chore: move vllm-xpu-run to intel/gpu-ai-skills@6e7bce819e2e

skills.yaml

@@ -313,7 +313,7 @@
 - name: vllm-xpu-run
   maintainer: "xaleryb"
   external-repo: "https://github.com/intel/gpu-ai-skills"
-  external-commit: "0b4fafd09c5eb4cc5daf532d915ef5984a919775"
+  external-commit: "6e7bce819e2e44a4aac692b0a49253f39c0f6957"
   external-path: "plugins/intel-gpu-ai-skills/skills/vllm-xpu-run"
   external-license: "Apache-2.0"
   intel-products: "Intel GPU"

skills/vllm-xpu-run/.source.json

@@ -1,6 +1,6 @@
 {
   "repo": "https://github.com/intel/gpu-ai-skills",
   "path": "plugins/intel-gpu-ai-skills/skills/vllm-xpu-run",
-  "commit": "0b4fafd09c5eb4cc5daf532d915ef5984a919775",
+  "commit": "6e7bce819e2e44a4aac692b0a49253f39c0f6957",
   "license": "Apache-2.0"
 }

skills/vllm-xpu-run/SKILL.md

@@ -51,7 +51,7 @@ general vLLM, CUDA, or Transformers support.
 | `vllm/vllm-openai:latest` | `vllm/vllm-openai-xpu:latest` |
 | `--gpus all` | `--device /dev/dri` + `-v /dev/dri/by-path:/dev/dri/by-path:ro` + `--ipc=host` |
 | `--dtype auto` | `--dtype bfloat16` (explicit) |
-| CUDA graphs default | `--enforce-eager` |
+| CUDA graphs default | graphs also capture by default from vLLM v0.31.0 — emit nothing. Add `--enforce-eager` only for pre-v0.31.0 images. |
 | `--tensor-parallel-size N` | same; pin N XPUs in `ZE_AFFINITY_MASK` |
 
 ## Quickstart (single GPU)
@@ -83,7 +83,6 @@ docker run -d --name vllm-xpu \
     vllm/vllm-openai-xpu:latest \
     Qwen/Qwen2.5-1.5B-Instruct \
         --dtype bfloat16 \
-        --enforce-eager \
         --block-size=64 \
         --max-model-len 4096 \
         --gpu-memory-utilization 0.85
@@ -114,7 +113,7 @@ remote-box workflow), see `references/remote-deploy.md`.
 | Flag | Why |
 |---|---|
 | `--dtype bfloat16` | Battlemage runs bf16 better than fp16; `auto` may pick fp16 from the checkpoint config. Set for unquantized serving only — combining with `--quantization` produces conflicts. |
-| `--enforce-eager` | Conservative default. Graph capture / `torch.compile` on XPU is experimental. After a stable eager run, drop it and re-bench; keep only if TPOT/TTFT improve and content stays correct. |
+| `--enforce-eager` | **Do not emit this on vLLM v0.31.0 or newer.** There, XPU graphs capture by default and `VLLM_XPU_ENABLE_XPU_GRAPH` no longer exists, so the flag disables exactly what the version enables — measured cost roughly 2x decode (TPOT 64 ms vs 36 ms, 4x Arc Pro B70 at concurrency 8). It does not change TTFT, so eager is never the explanation for a slow first token. Emit it only for pre-v0.31.0 images, where graph capture was experimental, or to isolate a suspected graph-capture bug. Check the image first: `docker run --rm --entrypoint python3 <image> -c "import vllm; print(vllm.__version__)"`. |
 | `--block-size=64` | Validated default for the XPU paged-attention path on Battlemage. Bench higher (128, 256) once 64 is correct. |
 | `--gpu-memory-utilization 0.85` | Default 0.92 fails on workstations with active GUI sessions. Drop to 0.70 with browsers open; raise to 0.92 on headless servers. |
 | `-v /dev/dri/by-path:/dev/dri/by-path:ro` | Some oneCCL/device-discovery configurations scan the host's `/dev/dri/by-path` symlinks even for a single-GPU launch. Include this read-only mount to support those configurations; if it is omitted, affected images can abort at engine initialization with `opendir failed: could not open device directory`. |

skills/vllm-xpu-run/references/multi-gpu-and-tuning.md

@@ -39,7 +39,6 @@ docker run -d --name vllm-xpu \
     vllm/vllm-openai-xpu:latest \
     <model-id> \
         --dtype bfloat16 \
-        --enforce-eager \
         --block-size=64 \
         --disable-custom-all-reduce \
         --tensor-parallel-size "$TP"

skills/vllm-xpu-run/references/remote-deploy.md

@@ -38,7 +38,6 @@ ssh "$GPU_HOST" 'docker run -d --name vllm-xpu \
     vllm/vllm-openai-xpu:latest \
     <model-id> \
         --dtype bfloat16 \
-        --enforce-eager \
         --block-size=64 \
         --max-model-len 4096 \
         --gpu-memory-utilization 0.85'

skills/vllm-xpu-run/scripts/emit_launch.sh

@@ -20,6 +20,7 @@ image="${VLLM_XPU_IMAGE:-vllm/vllm-openai-xpu:latest}"
 port=8000
 container_name="vllm-xpu"
 trust_remote_code=0
+enforce_eager=0
 
 usage() {
     cat <<'EOF'
@@ -38,6 +39,9 @@ Options:
   --port N              First host port to publish (default: 8000)
   --container-name N    Base container name (default: vllm-xpu)
   --trust-remote-code   Add --trust-remote-code
+  --enforce-eager       Add --enforce-eager, disabling XPU graph capture.
+                        Only for images before vLLM v0.31.0, where graph
+                        capture was experimental. Omitted by default.
   -h, --help            Show this help
 EOF
 }
@@ -72,6 +76,7 @@ while [ "$#" -gt 0 ]; do
         --port)              port="${2:-}"; shift 2 ;;
         --container-name)    container_name="${2:-}"; shift 2 ;;
         --trust-remote-code) trust_remote_code=1; shift ;;
+        --enforce-eager)     enforce_eager=1; shift ;;
         -h|--help)           usage; exit 0 ;;
         *) die "unknown argument: $1" ;;
     esac
@@ -165,11 +170,21 @@ if [ "$required_xpus" -gt "$num_xpus" ]; then
     die "topology requires $required_xpus XPU(s), but --num-xpus is $num_xpus"
 fi
 
-serve_flags="--enforce-eager \\
-        --block-size=64 \\
+serve_flags="--block-size=64 \\
         --max-model-len $ctx \\
         --gpu-memory-utilization 0.85"
 
+# XPU graph capture is left on by default. From vLLM v0.31.0 graphs capture
+# automatically and VLLM_XPU_ENABLE_XPU_GRAPH no longer exists; --enforce-eager
+# turns off exactly what that version enables, costing roughly 2x decode
+# (measured TPOT 64 ms vs 36 ms, 4x Arc Pro B70 at concurrency 8). It does not
+# change TTFT, so eager is never the explanation for a slow first token. Pass
+# --enforce-eager only for pre-v0.31.0 images, where capture was experimental.
+if [ "$enforce_eager" -eq 1 ]; then
+    serve_flags="--enforce-eager \\
+        $serve_flags"
+fi
+
 case "$quant" in
     bf16|fp16)
         serve_flags="--dtype bfloat16 \\