optimized-baseline-xpu.yaml

# OPTIMIZED BASELINE - Intel XPU (device-plugin) variant
# ============================================================================
# Adapted from config/scenarios/guides/optimized-baseline.yaml for Intel GPU
# (xe driver, classic device-plugin `gpu.intel.com/xe`). References the
# upstream guide modelserver overlay at:
#   llm-d/guides/optimized-baseline/modelserver/xpu/vllm
#
# Key differences vs the NVIDIA scenario:
#   * accelerator.type=intel-xe, resource=gpu.intel.com/xe (not nvidia.com/gpu)
#   * vLLM image = ghcr.io/llm-d/llm-d-xpu (XPU build, not CUDA vllm-openai)
#   * decode.vllm.customCommand has NO libcuda export and NO
#     --gpu-memory-utilization; uses --dtype float16 --disable-sliding-window
#     as in the upstream XPU overlay.
#   * model = Qwen/Qwen3-0.6B (small, fits a single Arc/B-series GPU)
#   * NO DRA (cluster uses the classic Intel GPU device plugin)
# ============================================================================

scenario:
  - name: "optimized-baseline-xpu"

    # =========================================================================
    # ACCELERATOR - Intel GPU via classic device plugin (gpu.intel.com/xe)
    # =========================================================================
    accelerator:
      type: intel-xe
      resource: "gpu.intel.com/xe"
      count: 1
      # The llm-d-modelservice chart injects accelerator-specific env for
      # intel-xe (VLLM_WORKER_MULTIPROC_METHOD=spawn). The benchmark macro
      # already adds VLLM_WORKER_MULTIPROC_METHOD to the container env, so the
      # chart default would produce a duplicate env key (rejected by
      # server-side apply). Override the chart's intel-xe env list to empty.
      env:
        intel-xe: []

    # =========================================================================
    # IMAGE - vLLM XPU build (overrides the CUDA default in defaults.yaml)
    # =========================================================================
    images:
      vllm:
        repository: ghcr.io/llm-d/llm-d-xpu
        tag: v0.7.0
        pullPolicy: IfNotPresent

    # =========================================================================
    # MODEL
    # =========================================================================
    model:
      name: Qwen/Qwen3-0.6B
      shortName: qwen-qwen3-06b
      path: models/Qwen/Qwen3-0.6B
      huggingfaceId: Qwen/Qwen3-0.6B
      size: 50Gi
      maxModelLen: 16000
      blockSize: 16
      gpuMemoryUtilization: 0.9

    # =========================================================================
    # DEPLOYMENT METHOD - modelservice with standalone (epponly) router
    # =========================================================================
    modelservice:
      enabled: true

    standalone:
      enabled: false

    kustomize:
      enabled: false

    gateway:
      className: epponly

    # =========================================================================
    # ROUTING / GAIE
    # =========================================================================
    inferenceExtension:
      pluginsConfigFile: "default-plugins.yaml"

    # =========================================================================
    # PREFILL - disabled (single-pool prefix/load-aware routing only)
    # =========================================================================
    prefill:
      enabled: false
      replicas: 0

    # =========================================================================
    # DECODE
    # =========================================================================
    decode:
      replicas: 2

      # Suppress the default NVIDIA nodeAffinity. defaults.yaml sets
      # decode.acceleratorType.labelKey=nvidia.com/gpu.product (value
      # NVIDIA-H100-80GB-HBM3), which the Intel node does not carry, making
      # decode pods Unschedulable. Empty dict => template skips the
      # acceleratorTypes nodeAffinity block entirely (Intel GPUs are selected
      # purely via the gpu.intel.com/xe resource request).
      acceleratorType: {}

      # Pod-level securityContext. supplementalGroups is a Pod field (not a
      # container field), so it must live here rather than in
      # extraContainerConfig.securityContext. Group 107 = render, for /dev/dri.
      # NOTE: do NOT set runAsUser/runAsGroup at the pod level. The modelservice
      # chart injects a routing-proxy sidecar that declares runAsNonRoot: true;
      # a pod-level runAsUser:0 would propagate to it and the kubelet rejects it
      # ("runAsUser breaks non-root policy"). Root is needed only by the vllm
      # container, which sets it via extraContainerConfig.securityContext below.
      podSecurityContext:
        supplementalGroups:
          - 107

      vllm:
        # XPU vLLM serve command. Mirrors the upstream xpu/vllm overlay
        # (no libcuda export, no --gpu-memory-utilization). Binds to the
        # metrics port so the llm-d routing sidecar can bridge it.
        # --enforce-eager: the XPU torch.compile/inductor path crashes during
        # warmup ("level_zero backend failed with error: 20
        # UR_RESULT_ERROR_DEVICE_LOST"); eager mode is the stable path on XPU.
        customCommand: |
          vllm serve /model-cache/$MODEL_PATH \
          --host 0.0.0.0 \
          --served-model-name $MODEL_NAME \
          --port $VLLM_METRICS_PORT \
          --dtype float16 \
          --enforce-eager \
          --max-model-len $VLLM_MAX_MODEL_LEN \
          --disable-sliding-window \
          --disable-uvicorn-access-log \
          --disable-access-log-for-endpoints=/health,/metrics,/v1/models

      initContainers:
        - name: preprocess
          imageKey: benchmark
          imagePullPolicy: Always
          command: ["set_llmdbench_environment.py", "-e", "/shared-config/llmdbench_env.sh", "-i"]
          volumeMounts:
            - name: shared-config
              mountPath: /shared-config

      parallelism:
        tensor: 1
        data: 1
        dataLocal: 1
        workers: 1

      resources:
        limits:
          memory: 24Gi
          cpu: "8"
          gpu.intel.com/xe: 1
        requests:
          memory: 12Gi
          cpu: "4"
          gpu.intel.com/xe: 1

      extraEnvVars: []

      # Metrics port only (no NIXL side-channel - single-node, no KV
      # disaggregation). Container-level securityContext runs as root; the
      # render group (107) for /dev/dri access is set at the pod level via
      # decode.podSecurityContext (supplementalGroups is a Pod field).
      extraContainerConfig:
        ports:
          - containerPort: 8200
            name: metrics
            protocol: TCP
        securityContext:
          runAsGroup: 0
          runAsUser: 0
        imagePullPolicy: IfNotPresent

      additionalVolumeMounts: []
      additionalVolumes: []

    # =========================================================================
    # COMMON (vLLM volumes)
    # =========================================================================
    vllmCommon:
      volumes:
        - name: shared-config
          type: emptyDir
          emptyDir: {}
        - name: dshm
          type: emptyDir
          emptyDir:
            medium: Memory
            sizeLimit: 16Gi

      volumeMounts:
        - name: dshm
          mountPath: /dev/shm
        - name: shared-config
          mountPath: /shared-config

    # =========================================================================
    # STORAGE
    # kind's rancher.io/local-path provisioner only supports ReadWriteOnce
    # (NodePath rejects ReadWriteMany), so override the RWX defaults to RWO.
    # Single-node kind => 2 decode replicas share the RWO volume on one node.
    # =========================================================================
    storage:
      modelPvc:
        size: 50Gi
        accessModes:
          - ReadWriteOnce
      workloadPvc:
        accessModes:
          - ReadWriteOnce

    # =========================================================================
    # WORKLOAD / HARNESS
    # =========================================================================
    workDir: "~/data/optimized-baseline-xpu"

    harness:
      name: inference-perf
      experimentProfile: shared_prefix_synthetic.yaml