apply for one GPU(i.e iluvatar.ai/vgpu=1 ). When requesting multiple GPUs, the system will automatically set the core resources based on the number of GPUs requested.
 
-3. The `iluvatar.ai/vcuda-memory` resource is only effective when `iluvatar.ai/vgpu=1`.
+3. The `iluvatar.ai/<card-type>.vMem` resource is only effective when `iluvatar.ai/<card-type>-vgpu=1`.
 
-4. Multi-device requests (`iluvatar.ai/vgpu > 1`) do not support vGPU mode.
+4. Multi-device requests (`iluvatar.ai/<card-type>-vgpu= > 1`) do not support vGPU mode.