Enable optimized-baseline benchmark on Intel XPU

config/scenarios/guides/optimized-baseline-xpu.yaml

@@ -0,0 +1,193 @@
+# 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)
+#     and vLLM image = ghcr.io/llm-d/llm-d-xpu -- both supplied by the shared
+#     Intel XPU values overlay (config/templates/values/overlays/xpu.yaml),
+#     wired in via the spec's values_file.overlays. They are NOT duplicated
+#     here so every XPU guide can share one accelerator/image layer.
+#   * 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"
+
+    # =========================================================================
+    # 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
+
+      # NOTE: node affinity for Intel GPUs (decode.acceleratorType ->
+      # intel.feature.node.kubernetes.io/gpu) is provided by the shared XPU
+      # overlay (config/templates/values/overlays/xpu.yaml), not here, so it
+      # can be reused by every XPU guide.
+
+      # 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

config/specification/guides/optimized-baseline-xpu.yaml.j2

@@ -0,0 +1,21 @@
+# Intel XPU variant of the optimized-baseline specification.
+# [REQUIRED]
+{% set base_dir = base_dir | default('../') -%}
+base_dir: {{ base_dir }}
+
+# [REQUIRED]
+values_file:
+  path: {{ base_dir }}/config/templates/values/defaults.yaml
+  # Shared Intel XPU machine layer (accelerator + vLLM XPU image), deep-merged
+  # on top of defaults before the scenario. Keeps those bits out of the guide
+  # scenario so every XPU guide reuses one overlay instead of duplicating them.
+  overlays:
+    - {{ base_dir }}/config/templates/values/overlays/xpu.yaml
+
+# [REQUIRED]
+template_dir:
+  path: {{ base_dir }}/config/templates/jinja
+
+# [OPTIONAL]
+scenario_file:
+  path: {{ base_dir }}/config/scenarios/guides/optimized-baseline-xpu.yaml

config/templates/jinja/13_ms-values.yaml.j2

@@ -339,7 +339,7 @@ decode:
 
 {% if decode.podSecurityContext is defined and decode.podSecurityContext %}
   podSecurityContext:
-{{ decode.podSecurityContext | toyaml | indent(4) }}
+{{ decode.podSecurityContext | toyaml | indent(4, first=True) }}
 {% endif %}
 
 {# LWS / Multinode specific options #}
@@ -759,7 +759,7 @@ prefill:
 
 {% if prefill.podSecurityContext is defined and prefill.podSecurityContext %}
   podSecurityContext:
-{{ prefill.podSecurityContext | toyaml | indent(4) }}
+{{ prefill.podSecurityContext | toyaml | indent(4, first=True) }}
 {% endif %}
 
 {# LWS / Multinode specific options #}

config/templates/values/overlays/xpu.yaml

@@ -0,0 +1,64 @@
+# ============================================================================
+# Intel XPU values overlay
+# ============================================================================
+# Reusable, machine-related overrides for running any guide on Intel GPUs
+# (xe driver, classic device-plugin `gpu.intel.com/xe`).
+#
+# This file is deep-merged ON TOP OF config/templates/values/defaults.yaml
+# BEFORE the guide scenario is applied (defaults -> overlay -> scenario).
+# It therefore only carries the accelerator/image bits that the guide
+# scenarios do NOT hardcode, so a single overlay can be shared by every XPU
+# guide instead of duplicating them in each scenario. Anything a guide
+# scenario sets explicitly (customCommand, resources, model, ...) still wins
+# over this overlay.
+#
+# Wire it into a spec via:
+#   values_file:
+#     path: {{ base_dir }}/config/templates/values/defaults.yaml
+#     overlays:
+#       - {{ base_dir }}/config/templates/values/overlays/xpu.yaml
+# ============================================================================
+
+# ----------------------------------------------------------------------------
+# 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: []
+
+# ----------------------------------------------------------------------------
+# NODE AFFINITY - select Intel GPU nodes
+# ----------------------------------------------------------------------------
+# defaults.yaml sets {decode,prefill}.acceleratorType to the NVIDIA GPU-product
+# label (nvidia.com/gpu.product = NVIDIA-H100-80GB-HBM3). The modelservice
+# template renders that into a REQUIRED nodeAffinity whenever `labelKey` is
+# defined, so on an Intel node (which lacks that label) the decode/prefill pods
+# stay Unschedulable. An empty dict in a scenario cannot clear it (deep-merge
+# keeps the populated default), so we override it here with the generic Intel
+# GPU node label emitted by NFD.
+decode:
+  acceleratorType:
+    labelKey: intel.feature.node.kubernetes.io/gpu
+    labelValue: "true"
+prefill:
+  acceleratorType:
+    labelKey: intel.feature.node.kubernetes.io/gpu
+    labelValue: "true"
+
+# ----------------------------------------------------------------------------
+# 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
+    sourceRepo: https://github.com/llm-d/llm-d

llmdbenchmark/cli.py

@@ -112,6 +112,7 @@ def dispatch_cli(args: argparse.Namespace, logger: logging.Logger) -> None:
             defaults_file=specification_as_dict["values_file"]["path"],
             scenarios_file=specification_as_dict["scenario_file"]["path"],
             output_dir=config.plan_dir,
+            values_overlays=specification_as_dict["values_file"].get("overlays", []),
             version_resolver=version_resolver,
             cluster_resource_resolver=cluster_resource_resolver,
             cli_namespace=getattr(args, "namespace", None),
@@ -1192,6 +1193,7 @@ def _render_plans_for_experiment(args, logger, setup_overrides=None):
         defaults_file=specification_as_dict["values_file"]["path"],
         scenarios_file=specification_as_dict["scenario_file"]["path"],
         output_dir=config.plan_dir,
+        values_overlays=specification_as_dict["values_file"].get("overlays", []),
         version_resolver=version_resolver,
         cluster_resource_resolver=cluster_resource_resolver,
         cli_namespace=getattr(args, "namespace", None),

llmdbenchmark/parser/render_plans.py

@@ -41,6 +41,7 @@ def __init__(
         defaults_file: Path,
         scenarios_file: Path,
         output_dir: Path,
+        values_overlays: list[Path] | None = None,
         logger=None,
         version_resolver=None,
         cluster_resource_resolver=None,
@@ -57,6 +58,11 @@ def __init__(
         self.defaults_file = Path(defaults_file)
         self.scenarios_file = Path(scenarios_file)
         self.output_dir = Path(output_dir)
+        # Optional values overlays deep-merged on top of `defaults_file`
+        # before the scenario is applied. Used to layer hardware-specific
+        # base values (e.g. an Intel XPU overlay) without duplicating the
+        # entire defaults file. Each later overlay wins over earlier ones.
+        self.values_overlays: list[Path] = [Path(p) for p in (values_overlays or [])]
         self.version_resolver = version_resolver
         self.cluster_resource_resolver = cluster_resource_resolver
         self.cli_namespace = cli_namespace
@@ -1314,6 +1320,8 @@ def eval(self) -> RenderResult:
 
         try:
             defaults = self._load_yaml(self.defaults_file)
+            for overlay in self.values_overlays:
+                defaults = self.deep_merge(defaults, self._load_yaml(overlay))
         except Exception as e:
             msg = f"Failed to load defaults file: {e}"
             self.logger.log_error(msg)