[CI/Build] Match perf warmups to benchmark concurrency

Use enough warmup requests to cover a full concurrent batch while preserving the existing two-request minimum.

Signed-off-by: Sy03 <24841857+Sy0307@users.noreply.github.com>

.buildkite/amd/scripts/bootstrap-amd-omni.sh

@@ -6,6 +6,8 @@
 
 set -euo pipefail
 
+source .buildkite/common/scripts/resolve_skip_ci.sh
+
 if [[ -z "${RUN_ALL:-}" ]]; then
     RUN_ALL=0
 fi
@@ -22,10 +24,6 @@ if [[ -z "${AMD_MIRROR_HW:-}" ]]; then
     AMD_MIRROR_HW="amdproduction"
 fi
 
-if [[ -z "${DOCS_ONLY_DISABLE:-}" ]]; then
-    DOCS_ONLY_DISABLE=0
-fi
-
 fail_fast() {
     DISABLE_LABEL="ci-no-fail-fast"
     # If BUILDKITE_PULL_REQUEST != "false", then we check the PR labels using curl and jq
@@ -72,14 +70,14 @@ upload_pipeline() {
     fi
 
     # Use local template file for vllm-omni
-    cp .buildkite/test-template-amd-omni.j2 .buildkite/test-template.j2
+    cp .buildkite/amd/test-template-amd-omni.j2 .buildkite/amd/test-template.j2
 
 
     # (WIP) Use pipeline generator instead of jinja template
-    if [ -e ".buildkite/pipeline_generator/pipeline_generator.py" ]; then
+    if [ -e ".buildkite/amd/pipeline_generator/pipeline_generator.py" ]; then
         python -m pip install click pydantic
-        python .buildkite/pipeline_generator/pipeline_generator.py --run_all=$RUN_ALL --list_file_diff="$LIST_FILE_DIFF" --nightly="$NIGHTLY" --mirror_hw="$AMD_MIRROR_HW"
-        buildkite-agent pipeline upload .buildkite/pipeline.yaml
+        python .buildkite/amd/pipeline_generator/pipeline_generator.py --run_all=$RUN_ALL --list_file_diff="$LIST_FILE_DIFF" --nightly="$NIGHTLY" --mirror_hw="$AMD_MIRROR_HW"
+        buildkite-agent pipeline upload .buildkite/amd/pipeline.yaml
         exit 0
     fi
     echo "List file diff: $LIST_FILE_DIFF"
@@ -89,13 +87,29 @@ upload_pipeline() {
 
     FAIL_FAST=$(fail_fast)
 
-    cd .buildkite
+    cd .buildkite/amd
 
-    # Select test definition file: merge suite for main, ready suite for PRs
-    if [[ $BUILDKITE_BRANCH == "main" ]]; then
+    # Select test definition file: merge suite for main, ready suite for PRs.
+    # For debugging, DEBUG_TEST_YAML can override the selection — accepts
+    # "merge" or "ready" (case-insensitive).
+    if [[ -n "${DEBUG_TEST_YAML:-}" ]]; then
+        case "${DEBUG_TEST_YAML,,}" in
+            merge)
+                TEST_YAML="test-amd-merge.yml"
+                ;;
+            ready)
+                TEST_YAML="test-amd-ready.yml"
+                ;;
+            *)
+                echo "ERROR: DEBUG_TEST_YAML must be 'merge' or 'ready', got '$DEBUG_TEST_YAML'" >&2
+                exit 1
+                ;;
+        esac
+        echo "DEBUG_TEST_YAML override: using $TEST_YAML"
+    elif [[ $BUILDKITE_BRANCH == "main" ]]; then
         TEST_YAML="test-amd-merge.yml"
     else
-        TEST_YAML="test-amd-ready.yaml"
+        TEST_YAML="test-amd-ready.yml"
     fi
 
     (
@@ -136,40 +150,13 @@ if [[ $BUILDKITE_BRANCH == "main" ]]; then
     file_diff=$(get_diff_main)
 fi
 
-# ----------------------------------------------------------------------
-# Early exit start: skip pipeline if conditions are met
-# ----------------------------------------------------------------------
-
-# skip pipeline if all changed files are under docs/
-if [[ "${DOCS_ONLY_DISABLE}" != "1" ]]; then
-  if [[ -n "${file_diff:-}" ]]; then
-    docs_only=1
-    # Robust iteration over newline-separated file_diff
-    while IFS= read -r f; do
-      [[ -z "$f" ]] && continue
-      # **Policy:** only skip if *every* path starts with docs/
-      if [[ "$f" != docs/* ]]; then
-        docs_only=0
-        break
-      fi
-    done < <(printf '%s\n' "$file_diff" | tr ' ' '\n' | tr -d '\r')
-
-    if [[ "$docs_only" -eq 1 ]]; then
-      buildkite-agent annotate ":memo: CI skipped — docs/** only changes detected
-
-\`\`\`
-${file_diff}
-\`\`\`" --style "info" || true
-      echo "[docs-only] All changes are under docs/. Exiting before pipeline upload."
-      exit 0
-    fi
-  fi
+# Early exit: unified skip-ci (docs / skip marks) and CI-yaml-only level targeting.
+if [[ $BUILDKITE_BRANCH == "main" ]]; then
+    gate_bootstrap_ci amd l3
+else
+    gate_bootstrap_ci amd l2
 fi
 
-# ----------------------------------------------------------------------
-# Early exit end
-# ----------------------------------------------------------------------
-
 patterns=(
     "docker/Dockerfile"
     "CMakeLists.txt"

.buildkite/amd/scripts/run-amd-test.sh

@@ -0,0 +1,103 @@
+#!/bin/bash
+
+# Run vLLM-Omni ROCm tests directly in the MI300 Kubernetes pod. The pod's
+# container image is selected by test-template-amd-omni.j2; MI300 has no DinD.
+set -euo pipefail
+
+: "${PYTHONFAULTHANDLER:=1}"
+: "${HF_HOME:=/home/buildkite-agent/huggingface}"
+: "${HF_HUB_DOWNLOAD_TIMEOUT:=300}"
+: "${HF_HUB_ETAG_TIMEOUT:=60}"
+: "${MIOPEN_DEBUG_CONV_DIRECT:=0}"
+: "${MIOPEN_DEBUG_CONV_GEMM:=0}"
+: "${VLLM_ROCM_USE_AITER:=0}"
+export PYTHONFAULTHANDLER HF_HOME HF_HUB_DOWNLOAD_TIMEOUT HF_HUB_ETAG_TIMEOUT
+export MIOPEN_DEBUG_CONV_DIRECT MIOPEN_DEBUG_CONV_GEMM VLLM_ROCM_USE_AITER
+export PYTORCH_ROCM_ARCH=""
+export PYTHONPATH="${PYTHONPATH:-..}"
+
+if [[ "${VLLM_CI_DOCKER_DISABLED:-0}" != "1" ]]; then
+    echo "Error: MI300 CI must run natively with Docker disabled." >&2
+    exit 1
+fi
+
+if [[ -n "${TEST_COMMANDS:-}" ]]; then
+    commands="${TEST_COMMANDS}"
+else
+    commands="$*"
+fi
+if [[ -z "${commands}" ]]; then
+    echo "Error: No test commands provided." >&2
+    exit 1
+fi
+
+echo "--- Native in-pod ROCm CI"
+
+job_id="${BUILDKITE_JOB_ID:-${BUILDKITE_PARALLEL_JOB:-local}}"
+job_id="${job_id//[^A-Za-z0-9_.-]/_}"
+native_cache_root="/tmp/vllm-omni-native-${job_id}"
+TMPDIR="${native_cache_root}/tmp"
+TORCHINDUCTOR_CACHE_DIR="${native_cache_root}/torchinductor"
+TRITON_CACHE_DIR="${native_cache_root}/triton"
+VLLM_CACHE_ROOT="${native_cache_root}/vllm"
+XDG_CACHE_HOME="${native_cache_root}/xdg"
+HF_DATASETS_CACHE="${native_cache_root}/huggingface/datasets"
+export TMPDIR TORCHINDUCTOR_CACHE_DIR TRITON_CACHE_DIR VLLM_CACHE_ROOT
+export XDG_CACHE_HOME HF_DATASETS_CACHE
+mkdir -p "${TMPDIR}" "${TORCHINDUCTOR_CACHE_DIR}" "${TRITON_CACHE_DIR}" \
+    "${VLLM_CACHE_ROOT}" "${XDG_CACHE_HOME}" "${HF_HOME}" \
+    "${HF_DATASETS_CACHE}"
+
+if [[ "${VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE:-0}" == "1" ]]; then
+    if ! command -v findmnt >/dev/null 2>&1; then
+        echo "Error: findmnt is required to verify the Hugging Face cache mount." >&2
+        exit 1
+    fi
+    hf_mount=$(findmnt -n -T "${HF_HOME}" -o TARGET 2>/dev/null || true)
+    if [[ -z "${hf_mount}" || "${hf_mount}" == "/" ]]; then
+        echo "Error: MI300 CI requires a persistent volume at or above ${HF_HOME}." >&2
+        exit 1
+    fi
+fi
+
+rocminfo
+
+expected_gpus="${VLLM_CI_EXPECTED_GPU_COUNT:-1}"
+python3 - <<'PY'
+import os
+
+import torch
+
+expected = int(os.environ.get("VLLM_CI_EXPECTED_GPU_COUNT", "1"))
+assert torch.version.hip, "PyTorch is not a ROCm build"
+assert torch.cuda.is_available(), "ROCm GPU is not available to PyTorch"
+actual = torch.cuda.device_count()
+assert actual == expected, f"Expected {expected} ROCm GPU(s), found {actual}"
+PY
+
+echo "Commands:${commands}"
+if /bin/bash -o pipefail -c '
+set -E
+test_status=0
+trap '\''
+    command_status=$?
+    # Keep running subsequent commands, but retain a failure for the final exit.
+    # Prefer a test failure over pytest "no tests collected" when both occur.
+    if (( test_status == 0 || (test_status == 5 && command_status != 5) )); then
+        test_status=${command_status}
+    fi
+'\'' ERR
+eval "$1"
+exit "${test_status}"
+' _ "${commands}"; then
+    exit 0
+else
+    exit_code=$?
+fi
+
+if [[ ${exit_code} -eq 5 && "${VLLM_CI_ALLOW_NO_TESTS:-0}" == "1" ]]; then
+    echo "Pytest collected no tests; VLLM_CI_ALLOW_NO_TESTS=1, treating exit code 5 as success."
+    exit 0
+fi
+
+exit "${exit_code}"

.buildkite/amd/test-amd-merge.yml

@@ -0,0 +1,244 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+
+- group: ":card_index_dividers: Simple Test"
+  steps:
+    - label: "Simple · Model Executor Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-model-executor-$$BUILDKITE_PARALLEL_JOB.xml"
+
+    - label: "Simple · Diffusion Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/diffusion -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-diffusion-$$BUILDKITE_PARALLEL_JOB.xml"
+
+    - label: "Simple · Engine&Entrypoints Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+    - label: "Simple · Other Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+- group: ":card_index_dividers: Diffusion Test"
+  steps:
+    - label: "Diffusion · LoRA Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      artifact_paths:
+        - lora-server.log
+      commands:
+        - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model" 2>&1 | tee lora-server.log
+
+    - label: "Diffusion Model CPU offloading Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - |
+          bash -c '
+            set +e
+            pytest -s -v tests/diffusion/offloader/test_diffusion_cpu_offload.py
+            EXIT1=\$?
+            pytest -s -v tests/diffusion/offloader/test_diffusion_layerwise_offload.py
+            EXIT2=\$?
+            exit \$((EXIT1 | EXIT2))
+          '
+
+    - label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
+      agent_pool: mi300_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
+
+    # merge-only tests
+    - label: "Diffusion Tensor Parallelism Test"
+      agent_pool: mi300_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - pytest -s -v tests/diffusion/distributed/test_tensor_parallel.py
+
+    - label: "Diffusion Model Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 30m pytest -s -v tests/e2e/offline_inference/test_z_image.py
+
+    - label: "Diffusion GPU Worker Test"
+      agent_pool: mi300_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
+
+    - label: "Diffusion · Wan22 Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 40m bash -c "
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+          "
+
+## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
+# - label: "Audio Generation Model Test"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
+
+- label: "Engine Test"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - timeout 20m pytest -s -v tests/engine/test_async_omni_engine_abort.py
+
+# This is from test-nightly.yml.
+# This model seems to be important, so I am trying to run it here.
+# Since this is from test-nightly.yml,
+# I keep this non-blocking.
+- group: ":card_index_dividers: E2E Test"
+  steps:
+    - label: "Omni Model Test Qwen2-5-Omni"
+      agent_pool: mi300_4
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      commands:
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+
+    - label: "Omni Model Test Qwen3-Omni"
+      agent_pool: mi300_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+
+    - label: "Qwen3-TTS CustomVoice E2E Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model" --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+          '
+
+    - label: "Qwen3-TTS Base E2E Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      timeout_in_minutes: 50
+      commands:
+        - |
+          timeout 40m bash -c '
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_WORKER_MULTIPROC_METHOD=spawn
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+            pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+          '
+
+# TODO: Bagel test on ROCm is very unstable. @tjtanaa
+# Need to debug before reneable numerical changes across large PRs
+# # split Bagel Model Test with H100 (Real Weights) into three tests
+# - label: "Bagel Text2Img Model Test (1/3)"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory" -k "rocm"
+
+# - label: "Bagel Img2Img Model Test (2/3)"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
+
+# - label: "Bagel Online Serving Test (3/3)"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel.py -m "advanced_model" --run-level "advanced_model" -k "rocm"

.buildkite/amd/test-amd-ready.yml

@@ -0,0 +1,236 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+
+- group: ":card_index_dividers: Simple Test"
+  steps:
+    - label: "Simple · Model Executor Test · Shard %N/%t"
+      agent_pool: mi300_1
+      parallelism: 2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/model_executor -m 'core_model and cpu' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml:coverage-model-executor-$$BUILDKITE_PARALLEL_JOB.xml"
+
+    - label: "Simple · Diffusion Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/diffusion -m 'core_model and cpu'  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+    - label: "Simple · Engine&Entrypoints Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        - "pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+    - label: "Simple · Other Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_ROCM_USE_AITER=0
+        # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
+        # that is not supported on CPU
+        - "pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine  --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+
+- label: "Custom Pipeline Test"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
+
+
+- label: "Voxtral TTS CUDA Unit Test"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - timeout 10m pytest -s -v tests/model_executor/models/voxtral_tts/test_cuda_graph_acoustic_transformer.py
+
+- group: ":card_index_dividers: Diffusion Test"
+  steps:
+    - label: "Diffusion · Batch Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
+
+    - label: "Diffusion · Offloader Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
+
+    - label: "Diffusion · Cache Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export GPU_ARCHS=gfx942
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - pytest -sv tests/diffusion/cache -m 'core_model and cuda' --run-level "core_model"
+
+    - label: "Diffusion Sequence Parallelism Test"
+      agent_pool: mi300_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - timeout 20m pytest -s -v tests/diffusion/distributed/test_sequence_parallel.py -m core_model
+
+    - label: "Diffusion GPU Worker Test"
+      agent_pool: mi300_2
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
+
+    - label: "Diffusion · Model Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
+
+    - label: "Diffusion · Wan22 Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 40m bash -c "
+            export VLLM_TEST_CLEAN_GPU_MEMORY=1
+            export VLLM_IMAGE_FETCH_TIMEOUT=60
+            pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
+          "
+
+## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
+# - label: "Audio Generation Model Test"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
+
+- label: "Engine Test"
+  agent_pool: mi300_1
+  depends_on: amd-build
+  mirror_hardwares: [amdproduction]
+  grade: Blocking
+  commands:
+    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    - |
+      timeout 15m bash -c '
+              pytest -s -v tests/engine/test_async_omni_engine_abort.py
+      '
+
+# - label: "Omni Model Test Qwen3-Omni"
+#   agent_pool: mi300_2
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
+#     - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
+
+- group: ":card_index_dividers: E2E Test"
+  steps:
+    - label: "Qwen3-TTS E2E Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - export VLLM_LOGGING_LEVEL=DEBUG
+        - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+        - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+        - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
+
+    - label: "CosyVoice3-TTS E2E Test"
+      agent_pool: mi300_1
+      depends_on: amd-build
+      mirror_hardwares: [amdproduction]
+      grade: Blocking
+      commands:
+        - |
+          timeout 20m bash -c '
+            pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py -m "slow" --run-level "core_model"
+          '
+
+# TODO: Bagel test on ROCm is very unstable. @tjtanaa
+# Need to debug before reneable numerical changes across large PRs
+# - label: "Bagel Text2Img Model Test"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"
+
+# - label: "Bagel Img2Img Model Test"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"
+
+# - label: "Bagel Online Serving Test"
+#   agent_pool: mi300_1
+#   depends_on: amd-build
+#   mirror_hardwares: [amdproduction]
+#   grade: Blocking
+#   commands:
+#     - export GPU_ARCHS=gfx942
+#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
+#     - export VLLM_LOGGING_LEVEL=DEBUG
+#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
+#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel.py -m "core_model" --run-level "core_model" -k "rocm"

.buildkite/amd/test-template-amd-omni.j2

@@ -0,0 +1,237 @@
+{# vllm-omni customized version
+   Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/test-template-amd.j2
+   Last synced: 2026-08-04
+   Modifications: Use the shared MI300 queues and Omni command-passing contract
+#}
+{% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
+{% set default_working_dir = "/app/vllm-omni" %}
+
+{# MI300 runs tests natively in Kubernetes; Docker-in-Docker is unavailable. #}
+{% macro amd_native_k8s_plugin(indent, step) -%}
+{{ indent }}plugins:
+{{ indent }}  - kubernetes:
+{{ indent }}      podSpecPatch:
+{{ indent }}        automountServiceAccountToken: false
+{{ indent }}        securityContext:
+{{ indent }}          seccompProfile:
+{{ indent }}            type: RuntimeDefault
+{{ indent }}        imagePullSecrets:
+{{ indent }}          - name: docker-config
+{{ indent }}        containers:
+{{ indent }}          - name: container-0
+{{ indent }}            image: {{ docker_image_amd }}
+{{ indent }}            imagePullPolicy: Always
+{{ indent }}            securityContext:
+{{ indent }}              allowPrivilegeEscalation: false
+{{ indent }}              capabilities:
+{{ indent }}                add:
+{{ indent }}                  - IPC_LOCK
+{{ indent }}            resources:
+{{ indent }}              limits:
+{{ indent }}                amd.com/gpu: "{{ step.agent_pool[-1:] }}"
+{{ indent }}              requests:
+{{ indent }}                amd.com/gpu: "{{ step.agent_pool[-1:] }}"
+{{ indent }}            volumeMounts:
+{{ indent }}              - name: devshm
+{{ indent }}                mountPath: /dev/shm
+{{ indent }}            env:
+{{ indent }}              - name: AMD_CI_RUNTIME
+{{ indent }}                value: native
+{{ indent }}              - name: NATIVE_CI
+{{ indent }}                value: "true"
+{{ indent }}              - name: VLLM_CI_DOCKER_DISABLED
+{{ indent }}                value: "1"
+{{ indent }}              - name: VLLM_CI_EXPECTED_GPU_COUNT
+{{ indent }}                value: "{{ step.agent_pool[-1:] }}"
+{{ indent }}              - name: HF_HOME
+{{ indent }}                value: /home/buildkite-agent/huggingface
+{{ indent }}              - name: MIOPEN_DEBUG_CONV_DIRECT
+{{ indent }}                value: "0"
+{{ indent }}              - name: MIOPEN_DEBUG_CONV_GEMM
+{{ indent }}                value: "0"
+{{ indent }}              - name: VLLM_ROCM_USE_AITER
+{{ indent }}                value: "0"
+{{ indent }}              - name: PYTORCH_ROCM_ARCH
+{{ indent }}                value: ""
+{{ indent }}        volumes:
+{{ indent }}          - name: devshm
+{{ indent }}            emptyDir:
+{{ indent }}              medium: Memory
+{{ indent }}              sizeLimit: 16Gi
+{%- endmacro %}
+
+{% if env %}
+env:
+{% for name in env %}
+  {{ name }}: "{{ env[name] }}"
+{% endfor %}
+{% endif %}
+steps:
+  - group: "AMD Tests"
+    depends_on: ~
+    steps:
+      - label: "AMD: :docker: build image"
+        depends_on: ~
+        soft_fail: false
+        commands:
+          - "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --target test --build-arg ARG_PYTORCH_ROCM_ARCH=gfx942 --progress plain ."
+          - "docker push {{ docker_image_amd }}"
+        key: "amd-build"
+        env:
+          DOCKER_BUILDKIT: "1"
+        retry:
+          automatic:
+            - exit_status: -1  # Agent was lost
+              limit: 2
+            - exit_status: -10  # Agent was lost
+              limit: 2
+            - exit_status: 128  # Git connectivity issues
+              limit: 2
+            - exit_status: 1  # Machine occasionally fail
+              limit: 1
+        agents:
+          queue: amd-cpu
+
+{% for step_group in steps %}
+{% if step_group.group %}
+  - group: "{{ step_group.group }}"
+{% if step_group.depends_on %}
+    depends_on: {{ step_group.depends_on }}
+{% endif %}
+    steps:
+{% for step in step_group.steps %}
+{% if step.mirror_hardwares and mirror_hw in step.mirror_hardwares %}
+      - label: "{{ step.agent_pool }}: {{ step.label }}"
+        depends_on: amd-build
+        agents:
+          {% if step.agent_pool %}
+          queue: amd_{{ step.agent_pool }}
+          {% else %}
+          queue: amd_mi300_1
+          {% endif %}
+{{ amd_native_k8s_plugin("        ", step) }}
+{% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
+{% set indented_cmd = cmd_body | replace("\n", "\n            ") %}
+        command: bash .buildkite/amd/scripts/run-amd-test.sh
+        env:
+          AMD_CI_RUNTIME: "native"
+          NATIVE_CI: "true"
+          VLLM_CI_DOCKER_DISABLED: "1"
+          VLLM_CI_EXPECTED_GPU_COUNT: "{{ step.agent_pool[-1:] }}"
+          VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE: "1"
+          HF_HOME: "/home/buildkite-agent/huggingface"
+          HF_HUB_DOWNLOAD_TIMEOUT: "300"
+          HF_HUB_ETAG_TIMEOUT: "60"
+          MIOPEN_DEBUG_CONV_DIRECT: "0"
+          MIOPEN_DEBUG_CONV_GEMM: "0"
+          VLLM_ROCM_USE_AITER: "0"
+          PYTORCH_ROCM_ARCH: ""
+{% if step.env %}
+{% for name in step.env %}
+          {{ name }}: "{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+          TEST_COMMANDS: |-
+            (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
+{% if env %}
+{% for name in env %}
+            export {{ name }}="{{ env[name] }}"
+{% endfor %}
+{% endif %}
+{% if step.env %}
+{% for name in step.env %}
+            export {{ name }}="{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+{% if "mi250" in step.agent_pool %}
+            python3 -m pip uninstall -y amd-aiter
+{% endif %}
+            {{ indented_cmd | safe }}
+        priority: 100
+{% if step.parallelism %}
+        parallelism: {{ step.parallelism }}
+{% endif %}
+{% if step.artifact_paths %}
+        artifact_paths:
+{% for path in step.artifact_paths %}
+          - "{{ path }}"
+{% endfor %}
+{% endif %}
+        {% if step.timeout_in_minutes %}
+        timeout_in_minutes: {{ step.timeout_in_minutes }}
+        {% endif %}
+        {% if step.grade and step.grade == "Blocking" %}
+        soft_fail: false
+        {% else %}
+        soft_fail: true
+        {% endif%}
+{% endif %}
+{% endfor %}
+{% elif step_group.mirror_hardwares and mirror_hw in step_group.mirror_hardwares %}
+{% set step = step_group %}
+  - label: "{{ step.agent_pool }}: {{ step.label }}"
+    depends_on: amd-build
+    agents:
+      {% if step.agent_pool %}
+      queue: amd_{{ step.agent_pool }}
+      {% else %}
+      queue: amd_mi300_1
+      {% endif %}
+{{ amd_native_k8s_plugin("    ", step) }}
+{% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
+{% set indented_cmd = cmd_body | replace("\n", "\n        ") %}
+    command: bash .buildkite/amd/scripts/run-amd-test.sh
+    env:
+      AMD_CI_RUNTIME: "native"
+      NATIVE_CI: "true"
+      VLLM_CI_DOCKER_DISABLED: "1"
+      VLLM_CI_EXPECTED_GPU_COUNT: "{{ step.agent_pool[-1:] }}"
+      VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE: "1"
+      HF_HOME: "/home/buildkite-agent/huggingface"
+      HF_HUB_DOWNLOAD_TIMEOUT: "300"
+      HF_HUB_ETAG_TIMEOUT: "60"
+      MIOPEN_DEBUG_CONV_DIRECT: "0"
+      MIOPEN_DEBUG_CONV_GEMM: "0"
+      VLLM_ROCM_USE_AITER: "0"
+      PYTORCH_ROCM_ARCH: ""
+{% if step.env %}
+{% for name in step.env %}
+      {{ name }}: "{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+      TEST_COMMANDS: |-
+        (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
+{% if env %}
+{% for name in env %}
+        export {{ name }}="{{ env[name] }}"
+{% endfor %}
+{% endif %}
+{% if step.env %}
+{% for name in step.env %}
+        export {{ name }}="{{ step.env[name] }}"
+{% endfor %}
+{% endif %}
+{% if "mi250" in step.agent_pool %}
+        python3 -m pip uninstall -y amd-aiter
+{% endif %}
+        {{ indented_cmd | safe }}
+    priority: 100
+{% if step.parallelism %}
+    parallelism: {{ step.parallelism }}
+{% endif %}
+{% if step.artifact_paths %}
+    artifact_paths:
+{% for path in step.artifact_paths %}
+      - "{{ path }}"
+{% endfor %}
+{% endif %}
+    {% if step.timeout_in_minutes %}
+    timeout_in_minutes: {{ step.timeout_in_minutes }}
+    {% endif %}
+    {% if step.grade and step.grade == "Blocking" %}
+    soft_fail: false
+    {% else %}
+    soft_fail: true
+    {% endif%}
+{% endif %}
+{% endfor %}

.buildkite/bootstrap-intel-omni.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-# vllm-omni Intel bootstrap
-# Uses static pipeline-intel.yaml for Intel XPU tests
-# Based on: bootstrap-amd-omni.sh
-
-set -euo pipefail
-
-if [[ -z "${DOCS_ONLY_DISABLE:-}" ]]; then
-    DOCS_ONLY_DISABLE=0
-fi
-
-upload_pipeline() {
-    echo "--- 🛠 Preparing Intel pipeline"
-    buildkite-agent pipeline upload .buildkite/pipeline-intel.yaml
-}
-
-get_diff() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR $(git merge-base origin/main HEAD))
-}
-
-get_diff_main() {
-    $(git add .)
-    echo $(git diff --name-only --diff-filter=ACMDR HEAD~1)
-}
-
-file_diff=$(get_diff)
-if [[ $BUILDKITE_BRANCH == "main" ]]; then
-    file_diff=$(get_diff_main)
-fi
-
-# ----------------------------------------------------------------------
-# Early exit: skip pipeline if all changed files are under docs/
-# ----------------------------------------------------------------------
-if [[ "${DOCS_ONLY_DISABLE}" != "1" ]] && [[ -n "${file_diff:-}" ]]; then
-    docs_only=1
-    while IFS= read -r f; do
-        [[ -z "$f" ]] && continue
-        if [[ "$f" != docs/* ]]; then
-            docs_only=0
-            break
-        fi
-    done < <(printf '%s\n' "$file_diff" | tr ' ' '\n' | tr -d '\r')
-
-    if [[ "$docs_only" -eq 1 ]]; then
-        buildkite-agent annotate ":memo: CI skipped — docs only" --style "info"
-        exit 0
-    fi
-fi
-
-upload_pipeline

.buildkite/common/ci_mirror_hardwares.yml

@@ -0,0 +1,246 @@
+# Uploader-only hardware presets for CUDA / NPU test YAML.
+# Referenced as ``mirror_hardwares: l4_1`` on a step; ``upload_pipeline.py`` expands to
+# ``agents`` (+ optional top-level ``image`` for NPU) + ``plugins``, then strips
+# ``mirror_hardwares`` before Buildkite upload.
+#
+# CUDA naming: {chip}_{count}
+#   l4_1, l4_4, h100_1 … h100_4
+# NPU naming: {queue}_{resource_class}
+#   a2b3_npu_1  — ascend-a2b3, resource_class npu-1 (A2/B3 CI image)
+#   a2b3_npu_4  — ascend-a2b3, resource_class npu-4
+#   a3_npu_2    — ascend-a3, resource_class npu-2
+#
+# The ``l4_*`` presets run under the ``docker`` plugin, which does not put
+# ``buildkite-agent`` on PATH inside the container the way the ``kubernetes``
+# presets do, so they set ``mount-buildkite-agent: true``; without it any step
+# calling ``buildkite-agent`` (artifact upload, annotate, meta-data) fails there.
+
+x-npu-k8s-plugins: &npu_k8s_plugins
+  - kubernetes:
+      podSpecPatch:
+        imagePullSecrets:
+          - name: swr-secret
+
+
+mirror_hardwares:
+  l4_1:
+    agents:
+      queue: gpu_1_queue
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          mount-buildkite-agent: true
+          shm-size: "8gb"
+          environment:
+            - HF_HOME=/fsx/hf_cache
+            - HF_TOKEN
+          volumes:
+            - /fsx/hf_cache:/fsx/hf_cache
+
+  l4_4:
+    agents:
+      queue: gpu_4_queue
+    plugins:
+      - docker#v5.2.0:
+          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+          always-pull: true
+          propagate-environment: true
+          mount-buildkite-agent: true
+          shm-size: "8gb"
+          environment:
+            - HF_HOME=/fsx/hf_cache
+            - HF_TOKEN
+          volumes:
+            - /fsx/hf_cache:/fsx/hf_cache
+
+  h100_1:
+    agents:
+      queue: mithril-h100-pool
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 1
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
+  h100_2:
+    agents:
+      queue: mithril-h100-pool
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 2
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
+  h100_3:
+    agents:
+      queue: mithril-h100-pool
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 3
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
+  h100_4:
+    agents:
+      queue: mithril-h100-pool
+    plugins:
+      - kubernetes:
+          podSpec:
+            containers:
+              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+                resources:
+                  limits:
+                    nvidia.com/gpu: 4
+                volumeMounts:
+                  - name: devshm
+                    mountPath: /dev/shm
+                  - name: hf-cache
+                    mountPath: /root/.cache/huggingface
+                env:
+                  - name: HF_HOME
+                    value: /root/.cache/huggingface
+                  - name: HF_TOKEN
+                    valueFrom:
+                      secretKeyRef:
+                        name: hf-token-secret
+                        key: token
+            nodeSelector:
+              node.kubernetes.io/instance-type: gpu-h100-sxm
+            volumes:
+              - name: devshm
+                emptyDir:
+                  medium: Memory
+              - name: hf-cache
+                hostPath:
+                  path: /mnt/hf-cache
+                  type: DirectoryOrCreate
+
+  a2b3_npu_1:
+    agents:
+      queue: "ascend-a2b3"
+      resource_class: "npu-1"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}"
+    plugins: *npu_k8s_plugins
+
+  a2b3_npu_4:
+    agents:
+      queue: "ascend-a2b3"
+      resource_class: "npu-4"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}"
+    plugins: *npu_k8s_plugins
+
+
+  a2b3_npu_8:
+    agents:
+      queue: "ascend-a2b3"
+      resource_class: "npu-8"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}"
+    plugins: *npu_k8s_plugins
+
+  a3_npu_2:
+    agents:
+      queue: "ascend-a3"
+      resource_class: "npu-2"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
+    plugins: *npu_k8s_plugins
+
+  a3_npu_4:
+    agents:
+      queue: "ascend-a3"
+      resource_class: "npu-4"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
+    plugins: *npu_k8s_plugins
+
+  a3_npu_8:
+    agents:
+      queue: "ascend-a3"
+      resource_class: "npu-8"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
+    plugins: *npu_k8s_plugins
+
+  a3_npu_16:
+    agents:
+      queue: "ascend-a3"
+      resource_class: "npu-16"
+    image: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry/vllm-omni-ci-npu:${BUILDKITE_COMMIT}-a3"
+    plugins: *npu_k8s_plugins

.buildkite/common/scripts/resolve_skip_ci.sh

@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+# Shared Buildkite skip-ci helpers for AMD / Intel bootstrap scripts.
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SKIP_CI_PY="${SCRIPT_DIR}/skip_ci.py"
+
+# Resolve git diff once. Exit 0 when this bootstrap should stop uploading.
+# Decision details are logged by skip_ci.py; this helper only annotates skip-all.
+# Usage: gate_bootstrap_ci <platform> <l2|l3>
+gate_bootstrap_ci() {
+    local platform="$1"
+    local level="$2"
+    local kind
+    if kind="$(python3 "${SKIP_CI_PY}" gate "${platform}" "${level}")"; then
+        if [[ "${kind}" == "skip-all" ]]; then
+            buildkite-agent annotate \
+                ":memo: CI skipped — docs or pytest skip-mark changes only" \
+                --style "info" 2>/dev/null || true
+        fi
+        exit 0
+    fi
+}

.buildkite/common/scripts/run_cov_split.sh

@@ -0,0 +1,82 @@
+#!/usr/bin/env bash
+# Run a model's E2E tests once per entry mode as separate coverage runs and upload
+# the reports, so per-mode attribution survives (see vllm-project/vllm-omni#5332).
+#
+# Usage: run_cov_split.sh --model-id <id> [--offline <paths>] [--online <paths>] \
+#                         -- <pytest args...>
+#
+#   --model-id  Names the artifacts: coverage-<id>-{offline,online}-<step id>.xml.gz.
+#               Use the model's directory name under vllm_omni/*/models/.
+#   --offline   Pytest path(s) for the offline half. Repeatable.
+#   --online    Pytest path(s) for the online half. Repeatable.
+#   --          Everything after this is forwarded verbatim to every pytest run,
+#               quoting preserved: the job's own -m expression, --run-level, and
+#               anything else it needs (e.g. --test-config-file).
+#
+# At least one of --offline/--online is required, so a job with only one mode runs
+# just that half. Total runtime is bounded by the step's timeout_in_minutes.
+#
+# Exits non-zero if any half or the upload failed.
+set -uo pipefail
+
+MODEL_ID=""
+OFFLINE=()
+ONLINE=()
+PYTEST_ARGS=()
+
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --model-id) MODEL_ID="$2"; shift 2 ;;
+        --offline)  OFFLINE+=("$2"); shift 2 ;;
+        --online)   ONLINE+=("$2"); shift 2 ;;
+        --)         shift; PYTEST_ARGS=("$@"); break ;;
+        *) echo "run_cov_split.sh: unknown argument: $1" >&2; exit 2 ;;
+    esac
+done
+
+if [[ -z "${MODEL_ID}" ]]; then
+    echo "run_cov_split.sh: missing --model-id" >&2
+    exit 2
+fi
+
+if ((${#OFFLINE[@]} == 0 && ${#ONLINE[@]} == 0)); then
+    echo "run_cov_split.sh: need at least one of --offline / --online" >&2
+    exit 2
+fi
+
+# Two steps can test the same model, and artifacts are scoped to the build (a
+# nightly one carries the ready, merge and nightly tiers at once), so unsuffixed
+# reports would share a path -- a glob download then races to rename onto one file.
+# Not BUILDKITE_JOB_ID: the step id survives retries, so attempts collapse under
+# Buildkite's latest-attempt lookup instead of accumulating. A parallel step would
+# need BUILDKITE_PARALLEL_JOB as well; no pilot is one.
+REPORT_SUFFIX="${BUILDKITE_STEP_ID:-local}"
+OFFLINE_REPORT="coverage-${MODEL_ID}-offline-${REPORT_SUFFIX}.xml"
+ONLINE_REPORT="coverage-${MODEL_ID}-online-${REPORT_SUFFIX}.xml"
+
+run_half() {
+    local mode="$1"
+    shift
+    local report="coverage-${MODEL_ID}-${mode}-${REPORT_SUFFIX}.xml"
+    echo "--- coverage: ${MODEL_ID} ${mode}"
+    pytest -s -v "$@" "${PYTEST_ARGS[@]}" --cov=vllm_omni --cov-report="xml:${report}"
+    local status=$?
+    # Cobertura lists every statement in vllm_omni whether or not it ran, so the
+    # report is ~7MB no matter how little the job covers. The repetition is what
+    # dominates it, and gzip takes that to ~400KB.
+    gzip -9 -f "${report}" || status=1
+    return "${status}"
+}
+
+# Clear both modes, not just the ones being run: the upload glob matches both, so
+# a report left by an earlier run would otherwise be published as if the mode it
+# belongs to had run this time.
+rm -f "${OFFLINE_REPORT}" "${ONLINE_REPORT}" "${OFFLINE_REPORT}.gz" "${ONLINE_REPORT}.gz"
+
+EXIT=0
+((${#OFFLINE[@]})) && { run_half offline "${OFFLINE[@]}" || EXIT=1; }
+((${#ONLINE[@]})) && { run_half online "${ONLINE[@]}" || EXIT=1; }
+
+buildkite-agent artifact upload "coverage-${MODEL_ID}-*-${REPORT_SUFFIX}.xml.gz" || EXIT=1
+
+exit "${EXIT}"

.buildkite/common/scripts/skip_ci.py

@@ -0,0 +1,617 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+"""Shared Buildkite skip-ci and CI-YAML level targeting logic.
+
+``resolve_ci_decision(changed_files, ...)`` buckets explicit file lists (tests, dry-run).
+``resolve_ci_context_from_git()`` resolves git diff once and returns decision + changed files
+for Buildkite bootstrap / upload scripts.
+
+CLI (for bootstrap scripts; exit code is the signal):
+  python3 skip_ci.py gate <platform> <level>
+      Exit 0 when bootstrap should stop (skip-all, or that L2/L3 target is off).
+      Prints ``skip-all`` or ``skip-l23`` on stdout; logs the decision once.
+  python3 skip_ci.py check-skip-all
+      Exit 0 when only docs/skip-mark changes → bootstrap skips entire CI upload.
+  python3 skip_ci.py check-skip-l2-l3 <platform> <level>
+      Exit 0 when that L2/L3 pipeline should be skipped (CI config YAML-only diffs).
+  python3 skip_ci.py print-annotate
+      Print decision message for Buildkite annotation.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import re
+import subprocess
+import sys
+from dataclasses import dataclass, field, replace
+from pathlib import Path
+
+LOG = "[skip-ci]"
+ROOT = Path(__file__).resolve().parent.parent.parent.parent
+
+PLATFORMS = ("cuda", "amd", "intel", "npu")
+L23_LEVELS = frozenset({"l2", "l3"})
+
+L2_YAML_FILES: dict[str, str] = {
+    ".buildkite/cuda/test-ready.yml": "cuda",
+    ".buildkite/npu/test-npu-ready.yml": "npu",
+    ".buildkite/amd/test-amd-ready.yml": "amd",
+    ".buildkite/intel/pipeline-intel.yml": "intel",
+}
+
+L3_YAML_FILES: dict[str, str] = {
+    ".buildkite/cuda/test-merge.yml": "cuda",
+    ".buildkite/amd/test-amd-merge.yml": "amd",
+}
+
+L45_YAML_FILES: dict[str, str] = {
+    ".buildkite/cuda/test-nightly.yml": "cuda",
+    ".buildkite/cuda/test-weekly.yml": "cuda",
+    ".buildkite/npu/test-npu-nightly.yml": "npu",
+}
+
+
+def _empty_platform_buckets() -> dict[str, list[str]]:
+    return {platform: [] for platform in PLATFORMS}
+
+
+@dataclass(frozen=True)
+class CiDevice:
+    """Per-platform / per-level skip flags for yaml-gated bootstrap (``skip_l2_l3``)."""
+
+    skip_cuda_l2: bool = False
+    skip_cuda_l3: bool = False
+    skip_amd_l2: bool = False
+    skip_amd_l3: bool = False
+    skip_intel_l2: bool = False
+    skip_intel_l3: bool = False
+    skip_npu_l2: bool = False
+    skip_npu_l3: bool = False
+
+    def is_skip(self, platform: str, level: str) -> bool:
+        return bool(getattr(self, f"skip_{platform}_{level}", True))
+
+
+@dataclass
+class DiffBuckets:
+    """Changed files grouped by doc / skip-mark / CI yaml / other."""
+
+    docs: list[str] = field(default_factory=list)
+    skip_tests: list[str] = field(default_factory=list)
+    other: list[str] = field(default_factory=list)
+    l2: dict[str, list[str]] = field(default_factory=_empty_platform_buckets)
+    l3: dict[str, list[str]] = field(default_factory=_empty_platform_buckets)
+    l45: dict[str, list[str]] = field(default_factory=_empty_platform_buckets)
+
+    @property
+    def has_doc_changes(self) -> bool:
+        return bool(self.docs)
+
+    @property
+    def has_skip_test_changes(self) -> bool:
+        return bool(self.skip_tests)
+
+    @property
+    def has_other_changes(self) -> bool:
+        """Product code or paths outside skip-ci scope."""
+        return bool(self.other)
+
+    @property
+    def has_l23_yaml_changes(self) -> bool:
+        """At least one whitelisted L2/L3 CI yaml file changed."""
+        return any(self.l2[platform] or self.l3[platform] for platform in PLATFORMS)
+
+    @property
+    def has_l45_yaml_changes(self) -> bool:
+        """At least one whitelisted L4/L5 (nightly/weekly) CI yaml file changed."""
+        return any(self.l45[platform] for platform in PLATFORMS)
+
+    @property
+    def has_skip_ci_scope_changes(self) -> bool:
+        """At least one doc / skip-mark / whitelisted CI yaml change."""
+        return (
+            self.has_doc_changes or self.has_skip_test_changes or self.has_l23_yaml_changes or self.has_l45_yaml_changes
+        )
+
+    def l2_platforms(self) -> frozenset[str]:
+        return frozenset(platform for platform in PLATFORMS if self.l2[platform])
+
+    def l3_platforms(self) -> frozenset[str]:
+        return frozenset(platform for platform in PLATFORMS if self.l3[platform])
+
+
+@dataclass(frozen=True)
+class SkipL2L3Basis:
+    skip_all_l2: bool
+    skip_all_l3: bool
+
+    @classmethod
+    def from_buckets(cls, buckets: DiffBuckets) -> SkipL2L3Basis | None:
+        """L4/L5 yaml → skip both L2 and L3 (L2/L3 buckets may rescue).
+
+        Docs / skip-mark changes do not block this path; they are ignored for
+        L2/L3 targeting when CI YAML is also present.
+        """
+        if buckets.has_other_changes:
+            return None
+        if not buckets.has_l45_yaml_changes:
+            return None
+        return cls(
+            skip_all_l2=not buckets.l2_platforms(),
+            skip_all_l3=not buckets.l3_platforms(),
+        )
+
+
+@dataclass(frozen=True)
+class SkipL3Basis:
+    enable_l2_platforms: frozenset[str]
+
+    @classmethod
+    def from_buckets(cls, buckets: DiffBuckets) -> SkipL3Basis | None:
+        """L2 yaml changed → skip L3; keep L2 on touched platforms."""
+        platforms = buckets.l2_platforms()
+        if not platforms:
+            return None
+        return cls(enable_l2_platforms=platforms)
+
+
+@dataclass(frozen=True)
+class SkipL2Basis:
+    enable_l3_platforms: frozenset[str]
+
+    @classmethod
+    def from_buckets(cls, buckets: DiffBuckets) -> SkipL2Basis | None:
+        """L3 yaml changed → skip L2; keep L3 on touched platforms."""
+        platforms = buckets.l3_platforms()
+        if not platforms:
+            return None
+        return cls(enable_l3_platforms=platforms)
+
+
+@dataclass(frozen=True)
+class CiDecision:
+    """Final CI skip decision for one diff."""
+
+    skip_all: bool = False
+    skip_l2_l3: bool = False
+    device: CiDevice = field(default_factory=CiDevice)
+    message: str = ""
+
+    @classmethod
+    def skip_all_basis(cls, buckets: DiffBuckets, *, diff_range: str | None) -> bool:
+        """Docs / pytest skip-mark only → suppress entire default CI."""
+        if buckets.has_other_changes:
+            return False
+        if (buckets.has_l23_yaml_changes or buckets.has_l45_yaml_changes) and (
+            buckets.has_doc_changes or buckets.has_skip_test_changes
+        ):
+            return False
+        if not buckets.has_doc_changes and not buckets.has_skip_test_changes:
+            return False
+        if diff_range is None and not buckets.has_doc_changes:
+            return False
+        return True
+
+    @classmethod
+    def from_yaml_basis(
+        cls,
+        l2_l3_basis: SkipL2L3Basis | None,
+        l3_basis: SkipL3Basis | None,
+        l2_basis: SkipL2Basis | None,
+    ) -> CiDecision:
+        """Build a yaml-gated decision by merging L2/L3 skip bases.
+
+        Bases are applied as a union of enables: L2 yaml keeps L2 on touched
+        platforms, L3 yaml keeps L3 on touched platforms. Cross-platform mixes
+        (e.g. CUDA ready + AMD merge) must preserve both enables. The resulting
+        per-platform / per-level matrix is stored on ``device`` as ``skip_*``.
+        """
+        run = {platform: {"l2": True, "l3": True} for platform in PLATFORMS}
+
+        if l2_l3_basis is not None:
+            if l2_l3_basis.skip_all_l2:
+                for platform in PLATFORMS:
+                    run[platform]["l2"] = False
+            if l2_l3_basis.skip_all_l3:
+                for platform in PLATFORMS:
+                    run[platform]["l3"] = False
+
+        if l3_basis is not None:
+            for platform in PLATFORMS:
+                run[platform]["l3"] = False
+                run[platform]["l2"] = platform in l3_basis.enable_l2_platforms
+
+        if l2_basis is not None:
+            for platform in PLATFORMS:
+                if platform in l2_basis.enable_l3_platforms:
+                    run[platform]["l3"] = True
+                    if l3_basis is None or platform not in l3_basis.enable_l2_platforms:
+                        run[platform]["l2"] = False
+                elif l3_basis is None:
+                    # L3-yaml-only: disable platforms that did not change L3.
+                    run[platform]["l2"] = False
+                    run[platform]["l3"] = False
+                # else: leave L2 enables from SkipL3Basis; keep this platform's L3 off.
+
+        return cls(
+            skip_l2_l3=True,
+            device=cls._device_from_run(run),
+        )
+
+    @staticmethod
+    def _device_from_run(run: dict[str, dict[str, bool]]) -> CiDevice:
+        return CiDevice(
+            **{f"skip_{platform}_{level}": not run[platform][level] for platform in PLATFORMS for level in ("l2", "l3")}
+        )
+
+    def is_run(self, platform: str, level: str) -> bool:
+        if self.skip_all:
+            return False
+        if not self.skip_l2_l3:
+            return True
+        if level not in L23_LEVELS:
+            return True
+        return not self.device.is_skip(platform, level)
+
+
+@dataclass(frozen=True)
+class CiContext:
+    """Skip-ci decision plus git diff inputs used to compute it."""
+
+    decision: CiDecision
+    changed_files: list[str] | None
+    diff_range: str | None
+
+
+def _log(message: str) -> None:
+    print(f"{LOG}: {message}", file=sys.stderr)
+
+
+def _git(*args: str) -> subprocess.CompletedProcess[str]:
+    return subprocess.run(
+        ["git", *args],
+        check=False,
+        capture_output=True,
+        text=True,
+    )
+
+
+# --- Diff helpers ---
+
+
+def _resolve_diff_range() -> str | None:
+    """Return a git diff range for PR or main builds, or None when unavailable."""
+    is_pr = os.environ.get("BUILDKITE_PULL_REQUEST", "false") != "false" and os.environ.get(
+        "BUILDKITE_PULL_REQUEST", ""
+    )
+    commit = os.environ.get("BUILDKITE_COMMIT", "")
+
+    if is_pr:
+        base_branch = os.environ.get("BUILDKITE_PULL_REQUEST_BASE_BRANCH", "main")
+        base_ref = f"origin/{base_branch}"
+        if _git("rev-parse", "--verify", base_ref).returncode != 0:
+            _log(f"origin/{base_branch} not found locally; trying fetch")
+            _git("fetch", "--depth=200", "origin", base_branch)
+        if _git("rev-parse", "--verify", base_ref).returncode != 0:
+            if _git("rev-parse", "--verify", base_branch).returncode == 0:
+                base_ref = base_branch
+            else:
+                _log(f"cannot resolve PR base {base_branch}; using safe defaults")
+                return None
+        return f"{base_ref}...{commit}"
+
+    if os.environ.get("BUILDKITE_BRANCH", "") == "main":
+        if _git("rev-parse", "--verify", f"{commit}^").returncode != 0:
+            _log("main commit has no parent; using safe defaults")
+            return None
+        return f"{commit}^..{commit}"
+
+    _log("not PR/main build; using safe defaults")
+    return None
+
+
+def _changed_files_for_diff_range(diff_range: str | None) -> list[str] | None:
+    """Return changed file paths for *diff_range*, or None when unavailable."""
+    if diff_range is None:
+        return None
+
+    result = _git("diff", "--name-only", diff_range)
+    if result.returncode != 0:
+        _log(f"git diff failed ({diff_range}); using safe defaults")
+        return None
+
+    files = [line for line in result.stdout.splitlines() if line.strip()]
+    _log(f"{len(files)} changed file(s)")
+    return files
+
+
+def resolve_ci_context_from_git() -> CiContext:
+    """Resolve git diff once and return skip-ci decision plus changed files."""
+    diff_range = _resolve_diff_range()
+    changed_files = _changed_files_for_diff_range(diff_range)
+    decision = resolve_ci_decision(changed_files, diff_range=diff_range)
+    return CiContext(decision=decision, changed_files=changed_files, diff_range=diff_range)
+
+
+# --- Doc / skip-mark helpers ---
+
+
+_SKIP_MARK_RE = re.compile(r"pytest\.mark\.skip(?:if)?\b|pytest\.skip\s*\(")
+_PYTESTMARK_SKIP_RE = re.compile(r"pytest\.mark\.skip\b")
+_PYTEST_MARK_ONLY_RE = re.compile(r"pytest\.mark\.\w+")
+
+
+def _diff_only_contains_skip_mark_changes(diff_text: str) -> bool:
+    """Return True when a unified diff only *adds* pytest skip marks.
+
+    Deleting a skip mark re-enables the test and must not suppress CI. Editing an
+    existing skip (``-`` old / ``+`` new) still qualifies when at least one skip
+    mark is added.
+    """
+
+    def paren_balance(line: str) -> int:
+        return line.count("(") - line.count(")")
+
+    def is_blank_or_comment(line: str) -> bool:
+        stripped = line.strip()
+        return not stripped or stripped.startswith("#")
+
+    def is_skip_mark_line(line: str) -> bool:
+        return _SKIP_MARK_RE.search(line.strip()) is not None
+
+    def is_pytestmark_adjacent_line(line: str) -> bool:
+        stripped = line.strip().rstrip(",")
+        if not stripped or stripped in {"[", "]"} or stripped.startswith("#"):
+            return True
+        if stripped.startswith("pytestmark"):
+            return True
+        return _PYTEST_MARK_ONLY_RE.search(stripped) is not None
+
+    pending_depth = 0
+    saw_change = False
+    has_added_skip_mark = False
+    for raw_line in diff_text.splitlines():
+        if raw_line.startswith("@@"):
+            pending_depth = 0
+            continue
+        if not (raw_line.startswith("+") or raw_line.startswith("-")):
+            continue
+        if raw_line.startswith("+++") or raw_line.startswith("---"):
+            continue
+
+        saw_change = True
+        is_add = raw_line.startswith("+")
+        content = raw_line[1:]
+
+        if pending_depth > 0:
+            pending_depth += paren_balance(content)
+            if pending_depth < 0:
+                pending_depth = 0
+            continue
+
+        if is_blank_or_comment(content):
+            continue
+
+        if is_skip_mark_line(content):
+            if is_add:
+                has_added_skip_mark = True
+            pending_depth = max(0, paren_balance(content))
+            continue
+
+        if is_pytestmark_adjacent_line(content):
+            continue
+
+        return False
+
+    return saw_change and has_added_skip_mark
+
+
+def _is_skip_testcase_change(file_path: str, *, diff_range: str) -> bool:
+    """Return True when a test file diff is skip-mark-only (including new-file cases)."""
+    diff_result = _git("diff", diff_range, "--", file_path)
+    if diff_result.returncode != 0:
+        _log(f"skip-mark-only: git diff failed for {file_path}")
+        return False
+
+    diff_text = diff_result.stdout
+    if not diff_text.strip():
+        _log(f"skip-mark-only: empty diff for {file_path}")
+        return False
+
+    if _diff_only_contains_skip_mark_changes(diff_text):
+        return True
+
+    if "new file mode" not in diff_text and "--- /dev/null" not in diff_text:
+        _log(f"skip-mark-only: non-skip changes in {file_path}")
+        return False
+
+    commit = os.environ.get("BUILDKITE_COMMIT", "")
+    if not commit:
+        return False
+
+    content_result = _git("show", f"{commit}:{file_path}")
+    if content_result.returncode != 0:
+        _log(f"skip-mark-only: cannot read {file_path} at {commit}")
+        return False
+
+    content = content_result.stdout
+    match = re.search(r"^pytestmark\s*=\s*\[(.*?)^\s*\]", content, re.MULTILINE | re.DOTALL)
+    if match is not None and _PYTESTMARK_SKIP_RE.search(match.group(1)) is not None:
+        _log(f"skip-mark-only: new test file with module-level pytest.mark.skip: {file_path}")
+        return True
+    if re.search(r"^pytestmark\s*=\s*pytest\.mark\.skip\b", content, re.MULTILINE):
+        _log(f"skip-mark-only: new test file with module-level pytest.mark.skip: {file_path}")
+        return True
+
+    _log(f"skip-mark-only: new test file without module-level pytest.mark.skip: {file_path}")
+    return False
+
+
+def _classify_changed_files_into_buckets(changed_files: list[str], *, diff_range: str | None) -> DiffBuckets:
+    """Classify each changed file path into a ``DiffBuckets`` category."""
+    buckets = DiffBuckets()
+    for file_path in changed_files:
+        if not file_path:
+            continue
+        if file_path.startswith("docs/") or file_path.endswith(".md") or file_path == "mkdocs.yml":
+            buckets.docs.append(file_path)
+            continue
+        path = Path(file_path)
+        if path.suffix == ".py" and path.parts and path.parts[0] == "tests":
+            if diff_range is not None and _is_skip_testcase_change(file_path, diff_range=diff_range):
+                buckets.skip_tests.append(file_path)
+            else:
+                buckets.other.append(file_path)
+            continue
+        if file_path in L2_YAML_FILES:
+            platform = L2_YAML_FILES[file_path]
+            buckets.l2[platform].append(file_path)
+        elif file_path in L3_YAML_FILES:
+            platform = L3_YAML_FILES[file_path]
+            buckets.l3[platform].append(file_path)
+        elif file_path in L45_YAML_FILES:
+            platform = L45_YAML_FILES[file_path]
+            buckets.l45[platform].append(file_path)
+        else:
+            buckets.other.append(file_path)
+    return buckets
+
+
+# --- Decision ---
+
+
+def _finish(decision: CiDecision, message: str) -> CiDecision:
+    finished = replace(decision, message=message)
+    _log(message)
+    return finished
+
+
+def _format_yaml_gated_message(buckets: DiffBuckets, decision: CiDecision) -> str:
+    """Human-readable summary of which CI YAML changed and which L2/L3 targets run."""
+    changed_parts: list[str] = []
+    for level_name, by_platform in (("L2", buckets.l2), ("L3", buckets.l3), ("L4/L5", buckets.l45)):
+        files = [path for platform in PLATFORMS for path in by_platform[platform]]
+        if files:
+            changed_parts.append(f"{level_name}=[{', '.join(files)}]")
+
+    run_targets = [
+        f"{platform}/{level}" for platform in PLATFORMS for level in ("l2", "l3") if decision.is_run(platform, level)
+    ]
+    skip_targets = [
+        f"{platform}/{level}"
+        for platform in PLATFORMS
+        for level in ("l2", "l3")
+        if not decision.is_run(platform, level)
+    ]
+
+    changed = "; ".join(changed_parts) if changed_parts else "none"
+    run = ", ".join(run_targets) if run_targets else "none"
+    skip = ", ".join(skip_targets) if skip_targets else "none"
+    if buckets.has_doc_changes or buckets.has_skip_test_changes:
+        return (
+            "CI config YAML + docs/skip-mark changed — follow CI YAML gating; "
+            f"changed: {changed}; run: {run}; skip: {skip}"
+        )
+    return f"only CI config YAML changed — changed: {changed}; run: {run}; skip: {skip}"
+
+
+def resolve_ci_decision(
+    changed_files: list[str] | None = None,
+    *,
+    diff_range: str | None = None,
+) -> CiDecision:
+    if changed_files is None:
+        return _finish(CiDecision(), "could not resolve changed files; run normal CI")
+
+    buckets = _classify_changed_files_into_buckets(changed_files, diff_range=diff_range)
+    if buckets.has_other_changes or not buckets.has_skip_ci_scope_changes:
+        return _finish(CiDecision(), "product or non-whitelisted changes present; run normal CI")
+
+    if CiDecision.skip_all_basis(buckets, diff_range=diff_range):
+        return _finish(
+            CiDecision(skip_all=True),
+            "CI skipped — docs or pytest skip-mark changes only",
+        )
+
+    # Docs / skip-mark mixed with whitelisted CI YAML → same yaml-gated path
+    # as CI-YAML-only (docs/skip-mark do not widen to normal CI).
+    if not buckets.has_l23_yaml_changes and not buckets.has_l45_yaml_changes:
+        if buckets.has_skip_test_changes and diff_range is None:
+            message = (
+                "only pytest skip-mark test changes detected, but no git diff range "
+                "is available to verify them (skip-all requires diff_range); run normal CI"
+            )
+        elif buckets.has_skip_test_changes:
+            message = "pytest skip-mark test changes did not qualify for skip-all and no ci yaml changed; run normal CI"
+        else:
+            message = "doc/skip-mark changes did not qualify for skip-all; run normal CI"
+        return _finish(CiDecision(), message)
+
+    decision = CiDecision.from_yaml_basis(
+        SkipL2L3Basis.from_buckets(buckets),
+        SkipL3Basis.from_buckets(buckets),
+        SkipL2Basis.from_buckets(buckets),
+    )
+    return _finish(decision, _format_yaml_gated_message(buckets, decision))
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+
+    gate_parser = subparsers.add_parser(
+        "gate",
+        help="exit 0 when bootstrap should stop (skip-all or PLATFORM/LEVEL disabled); logs once",
+    )
+    gate_parser.add_argument("platform", help="cuda, amd, intel, or npu")
+    gate_parser.add_argument("level", choices=("l2", "l3"), help="test level")
+
+    subparsers.add_parser(
+        "check-skip-all",
+        help="exit 0 when docs/skip-mark-only diff should skip entire CI upload",
+    )
+
+    skip_parser = subparsers.add_parser(
+        "check-skip-l2-l3",
+        help="exit 0 when PLATFORM l2/l3 pipeline should be skipped (CI config YAML-only diffs)",
+    )
+    skip_parser.add_argument("platform", help="cuda, amd, intel, or npu")
+    skip_parser.add_argument("level", choices=("l2", "l3"), help="test level")
+
+    subparsers.add_parser(
+        "print-annotate",
+        help="print human-readable skip reason for Buildkite annotation",
+    )
+
+    args = parser.parse_args()
+    decision = resolve_ci_context_from_git().decision
+
+    if args.command == "gate":
+        if decision.skip_all:
+            print("skip-all")
+            return 0
+        if not decision.is_run(args.platform, args.level):
+            print("skip-l23")
+            return 0
+        return 1
+
+    if args.command == "check-skip-all":
+        return 0 if decision.skip_all else 1
+
+    if args.command == "check-skip-l2-l3":
+        return 0 if not decision.is_run(args.platform, args.level) else 1
+
+    if args.command == "print-annotate":
+        print(decision.message or "CI will run normally")
+        return 0
+
+    return 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.buildkite/common/scripts/upload_pipeline.py

@@ -0,0 +1,449 @@
+#!/usr/bin/env python3
+# SPDX-License-Identifier: Apache-2.0
+"""Render and optionally upload Buildkite pipeline YAML with diff-aware logic.
+
+Bootstrap mode (``bootstrap-upload-steps.yml``):
+  - Hook uploads the entry YAML (``pipeline.yml`` / ``pipeline-npu.yml``) with one step that runs
+    ``upload_pipeline.py --upload <platform>/bootstrap-upload-steps.yml``.
+  - Injects ``if`` by step ``key`` from skip-ci and uploads child steps (image build, L2–L5 upload).
+  - Detect docs-only, pytest skip-mark-only, or combined skip-ci from git diff.
+  - When only CI level YAML changes, enable **L2/L3** upload steps for affected levels only.
+
+Test pipeline mode (e.g. test-merge.yml):
+  - Drop steps whose ``source_file_dependencies`` do not match changed files.
+  - Expand uploader-only ``mirror_hardwares: l4_1`` into ``agents`` (+ optional ``image``
+    for NPU) + ``plugins`` (see ci_mirror_hardwares.yml).
+
+Usage:
+  python3 upload_pipeline.py [--upload] [--all | --e2e] <pipeline.yml>
+
+Requires PyYAML (``pip install pyyaml``); installs it automatically when missing.
+"""
+
+from __future__ import annotations
+
+import argparse
+import copy
+import subprocess
+import sys
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+try:
+    import yaml
+except ModuleNotFoundError:
+    subprocess.run(
+        [sys.executable, "-m", "pip", "install", "-q", "pyyaml"],
+        check=True,
+    )
+    import yaml
+
+from skip_ci import (
+    ROOT,
+    resolve_ci_context_from_git,
+)
+
+# --- Constants ---
+
+LOG = "upload_pipeline"
+BOOTSTRAP_STEPS_FILENAME = "bootstrap-upload-steps.yml"
+BOOTSTRAP_IMAGE_BUILD_KEYS = frozenset({"image-build", "image-build-a2", "image-build-a3"})
+BOOTSTRAP_UPLOAD_IF_KEYS = {
+    "upload-ready-pipeline": "ready",
+    "upload-merge-pipeline": "merge",
+    "upload-nightly-pipeline": "nightly",
+    "upload-weekly-pipeline": "weekly",
+}
+E2E_GROUP_MARKER = "E2E Test"
+CI_MIRROR_HARDWARES_PATH = ROOT / ".buildkite/common/ci_mirror_hardwares.yml"
+
+CUDA_NIGHTLY_ONLY = (
+    '(build.pull_request.labels includes "nightly-test") || (build.branch == "main" && build.env("NIGHTLY") == "1")'
+)
+NPU_NIGHTLY_ONLY = (
+    '(build.branch == "main" && build.env("NIGHTLY") == "1") || '
+    '(build.branch != "main" && ('
+    'build.pull_request.labels includes "nightly-test" || '
+    'build.pull_request.labels includes "omni-test" || '
+    'build.pull_request.labels includes "tts-test" || '
+    'build.pull_request.labels includes "diffusion-x2iat-test" || '
+    'build.pull_request.labels includes "diffusion-x2v-test"'
+    "))"
+)
+
+
+# --- Logging ---
+
+
+def _log(message: str) -> None:
+    print(f"{LOG}: {message}", file=sys.stderr)
+
+
+# --- Bootstrap pipeline (bootstrap-upload-steps.yml) ---
+
+
+def _get_bootstrap_platform(path: Path) -> str:
+    parts = path.as_posix().split("/")
+    return "npu" if "npu" in parts else "cuda"
+
+
+def _load_bootstrap_steps(path: Path) -> str:
+    if path.name != BOOTSTRAP_STEPS_FILENAME:
+        raise ValueError(f"expected {BOOTSTRAP_STEPS_FILENAME}, got {path.name}")
+    return path.read_text(encoding="utf-8")
+
+
+def _format_bootstrap_if(expr: str) -> str:
+    """Return a Buildkite ``if`` string. Buildkite rejects YAML bool ``if`` values."""
+    if expr in ("true", "false"):
+        return expr
+    return f"({expr})"
+
+
+def _compute_bootstrap_if_exprs(*, decision, platform: str) -> dict[str, str]:
+    disabled = "false"
+    nightly_main = 'build.branch == "main" && build.env("NIGHTLY") == "1"'
+    nightly_only = NPU_NIGHTLY_ONLY if platform == "npu" else CUDA_NIGHTLY_ONLY
+
+    if platform == "npu":
+        ready_pr = (
+            'build.branch != "main" && ('
+            'build.pull_request.labels includes "npu-test" || '
+            'build.pull_request.labels includes "ready"'
+            ")"
+        )
+        nightly_label_if = nightly_only
+        weekly_label_if = disabled
+        merge_base = disabled
+    else:
+        ready_pr = 'build.branch != "main" && build.pull_request.labels includes "ready"'
+        merge_main = 'build.branch == "main" && build.env("NIGHTLY") != "1" && build.env("WEEKLY") != "1"'
+        merge_pr = 'build.branch != "main" && build.pull_request.labels includes "merge-test"'
+        nightly_label_if = (
+            '(build.branch == "main" && build.env("NIGHTLY") == "1") || '
+            '(build.branch != "main" && ('
+            'build.pull_request.labels includes "nightly-test" || '
+            'build.pull_request.labels includes "omni-test" || '
+            'build.pull_request.labels includes "tts-test" || '
+            'build.pull_request.labels includes "diffusion-x2iat-test" || '
+            'build.pull_request.labels includes "diffusion-x2v-test"'
+            "))"
+        )
+        weekly_label_if = (
+            '(build.branch == "main" && build.env("WEEKLY") == "1") || '
+            '(build.branch != "main" && build.pull_request.labels includes "weekly-test")'
+        )
+        merge_base = f"({nightly_main}) || (({merge_main}) || ({merge_pr}))"
+
+    ready_base = f"({nightly_main}) || ({ready_pr})"
+    weekly_main = 'build.branch == "main" && build.env("WEEKLY") == "1"'
+
+    if decision.skip_all:
+        # Docs / skip-mark only: no PR-label escape hatch. Main scheduled
+        # NIGHTLY=1 still runs L4 + L2/L3 (--e2e); WEEKLY=1 still runs L5.
+        image_expr = f"({nightly_main}) || ({weekly_main})" if platform == "cuda" else nightly_main
+        ready_expr = nightly_main
+        merge_expr = nightly_main if platform == "cuda" else disabled
+        nightly_expr = nightly_main
+        weekly_expr = weekly_main if platform == "cuda" else disabled
+    elif decision.skip_l2_l3:
+        l2_enabled = decision.is_run("npu", "l2") if platform == "npu" else decision.is_run("cuda", "l2")
+        l3_enabled = platform == "cuda" and decision.is_run("cuda", "l3")
+
+        ready_expr = ready_base if l2_enabled else disabled
+        merge_expr = merge_base if l3_enabled else disabled
+        nightly_expr = nightly_label_if
+        weekly_expr = weekly_label_if if platform == "cuda" else disabled
+
+        image_parts = [f"({nightly_label_if})"]
+        if platform == "cuda":
+            image_parts.append(f"({weekly_label_if})")
+        if l2_enabled:
+            image_parts.insert(0, f"({ready_base})")
+        if l3_enabled:
+            image_parts.insert(1 if l2_enabled else 0, f"({merge_base})")
+        image_expr = " || ".join(image_parts)
+    else:
+        image_expr = "true"
+        ready_expr = ready_base
+        merge_expr = merge_base if platform == "cuda" else disabled
+        nightly_expr = nightly_label_if
+        weekly_expr = weekly_label_if if platform == "cuda" else disabled
+
+    return {
+        "image": _format_bootstrap_if(image_expr),
+        "ready": _format_bootstrap_if(ready_expr),
+        "merge": _format_bootstrap_if(merge_expr),
+        "nightly": _format_bootstrap_if(nightly_expr),
+        "weekly": _format_bootstrap_if(weekly_expr),
+    }
+
+
+def _apply_bootstrap_if(steps: list[Any], if_exprs: dict[str, str]) -> list[Any]:
+    """Inject ``if`` by step key; drop steps that are unconditionally disabled."""
+    kept: list[Any] = []
+    for step in steps:
+        if not isinstance(step, dict):
+            kept.append(step)
+            continue
+        nested = step.get("steps")
+        if isinstance(nested, list):
+            nested_kept = _apply_bootstrap_if(nested, if_exprs)
+            if nested_kept:
+                kept.append({**step, "steps": nested_kept})
+            continue
+        key = step.get("key")
+        if key in BOOTSTRAP_IMAGE_BUILD_KEYS:
+            step["if"] = if_exprs["image"]
+        elif key in BOOTSTRAP_UPLOAD_IF_KEYS:
+            step["if"] = if_exprs[BOOTSTRAP_UPLOAD_IF_KEYS[key]]
+        if_expr = step.get("if")
+        if if_expr == "false":
+            _log(f"omit disabled bootstrap step {key!r}")
+            continue
+        if if_expr == "true":
+            # Unconditional step: omit ``if`` (Buildkite requires string ``if``, not YAML bool).
+            step.pop("if", None)
+        kept.append(step)
+    return kept
+
+
+def _render_bootstrap_pipeline(
+    steps_yaml: str,
+    *,
+    decision,
+    path: Path,
+) -> str:
+    """Load bootstrap steps YAML and inject ``if`` expressions from skip-ci decision."""
+    doc = yaml.safe_load(steps_yaml)
+    if not isinstance(doc, dict):
+        raise ValueError(f"invalid bootstrap steps YAML for {path}")
+    steps = doc.get("steps")
+    if not isinstance(steps, list):
+        raise ValueError(f"bootstrap steps YAML must contain steps: list in {path}")
+
+    platform = _get_bootstrap_platform(path)
+    if_exprs = _compute_bootstrap_if_exprs(decision=decision, platform=platform)
+    doc["steps"] = _apply_bootstrap_if(steps, if_exprs)
+    return yaml.safe_dump(doc, sort_keys=False)
+
+
+# --- Test pipeline (test-ready.yml, test-merge.yml) ---
+
+
+@lru_cache(maxsize=1)
+def _load_mirror_hardwares() -> dict[str, dict[str, Any]]:
+    if not CI_MIRROR_HARDWARES_PATH.is_file():
+        raise FileNotFoundError(f"missing CI mirror_hardwares registry: {CI_MIRROR_HARDWARES_PATH}")
+    doc = yaml.safe_load(CI_MIRROR_HARDWARES_PATH.read_text(encoding="utf-8"))
+    if not isinstance(doc, dict):
+        raise ValueError(f"invalid CI mirror_hardwares registry: {CI_MIRROR_HARDWARES_PATH}")
+    presets = doc.get("mirror_hardwares")
+    if not isinstance(presets, dict):
+        raise ValueError(f"mirror_hardwares must be a mapping in {CI_MIRROR_HARDWARES_PATH}")
+    return presets
+
+
+def _expand_mirror_hardwares(step: dict[str, Any]) -> dict[str, Any]:
+    """Replace uploader-only ``mirror_hardwares`` with preset fields from ci_mirror_hardwares.yml."""
+    hardware = step.get("mirror_hardwares")
+    if hardware is None:
+        return step
+
+    if not isinstance(hardware, str) or not hardware.strip():
+        raise ValueError(
+            f"mirror_hardwares must be a non-empty string in step {_get_step_label(step)!r}",
+        )
+
+    preset = _load_mirror_hardwares().get(hardware)
+    if preset is None:
+        known = ", ".join(sorted(_load_mirror_hardwares()))
+        raise ValueError(
+            f"unknown mirror_hardwares {hardware!r} in step {_get_step_label(step)!r}; known: {known}",
+        )
+
+    if step.get("agents") is not None or step.get("plugins") is not None or step.get("image") is not None:
+        raise ValueError(
+            f"step {_get_step_label(step)!r} sets mirror_hardwares together with agents/plugins/image; "
+            "use mirror_hardwares only",
+        )
+
+    expanded = copy.deepcopy(preset)
+    return {key: value for key, value in step.items() if key != "mirror_hardwares"} | expanded
+
+
+def _match_source_file(changed_files: list[str], prefixes: list[str]) -> bool:
+    for path in changed_files:
+        for prefix in prefixes:
+            normalized = prefix.rstrip("/")
+            if path == normalized or path.startswith(f"{normalized}/"):
+                return True
+    return False
+
+
+def _get_step_label(step: dict[str, Any]) -> str:
+    return str(step.get("group") or step.get("label") or "<step>")
+
+
+def _process_test_steps(
+    steps: list[Any],
+    changed_files: list[str] | None,
+) -> list[Any]:
+    """Drop steps by ``source_file_dependencies`` when *changed_files* is set; always strip that field."""
+    processed: list[Any] = []
+    for step in steps:
+        if not isinstance(step, dict):
+            processed.append(step)
+            continue
+
+        deps = step.get("source_file_dependencies")
+        if deps is not None and not isinstance(deps, list):
+            raise ValueError(
+                f"source_file_dependencies must be a list in step {_get_step_label(step)!r}",
+            )
+        if changed_files is not None and deps is not None and not _match_source_file(changed_files, deps):
+            _log(f"skip {_get_step_label(step)!r} (no changes under {deps})")
+            continue
+
+        nested = step.get("steps")
+        if nested is not None:
+            kept_nested = _process_test_steps(nested, changed_files)
+            if changed_files is not None and not kept_nested:
+                _log(f"omit empty group {_get_step_label(step)!r}")
+                continue
+            new_step = {key: value for key, value in step.items() if key != "source_file_dependencies"}
+            new_step["steps"] = kept_nested
+            processed.append(new_step)
+            continue
+
+        if deps is not None:
+            processed.append(
+                _expand_mirror_hardwares(
+                    {key: value for key, value in step.items() if key != "source_file_dependencies"},
+                ),
+            )
+        else:
+            processed.append(_expand_mirror_hardwares(step))
+
+    return processed
+
+
+def _select_e2e_group_steps(steps: list[Any]) -> list[Any]:
+    """Keep only top-level groups whose name contains ``E2E_GROUP_MARKER``."""
+    selected = [
+        step
+        for step in steps
+        if isinstance(step, dict) and isinstance(step.get("group"), str) and E2E_GROUP_MARKER in step["group"]
+    ]
+    if not selected:
+        _log(f"no group matching {E2E_GROUP_MARKER!r} found")
+    else:
+        _log(f"keep {len(selected)} group(s) matching {E2E_GROUP_MARKER!r}")
+    return selected
+
+
+def _render_test_pipeline(
+    doc: dict[str, Any],
+    changed_files: list[str] | None,
+    *,
+    e2e_only: bool = False,
+) -> dict[str, Any]:
+    """Filter steps by PR diff and strip uploader-only ``source_file_dependencies`` metadata."""
+    steps = doc.get("steps")
+    if not isinstance(steps, list):
+        return doc
+    if e2e_only:
+        steps = _select_e2e_group_steps(steps)
+    steps = _process_test_steps(steps, changed_files)
+    return {**doc, "steps": steps}
+
+
+# --- Entry (read file → bootstrap or test render → YAML string) ---
+
+
+def _render_pipeline(
+    path: Path,
+    *,
+    force_all: bool = False,
+    e2e_only: bool = False,
+) -> str:
+    if path.name == BOOTSTRAP_STEPS_FILENAME:
+        ctx = resolve_ci_context_from_git()
+        continuation = _load_bootstrap_steps(path)
+        return _render_bootstrap_pipeline(
+            continuation,
+            decision=ctx.decision,
+            path=path,
+        )
+
+    text = path.read_text(encoding="utf-8")
+    ctx = resolve_ci_context_from_git()
+    if force_all or e2e_only:
+        changed_files = None
+    else:
+        changed_files = ctx.changed_files
+
+    doc = yaml.safe_load(text)
+    if not isinstance(doc, dict):
+        raise ValueError(f"invalid pipeline YAML: {path}")
+
+    doc = _render_test_pipeline(doc, changed_files, e2e_only=e2e_only)
+    return yaml.safe_dump(doc, sort_keys=False)
+
+
+def _upload_to_buildkite(content: str) -> None:
+    subprocess.run(
+        ["buildkite-agent", "pipeline", "upload"],
+        input=content,
+        text=True,
+        check=True,
+    )
+
+
+# --- CLI ---
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "pipeline",
+        nargs="?",
+        default=".buildkite/cuda/bootstrap-upload-steps.yml",
+        help="Pipeline YAML path (default: .buildkite/cuda/bootstrap-upload-steps.yml)",
+    )
+    parser.add_argument(
+        "--upload",
+        action="store_true",
+        help="Pipe rendered YAML to buildkite-agent pipeline upload",
+    )
+    mode = parser.add_mutually_exclusive_group()
+    mode.add_argument(
+        "--all",
+        action="store_true",
+        help="Keep all steps (disable diff-aware skipping)",
+    )
+    mode.add_argument(
+        "--e2e",
+        action="store_true",
+        help="Keep only the E2E Test group",
+    )
+    args = parser.parse_args()
+
+    path = Path(args.pipeline)
+    if not path.is_absolute():
+        path = ROOT / path
+    if not path.is_file():
+        _log(f"missing pipeline file: {path}")
+        return 1
+
+    rendered = _render_pipeline(path, force_all=args.all, e2e_only=args.e2e)
+    if args.upload:
+        _upload_to_buildkite(rendered)
+    else:
+        sys.stdout.write(rendered)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

.buildkite/cuda/bootstrap-upload-steps.yml

@@ -0,0 +1,55 @@
+# Uploaded by upload_pipeline.py --upload (not the Buildkite hook).
+# ``if`` is injected by step ``key`` from skip-ci at upload time.
+steps:
+  - label: ":docker: Build image"
+    key: image-build
+    commands:
+      - "bash .buildkite/cuda/scripts/build-ci-image.sh"
+    agents:
+      queue: "cpu_queue_premerge"
+
+  # L2 Test — PR ready label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  - label: "Upload Ready Pipeline"
+    depends_on: image-build
+    key: upload-ready-pipeline
+    commands:
+      - |
+        EXTRA=""
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+          EXTRA="--e2e"
+        fi
+        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-ready.yml
+    agents:
+      queue: "cpu_queue_premerge"
+
+  # L3 Test — PR merge-test label (diff-aware) or main+NIGHTLY=1 (--e2e, full E2E group)
+  - label: "Upload Merge Pipeline"
+    depends_on: image-build
+    key: upload-merge-pipeline
+    commands:
+      - |
+        EXTRA=""
+        if [ "${NIGHTLY:-}" = "1" ] && [ "${BUILDKITE_BRANCH}" = "main" ]; then
+          EXTRA="--e2e"
+        fi
+        python3 .buildkite/common/scripts/upload_pipeline.py --upload $$EXTRA .buildkite/cuda/test-merge.yml
+    agents:
+      queue: "cpu_queue_premerge"
+
+  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  - label: "Upload Nightly Pipeline"
+    depends_on: image-build
+    key: upload-nightly-pipeline
+    commands:
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/cuda/test-nightly.yml
+    agents:
+      queue: "cpu_queue_premerge"
+
+  # L5 Test — main+WEEKLY=1 (scheduled), or PR with weekly-test label
+  - label: "Upload Weekly Pipeline"
+    depends_on: image-build
+    key: upload-weekly-pipeline
+    commands:
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/cuda/test-weekly.yml
+    agents:
+      queue: "cpu_queue_premerge"

.buildkite/cuda/pipeline.yml

@@ -0,0 +1,9 @@
+# Hook uploads this entry YAML; upload_pipeline.py --upload reads bootstrap-upload-steps.yml
+# and injects ``if`` by step ``key`` from skip-ci.
+steps:
+  - label: ":github: Resolve skip-ci & upload pipeline"
+    key: upload-ci-pipeline
+    commands:
+      - "python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/cuda/bootstrap-upload-steps.yml"
+    agents:
+      queue: "cpu_queue_premerge"

.buildkite/cuda/rebase-pipeline.yml

@@ -2,10 +2,7 @@ steps:
   - label: ":docker: Build image"
     key: image-build
     commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "docker build --progress=plain --file docker/Dockerfile.ci -t vllm-omni-ci ."
-      - "docker tag vllm-omni-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
+      - "bash .buildkite/cuda/scripts/build-ci-image.sh"
     agents:
       queue: "cpu_queue_premerge"
 
@@ -14,7 +11,7 @@ steps:
     depends_on: image-build
     key: upload-ready-pipeline
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-ready.yml
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload --all .buildkite/cuda/test-ready.yml
     agents:
       queue: "cpu_queue_premerge"
 
@@ -23,7 +20,7 @@ steps:
     depends_on: image-build
     key: upload-merge-pipeline
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-merge.yml
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload --all .buildkite/cuda/test-merge.yml
     agents:
       queue: "cpu_queue_premerge"
 
@@ -33,6 +30,6 @@ steps:
     key: upload-nightly-pipeline
     if: build.env("NIGHTLY") == "1"
     commands:
-      - buildkite-agent pipeline upload .buildkite/test-nightly.yml
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload --all .buildkite/cuda/test-nightly.yml
     agents:
       queue: "cpu_queue_premerge"

.buildkite/cuda/scripts/build-ci-image.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+set -euo pipefail
+
+# Build and push the CI Docker image with registry-based layer caching.
+#
+# Usage: build-ci-image.sh
+#
+# Expects the following environment variables (set by Buildkite):
+#   BUILDKITE_COMMIT          - commit SHA used as the image tag
+ECR_NAMESPACE="public.ecr.aws/q9t5s3a7"
+REGISTRY="${ECR_NAMESPACE}/vllm-ci-test-repo"
+REGION="us-east-1"
+DOCKERFILE="docker/Dockerfile.ci"
+BUILDER_NAME="vllm-omni-builder"
+
+# Ensure that the env vars are actually set, otherwise exit early
+if [ -z "${BUILDKITE_COMMIT:-}" ]; then
+    echo "ERROR: BUILDKITE_COMMIT is not set"
+    exit 1
+fi
+echo "BUILDKITE_COMMIT: ${BUILDKITE_COMMIT}"
+
+# Authenticate to ECR Public
+aws ecr-public get-login-password --region "$REGION" \
+    | docker login --username AWS --password-stdin "$ECR_NAMESPACE"
+
+# Compute the cache tag for the dependencies image as the hash of the contents.
+# This is because dependencies don't change often, so most PRs will have the same hash.
+#
+# The Dockerfile is also included to avoid churn in the cached image based on the vLLM version.
+DEP_FILES="pyproject.toml setup.py requirements/*.txt docker/Dockerfile.ci"
+CACHE_KEY=$(cat ${DEP_FILES} | sha256sum | cut -c1-16)
+CACHE_TAG="deps-cache-${CACHE_KEY}"
+echo "Cache key: ${CACHE_TAG}"
+
+# Set up buildx with docker-container driver; we need to do this
+# since cache export is not supported for the default docker driver
+# that is running in the CI.
+docker buildx inspect $BUILDER_NAME >/dev/null 2>&1 \
+|| docker buildx create --name $BUILDER_NAME --driver docker-container
+docker buildx use $BUILDER_NAME
+
+echo "Building image tag: ${REGISTRY}:${BUILDKITE_COMMIT}"
+
+# Build + push the image to the registry, using the hash from the dependencies.
+# Note that the caching here is layerwise, so we're just making sure we always
+# can cache hit on everything but the last copy at the moment.
+docker buildx build --push --progress=plain \
+    --cache-from "type=registry,ref=${REGISTRY}:${CACHE_TAG}" \
+    --cache-to "type=registry,ref=${REGISTRY}:${CACHE_TAG},mode=max,compression=zstd" \
+    --file "$DOCKERFILE" \
+    -t "${REGISTRY}:${BUILDKITE_COMMIT}" .

.buildkite/cuda/test-merge.yml

@@ -0,0 +1,213 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+  - group: ":card_index_dividers: Simple Test"
+    depends_on: upload-merge-pipeline
+    steps:
+      - label: "Simple · Model Executor Test"
+        commands:
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Other Test"
+        commands:
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: Diffusion Test"
+    depends_on: upload-merge-pipeline
+    steps:
+      - label: "Diffusion · LoRA Test"
+        timeout_in_minutes: 30
+        commands:
+          - pytest -sv tests/diffusion/lora/ -m 'advanced_model and cuda' --run-level "advanced_model"
+        mirror_hardwares: l4_1
+
+  - label: "Distributed Test"
+    depends_on: upload-merge-pipeline
+    timeout_in_minutes: 60
+    commands:
+      - |
+        timeout 55m bash -c "
+          set -e
+          export VLLM_IMAGE_FETCH_TIMEOUT=60
+          pytest -s -v tests/distributed -m 'advanced_model and cuda and H100' --run-level 'advanced_model'
+        "
+    mirror_hardwares: h100_1
+
+  - group: ":card_index_dividers: Entrypoints Test"
+    depends_on: upload-merge-pipeline
+    steps:
+      - label: "Entrypoint Test with H100"
+        # TEMPORARY 60 -> 90: engine startup/sleep-wake is ~13-15 min slower per
+        # run on the vLLM 0.26 image (model loading ~2x, init ~2x; see builds
+        # 2915 vs 2916), which pushed this suite past 60 min. Revert once the
+        # 0.26 startup regression is understood and fixed.
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and H100" --run-level "advanced_model"
+        mirror_hardwares: h100_2
+
+      - label: "Entrypoint Test with L4"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -s -v tests/entrypoints/ -m "advanced_model and cuda and L4" --run-level "advanced_model"
+        mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: E2E Test"
+    depends_on: upload-merge-pipeline
+    steps:
+      - label: "TTS · Qwen3-TTS CustomVoice Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
+          - tests/e2e/offline_inference/test_qwen3_tts_customvoice.py
+          - vllm_omni/model_executor/models/qwen3_tts/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/common/alias_free_activation.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+          - vllm_omni/deploy/qwen3_tts.yaml
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_LOGGING_LEVEL=DEBUG
+              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "TTS · Qwen3-TTS Base Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_tts_base.py
+          - tests/e2e/offline_inference/test_qwen3_tts_base.py
+          - vllm_omni/model_executor/models/qwen3_tts/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/common/alias_free_activation.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+          - vllm_omni/deploy/qwen3_tts.yaml
+          - .buildkite/common/scripts/run_cov_split.sh
+          - pyproject.toml
+        # 40 -> 60: the coverage split runs the model twice.
+        timeout_in_minutes: 60
+        commands:
+          - |
+            export VLLM_LOGGING_LEVEL=DEBUG
+            export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+            .buildkite/common/scripts/run_cov_split.sh \
+              --model-id qwen3_tts \
+              --offline tests/e2e/offline_inference/test_qwen3_tts_base.py \
+              --online tests/e2e/online_serving/test_qwen3_tts_base.py \
+              -- -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: l4_1
+
+      - label: "Omni · Qwen3-Omni Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_qwen3_omni.py
+          - tests/e2e/online_serving/test_qwen3_omni.py
+          - vllm_omni/model_executor/models/qwen3_omni/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
+          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+          - vllm_omni/deploy/qwen3_omni_moe.yaml
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_minicpmo_4_5.py
+          - tests/e2e/online_serving/test_minicpmo_4_5.py
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        # h100_2, not h100_1: advanced_model asserts transcribe every audio
+        # response with Whisper, and tests/helpers/media.py only uses an
+        # accelerator for Whisper when a spare GPU exists. On a single card it
+        # grades on CPU: ~8-10 min per clip vs ~30 s, which alone overruns the
+        # 50-minute job timeout (build 2956).
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/engine/orchestrator.py
+          - vllm_omni/entrypoints/cli/serve.py
+          - vllm_omni/entrypoints/openai/api_server.py
+          - vllm_omni/entrypoints/openai/duplex_capability.py
+          - vllm_omni/config/stage_config.py
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Qwen Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image.py
+          - vllm_omni/diffusion/models/qwen_image/
+          - vllm_omni/diffusion/models/dmd2/
+          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
+        commands:
+          - |
+            timeout 40m bash -c '
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'advanced_model and cuda' --run-level 'advanced_model'
+            '
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Wan22 Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_wan22_t2v.py
+          - tests/e2e/online_serving/test_wan22_t2v.py
+          - vllm_omni/diffusion/models/wan2_2/
+          - vllm_omni/diffusion/models/dmd2/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/offline_inference/test_wan22_t2v.py tests/e2e/online_serving/test_wan22_t2v.py -m 'advanced_model and cuda and diffusion' --run-level 'advanced_model'
+            "
+        mirror_hardwares: h100_1

.buildkite/cuda/test-nightly.yml

@@ -0,0 +1,432 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+  # Group: collapses under one heading in the Buildkite UI; child steps still run in parallel.
+  - group: ":card_index_dividers: Omni Model Test"
+    key: nightly-omni-test-group
+    depends_on: upload-nightly-pipeline
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
+    steps:
+
+      - label: ":full_moon: Omni · Function Test with H100"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -sv tests/e2e --run-level "full_model" -m "full_model and H100 and omni" --ignore=tests/e2e/accuracy --ignore=tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py --ignore=tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 Duplex Test"
+        timeout_in_minutes: 50
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py -m "full_model and cuda and H100 and omni" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · Doc Test with L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      - label: ":full_moon: Omni · Doc Test with H100"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=cuda:1
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=cuda:1
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and cuda" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        key: nightly-omni-performance-no-async-chunk
+        timeout_in_minutes: 300
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        key: nightly-omni-performance-async-chunk
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · Perf Test · vLLM Text"
+        key: nightly-omni-performance-vllm-text
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_vllm_text.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Omni · Perf Test · Multi-Replica"
+        key: nightly-omni-performance-multi-replicas
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen3_omni_multi_replicas.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_3
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Seed-TTS Perf Test"
+        key: nightly-omni-performance-minicpmo-4-5-duplex-seed-tts
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_duplex_seed_tts.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Omni · Multi-Replica Startup Test with 4x H100"
+        timeout_in_minutes: 45
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_omni_multi_replicas.py -m "full_model" --run-level "core_model"
+        mirror_hardwares: h100_4
+
+  - group: ":card_index_dividers: TTS Model Test"
+    key: nightly-tts-test-group
+    depends_on: upload-nightly-pipeline
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
+    steps:
+
+      - label: ":full_moon: TTS · Function Test with L4"
+        timeout_in_minutes: 120
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - pytest -s -v tests/e2e/ -m "full_model and L4 and tts" --run-level "full_model" --ignore=tests/e2e/accuracy
+        mirror_hardwares: l4_1
+
+      - label: ":full_moon: TTS · Perf Test"
+        key: nightly-tts-performance
+        timeout_in_minutes: 180
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+        mirror_hardwares: h100_2
+
+  - group: ":card_index_dividers: Tiny Model Tests [Multi-GPU]"
+    key: nightly-tiny-model-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test"
+    steps:
+
+      - label: ":full_moon: Tiny Model Tests · Diffusion (Multi-GPU accelerations)"
+        timeout_in_minutes: 30
+        # NOTE: Single-GPU (core_model) tiny model tests run on the ready label,
+        # so nightly only adds the multi-GPU parallelism configurations.
+        #
+        # This is a bit confusing, but `full_model` is used to mark the multigpu tests, and
+        # --run-level `core_model` is used to indicate that we should use tiny model weights
+        # for the CI, since these tests should pass irrespective of whether or not we use
+        # the full weights or not.
+        commands:
+          - timeout 25m pytest -sv tests/model_tests/diffusion -m 'full_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_4
+
+  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
+    key: nightly-diffusion-x2iat-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      # Use explicit file shards so single-card cases do not reserve 2+ GPUs.
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_qwen_image_expansion.py
+            -m "full_model and diffusion and H100 and not distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100 · Multi-GPU Qwen-Image"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/online_serving/test_qwen_image_expansion.py
+            -m "full_model and diffusion and H100 and distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -sv tests/e2e/ -k "not test_wan and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
+        mirror_hardwares: l4_4
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export VLLM_HTTP_TIMEOUT_KEEP_ALIVE=120
+          - pytest -s -v tests/e2e/accuracy/test_qwen_image.py --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2i_matches_diffusers' -m full_model --run-level full_model
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - export HUNYUAN_IMAGE3_MODEL="tencent/HunyuanImage-3.0-Instruct"
+          - export HUNYUAN_IMAGE3_DEVICES="0,1,2,3"
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m full_model --run-level full_model
+        mirror_hardwares: h100_4
+
+      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test · Qwen-Image"
+        key: nightly-diffusion-x2iat-performance-qwen-image
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - export CACHE_DIT_VERSION=1.5.0
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_4
+
+  - group: ":card_index_dividers: Diffusion X2V Model Test"
+    key: nightly-diffusion-x2v-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2v-test"
+    steps:
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 T2V Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "t2v" -m "full_model and cuda" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 I2V/TI2V Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k "i2v" -m "full_model and cuda" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Doc Test"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -s -v tests/examples/offline_inference/test_image_to_video.py -m "full_model and example and H100" --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion X2V · Other Function Test"
+        timeout_in_minutes: 90
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_wan22_autoround_w4a16_expansion.py -m "full_model and cuda" --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Wan2.2 Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/test_diffusers_backend_similarity.py -k '2v_matches_diffusers' -m full_model --run-level full_model
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · MiniMax H3 I2VA/Ref2VA Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/minimax_h3/test_minimax_h3_i2va_ref2va_similarity.py -m full_model --run-level full_model
+        mirror_hardwares: h100_4
+
+      - label: ":full_moon: Diffusion X2V · Other Accuracy Test"
+        timeout_in_minutes: 180
+        commands:
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_t2v/test_hunyuanvideo15_t2v_video_similarity.py -m full_model --run-level full_model
+          - pytest -s -v tests/e2e/accuracy/hunyuanvideo15_i2v/test_hunyuanvideo15_i2v_video_similarity.py -m full_model --run-level full_model
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Perf Test"
+        key: nightly-diffusion-x2v-performance
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            EXIT1=$$?
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_cosmos3_vllm_omni.json
+            EXIT2=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            if [ $$EXIT1 -ne 0 ]; then exit $$EXIT1; fi
+            exit $$EXIT2
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion X2V · Perf Test · MiniMax-H3"
+        key: nightly-diffusion-x2v-performance-minimax-h3
+        timeout_in_minutes: 180
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_minimax_h3_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+        mirror_hardwares: h100_4
+
+  - group: ":card_index_dividers: Diffusion Test"
+    key: nightly-diffusion-test-group
+    depends_on: upload-nightly-pipeline
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
+    steps:
+      - label: ":full_moon: Diffusion · Distributed Test with L4"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and L4' --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      - label: ":full_moon: Diffusion · Distributed Test with H100"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/distributed/ -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_4
+
+      - label: ":full_moon: Diffusion · Offloader Test"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/offloader -m "full_model and cuda"
+        mirror_hardwares: l4_1
+
+      - label: ":full_moon: Diffusion · Quantization Test with H100 · Single-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: ":full_moon: Diffusion · MiniMax H3 FP8 Quality Test with H100 · 2-GPU"
+        timeout_in_minutes: 60
+        commands:
+          - pytest -sv tests/diffusion/models/minimax_h3/test_minimax_h3_quantization_quality.py -m 'full_model and cuda and H100' --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: ":full_moon: Diffusion · Quantization Test with L4"
+        timeout_in_minutes: 60
+        commands:
+          - pip install "vllm-gguf-plugin>=0.0.3"
+          - pytest -sv tests/diffusion/quantization -m 'full_model and cuda and L4' --run-level "full_model"
+        mirror_hardwares: l4_1
+
+  # NOTE: ":email: Nightly Collection & Email" is deprecated and will be removed soon.
+  # Do not add new depends_on entries or extend this step.
+  - label: ":email: Nightly Collection & Email"
+    key: nightly-perf-distribution
+    depends_on:
+      - nightly-omni-performance-no-async-chunk
+      - nightly-omni-performance-async-chunk
+      - nightly-omni-performance-vllm-text
+      - nightly-omni-performance-multi-replicas
+      - nightly-tts-performance
+      - nightly-diffusion-x2iat-performance-qwen-image
+      - nightly-diffusion-x2v-performance
+    if: build.env("NIGHTLY") == "1" && build.env("EMAIL_DISTRIBUTION") == "1"
+    commands:
+      - pip install openpyxl
+      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
+      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-no-async-chunk
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-async-chunk
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-vllm-text
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance-multi-replicas
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance-qwen-image
+      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
+      - python tools/nightly/generate_nightly_perf_excel.py
+      - python tools/nightly/generate_nightly_perf_html.py
+      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
+      - buildkite-agent artifact upload "tests/dfx/perf/results/*.xlsx"
+      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
+    agents:
+      queue: "cpu_queue_premerge"

.buildkite/cuda/test-ready.yml

@@ -0,0 +1,340 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+  - group: ":card_index_dividers: Simple Test"
+    depends_on: upload-ready-pipeline
+    steps:
+      - label: "Simple · Model Executor Test"
+        commands:
+          - "timeout 20m pytest -sv tests/model_executor -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Diffusion Test"
+        commands:
+          - "timeout 20m pytest -sv tests/diffusion -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Engine&Entrypoints Test"
+        commands:
+          - "timeout 20m pytest -sv tests/entrypoints tests/engine -m 'core_model and cpu' --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+      - label: "Simple · Other Test"
+        commands:
+          - "timeout 20m pytest -sv tests/ -m 'core_model and cpu' --ignore=tests/diffusion --ignore=tests/model_executor --ignore=tests/entrypoints --ignore=tests/engine --cov=vllm_omni --cov-report=term-missing:skip-covered --cov-report=xml"
+        mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: Diffusion Test"
+    depends_on: upload-ready-pipeline
+    steps:
+
+      - label: "Diffusion · Offloader Test"
+        commands:
+          - timeout 40m pytest -s -v tests/diffusion/offloader -m "core_model and cuda" --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Cache Test"
+        commands:
+          - timeout 15m pytest -sv tests/diffusion/cache -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Batch Test"
+        commands:
+          - timeout 15m pytest -sv tests/diffusion/batching -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Distributed & Attention Test"
+        source_file_dependencies:
+          - tests/diffusion/distributed/
+          - tests/diffusion/attention/test_attention_sp.py
+          - vllm_omni/diffusion/distributed/
+          - vllm_omni/diffusion/attention/
+          - vllm_omni/diffusion/data.py
+          - vllm_omni/diffusion/models/
+        commands:
+          - timeout 20m pytest -sv tests/diffusion/distributed -m 'core_model and cuda' --run-level "core_model"
+          - timeout 20m pytest -sv tests/diffusion/attention/test_attention_sp.py -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_4
+
+      - label: "Diffusion · Model Test"
+        commands:
+          - timeout 15m pytest -sv tests/diffusion/models/ -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Other Test"
+        commands:
+          - timeout 15m pytest -sv tests/diffusion/*.py -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: Tiny Model Tests [Single GPU]"
+    depends_on: upload-ready-pipeline
+    steps:
+
+      - label: "Tiny Model Tests · Diffusion (base)"
+        commands:
+          - timeout 15m pytest -sv tests/model_tests/diffusion -m 'core_model and cuda' --run-level "core_model"
+        mirror_hardwares: l4_1
+
+  - label: "Engine&Model Executor Test"
+    depends_on: upload-ready-pipeline
+    commands:
+      - |
+        timeout 15m bash -c "
+                  pytest -sv tests/engine/ tests/model_executor/ -m 'core_model and cuda' --run-level 'core_model'
+        "
+    mirror_hardwares: l4_1
+
+  - label: "Distributed Test"
+    depends_on: upload-ready-pipeline
+    commands:
+      - |
+        timeout 20m bash -c "
+          pytest -sv tests/distributed/ -m 'core_model and cuda and L4' --run-level 'core_model'
+        "
+    mirror_hardwares: l4_1
+
+  - label: "Entrypoints Test"
+    depends_on: upload-ready-pipeline
+    commands:
+      - |
+        timeout 30m bash -c "
+          export VLLM_WORKER_MULTIPROC_METHOD=spawn
+          pytest -sv tests/entrypoints/ -m 'core_model and cuda' --run-level 'core_model'
+        "
+    mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: E2E Test"
+    depends_on: upload-ready-pipeline
+    steps:
+      - label: "Custom Pipeline Test"
+        source_file_dependencies:
+          - tests/e2e/features/custom_pipeline/
+          - tests/e2e/features/helpers/custom_pipeline.py
+          - vllm_omni/diffusion/
+          - vllm_omni/engine/
+          - vllm_omni/entrypoints/
+          - vllm_omni/inputs/
+          - vllm_omni/outputs/
+        commands:
+          - timeout 20m pytest -s -v tests/e2e/features/custom_pipeline/ -m "core_model"
+        agents:
+          queue: "gpu_1_queue"
+        plugins:
+          - docker#v5.2.0:
+              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
+              always-pull: true
+              propagate-environment: true
+              # vLLM 0.27 added a pre-flight capacity check in
+              # distributed/device_communicators/shm_broadcast.py. Without this
+              # the container gets Docker's 64 MiB default and every test in
+              # this step dies at engine start with
+              #   RuntimeError: Insufficient space in /dev/shm:
+              #                 240 MiB required, 64 MiB available
+              # (9 failed in 8.7s on build #2949). vLLM 0.26 had no such check
+              # and allocated regardless, so this step passed on main while
+              # being under-provisioned the whole time — 0.27 turned a silent
+              # risk into an explicit error.
+              #
+              # This is the only inline docker plugin block in .buildkite/cuda/;
+              # every other step inherits common/ci_mirror_hardwares.yml, which
+              # already sets 8gb. Matching that rather than inventing a value.
+              shm-size: "8gb"
+              environment:
+                - "HF_HOME=/fsx/hf_cache"
+              volumes:
+                - "/fsx/hf_cache:/fsx/hf_cache"
+
+      - label: "RLHF · VeRL-Omni E2E Test"
+        source_file_dependencies:
+          - tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
+          - tests/e2e/features/helpers/verl_omni_server.py
+          - tests/e2e/features/helpers/custom_pipeline.py
+          - vllm_omni/diffusion/
+          - vllm_omni/engine/
+          - vllm_omni/entrypoints/
+          - vllm_omni/inputs/
+          - vllm_omni/outputs/
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/features/rlhf_test/test_verl_omni_e2e.py
+            "
+        mirror_hardwares: l4_1
+
+      - label: "Omni · Qwen3-Omni Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_omni.py
+          - vllm_omni/model_executor/models/qwen3_omni/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/qwen2_5_omni/qwen2_5_omni_thinker.py
+          - vllm_omni/model_executor/models/qwen3_tts/cuda_graph_decoder_wrapper.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_omni.py
+          - vllm_omni/deploy/qwen3_omni_moe.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_2
+
+      - label: "Omni · MiniCPM-o 4.5 Offline Test"
+        source_file_dependencies:
+          - tests/e2e/offline_inference/test_minicpmo_4_5.py
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Omni · MiniCPM-o 4.5 Online Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_minicpmo_4_5.py
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        source_file_dependencies:
+          - tests/assets/minicpmo_4_5/response_required_16k.wav
+          - tests/assets/minicpmo_4_5/soft_interrupt_16k.wav
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+          - tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+          - tests/e2e/online_serving/test_minicpmo_realtime_duplex_drivers.py
+          - tests/e2e/online_serving/helpers/minicpmo_realtime_duplex_scenarios.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_multi_session.py
+          - tests/e2e/online_serving/run_minicpmo_realtime_duplex_soft_interrupt.py
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+          - tests/e2e/online_serving/helpers/minicpmo_4_5_duplex.py
+          - vllm_omni/deploy/minicpmo_4_5_duplex.yaml
+          - vllm_omni/experimental/fullduplex/
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/models/step_audio2/
+          - vllm_omni/model_executor/models/cosyvoice3/code2wav_core/
+          - vllm_omni/model_executor/models/cosyvoice3/utils.py
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+          - vllm_omni/core/sched/
+          - vllm_omni/outputs/multimodal_accumulation.py
+          - vllm_omni/engine/stage_init_utils.py
+          - vllm_omni/engine/async_omni_engine.py
+          - vllm_omni/engine/orchestrator.py
+          - vllm_omni/entrypoints/cli/serve.py
+          - vllm_omni/entrypoints/openai/api_server.py
+          - vllm_omni/entrypoints/openai/duplex_capability.py
+          - vllm_omni/config/stage_config.py
+        commands:
+          - |
+            timeout 20m bash -c "
+              pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and cuda' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        source_file_dependencies:
+          - tests/dfx/perf/tests/test_minicpmo_4_5_ready.json
+          - vllm_omni/deploy/minicpmo_4_5.yaml
+          - vllm_omni/model_executor/models/minicpmo_4_5/
+          - vllm_omni/model_executor/stage_input_processors/minicpmo_4_5_omni.py
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and cuda'
+        mirror_hardwares: h100_1
+
+      - label: "TTS · Qwen3-TTS CustomVoice Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_tts_customvoice.py
+          - vllm_omni/model_executor/models/qwen3_tts/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/common/alias_free_activation.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+          - vllm_omni/deploy/qwen3_tts.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "TTS · Qwen3-TTS Base Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen3_tts_base.py
+          - vllm_omni/model_executor/models/qwen3_tts/
+          - vllm_omni/model_executor/models/common/qwen3_code_predictor.py
+          - vllm_omni/model_executor/models/common/snake_activation.py
+          - vllm_omni/model_executor/models/common/alias_free_activation.py
+          - vllm_omni/model_executor/stage_input_processors/qwen3_tts.py
+          - vllm_omni/deploy/qwen3_tts.yaml
+        commands:
+          - |
+            timeout 20m bash -c "
+              export VLLM_WORKER_MULTIPROC_METHOD=spawn
+              export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+              export VLLM_OMNI_USE_V2_RUNNER=1
+              pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · Wan22 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_wan22_t2v.py
+          - vllm_omni/diffusion/models/wan2_2/
+          - vllm_omni/diffusion/models/dmd2/
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_wan22_t2v.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Cosmos3 Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_cosmos3.py
+          - vllm_omni/diffusion/models/cosmos3/
+          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_wan.py
+          - vllm_omni/experimental/world_models/
+          - vllm_omni/model_extras/cosmos3.py
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_cosmos3.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · Qwen Image Test"
+        source_file_dependencies:
+          - tests/e2e/online_serving/test_qwen_image.py
+          - vllm_omni/diffusion/models/qwen_image/
+          - vllm_omni/diffusion/models/dmd2/
+          - vllm_omni/diffusion/distributed/autoencoders/autoencoder_kl_qwenimage.py
+        commands:
+          - |
+            timeout 40m bash -c "
+              export VLLM_IMAGE_FETCH_TIMEOUT=60
+              pytest -s -v tests/e2e/online_serving/test_qwen_image.py -m 'core_model' --run-level 'core_model'
+            "
+        mirror_hardwares: h100_1

.buildkite/cuda/test-weekly.yml

@@ -0,0 +1,144 @@
+env:
+  VLLM_WORKER_MULTIPROC_METHOD: spawn
+  VLLM_USE_FLASHINFER_MOE_FP16: 0
+  HF_HUB_DOWNLOAD_TIMEOUT: 300
+  HF_HUB_ETAG_TIMEOUT: 60
+
+steps:
+
+  - label: "Reliability Test - qwen3-omni"
+    timeout_in_minutes: 180
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    commands:
+      - pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
+    mirror_hardwares: h100_2
+
+  - label: "Reliability Test - wan22"
+    timeout_in_minutes: 180
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    commands:
+      - pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
+    mirror_hardwares: h100_2
+
+  - label: "Reliability Test - hunyuan-image"
+    timeout_in_minutes: 180
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    commands:
+      - pytest -s -v tests/dfx/reliability/test_reliability_hunyuan_image.py -m "slow"
+    mirror_hardwares: h100_4
+
+  - group: "Reliability Test - Invalid parameters Test"
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    steps:
+
+      - label: "Invalid parameters Test · H100"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and H100"
+        mirror_hardwares: h100_2
+
+      - label: "Invalid parameters Test · L4"
+        timeout_in_minutes: 120
+        commands:
+          - pytest -s -v tests/dfx/reliability/invalid_param_test/ -m "slow and L4"
+        mirror_hardwares: l4_1
+
+  - group: ":card_index_dividers: E2E Tests"
+    depends_on: upload-weekly-pipeline
+    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
+    steps:
+
+      - label: "Omni · L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
+          - >-
+            pytest -s -v tests/e2e/
+            --ignore=tests/e2e/online_serving/test_qwen2_5_omni_expansion.py
+            --ignore=tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
+            -m "slow and L4 and omni"
+            --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      - label: "Omni · H100"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and omni"
+            --run-level "full_model"
+        mirror_hardwares: h100_2
+
+      - label: "TTS · L4"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - |
+            failed=0
+            for f in $(pytest --collect-only -q tests/e2e/ -m "slow and L4 and tts" --run-level "full_model" | grep '\.py::' | sed -E 's|::.*||' | sort -u); do
+              pytest -s -v "$$f" -m "slow and L4 and tts" --run-level "full_model" || failed=1
+            done
+            exit $$failed
+        mirror_hardwares: l4_1
+
+      - label: "TTS · H100"
+        timeout_in_minutes: 90
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - export VLLM_USE_DEEP_GEMM="0"
+          - export VLLM_MOE_USE_DEEP_GEMM="0"
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and H100 and tts"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      # Own step: the image has no NanoCodec, so this file import-skips out of
+      # the mark-selected TTS steps above. The overrides keep nemo_toolkit[tts]
+      # from downgrading transformers; see
+      # examples/offline_inference/text_to_speech/README.md.
+      - label: "TTS · Gepard-1.0 · L4"
+        timeout_in_minutes: 60
+        commands:
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - printf '%s\n' "transformers>=5.5.3" "huggingface-hub>=1.0" > /tmp/gepard-overrides.txt
+          - uv pip install --python "$(python3 -c 'import sys; print(sys.executable)')" "nemo_toolkit[tts]==2.7.3" --overrides /tmp/gepard-overrides.txt
+          - >-
+            pytest -s -v tests/e2e/offline_inference/test_gepard_tts.py
+            -m "tts and cuda"
+            --run-level "full_model"
+        mirror_hardwares: l4_1
+
+      - label: "Diffusion · L4"
+        timeout_in_minutes: 60
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and L4"
+            --run-level "full_model"
+        mirror_hardwares: l4_4
+
+      - label: "Diffusion · H100 · Single-GPU"
+        timeout_in_minutes: 120
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and not distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_1
+
+      - label: "Diffusion · H100 · Multi-GPU"
+        timeout_in_minutes: 90
+        commands:
+          - >-
+            pytest -sv
+            tests/e2e/ -m "slow and diffusion and H100 and distributed_cuda"
+            --run-level "full_model"
+        mirror_hardwares: h100_4

.buildkite/intel/pipeline-intel.yml

@@ -1,16 +1,16 @@
-# .buildkite/pipeline-intel.yaml
+# .buildkite/intel/pipeline-intel.yml
 steps:
   - group: "INTEL Tests"
     steps:
       - label: "Intel: XPU Test"
-        command: "bash .buildkite/scripts/hardware_ci/run-xpu-test.sh"
+        command: "bash .buildkite/intel/scripts/run-xpu-test.sh"
         agents:
           queue: "intel-gpu-omni"
         env:
           DOCKER_BUILDKIT: "1"
           # Buildkite will automatically replace this with the actual commit hash
           VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
-          VLLM_VERSION: "v0.20.0"
+          VLLM_VERSION: "v0.27.0"
         priority: 100
         timeout_in_minutes: 60
-        soft_fail: true
+        soft_fail: false

.buildkite/intel/scripts/bootstrap-intel-omni.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+# vllm-omni Intel bootstrap
+# Uses static pipeline-intel.yml for Intel XPU tests
+
+set -euo pipefail
+
+source .buildkite/common/scripts/resolve_skip_ci.sh
+
+upload_pipeline() {
+    echo "--- 🛠 Preparing Intel pipeline"
+    buildkite-agent pipeline upload .buildkite/intel/pipeline-intel.yml
+}
+
+gate_bootstrap_ci intel l2
+
+upload_pipeline

.buildkite/intel/scripts/run-xpu-test.sh

@@ -0,0 +1,95 @@
+#!/bin/bash
+
+# This script build the XPU docker image and run the offline inference inside the container.
+set -ex
+
+omni_source_dir=$(git rev-parse --show-toplevel)
+
+base_image_name="xpu/vllm-omni-ci-base:${VLLM_VERSION:?VLLM_VERSION must be set}"
+image_name="xpu/vllm-omni-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT must be set}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(
+    tr -dc A-Za-z0-9 </dev/urandom | head -c 10
+    echo
+)"
+
+cd "${omni_source_dir}"
+
+# The XPU base image is ~37GB; the default gzip layer exporter is single-threaded
+# and takes ~16min to compress it. zstd is multi-threaded (uses all cores) at a
+# similar ratio; set EXPORT_COMPRESSION=uncompressed to skip compression entirely
+# for local-only images. This requires the containerd image store (buildx docker
+# driver), which is the default here.
+EXPORT_COMPRESSION="${EXPORT_COMPRESSION:-zstd}"
+if [ "${EXPORT_COMPRESSION}" = "uncompressed" ]; then
+    export_args=(--output "type=image,name={{IMAGE}},compression=uncompressed")
+else
+    export_args=(--output "type=image,name={{IMAGE}},compression=${EXPORT_COMPRESSION},compression-level=3,force-compression=true")
+fi
+
+docker_build() {
+    # $1 = image name; remaining args passed through to docker build.
+    local image="$1"
+    shift
+    local out=("${export_args[@]/'{{IMAGE}}'/${image}}")
+    docker build "${out[@]}" "$@" -f docker/Dockerfile.xpu .
+}
+
+build_vllm_base() (
+    local vllm_source_dir
+    vllm_source_dir=$(mktemp -d)
+    trap 'rm -rf "${vllm_source_dir}"' EXIT
+
+    git clone --depth 1 --branch "${VLLM_VERSION}" \
+        https://github.com/vllm-project/vllm "${vllm_source_dir}"
+
+    local out=("${export_args[@]/'{{IMAGE}}'/${base_image_name}}")
+    docker build "${out[@]}" \
+        --target vllm-openai \
+        -f "${vllm_source_dir}/docker/Dockerfile.xpu" \
+        "${vllm_source_dir}"
+)
+
+if [ -z "$(docker images -q "${base_image_name}")" ]; then
+    build_vllm_base
+fi
+
+# Try building the docker image
+docker_build "${image_name}" --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}"
+
+# Setup cleanup
+remove_docker_container() {
+    docker rm -f "${container_name}" || true
+    docker image rm -f "${image_name}" || true
+    docker system prune -f || true
+}
+trap remove_docker_container EXIT
+
+HF_CACHE="${HF_CACHE:-$(realpath ~)/.cache/huggingface}"
+mkdir -p "${HF_CACHE}"
+HF_MOUNT="/root/.cache/huggingface"
+
+time timeout -k 30 30m docker run \
+    --device /dev/dri:/dev/dri \
+    --net=host \
+    --ipc=host \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    -v "${HF_CACHE}:${HF_MOUNT}" \
+    --security-opt seccomp=unconfined \
+    --entrypoint="" \
+    -e VLLM_LOGGING_LEVEL \
+    -e VLLM_OMNI_LOGGING_LEVEL \
+    -e HF_TOKEN \
+    -e ZE_AFFINITY_MASK \
+    --name "${container_name}" \
+    "${image_name}" \
+    bash -c '
+    set -e
+    echo $ZE_AFFINITY_MASK
+    pip install tblib==3.1.0
+    cd /workspace/vllm-omni
+    XPU_TEST_PATHS="tests/diffusion tests/dfx tests/e2e"
+    pytest -v -s $XPU_TEST_PATHS -m "core_model and xpu and B60"
+    pytest -v -s tests/diffusion/quantization/test_mxfp8_config.py
+    pytest -v -s $XPU_TEST_PATHS -m "advanced_model and xpu and B60"
+    pytest -v -s $XPU_TEST_PATHS -m "omni and xpu and B60"
+'

.buildkite/nightly-release-pipeline.yaml

@@ -1,20 +0,0 @@
-steps:
-  - label: "Build and upload wheel"
-    key: "build-wheel"
-    agents:
-      queue: cpu_queue_release
-    commands:
-      - "curl -LsSf https://astral.sh/uv/install.sh | sh"
-      - 'export PATH="$HOME/.local/bin:$PATH"'
-      - "uv venv --python=3.12 && source .venv/bin/activate"
-      - "uv pip install --upgrade build"
-      - "python3 -m build"
-      - "bash .buildkite/scripts/upload-nightly-wheels.sh"
-
-  - label: "Generate and upload wheel indices"
-    depends_on: "build-wheel"
-    allow_dependency_failure: true
-    agents:
-      queue: small_cpu_queue_release
-    commands:
-      - "bash .buildkite/scripts/generate-and-upload-nightly-index.sh"

.buildkite/npu/bootstrap-upload-steps.yml

@@ -0,0 +1,181 @@
+# Uploaded by upload_pipeline.py --upload (not the Buildkite hook).
+# ``if`` is injected by step ``key`` from skip-ci at upload time.
+steps:
+  - label: ":buildkit: Build and Push NPU Test Image - A2"
+    key: image-build-a2
+    agents:
+      queue: "ascend-a2b3"
+      resource_class: "npu-1"
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              vault.hashicorp.com/agent-init-first: "true"
+              vault.hashicorp.com/agent-inject: "true"
+              vault.hashicorp.com/agent-inject-perms-ca.pem: "0400"
+              vault.hashicorp.com/agent-inject-perms-cert.pem: "0400"
+              vault.hashicorp.com/agent-inject-perms-config.json: "0400"
+              vault.hashicorp.com/agent-inject-perms-key.pem: "0400"
+              vault.hashicorp.com/agent-inject-secret-ca.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-cert.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-config.json: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-key.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-template-ca.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.RootCA }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-cert.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaCert }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-config.json: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.dockerConfig }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-key.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaKey }}\n{{- end }}"
+              vault.hashicorp.com/agent-pre-populate-only: "true"
+              vault.hashicorp.com/agent-run-as-group: "1000"
+              vault.hashicorp.com/agent-run-as-user: "1000"
+              vault.hashicorp.com/agent-service-account-token-volume-name: token-vol
+              vault.hashicorp.com/role: ascend-gha-runners
+              vault.hashicorp.com/secret-volume-path: /home/user/.docker/
+              vault.hashicorp.com/tls-skip-verify: "true"
+          podSpecPatch:
+            volumes:
+            - name: token-vol
+              projected:
+                defaultMode: 420
+                sources:
+                - serviceAccountToken:
+                    audience: api
+                    expirationSeconds: 600
+                    path: token
+    env:
+      VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
+      IMAGE_NAME: "vllm-omni-ci-npu"
+      IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
+      BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
+    command: |
+      set -ex
+
+      echo "--- Building and pushing NPU Test Image"
+      echo "Image: $${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+      echo "buildkitd address: $${BUILDKITD_ADDR}"
+
+      if ! command -v buildctl &> /dev/null; then
+        echo "Installing buildctl..."
+        mkdir -p /tmp/buildkit
+        BUILDKIT_VERSION="v0.29.0"
+        wget -q "https://github.com/moby/buildkit/releases/download/$${BUILDKIT_VERSION}/buildkit-$${BUILDKIT_VERSION}.linux-arm64.tar.gz" -O /tmp/buildkit.tar.gz
+        tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
+        cp /tmp/buildkit/bin/buildctl /usr/local/bin/
+      fi
+
+      export DOCKER_CONFIG=/home/user/.docker
+      buildctl \
+        --addr="$${BUILDKITD_ADDR}" \
+        --tlscacert=/home/user/.docker/ca.pem \
+        --tlscert=/home/user/.docker/cert.pem \
+        --tlskey=/home/user/.docker/key.pem \
+        build \
+        --frontend dockerfile.v0 \
+        --local context=. \
+        --local dockerfile=./docker \
+        --opt filename=Dockerfile.npu.ci \
+        --secret id=dockerconfig,src=/home/user/.docker/config.json \
+        --output type=image,name=$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG},push=true \
+        --progress=plain
+
+      echo "--- Image pushed successfully"
+      echo "$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+
+  - label: ":buildkit: Build and Push NPU Test Image - A3"
+    key: image-build-a3
+    agents:
+      queue: "ascend-a2b3"
+      resource_class: "npu-1"
+    plugins:
+      - kubernetes:
+          metadata:
+            annotations:
+              vault.hashicorp.com/agent-init-first: "true"
+              vault.hashicorp.com/agent-inject: "true"
+              vault.hashicorp.com/agent-inject-perms-ca.pem: "0400"
+              vault.hashicorp.com/agent-inject-perms-cert.pem: "0400"
+              vault.hashicorp.com/agent-inject-perms-config.json: "0400"
+              vault.hashicorp.com/agent-inject-perms-key.pem: "0400"
+              vault.hashicorp.com/agent-inject-secret-ca.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-cert.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-config.json: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-secret-key.pem: internal/data/ascend/buildkitd
+              vault.hashicorp.com/agent-inject-template-ca.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.RootCA }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-cert.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaCert }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-config.json: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.dockerConfig }}\n{{- end }}"
+              vault.hashicorp.com/agent-inject-template-key.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaKey }}\n{{- end }}"
+              vault.hashicorp.com/agent-pre-populate-only: "true"
+              vault.hashicorp.com/agent-run-as-group: "1000"
+              vault.hashicorp.com/agent-run-as-user: "1000"
+              vault.hashicorp.com/agent-service-account-token-volume-name: token-vol
+              vault.hashicorp.com/role: ascend-gha-runners
+              vault.hashicorp.com/secret-volume-path: /home/user/.docker/
+              vault.hashicorp.com/tls-skip-verify: "true"
+          podSpecPatch:
+            volumes:
+            - name: token-vol
+              projected:
+                defaultMode: 420
+                sources:
+                - serviceAccountToken:
+                    audience: api
+                    expirationSeconds: 600
+                    path: token
+    env:
+      VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}-a3"
+      IMAGE_NAME: "vllm-omni-ci-npu"
+      IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
+      BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
+    command: |
+      set -ex
+
+      echo "--- Building and pushing NPU Test Image"
+      echo "Image: $${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+      echo "buildkitd address: $${BUILDKITD_ADDR}"
+
+      if ! command -v buildctl &> /dev/null; then
+        echo "Installing buildctl..."
+        mkdir -p /tmp/buildkit
+        BUILDKIT_VERSION="v0.29.0"
+        wget -q "https://github.com/moby/buildkit/releases/download/$${BUILDKIT_VERSION}/buildkit-$${BUILDKIT_VERSION}.linux-arm64.tar.gz" -O /tmp/buildkit.tar.gz
+        tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
+        cp /tmp/buildkit/bin/buildctl /usr/local/bin/
+      fi
+
+      export DOCKER_CONFIG=/home/user/.docker
+      buildctl \
+        --addr="$${BUILDKITD_ADDR}" \
+        --tlscacert=/home/user/.docker/ca.pem \
+        --tlscert=/home/user/.docker/cert.pem \
+        --tlskey=/home/user/.docker/key.pem \
+        build \
+        --frontend dockerfile.v0 \
+        --local context=. \
+        --local dockerfile=./docker \
+        --opt filename=Dockerfile.npu.ci.a3 \
+        --secret id=dockerconfig,src=/home/user/.docker/config.json \
+        --output type=image,name=$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG},push=true \
+        --progress=plain
+
+      echo "--- Image pushed successfully"
+      echo "$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+
+  - label: "Upload Ready Pipeline"
+    depends_on:
+      - image-build-a2
+      - image-build-a3
+    key: upload-ready-pipeline
+    agents:
+      queue: "cpu_queue_premerge"
+    command: |
+      python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/npu/test-npu-ready.yml
+
+  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  - label: "Upload Nightly Pipeline"
+    depends_on:
+      - image-build-a2
+      - image-build-a3
+    key: upload-nightly-pipeline
+    commands:
+      - python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/npu/test-npu-nightly.yml
+    agents:
+      queue: "cpu_queue_premerge"

.buildkite/npu/pipeline-npu-a3.yml

@@ -0,0 +1,110 @@
+# steps:
+#   - label: ":buildkit: Build and Push NPU Test Image - A3"
+#     key: image-build-a3
+#     agents:
+#       queue: "ascend-a3"
+#       resource_class: "npu-2"
+#     plugins:
+#       - kubernetes:
+#           metadata:
+#             annotations:
+#               vault.hashicorp.com/agent-init-first: "true"
+#               vault.hashicorp.com/agent-inject: "true"
+#               vault.hashicorp.com/agent-inject-perms-ca.pem: "0400"
+#               vault.hashicorp.com/agent-inject-perms-cert.pem: "0400"
+#               vault.hashicorp.com/agent-inject-perms-config.json: "0400"
+#               vault.hashicorp.com/agent-inject-perms-key.pem: "0400"
+#               vault.hashicorp.com/agent-inject-secret-ca.pem: internal/data/ascend/buildkitd
+#               vault.hashicorp.com/agent-inject-secret-cert.pem: internal/data/ascend/buildkitd
+#               vault.hashicorp.com/agent-inject-secret-config.json: internal/data/ascend/buildkitd
+#               vault.hashicorp.com/agent-inject-secret-key.pem: internal/data/ascend/buildkitd
+#               vault.hashicorp.com/agent-inject-template-ca.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.RootCA }}\n{{- end }}"
+#               vault.hashicorp.com/agent-inject-template-cert.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaCert }}\n{{- end }}"
+#               vault.hashicorp.com/agent-inject-template-config.json: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.dockerConfig }}\n{{- end }}"
+#               vault.hashicorp.com/agent-inject-template-key.pem: "{{- with secret \"internal/data/ascend/buildkitd\" -}}\n{{ .Data.data.ClientCaKey }}\n{{- end }}"
+#               vault.hashicorp.com/agent-pre-populate-only: "true"
+#               vault.hashicorp.com/agent-run-as-group: "1000"
+#               vault.hashicorp.com/agent-run-as-user: "1000"
+#               vault.hashicorp.com/agent-service-account-token-volume-name: token-vol
+#               vault.hashicorp.com/role: ascend-gha-runners
+#               vault.hashicorp.com/secret-volume-path: /home/user/.docker/
+#               vault.hashicorp.com/tls-skip-verify: "true"
+#           podSpecPatch:
+#             volumes:
+#             - name: token-vol
+#               projected:
+#                 defaultMode: 420
+#                 sources:
+#                 - serviceAccountToken:
+#                     audience: api
+#                     expirationSeconds: 600
+#                     path: token
+#     env:
+#       VLLM_IMAGE_TAG: "${BUILDKITE_COMMIT}"
+#       IMAGE_NAME: "vllm-omni-ci-npu"
+#       IMAGE_REGISTRY: "swr.cn-southwest-2.myhuaweicloud.com/modelfoundry"
+#       BUILDKITD_ADDR: "tcp://buildkitd-service.buildkitd:1234"
+#     command: |
+#       set -ex
+
+#       echo "--- Building and pushing NPU Test Image"
+#       echo "Image: $${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+#       echo "buildkitd address: $${BUILDKITD_ADDR}"
+
+#       git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/"
+
+#       if ! command -v buildctl &> /dev/null; then
+#         echo "Installing buildctl..."
+#         mkdir -p /tmp/buildkit
+#         BUILDKIT_VERSION="v0.29.0"
+#         wget -q "https://gh-proxy.test.osinfra.cn/https://github.com/moby/buildkit/releases/download/$${BUILDKIT_VERSION}/buildkit-$${BUILDKIT_VERSION}.linux-arm64.tar.gz" -O /tmp/buildkit.tar.gz
+#         tar -xzf /tmp/buildkit.tar.gz -C /tmp/buildkit
+#         cp /tmp/buildkit/bin/buildctl /usr/local/bin/
+#       fi
+
+#       export DOCKER_CONFIG=/home/user/.docker
+#       buildctl \
+#         --addr="$${BUILDKITD_ADDR}" \
+#         --tlscacert=/home/user/.docker/ca.pem \
+#         --tlscert=/home/user/.docker/cert.pem \
+#         --tlskey=/home/user/.docker/key.pem \
+#         build \
+#         --frontend dockerfile.v0 \
+#         --local context=. \
+#         --local dockerfile=./docker \
+#         --opt filename=Dockerfile.npu.ci.a3 \
+#         --secret id=dockerconfig,src=/home/user/.docker/config.json \
+#         --output type=image,name=$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG},push=true \
+#         --progress=plain
+
+#       echo "--- Image pushed successfully"
+#       echo "$${IMAGE_REGISTRY}/$${IMAGE_NAME}:$${VLLM_IMAGE_TAG}"
+
+#   # - label: "Upload Ready Pipeline - A3"
+#   #   depends_on: image-build-a3
+#   #   key: upload-ready-pipeline-a3
+#   #   if: build.branch != "main" && build.pull_request.labels includes "npu-test"
+#   #   agents:
+#   #     queue: "ascend-a3"
+#   #     resource_class: "npu-2"
+#   #   command: |
+#   #     buildkite-agent pipeline upload .buildkite/npu/test-npu-ready.yml
+
+  # # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
+  # - label: "Upload Nightly Pipeline"
+  #   depends_on: image-build
+  #   key: upload-nightly-pipeline
+  #   if: >-
+  #     (build.branch == "main" && build.env("NIGHTLY") == "1") ||
+  #     (build.branch != "main" && (
+  #       build.pull_request.labels includes "nightly-test" ||
+  #       build.pull_request.labels includes "omni-test" ||
+  #       build.pull_request.labels includes "tts-test" ||
+  #       build.pull_request.labels includes "diffusion-x2iat-test" ||
+  #       build.pull_request.labels includes "diffusion-x2v-test"
+  #     ))
+  #   commands:
+  #     - buildkite-agent pipeline upload .buildkite/npu/test-npu-nightly.yml
+  #   agents:
+  #     queue: "ascend-a3"
+  #     resource_class: "npu-2"

.buildkite/npu/pipeline-npu.yml

@@ -0,0 +1,9 @@
+# Hook uploads this entry YAML; upload_pipeline.py --upload reads bootstrap-upload-steps.yml
+# and injects ``if`` by step ``key`` from skip-ci.
+steps:
+  - label: ":github: Resolve skip-ci & upload pipeline"
+    key: upload-ci-pipeline
+    commands:
+      - "python3 .buildkite/common/scripts/upload_pipeline.py --upload .buildkite/npu/bootstrap-upload-steps.yml"
+    agents:
+      queue: "cpu_queue_premerge"

.buildkite/npu/test-npu-nightly.yml

@@ -0,0 +1,212 @@
+steps:
+  - group: ":card_index_dividers: Omni Model Test"
+    key: nightly-omni-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "omni-test"
+    steps:
+      - label: ":full_moon: Omni · Perf Test · No Async Chunk"
+        key: nightly-omni-performance-no-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_no_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · Perf Test · Async Chunk"
+        key: nightly-omni-performance-async-chunk
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_qwen3_omni_async_chunk.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5_expansion.py -m 'full_model' --run-level 'full_model'
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Duplex Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - >-
+            pytest -s -v
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex.py
+            tests/e2e/online_serving/test_minicpmo_4_5_duplex_expansion.py
+            -m "npu and A3 and omni"
+            --run-level full_model
+      - label: ":full_moon: Omni · MiniCPM-o 4.5 · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          # Pin the Seed-TTS scorer to the second card: _get_eval_device() otherwise
+          # auto-picks npu:0, which the omni server under test already fills with its
+          # KV cache, so Whisper OOMs on `.to(device)` and every clip reports
+          # "ASR / WER failed".
+          - export SEED_TTS_WER_EVAL=1
+          - export SEED_TTS_EVAL_DEVICE=npu:1
+          - |
+            set +e
+            pytest -s -v tests/e2e/accuracy/minicpmo_4_5/test_minicpmo_4_5.py -m "full_model and npu and A3" --run-level full_model
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/e2e/accuracy/minicpmo_4_5/results/*.json"
+            exit $$EXIT
+  - group: ":card_index_dividers: TTS Model Test"
+    key: nightly-tts-test-group
+    depends_on: upload-nightly-pipeline
+    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
+    steps:
+      - label: ":full_moon: TTS · Perf Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_tts.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion I2I Model Test"
+    key: nightly-HunyuanImage3-x2i-test-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: HunyuanImage3-DIT · Accuracy Test"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HUNYUAN_IMAGE3_MODEL: "tencent/HunyuanImage-3.0-Instruct"
+          HUNYUAN_IMAGE3_DEVICES: "0,1,2,3"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - pytest -s -v tests/e2e/accuracy/test_hunyuan_image3_pixel_accuracy.py -m "full_model and A3" --run-level full_model
+  - group: ":card_index_dividers: Diffusion Benchmark Perf Test"
+    key: nightly-diffusion-benchmark-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2iat-test"
+    steps:
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 CFGP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_cfgp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=2 SP=2"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp2_sp2.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+      - label: ":full_moon: Diffusion Perf · HunyuanImage3 TP=4"
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m A3 --test-config-file tests/dfx/perf/tests/test_hunyuan_image_tp4.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT
+  - group: ":card_index_dividers: Diffusion X2V Model Test"
+    key: nightly-diffusion-x2v-group
+    depends_on: upload-nightly-pipeline
+    if: >-
+      build.env("NIGHTLY") == "1" ||
+      build.pull_request.labels includes "nightly-test" ||
+      build.pull_request.labels includes "diffusion-x2v-test"
+    steps:
+      - label: ":full_moon: Diffusion X2V · Function Test"
+        mirror_hardwares: a2b3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py -k npu_i2v --run-level "full_model"
+      - label: ":full_moon: Diffusion X2V · Perf Test"
+        key: nightly-diffusion-x2v-performance
+        timeout_in_minutes: 180
+        mirror_hardwares: a3_npu_4
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+          DIFFUSION_ATTENTION_BACKEND: TORCH_SDPA
+        commands:
+          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
+          - |
+            set +e
+            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py -m npu --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
+            EXIT=$$?
+            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
+            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
+            exit $$EXIT

.buildkite/npu/test-npu-ready.yml

@@ -0,0 +1,52 @@
+steps:
+  - label: "Platforms Test"
+    key: ready-platforms-test
+    depends_on: upload-ready-pipeline
+    mirror_hardwares: a3_npu_2
+    commands:
+      - >-
+        pytest -s -v
+        tests/platforms/npu/test_minicpmo_code2wav_npugraph.py
+        -m 'core_model and npu and A3 and omni'
+        --run-level 'core_model'
+
+  - group: ":card_index_dividers: Omni Model Test"
+    key: ready-omni-group
+    depends_on: upload-ready-pipeline
+    steps:
+      - label: "Omni · MiniCPM-o 4.5 Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - pytest -s -v tests/e2e/offline_inference/test_minicpmo_4_5.py tests/e2e/online_serving/test_minicpmo_4_5.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Duplex Test"
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_minicpmo_4_5_duplex.py -m 'core_model and npu and A3 and omni' --run-level 'core_model'
+      - label: "Omni · MiniCPM-o 4.5 Perf Test"
+        timeout_in_minutes: 60
+        mirror_hardwares: a3_npu_2
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+        commands:
+          - export BENCHMARK_DIR=tests/dfx/perf/results
+          - pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_minicpmo_4_5_ready.json -m 'core_model and npu and A3 and omni'
+
+  - group: ":card_index_dividers: TTS Model Test"
+    key: ready-tts-group
+    depends_on: upload-ready-pipeline
+    steps:
+      - label: ":sound: TTS · Qwen3-TTS CustomVoice Test"
+        mirror_hardwares: a2b3_npu_1
+        env:
+          VLLM_WORKER_MULTIPROC_METHOD: spawn
+          VLLM_ALLOW_LONG_MAX_MODEL_LEN: "1"
+          HF_TOKEN: "${HF_TOKEN}"
+        commands:
+          - pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m 'core_model and npu' --run-level 'core_model'

.buildkite/pipeline.yml

@@ -1,76 +0,0 @@
-# Document 1: Buildkite loads only this block on first parse. The next step resolves docs-only skip-ci
-# from git diff, then uploads document 2. When docs-only skip applies, image-build still runs for the nightly
-# exception in upload_pipeline_with_skip_ci.sh (nightly-test label or main NIGHTLY=1); otherwise upload-nightly
-# can be skipped together with test-ready/test-merge. Weekly uploads also depend on image-build.
-
-# Document 2: appended after `---`; same file, read by upload_pipeline_with_skip_ci.sh (not evaluated as a second pipeline by Buildkite). Child uploads include test-ready.yml, test-merge.yml, test-nightly.yml, and test-weekly.yml.
-steps:
-  - label: ":github: Resolve skip-ci & upload pipeline"
-    key: upload-ci-pipeline
-    commands:
-      - "bash .buildkite/scripts/upload_pipeline_with_skip_ci.sh"
-    agents:
-      queue: "cpu_queue_premerge"
-
----
-steps:
-  - label: ":docker: Build image"
-    key: image-build
-    if: __IMAGE_BUILD_IF__
-    commands:
-      - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "docker build --progress=plain --file docker/Dockerfile.ci -t vllm-omni-ci ."
-      - "docker tag vllm-omni-ci public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT"
-    agents:
-      queue: "cpu_queue_premerge"
-
-  # L2 Test
-  - label: "Upload Ready Pipeline"
-    depends_on: image-build
-    key: upload-ready-pipeline
-    if: __UPLOAD_READY_IF__
-    commands:
-      - buildkite-agent pipeline upload .buildkite/test-ready.yml
-    agents:
-      queue: "cpu_queue_premerge"
-
-  # L3 Test
-  - label: "Upload Merge Pipeline"
-    depends_on: image-build
-    key: upload-merge-pipeline
-    if: __UPLOAD_MERGE_IF__
-    commands:
-      - buildkite-agent pipeline upload .buildkite/test-merge.yml
-    agents:
-      queue: "cpu_queue_premerge"
-
-  # L4 Test — main+NIGHTLY=1 (scheduled), or PR with specific label (e.g. add label then Rebuild)
-  - label: "Upload Nightly Pipeline"
-    depends_on: image-build
-    key: upload-nightly-pipeline
-    if: >-
-      (build.branch == "main" && build.env("NIGHTLY") == "1") ||
-      (build.branch != "main" && (
-        build.pull_request.labels includes "nightly-test" ||
-        build.pull_request.labels includes "omni-test" ||
-        build.pull_request.labels includes "tts-test" ||
-        build.pull_request.labels includes "diffusion-x2iat-test" ||
-        build.pull_request.labels includes "diffusion-x2v-test"
-      ))
-    commands:
-      - buildkite-agent pipeline upload .buildkite/test-nightly.yml
-    agents:
-      queue: "cpu_queue_premerge"
-
-  # L5 Test — main+WEEKLY=1 (scheduled), or PR with weekly-test label
-  - label: "Upload Weekly Pipeline"
-    depends_on: image-build
-    key: upload-weekly-pipeline
-    if: >-
-      (build.branch == "main" && build.env("WEEKLY") == "1") ||
-      (build.branch != "main" && build.pull_request.labels includes "weekly-test")
-    commands:
-      - buildkite-agent pipeline upload .buildkite/test-weekly.yml
-    agents:
-      queue: "cpu_queue_premerge"

.buildkite/release/release-pipeline.yml

@@ -0,0 +1,132 @@
+steps:
+  - input: "Provide Release version here"
+    id: input-release-version
+    fields:
+      - text: "What is the release version?"
+        key: release-version
+
+  - label: "Build and upload wheel"
+    depends_on: ~
+    key: "build-wheel"
+    agents:
+      queue: cpu_queue_release
+    commands:
+      - "curl -LsSf https://astral.sh/uv/install.sh | sh"
+      - 'export PATH="$HOME/.local/bin:$PATH"'
+      - "uv venv --python=3.12 && source .venv/bin/activate"
+      - "uv pip install --upgrade build"
+      - "python3 -m build"
+      - "bash .buildkite/release/scripts/upload-nightly-wheels.sh"
+
+  - label: "Generate and upload wheel indices"
+    depends_on: "build-wheel"
+    allow_dependency_failure: true
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/release/scripts/generate-and-upload-nightly-index.sh"
+
+  - block: "Upload release wheels to PyPI"
+    depends_on: "build-wheel"
+    key: block-upload-release-wheels
+
+  - label: "Upload release wheels to PyPI"
+    depends_on:
+      - block-upload-release-wheels
+      - input-release-version
+    key: upload-release-wheels
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/release/scripts/publish-release-wheels-pypi.sh"
+
+  # Auto-skip when NIGHTLY=1 (scheduled); otherwise require manual unblock.
+  - block: "Unblock to build release Docker images"
+    depends_on: ~
+    key: block-build-release-images
+    if: build.env("NIGHTLY") != "1"
+
+  - group: "Build release Docker images"
+    key: "build-release-images"
+    depends_on: block-build-release-images
+    allow_dependency_failure: true
+    steps:
+      - label: "Build release image - x86_64"
+        key: build-release-image-x86
+        depends_on: ~
+        agents:
+          queue: cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              --build-arg max_jobs=16 \
+              --tag public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m) \
+              --progress plain \
+              -f docker/Dockerfile.cuda .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+          # Fallback default tag (no arch suffix) if arm64 build fails.
+          - "docker tag public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT"
+        env:
+          DOCKER_BUILDKIT: "1"
+
+      - label: "Build release image - aarch64"
+        key: build-release-image-arm64
+        depends_on: ~
+        agents:
+          queue: arm64_cpu_queue_release
+        commands:
+          - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
+          - |
+            DOCKER_BUILDKIT=1 docker build \
+              --build-arg max_jobs=16 \
+              --tag public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m) \
+              --progress plain \
+              -f docker/Dockerfile.cuda .
+          - "docker push public.ecr.aws/q9t5s3a7/vllm-omni-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
+        env:
+          DOCKER_BUILDKIT: "1"
+
+  # NIGHTLY=1 only: promote ECR images to DockerHub nightly tags, then prune.
+  - label: "Publish nightly multi-arch image to DockerHub"
+    depends_on:
+      - build-release-image-x86
+      - build-release-image-arm64
+    if: build.env("NIGHTLY") == "1"
+    key: publish-nightly-images-dockerhub
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/release/scripts/publish-release-images.sh nightly"
+      - "bash .buildkite/release/scripts/cleanup-nightly-builds.sh"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"
+
+  - block: "Publish release images to DockerHub"
+    key: block-publish-release-images
+    depends_on:
+      - build-release-image-x86
+      - build-release-image-arm64
+      - input-release-version
+
+  - label: "Publish release images to DockerHub"
+    depends_on:
+      - block-publish-release-images
+    key: publish-release-images-dockerhub
+    agents:
+      queue: small_cpu_queue_release
+    commands:
+      - "bash .buildkite/release/scripts/publish-release-images.sh"
+    plugins:
+      - docker-login#v3.0.0:
+          username: vllmbot
+          password-env: DOCKERHUB_TOKEN
+    env:
+      DOCKER_BUILDKIT: "1"
+      DOCKERHUB_USERNAME: "vllmbot"

.buildkite/release/scripts/cleanup-nightly-builds.sh

@@ -0,0 +1,147 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+#
+# Clean up old nightly builds from DockerHub (vllm/vllm-omni), keeping the
+# newest 14 tags with the nightly- prefix.
+
+set -ex
+
+TAG_PREFIX="nightly-"
+REPO="vllm/vllm-omni"
+REPO_API_URL="https://hub.docker.com/v2/repositories/${REPO}/tags"
+
+echo "Cleaning up tags with prefix: $TAG_PREFIX in repository: $REPO"
+
+if [ -z "$DOCKERHUB_TOKEN" ]; then
+  echo "Error: DOCKERHUB_TOKEN environment variable is not set"
+  exit 1
+fi
+
+if [ -z "$DOCKERHUB_USERNAME" ]; then
+  echo "Error: DOCKERHUB_USERNAME environment variable is not set"
+  exit 1
+fi
+
+echo "Getting DockerHub bearer token..."
+set +x
+BEARER_TOKEN=$(curl -s -X POST \
+  -H "Content-Type: application/json" \
+  -d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \
+  "https://hub.docker.com/v2/users/login" | jq -r '.token')
+set -x
+
+if [ -z "$BEARER_TOKEN" ] || [ "$BEARER_TOKEN" = "null" ]; then
+  echo "Error: Failed to get DockerHub bearer token"
+  exit 1
+fi
+
+get_all_tags() {
+  local page=1
+  local all_tags=""
+  # Hub `name` is a partial match (not a strict prefix). Use it to narrow the
+  # list, then enforce startswith() client-side. Exhaust API pages; do not
+  # stop merely because a page has no matching tags after filtering.
+  local name_query
+  name_query=$(printf '%s' "$TAG_PREFIX" | jq -sRr @uri)
+
+  while true; do
+    set +x
+    local response
+    response=$(curl -s -H "Authorization: Bearer $BEARER_TOKEN" \
+      "$REPO_API_URL?page=$page&page_size=100&name=${name_query}")
+    set -x
+
+    local page_count
+    page_count=$(echo "$response" | jq -r '.results | length')
+    if [ -z "$page_count" ] || [ "$page_count" = "0" ] || [ "$page_count" = "null" ]; then
+      break
+    fi
+
+    local tags
+    tags=$(echo "$response" | jq -r --arg prefix "$TAG_PREFIX" \
+      '.results[] | select(.name | startswith($prefix)) | "\(.last_updated)|\(.name)"')
+
+    if [ -n "$tags" ]; then
+      all_tags="$all_tags$tags"$'\n'
+    fi
+    page=$((page + 1))
+  done
+
+  echo "$all_tags" | sort -r | cut -d'|' -f2
+}
+
+delete_tag() {
+  local tag_name="$1"
+  echo "Deleting tag: $tag_name"
+
+  local delete_url="https://hub.docker.com/v2/repositories/${REPO}/tags/$tag_name"
+  set +x
+  local response http_code body
+  response=$(curl -s -w "\n%{http_code}" -X DELETE \
+    -H "Authorization: Bearer $BEARER_TOKEN" "$delete_url")
+  set -x
+  http_code=$(printf '%s\n' "$response" | tail -n1)
+  body=$(printf '%s\n' "$response" | sed '$d')
+
+  if [ "$http_code" -ge 200 ] && [ "$http_code" -lt 300 ]; then
+    echo "Successfully deleted tag: $tag_name (HTTP ${http_code})"
+    return 0
+  fi
+
+  local detail=""
+  if echo "$body" | jq -e '.detail' > /dev/null 2>&1; then
+    detail=$(echo "$body" | jq -r '.detail')
+  elif [ -n "$body" ]; then
+    detail="$body"
+  else
+    detail="(empty response body)"
+  fi
+  echo "Error: Failed to delete tag $tag_name (HTTP ${http_code}): ${detail}"
+  return 1
+}
+
+echo "Fetching all tags from DockerHub..."
+all_tags=$(get_all_tags)
+
+if [ -z "$all_tags" ]; then
+  echo "No tags found to clean up"
+  exit 0
+fi
+
+total_tags=$(echo "$all_tags" | wc -l)
+echo "Found $total_tags tags"
+
+tags_to_keep=14
+tags_to_delete=$((total_tags - tags_to_keep))
+
+if [ $tags_to_delete -le 0 ]; then
+  echo "No tags need to be deleted (only $total_tags tags found, keeping $tags_to_keep)"
+  exit 0
+fi
+
+echo "Will delete $tags_to_delete old tags, keeping the newest $tags_to_keep"
+
+tags_to_delete_list=$(echo "$all_tags" | tail -n +$((tags_to_keep + 1)))
+
+if [ -z "$tags_to_delete_list" ]; then
+  echo "No tags to delete"
+  exit 0
+fi
+
+echo "Deleting old tags..."
+delete_failures=0
+while IFS= read -r tag; do
+  if [ -n "$tag" ]; then
+    if ! delete_tag "$tag"; then
+      delete_failures=$((delete_failures + 1))
+    fi
+    sleep 1
+  fi
+done <<< "$tags_to_delete_list"
+
+if [ "$delete_failures" -gt 0 ]; then
+  echo "Cleanup finished with ${delete_failures} deletion failure(s)"
+  exit 1
+fi
+
+echo "Cleanup completed successfully"

.buildkite/release/scripts/generate-and-upload-nightly-index.sh

@@ -36,12 +36,12 @@ mkdir -p "$INDICES_OUTPUT_DIR"
 
 # HACK: we do not need regex module here, but it is required by pre-commit hook
 # To avoid any external dependency, we simply replace it back to the stdlib re module
-sed -i.bak 's/import regex as re/import re/g' .buildkite/scripts/generate-nightly-index.py && rm -f .buildkite/scripts/generate-nightly-index.py.bak
+sed -i.bak 's/import regex as re/import re/g' .buildkite/release/scripts/generate-nightly-index.py && rm -f .buildkite/release/scripts/generate-nightly-index.py.bak
 
 # Generate indices -- the version is just the commit hash (not omni/{commit})
 # because relative paths are computed between the index and wheel directories,
 # both of which live under the omni/ prefix in S3.
-$PYTHON .buildkite/scripts/generate-nightly-index.py \
+$PYTHON .buildkite/release/scripts/generate-nightly-index.py \
     --version "$BUILDKITE_COMMIT" \
     --current-objects "$obj_json" \
     --output-dir "$INDICES_OUTPUT_DIR" \
@@ -78,7 +78,7 @@ if [[ "$version" != *"dev"* ]]; then
     rm -rf "${INDICES_OUTPUT_DIR:?}"
     mkdir -p "$INDICES_OUTPUT_DIR"
     # wheel-dir is overridden to be the commit directory, so that the indices point to the correct wheel path
-    $PYTHON .buildkite/scripts/generate-nightly-index.py \
+    $PYTHON .buildkite/release/scripts/generate-nightly-index.py \
         --version "$s3_version" \
         --wheel-dir "$BUILDKITE_COMMIT" \
         --current-objects "$obj_json" \

.buildkite/release/scripts/publish-release-images.sh

@@ -0,0 +1,87 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+#
+# Publish Docker images from ECR staging to DockerHub.
+#
+# Usage:
+#   publish-release-images.sh            # release mode (default)
+#   publish-release-images.sh release
+#   publish-release-images.sh nightly
+#
+# Release tags: latest, v<version>
+# Nightly tags: nightly, nightly-<commit>
+
+set -euo pipefail
+
+MODE="${1:-release}"
+
+DOCKERHUB_REPO="vllm/vllm-omni"
+ECR_REPO="public.ecr.aws/q9t5s3a7/vllm-omni-release-repo"
+COMMIT="$BUILDKITE_COMMIT"
+PRIMARY_TAGS=()
+EXTRA_MANIFEST_TAGS=()
+
+case "${MODE}" in
+  release)
+    RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
+    if [ -z "${RELEASE_VERSION}" ]; then
+      echo "ERROR: release-version metadata not set"
+      exit 1
+    fi
+    PRIMARY_TAGS=("latest" "v${RELEASE_VERSION}")
+    ;;
+  nightly)
+    # Commit-pinned manifest reuses nightly-{x86_64,aarch64} arch images.
+    PRIMARY_TAGS=("nightly")
+    EXTRA_MANIFEST_TAGS=("nightly-${COMMIT}")
+    ;;
+  *)
+    echo "Usage: $0 {release|nightly}"
+    exit 2
+    ;;
+esac
+
+echo "========================================"
+echo "Publishing ${MODE} images to ${DOCKERHUB_REPO}"
+echo "  Commit: ${COMMIT}"
+echo "  Primary tags: ${PRIMARY_TAGS[*]}"
+if [ ${#EXTRA_MANIFEST_TAGS[@]} -gt 0 ]; then
+  echo "  Extra manifests: ${EXTRA_MANIFEST_TAGS[*]}"
+fi
+echo "========================================"
+
+aws ecr-public get-login-password --region us-east-1 | \
+  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
+
+SRC_X86="${ECR_REPO}:${COMMIT}-x86_64"
+SRC_ARM="${ECR_REPO}:${COMMIT}-aarch64"
+
+docker pull "${SRC_X86}"
+docker pull "${SRC_ARM}"
+
+publish_manifest() {
+  local tag="$1"
+  local arch_tag_base="$2"
+  docker manifest rm "${DOCKERHUB_REPO}:${tag}" || true
+  docker manifest create \
+    "${DOCKERHUB_REPO}:${tag}" \
+    "${DOCKERHUB_REPO}:${arch_tag_base}-x86_64" \
+    "${DOCKERHUB_REPO}:${arch_tag_base}-aarch64"
+  docker manifest push "${DOCKERHUB_REPO}:${tag}"
+}
+
+for tag in "${PRIMARY_TAGS[@]}"; do
+  docker tag "${SRC_X86}" "${DOCKERHUB_REPO}:${tag}-x86_64"
+  docker tag "${SRC_ARM}" "${DOCKERHUB_REPO}:${tag}-aarch64"
+  docker push "${DOCKERHUB_REPO}:${tag}-x86_64"
+  docker push "${DOCKERHUB_REPO}:${tag}-aarch64"
+  publish_manifest "${tag}" "${tag}"
+done
+
+ARCH_BASE="${PRIMARY_TAGS[0]}"
+for tag in "${EXTRA_MANIFEST_TAGS[@]}"; do
+  publish_manifest "${tag}" "${ARCH_BASE}"
+done
+
+echo ""
+echo "Successfully published ${MODE} images"

.buildkite/release/scripts/publish-release-wheels-pypi.sh

@@ -0,0 +1,52 @@
+#!/usr/bin/env bash
+# SPDX-License-Identifier: Apache-2.0
+
+set -e
+
+BUCKET="vllm-wheels"
+SUBPATH="omni/$BUILDKITE_COMMIT"
+S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
+
+RELEASE_VERSION=$(buildkite-agent meta-data get release-version | sed 's/^v//')
+if [[ -z "$RELEASE_VERSION" ]]; then
+  echo "[FATAL] release-version metadata not set."
+  exit 1
+fi
+echo "Release version: $RELEASE_VERSION"
+
+if [[ -z "$PYPI_TOKEN" ]]; then
+  echo "[FATAL] PYPI_TOKEN is not set."
+  exit 1
+else
+  export TWINE_USERNAME="__token__"
+  export TWINE_PASSWORD="$PYPI_TOKEN"
+fi
+
+set -x
+
+if ! command -v uv &> /dev/null; then
+  curl -LsSf https://astral.sh/uv/install.sh | sh
+  export PATH="$HOME/.local/bin:$PATH"
+fi
+
+uv venv --python 3.12 /tmp/vllm-omni-release-env
+source /tmp/vllm-omni-release-env/bin/activate
+uv pip install twine
+
+DIST_DIR=/tmp/vllm-omni-release-dist
+mkdir -p "$DIST_DIR"
+
+echo "Downloading wheels from S3:"
+aws s3 ls "$S3_COMMIT_PREFIX"
+aws s3 cp --recursive --exclude "*" --include "vllm_omni-${RELEASE_VERSION}*.whl" --exclude "*dev*" "$S3_COMMIT_PREFIX" "$DIST_DIR"
+ls -la "$DIST_DIR"
+
+PYPI_WHEEL_FILES=$(find "$DIST_DIR" -name "vllm_omni-${RELEASE_VERSION}*.whl" -not -name "*+*")
+if [[ -z "$PYPI_WHEEL_FILES" ]]; then
+  echo "[FATAL] No wheels found for version ${RELEASE_VERSION}"
+  exit 1
+fi
+
+python3 -m twine check $PYPI_WHEEL_FILES
+python3 -m twine upload --non-interactive --verbose $PYPI_WHEEL_FILES
+echo "Wheels uploaded to PyPI"

.buildkite/scripts/docker_login_ecr_public.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-# Helper function to safely login to ECR Public with per-job config isolation
-# Uses DOCKER_CONFIG environment variable to prevent race conditions
-#
-# This script prevents the "device or resource busy" error by giving each
-# Buildkite job its own isolated Docker config directory.
-#
-# Usage:
-#   source docker_login_ecr_public.sh && safe_docker_login_ecr_public
-
-set -euo pipefail
-
-# Configuration
-ECR_REGISTRY="public.ecr.aws"
-
-setup_isolated_docker_config() {
-    # Use BUILDKITE_JOB_ID for job-specific isolation
-    # Fallback to PID if running outside Buildkite
-    local job_id="${BUILDKITE_JOB_ID:-$$}"
-
-    # Set Docker config to job-specific directory
-    export DOCKER_CONFIG="/tmp/docker-config-${job_id}"
-
-    # Create directory if it doesn't exist
-    mkdir -p "$DOCKER_CONFIG"
-
-    echo "[docker-config] Using isolated Docker config: $DOCKER_CONFIG"
-}
-
-check_docker_auth() {
-    # Check if already authenticated to the given registry
-    # Returns 0 if authenticated, 1 if not
-    local registry="$1"
-
-    # Check if credentials exist in the isolated config
-    if [[ -f "$DOCKER_CONFIG/config.json" ]]; then
-        # Check if registry is present in config
-        if grep -q "$registry" "$DOCKER_CONFIG/config.json" 2>/dev/null; then
-            return 0
-        fi
-    fi
-
-    return 1
-}
-
-safe_docker_login_ecr_public() {
-    # Setup isolated config first
-    setup_isolated_docker_config
-
-    local registry="$ECR_REGISTRY"
-
-    # Check if already authenticated (within this job)
-    if check_docker_auth "$registry"; then
-        echo "[docker-login] Already authenticated to $registry in this job"
-        return 0
-    fi
-
-    # Perform login to isolated config directory
-    echo "[docker-login] Logging in to $ECR_REGISTRY (isolated config)..."
-    if aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin "$ECR_REGISTRY"; then
-        echo "[docker-login] Login successful (config: $DOCKER_CONFIG)"
-        return 0
-    else
-        local exit_code=$?
-        echo "[docker-login] ERROR: Login failed with exit code $exit_code" >&2
-        return $exit_code
-    fi
-}
-
-# Execute if run as script (not sourced)
-if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
-    safe_docker_login_ecr_public
-fi

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -1,154 +0,0 @@
-#!/bin/bash
-# vllm-omni customized version
-# Based on: vllm/.buildkite/scripts/hardware_ci/run-amd-test.sh
-# Last synced: 2025-12-15
-# Modifications: docker image name for vllm-omni
-
-# This script runs test inside the corresponding ROCm docker container.
-set -o pipefail
-
-# Export Python path
-export PYTHONPATH=".."
-
-# Print ROCm version
-echo "--- ROCm info"
-rocminfo
-
-# cleanup older docker images
-cleanup_docker() {
-  # Get Docker's root directory
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
-
-# Call the cleanup docker function
-cleanup_docker
-
-echo "--- Pulling container"
-## Temporary change to use AMD Docker Hub to store the vllm-omni image
-# to bypass the rate limit issue with ECR Public Gallery.
-# Images are now stored in a separate repository for vllm-omni, instead of vllm-ci.
-# TODO: @tjtanaa point back to ECR Public Gallery
-# once the amd agents are configured to use ECR Public Gallery.
-# image_name="public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:${BUILDKITE_COMMIT}-rocm-omni"
-image_name="rocm/vllm-omni:${BUILDKITE_COMMIT}"
-container_name="rocm_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-
-# TODO: @tjtanaa uncomment this once the amd agents are configured to use ECR Public Gallery.
-# # Install AWS CLI to authenticate to ECR Public Gallery to get higher rate limit for pulling images
-# sudo apt-get update && sudo apt-get install -y awscli
-##  Use safe docker login helper to prevent race conditions
-# source "$(dirname "${BASH_SOURCE[0]}")/../docker_login_ecr_public.sh"
-# safe_docker_login_ecr_public
-
-## Pull the container from AMD Docker Hub
-
-docker pull "${image_name}"
-
-remove_docker_container() {
-   docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true
-}
-trap remove_docker_container EXIT
-
-echo "--- Running container"
-
-HF_CACHE="$(realpath ~)/huggingface"
-mkdir -p "${HF_CACHE}"
-HF_MOUNT="/root/.cache/huggingface"
-
-if [[ -n "${TEST_COMMAND:-}" ]]; then
-    commands="$TEST_COMMAND"
-else
-    commands="$@"
-fi
-echo "Commands:$commands"
-
-PARALLEL_JOB_COUNT=8
-MYPYTHONPATH=".."
-
-# Test that we're launching on the machine that has
-# proper access to GPUs
-render_gid=$(getent group render | cut -d: -f3)
-if [[ -z "$render_gid" ]]; then
-  echo "Error: 'render' group not found. This is required for GPU access." >&2
-  exit 1
-fi
-
-# check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs.
-# TODO: @tjtanaa reenable to run VLLM_ROCM_USE_AITER=1 when AITER is shipped with prebuilt kernels.
-if [[ $commands == *"--shard-id="* ]]; then
-  # assign job count as the number of shards used
-  commands=$(echo "$commands" | sed -E "s/--num-shards[[:blank:]]*=[[:blank:]]*[0-9]*/--num-shards=${PARALLEL_JOB_COUNT} /g" | sed 's/ \\ / /g')
-  for GPU in $(seq 0 $(($PARALLEL_JOB_COUNT-1))); do
-    # assign shard-id for each shard
-    commands_gpu=$(echo "$commands" | sed -E "s/--shard-id[[:blank:]]*=[[:blank:]]*[0-9]*/--shard-id=${GPU} /g" | sed 's/ \\ / /g')
-    echo "Shard ${GPU} commands:$commands_gpu"
-    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
-    docker run \
-        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-        --network=host \
-        --shm-size=16gb \
-        --group-add "$render_gid" \
-        --rm \
-        -e MIOPEN_DEBUG_CONV_DIRECT=0 \
-        -e MIOPEN_DEBUG_CONV_GEMM=0 \
-        -e VLLM_ROCM_USE_AITER=0 \
-        -e HIP_VISIBLE_DEVICES="${GPU}" \
-        -e HF_TOKEN \
-        -v "${HF_CACHE}:${HF_MOUNT}" \
-        -e "HF_HOME=${HF_MOUNT}" \
-        -e "PYTHONPATH=${MYPYTHONPATH}" \
-        --name "${container_name}_${GPU}" \
-        "${image_name}" \
-        /bin/bash -c "${commands_gpu}" \
-        |& while read -r line; do echo ">>Shard $GPU: $line"; done &
-    PIDS+=($!)
-  done
-  #wait for all processes to finish and collect exit codes
-  for pid in "${PIDS[@]}"; do
-    wait "${pid}"
-    STATUS+=($?)
-  done
-  for st in "${STATUS[@]}"; do
-    if [[ ${st} -ne 0 ]]; then
-      echo "One of the processes failed with $st"
-      exit "${st}"
-    fi
-  done
-else
-  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
-  docker run \
-          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-          --network=host \
-          --shm-size=16gb \
-          --group-add "$render_gid" \
-          --rm \
-          -e MIOPEN_DEBUG_CONV_DIRECT=0 \
-          -e MIOPEN_DEBUG_CONV_GEMM=0 \
-          -e VLLM_ROCM_USE_AITER=0 \
-          -e HF_TOKEN \
-          -v "${HF_CACHE}:${HF_MOUNT}" \
-          -e "HF_HOME=${HF_MOUNT}" \
-          -e "PYTHONPATH=${MYPYTHONPATH}" \
-          --name "${container_name}" \
-          "${image_name}" \
-          /bin/bash -c "${commands}"
-fi

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -1,56 +0,0 @@
-#!/bin/bash
-
-# This script build the XPU docker image and run the offline inference inside the container.
-set -ex
-
-omni_source_dir=$(git rev-parse --show-toplevel)
-
-base_image_name="xpu/vllm-omni-ci-base:${VLLM_VERSION:?VLLM_VERSION must be set}"
-image_name="xpu/vllm-omni-ci:${BUILDKITE_COMMIT:?BUILDKITE_COMMIT must be set}"
-container_name="xpu_${BUILDKITE_COMMIT}_$(
-    tr -dc A-Za-z0-9 </dev/urandom | head -c 10
-    echo
-)"
-
-cd "${omni_source_dir}"
-if [ -z "$(docker images -q "${base_image_name}")" ]; then
-    docker build --target vllm-base -t "${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}" -f docker/Dockerfile.xpu .
-fi
-
-# Try building the docker image
-docker build --build-arg "VLLM_BASE=${base_image_name}" --build-arg "VLLM_VERSION=${VLLM_VERSION}" -t "${image_name}" -f docker/Dockerfile.xpu .
-
-# Setup cleanup
-remove_docker_container() {
-    docker rm -f "${container_name}" || true
-    docker image rm -f "${image_name}" || true
-    docker system prune -f || true
-}
-trap remove_docker_container EXIT
-
-HF_CACHE="${HF_CACHE:-$(realpath ~)/.cache/huggingface}"
-mkdir -p "${HF_CACHE}"
-HF_MOUNT="/root/.cache/huggingface"
-
-time timeout -k 30 30m docker run \
-    --device /dev/dri:/dev/dri \
-    --net=host \
-    --ipc=host \
-    -v /dev/dri/by-path:/dev/dri/by-path \
-    -v "${HF_CACHE}:${HF_MOUNT}" \
-    --security-opt seccomp=unconfined \
-    --entrypoint="" \
-    -e VLLM_LOGGING_LEVEL \
-    -e VLLM_OMNI_LOGGING_LEVEL \
-    -e HF_TOKEN \
-    -e ZE_AFFINITY_MASK \
-    --name "${container_name}" \
-    "${image_name}" \
-    bash -c '
-    set -e
-    echo $ZE_AFFINITY_MASK
-    pip install tblib==3.1.0
-    cd /workspace/vllm-omni
-    pytest -v -s -m "core_model and xpu and B60"
-    pytest -v -s -m "advanced_model and xpu and B60"
-'

.buildkite/scripts/hardware_ci/run_npu_test.sh

@@ -1,145 +0,0 @@
-#!/bin/bash
-
-# This script build the Ascend NPU docker image and run the offline inference inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -ex
-
-# Base ubuntu image with basic ascend development libraries and python installed
-VLLM_OMNI_REPO="https://github.com/vllm-project/vllm-omni.git"
-BASE_IMAGE_NAME="quay.nju.edu.cn/ascend/vllm-ascend:v0.11.0rc2"
-image_name="npu/vllm-omni-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
-# image_name="npu/vllm-ci:${BUILDKITE_COMMIT}_${EPOCHSECONDS}"
-container_name="npu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
-
-# BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards
-agent_idx=$(echo "${BUILDKITE_AGENT_NAME}" | awk -F'-' '{print $(NF-1)}')
-echo "agent_idx: ${agent_idx}"
-builder_name="cachebuilder${agent_idx}"
-builder_cache_dir="/mnt/docker-cache${agent_idx}"
-mkdir -p ${builder_cache_dir}
-
-# Try building the docker image
-cat <<EOF | DOCKER_BUILDKIT=1 docker build \
-    --add-host pypi-cache:${PYPI_CACHE_HOST} \
-    --builder ${builder_name} --cache-from type=local,src=${builder_cache_dir} \
-                           --cache-to type=local,dest=${builder_cache_dir},mode=max \
-    --build-arg BUILDKITE_PULL_REQUEST="${BUILDKITE_PULL_REQUEST}" \
-    --build-arg BUILDKITE_PULL_REQUEST_REPO="${BUILDKITE_PULL_REQUEST_REPO}" \
-    --progress=plain --load -t ${image_name} -f - .
-FROM ${BASE_IMAGE_NAME}
-
-# Define environments
-ENV DEBIAN_FRONTEND=noninteractive
-
-RUN pip config set global.index-url http://pypi-cache:${PYPI_CACHE_PORT}/pypi/simple && \
-    pip config set global.trusted-host pypi-cache && \
-    apt-get update -y && \
-    apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev && \
-    rm -rf /var/cache/apt/* && \
-    rm -rf /var/lib/apt/lists/*
-
-# Install for pytest to make the docker build cache layer always valid
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install pytest>=6.0  pytest-cov modelscope
-
-COPY . .
-
-# Install vllm-omni
-WORKDIR /workspace
-ARG VLLM_OMNI_REPO=https://github.com/vllm-project/vllm-omni.git
-ARG VLLM_OMNI_TAG=main
-ARG BUILDKITE_PULL_REQUEST
-ARG BUILDKITE_PULL_REQUEST_REPO
-RUN git config --global url."https://gh-proxy.test.osinfra.cn/https://github.com/".insteadOf "https://github.com/" && \
-    if [ "\$BUILDKITE_PULL_REQUEST" != "false" ] && [ -n "\$BUILDKITE_PULL_REQUEST" ]; then \
-        echo "Cloning and checking out PR #\$BUILDKITE_PULL_REQUEST..." && \
-        git clone \$VLLM_OMNI_REPO /workspace/vllm-omni && \
-        cd /workspace/vllm-omni && \
-        git fetch origin pull/\$BUILDKITE_PULL_REQUEST/head:pr-\$BUILDKITE_PULL_REQUEST && \
-        git checkout pr-\$BUILDKITE_PULL_REQUEST; \
-    else \
-        echo "Not a PR build, using main branch" && \
-        git clone --depth 1 \$VLLM_OMNI_REPO /workspace/vllm-omni; \
-    fi
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    export PIP_EXTRA_INDEX_URL=https://mirrors.huaweicloud.com/ascend/repos/pypi && \
-    source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
-    source /usr/local/Ascend/nnal/atb/set_env.sh && \
-    export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/`uname -i`-linux/devlib && \
-    python3 -m pip install -v -e /workspace/vllm-omni/
-
-ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
-
-WORKDIR /workspace/vllm-omni
-CMD ["/bin/bash"]
-
-EOF
-
-# Setup cleanup
-remove_docker_container() {
-  docker rm -f "${container_name}" || true;
-  docker image rm -f "${image_name}" || true;
-  docker system prune -f || true;
-}
-trap remove_docker_container EXIT
-
-# Generate corresponding --device args based on BUILDKITE_AGENT_NAME
-# Ascend NPU BUILDKITE_AGENT_NAME format is {hostname}-{agent_idx}-{npu_card_num}cards, and agent_idx starts from 1.
-#   e.g. atlas-a2-001-1-2cards means this is the 1-th agent on atlas-a2-001 host, and it has 2 NPU cards.
-#   returns --device /dev/davinci0 --device /dev/davinci1
-parse_and_gen_devices() {
-    local input="$1"
-    local index cards_num
-    if [[ "$input" =~ ([0-9]+)-([0-9]+)cards$ ]]; then
-        index="${BASH_REMATCH[1]}"
-        cards_num="${BASH_REMATCH[2]}"
-    else
-        echo "parse error" >&2
-        return 1
-    fi
-
-    local devices=""
-    local i=0
-    while (( i < cards_num )); do
-        local dev_idx=$(((index - 1)*cards_num + i ))
-        devices="$devices --device /dev/davinci${dev_idx}"
-        ((i++))
-    done
-
-    # trim leading space
-    devices="${devices#"${devices%%[![:space:]]*}"}"
-    # Output devices: assigned to the caller variable
-    printf '%s' "$devices"
-}
-
-devices=$(parse_and_gen_devices "${BUILDKITE_AGENT_NAME}") || exit 1
-
-# Run the image and execute the Out-Of-Tree (OOT) platform interface test case on Ascend NPU hardware.
-# This test checks whether the OOT platform interface is functioning properly in conjunction with
-# the hardware plugin vllm-ascend.
-hf_model_cache_dir=/mnt/hf_cache${agent_idx}
-ms_model_cache_dir=/mnt/modelscope${agent_idx}
-mkdir -p ${hf_model_cache_dir}
-mkdir -p ${ms_model_cache_dir}
-docker run \
-    --init \
-    ${devices} \
-    --device /dev/davinci_manager \
-    --device /dev/devmm_svm \
-    --device /dev/hisi_hdc \
-    -v /usr/local/dcmi:/usr/local/dcmi \
-    -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-    -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-    -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-    -v /etc/ascend_install.info:/etc/ascend_install.info \
-    -v ${hf_model_cache_dir}:/root/.cache/huggingface \
-    -v ${ms_model_cache_dir}:/root/.cache/modelscope \
-    --network host \
-    --entrypoint="" \
-    --name "${container_name}" \
-    "${image_name}" \
-    bash -c '
-    set -e
-    VLLM_USE_MODELSCOPE=True pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-'

.buildkite/scripts/upload_pipeline_with_skip_ci.sh

@@ -1,137 +0,0 @@
-#!/usr/bin/env bash
-# Evaluate docs-only skip-ci and upload continuation steps from the same `.buildkite/pipeline.yml`
-# (YAML document after the first `---`). Buildkite `if` is evaluated at upload time.
-set -euo pipefail
-
-ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
-PIPELINE_YML="${ROOT}/.buildkite/pipeline.yml"
-
-# Prints a single digit to stdout: 1 = skip image CI, 0 = run. Logs go to stderr.
-is_docs_only_change() {
-  local file_path
-  local has_any=0
-
-  while IFS= read -r file_path; do
-    [[ -z "${file_path}" ]] && continue
-    has_any=1
-
-    if [[ "${file_path}" == docs/* ]]; then
-      continue
-    fi
-    if [[ "${file_path}" == *.md ]]; then
-      continue
-    fi
-    if [[ "${file_path}" == "mkdocs.yaml" ]]; then
-      continue
-    fi
-    return 1
-  done
-
-  [[ "${has_any}" -eq 1 ]]
-}
-
-resolve_skip_ci() {
-  local is_pr_build=0
-  local files
-  local base_branch base_ref
-
-  if [[ "${BUILDKITE_PULL_REQUEST:-false}" != "false" && -n "${BUILDKITE_PULL_REQUEST:-}" ]]; then
-    is_pr_build=1
-  fi
-
-  if [[ "${is_pr_build}" -eq 1 ]]; then
-    base_branch="${BUILDKITE_PULL_REQUEST_BASE_BRANCH:-main}"
-    if ! git rev-parse --verify "origin/${base_branch}" >/dev/null 2>&1; then
-      echo "resolve_skip_ci: origin/${base_branch} not found locally; trying fetch" >&2
-      git fetch --depth=200 origin "${base_branch}" >/dev/null 2>&1 || true
-    fi
-
-    base_ref=""
-    if git rev-parse --verify "origin/${base_branch}" >/dev/null 2>&1; then
-      base_ref="origin/${base_branch}"
-    elif git rev-parse --verify "${base_branch}" >/dev/null 2>&1; then
-      base_ref="${base_branch}"
-    else
-      echo "resolve_skip_ci: cannot resolve PR base ${base_branch}; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-
-    if ! files="$(git diff --name-only "${base_ref}...${BUILDKITE_COMMIT}" 2>/dev/null)"; then
-      echo "resolve_skip_ci: failed to compute PR changed files; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-  elif [[ "${BUILDKITE_BRANCH:-}" == "main" ]]; then
-    if ! git rev-parse --verify "${BUILDKITE_COMMIT}^" >/dev/null 2>&1; then
-      echo "resolve_skip_ci: commit has no parent on main; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-    if ! files="$(git diff --name-only "${BUILDKITE_COMMIT}^..${BUILDKITE_COMMIT}" 2>/dev/null)"; then
-      echo "resolve_skip_ci: failed to compute main changed files; skip-ci=0" >&2
-      echo -n 0
-      return 0
-    fi
-  else
-    echo "resolve_skip_ci: not PR/main build; skip-ci=0" >&2
-    echo -n 0
-    return 0
-  fi
-
-  if is_docs_only_change <<< "${files}"; then
-    echo "resolve_skip_ci: docs-only change detected; skip-ci=1" >&2
-    echo -n 1
-    return 0
-  fi
-
-  echo "resolve_skip_ci: non-doc changes detected; skip-ci=0" >&2
-  echo -n 0
-}
-
-SKIP_CI="$(resolve_skip_ci)"
-
-if [[ ! -f "${PIPELINE_YML}" ]]; then
-  echo "upload_pipeline_with_skip_ci: missing ${PIPELINE_YML}" >&2
-  exit 1
-fi
-
-export ROOT SKIP_CI PIPELINE_YML
-python3 <<'PY' | buildkite-agent pipeline upload
-import os
-import pathlib
-
-path = pathlib.Path(os.environ["PIPELINE_YML"])
-text = path.read_text(encoding="utf-8")
-sep = "\n---\n"
-if sep not in text:
-    raise SystemExit(
-        "upload_pipeline_with_skip_ci: .buildkite/pipeline.yml must contain a '\\n---\\n' separator "
-        "(document 1 = bootstrap, document 2 = uploaded steps)"
-    )
-_, continuation = text.split(sep, 1)
-
-skip = os.environ.get("SKIP_CI") == "1"
-# When docs-only skip-ci: skip default CI image, but still build for L4 nightly (PR label nightly-test or
-# main NIGHTLY=1), otherwise upload-nightly (depends_on image-build) would be skipped too.
-nightly_only = (
-    '(build.pull_request.labels includes "nightly-test") '
-    '|| (build.branch == "main" && build.env("NIGHTLY") == "1")'
-)
-# Placeholder in pipeline.yml is `if: __IMAGE_BUILD_IF__` (valid YAML); replace value only.
-if skip:
-    rep = f"'{nightly_only}'"
-    ready_rep = "'false'"
-    merge_rep = "'false'"
-else:
-    rep = "'true'"
-    ready_rep = "'build.branch != \"main\" && build.pull_request.labels includes \"ready\"'"
-    merge_rep = "'(build.branch == \"main\" && build.env(\"NIGHTLY\") != \"1\") || (build.branch != \"main\" && build.pull_request.labels includes \"merge-test\")'"
-rendered = (
-    continuation
-    .replace("__IMAGE_BUILD_IF__", rep)
-    .replace("__UPLOAD_READY_IF__", ready_rep)
-    .replace("__UPLOAD_MERGE_IF__", merge_rep)
-)
-print(rendered, end="")
-PY

.buildkite/test-amd-merge.yml

@@ -1,223 +0,0 @@
-steps:
-
-- label: "Simple Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_ROCM_USE_AITER=0
-    # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-    # that is not supported on CPU
-    - "timeout 20m pytest -v -s -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-
-- label: "Diffusion Model Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "advanced_model and diffusion" --run-level "advanced_model"
-
-- label: "Diffusion Images API LoRA E2E"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-
-- label: "Diffusion Model CPU offloading Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - |
-      timeout 20m bash -c '
-        set +e
-        pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-        EXIT1=\$?
-        pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-        EXIT2=\$?
-        exit \$((EXIT1 | EXIT2))
-      '
-
-## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
-# - label: "Audio Generation Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
-
-- label: "Diffusion Cache Backend Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"
-
-- label: "Diffusion Sequence Parallelism Test (Need 4 GPUs)"
-  agent_pool: mi325_4
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py
-    - timeout 20m pytest -s -v tests/diffusion/distributed/test_ulysses_uaa_perf.py
-
-# merge-only tests
-- label: "Diffusion Tensor Parallelism Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - pytest -s -v tests/e2e/offline_inference/test_zimage_parallelism.py
-
-- label: "Diffusion GPU Worker Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
-
-- label: "Engine Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/engine/test_async_omni_engine_abort.py
-
-- label: "Omni Model Test Qwen2-5-Omni"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-    - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen2_5_omni_expansion.py -m "advanced_model" --run-level "core_model"
-
-- label: "Omni Model Test Qwen3-Omni"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_mimo_audio.py -m "advanced_model" --run-level "advanced_model"
-
-- label: "Qwen3-TTS CustomVoice E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
-      '
-
-- label: "Qwen3-TTS Base E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 30m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-        pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
-      '
-
-- label: "Diffusion Image Edit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-
-# TODO: Bagel test on ROCm is very unstable. @tjtanaa
-# Need to debug before reneable numerical changes across large PRs
-# # split Bagel Model Test with H100 (Real Weights) into three tests
-# - label: "Bagel Text2Img Model Test (1/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "advanced_model" --run-level "advanced_model" -k "shared_memory" -k "rocm"
-
-# - label: "Bagel Img2Img Model Test (2/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
-
-# - label: "Bagel Online Serving Test (3/3)"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "advanced_model" --run-level "advanced_model" -k "rocm"
-
-- label: "Voxtral-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '

.buildkite/test-amd-ready.yaml

@@ -1,229 +0,0 @@
-steps:
-
-- label: "Simple Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_ROCM_USE_AITER=0
-    # ignore test_teacache_extractors.py because it use rocm gemm kernel from vLLM
-    # that is not supported on CPU
-    - "timeout 20m pytest -vvvv -s -m 'core_model and cpu' --ignore=tests/diffusion/cache/test_teacache_extractors.py --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-
-- label: "Voxtral TTS CUDA Unit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 10m pytest -s -v tests/model_executor/models/voxtral_tts/test_cuda_graph_acoustic_transformer.py
-
-- label: "Diffusion Model Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 30m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
-
-- label: "Diffusion Batching Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen_image_diffusion_batching.py -m "core_model and diffusion" --run-level "core_model"
-
-- label: "Custom Pipeline Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
-
-- label: "Diffusion Model CPU offloading Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - |
-      timeout 20m bash -c '
-        set +e
-        pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-        EXIT1=\$?
-        pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-        EXIT2=\$?
-        exit \$((EXIT1 | EXIT2))
-      '
-
-## ISSUE depends on `diffusers` package: https://github.com/huggingface/diffusers/issues/13274
-# - label: "Audio Generation Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - timeout 20m pytest -s -v tests/e2e/offline_inference/test_stable_audio_expansion.py -m "advanced_model and diffusion and L4" --run-level advanced_model
-
-- label: "Diffusion Cache Backend Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 15m pytest -s -v -m "core_model and cache and diffusion and not distributed_cuda and L4"
-
-- label: "Diffusion Sequence Parallelism Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py -m core_model
-
-- label: "Diffusion GPU Worker Test"
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - timeout 20m pytest -s -v tests/diffusion/test_diffusion_worker.py
-
-- label: "Engine Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - |
-      timeout 15m bash -c '
-              pytest -s -v tests/engine/test_async_omni_engine_abort.py
-      '
-
-# - label: "Omni Model Test Qwen3-Omni"
-#   agent_pool: mi325_2
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - timeout 10m pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-#     - timeout 20m pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
-
-- label: "MiMo-Audio E2E Test with H100"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 30m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
-      '
-
-- label: "Qwen3-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-    - timeout 30m pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
-
-- label: "Voxtral-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        export VLLM_LOGGING_LEVEL=DEBUG
-        export VLLM_WORKER_MULTIPROC_METHOD=spawn
-        pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-        pytest -s -v tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-      '
-
-- label: "Diffusion Image Edit Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - timeout 20m pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-
-# TODO: Bagel test on ROCm is very unstable. @tjtanaa
-# Need to debug before reneable numerical changes across large PRs
-# - label: "Bagel Text2Img Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "core_model" --run-level "core_model" -k "rocm"
-
-# - label: "Bagel Img2Img Model Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 30m pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "core_model" --run-level "core_model" -k "rocm"
-
-# - label: "Bagel Online Serving Test"
-#   agent_pool: mi325_1
-#   depends_on: amd-build
-#   mirror_hardwares: [amdproduction]
-#   grade: Blocking
-#   commands:
-#     - export GPU_ARCHS=gfx942
-#     - export VLLM_TEST_CLEAN_GPU_MEMORY=1
-#     - export VLLM_IMAGE_FETCH_TIMEOUT=60
-#     - export VLLM_LOGGING_LEVEL=DEBUG
-#     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-#     - export VLLM_ROCM_USE_AITER_RMSNORM=0
-#     - timeout 40m pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "core_model" --run-level "core_model" -k "rocm"
-
-- label: "CosyVoice3-TTS E2E Test"
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - |
-      timeout 20m bash -c '
-        pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts.py -m "core_model" --run-level "core_model"
-      '

.buildkite/test-amd.yaml

@@ -1,133 +0,0 @@
-steps:
-
-- label: "Diffusion Model Test"
-  timeout_in_minutes: 30
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py
-
-- label: "Diffusion Images API LoRA E2E"
-  timeout_in_minutes: 30
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-
-- label: "Diffusion Model CPU offloading Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-    - pytest -s -v tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-
-- label: "Diffusion Cache Backend Test"
-  timeout_in_minutes: 15
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v -m 'core_model and cache and diffusion and not distributed_rocm and MI325'
-
-- label: "Diffusion Sequence Parallelism Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py -m core_model
-
-- label: "Diffusion Tensor Parallelism Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_2
-  depends_on: amd-build
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_zimage_parallelism.py
-
-- label: "Diffusion GPU Worker Test"
-  timeout_in_minutes: 20
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - pytest -s -v tests/diffusion/test_diffusion_worker.py
-
-- label: "Omni Model Test Qwen2-5-Omni"
-  timeout_in_minutes: 15
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/offline_inference/test_qwen2_5_omni_expansion.py
-    - pytest -s -v tests/engine/test_async_omni_engine_abort.py
-
-- label: "Omni Model Test Qwen3-Omni"
-  timeout_in_minutes: 15
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-    - export VLLM_ROCM_USE_AITER=0
-    - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py
-    - pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py
-
-
-- label: "Diffusion Image Edit Test"
-  timeout_in_minutes: 15
-  agent_pool: mi325_1
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-
-
-- label: "Omni Sleep Mode Test"
-  timeout_in_minutes: 40
-  agent_pool: mi325_2
-  depends_on: amd-build
-  mirror_hardwares: [amdproduction]
-  grade: Blocking
-  commands:
-    - export GPU_ARCHS=gfx942
-    - export VLLM_LOGGING_LEVEL=DEBUG
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-    - pytest -s -v tests/e2e/offline_inference/test_omni_sleep_mode.py -m "advanced_model and omni and MI325" --run-level "advanced_model"

.buildkite/test-merge.yml

@@ -1,487 +0,0 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
-steps:
-  - label: "Simple Unit Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - "pytest -v -s -m 'core_model and cpu' --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model Test"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "advanced_model and diffusion" --run-level "advanced_model"
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Images API LoRA E2E"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/online_serving/test_images_generations_lora.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model CPU offloading Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      # Single pytest session for one combined summary at end of log.
-      - pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py tests/e2e/offline_inference/test_diffusion_layerwise_offload.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Cache Backend Test"
-    timeout_in_minutes: 15
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v -m 'core_model and cache and diffusion and not distributed_cuda and L4'
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Sequence Parallelism Test"
-    timeout_in_minutes: 25
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py tests/diffusion/distributed/test_ulysses_uaa_perf.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Tensor Parallelism Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/offline_inference/test_zimage_parallelism.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion GPU Worker Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/diffusion/test_diffusion_worker.py
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Engine Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 15m bash -c '
-                pytest -s -v tests/engine/test_async_omni_engine_abort.py
-        '
-    agents:
-      queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Qwen3-TTS CustomVoice E2E Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py tests/e2e/offline_inference/test_qwen3_tts_customvoice.py -m "advanced_model" --run-level "advanced_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Qwen3-TTS Base E2E Test"
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py tests/e2e/offline_inference/test_qwen3_tts_base.py -m "advanced_model" --run-level "advanced_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  # - label: "MOSS-TTS-Nano E2E Test"
-  #   depends_on: upload-merge-pipeline
-  #   commands:
-  #     - |
-  #       timeout 20m bash -c '
-  #         export VLLM_LOGGING_LEVEL=DEBUG
-  #         export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-  #         pytest -s -v tests/e2e/online_serving/test_moss_tts_nano.py tests/e2e/offline_inference/test_moss_tts_nano.py -m "advanced_model" --run-level "advanced_model"
-  #       '
-  #   agents:
-  #     queue: "gpu_1_queue"
-  #   plugins:
-  #     - docker#v5.2.0:
-  #         image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #         always-pull: true
-  #         propagate-environment: true
-  #         shm-size: "8gb"
-  #         environment:
-  #           - "HF_HOME=/fsx/hf_cache"
-  #         volumes:
-  #           - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Omni Model Test with H100"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/e2e/offline_inference/test_qwen3_omni.py tests/e2e/online_serving/test_qwen3_omni.py tests/e2e/online_serving/test_mimo_audio.py -m "advanced_model" --run-level "advanced_model"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Audio Streaming Input Test with H100"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/entrypoints/openai_api/test_qwen3_omni_realtime_websocket.py -m "advanced_model" --run-level "advanced_model"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Diffusion Image Edit Test with H100 (1 GPU)"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Bagel Model Test with H100 (Real Weights)"
-    timeout_in_minutes: 60
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 55m bash -c '
-          set -e
-          export VLLM_TEST_CLEAN_GPU_MEMORY=1
-          export VLLM_IMAGE_FETCH_TIMEOUT=60
-          pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "advanced_model" --run-level "advanced_model"
-          pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py tests/e2e/online_serving/test_bagel_online.py -m "advanced_model" --run-level "advanced_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Omni Sleep Mode Test with H100"
-    timeout_in_minutes: 30
-    depends_on: upload-merge-pipeline
-    commands:
-      - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-      - pytest -s -v tests/e2e/offline_inference/test_omni_sleep_mode.py -m "advanced_model and H100 and omni" --run-level "advanced_model"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Voxtral-TTS E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-merge-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py tests/e2e/offline_inference/test_voxtral_tts.py -m "advanced_model" --run-level "advanced_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate

.buildkite/test-nightly.yml

@@ -1,807 +0,0 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
-steps:
-  # Group: collapses under one heading in the Buildkite UI; child steps still run in parallel.
-  - group: ":card_index_dividers: Omni Model Test"
-    key: nightly-omni-test-group
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "omni-test"
-    steps:
-      - label: ":full_moon: Omni · Function Test with H100"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/ -m "full_model and H100 and omni" --run-level "full_model" --ignore=tests/e2e/accuracy
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Omni · Function Test with L4"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/*/test_qwen2_5_omni_expansion.py --run-level "core_model"
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: Omni · Doc Test with L4"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/examples/ -m "full_model and omni and L4" --run-level "full_model"
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: Omni · Doc Test with H100"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/examples/ -m "full_model and omni and H100" --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Omni · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - export SEED_TTS_WER_EVAL=1
-          - export SEED_TTS_EVAL_DEVICE=cuda:1
-          - |
-            set +e
-            pytest -s -v tests/e2e/accuracy/qwen3_omni/test_qwen3_omni.py -m "full_model" --run-level full_model
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/e2e/accuracy/qwen3_omni/results/qwen_omni_acc/*.json"
-            exit $$EXIT
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Omni · Perf Test"
-        key: nightly-omni-performance
-        timeout_in_minutes: 180
-        commands:
-          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_omni.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-
-  - group: ":card_index_dividers: TTS Model Test"
-    key: nightly-tts-test-group
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test" || build.pull_request.labels includes "tts-test"
-    steps:
-      - label: ":full_moon: TTS · Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - pytest -s -v tests/e2e/ -m "full_model and L4 and omni" -k "not test_qwen2_5" --run-level "full_model" --ignore=tests/e2e/accuracy
-        agents:
-          queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: TTS · Perf Test"
-        key: nightly-tts-performance
-        timeout_in_minutes: 180
-        commands:
-          - export BENCHMARK_DIR=tests/dfx/perf/results
-          - export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_benchmark.py --test-config-file tests/dfx/perf/tests/test_tts.json
-            EXIT=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/*.json"
-            exit $$EXIT
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-  # Diffusion X2I suite: x2i / x2a / x2t and related non-video paths; x2v is only in "Diffusion X2V Model Test" below.
-  - group: ":card_index_dividers: Diffusion X2I(&A&T) Model Test"
-    key: nightly-diffusion-x2iat-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2iat-test"
-    steps:
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with H100"
-        timeout_in_minutes: 120
-        commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and H100" --run-level "full_model" --ignore=tests/e2e/accuracy
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Bagel Function Test with H100"
-        timeout_in_minutes: 120
-        commands:
-          - pytest -sv tests/e2e/online_serving/test_bagel_expansion.py -m "full_model and diffusion and H100" --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 3
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Function Test with L4"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -sv tests/e2e/ -k "not test_wan and not test_bagel_expansion and not hunyuan" -m "full_model and diffusion and L4" --run-level "full_model" --ignore=tests/e2e/accuracy
-        agents:
-          queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-        plugins:
-          - docker#v5.2.0:
-              image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-              always-pull: true
-              propagate-environment: true
-              shm-size: "8gb"
-              environment:
-                - "HF_HOME=/fsx/hf_cache"
-                - "HF_TOKEN"
-              volumes:
-                - "/fsx/hf_cache:/fsx/hf_cache"
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Doc Test"
-        timeout_in_minutes: 60
-        commands:
-          - export VLLM_TEST_CLEAN_GPU_MEMORY="1"
-          - pytest -s -v tests/examples/*/test_text_to_image.py -m "full_model and example and H100" --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEBench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gebench_h100_smoke.py --run-level full_model --gebench-model Qwen/Qwen-Image-2512 --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gebench-port 8093 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gebench_qwen-image-2512/summary*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · GEdit-Bench Accuracy Test"
-        timeout_in_minutes: 60
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_gedit_bench_h100_smoke.py --run-level full_model --gedit-model Qwen/Qwen-Image-Edit --accuracy-judge-model QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ --accuracy-gpu 0 --gedit-port 8093 --gedit-samples-per-group 20 --accuracy-workers 1
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_vie_score_*.csv"
-          - buildkite-agent artifact upload "tests/e2e/accuracy/artifacts/gedit_scores_qwen-image-edit/qwen-image-edit_all_all_summary_*.json"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
-                        value: "120"
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/test_qwen_image*.py --run-level full_model
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 1
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: VLLM_HTTP_TIMEOUT_KEEP_ALIVE
-                        value: "120"
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2I(&A&T) · Perf Test"
-        key: nightly-diffusion-x2iat-performance
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - export CACHE_DIT_VERSION=1.3.0
-          # [HACK]: run upload in the same command block as pytest.
-          # Because `exit` aborts the entire commands list.
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_vllm_omni.json
-            EXIT1=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_vllm_omni.json
-            EXIT2=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_edit_2509_vllm_omni.json
-            EXIT3=$$?
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_qwen_image_layered_vllm_omni.json
-            EXIT4=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$((EXIT1 | EXIT2 | EXIT3 | EXIT4))
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 4
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-  # Diffusion x2v only (Wan, HunyuanVideo, …). x2i/x2a/x2t live in the X2I group above, not here.
-  - group: ":card_index_dividers: Diffusion X2V Model Test"
-    key: nightly-diffusion-x2v-group
-    depends_on: upload-nightly-pipeline
-    if: >-
-      build.env("NIGHTLY") == "1" ||
-      build.pull_request.labels includes "nightly-test" ||
-      build.pull_request.labels includes "diffusion-x2v-test"
-    steps:
-      - label: ":full_moon: Diffusion X2V · Function Test"
-        timeout_in_minutes: 90
-        commands:
-          - pytest -s -v tests/e2e/online_serving/test_wan22_expansion.py tests/e2e/online_serving/test_wan_2_1_vace_expansion.py tests/e2e/online_serving/test_hunyuan_video_15_expansion.py  --run-level "full_model"
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2V · Accuracy Test"
-        timeout_in_minutes: 180
-        commands:
-          - pytest -s -v tests/e2e/accuracy/wan22_i2v/test_wan22_i2v_video_similarity.py -m full_model --run-level full_model
-          - pytest -s -v tests/e2e/accuracy/test_ltx2_3_video_similarity.py -m advanced_model --run-level advanced_model
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-      - label: ":full_moon: Diffusion X2V · Perf Test"
-        key: nightly-diffusion-x2v-performance
-        timeout_in_minutes: 180
-        commands:
-          - export DIFFUSION_BENCHMARK_DIR=tests/dfx/perf/results
-          - export DIFFUSION_ATTENTION_BACKEND=FLASH_ATTN
-          - |
-            set +e
-            pytest -s -v tests/dfx/perf/scripts/run_diffusion_benchmark.py --test-config-file tests/dfx/perf/tests/test_wan22_i2v_vllm_omni.json
-            EXIT1=$$?
-            buildkite-agent artifact upload "tests/dfx/perf/results/diffusion_result_*.json"
-            buildkite-agent artifact upload "tests/dfx/perf/results/logs/*.log"
-            exit $$EXIT1
-        agents:
-          queue: "mithril-h100-pool"
-        plugins:
-          - kubernetes:
-              podSpec:
-                containers:
-                  - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                    resources:
-                      limits:
-                        nvidia.com/gpu: 2
-                    volumeMounts:
-                      - name: devshm
-                        mountPath: /dev/shm
-                      - name: hf-cache
-                        mountPath: /root/.cache/huggingface
-                    env:
-                      - name: HF_HOME
-                        value: /root/.cache/huggingface
-                      - name: HF_TOKEN
-                        valueFrom:
-                          secretKeyRef:
-                            name: hf-token-secret
-                            key: token
-                nodeSelector:
-                  node.kubernetes.io/instance-type: gpu-h100-sxm
-                volumes:
-                  - name: devshm
-                    emptyDir:
-                      medium: Memory
-                  - name: hf-cache
-                    hostPath:
-                      path: /mnt/hf-cache
-                      type: DirectoryOrCreate
-
-  - label: ":bar_chart: Testcase Statistics"
-    key: nightly-testcase-statistics
-    timeout_in_minutes: 120
-    depends_on: upload-nightly-pipeline
-    if: build.env("NIGHTLY") == "1" || build.pull_request.labels includes "nightly-test"
-    commands:
-      - python tools/nightly/buildkite_testcase_statistics.py -o tests/dfx/perf/results/buildkite_testcase_statistics.html
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  # No need to run this step for PRs with label nightly-test
-  - label: ":email: Nightly Collection & Email"
-    key: nightly-perf-distribution
-    depends_on:
-      - nightly-omni-performance
-      - nightly-tts-performance
-      - nightly-diffusion-x2iat-performance
-      - nightly-diffusion-x2v-performance
-      - nightly-testcase-statistics
-    if: build.env("NIGHTLY") == "1"
-    commands:
-      - pip install openpyxl
-      - export DEFAULT_INPUT_DIR=tests/dfx/perf/results
-      - export DEFAULT_OUTPUT_DIR=tests/dfx/perf/results
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-tts-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-omni-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2iat-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.json" . --step nightly-diffusion-x2v-performance
-      - buildkite-agent artifact download "tests/dfx/perf/results/*.html" . --step nightly-testcase-statistics
-      - python tools/nightly/generate_nightly_perf_excel.py
-      - python tools/nightly/generate_nightly_perf_html.py
-      - python tools/nightly/send_nightly_email.py --report-file "tests/dfx/perf/results/*.xlsx, tests/dfx/perf/results/*.html"
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.xlsx"
-      - buildkite-agent artifact upload "tests/dfx/perf/results/*.html"
-    agents:
-      queue: "cpu_queue_premerge"

.buildkite/test-ready.yml

@@ -1,621 +0,0 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
-steps:
-  - label: "Simple Unit Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - "timeout 20m pytest -v -s -m 'core_model and cpu' --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "CUDA Unit Test with single card"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 10m pytest -v -s -m 'core_model and cuda and L4 and not distributed_cuda' --ignore=tests/e2e --ignore=tests/engine/test_async_omni_engine_abort.py --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "CUDA Unit Test with multi cards"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 10m pytest -v -s -m 'core_model and cuda and L4 and distributed_cuda' --ignore=tests/e2e --cov=vllm_omni --cov-branch --cov-report=term-missing --cov-report=html --cov-report=xml
-    agents:
-      queue: "gpu_4_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 30m pytest -s -v tests/e2e/offline_inference/test_t2i_model.py -m "core_model and diffusion" --run-level "core_model"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Batching Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_qwen_image_diffusion_batching.py -m "core_model and diffusion" --run-level "core_model"
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Custom Pipeline Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/custom_pipeline/ -m "core_model"
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Model CPU offloading Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 10m pytest -s -v tests/e2e/offline_inference/test_diffusion_cpu_offload.py
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Cache Backend Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 15m pytest -s -v -m 'core_model and cache and diffusion and not distributed_cuda and L4'
-    agents:
-      queue: "gpu_1_queue" # g6.4xlarge instance on AWS, has 1 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Diffusion Sequence Parallelism Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - timeout 20m pytest -s -v tests/e2e/offline_inference/test_sequence_parallel.py -m core_model
-    agents:
-      queue: "gpu_4_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-
-  - label: "Engine Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 15m bash -c '
-                pytest -s -v tests/engine/test_async_omni_engine_abort.py
-        '
-    agents:
-      queue: "gpu_1_queue" # g6.12xlarge instance on AWS, has 4 L4 GPU
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Omni Model Test with H100"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pytest -s -v tests/e2e/online_serving/test_qwen3_omni.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "MiMo-Audio E2E Test with H100"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 30m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          pytest -s -v tests/e2e/online_serving/test_mimo_audio.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Qwen3-TTS E2E Test"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_customvoice.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "VoxCPM E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pip install voxcpm
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/offline_inference/test_voxcpm.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "VoxCPM2 Native AR E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pip install voxcpm
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          pytest -s -v tests/e2e/offline_inference/test_voxcpm2.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "OmniVoice E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          pytest -s -v tests/e2e/online_serving/test_omnivoice.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Qwen3-TTS Base E2E Test (ModelRunner V2)"
-    depends_on: upload-ready-pipeline
-    soft_fail:
-      - exit_status: 1
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          export VLLM_WORKER_MULTIPROC_METHOD=spawn
-          export VLLM_ALLOW_LONG_MAX_MODEL_LEN="1"
-          export VLLM_OMNI_USE_V2_RUNNER="1"
-          pytest -s -v tests/e2e/online_serving/test_qwen3_tts_base.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "gpu_1_queue"
-    plugins:
-      - docker#v5.2.0:
-          image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-          always-pull: true
-          propagate-environment: true
-          shm-size: "8gb"
-          environment:
-            - "HF_HOME=/fsx/hf_cache"
-            - "HF_TOKEN"
-          volumes:
-            - "/fsx/hf_cache:/fsx/hf_cache"
-
-  - label: "Voxtral-TTS E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          export VLLM_LOGGING_LEVEL=DEBUG
-          pytest -s -v tests/e2e/online_serving/test_voxtral_tts.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  # - label: "Diffusion Image Edit Test with H100 (1 GPU)"
-  #   depends_on: upload-ready-pipeline
-  #   commands:
-  #     - |
-  #       timeout 20m bash -c '
-  #         pytest -s -v tests/e2e/online_serving/test_image_gen_edit.py
-  #       '
-  #   agents:
-  #     queue: "mithril-h100-pool"
-  #   plugins:
-  #     - kubernetes:
-  #         podSpec:
-  #           containers:
-  #             - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-  #               resources:
-  #                 limits:
-  #                   nvidia.com/gpu: 1
-  #               volumeMounts:
-  #                 - name: devshm
-  #                   mountPath: /dev/shm
-  #                 - name: hf-cache
-  #                   mountPath: /root/.cache/huggingface
-  #               env:
-  #                 - name: HF_HOME
-  #                   value: /root/.cache/huggingface
-  #           nodeSelector:
-  #             node.kubernetes.io/instance-type: gpu-h100-sxm
-  #           volumes:
-  #             - name: devshm
-  #               emptyDir:
-  #                 medium: Memory
-  #             - name: hf-cache
-  #               hostPath:
-  #                 path: /mnt/hf-cache
-  #                 type: DirectoryOrCreate
-
-  - label: "Bagel Text2Img Model Test with H100"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 30m bash -c '
-          export VLLM_TEST_CLEAN_GPU_MEMORY=1
-          pytest -s -v tests/e2e/offline_inference/test_bagel_text2img.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Bagel Img2Img Model Test with H100"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 30m bash -c '
-          export VLLM_TEST_CLEAN_GPU_MEMORY=1
-          pytest -s -v tests/e2e/offline_inference/test_bagel_img2img.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Bagel Online Serving Test with H100"
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 40m bash -c '
-          export VLLM_TEST_CLEAN_GPU_MEMORY=1
-          export VLLM_IMAGE_FETCH_TIMEOUT=60
-          pytest -s -v tests/e2e/online_serving/test_bagel_online.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "CosyVoice3-TTS E2E Test"
-    timeout_in_minutes: 20
-    depends_on: upload-ready-pipeline
-    commands:
-      - |
-        timeout 20m bash -c '
-          pytest -s -v tests/e2e/online_serving/test_cosyvoice3_tts.py -m "core_model" --run-level "core_model"
-        '
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 1
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate

.buildkite/test-template-amd-omni.j2

@@ -1,62 +0,0 @@
-{# vllm-omni customized version
-   Based on: https://github.com/vllm-project/ci-infra/blob/main/buildkite/test-template-amd.j2
-   Last synced: 2025-12-15
-   Modifications: Removed unused CUDA/NVIDIA logic, keeping only AMD tests
-#}
-{% set docker_image_amd = "rocm/vllm-omni:$BUILDKITE_COMMIT" %}
-{% set default_working_dir = "/app/vllm-omni" %}
-
-  - group: "AMD Tests"
-    depends_on: ~
-    steps:
-      - label: "AMD: :docker: build image"
-        depends_on: ~
-        soft_fail: false
-        commands:
-          - "docker build -f docker/Dockerfile.rocm -t {{ docker_image_amd }} --target test --build-arg ARG_PYTORCH_ROCM_ARCH=gfx942 --progress plain ."
-          - "docker push {{ docker_image_amd }}"
-        key: "amd-build"
-        env:
-          DOCKER_BUILDKIT: "1"
-        retry:
-          automatic:
-            - exit_status: -1  # Agent was lost
-              limit: 2
-            - exit_status: -10  # Agent was lost
-              limit: 2
-            - exit_status: 128  # Git connectivity issues
-              limit: 2
-            - exit_status: 1  # Machine occasionally fail
-              limit: 1
-        agents:
-          queue: amd-cpu
-
-    {% for step in steps %}
-    {% if step.mirror_hardwares and mirror_hw in step.mirror_hardwares %}
-      - label: "{{ step.agent_pool }}: {{ step.label }}"
-        depends_on: amd-build
-        agents:
-          {% if step.agent_pool %}
-          queue: amd_{{ step.agent_pool }}
-          {% else %}
-          queue: amd_mi325_1
-          {% endif %}
-{% set cmd_body = (step.command or (step.commands | join("\n"))) | trim %}
-{% set indented_cmd = cmd_body | replace("\n", "\n            ") %}
-        command: bash .buildkite/scripts/hardware_ci/run-amd-test.sh
-        env:
-          DOCKER_BUILDKIT: "1"
-          TEST_COMMAND: |-
-            (command rocm-smi || true) && cd {{ (step.working_dir or default_working_dir) | safe }}
-{% if "mi250" in step.agent_pool %}
-            python3 -m pip uninstall -y amd-aiter
-{% endif %}
-            {{ indented_cmd | safe }}
-        priority: 100
-        {% if step.grade and step.grade == "Blocking" %}
-        soft_fail: false
-        {% else %}
-        soft_fail: true
-        {% endif%}
-    {% endif %}
-    {% endfor %}

.buildkite/test-weekly.yml

@@ -1,85 +0,0 @@
-env:
-  VLLM_WORKER_MULTIPROC_METHOD: spawn
-  HF_HUB_DOWNLOAD_TIMEOUT: 300
-  HF_HUB_ETAG_TIMEOUT: 60
-
-steps:
-  - label: "Reliability Test - qwen3-omni"
-    timeout_in_minutes: 180
-    depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
-    commands:
-      - pytest -s -v tests/dfx/reliability/test_reliability_qwen3_omni.py -m "slow"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate
-
-  - label: "Reliability Test - wan22"
-    timeout_in_minutes: 180
-    depends_on: upload-weekly-pipeline
-    if: build.env("WEEKLY") == "1" || build.pull_request.labels includes "weekly-test"
-    commands:
-      - pytest -s -v tests/dfx/reliability/test_reliability_wan22.py -m "slow"
-    agents:
-      queue: "mithril-h100-pool"
-    plugins:
-      - kubernetes:
-          podSpec:
-            containers:
-              - image: 936637512419.dkr.ecr.us-west-2.amazonaws.com/vllm-ci-pull-through-cache/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-                resources:
-                  limits:
-                    nvidia.com/gpu: 2
-                volumeMounts:
-                  - name: devshm
-                    mountPath: /dev/shm
-                  - name: hf-cache
-                    mountPath: /root/.cache/huggingface
-                env:
-                  - name: HF_HOME
-                    value: /root/.cache/huggingface
-                  - name: HF_TOKEN
-                    valueFrom:
-                      secretKeyRef:
-                        name: hf-token-secret
-                        key: token
-            nodeSelector:
-              node.kubernetes.io/instance-type: gpu-h100-sxm
-            volumes:
-              - name: devshm
-                emptyDir:
-                  medium: Memory
-              - name: hf-cache
-                hostPath:
-                  path: /mnt/hf-cache
-                  type: DirectoryOrCreate

.claude/skills/add-diffusion-model/SKILL.md

@@ -24,7 +24,7 @@ Check the model's HF repo for `model_index.json`. This determines your path:
 
 | Scenario | How to identify | Migration path |
 |----------|----------------|----------------|
-| **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip to Step 5 (test) and Step 7 (docs) |
+| **Already supported** | `_class_name` in `model_index.json` matches a key in `_DIFFUSION_MODELS` in `registry.py` | Skip implementation, then validate model-specific examples, tests, and docs as needed |
 | **Diffusers-based** | Has standard `model_index.json` with `_diffusers_version`, subfolders for `transformer/`, `vae/`, etc. | Follow **Path A** below |
 | **Custom/private repo** | No diffusers `model_index.json`, weights in non-standard format, custom model code in a separate git repo | Follow **Path B** below |
 | **Hybrid** | Has some diffusers components (VAE) but custom transformer/fusion | Mix of Path A and Path B |
@@ -52,7 +52,7 @@ vllm_omni/diffusion/models/your_model_name/
 2. Replace attention with `vllm_omni.diffusion.attention.layer.Attention` (QKV shape: `[B, seq, heads, head_dim]`).
 3. Add `od_config: OmniDiffusionConfig | None = None` to `__init__`.
 4. Add `load_weights()` method mapping diffusers weight names to vllm-omni names.
-5. Add class attributes: `_repeated_blocks`, `_layerwise_offload_blocks_attr`.
+5. Add class attributes for acceleration features such as `_repeated_blocks` and `_layerwise_offload_blocks_attrs` (see [references/transformer-adaptation.md](references/transformer-adaptation.md) for examples).
 
 ### A4. Adapt pipeline
 
@@ -78,13 +78,34 @@ class YourPipeline(nn.Module):
 
 Add post/pre-process functions in the same pipeline file. Register them in `registry.py`.
 
+### A4.1 Add progress bar support (recommended)
+
+For pipelines with a standard denoising loop, prefer the existing progress bar pattern instead of hand-rolled logging.
+
+```python
+from vllm_omni.diffusion.models.progress_bar import ProgressBarMixin
+
+class YourPipeline(nn.Module, ProgressBarMixin):
+    def forward(self, req: OmniDiffusionRequest) -> DiffusionOutput:
+        # ... prepare timesteps / latents ...
+        with self.progress_bar(total=len(timesteps)) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # predict noise / scheduler step
+                latents = ...
+                progress_bar.update()
+
+        return DiffusionOutput(output=output)
+```
+
+For custom loop structures, follow `vllm_omni/diffusion/models/progress_bar.py` and existing pipelines using `ProgressBarMixin`.
+
 ### A5. Register, test, docs → continue at Step 4 below.
 
 ---
 
 ## Path B: Custom/Private Repo Model
 
-For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: DreamID-Omni, BAGEL, HunyuanImage3.
+For models without a diffusers pipeline — weights in custom format, model code in a private repo. Real examples: DreamID-Omni, BAGEL, HunyuanImage3. See [references/custom-model-patterns.md](references/custom-model-patterns.md) for concrete custom-model integration patterns.
 
 ### B1. Understand the reference repo
 
@@ -134,7 +155,7 @@ Copy the essential model files into `vllm_omni/diffusion/models/<name>/` and ada
 
 ### B4. Handle custom weight loading
 
-Custom models have two patterns for weight loading:
+Custom models have two common patterns for weight loading:
 
 **Pattern 1: Bypass standard loader** (DreamID-Omni style)
 
@@ -149,8 +170,10 @@ class CustomPipeline(nn.Module):
         self.vae = custom_init_vae(model, device=self.device)
         self.text_encoder = custom_init_text_encoder(model, device=self.device)
         self.transformer = CustomFusionModel(CONFIG)
-        load_custom_checkpoint(self.transformer,
-            checkpoint_path=os.path.join(model, "model.safetensors"))
+        load_custom_checkpoint(
+            self.transformer,
+            checkpoint_path=os.path.join(model, "model.safetensors"),
+        )
         # NO weights_sources defined — bypasses standard loader
 
     def load_weights(self, weights):
@@ -162,6 +185,8 @@ class CustomPipeline(nn.Module):
 When weights are in safetensors format but need name remapping:
 
 ```python
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+
 class CustomPipeline(nn.Module):
     def __init__(self, *, od_config, prefix=""):
         super().__init__()
@@ -187,7 +212,9 @@ class CustomPipeline(nn.Module):
             # Remap original weight names to vllm-omni module names
             name = self._remap_weight_name(name)
             if name in params:
-                default_weight_loader(params[name], tensor)
+                param = params[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, tensor)
                 loaded.add(name)
         return loaded
 ```
@@ -281,16 +308,43 @@ For Omni or custom models, create:
 ### Step 7: Update Documentation
 
 Required updates:
-1. `docs/user_guide/diffusion/parallelism_acceleration.md` — parallelism support table
-2. `docs/user_guide/diffusion/cpu_offload_diffusion.md` — if CPU offload supported (add to supported models table)
-3. `docs/user_guide/diffusion/teacache.md` — if TeaCache supported
-4. `docs/user_guide/diffusion/cache_dit_acceleration.md` — if Cache-DiT supported
-5. `examples/offline_inference/xxx/README.md` — offline example docs
-6. `examples/online_serve/xxx/README.md` — online serve docs
+1. `docs/user_guide/diffusion/parallelism/overview.md` — parallelism support overview/table
+2. `docs/user_guide/diffusion/cpu_offload.md` — if CPU offload supported (add to supported models table)
+3. `docs/user_guide/diffusion/cache_acceleration/teacache.md` — if TeaCache supported
+4. `docs/user_guide/diffusion/cache_acceleration/cache_dit.md` — if Cache-DiT supported
+5. Offline example docs under `examples/offline_inference/<name>/` (`README.md` or category-specific `.md`)
+6. `examples/online_serving/<name>/README.md` — online serving docs
 
-### Step 8: Add E2E Tests (Recommended)
+### Step 8: Add E2E Tests
 
-Create `tests/e2e/online_serving/test_your_model_expansion.py`.
+**Follow the [vllm-omni-test skill](../vllm-omni-test/SKILL.md)** for markers, file naming, Buildkite wiring, and run commands. Also read [l4_functionality_tests.inc.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_examples/l4_functionality_tests.inc.md), [test_system_overview.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_system_overview.md), and [test_writing_guide.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_writing_guide.md).
+
+Classify the model's **CI priority** first:
+
+| Priority | Required test levels | Files & markers |
+|----------|---------------------|-----------------|
+| **High** (listed in [#1832](https://github.com/vllm-project/vllm-omni/issues/1832) or on the diffusion hot path) | **L1** · **L2** online · **L3** online + offline · **L4** feature + performance | See table below |
+| **Medium** (*normal priority* in L4 docs) | **L3** online + offline · **L4** feature only | Fewer L4 parametrized rows |
+| **Low** | **L4** feature only | One or two `*_expansion.py` cases |
+
+**Per-level deliverables (diffusion / `pytest.mark.diffusion`):**
+
+| Level | Location | Marker | CI pipeline | Notes |
+|-------|----------|--------|-------------|-------|
+| **L1** | `tests/diffusion/models/{slug}/`, `tests/diffusion/cache/`, transformer unit tests | `core_model` + `cpu` | `test-ready.yml` | Weight remap, `_sp_plan`, cache enabler registration, shape contracts |
+| **L2** | `tests/e2e/online_serving/test_{slug}.py` (and offline if the category is offline-first) | **`core_model` + `advanced_model`** (both on baseline smoke) + `diffusion` + `@hardware_test` / `hardware_marks` | `test-ready.yml` | Default deploy smoke — minimal `num_inference_steps`, single prompt |
+| **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` when offline matters | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `diffusion`) | `test-merge.yml` **or** merged into nightly diffusion function job | Real weights, streaming/API paths, LoRA/offload smoke |
+| **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py` (+ offline expansion if needed) | `full_model` + `diffusion` | `test-nightly.yml` (X2I / X2V / X2A function groups) | Feature combos per [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); perf → `tests/dfx/perf/tests/test_{model}_vllm_omni.json` with per-case **`mark`** (`hardware_marks` + `full_model` + `diffusion`) |
+
+**L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, minimal steps, single prompt) should carry **both** `@pytest.mark.core_model` and `@pytest.mark.advanced_model` on the **same** function so L2 (`test-ready.yml`) and L3 (`test-merge.yml`) share one smoke test. Heavier deploy variants or API paths in the same file use **`advanced_model` only**. When L3 moves to nightly, migrate those heavier cases into `test_{slug}_expansion.py` with `full_model` and remove the dedicated `test-merge.yml` job (see `test_longcat_image_expansion.py`, `test_qwen_image_expansion.py`).
+
+**L4 design (high priority):** Combine multiple supported features (Cache-DiT, TP, USP, CFG, HSDP, CPU offload, quantization) into **few parametrized** `OmniServerParams` rows so each feature appears in at least one case without exploding GPU jobs. Shard single-GPU vs multi-GPU cases across the nightly X2I/X2V function steps (1-GPU vs `distributed_cuda`).
+
+**L4 design (medium / low):** One or two parametrized rows covering the best quality/perf trade-off; skip perf JSON unless the model is high priority.
+
+**Reference implementations:** `tests/e2e/online_serving/test_qwen_image_edit_expansion.py`, `tests/e2e/online_serving/test_longcat_image_expansion.py`, `tests/e2e/online_serving/test_hunyuan_video_15_expansion.py`.
+
+**Keep model-specific code inside test modules — not `tests/helpers/{slug}.py`:** deploy constants, prompts, sampling dicts, and inline `request_config` / `form_data` belong in each `test_{slug}.py` and `test_{slug}_expansion.py`. Do not add per-model files under `tests/helpers/`; reuse only repo-wide harness (`mark`, `media`, `runtime`, `stage_config`, `assertions`). **L2+ online/offline e2e:** reuse or add `send_*_request` in `tests/helpers/runtime.py` — tests call the handler, not raw `omni.generate` / HTTP. See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers**.
 
 ### Step 9: Add Cache-DiT Acceleration
 
@@ -364,7 +418,8 @@ def enable_cache_for_your_model(pipeline, cache_config):
 
 #### 9d. Register the custom enabler
 
-Add your enabler to `CUSTOM_DIT_ENABLERS` in `vllm_omni/diffusion/cache/cache_dit_backend.py`:
+Add your enabler to `CUSTOM_DIT_ENABLERS` in
+`vllm_omni/diffusion/cache/cachedit/model_specific.py`:
 
 ```python
 CUSTOM_DIT_ENABLERS = {
@@ -510,20 +565,20 @@ class YourTransformer(nn.Module):
 #### 10e. Update parallelism documentation
 
 After adding parallelism support, update:
-1. `docs/user_guide/diffusion/parallelism_acceleration.md` — add your model to the support table
+1. `docs/user_guide/diffusion/parallelism/overview.md` — add your model to the support overview/table
 2. Record which parallelism methods are supported (USP, Ring, CFG, TP, HSDP, VAE-Patch)
 
 ### Step 11: Add CPU Offload Support
 
-Implement `SupportsModuleOffload` on your pipeline class to enable
+Implement `SupportsComponentDiscovery` on your pipeline class to enable
 `--enable-cpu-offload` and `--enable-layerwise-offload`. The protocol
 declares which submodules the offloader should manage:
 
 ```python
 from typing import ClassVar
-from vllm_omni.diffusion.models.interface import SupportsModuleOffload
+from vllm_omni.diffusion.models.interface import SupportsComponentDiscovery
 
-class YourPipeline(nn.Module, SupportsModuleOffload):
+class YourPipeline(nn.Module, SupportsComponentDiscovery):
     _dit_modules: ClassVar[list[str]] = ["transformer"]
     _encoder_modules: ClassVar[list[str]] = ["text_encoder"]
     _vae_modules: ClassVar[list[str]] = ["vae"]
@@ -540,7 +595,7 @@ class YourPipeline(nn.Module, SupportsModuleOffload):
 All attribute names support dotted paths for nested submodules
 (e.g. `"pipe.transformer"`, `"bagel.time_embedder"`).
 
-Pipelines without `SupportsModuleOffload` fall back to scanning
+Pipelines without `SupportsComponentDiscovery` fall back to scanning
 well-known attribute names (`transformer`, `text_encoder`, `vae`,
 etc.), which fails for non-standard names.
 
@@ -568,6 +623,7 @@ See the [Profiling Single-Stage Diffusion](../../../docs/contributing/profiling.
 
 ## Reference Files
 
+- [vllm-omni-test skill](../vllm-omni-test/SKILL.md) — L1–L4 markers, naming, Buildkite wiring, run commands
 - [Transformer Adaptation](references/transformer-adaptation.md) — porting transformers from diffusers
 - [Custom Model Patterns](references/custom-model-patterns.md) — patterns for non-diffusers models
 - [Parallelism Patterns](references/parallelism-patterns.md) — TP, SP/USP, CFG parallel, HSDP implementation details

.claude/skills/add-diffusion-model/references/cache-dit-patterns.md

@@ -29,8 +29,13 @@ vLLM-Omni integrates cache-dit through `CacheDiTBackend`:
 | `cache_dit.refresh_context()` | Updates cache context when `num_inference_steps` changes |
 
 **Source files**:
-- `vllm_omni/diffusion/cache/cache_dit_backend.py` — `CacheDiTBackend`, enablers, `CUSTOM_DIT_ENABLERS`
-- `vllm_omni/diffusion/cache/` — cache backend implementations
+- `vllm_omni/diffusion/cache/cachedit/__init__.py` — public package API
+- `vllm_omni/diffusion/cache/cachedit/backend.py` — generic backend lifecycle and integration
+- `vllm_omni/diffusion/cache/cachedit/config.py` — Cache-DiT configuration types
+- `vllm_omni/diffusion/cache/cachedit/model_specific.py` — model-specific adapters, enablers, and registry
+
+Outside `vllm_omni/diffusion/cache`, import Cache-DiT symbols only from
+`vllm_omni.diffusion.cache.cachedit`.
 
 ## Standard Models: Automatic Support
 
@@ -175,7 +180,8 @@ Inspect your block's `forward()` return type and residual connection pattern to
 
 ## Registering Custom Enablers
 
-Add your enabler to `CUSTOM_DIT_ENABLERS` in `vllm_omni/diffusion/cache/cache_dit_backend.py`:
+Add your enabler to `CUSTOM_DIT_ENABLERS` in
+`vllm_omni/diffusion/cache/cachedit/model_specific.py`:
 
 ```python
 CUSTOM_DIT_ENABLERS = {
@@ -248,7 +254,7 @@ Models listed in `_NO_CACHE_ACCELERATION` in `vllm_omni/diffusion/registry.py` d
 
 | Model | Path | Notes |
 |-------|------|-------|
-| Standard DiT | `cache_dit_backend.py::enable_cache_for_dit` | Default enabler, automatic |
-| Wan2.2 | `cache_dit_backend.py::enable_cache_for_wan22` | Dual-transformer, auto-detects mode |
-| LongCat | `cache_dit_backend.py::enable_cache_for_longcat_image` | Multi-block-list |
-| BAGEL | `cache_dit_backend.py::enable_cache_for_bagel` | Complex omni model |
+| Standard DiT | `cachedit.backend::enable_cache_for_dit` | Default enabler, automatic |
+| Wan2.2 | `cachedit.model_specific::enable_cache_for_wan22` | Dual-transformer, auto-detects mode |
+| LongCat | `cachedit.config::CacheDiTAdapterConfig` | Declarative multi-block-list adapter |
+| BAGEL | `cachedit.model_specific::BagelCachedAdapter` | Custom cached adapter |

.claude/skills/add-diffusion-model/references/parallelism-patterns.md

@@ -92,6 +92,7 @@ class TPSelfAttention(nn.Module):
             softmax_scale=1.0 / (self.head_dim ** 0.5),
             causal=False,
             num_kv_heads=self.to_qkv.num_kv_heads,  # Local KV heads per GPU
+            role="self",
         )
 
     def forward(self, x):
@@ -568,4 +569,4 @@ Check which parallelism methods a model supports:
 | **TP** | Uses `ColumnParallelLinear` / `QKVParallelLinear`. Search: `grep -r 'ParallelLinear\|QKVParallel' vllm_omni/diffusion/models/<model>/` |
 | **HSDP** | Transformer defines `_hsdp_shard_conditions`. Search: `grep -r '_hsdp_shard_conditions' vllm_omni/diffusion/models/` |
 
-The canonical per-model support table is in `docs/user_guide/diffusion/parallelism_acceleration.md`.
+The canonical per-model support overview/table is in `docs/user_guide/diffusion/parallelism/overview.md`.

.claude/skills/add-diffusion-model/references/transformer-adaptation.md

@@ -61,6 +61,7 @@ class SelfAttentionBlock(nn.Module):
             softmax_scale=1.0 / (self.head_dim ** 0.5),
             causal=False,
             num_kv_heads=num_heads,
+            role="self",
         )
 
     def forward(self, x, attn_mask=None):
@@ -75,6 +76,24 @@ class SelfAttentionBlock(nn.Module):
         return self.to_out(out)
 ```
 
+**Declaring the attention role.** Always pass a `role` string to `Attention(...)`. It is the lookup key users target with `--diffusion-attention-config.per_role.<role>.backend` to swap kernels per site without touching model code.
+
+| Convention | When to use |
+|---|---|
+| `role="self"` | Q/K/V come from the same hidden state |
+| `role="cross"` | K/V come from a separate `encoder_hidden_states`; pair with `skip_sequence_parallel=True` if K/V is replicated across SP ranks |
+
+For multi-modal sites that don't fit `self` / `cross`, use a dot-namespaced role and provide `role_category` so user config can fall back to the generic category:
+
+```python
+self.audio_to_video_attn = Attention(
+    num_heads=num_heads, head_size=self.head_dim,
+    softmax_scale=1.0 / (self.head_dim ** 0.5), causal=False,
+    role="mymodel.audio_to_video",
+    role_category="cross",
+)
+```
+
 #### Fused QKV with TP (Advanced)
 
 For tensor parallelism, use vLLM's parallel linear layers:
@@ -104,6 +123,7 @@ class TPSelfAttention(nn.Module):
             softmax_scale=1.0 / (self.head_dim ** 0.5),
             causal=False,
             num_kv_heads=num_heads,
+            role="self",
         )
 ```
 
@@ -199,13 +219,14 @@ def load_weights(self, weights):
 
 ### Class-Level Attributes for Features
 
+
 ```python
 class MyTransformer(nn.Module):
     # torch.compile: list block class names that repeat and can be compiled
     _repeated_blocks = ["MyTransformerBlock"]
 
     # CPU offload: attribute name of the nn.ModuleList containing blocks
-    _layerwise_offload_blocks_attr = "blocks"
+    _layerwise_offload_blocks_attrs = ["blocks"]
 
     # LoRA: mapping of fused param names to original param names
     packed_modules_mapping = {"to_qkv": ["to_q", "to_k", "to_v"]}

.claude/skills/add-diffusion-model/references/troubleshooting.md

@@ -68,7 +68,7 @@ out = out.reshape(batch, seq_len, -1)
 ### Different Output vs Diffusers Reference
 
 **Common causes**:
-1. **Attention backend difference**: FlashAttention vs SDPA may produce slightly different results. Set `DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA` to match diffusers
+1. **Attention backend difference**: FlashAttention vs SDPA may produce slightly different results. Force SDPA via `--diffusion-attention-backend TORCH_SDPA` (or `DIFFUSION_ATTENTION_BACKEND=TORCH_SDPA`) to match diffusers; for finer-grained per-role overrides see `--diffusion-attention-config.per_role.<role>.backend`
 2. **Float precision**: vLLM-Omni may use bfloat16 where diffusers uses float32 for some operations
 3. **Missing normalization**: Check all LayerNorm/RMSNorm are preserved
 4. **Scheduler rounding**: Some schedulers have numerical sensitivity

.claude/skills/add-tts-model/SKILL.md

@@ -211,8 +211,8 @@ See `plan/voxcpm2_native_ar_design.md`.
 
 - Model files in `vllm_omni/model_executor/models/<model_name>/`
 - Stage config YAML
-- Working `end2end.py` with correct audio output
-- README.md in the example directory
+- Working `end2end.py` at `examples/offline_inference/text_to_speech/<model>/end2end.py`
+- New section in `examples/offline_inference/text_to_speech/README.md` (table row + per-model section). Do **not** create a top-level `examples/offline_inference/<model>/` dir or a per-model `README.md` inside `text_to_speech/<model>/` — the hub README is the documented surface and the mkdocs `generate_examples` hook only descends one level into `examples/<category>/`.
 
 ## Phase 3: Online Serving
 
@@ -220,65 +220,70 @@ See `plan/voxcpm2_native_ar_design.md`.
 
 ### Steps
 
-1. **Register in `serving_speech.py`** — add all 5 points in a **single commit**;
-   partial integration causes hard-to-debug failures. This file is modified by every
-   model PR and is the most common source of rebase conflicts — see conflict note below.
+1. **Write one adapter** under `vllm_omni/entrypoints/openai/tts_adapters/`.
+   `serving_speech.py` should not need an edit — detection, stage discovery and
+   dispatch are all derived from what the adapter declares.
 
-   **Point 1** — stage constant (near the top, alongside the other `_*_TTS_MODEL_STAGES` sets):
+   Create `vllm_omni/entrypoints/openai/tts_adapters/your_model.py`:
    ```python
-   _YOUR_MODEL_TTS_MODEL_STAGES = {"your_stage_key"}
+   @register_tts_adapter
+   class YourModelAdapter(ARTTSAdapter):
+       name = "your_model"                              # registry key + log label
+       stage_keys = frozenset({"your_stage_key"})       # the deploy yaml's model_stage
+
+       def validate(self, request) -> str | None:
+           if not request.input or not request.input.strip():
+               return "Input text cannot be empty"
+           return None
+
+       async def build(self, request, sampling_params_list, has_inline_ref_audio):
+           params = {"text": [request.input]}
+           if request.voice is not None:
+               params["voice"] = [request.voice]
+           return PreparedRequest(
+               prompt={"prompt": request.input},
+               tts_params=params,
+               model_type=self.name,
+           )
    ```
 
-   **Point 2** — union into `_TTS_MODEL_STAGES`:
-   ```python
-   _TTS_MODEL_STAGES: set[str] = (
-       ...
-       | _YOUR_MODEL_TTS_MODEL_STAGES
-   )
-   ```
+   Then add the module to the import block at the bottom of
+   `tts_adapters/__init__.py` so it registers. That import line is the only shared
+   file a new model touches — which is also why the old rebase-conflict hotspot is
+   gone.
 
-   **Point 3** — model type detection in `_detect_tts_model_type()`:
-   ```python
-   if model_stage in _YOUR_MODEL_TTS_MODEL_STAGES:
-       return "your_model"
-   ```
+   > **Pure-diffusion TTS does not go through adapters yet.** Under
+   > `for_diffusion()`, `create_speech()` routes straight to
+   > `_create_diffusion_speech()` and never calls `validate()`/`build()`.
+   > `DiffusionTTSAdapter` is scaffolding with no production subclass, so logic
+   > placed in one would silently never run. Diffusion-engine models follow the
+   > existing diffusion path; wiring it through adapters is open work (#4855).
 
-   **Point 4** — validation dispatch in `_validate_tts_request()`:
-   ```python
-   if self._tts_model_type == "your_model":
-       return self._validate_your_model_request(request)
-   ```
+   **If a stage key alone cannot identify the model**, declare
+   `model_archs = frozenset({"YourModelForConditionalGeneration"})`; add
+   `arch_identifies_entry_stage = True` when the model owns no stage key at all
+   (Ming dense). For a rule that is not set membership, override `matches()` (see
+   `covo_audio.py`). For a genuine overlap with another adapter, give one an
+   explicit `detect_priority` — `test_tts_detection.py` fails on an unordered
+   overlap. Models that only serve speech in some topologies override
+   `stage_serves_speech()` (see `audex.py`).
 
-   **Point 5** — validation + parameter-builder methods:
-   ```python
-   def _validate_your_model_request(self, request) -> str | None:
-       if not request.input or not request.input.strip():
-           return "Input text cannot be empty"
-       return None
+   **Reuse shared helpers via `self.ctx.server`** rather than reimplementing them:
+   `_resolve_ref_audio`, `_apply_uploaded_speaker`, `_validate_ref_audio_format`,
+   `_max_instructions_length`. Read a comparable adapter first — `fish_speech.py`
+   (voice cloning), `higgs_audio_v3.py` (parameter-heavy), `moss_tts.py` (family
+   sharing a base class).
 
-   def _build_your_model_params(self, request) -> dict:
-       params = {"text": [request.input]}
-       if request.voice is not None:
-           params["voice"] = [request.voice]
-       return params
-   ```
-   Wire `_build_your_model_params` into `_create_tts_request()` alongside the other
-   model-specific param builders.
+   > **Do not add `self._tts_model_type == ...` branches to `serving_speech.py`.**
+   > Older models predate the adapter framework and still have them; they are being
+   > migrated out (RFC #4327, #4855). `tools/pre_commit/check_tts_adapter.py` is a
+   > ratchet on the remaining count and fails the commit if it grows. Behaviour that
+   > no adapter hook can express is a missing hook — propose it on the RFC.
 
-   > **Two dispatch patterns coexist**: Fish Speech uses a `self._is_fish_speech` boolean
-   > instance attribute checked before `elif self._is_tts`, while all newer models
-   > (CosyVoice3, MOSS-TTS-Nano) use the `_tts_model_type` string returned by
-   > `_detect_tts_model_type()`. For new models, always use the `_tts_model_type` string
-   > pattern — do not add new `_is_*` flags.
-
-   > **Unused variable rule**: only extract fields in `_build_your_model_params` that
-   > are actually forwarded to the model. Unused extractions fail `ruff F841`.
-   > For voice-cloning fields (`ref_audio` → `prompt_audio_path`, `ref_text` →
-   > `prompt_text`), add them to the param builder and verify they reach the model call.
-
-   **Rebase conflict note**: when rebasing onto `main` after another model was merged,
-   `serving_speech.py` will conflict. Resolution: always keep *both* the upstream
-   model's additions and your own — never discard either side.
+   > **Unused variable rule**: only extract fields in `build()` that are actually
+   > forwarded to the model. Unused extractions fail `ruff F841`. For voice-cloning
+   > fields (`ref_audio` -> `prompt_audio_path`, `ref_text` -> `prompt_text`), add
+   > them to the params and verify they reach the model call.
 
 2. **Handle model-specific parameters**:
    - Voice cloning: `ref_audio` encoding and prompt injection
@@ -295,7 +300,7 @@ import base64
 from pathlib import Path
 
 def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
-    """Build prompt with reference audio for voice cloning in serving_speech.py."""
+    """Build prompt with reference audio for voice cloning, called from the adapter."""
     audio_bytes = Path(ref_audio_path).read_bytes()
     codes = codec.encode(audio_bytes)  # Encode on CPU using model's codec (e.g., DAC)
     token_ids = [code + codec.vocab_offset for code in codes.flatten().tolist()]
@@ -305,14 +310,73 @@ def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
     ]
 ```
 
+### Test Case Writing (CI Levels)
+
+**Follow the [vllm-omni-test skill](../vllm-omni-test/SKILL.md)** for markers, file naming (`test_{slug}.py` / `test_{slug}_expansion.py`), Buildkite wiring, and copy-paste run commands. Also read [test_system_overview.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_system_overview.md) and [test_writing_guide.md](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/ci/test_writing_guide.md).
+
+Classify the model's **CI priority** first (high / medium / low). High-priority TTS models are typically those on the integration hot path or listed in tracking issues such as [#1832](https://github.com/vllm-project/vllm-omni/issues/1832); medium and low tiers cover the long tail. When unsure, ask the reviewer which tier applies.
+
+| Priority | Required test levels | Files & markers |
+|----------|---------------------|-----------------|
+| **High** | **L1** unit/logic · **L2** online smoke · **L3** online + offline integration · **L4** feature + performance | See table below |
+| **Medium** | **L3** online + offline · **L4** feature only | Skip dedicated L1/L2 unless fixing a logic bug |
+| **Low** | **L4** feature only | One or two `*_expansion.py` parametrized cases |
+
+**Per-level deliverables (TTS / `pytest.mark.tts`):**
+
+| Level | Location | Marker | CI pipeline | Notes |
+|-------|----------|--------|-------------|-------|
+| **L1** | `tests/model_executor/…`, `tests/entrypoints/openai_api/…`, stage-processor tests | `core_model` + `cpu` | `test-ready.yml` | Prompt assembly, async_chunk helpers, adapter validation — no GPU |
+| **L2** | `tests/e2e/online_serving/test_{slug}.py` | **`core_model` + `advanced_model`** (both on baseline smoke) + `tts` + `@hardware_test(...)` | `test-ready.yml` (`ready` label) | Default deploy smoke: single `/v1/audio/speech` or offline `OmniRunner` path |
+| **L3** | `tests/e2e/online_serving/test_{slug}.py` **and** `tests/e2e/offline_inference/test_{slug}.py` | Baseline smoke: **`core_model` + `advanced_model`**; heavier cases: `advanced_model` only (+ `tts`) | `test-merge.yml` **or** merged into nightly TTS function job | Streaming, voice clone, batch/queue, async_chunk |
+| **L4** | `tests/e2e/online_serving/test_{slug}_expansion.py`, optional offline expansion | `full_model` + `tts` | `test-nightly.yml` (`:full_moon: TTS · Function Test with L4`) | Feature matrix; perf → `tests/dfx/perf/tests/test_tts.json` |
+
+**L2 & L3 online — same file, dual marks on the baseline smoke:** The **first / simplest** case in `test_{slug}.py` (default deploy, single non-streaming `/v1/audio/speech` or equivalent offline path) should carry **both** `@pytest.mark.core_model` **and** `@pytest.mark.advanced_model` on the **same** function so it runs in L2 (`test-ready.yml`, `--run-level core_model`, basic validation) and L3 (`test-merge.yml`, `--run-level advanced_model`, deeper validation) without duplicating the test. In-tree examples: `test_voxcpm2_tts.py::test_text_to_audio_001`, `test_qwen3_tts_customvoice.py::test_text_to_audio_001`.
+
+Heavier scenarios in the same file use **`advanced_model` only** (streaming, extra languages, concurrency, async_chunk, batch). Example: `test_voice_clone_en_streaming_001` → `advanced_model` only. When migrating L3 to nightly, move those heavier cases into `test_{slug}_expansion.py` with `full_model` and drop the dedicated merge job (see `test_ming_tts_expansion.py`, `test_glm_tts_expansion.py`).
+
+```python
+@pytest.mark.core_model
+@pytest.mark.advanced_model
+@pytest.mark.tts
+@hardware_test(res={"cuda": "L4"}, num_cards=1)
+@pytest.mark.parametrize("omni_server", tts_server_params, indirect=True)
+def test_voice_clone_en_non_streaming_001(omni_server, openai_client) -> None:
+    openai_client.send_audio_speech_request({...})
+```
+
+**L4 consolidation:** Prefer parametrized `OmniServerParams` rows (`default`, `async_chunk`, feature flags) in one expansion module rather than many merge-only files ([#1832](https://github.com/vllm-project/vllm-omni/issues/1832)).
+
+**L4 performance (high-priority models):** Add latency / throughput / stress rows in `tests/dfx/perf/tests/test_tts.json`, or a dedicated `tests/dfx/perf/tests/test_{slug}.json` when the model must not join the shared nightly server matrix before integration lands (see VoxCPM2 / Coqui XTTS pattern). Register the model in `benchmarks/tts/model_configs.yaml` for local `bench_tts.py`. Wire a **separate** `test-nightly.yml` Perf Test step when the JSON is not merged into `test_tts.json` yet.
+
+**Keep model-specific code inside test modules — not `tests/helpers/{slug}.py`:**
+
+- Put `MODEL`, deploy path, vendored `REF_AUDIO_URL`, `get_prompt()`, and inline `request_config` dicts **in each** `test_{slug}.py`, `test_{slug}_expansion.py`, offline `test_{slug}.py`, and L1 `test_{slug}_*.py` as needed.
+- **Do not** add `tests/helpers/{slug}.py` (or `tests/helpers/{model_name}.py`) to deduplicate constants or request builders across those files. A little duplication is intentional; follow in-tree references such as `tests/e2e/online_serving/test_glm_tts.py` and `tests/e2e/online_serving/test_cosyvoice3_tts_expansion.py`.
+- `tests/helpers/` is for **repo-wide** harness code only (`mark.py`, `media.py`, `runtime.py`, `stage_config.py`, `assertions.py`, `fixtures/`). Import those; do not extend the tree with per-model modules.
+
+**Runtime send helpers (`tests/helpers/runtime.py`) — online and offline e2e:**
+
+| Path | Fixture | Call |
+|------|---------|------|
+| Online `/v1/*` | `openai_client` | `openai_client.send_*_request(request_config)` |
+| Offline inference | `omni_runner_handler` | `omni_runner_handler.send_*_request(request_config)` |
+
+1. **Grep `runtime.py` first** — reuse `send_omni_request`, `send_diffusion_request`, `send_audio_speech_request` (online + offline Qwen-style TTS), `send_single_stage_tts_request` (Coqui XTTS / MOSS-TTS-Nano offline), etc.
+2. **No matching helper** → add `send_<feature>_request` (or `send_<route>_http_request` for negative/dfx) in **`runtime.py`** with general `assert_*` bundled inside, **then** call it from the test.
+3. **Test file** holds `request_config` dicts only — not `omni.generate`, not `_collect_audio()`, not raw HTTP/SDK.
+4. Wire `tests/helpers/runtime.py` into Buildkite `source_file_dependencies` when you add helpers.
+
+See [vllm-omni-test skill](../vllm-omni-test/SKILL.md) § **Runtime send helpers** for full tables and exceptions.
+
 ### Deliverables
 
-- Updated `serving_speech.py` with all 5 integration points (single commit)
-- Client scripts and server launcher
-- Gradio demo with streaming and voice cloning UI
-- E2E online serving test (`tests/e2e/online_serving/test_<model>.py`)
-- Buildkite CI entry in `.buildkite/test-merge.yml`
-- Documentation (offline + online serving docs)
+- One adapter file under `tts_adapters/` plus its line in the package import block
+- Client scripts and server launcher under `examples/online_serving/text_to_speech/<model>/`
+- Gradio demo with streaming and voice cloning UI in the same dir
+- E2E tests per **Test Case Writing (CI Levels)** above (priority tier determines L1–L4 scope)
+- Buildkite wired per level: `test-ready.yml` (L1/L2), `test-merge.yml` or nightly function job (L3), `test-nightly.yml` (L4) — see [vllm-omni-test skill](../vllm-omni-test/SKILL.md)
+- New section in `examples/online_serving/text_to_speech/README.md` (table row + per-model section). Do **not** create a top-level `examples/online_serving/<model>/` dir or a per-model `README.md` inside `text_to_speech/<model>/`.
 
 ### E2E test pitfalls to avoid
 
@@ -326,9 +390,9 @@ def build_voice_clone_prompt(ref_audio_path: str, text: str, codec) -> list:
 - **Use the harness readiness gate.** The fixture waits for HTTP 200 on
   `/health`; don't add `time.sleep` in tests. If warmup is incomplete, make
   `/health` return non-200 until you're actually ready.
-- **Mark with `@pytest.mark.core_model` + `hardware_test(res={"cuda": "H100"})`**
-  so the test lands in `test-ready.yml` (triggered by the `ready` label) rather
-  than only nightly.
+- **Mark tests per the CI Levels table** — baseline smoke: `core_model` + `advanced_model`; heavier cases: `advanced_model` only; L4 expansion: `full_model`
+- **No per-model helper modules** — do not create `tests/helpers/{slug}.py`; keep constants and `request_config` payloads in the test file
+- **Online and offline e2e go through `runtime.py`** — `openai_client.send_audio_speech_request` (online); `omni_runner_handler.send_audio_speech_request` (Qwen-style offline) or `send_single_stage_tts_request` (single-stage offline). Add a new `send_*_request` in `runtime.py` when none fits; do not embed `omni.generate` or HTTP in tests
 
 ## Phase 4: Async Chunk (Streaming)
 
@@ -455,14 +519,14 @@ Use this checklist when integrating a new TTS model:
 - [ ] README.md written
 
 ### Phase 3: Online Serving
-- [ ] All 5 `serving_speech.py` integration points added in one commit
-- [ ] Only extract params in `_build_*_params` that are forwarded to the model call (ruff F841)
+- [ ] Adapter written under `tts_adapters/` and registered in the import block
+- [ ] Only extract params in `build()` that are forwarded to the model call (ruff F841)
 - [ ] Prompt builder handles text input correctly
 - [ ] Voice cloning works (if supported)
 - [ ] All response formats work (wav, mp3, flac, pcm)
 - [ ] Client scripts and server launcher created
-- [ ] E2E online serving test written (`tests/e2e/online_serving/test_<model>.py`)
-- [ ] Buildkite CI entry added to `.buildkite/test-merge.yml`
+- [ ] E2E tests added per model priority tier (see **Test Case Writing (CI Levels)**)
+- [ ] Buildkite entries match level: `test-ready.yml` / `test-merge.yml` or nightly TTS job / `test-nightly.yml`
 - [ ] Gradio demo working
 - [ ] Documentation added (offline + online docs, nav, supported models)
 
@@ -500,5 +564,6 @@ Project docs and adjacent skills:
 - [TTS audio skill](../vllm-omni-audio-tts/SKILL.md) — supported models and usage
 - [Fish Speech integration](../vllm-omni-audio-tts/references/fish-speech.md) — complete example of Phases 1–3
 - [Qwen3-TTS reference](../vllm-omni-audio-tts/references/qwen-tts.md) — complete example of all 5 phases
+- [vllm-omni-test skill](../vllm-omni-test/SKILL.md) — L1–L4 markers, naming, Buildkite wiring, run commands
 - [Adding a TTS model (developer guide)](https://github.com/vllm-project/vllm-omni/blob/main/docs/contributing/model/adding_tts_model.md)
 - `plan/voxcpm2_native_ar_design.md` — VoxCPM2's vLLM-native AR + side-computation pattern (distinct from the generator-based single-stage described above)

.claude/skills/diffusion-perf-opt/SKILL.md

@@ -0,0 +1,510 @@
+---
+name: diffusion-perf-opt
+description: Diagnose and optimize vLLM Omni diffusion workloads, especially Wan/Qwen/Flux-style image and video generation. Use when Codex is asked to analyze profiling traces, choose parallel strategies, inspect torch profiler trace.json or trace.json.gz timelines, estimate optimization ROI, investigate GPU idle/free bubbles, compare USP/CFG/HSDP/VAE parallelism, or design operator/host/quantization optimizations for vLLM Omni.
+---
+
+# vLLM Omni Performance Optimization
+
+Use this skill to run a disciplined optimization loop for vLLM Omni diffusion workloads. Keep two ideas separate: real performance baselines are collected with low overhead, while torch profiler traces are diagnostic artifacts and may distort latency.
+
+## First Questions
+
+Before proposing changes, ask for the optimization scene if it is not already known:
+
+- GPU model, card count, topology, and whether NVLink is present.
+- Model and pipeline, for example Wan2.2 I2V A14B.
+- User workload: resolution, frames, steps, batch/concurrency, CFG scales, prompt/image inputs.
+- Three runnable commands for each model/workload, or scripts that generate them:
+  - **Server startup command**: exact `vllm serve` command, environment variables, model path, port, parallelism flags, profiler flags, and precision/compile settings.
+  - **User request command**: exact single-request client command (for example curl or Python client) with fixed prompt/media/seed/size/steps. Use this to validate correctness and collect per-request stage timings.
+  - **User benchmark command**: exact repeatable benchmark command or script with warmup count, measured iteration count, concurrency/batch policy, output directory, and summary format.
+- Current enabled strategies: USP/SP, CFG parallel, HSDP/FSDP, VAE patch parallel, torch.compile, profiler options.
+- Optimization target: latency, throughput, memory, cost, or quality-preserving speed.
+- Precision/quality tolerance: bf16/fp8/quantization/sparsity/approximate attention allowed or not.
+- Quality validation method: exact-output comparison when feasible, or metrics
+  such as SSIM, PSNR, LPIPS, cosine similarity, MAE, MSE, temporal flicker
+  checks, baseline self-run variance, and reviewable output artifacts.
+
+## Workflow
+
+1. **Freeze the measurement protocol and commands.**
+   - Before analyzing parallel strategies or traces, establish the three commands for the target model/workload: server startup, single user request, and user benchmark.
+   - Prefer checked-in scripts or one-off shell scripts over free-form commands in chat. The scripts should make workload variables explicit, including model path, port/base URL, prompt/media inputs, resolution, frames, steps, seed, warmup, measured iterations, concurrency, and output path.
+   - Measurement reports must preserve the concrete process, not just the final numbers. For each tested configuration, write down the server startup command, the user request command, the user benchmark command or polling loop, the final server response metrics, and the client-side timing/HTTP result.
+   - Present all duration and latency values in **milliseconds (ms)** in tables and summaries. If an API returns seconds, convert to ms before reporting. Keep raw units only inside quoted raw logs or source snippets.
+   - Keep profiler modes separate in the command set:
+     - Baseline/benchmark commands should avoid torch profiler and stack collection.
+     - Diagnostic commands may enable torch profiler after a baseline identifies a bottleneck.
+   - If any of the three commands is missing, ask for it or create a proposed script before proceeding.
+   - Make clear which command is authoritative for correctness validation and which command is authoritative for performance numbers.
+
+2. **Collect a real baseline.**
+   - Disable PyTorch profiler and stack collection.
+   - Disable or fix torch.compile state so A/B is fair.
+   - Avoid `--enforce-eager` for production-speed baselines unless eager is the target.
+   - Prefer `--log-stats` and `--enable-diffusion-pipeline-profiler` for low-overhead stage timing. PR 3069 has the relevant metrics/log-stats changes; if local code does not include them, fetch or cherry-pick the minimal metrics changes rather than merging unrelated PR drift.
+   - Run warmup requests and exclude first-request lazy init.
+
+3. **Model the parallel strategy before testing.**
+   - Estimate compute and communication for self-attention, cross-attention, FFN, CFG, VAE encode/decode, and HSDP.
+   - Select a small candidate matrix rather than exhaustively testing everything.
+   - Typical 2-card candidates: `USP=2`, `CFG=2`, `USP=1/HSDP on-off`, VAE parallel on-off if memory allows.
+   - Typical 4-card candidates: `USP=4`, `CFG=2 x USP=2`, `USP=2 x HSDP`, VAE parallel on-off.
+   - Typical 8-card candidates for official CFG-enabled video diffusion models:
+     - Primary candidate: `CFG=2 x USP=4` with VAE patch parallel across all 8 ranks.
+     - Compare against: `CFG=1 x USP=8` to test whether larger sequence parallel groups beat CFG branch parallelism.
+     - Isolate VAE parallelism: keep the DiT strategy fixed and compare VAE patch parallel on/off or different VAE patch sizes.
+     - If long-video `diffuse` remains dominant and Ulysses all-to-all is suspected, test a hybrid sequence strategy such as `CFG=2 x USP=2 x Ring=2`.
+     - Test HSDP off only if the model fits without it; treat HSDP primarily as a memory strategy until A/B proves otherwise.
+   - Prefer USP/SP for long video token sequences; prefer CFG parallel when CFG doubles transformer forwards and sequence length is modest.
+   - Convert workload shape to latent/patch token counts before choosing candidates. For video models, record frames, latent frames, latent HxW, patch size, approximate token count, and which stages should scale with the token count.
+
+4. **Search for the best parallel configuration.**
+   - Start with a small matrix that answers one question per comparison:
+     - `CFG parallel vs larger USP`: compare `CFG=2 x USP=world/2` against `CFG=1 x USP=world` for CFG-enabled workloads.
+     - `VAE patch parallel value`: compare the best DiT strategy with VAE patch parallel enabled and disabled.
+     - `HSDP cost`: compare HSDP on/off only when both configurations fit memory.
+     - `Ulysses vs Ring`: test a Ring/Ulysses hybrid only after long-sequence `diffuse` is confirmed dominant.
+   - For each configuration, create or record a stable config id, for example `A_cfg2_usp4_vaepp8_hsdp_tiling`.
+   - For each config id, capture:
+     - exact server command and environment variables,
+     - observed distributed setup from logs, such as SP groups, CFG groups, HSDP shard/replicate sizes, VAE patch size,
+     - exact request command for every scenario,
+     - final server response metrics and client-side elapsed time,
+     - output artifact paths and any failed/empty responses.
+   - Use one warmup request per scenario, then at least three measured repeats for the shortlisted config. For exploratory matrix pruning, one measured request is acceptable only if the margin is large; label it as one-shot.
+   - Compare configurations by stage, not only end-to-end latency. A configuration can improve `diffuse` while hurting `vae.decode`; record both effects.
+   - Select the best config only after checking the target metric, dominant stages, memory headroom, and output correctness.
+
+5. **Run targeted A/B tests.**
+   - Change one variable per test.
+   - Keep model, input, seed, request parameters, GPU placement, and warmup policy fixed.
+   - Record latency, stage timings, memory, output quality, and logs.
+   - Report comparison tables in ms. Include at least end-to-end client time, server `inference_time`, server stage generation time if available, `vae.encode`, `diffuse`, `vae.decode`, and peak memory.
+
+6. **Enforce the quality and precision gate for every optimization.**
+   - Do not mark an optimization ready until quality is checked on the same
+     workload: model, prompt/media, seed, shape, steps, scheduler, dtype,
+     backend, parallelism, and output encoding.
+   - For math-preserving changes, require exact/near-exact agreement or show
+     that differences are within baseline self-run variance.
+   - For precision, quantization, approximate attention, sparse/custom kernels,
+     or backend changes, include reviewable artifacts and metrics such as SSIM,
+     PSNR, LPIPS, cosine similarity, MAE, MSE, and temporal flicker checks.
+   - If quality tolerance is not stated, default to quality-preserving behavior.
+     Failed or inconclusive quality validation blocks a ready-to-merge claim.
+
+7. **Collect diagnostic trace only after narrowing hypotheses.**
+   - Use torch profiler for a small number of requests.
+   - Run two separate diagnostic traces instead of mixing concerns:
+     - **Operator/shape trace**: enable `torch_profiler_record_shapes=True` and keep stack collection disabled. Use this to rank CUDA kernels, NCCL collectives, attention/MLP/norm/RoPE work, and shape-specific hot operators.
+     - **Host-stack trace**: enable `torch_profiler_with_stack=True` and normally keep shape collection disabled. Use this to map CPU/Python host gaps, synchronization points, scheduler paths, and request handling overhead.
+   - Keep both trace commands and reports separate from baseline/benchmark commands. Torch profiler latency is diagnostic only and must not be used as the final latency claim.
+   - Prefer profiling only the narrowed dominant scenario, for example the highest-resolution/video-length case where `diffuse` dominates.
+   - If profiler endpoints are available, run one warmup request first, then call `/start_profile`, run one profiled request, and call `/stop_profile`. This keeps model initialization and warmup out of the diagnostic trace.
+   - Start by analyzing rank 0 only. Expand to more ranks only if rank 0 suggests imbalance, unclear GPU idle/free bubbles, high NCCL wait, server timing mismatch, CFG branch imbalance, or USP group stragglers.
+   - Diagnostic reports must be written to disk and preserve: server command, profiler config, warmup/request/polling commands, trace artifact paths, rank analyzed, analyzer output or summary, and the decision about whether additional ranks are necessary.
+   - Analyze both rank-level balance and device-level free bubbles when additional ranks are opened.
+
+8. **Analyze host, communication, and operators.**
+   - Find GPU idle/free intervals and map each large gap to the enclosing CPU/Python code.
+   - Separate real GPU idle from profiler overhead such as CUPTI `Command Buffer Full`.
+   - Compare NCCL kernel time to user annotations; annotations can overcount nested intervals.
+   - Rank operator work by total CUDA time and by repeated small-kernel launch count.
+
+9. **Produce an optimization plan.**
+   - Classify candidates as P0/P1/P2.
+   - For each candidate, state necessity, expected benefit, implementation path, validation plan, and quality risk.
+   - Include a concrete quality/precision validation gate for each candidate.
+   - Do not implement high-risk operator rewrites before proving the operator is a bottleneck for the target shapes.
+   - End the plan with a user-facing candidate selection table. The assistant
+     should not automatically choose a risky optimization just because it is
+     technically possible. Present the options clearly and let the user decide
+     which item is worth implementing based on latency target, engineering
+     budget, memory headroom, and quality tolerance.
+   - Organize the plan by optimization layer, not by a flat list of ideas:
+     - host/runtime optimization,
+     - measurement/benchmark reliability,
+     - parallelism and communication,
+     - VAE encode/decode and media pre/post processing,
+     - operator fusion and layout cleanup,
+     - attention main-path optimization,
+     - algorithmic, precision, or approximation changes.
+   - For each layer, tie every candidate back to evidence from baseline
+     metrics, diagnostic trace, source code, or output quality requirements.
+     Do not list generic optimizations without a trace or workload reason.
+
+## Priority Rules
+
+- **P0:** low risk, likely useful, or required for trustworthy measurement. Examples: real baseline, warmup, targeted parallel A/B, disabling avoidable `empty_cache`, scheduler coefficient caching.
+- **P1:** meaningful code changes with contained risk. Examples: cross-attention KV caching, VAE gather/broadcast reduction, AdaLayerNorm/RMSNorm/RoPE fusion after trace evidence.
+- **P2:** high implementation or quality risk. Examples: FA to LA replacement, custom Triton/CUDA fused kernels, FP8/quantization, sparsity/Rainfusion-style acceleration.
+
+Every implemented optimization needs A/B validation and a passing
+quality/precision gate, including math-preserving changes such as padding trim,
+layout cleanup, cache reuse, or host/runtime cleanup. Use objective metrics
+when available, keep reviewable artifacts, and compare against baseline
+self-run variance for precision, quantization, approximate kernels, or backend
+changes. If quality validation fails or is inconclusive, do not present the
+optimization as ready to merge.
+
+## Optimization Layers
+
+After baseline, parallel-search, and diagnostic traces, summarize optimization
+opportunities by layer. This is the core of the performance analysis: the goal
+is to connect evidence to a scoped implementation and a validation plan.
+
+### Host and Runtime Optimization
+
+Purpose: remove CPU/Python stalls, synchronization points, allocator overhead,
+and request-path overhead that leave GPU lanes empty.
+
+Evidence to look for:
+
+- High `idle_pct` or large `GAP` blocks in `trace_analyzer.py`.
+- Host-stack trace lines such as `torch.cuda.empty_cache`,
+  `cudaStreamSynchronize`, `cudaDeviceSynchronize`, Python locks, scheduler
+  waits, image/video preprocessing, or repeated small allocation paths.
+- Difference between client wall-clock time and server `inference_time_s`.
+
+Typical candidates:
+
+- Make avoidable `torch.cuda.empty_cache()` optional or guard it by memory
+  headroom.
+- Cache scheduler coefficients, timesteps, masks, or other tiny repeated CPU
+  computations when the request shape/steps are fixed.
+- Remove avoidable host-device synchronizations and blocking logging/stat calls.
+- Move expensive preprocessing out of the critical path or cache fixed prompt,
+  image, and transform work for benchmark scenarios.
+- Ensure benchmark scripts record client-side elapsed time, HTTP status, output
+  path, and server response metrics.
+
+Priority guidance:
+
+- Usually P0 when the change is measurement reliability or an obvious removable
+  synchronization.
+- Usually P1 when it changes scheduling, memory lifetime, or request execution
+  order.
+
+Validation:
+
+- Re-run non-profiler baseline with same workload and seed.
+- Confirm peak memory headroom if disabling cache cleanup.
+- Confirm generated output exists and quality/seed behavior is unchanged.
+
+### Parallelism and Communication Optimization
+
+Purpose: choose the right decomposition for CFG branches, sequence tokens, model
+weights, VAE tiles, and rank topology.
+
+Evidence to look for:
+
+- Baseline A/B across `CFG`, `USP/SP`, `Ring`, `HSDP/FSDP`, and VAE patch
+  parallelism.
+- Stage timing shifts: `diffuse`, `vae.encode`, `vae.decode`, and server
+  end-to-end.
+- NCCL kernel time from trace, not only `user_annotation` time.
+- Rank imbalance across SP group ranks or CFG branch ranks.
+- Memory headroom and OOM risk.
+
+Typical candidates:
+
+- `CFG=2 x USP=world/2` versus `CFG=1 x USP=world` for CFG-enabled models.
+- VAE patch parallel on/off or patch size tuning.
+- HSDP/FSDP on/off only if both configurations fit memory.
+- Ulysses versus Ulysses+Ring only after long-sequence `diffuse` is confirmed
+  dominant and all-to-all is suspected.
+- Rank mapping/topology changes if all-rank traces show stragglers or NCCL wait.
+- Buffer reuse or preallocation for FSDP/HSDP all-gather paths.
+
+Priority guidance:
+
+- P0/P1 for configuration-only changes with strong measured wins.
+- P1 for buffer reuse or rank mapping changes.
+- P2 for invasive distributed algorithm changes.
+
+Validation:
+
+- Measure by stage and memory, not only end-to-end.
+- Use one-variable A/B with identical prompt/media/seed/shape/steps.
+- When communication is suspected, compare rank0-3 in one USP group and rank0
+  versus rank4 across CFG branches for `CFG=2 x USP=4`.
+
+### VAE and Media Pipeline Optimization
+
+Purpose: reduce encode/decode, tiling, split/gather, and media conversion time.
+
+Evidence to look for:
+
+- Large `vae.encode` or `vae.decode` in low-overhead stage timings.
+- Host-stack gaps in VAE tile split, gather, merge, broadcast, or image/video
+  transforms.
+- VAE kernels or cuDNN convolution in operator trace.
+- Whether every rank needs the final decoded tensor.
+
+Typical candidates:
+
+- Keep VAE patch parallel enabled when it has clear measured benefit.
+- Reduce VAE gather/broadcast to only ranks that need the final media output.
+- Reuse tile metadata, split buffers, or gather buffers.
+- Evaluate bf16/autocast behavior for VAE only with visual quality checks.
+- Avoid redundant image conversion, resize, or tensor construction in repeated
+  benchmark runs.
+
+Priority guidance:
+
+- P0/P1 if VAE is a large share of the target workload or if a host gap is
+  obvious and low risk.
+- Lower priority when `diffuse` dominates and VAE is already patch-parallelized.
+
+Validation:
+
+- Compare `vae.encode`, `vae.decode`, server end-to-end, and peak memory.
+- Check output video integrity, artifacts, flicker, and seed stability.
+
+### Operator Fusion and Layout Cleanup
+
+Purpose: reduce high-frequency small kernels, memory bandwidth pressure, layout
+conversions, and launch overhead in transformer and VAE blocks.
+
+Evidence to look for:
+
+- Top operator tables showing many `aten::copy_`, `aten::cat`,
+  `split_with_sizes_copy`, `aten::add`, `aten::mul`, `aten::div`, norm,
+  activation, RoPE, or reshape/layout kernels.
+- `ops_rankN.xlsx` `by_shape` sheet showing repeated small shapes inside the
+  same block path.
+- Trace lanes showing many short kernels between larger GEMM/attention kernels.
+- Source code patterns with repeated elementwise chains or layout conversions.
+
+Typical fusion targets:
+
+- AdaLayerNorm / RMSNorm / LayerNorm plus scale/shift fusion.
+- RoPE fusion with Q/K layout preparation when shapes are stable.
+- Residual add, scale, gate, and elementwise chains.
+- MLP gate/up/down cleanup, such as fusing activation and multiply around
+  GEMM outputs when feasible.
+- QKV projection and reshape/split/cat path cleanup.
+- Attention pre/post layout cleanup to avoid unnecessary copies, cats, and
+  splits around sequence parallel all-to-all.
+
+Priority guidance:
+
+- P1 when implemented with existing PyTorch/Triton/local helper patterns and
+  validated against exact outputs or tolerances.
+- P2 when it requires custom CUDA/Triton kernels, changes numerics, or touches
+  attention math directly.
+
+Validation:
+
+- First prove the operator family is material for the target shape.
+- Use non-profiler A/B for latency and stage timing.
+- Use quality regression checks for generated video stability.
+- Check compile behavior and graph breaks if using `torch.compile`.
+
+### Attention Main-Path Optimization
+
+Purpose: address the dominant self-attention cost when FlashAttention or other
+attention kernels dominate CUDA time.
+
+Evidence to look for:
+
+- Operator trace where FlashAttention/SDPA kernels dominate total CUDA time.
+- Attention shape from `ops_rankN.xlsx` `by_shape`, model code, or trace
+  metadata.
+- Whether attention cost scales with latent frames, latent H/W, patch size, or
+  CFG duplication.
+- Layout/copy/all-to-all work around attention.
+
+Typical candidates:
+
+- Verify the attention backend and shape are on the intended fast kernel path.
+- Compare supported attention backends only with identical workload and quality
+  settings.
+- Reduce attention input size by safe model/config choices when allowed:
+  latent resolution, frame count, patching, boundary ratio, or windowing.
+- Remove avoidable layout conversions before/after attention.
+- Reuse condition-side KV or other static inputs if the model structure allows.
+- Consider custom kernels, sparse/window/linear attention, or approximation only
+  after quality risk is accepted.
+
+Priority guidance:
+
+- P1 for backend/config/layout changes with preserved math.
+- P2 for approximate attention, sparsity, custom kernels, or any change that can
+  alter quality/temporal consistency.
+
+Validation:
+
+- Always include output quality and seed behavior checks.
+- Compare `diffuse`, server end-to-end, peak memory, and attention kernel time
+  in diagnostic traces if needed.
+
+### Algorithmic, Precision, and Approximation Optimization
+
+Purpose: reduce mathematical work or precision cost beyond local code cleanup.
+
+Evidence to look for:
+
+- A single operator family dominates even after low-risk runtime, parallel, and
+  fusion work.
+- Memory bandwidth or compute utilization suggests precision or quantization
+  could matter.
+- The user explicitly allows quality-preserving or approximate methods.
+
+Typical candidates:
+
+- FP8/quantization for transformer or selected projections.
+- Sparsity or Rainfusion-style acceleration.
+- Reduced steps, scheduler changes, distillation, or caching across frames.
+- Approximate attention or linear attention.
+
+Priority guidance:
+
+- Usually P2 because quality, numerics, and implementation risk are high.
+
+Validation:
+
+- Requires strict A/B, visual quality review, temporal flicker checks, seed
+  stability, and possibly human evaluation.
+
+### Interpolation, Super-Resolution, and E2E Pipeline Optimization
+
+Purpose: optimize the whole user-visible video product, not only the base
+diffusion invocation. Some deployments trade base-model latency against
+post-processing, interpolation, or super-resolution stages.
+
+Evidence to look for:
+
+- E2E latency breakdown across base generation, interpolation, super-resolution,
+  encoding, storage, and response streaming.
+- Fast/slow GPU or fast/slow stage analysis across multiple cards and pipeline
+  stages.
+- User quality target: resolution, FPS, temporal smoothness, and acceptable
+  post-processing artifacts.
+
+Typical candidates:
+
+- Add or optimize a frame interpolation stage when it reduces required base
+  model frames for the same perceived FPS.
+- Add or optimize a super-resolution model when generating lower base
+  resolution plus SR is faster for the target quality.
+- Analyze E2E2 pipeline behavior: client request, service scheduling, diffusion,
+  VAE/media, post-process, file write, and response.
+- Identify fast/slow cards or stages and rebalance pipeline placement.
+
+Priority guidance:
+
+- P1 when using proven interpolation/SR components without changing diffusion
+  math.
+- P2 when quality risk is high or the pipeline adds significant operational
+  complexity.
+
+Validation:
+
+- Measure E2E wall-clock, per-stage server timings, output FPS/resolution,
+  artifacts, flicker, and user-visible quality.
+
+### Optimization Candidate Library
+
+Use this table as a compact menu, not as automatic recommendations. Pick items
+only when baseline metrics, trace evidence, source inspection, or quality
+tolerance supports them.
+
+| Layer | Candidate | Evidence | Priority | Validation focus |
+|---|---|---|---|---|
+| Measurement | Freeze server/request/benchmark commands | Missing or drifting commands | P0 | Repeatable non-profiler A/B |
+| Measurement | Separate baseline and diagnostic profiler runs | Profiler used for latency claims | P0 | Low-overhead stage timings |
+| Host/runtime | Guard or remove avoidable `empty_cache` | Host-stack gaps or sync stalls | P0/P1 | Latency, peak memory, OOM safety |
+| Host/runtime | Cache scheduler coefficients/timesteps | Repeated tiny CPU/GPU work | P0/P1 | Same seed/output, stage timing |
+| Host/runtime | Reduce framework scheduling overhead | Client time exceeds server time | P1 | E2E latency, throughput |
+| Parallel | `CFG=2 x USP=world/2` vs `USP=world` | CFG doubles forward work | P0/P1 | `diffuse`, NCCL, memory |
+| Parallel | Tune VAE patch parallelism | VAE encode/decode is material | P0/P1 | VAE time, output correctness |
+| Parallel | HSDP on/off or buffer reuse | HSDP affects memory/all-gather | P1 | Memory, latency, OOM risk |
+| Parallel | Ulysses vs Ulysses+Ring | Long sequence all-to-all suspected | P1/P2 | Rank balance, NCCL kernels |
+| Cross-attn | Disable SP for short condition tokens | Cross-attn comm exceeds compute | P1 | `diffuse`, correctness |
+| VAE/media | Reduce VAE gather/broadcast | Rank traces show VAE wait | P1 | Rank balance, output file |
+| VAE/media | Reuse tile metadata/buffers | Tile split/merge host gaps | P1 | `vae.encode/decode`, memory |
+| VAE/media | VAE bf16/autocast | VAE float kernels are slow | P1/P2 | Artifacts, flicker, seed stability |
+| Operator fusion | AdaLayerNorm/LayerNorm fusion | Norm plus scale/shift kernels | P1 | Numeric tolerance, latency |
+| Operator fusion | RMSNorm fusion | Many small RMSNorm kernels | P1 | Numeric tolerance, latency |
+| Operator fusion | RoPE cache/fuse/layout cleanup | RoPE copy/reshape kernels | P1/P2 | Kernel count, correctness |
+| Operator layout | QKV or attention layout cleanup | Copy/cat/split around attention | P1 | Copy kernels, compile behavior |
+| Attention | Verify backend fast path | FA/SDPA dominates trace | P1 | `diffuse`, attention kernels |
+| Attention | FA to LA or selected-head LA | Attention remains dominant | P2 | Quality, temporal stability |
+| Precision | Transformer FP8/quantization | Compute/bandwidth bound and allowed | P2 | Quality, speed, stability |
+| Sparsity | Rainfusion-style acceleration | DiT compute remains dominant | P2 | Prompt diversity, quality |
+| Pipeline | Frame interpolation | Fewer base frames can meet FPS | P1/P2 | E2E latency, motion artifacts |
+| Pipeline | Super-resolution | Lower base res plus SR may win | P1/P2 | Detail quality, artifacts |
+| E2E | Fast/slow-card analysis | Multi-card stragglers | P0/P1 | Per-rank/stage wall-clock |
+
+### Optimization Plan Template
+
+Use this table shape when reporting the next work items:
+
+| Priority | Layer | Candidate | Evidence | Expected benefit | Implementation path | Validation | Quality risk |
+|---|---|---|---|---|---|---|---|
+| P0 | Host/runtime | Guard `empty_cache` | Host-stack gap points to `torch.cuda.empty_cache` | Small latency reduction, less idle | Add config/env guard | Non-profiler A/B, memory check | Low |
+| P1 | Operator fusion | RMSNorm/AdaLayerNorm fusion | High-frequency norm/elementwise kernels | Lower launch/bandwidth overhead | Use existing fusion helper or targeted Triton | A/B + output check | Medium |
+| P1/P2 | Attention | Attention layout/backend investigation | FA kernel dominates CUDA time | Potentially large | Inspect shapes/backend and remove layout copies | A/B + trace + quality | Medium/high |
+
+Then present a short selection prompt using the same rows:
+
+```text
+Which candidate should we implement next?
+
+1. P0 Host/runtime: guard empty_cache
+   - Expected benefit: small but low-risk latency reduction.
+   - Risk: possible memory increase/OOM if memory headroom is insufficient.
+
+2. P1 Operator fusion: inspect by_shape and implement first norm/RoPE/layout fusion
+   - Expected benefit: medium if high-frequency small kernels are confirmed.
+   - Risk: numerical/compile/quality validation needed.
+
+3. P1/P2 Attention: FA/LA/backend/layout investigation
+   - Expected benefit: potentially large.
+   - Risk: high quality and implementation risk.
+```
+
+If the user has not chosen an item, default to explaining tradeoffs and asking
+which candidate to execute. Only proceed autonomously on low-risk P0 measurement
+or instrumentation fixes.
+
+## Analysis Helpers
+
+PyTorch profiler traces are Chrome/Perfetto-compatible JSON files, usually
+`trace_rankN.json` or `trace_rankN.json.gz`. They normally contain a top-level
+`traceEvents` list, though some exporters emit the raw event list directly.
+
+Use the checked-in analyzer from the repository root:
+
+```bash
+python3 .claude/skills/diffusion-perf-opt/scripts/trace_analyzer.py \
+  vllm_profile/.../trace_rank0.json.gz \
+  --min-gap-ms 5 \
+  --topn 20
+```
+
+For rank imbalance or communication questions, pass all relevant rank traces in
+one command. For host gaps, lower `--min-gap-ms` to `1` and use a host-stack
+trace. Read `gpu_span_s`, `busy_union_s`, `idle_union_s`, `idle_pct`, `GAP`
+blocks, `Top GPU/operator events by total duration`, and `Top NCCL-like events
+by category`. Treat `cat=user_annotation` NCCL ranges as enclosing annotations;
+prefer `cat=kernel` or `cat=gpu_user_annotation` for real device work.
+
+The analyzer summarizes timing only. It does not parse tensor shapes, attribute
+overlap to individual streams, prove quality, or provide final latency claims.
+Use `ops_rankN.xlsx` or PyTorch key averages for shape analysis, and re-test any
+optimization with non-profiler baseline commands.
+
+Read `references/optimization-playbook.md` when drafting the optimization table or comparing candidate techniques.
+
+## vLLM Omni Heuristics
+
+- Cross-attention usually should not use USP/SP when text/image condition token count is much smaller than latent video tokens. Confirm via trace; in Wan2.2 I2V, self-attention dominates cross-attention.
+- VAE bf16/autocast is often worthwhile but requires visual quality checks.
+- VAE patch parallel can help decode/encode but may add gather/merge/broadcast overhead. Check whether all ranks need the final decoded tensor.
+- HSDP/FSDP is primarily a memory strategy. If the model fits without it, run an on/off latency comparison.
+- Scheduler work can create small host/device gaps; cache tiny solve coefficients when timesteps/order are known.
+- `torch.cuda.empty_cache()` can prevent OOM but creates synchronization/idle. Make it optional if memory headroom is sufficient.
+- `Command Buffer Full` in profiler output is profiler overhead, not a model optimization target.

.claude/skills/diffusion-perf-opt/agents/openai.yaml

@@ -0,0 +1,4 @@
+interface:
+  display_name: "vLLM Omni Perf Opt"
+  short_description: "Profile and optimize vLLM Omni diffusion performance."
+  default_prompt: "Analyze and optimize a vLLM Omni diffusion workload using baselines, traces, parallel strategy modeling, and prioritized optimization plans."

.claude/skills/diffusion-perf-opt/references/optimization-playbook.md

@@ -0,0 +1,82 @@
+# Optimization Playbook
+
+Use this reference when turning trace evidence into prioritized vLLM Omni optimizations.
+
+## Parallel Strategy
+
+Start from workload shape:
+
+- Long video tokens, high resolution, many frames: self-attention and FFN dominate. USP/SP is usually valuable.
+- CFG enabled with positive and negative prompts: transformer forward count doubles. CFG parallel may beat USP on small/moderate sequence lengths.
+- Cross-attention: skip SP/USP when condition tokens are much smaller than latent tokens; extra all-to-all can exceed compute saved.
+- HSDP/FSDP: use to fit memory first. If memory allows, compare without HSDP because all-gathers and lazy init add overhead.
+- VAE parallel: useful when encode/decode is material. Check gather/merge/broadcast and whether every rank needs final decoded output.
+
+Candidate matrices:
+
+- 2 cards: `USP=2`, `CFG=2`, HSDP on/off, VAE PP on/off.
+- 4 cards: `USP=4`, `CFG=2 x USP=2`, `USP=2 x HSDP`, VAE PP on/off.
+- 8 cards: add pipeline/stage parallel only after measuring orchestration and inter-stage transfer overhead.
+
+## Host And Free Bubble Analysis
+
+Look for device intervals where no kernel, memcpy, or memset is active.
+
+Common causes:
+
+- First request FSDP/HSDP lazy init.
+- Python preprocessing: image load, resize, `to_tensor`, tokenizer, request marshaling.
+- `torch.cuda.empty_cache()` or explicit synchronizations.
+- Scheduler small linear algebra such as tiny `torch.linalg.solve`.
+- VAE tile split/merge Python loops.
+- Profiler overhead such as CUPTI command buffer flush/full.
+
+Also inspect trace-external time:
+
+- API request parsing and multipart upload.
+- Engine/orchestrator dispatch.
+- Worker RPC and queue wait.
+- Output serialization, video write, base64, HTTP response.
+
+## Operator Optimization Candidates
+
+Evaluate necessity before implementation:
+
+- **FA to LA / LA for head:** high potential only if attention is the main CUDA hotspot and target shapes fit LA well.
+- **Cross-attention BSND/no transpose:** do only if transpose/copy around cross-attn is measurable.
+- **AdaLayerNorm fusion:** fuse norm, scale/shift, gate, residual, and dtype conversion if repeated elementwise kernels are significant.
+- **RMSNorm fusion:** useful for Q/K norms and VAE norms when small-kernel launch count is high.
+- **FP32 LayerNorm to bf16:** quality-sensitive; run visual regression.
+- **LA preprocess:** integrate only if preprocess overhead is cached or amortized.
+- **RoPE move/cache/fuse:** useful when RoPE small kernels are visible around every self-attn block.
+- **VAE RMSNorm replacement:** only after VAE internal trace shows norm as a real component of decode/encode.
+- **Scheduler fusion/cache:** cache coefficient solves and fuse CFG combine with scheduler arithmetic when possible.
+
+Before writing a custom kernel, check:
+
+- Local vLLM Omni implementations.
+- Upstream vLLM kernels and attention backends.
+- SGLang, FlashInfer, FlashAttention, and Triton examples.
+- Shape constraints and layout expectations.
+
+## Quantization And Sparsity
+
+Treat these as high-risk optimizations:
+
+- Rainfusion/sparse diffusion can be high reward but needs model-specific quality validation.
+- VAE decode quantization can reduce decode time but usually has smaller end-to-end upside than transformer attention/FFN work.
+- FP8 or approximate kernels require seed-stable visual regression, temporal flicker checks, and prompt diversity.
+
+## Output Format For Recommendations
+
+Return a table with:
+
+- Priority: P0/P1/P2.
+- Optimization.
+- Evidence from trace or code.
+- Expected benefit.
+- Risk/quality impact.
+- Implementation path.
+- Validation command or A/B test.
+
+State clearly when a recommendation is a hypothesis requiring new trace with shapes.

.claude/skills/diffusion-perf-opt/scripts/trace_analyzer.py

@@ -0,0 +1,144 @@
+#!/usr/bin/env python3
+"""Summarize torch profiler Chrome traces for vLLM Omni diffusion optimization."""
+
+from __future__ import annotations
+
+import argparse
+import collections
+import gzip
+import json
+from pathlib import Path
+from typing import Any
+
+GPU_CATS = {"kernel", "gpu_memcpy", "gpu_memset"}
+CPU_CATS = {"python_function", "user_annotation", "cpu_op", "cuda_runtime", "cuda_driver"}
+
+
+def open_trace(path: Path):
+    if path.suffix == ".gz":
+        return gzip.open(path, "rt")
+    return path.open("rt")
+
+
+def event_name(event: dict[str, Any]) -> str:
+    return str(event.get("name", ""))
+
+
+def summarize_trace(path: Path, min_gap_us: float, topn: int) -> None:
+    with open_trace(path) as f:
+        data = json.load(f)
+
+    events = data if isinstance(data, list) else data.get("traceEvents", [])
+    gpu: list[tuple[float, float, float, str, str, Any, Any]] = []
+    cpu: list[tuple[float, float, float, str, str, Any, Any]] = []
+    by_gpu_name: dict[str, list[float]] = collections.defaultdict(lambda: [0, 0.0, 0.0])
+    by_name: dict[str, list[float]] = collections.defaultdict(lambda: [0, 0.0, 0.0])
+    nccl: dict[tuple[str, str], list[float]] = collections.defaultdict(lambda: [0, 0.0, 0.0])
+
+    for event in events:
+        dur = event.get("dur")
+        ts = event.get("ts")
+        if dur is None or ts is None or dur <= 0:
+            continue
+        cat = str(event.get("cat", ""))
+        name = event_name(event)
+        row = (float(ts), float(ts + dur), float(dur), name, cat, event.get("pid"), event.get("tid"))
+        stat = by_name[name]
+        stat[0] += 1
+        stat[1] += dur
+        stat[2] = max(stat[2], dur)
+        if cat in GPU_CATS:
+            gpu.append(row)
+            gstat = by_gpu_name[name]
+            gstat[0] += 1
+            gstat[1] += dur
+            gstat[2] = max(gstat[2], dur)
+        elif cat in CPU_CATS:
+            cpu.append(row)
+        if "nccl" in name.lower():
+            nstat = nccl[(cat, name)]
+            nstat[0] += 1
+            nstat[1] += dur
+            nstat[2] = max(nstat[2], dur)
+
+    print(f"\n== {path}")
+    print(f"events={len(events)} gpu_events={len(gpu)} cpu_events={len(cpu)}")
+    if not gpu:
+        print("No GPU events found.")
+        return
+
+    gpu.sort()
+    merged: list[list[Any]] = []
+    for start, end, dur, name, cat, pid, tid in gpu:
+        if not merged or start > merged[-1][1]:
+            merged.append([start, end, [(start, end, dur, name, cat, pid, tid)]])
+        else:
+            merged[-1][1] = max(merged[-1][1], end)
+            merged[-1][2].append((start, end, dur, name, cat, pid, tid))
+
+    span = max(end for _, end, *_ in gpu) - min(start for start, *_ in gpu)
+    busy = sum(end - start for start, end, _ in merged)
+    idle = span - busy
+    print(
+        f"gpu_span_s={span / 1e6:.3f} "
+        f"busy_union_s={busy / 1e6:.3f} "
+        f"idle_union_s={idle / 1e6:.3f} "
+        f"idle_pct={idle / span * 100:.2f}"
+    )
+
+    interesting_cpu = [
+        row
+        for row in cpu
+        if row[2] >= 1000
+        and (
+            row[4] in {"python_function", "user_annotation"}
+            or "cudaStreamSynchronize" in row[3]
+            or "cudaDeviceSynchronize" in row[3]
+            or "cudaLaunch" in row[3]
+            or "cudaMemcpy" in row[3]
+        )
+    ]
+
+    gaps = []
+    for idx in range(1, len(merged)):
+        gap_start = merged[idx - 1][1]
+        gap_end = merged[idx][0]
+        gap_dur = gap_end - gap_start
+        if gap_dur >= min_gap_us:
+            prev_event = max(merged[idx - 1][2], key=lambda x: x[1])
+            next_event = min(merged[idx][2], key=lambda x: x[0])
+            mid = (gap_start + gap_end) / 2
+            containers = [row for row in interesting_cpu if row[0] <= mid <= row[1]]
+            containers = sorted(containers, key=lambda x: x[2])[:8]
+            gaps.append((gap_dur, gap_start, gap_end, prev_event, next_event, containers))
+
+    print(f"gaps_ge_{min_gap_us / 1000:.3f}ms count={len(gaps)} sum_s={sum(g[0] for g in gaps) / 1e6:.3f}")
+    for gap_dur, gap_start, gap_end, prev_event, next_event, containers in sorted(gaps, reverse=True)[:topn]:
+        print(f"\nGAP {gap_dur / 1000:.3f} ms ts={gap_start:.0f}->{gap_end:.0f}")
+        print(f"  prev {prev_event[4]} {prev_event[2] / 1000:.3f} ms {prev_event[3][:160]}")
+        print(f"  next {next_event[4]} {next_event[2] / 1000:.3f} ms {next_event[3][:160]}")
+        for row in containers:
+            print(f"  in   {row[4]} {row[2] / 1000:.3f} ms {row[3][:180]}")
+
+    print("\nTop GPU/operator events by total duration:")
+    for name, (count, total, max_dur) in sorted(by_gpu_name.items(), key=lambda kv: kv[1][1], reverse=True)[:topn]:
+        print(f"  {int(count):8d} total={total / 1e6:9.3f}s max={max_dur / 1000:9.3f}ms {name[:180]}")
+
+    print("\nTop NCCL-like events by category:")
+    for (cat, name), (count, total, max_dur) in sorted(nccl.items(), key=lambda kv: kv[1][1], reverse=True)[:topn]:
+        print(f"  {int(count):8d} total={total / 1e6:9.3f}s max={max_dur / 1000:9.3f}ms cat={cat} {name[:160]}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("traces", nargs="+", type=Path, help="trace.json or trace.json.gz files")
+    parser.add_argument("--min-gap-ms", type=float, default=5.0, help="minimum GPU idle gap to print")
+    parser.add_argument("--topn", type=int, default=20, help="number of gaps/hotspots to print")
+    args = parser.parse_args()
+
+    for trace in args.traces:
+        summarize_trace(trace, args.min_gap_ms * 1000.0, args.topn)
+
+
+if __name__ == "__main__":
+    main()

.claude/skills/precheck-pr/SKILL.md

@@ -0,0 +1,103 @@
+---
+name: precheck-pr
+description: Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness. Use when the user says "precheck", "self review", "pre-submit check", or "check my PR before I open it." Never posts to GitHub.
+---
+
+# PR Pre-Check
+
+Self-review your branch before creating a PR against `vllm-project/vllm-omni`. Two modes: **quick** catches showstoppers, **full** does a thorough maintainer-grade review. Never posts to GitHub; the report is for the contributor's terminal only.
+
+## Mode Selection
+
+| Mode | When | Time |
+|------|------|------|
+| **Quick** | About to push, final sanity check | ~3 min |
+| **Full** | Ready for review, want maintainer-level scan | ~10 min |
+
+Default to quick if unsure. Run full before marking a PR "ready for review."
+
+## Workflow
+
+### Step 1: Detect Base Branch
+
+```bash
+BASE_SHA=$(git merge-base HEAD origin/main 2>/dev/null \
+         || git merge-base HEAD main 2>/dev/null \
+         || echo origin/main)
+echo "diffing against ${BASE_SHA}"
+git diff --name-only ${BASE_SHA}...HEAD
+```
+
+### Step 2: Validate PR Title
+
+Check the most recent commit message (or branch name if no commit yet) against the project convention. Valid prefixes:
+
+| Prefix | Applies to |
+|--------|-----------|
+| `[Bugfix]` | Bug fixes |
+| `[CI/Build]` | Build or CI improvements |
+| `[Doc]` | Documentation changes |
+| `[Model]` | New/improved models (include model name) |
+| `[Frontend]` | Frontend changes (API server, OmniLLM class, etc.) |
+| `[Kernel]` | CUDA/kernel changes |
+| `[Core]` | Core logic changes (OmniProcessor, OmniARScheduler, etc.) |
+| `[Hardware][Vendor]` | Hardware-specific (e.g., `[Hardware][Ascend]`) |
+| `[Misc]` | Other changes (use sparingly) |
+
+✗ if: missing prefix, wrong case (`[bugfix]`), or WIP/Draft in title.
+⚠ if: `[Model]` prefix without the model identifier (e.g., `[Model] Add new model` — should be `[Model] Add <ModelName> ...`).
+
+### Step 3: Categorize the PR
+
+| Diff contains | PR type |
+|---------------|---------|
+| New files under `vllm_omni/model_executor/models/<name>/` | **New Model** |
+| Changes to `vllm_omni/diffusion/` | **Diffusion Model** |
+| `[Bugfix]` prefix or single-file fix | **Bug Fix** |
+| Perf/benchmark/throughput claims in commit msg or diff | **Performance** |
+| Everything else | **General** |
+
+If multiple rows apply (e.g., a diffusion model is also a new model), union the checklists.
+
+### Step 4: Run Checklist
+
+Ask: "Quick mode or full mode?" Then walk the checklist for the detected PR type from [references/checklists.md](references/checklists.md). Each item produces ✓, ✗, or ⚠.
+
+**Also run the Code-Quality sweep on every PR**, regardless of type or mode: the five diff-scoped checks in [references/code-quality.md](references/code-quality.md) — kwargs fragility, broad-except swallow, `Any`/wrong type hints, hot-path `.clone()`/`deepcopy`, and event-loop blocking — plus the advisory conventions (log level, structured logging, synchronization, cleanup, dependencies, naming) in [checklists.md](references/checklists.md). These count only lines the PR *adds* — the pre-existing backlog across the repo is out of scope.
+
+**Also run the Examples-Policy check on every PR** using [references/examples-policy.md](references/examples-policy.md). Inspect only Python paths introduced by the diff. Treat a new model-, checkpoint-, vendor-, or family-specific Python example as blocking; do not report pre-existing example debt.
+
+### Step 5: Print Report
+
+```
+Pre-check report for <branch>
+
+  Mode: quick | full
+  Type: <new-model | diffusion-model | bug-fix | perf | general>
+
+  Dimension          Result
+  ─────────────────  ──────
+  PR title format    ✓
+  Code quality       ⚠ 1 broad except, 2 Any hints
+  Examples policy    ✗ new model-specific Python example
+  PR desc integrity  ✓
+  Registry/config    ✓
+  Dead code          ⚠ 2 warnings
+  Accuracy           ✓
+  Benchmark          ✗ missing software versions
+
+  Verdict: 1 blocking | 2 warnings | recommend fixing ✗ before PR
+```
+
+**Severity:**
+
+| Mark | Meaning |
+|------|---------|
+| ✗ | Blocking — fix before opening PR |
+| ⚠ | Warning — consider fixing |
+| ✓ | Pass |
+| — | Skipped (not applicable) |
+
+### Stop Here
+
+Do not post comments, open PRs, or modify files. The report is for the contributor's terminal only.

.claude/skills/precheck-pr/references/checklists.md

@@ -0,0 +1,173 @@
+# Pre-Check Checklists
+
+Each item is a concrete check to run against the diff. ✗ = fix before PR. ⚠ = consider fixing.
+
+---
+
+## All PRs: Title Format (check first)
+
+PR title must follow the project convention documented in `docs/contributing/README.md`:
+
+| Prefix | Applies to |
+|--------|-----------|
+| `[Bugfix]` | Bug fixes |
+| `[CI/Build]` | Build or CI improvements |
+| `[Doc]` | Documentation changes |
+| `[Model]` | New/improved models (include model name) |
+| `[Frontend]` | Frontend changes (API server, OmniLLM class, etc.) |
+| `[Kernel]` | CUDA/kernel changes |
+| `[Core]` | Core logic changes (OmniProcessor, OmniARScheduler, etc.) |
+| `[Hardware][Vendor]` | Hardware-specific (e.g., `[Hardware][Ascend]`) |
+| `[Misc]` | Other changes (use sparingly) |
+
+**Examples:**
+```
+✓ [Model] Add MiniCPM-o 4.5 support
+✓ [Bugfix] Fix Ovis image text encoder dtype
+✓ [Core] Qwen3-Omni performance optimization
+✓ [Hardware][Ascend] Fix VoxCPM2 on Ascend
+✗ Fix Ovis image text encoder dtype              ← missing prefix
+✗ [bugfix] Fix Ovis image text encoder           ← wrong case
+✗ [WIP] Add model                                ← WIP in title
+```
+
+- [ ] **Title has valid prefix** — ✗ if missing prefix, wrong case, or WIP/Draft in title. For `[Model]` PRs, the title MUST also include the model identifier (e.g., `[Model] Add <ModelName> ...`). ⚠ if missing the model name.
+
+---
+
+## All PRs: Code Quality (run on every PR, quick & full)
+
+Diff-scoped sweep for five fragility patterns that are pervasive in the existing codebase — **only lines the PR adds count**, not the pre-existing backlog. Detection commands, codebase examples, and full severity rules live in [code-quality.md](code-quality.md).
+
+- [ ] **No new `**kwargs` string-lookup plumbing:** the diff adds no `**kwargs` + `kwargs["..."]` / `kwargs.get("...")` / `"..." in kwargs`. ⚠ any new instance; ✗ if a new string key is duplicated across ≥2 files without a shared constant, or unknown keys are silently dropped on a fail-fast path.
+- [ ] **No new broad exception swallow:** the diff adds no `except Exception:` / bare `except:`. ⚠ any new instance; ✗ for `except: pass` / `except Exception: return None` / `continue` on a fail-fast path (init, config validation, weight loading, request handling).
+- [ ] **No new `Any` / wrong type hints:** the diff adds no `: Any` / `-> Any` / untyped production signature, and no new `SimpleNamespace` in tests faking an object that already has a real typed stub. ⚠ any new instance; ✗ for a *wrong* (actively misleading) annotation or a `SimpleNamespace` test fake of a typed object.
+- [ ] **No new hot-path copy:** the diff adds no `.clone()` / `.copy_()` / `copy.deepcopy(...)` inside a per-step loop or per-request path. ⚠ any new instance; ✗ for `.clone()` of a full latent/activations tensor inside an AR or diffusion step loop without a comment, or `deepcopy` of a scheduler / sampling-config on the request path.
+- [ ] **No new event-loop blocking:** the diff adds no `time.sleep` / blocking HTTP (`requests`, `urllib`) / `await` inside a held lock in `async def` code. ⚠ any new instance; ✗ for blocking sleep or HTTP on the serving path (`engine/`, `entrypoints/`, async `connectors/`), or a lock held across an `await`. Blocking inside a dedicated worker thread is fine.
+
+Roll the ⚠/✗ counts into the report as a single **Code quality** dimension row.
+
+---
+
+## All PRs: Examples Policy (run on every PR, quick & full)
+
+Inspect Python files introduced under `examples/` using
+[examples-policy.md](examples-policy.md). This is a diff-scoped ratchet: existing
+model-specific examples may be edited or removed without triggering the check.
+
+- [ ] **No new model-specific Python examples:** the diff adds, copies, or renames no Python file whose path or behavior is specific to one model, checkpoint, vendor, or model family. ✗ for any violation. Route inference through a shared task/protocol entrypoint, model request behavior through `vllm_omni/model_extras`, and commands through task docs or `recipes/`.
+- [ ] **New generic example code is justified:** any new task-neutral Python file under `examples/` is an actual user-facing entrypoint rather than reusable production logic, a benchmark, an app, a setup tool, or a reproducer. ⚠ when the placement is questionable.
+
+Roll the result into the report as a separate **Examples policy** dimension.
+
+### Conventions (eyeball — no grep)
+
+Judgment calls that don't grep cleanly — apply them while reading the diff, not as a mechanical check:
+
+- **Right log level** — `info` is for end-user-visible events; routine internal events are `debug`; hot paths are `trace` or removed. Logging is not free.
+- **Structured fields over f-strings** — pass dynamic values as structured fields (`logger.info("msg", extra={...})`), not interpolated strings, so they stay greppable and parseable.
+- **No reflexive synchronization** — don't add `threading.Lock` / `asyncio.Lock` / `deepcopy` / re-wrap shared state unless there is real concurrent mutation; the GIL covers most Python code. Owners decide their own synchronization — don't pre-lock in a constructor.
+- **Context managers for cleanup** — use `with` / `contextlib` for files, locks, CUDA streams, and profilers; not raw `open()` + manual `.close()` or ad-hoc `finally`.
+- **No new dependencies without justification** — prefer the stdlib and existing vllm-omni deps; a new top-level dep needs a reason (check `pyproject.toml` / `requirements*.txt`).
+- **Naming / interface discipline** — don't rename or change interfaces just for taste in this PR, but do fix names that actively mislead (e.g. `serve` implying a long-running server, `Instance` where multiple instance kinds coexist).
+
+---
+
+## Bug Fix PRs
+
+### Quick
+
+- [ ] **PR body has 4 sections:** what broke (error message), repro steps, root cause, fix description
+- [ ] **Fix is minimal:** diff touches only files directly related to the bug. No unrelated refactors or style changes.
+- [ ] **Repro is runnable:** the PR body includes a copy-paste command that reproduces the bug
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Regression test exists:** `git diff --name-only` includes at least one `tests/` file
+- [ ] **Regression test reproduces the original error:** the test would fail on main, pass on this branch
+- [ ] **Fix matches root cause exactly:** no "fix the symptom + something else" — if the root cause is dtype, the fix is dtype, not dtype + reformatting
+- [ ] **No silent failure risk:** no bare `except: pass`, no `try: ... except: return None`, no empty fallback added. See the broad-except pattern in [code-quality.md](code-quality.md#2-broad-exception-swallow) for the full severity rule (⚠ any new broad catch; ✗ for a swallow on a fail-fast path).
+- [ ] **Upstream pattern match:** the fix follows the same pattern as existing code for similar cases (grep for analogous `from_pretrained` calls, etc.)
+- [ ] **Environment documented:** torch/transformers/vllm versions listed if the bug is version-dependent
+
+---
+
+## Performance PRs
+
+### Quick
+
+- [ ] **Before/after numbers in PR body:** at minimum, one metric (RTF, RPS, latency, VRAM) with main vs PR values
+- [ ] **Hardware specified:** GPU model, count, VRAM. "L4" or "H20" is sufficient. "tested on GPU" is not.
+- [ ] **Concurrency scaling:** if the optimization claims better throughput, numbers at 2+ concurrency levels
+- [ ] **No unexplained regressions:** if any metric goes down at any concurrency level, explain why
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Software versions:** torch, CUDA, vllm, vllm-omni versions in PR body
+- [ ] **Warmup stated:** "1 warmup + 3 measured" or equivalent
+- [ ] **Stddev or range:** not just a single best run. "average of 2 rounds" is minimum.
+- [ ] **VRAM measured:** peak GPU memory before and after
+- [ ] **Benchmark script checked in:** under `tests/` or `examples/`, runnable by reviewer
+- [ ] **Exact command line in PR:** not pseudocode, not "run the benchmark"
+- [ ] **Pytest summary included:** `36 passed, 3 skipped, 17 warnings in 2897.66s` — proves the whole suite passes
+- [ ] **Config changes documented:** if `max_num_seqs`, `gpu_memory_utilization`, or deploy YAML changed, old and new values stated with reasoning
+
+---
+
+## New Model PRs
+
+### Quick
+
+- [ ] **PR body file list matches `git diff --name-only`:** every file path in the description exists in the diff
+- [ ] **Registry entries match claimed count:** count `_OMNI_MODELS` or `_DIFFUSION_MODELS` entries added vs claimed in body
+- [ ] **Pipeline config `custom_process_input_func` paths resolve:** each referenced module.function exists
+- [ ] **`__init__.py` exports match registry:** each registered class appears in `__init__.py` and `__all__`
+- [ ] **Shell script modes match README:** `run_curl.sh` supports every mode the README documents
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+
+### Full (adds)
+
+- [ ] **Dead code scan (5 patterns):**
+  1. Dead `forward()` — defined but never called through the pipeline path
+  2. Dead factory/builder functions — instantiated nowhere
+  3. Dead wrappers — thin wrappers with no callers
+  4. Dead branch guards — `if version > X` where X is always satisfied
+  5. Unused parameters — `__init__` args never accessed
+- [ ] **Copy-paste detection:** no string constant defined in 3+ files, no cross-module validation duplication, no near-identical shape coercion fns
+- [ ] **Import hygiene:** no re-export-only imports, no module-level side effects without docs, no `import os` in function body when already at top. Function-body imports are acceptable when the import is only reachable from a closure or factory return value (i.e., `import` would otherwise execute at module load even though the user may never trigger that path). Either lift to top-of-file or annotate with `# noqa: PLC0415`.
+- [ ] **Accuracy:** at least one test compares output against reference; audio = valid WAV at correct sample rate; determinism verified if seed claimed. ⚠ if missing and not explicitly deferred to a follow-up PR or RFC section; — if explicitly deferred (the deferral target SHOULD be linked in the PR body).
+- [ ] **Performance:** hardware, software versions, warmup, and metrics (RTF, VRAM, RPS, latency) stated. ⚠ if missing and not explicitly deferred; — if explicitly deferred.
+- [ ] **Benchmark settings:** model config (TP, PP, max_model_len, enforce_eager, quant), runtime config (batch, input/output spec), environment versions
+- [ ] **Benchmark script checked in + exact command line + pytest summary**
+
+---
+
+## Diffusion Model PRs
+
+Diffusion models live under `vllm_omni/diffusion/`. In addition to the New Model checklist above, verify:
+
+- [ ] **Transformer adapter follows contract:** inherits from `nn.Module`, implements `load_weights()`, uses `vllm_omni.diffusion.attention.layer.Attention`
+- [ ] **Pipeline has both offline and online paths:** `forward()` for batch generation, streaming path for online serving
+- [ ] **Cache-DiT integration:** at least one acceleration (TeaCache, FBCache, etc.) is wired up. ✗ for new **top-level** pipelines. For **subclass** pipelines that inherit `__call__` from a parent where Cache-DiT is already absent, downgrade to ⚠ and recommend filing a follow-up issue against the parent pipeline family.
+- [ ] **Parallelism config:** TP/SP/USP/CFG-Parallel options exposed in pipeline config
+- [ ] **Docs table updated:** model added to `docs/models/supported_models.md` with correct metadata
+- [ ] **E2E test exists:** at least one test exercises the full generate path
+
+---
+
+## General PRs
+
+### Quick
+
+- [ ] **PR body matches diff:** description doesn't claim changes not in the diff
+- [ ] **Branch is rebased:** `git merge-base HEAD origin/main` is recent, no merge conflicts
+- [ ] **CI gates passing:** DCO, pre-commit, build — check `gh pr view --json statusCheckRollup` if a PR already exists
+
+### Full (adds)
+
+- [ ] **No new dead code in changed files:** scan for unreachable paths, unused imports
+- [ ] **Test coverage:** if core code changed (`engine/`, `stages/`, `connectors/`), tests are added or existing tests cover the path
+- [ ] **Import hygiene:** no redundant imports added
+- [ ] **No unrelated changes:** diff scope matches PR description

.claude/skills/precheck-pr/references/code-quality.md

@@ -0,0 +1,179 @@
+# Code-Quality Patterns
+
+Five fragility patterns to sweep on **every** PR, in both quick and full mode. These are pervasive in the existing codebase (hundreds of pre-existing sites) — so the checks are **diff-scoped**: only lines the PR *adds* count. Whole-repo greps light up every PR with the backlog and are useless.
+
+All commands assume:
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+```
+
+**Severity:** ⚠ for any *new* instance introduced by the diff; ✗ for the acute sub-cases defined per pattern. Acute ✗ findings belong in the report's blocking count.
+
+---
+
+## 1. `**kwargs` string-lookup passthrough
+
+### Anti-pattern
+```python
+def forward(self, **kwargs):
+    # BLOCKER-prone: raw dict + string keys duplicated across files
+    if "runtime_additional_information" in kwargs and \
+       "model_intermediate_buffer" not in kwargs:
+        ...
+```
+Real instances of this exact guard are copy-pasted across `cosyvoice3.py`, `indextts2_talker.py` (×2), `indextts2_s2mel_decoder.py`, `qwen3_tts_talker.py`, `qwen3_omni.py`. A typo in any string key silently no-ops; there is no type check, no IDE completion, and unknown keys are silently dropped.
+
+### Why dangerous
+Raw `dict` + `**kwargs` + lists of param-name strings is **fragile by construction**: the compiler can't see a missing or misspelled key, so failures are silent. Each duplicated copy is a new place for the strings to drift.
+
+### Severity
+- ⚠ any *new* `**kwargs` signature whose body does `kwargs["..."]` / `kwargs.get("...")` / `"..." in kwargs`.
+- ✗ when the diff introduces a **new** string key that is duplicated across ≥2 files without a shared module constant, or when `**kwargs` plumbing **silently drops unknown keys** on a fail-fast path (init, config validation, weight loading).
+
+### Detect (diff-scoped)
+```bash
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
+  | grep -E '\*\*kwargs|kwargs(\.get|\[)|"[a-z_]+"\s+in\s+kwargs'
+```
+
+### Fix
+Prefer explicit typed params. The repo already has the right primitive: `msgspec.Struct` with `forbid_unknown_fields=True` rejects unknown keys instead of silently dropping them — see `_StructBase` in `vllm_omni/data_entry_keys.py:105` and `MoriPullRequest` in `vllm_omni/distributed/omni_connectors/connectors/mori_transfer_engine_connector.py:81`. A `@dataclass` or `TypedDict` works too.
+
+If `**kwargs` is unavoidable (vLLM base-class compat), centralize the key strings as module-level constants behind **one** typed accessor, instead of repeating the literal across files.
+
+---
+
+## 2. Broad exception swallow
+
+### Anti-pattern
+```python
+# BLOCKER-prone: catches everything, returns None on any failure
+try:
+    evt = build_event(...)
+    return evt
+except Exception:
+    return None
+```
+Real clusters: `vllm_omni/metrics/stats.py:240,274,367,450` and `vllm_omni/metrics/{modality,utils}.py` — all `except Exception: return None`.
+
+### Why dangerous
+A generic `except Exception:` is **too flexible**. If we catch an exception type we don't expect, it is almost always an unhandled edge case that needs to be *fixed* — not swallowed into `None`/`pass`/`continue`. Broad catches turn fixable bugs into silent wrong behavior and make debugging impossible.
+
+The repo backs this with ruff `BLE001` (broad-exception catch); it is currently suppressed via `# noqa: BLE001` in `vllm_omni/patch.py:413`. Prefer fixing over suppressing.
+
+### Severity
+- ⚠ any *new* `except Exception:` / `except BaseException:` / bare `except:`.
+- ✗ for `except:` / `except Exception: pass|return None|continue` on a **fail-fast path** (init, config validation, weight loading, request handling, connector setup). This extends the Bug-Fix "No silent failure risk" ✗ in [checklists.md](checklists.md).
+
+### Detect (diff-scoped)
+```bash
+# Any new broad catch (⚠ baseline):
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
+  | grep -E 'except\s*(Exception|BaseException)?\s*:'
+# Note: this matches only the immediate-colon swallow forms (`except :`,
+# `except Exception:`) — it deliberately excludes specific catches like
+# `except ValueError:` and the `except Exception as e:` variant. Eyeball
+# the latter; `as e` usually means it is logged, but still verify.
+```
+
+### Fix
+Catch the **specific** types you actually expect (`ValueError`, `KeyError`, `AttributeError`, `OSError`, `torch.cuda.OutOfMemoryError`, `msgspec.ValidationError`, …). At a genuine top-level / best-effort boundary (metrics, signal handlers), at minimum **log** the exception; never swallow into a bare `pass`/`return None` on a path that should fail loudly.
+
+---
+
+## 3. `Any` / wrong type annotations
+
+### Anti-pattern
+```python
+# ⚠ Any param + Any return — no contract, no narrowing
+def _extract_mm_output(engine_outputs: Any) -> dict[str, Any]:
+    ...
+```
+
+### Why dangerous (root cause)
+`Any` disables the type checker for that position, so typos and wrong shapes pass silently. The repo has 636 `: Any` params, 244 `-> Any` returns, and **1,173 `SimpleNamespace` in `tests/`**. The `Any` leak is largely test-driven: vibe-coded unit tests fake objects with `SimpleNamespace`, which forces the production code consuming them to be typed `Any`, and the `Any` then propagates outward into public signatures.
+
+### Severity
+- ⚠ any *new* `: Any` / `-> Any` / fully-untyped signature in production code.
+- ✗ for a **wrong** annotation (actively misleading — e.g. `-> bool` that returns `Optional[bool]`), or a *new* `SimpleNamespace` in a test that mimics an object which already has a real typed stub / `@dataclass` / `TypedDict` / `Protocol`.
+
+### Detect (diff-scoped)
+```bash
+# Any leaking into new signatures:
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
+  | grep -E ':\s*Any\b|->\s*Any\b'
+# SimpleNamespace newly added in tests (the Any-leak source):
+git diff ${BASE}...HEAD -- "tests/" | grep "^+[^+]" | grep "SimpleNamespace"
+```
+
+### Fix
+Replace `Any` with the concrete type, a `Protocol`, or a `Union`. If a type is genuinely dynamic, prefer `object` + `isinstance` narrowing over `Any` — `object` keeps the checker engaged. In tests, replace `SimpleNamespace` with the real class, a small `@dataclass`/`TypedDict`, or a `Protocol` + fake, so the system under test stays fully typed.
+
+---
+
+## 4. Unnecessary copy — `.clone()` / `copy.deepcopy` in hot paths
+
+### Anti-pattern
+```python
+# Per-step in a diffusion/AR loop: clones the full latent every iteration
+for t in timesteps:
+    trajectory_latents.append(x_t.clone())   # O(N) GPU alloc + memcpy each step
+```
+Real clusters: `vllm_omni/diffusion/models/bagel/bagel_transformer.py` calls `x_t.clone()` ~9× across a trajectory loop (lines 1820–2186); the cache hooks `vllm_omni/diffusion/cache/teacache/hook.py:155` and `magcache/hook.py:182` clone `hidden_states` per step. Separately, `copy.deepcopy(self.scheduler)` / `copy.deepcopy(req.sampling_params)` is copy-pasted across diffusion pipelines — `pipeline_qwen_image.py:786`, `pipeline_hunyuan_image3.py:1904`, `pipeline_ltx2*.py` (×3), `pipeline_dreamzero.py:1085`, `diffusion_model_runner.py:398` — a full object-graph deepcopy on the request path. (Contrast `dreamzero/causal_wan_model.py:203`, which carries a `# No .clone() needed.` comment — the aware pattern.)
+
+### Why dangerous
+A `.clone()` of a GPU tensor or a `copy.deepcopy` of a config object inside a per-step / per-request path is a hidden allocator: it reads as one Python line but triggers a GPU memcpy or a deep object-graph walk every iteration. Across 50 diffusion steps or hundreds of AR steps it dominates latency and VRAM bandwidth. `deepcopy` additionally ignores any custom device / `__copy__` logic and can drag tensors through CPU.
+
+### Severity
+- ⚠ any *new* `.clone()` / `.copy_()` / `copy.deepcopy(...)` inside a per-step loop or per-request path.
+- ✗ for `.clone()` of a full latent/activations tensor inside an AR or diffusion step loop without an explanatory comment, or `copy.deepcopy` of a scheduler / sampling-config on the request hot path.
+
+### Detect (diff-scoped)
+```bash
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
+  | grep -E '\.clone\(\)|\.copy_\(|copy\.deepcopy|deepcopy\('
+```
+
+### Fix
+Tensors: prefer a view (`x[..., :k]`, `torch.narrow`, `as_strided`) or write into a pre-allocated buffer with `dst.copy_(src)` instead of `.clone()`; if the caller no longer needs the original, `move`/`del` it rather than copy. Schedulers / configs: build a fresh lightweight instance per request, or split out the mutable per-request bits so the shared immutable part isn't deepcopied. If a copy is genuinely required (e.g. the source is mutated in place later), keep it and add a one-line comment saying why.
+
+---
+
+## 5. Blocking the asyncio event loop / lock held across `await`
+
+### Anti-pattern
+```python
+# BLOCKER-prone: blocking sleep / blocking HTTP / lock held across an await,
+# all inside an async (event-loop) function
+async def handle(self, req):
+    with self._lock:
+        resp = await self._client.call(req)   # lock held across await -> serializes all requests
+    time.sleep(0.1)                           # stalls the single loop for every concurrent request
+```
+
+### Why dangerous
+vLLM-Omni serves over a single asyncio event loop. Any synchronous blocking work on an `async def` path — `time.sleep`, blocking HTTP (`requests`/`urllib`), heavy CPU work, or a lock held across an `await` — stalls the loop for *every* concurrent request, not just the blocking one. A lock held across `await` silently serializes the whole pipeline; blocking I/O tanks throughput and inflates tail latency.
+
+### Severity
+- ⚠ any *new* `time.sleep(`, blocking HTTP call (`requests.get/post`, `urllib.request`, `urlopen`), or `await` inside a `with Lock` / `async with` critical section, in `async def` code.
+- ✗ for `time.sleep` or blocking HTTP on the serving path (`engine/`, `entrypoints/`, async `connectors/`), or a lock acquired and held across an `await` on the request path.
+
+### Detect (diff-scoped)
+```bash
+# New blocking primitives introduced by the diff — then eyeball whether each
+# sits in an async function on the loop vs a dedicated worker thread:
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" \
+  | grep -E 'time\.sleep|requests\.(get|post|put|delete)|urllib\.request|urlopen'
+# Lock acquired with an await potentially inside the critical section (manual eyeball):
+git diff ${BASE}...HEAD -- "*.py" | grep "^+[^+]" | grep -E 'async with|\.acquire\(\)'
+```
+Note: blocking inside a **dedicated worker thread is correct** — the repo already does this (`diffusion/model_loader/hub_prefetch.py`, `distributed/omni_connectors/kv_transfer_manager.py`, the `mooncake`/`mori` connectors poll with `time.sleep` on their own threads). The check targets the *event-loop* path only.
+
+### Fix
+Move blocking work off the loop with `asyncio.to_thread(...)` / `run_in_executor`, or use a native async client (`httpx.AsyncClient` / `aiohttp`). Never `await` while holding a lock — drop the lock before the `await` and re-acquire after, or restructure so the critical section contains no `await`.
+
+---
+
+## Running the sweep
+
+Run all five detections against `${BASE}...HEAD`, then for each hit decide ⚠ vs ✗ using the severity rules above. Roll the results into the report as a single **Code quality** dimension row (count of ⚠ and ✗), alongside the type-specific checklist from [checklists.md](checklists.md).

.claude/skills/precheck-pr/references/examples-policy.md

@@ -0,0 +1,68 @@
+# Examples Python Policy
+
+Prevent new model-specific Python examples without forcing cleanup of the
+existing backlog. Run this check on every PR in quick and full modes.
+
+## Scope
+
+Set the merge base as in the main workflow, then list Python paths introduced
+by the PR, including additions, copies, and renames:
+
+```bash
+BASE="$(git merge-base HEAD origin/main)"
+git diff --name-status --diff-filter=ACR --find-renames --find-copies \
+  "${BASE}"...HEAD -- 'examples/**/*.py'
+```
+
+Inspect destination paths for copies and renames. Existing model-specific files
+that are only modified or deleted are grandfathered and must not be reported.
+
+## Blocking classification
+
+Mark ✗ when a newly introduced Python file does any of the following:
+
+- Names or scopes a path to one model, checkpoint, vendor, or model family.
+- Hard-codes a model identifier, model-specific prompt, request shape, output
+  conversion, or launch configuration behind an otherwise generic filename.
+- Duplicates an existing task/protocol runner to demonstrate one model.
+- Adds a per-model helper below a generic task hub such as `text_to_speech/`.
+
+Do not infer safety from a generic filename alone. For example, a file named
+`text_to_audio.py` is still model-specific if it only implements one model's
+contract.
+
+## Acceptable additions
+
+A new Python example may pass when it is a genuinely model-neutral,
+user-facing task or protocol entrypoint. It must accept model selection through
+configuration and keep model-specific behavior out of the script.
+
+Use ⚠ when the file is model-neutral but reusable logic appears misplaced under
+`examples/`. Route code by purpose:
+
+| Code purpose | Preferred location |
+|---|---|
+| Model prompt, defaults, request/output adaptation | `vllm_omni/model_extras/` or production model modules |
+| Runnable model command and validation evidence | Task documentation or `recipes/` |
+| UI or interactive application | `apps/` |
+| Benchmark or evaluator | `benchmarks/` |
+| Regression reproducer | `tests/` |
+| Download, conversion, or setup utility | `tools/` |
+
+If a model cannot use an existing shared runner, report the missing generic
+capability. Do not solve the gap by adding a model-specific Python example.
+
+## Report format
+
+Add one row to the pre-check report:
+
+```text
+Examples policy    ✓ no new Python example paths
+Examples policy    ⚠ generic helper belongs in tools/
+Examples policy    ✗ examples/offline_inference/new_model/end2end.py is model-specific
+Examples policy    ✗ examples/offline_inference/x_to_y_model_name.py is model-specific
+```
+
+For a blocker, name the path, the model-specific behavior, and the appropriate
+shared runner or destination. Do not propose deleting unrelated existing
+examples as part of the PR.

.claude/skills/quantization/SKILL.md

@@ -0,0 +1,103 @@
+---
+name: quantization
+description: Work on vLLM-Omni quantization for diffusion, autoregressive, omni, or multi-stage models. Use when choosing or adding methods such as fp8, int8, gguf, mxfp8, mxfp4, mxfp4_dualscale, ModelOpt, AutoRound, INC, msModelSlim, awq, or gptq; debugging quantized loading; or validating memory, speed, and output quality.
+---
+
+# vLLM-Omni Quantization
+
+Use this skill for quantization work in `vllm-omni`. Start from the local
+code and docs, then pick the smallest validated path for the model, method,
+hardware, and quality target.
+
+## First Checks
+
+Before changing code or recommending a command, identify:
+
+- Model and task: AR text, omni/TTS, text-to-image, text-to-video, or multi-stage diffusion.
+- Hardware backend: CUDA generation, Ascend NPU, Intel XPU, or another platform.
+- Quantization mode: online from BF16/FP16 weights, pre-quantized checkpoint, runtime KV/FA quantization, or per-component routing.
+- Scope: transformer/DiT, thinker language model, text encoder, VAE, audio/vision encoder, talker, or a specific stage.
+- Validation target: memory reduction, latency/throughput, output quality, or all three.
+
+Do not assume a method is supported just because the CLI accepts the string.
+Check `docs/user_guide/quantization/` and the closest implementation first.
+
+## Quick Decision
+
+| Task | Start With |
+|------|------------|
+| Choose a method or command | `references/methods.md` and `references/modality-compat.md` |
+| Use `build_quant_config()` or per-component routing | `references/methods.md` |
+| Work on diffusion quantization | `references/diffusion.md` |
+| Add quantization to a new model | `references/adding-models.md` |
+| Convert or load ModelOpt FP8 checkpoints | `references/modelopt-fp8.md` |
+| Debug ModelOpt, GGUF, AutoRound, MXFP, or serialized Int8 loading | `references/diffusion.md` and method docs under `docs/user_guide/quantization/` |
+
+## Current Runtime Shape
+
+The unified entrypoint is:
+
+```python
+from vllm_omni.quantization import build_quant_config
+```
+
+It supports method strings, flat method dictionaries, per-component
+dictionaries, existing `QuantizationConfig` objects, and `None`. The factory
+delegates generic methods to upstream `vllm` and keeps vLLM-Omni overrides for
+diffusion or omni-specific routing:
+
+- `gguf`
+- `int8`
+- `mxfp8`
+- `mxfp4`
+- `mxfp4_dualscale`
+- `inc`, `auto-round`, `auto_round`
+- ModelOpt auto-detected configs: `modelopt`, `modelopt_fp4`, `modelopt_mixed`
+
+Use `ComponentQuantizationConfig` when only one stage or component should be
+quantized. Pre-quantized ModelOpt-style checkpoints should not spill into
+vision/audio encoders that have no corresponding scale tensors.
+
+## Ownership Boundary
+
+- Upstream `vllm` owns generic quantization configs, kernels, loader semantics,
+  hardware capability rules, and generic AR quantization methods.
+- `vllm-omni` owns unified config routing, diffusion-specific wrappers,
+  component scoping, GGUF or ModelOpt checkpoint adapters, model-specific
+  prefix mapping, docs, examples, and validation.
+
+If a new method needs missing generic kernels or loader behavior, fix upstream
+`vllm` first. In `vllm-omni`, add thin integration and model wiring.
+
+## Working Loop
+
+1. Read the local method doc under `docs/user_guide/quantization/`.
+2. Inspect the active factory and config class in `vllm_omni/quantization/`.
+3. Inspect the target model pipeline and transformer for stable prefixes and
+   whether every relevant vLLM linear layer receives `quant_config`.
+4. For pre-quantized checkpoints, inspect `transformer/config.json` and the
+   checkpoint tensor names before running full generation.
+5. Validate with the same prompt, seed, size, scheduler, steps, dtype, eager or
+   non-eager mode, and parallelism as the BF16 baseline.
+6. Report quality, latency, and memory separately. Do not call a method
+   supported until quality is checked.
+
+## Common Mistakes
+
+| Symptom | Likely Cause | Fix |
+|---------|--------------|-----|
+| `--quantization` has no visible effect | Wrong scope or unsupported model path | Check component routing and method docs |
+| Some layers stay BF16 unexpectedly | `quant_config` was not threaded into all vLLM linear layers | Audit transformer constructors and prefixes |
+| Quality collapses but loading succeeds | Too many sensitive layers were quantized | Add model-specific `ignored_layers` and compare to BF16 |
+| ModelOpt checkpoint loads but output is corrupted | Prefixes, packed-module mapping, scale routing, or BF16 fallback is wrong | Use `references/modelopt-fp8.md` |
+| GGUF shape or tensor mismatch | Missing architecture-specific adapter | Add explicit adapter mapping; avoid generic fallback |
+| Online and offline MXFP4 disagree | Wrong `mxfp4` vs `mxfp4_dualscale` mode | Check `docs/user_guide/quantization/mxfp4.md` |
+| Quantized path is slower than BF16 | Kernel path, compile mode, dtype, or shape mismatch | Compare eager-to-eager and non-eager-to-non-eager |
+
+## References
+
+- General methods and config routing: [references/methods.md](references/methods.md)
+- Modality and model scope: [references/modality-compat.md](references/modality-compat.md)
+- Diffusion implementation patterns: [references/diffusion.md](references/diffusion.md)
+- Adding model support: [references/adding-models.md](references/adding-models.md)
+- ModelOpt FP8 conversion and loading: [references/modelopt-fp8.md](references/modelopt-fp8.md)

.claude/skills/quantization/references/adding-models.md

@@ -0,0 +1,70 @@
+# Adding Quantization Support to a Model
+
+Use this checklist when extending quantization to a new architecture or stage.
+
+## Classify Ownership
+
+| Case | Owner |
+|------|-------|
+| New generic AR method, kernel, or loader semantics | upstream `vllm` first |
+| Existing upstream method needs diffusion wiring | `vllm-omni` model and config integration |
+| GGUF or ModelOpt tensor mapping for a diffusion architecture | `vllm-omni` adapter |
+| NPU MXFP or msModelSlim diffusion path | `vllm-omni` platform/method wrapper and docs |
+| Multi-stage omni checkpoint quantizes only the thinker LM | `vllm-omni` component scoping |
+
+Avoid building a private quantization stack in model code.
+
+## Model Wiring Checklist
+
+1. Find the closest supported model implementation.
+2. Confirm the model uses vLLM linear layers that accept `quant_config`.
+3. Add `quant_config` to the transformer constructor.
+4. Pass stable `prefix` values through every linear layer.
+5. Handle fused modules and packed mappings explicitly.
+6. Ensure `load_weights()` and any `WeightsMapper` remap `ignored_layers` or
+   `modules_to_not_convert`.
+7. Keep tokenizer, scheduler, VAE, text encoder, audio/vision encoders, and
+   talker BF16 unless method-specific docs validate them.
+8. Add or update a method doc under `docs/user_guide/quantization/`.
+
+## Checkpoint Adapter Checklist
+
+For pre-quantized checkpoints:
+
+- Inspect `transformer/config.json` first.
+- Check `quant_method`, `method`, `producer.name`, `quant_algo`, serialized
+  flags, `ignored_layers`, and any method-specific fields.
+- Verify tensor name mapping before generation.
+- Verify packed modules such as `to_qkv`, `add_kv_proj`, and `w13`.
+- Confirm scale tensor names and shapes.
+- Allow dequantization back to BF16 for intentionally skipped target layers.
+
+## Sensitive Layer Discovery
+
+Start conservative. Quantize the backbone first and keep output-side or routing
+layers BF16 until quality is proven.
+
+Frequently sensitive areas:
+
+- attention output projections such as `to_out`
+- FFN down projections such as `w2`
+- final projections such as `proj_out`, `final_layer`, and output heads
+- modulation, timestep, position, image, or text embedders
+- refiner blocks
+- MoE routers, experts selected by routing, and layernorm-adjacent modules
+- VAE, text/vision towers, audio encoders, and codec/talker stages
+
+Do not reuse one model family's ignore list blindly.
+
+## Tests and Evidence
+
+Minimum evidence for a PR:
+
+- loader/config test or smoke command
+- fixed-seed BF16 vs quantized output comparison
+- quality metrics and saved outputs for precision-changing work
+- memory and latency comparison under identical execution mode
+- failure behavior for unsupported models or missing checkpoint fields
+
+When the PR adds a method to a support table, include the exact model,
+hardware, command, quality threshold, and artifact path used for validation.

.claude/skills/quantization/references/diffusion.md

@@ -0,0 +1,113 @@
+# Diffusion Quantization Reference
+
+Use this file for DiT quantization, config routing, checkpoint adapters, and
+quality or loader debugging.
+
+## Supported Method Families
+
+The current local diffusion quantization families are:
+
+- CUDA/GPU online or checkpoint: `fp8`, `int8`, `gguf`, ModelOpt
+- Ascend NPU: `int8`, `mxfp8`, `mxfp4`, `mxfp4_dualscale`, msModelSlim paths
+- Intel XPU: AutoRound/INC and online MXFP8 paths where documented
+
+Always cross-check the method doc before claiming model support. The CLI
+accepting a method string is not enough.
+
+## Implementation Starting Points
+
+| Area | Files |
+|------|-------|
+| Unified factory | `vllm_omni/quantization/factory.py` |
+| Per-component routing | `vllm_omni/quantization/component_config.py` |
+| GGUF diffusion method | `vllm_omni/quantization/gguf_config.py` |
+| Int8 diffusion method | `vllm_omni/quantization/int8_config.py` |
+| MXFP8 diffusion method | `vllm_omni/quantization/mxfp8_config.py` |
+| MXFP4 and dual-scale method | `vllm_omni/quantization/mxfp4_config.py` |
+| AutoRound/INC omni extension | `vllm_omni/quantization/inc_config.py` |
+| ModelOpt checkpoint adapters | `vllm_omni/diffusion/model_loader/checkpoint_adapters/modelopt.py` |
+| FP8 scaled-mm runtime patches | `vllm_omni/patch.py` |
+| Similarity tool | `vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py` |
+
+## Threading `quant_config`
+
+For a diffusion transformer:
+
+1. Accept `quant_config: QuantizationConfig | None` in the transformer.
+2. Pass `quant_config` and stable `prefix` values to every vLLM linear layer.
+3. Preserve packed-module mappings for fused QKV, FFN, or model-specific fused
+   projections.
+4. Call `apply_vllm_mapper()` on configs that need HF-to-vLLM name remapping.
+5. For component configs, keep prefixes specific enough to avoid quantizing VAE
+   or encoders by accident.
+
+Common skipped areas are `proj_out`, output projections, modulation layers,
+embedders, refiner blocks, MoE routers, and layernorm-adjacent paths.
+
+## Online vs Offline
+
+Online quantization:
+
+- starts from BF16/FP16 checkpoint weights
+- computes or stores quantized weights at load time
+- typically uses `--quantization <method>` or `quantization="<method>"`
+
+Offline or pre-quantized checkpoints:
+
+- include quantized weights and scales on disk
+- usually carry `quantization_config` in `transformer/config.json`
+- should auto-detect when the config is complete
+- may require model-specific adapter logic for tensor names and packed modules
+
+Do not mix online flags with incompatible offline checkpoint configs. If the
+active config and disk config differ, `resolve_quant_config_from_disk()` should
+raise or rebuild rather than silently loading corrupted weights.
+
+## GGUF Rules
+
+- Require `quantization_config.gguf_model`.
+- Use the normal base model for tokenizer, scheduler, text encoder, and VAE.
+- Add explicit architecture adapters for tensor-name mapping.
+- Use `named_parameters()` and `named_buffers()`, not `state_dict()`, to
+  discover loadable names.
+- Guard fused QKV/KV rewrites so tensors are not rewritten twice.
+- Flatten N-D activations around the GGUF matmul and restore shape.
+
+Unsupported architectures should fail clearly instead of falling back to a fake
+generic mapper.
+
+## MXFP Rules
+
+- `mxfp8` is W8A8 microscaling, currently centered on NPU and documented XPU
+  behavior.
+- `mxfp4` is online single-scale W4A4 on NPU.
+- `mxfp4_dualscale` is online/offline dual-scale W4A4 with BF16 fallback; the
+  offline path is the production path when calibrated `mul_scale` exists.
+- Offline dual-scale checkpoints must inject per-transformer `ignored_layers`
+  into config because cascade transformers can have different BF16 fallback
+  sets.
+
+Do not load an offline dual-scale checkpoint with the online single-scale path.
+
+## Common Failures
+
+| Symptom | Likely Cause | Fix |
+|---------|--------------|-----|
+| `Unknown quantization method` | Factory override or upstream registration is missing | Add the right override or upstream method |
+| Missing scale tensors | Wrong scope, wrong adapter, or BF16 layer not allowed to dequantize | Check config scope and adapter scale routing |
+| Output rank collapses from 3-D to 2-D | FP8 scaled-mm kernel ignored output shape | Check the patch in `vllm_omni/patch.py` |
+| Quality degrades silently | Sensitive layers quantized | Add `ignored_layers`; compare to BF16 |
+| Only one cascade transformer works | Disk config differs per transformer but active config is reused | Rebuild from each transformer's config |
+| Quantized path slower than BF16 | Kernel, dtype, compile, or benchmark mode mismatch | Compare eager-to-eager and non-eager-to-non-eager |
+
+## Verification
+
+For a new or changed diffusion quantization path, run:
+
+1. Config and loader smoke test.
+2. One fixed-seed generation against BF16.
+3. Numeric comparison with the similarity tool or equivalent metrics.
+4. Serving test if the path is user-facing online.
+5. Memory and latency check under the same execution mode as the baseline.
+
+Keep output artifacts when precision changes are part of the PR.

.claude/skills/quantization/references/methods.md

@@ -0,0 +1,82 @@
+# General Quantization Methods
+
+Use this file for method selection and unified `vllm-omni` config syntax. For
+diffusion-specific wiring, see `diffusion.md`. For ModelOpt checkpoint export
+and loading details, see `modelopt-fp8.md`.
+
+## Unified Entry Point
+
+`vllm_omni.quantization.build_quant_config()` accepts:
+
+- `None` or `"none"` to disable quantization
+- a method string such as `"fp8"`, `"int8"`, `"mxfp8"`, `"awq"`, or `"auto-round"`
+- a flat dict with `"method"` or `"quant_method"`
+- a per-component dict such as `{"transformer": {"method": "fp8"}, "vae": None}`
+- an already-built `QuantizationConfig`
+
+Per-component dicts are routed by `ComponentQuantizationConfig` using
+longest-prefix matching. Prefix stability matters: if a model remaps HF names
+to vLLM names, apply the mapper to `ignored_layers` or component prefixes.
+
+## Local Overrides
+
+The local factory delegates most methods to upstream `vllm`, but has vLLM-Omni
+overrides for methods that need diffusion, NPU, XPU, or component behavior:
+
+| Method | Local Config | Main Use |
+|--------|--------------|----------|
+| `gguf` | `DiffusionGGUFConfig` | Pre-quantized diffusion transformer weights |
+| `int8` | `DiffusionInt8Config` | Online or serialized W8A8 diffusion transformers |
+| `mxfp8` | `DiffusionMXFP8Config` | W8A8 MXFP8 diffusion on NPU and online XPU path |
+| `mxfp4` | `DiffusionMXFP4Config` | Online W4A4 MXFP4 diffusion on NPU |
+| `mxfp4_dualscale` | `DiffusionMXFP4DualScaleMixedConfig` | Online/offline W4A4 dual-scale plus BF16 fallback on NPU |
+| `inc`, `auto-round`, `auto_round` | `OmniINCConfig` | AutoRound/INC checkpoint loading, including MXFP8 checkpoint metadata |
+| ModelOpt config dicts | upstream ModelOpt configs through auto-detect | FP8, NVFP4, or mixed ModelOpt checkpoints |
+
+Check `SUPPORTED_QUANTIZATION_METHODS` in `vllm_omni/quantization/factory.py`
+before adding a new method name.
+
+## AR and Generic Quantization
+
+For AR-backed models, upstream `vllm` methods usually apply:
+
+- `awq` and `gptq`: 4-bit or 8-bit weight quantization with calibration,
+  commonly used for text and AR stages.
+- `fp8`: FP8 weight path or KV-cache FP8 depending on flags and hardware.
+- `inc` or `auto-round`: pre-quantized checkpoints with Intel Neural
+  Compressor / AutoRound metadata.
+
+Use these for the AR language model stage only when the checkpoint and model
+guide support it. Omni and TTS models often keep audio, vision, talker, and
+codec stages in BF16.
+
+## Diffusion and Offline Methods
+
+For diffusion transformers, prefer the method-specific docs:
+
+- `docs/user_guide/quantization/fp8.md`
+- `docs/user_guide/quantization/int8.md`
+- `docs/user_guide/quantization/gguf.md`
+- `docs/user_guide/quantization/modelopt.md`
+- `docs/user_guide/quantization/mxfp8.md`
+- `docs/user_guide/quantization/mxfp4.md`
+- `docs/user_guide/quantization/autoround.md`
+- `docs/user_guide/quantization/msmodelslim.md`
+
+Pre-quantized checkpoints should normally auto-detect from config. Runtime
+flags such as `--force-cutlass-fp8`, `--linear-backend cutlass`, and
+`--moe-backend cutlass` select kernels; they do not quantize a BF16 checkpoint.
+
+## Validation
+
+Use fixed-seed A/B comparisons and record:
+
+- model path and quantized checkpoint path
+- method config and `ignored_layers`
+- prompt, size, frame count, steps, scheduler, dtype, and seed
+- eager/non-eager mode, compile flags, and parallelism
+- output metrics such as PSNR, MAE, cosine similarity, SSIM, LPIPS, plus visual artifacts
+- latency and peak memory, reported separately from quality
+
+The helper `vllm_omni.quantization.tools.compare_diffusion_trajectory_similarity`
+is the default lightweight image/video comparison tool for diffusion paths.

.claude/skills/quantization/references/modality-compat.md

@@ -0,0 +1,47 @@
+# Quantization Compatibility by Modality
+
+Use this as a routing guide. The source of truth is the local method docs under
+`docs/user_guide/quantization/` and the current model implementation.
+
+## Summary
+
+- AR and omni language-model quantization mostly follows upstream `vllm`, with
+  vLLM-Omni scoping for multi-stage models.
+- Diffusion quantization is method-specific and model-specific. It usually
+  targets the diffusion transformer only.
+- Pre-quantized checkpoints should quantize only the component that has
+  quantized weights and scale tensors.
+
+## Model Type Matrix
+
+| Model Type | Typical Quantization Scope | Notes |
+|------------|----------------------------|-------|
+| AR text models | Language model | Upstream `vllm` methods usually apply |
+| Omni/TTS models | Thinker or AR language-model stage | Audio, vision, talker, and codec stages usually stay BF16 |
+| DiT image models | Diffusion transformer | Check FP8, Int8, GGUF, ModelOpt, AutoRound docs |
+| DiT video models | Diffusion transformer | HunyuanVideo FP8 and Wan2.2 MXFP paths are documented; validate other video paths |
+| Multi-stage diffusion | Selected stage only | Use per-component routing and per-stage validation |
+| Audio diffusion | Usually unsupported | Prefer dtype, offload, cache, or parallelism unless docs say otherwise |
+
+## Method Hints
+
+| Goal | Candidate |
+|------|-----------|
+| AR memory reduction | AWQ, GPTQ, AutoRound, ModelOpt where checkpoint supports it |
+| CUDA diffusion W8A8 | FP8 or Int8 |
+| Pre-quantized CUDA diffusion checkpoint | ModelOpt or GGUF |
+| Ascend NPU diffusion | Int8, MXFP8, MXFP4, MXFP4 DualScale, msModelSlim |
+| Intel XPU checkpoint | AutoRound/INC paths |
+| Conservative image quality | selective FP8/Int8 with `ignored_layers` |
+| Unsupported diffusion model | cache acceleration, CPU/layerwise offload, dtype, TP/SP/CFG parallelism |
+
+## Validation Rule
+
+Compatibility means more than successful loading. A method is compatible only
+after it has:
+
+- correct component scope
+- complete checkpoint/config metadata if offline
+- fixed-seed quality comparison against BF16
+- acceptable latency and memory behavior
+- docs or examples that state model, method, hardware, and caveats

.claude/skills/quantization/references/modelopt-fp8.md

@@ -0,0 +1,122 @@
+# ModelOpt FP8 Conversion and Loading
+
+Use this when exporting or loading ModelOpt FP8 checkpoints for diffusion
+transformers. This reference captures the practical conversion lessons from the
+2026-04-21 ModelOpt FP8 paste and aligns them with the current
+`vllm-omni` ModelOpt adapter.
+
+## Core Rule
+
+Treat ModelOpt FP8 as selective checkpoint quantization, not one-click
+whole-model FP8. Quantize the main transformer or DiT backbone, keep sensitive
+or peripheral modules BF16, serialize a complete ModelOpt checkpoint, and let
+vLLM-Omni auto-detect it from config.
+
+Do not pass `--quantization fp8` to force a ModelOpt checkpoint path. The
+checkpoint's `quantization_config` should select `modelopt`.
+
+## Conversion Strategy
+
+Prefer:
+
+- backbone attention Q/K/V and FFN expansion projections
+- conservative per-model ignore lists
+- MHA quantizers disabled until basic linear quantization is stable
+- full module-name prefixes in ignore/routing logic
+- BF16 fallback for output-side and routing-sensitive layers
+
+Avoid quantizing by default:
+
+- VAE
+- text, vision, or audio encoders
+- embedders and modulation layers
+- final heads and output projections
+- refiner modules
+- MoE routers and layernorm-sensitive blocks
+
+## Model-Specific Starting Points
+
+| Model Family | Quantize First | Keep BF16 First |
+|--------------|----------------|-----------------|
+| Z-Image | attention q/k/v, FFN w1/w3 | `to_out`, `w2`, `noise_refiner`, `context_refiner`, embedders, modulation, final/output |
+| Qwen-Image | transformer backbone linear layers | `proj_out`, `img_in`, `txt_in`, norms, modulation, position/time/embed layers |
+| FLUX2-dev / FLUX2-klein | backbone linear layers | `proj_out`, context/x embedders, `norm_out`, stream modulation, time embeddings |
+| HunyuanImage-3.0 | `model.model` backbone | VAE, vision tower, token/time/image embedders, final image head, LM head, routers, layernorm-sensitive blocks |
+
+These are starting points, not support claims. Validate with BF16 outputs.
+
+## Required Checkpoint Metadata
+
+`transformer/config.json` must include a complete `quantization_config`.
+At minimum it should identify ModelOpt and the algorithm:
+
+```json
+{
+  "quantization_config": {
+    "producer": {"name": "modelopt"},
+    "quant_method": "modelopt",
+    "quant_algo": "FP8"
+  }
+}
+```
+
+Current vLLM-Omni auto-detects:
+
+- `FP8` and `FP8_PER_CHANNEL_PER_TOKEN` -> `modelopt`
+- `NVFP4` -> `modelopt_fp4`
+- `MIXED_PRECISION` -> `modelopt_mixed`
+
+Packed modules and scales must map correctly:
+
+- `to_qkv` from `to_q`, `to_k`, `to_v`
+- `add_kv_proj` from `add_q_proj`, `add_k_proj`, `add_v_proj`
+- `w13` from `w1`, `w3`
+- `.input_scale`, `.weight_scale`, `.weight_scale_2`, `.weight_scale_inv`
+
+For selective FP8, target layers that remain BF16 must be allowed to
+dequantize from FP8 weights using the matching `weight_scale`, or the adapter
+must skip the quantized scale tensors for that BF16 target.
+
+## Runtime Signals
+
+Healthy loading should show ModelOpt auto-detection and a ModelOpt linear
+kernel path. Useful signals include:
+
+- auto-detected quantization from config
+- selected ModelOpt FP8 linear method
+- CUTLASS or FlashInfer FP8 scaled-mm backend when enabled and supported
+- non-eager path compiling the transformer when expected
+
+If online serving fails while offline inference works, inspect warmup and
+serving initialization before changing checkpoint conversion.
+
+## Validation Order
+
+1. Check config completeness.
+2. Run an export checker such as `examples/quantization/check_modelopt_fp8_export.py`.
+3. Confirm actual FP8 tensors and expected disk-size reduction.
+4. Run one offline generation with fixed prompt, seed, size, and steps.
+5. Run serving with `vllm serve --omni` and verify `/v1/images/generations`.
+6. Compare against BF16 using visual review plus metrics such as LPIPS, PSNR,
+   MAE, cosine similarity, and SSIM.
+7. Compare performance only under matched modes:
+   - BF16 eager vs FP8 eager
+   - BF16 non-eager vs FP8 non-eager
+
+FP8 is not automatically faster. Check whether execution reaches FP8 kernels,
+whether compile mode is comparable, and whether the workload shape fits the
+kernel path.
+
+## Triage
+
+| Symptom | First Check |
+|---------|-------------|
+| Startup failure | `config.json`, `model_index.json`, `quantization_config`, missing scales |
+| Loads but output is corrupted | Over-quantized sensitive layers or wrong full-name prefixes |
+| Offline works but online fails | Dummy warmup, serving init, compile path |
+| FP8 is not faster | Kernel selection, dtype, compile mode, workload shape |
+| BF16 fallback fails | Missing `weight_scale` for dequantization or wrong packed-module mapping |
+
+The success criterion is stable, correctly scoped checkpoint loading with
+quality preserved enough for the target use case, not maximizing the number of
+FP8 layers.

.claude/skills/readme.md

@@ -1,9 +1,9 @@
-# Claude Skills for vLLM-Omni
+# Repository Skills for vLLM-Omni
 
-This directory contains Claude Code skills maintained for the `vllm-omni`
-repository. These skills capture repeatable workflows for common contributor
-tasks such as model integration, pull request review, and release note
-generation.
+This directory contains repository-scale skills maintained for `vllm-omni`.
+They capture repeatable workflows for common contributor and maintainer tasks
+such as model integration, CI-aligned testing, performance optimization, and
+pull request review.
 
 ## Directory Structure
 
@@ -14,15 +14,41 @@ include:
 - `references/`: focused reference material used by the skill
 - `scripts/`: small helper scripts used by the skill
 
+## Using the Skills
+
+Coding agents that discover repository skills can invoke the relevant skill by
+name. For other agents, point them to the skill's `SKILL.md` and ask them to
+read it before changing code. Combine a domain skill with `vllm-omni-test` when
+tests or CI coverage change, then use `precheck-pr` for the contributor's final
+self-check.
+
 ## Available Skills
 
-- `add-diffusion-model`: guides integration of a new diffusion model into
-  `vllm-omni`
-- `add-omni-model`: covers addition of new omni-modality model support
-- `add-tts-model`: covers integration of new TTS models and related serving
-  workflows
-- `generate-release-note`: helps prepare release notes for repository changes
-- `review-pr`: provides a structured workflow for reviewing pull requests
+- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
+  a new diffusion model into `vllm-omni`
+- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
+  models and related serving workflows
+- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
+  performance optimization, including profiling traces, parallel strategies,
+  stage timing analysis, and benchmark-driven tuning
+- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
+  PR by validating title format, dead code, accuracy and performance claims,
+  and merge readiness
+- [`quantization`](quantization/SKILL.md): guides quantization method selection,
+  model integration, checkpoint loading, and quality/performance validation
+  for vLLM-Omni
+- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
+  workflow for maintainers and reviewers; contributors should use
+  `precheck-pr` for self-review
+- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
+  upgrades NPU model runners to align with the latest vllm-ascend
+  `NPUModelRunner`
+- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
+  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
+  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
+  wiring (`test-ready.yml`, `test-merge.yml`, `test-nightly.yml`,
+  `test-weekly.yml`), and copy-paste local plus CI-like `pytest` commands; see
+  `references/test-routing.md` for level-to-command mapping
 
 ## Maintenance Guidelines
 

.claude/skills/review-pr/SKILL.md

@@ -0,0 +1,239 @@
+---
+name: review-pr
+description: Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Use for default, detailed, or repeat maintainer reviews; checking correctness, compatibility, tests, benchmarks, model additions, distributed changes, or breaking behavior; and identifying or explicitly requesting the most relevant code-owner reviewers. Use precheck-pr instead for an author's pre-submit self-check.
+---
+
+# Review vLLM-Omni Pull Requests
+
+Review like a maintainer: direct, selective, and focused on issues that CI does
+not prove. Prefer a few high-confidence findings over exhaustive commentary.
+Zero findings is a valid result.
+
+## Quality contract
+
+Make every finding:
+
+- **Correct:** prove a reachable failure, not a suspicion.
+- **Prioritized:** lead with merge blockers and high-impact defects.
+- **Actionable:** identify the smallest safe fix direction.
+- **Evidence-based:** cite code, tests, docs, CI, or measurements.
+- **Concise:** avoid review templates and repeated summaries.
+- **Calibrated:** match severity to user and maintainer impact.
+
+Do not report unrelated backlog, style already enforced by pre-commit, or a
+missing test that would not protect changed behavior.
+
+## Select the input and depth
+
+Use `vllm-project/vllm-omni` as the base repository. Accept its forks and local
+checkouts; use another skill for unrelated repositories.
+
+| Input | Review surface |
+| --- | --- |
+| PR number or URL | Frozen PR metadata, full diff, and relevant threads. |
+| Local branch/worktree | Frozen target-base SHA through committed, staged, unstaged, and in-scope untracked changes. |
+| Pre-filled context | Reuse supplied metadata; fetch only missing facts and the full diff. |
+
+Default to maintainer brevity. A detailed or audit request expands coverage and
+lists `path:line` findings, but keeps the same confidence and severity bar.
+
+## Reference guide
+
+Load references in review order: process, one primary module contract, matching
+feature designs, evidence checks, then delivery. Every file is linked directly
+below; do not load unrelated module references.
+
+Each concise reference links to the maintained
+[vLLM-Omni documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/).
+For branch-specific behavior, inspect the matching `docs/` file in the reviewed
+checkout first; use the published latest docs for current guidance and discovery.
+If docs and live code disagree, verify the code/tests and report the drift.
+
+### Review process
+
+| Reference | Read when |
+| --- | --- |
+| [review-execution.md](references/process/review-execution.md) | Every review; freeze inputs, inspect safely, and deliver against the same snapshot. |
+| [general-checks.md](references/process/general-checks.md) | Every review; apply repository-wide correctness and evidence rules. |
+| [design-contracts.md](references/process/design-contracts.md) | Every production review; resolve branch-local module and feature design status. |
+| [review-routing.md](references/process/review-routing.md) | After the diff census; select one primary module and conditional overlays. |
+
+### Primary module contract
+
+| Reference | Read when |
+| --- | --- |
+| [entrypoints.md](references/modules/entrypoints.md) | Offline/CLI/API ingress, validation, rendering, streaming, or sessions change. |
+| [configuration.md](references/modules/configuration.md) | Config construction, deploy/stage schema, defaults, registry, or topology changes. |
+| [input-output-modality.md](references/modules/input-output-modality.md) | Requests, messages, serialization, output types, accumulation, or completion change. |
+| [error-contracts.md](references/modules/error-contracts.md) | Error classification, fatality, propagation, sanitization, or rendering changes. |
+| [engine-orchestration.md](references/modules/engine-orchestration.md) | Cross-stage routing, request state, output ordering, RPC correlation, or terminal convergence changes. |
+| [stage-runtime.md](references/modules/stage-runtime.md) | Placement, startup, readiness, replica identity, affinity, membership, or shutdown changes. |
+| [omni-connector.md](references/modules/omni-connector.md) | Cross-stage/process/device/node transport or synchronization changes. |
+| [model-integration.md](references/modules/model-integration.md) | Registration, preprocessing, loading, runners, or model-specific execution changes. |
+| [ar-runtime.md](references/modules/ar-runtime.md) | AR scheduling, request/cache state, adapters, workers, or upstream vLLM semantics change. |
+| [diffusion.md](references/modules/diffusion.md) | Diffusion runtime, models, batching, parallelism, or offload changes. |
+| [execution-platforms.md](references/modules/execution-platforms.md) | Hardware selection, capabilities, vendor workers, kernels, or patches change. |
+| [cache-management.md](references/modules/cache-management.md) | Cache identity, reuse, validity, reset, eviction, or teardown changes. |
+| [quantization.md](references/modules/quantization.md) | Quantization selection, checkpoint metadata, layer mapping, precision, or constraints change. |
+| [observability.md](references/modules/observability.md) | Metrics, logs, units, labels, correlation, or lifecycle changes. |
+| [profiling.md](references/modules/profiling.md) | Profiling instrumentation, traces, start/stop lifecycle, or overhead changes. |
+| [benchmarking.md](references/modules/benchmarking.md) | Benchmark workload, metric calculation, CLI, or result metadata changes. |
+
+### Feature-design overlays
+
+| Reference | Read when |
+| --- | --- |
+| [runtime-stage-execution.md](references/features/runtime-stage-execution.md) | Disaggregated inference, async chunk/output/materialization, or prefix caching changes. |
+| [communication.md](references/features/communication.md) | A concrete OmniConnector backend or its deployment contract changes. |
+| [diffusion-acceleration.md](references/features/diffusion-acceleration.md) | Diffusion parallelism, attention, quantization, cache, batching, or offload changes. |
+| [infrastructure-performance.md](references/features/infrastructure-performance.md) | Metrics infrastructure or documented speech optimization stacks change. |
+
+### Evidence and quality checks
+
+| Reference | Read when |
+| --- | --- |
+| [model-addition-checklist.md](references/checks/model-addition-checklist.md) | A model, architecture, loader, processor, registry, or stage config is added. |
+| [perf-verification.md](references/checks/perf-verification.md) | The PR makes a latency, throughput, memory, or quality claim. |
+| [test-quality-evaluation.md](references/checks/test-quality-evaluation.md) | Tests change, are absent for risky code, or may not exercise production behavior. |
+| [tests-docs-checklist.md](references/checks/tests-docs-checklist.md) | Coverage, CI markers, examples, user docs, or PR evidence need review. |
+| [verification.md](references/checks/verification.md) | Hardware, a server, or a runnable affected path is available for active verification. |
+| [examples-policy.md](../precheck-pr/references/examples-policy.md) | The PR adds, copies, or renames Python under `examples/`; apply the canonical policy shared with `precheck-pr`. |
+
+### Delivery and reviewer coordination
+
+| Reference | Read when |
+| --- | --- |
+| [maintainer-style-study.md](references/delivery/maintainer-style-study.md) | Findings are ready for concise maintainer-style delivery. |
+| [review-requests.md](references/delivery/review-requests.md) | The user asks to identify, suggest, request, or ping code-owner reviewers. |
+
+## Workflow
+
+### 1. Freeze and report the snapshot
+
+Pin the base and head before reading source or running validation. Within 60 seconds,
+report the pinned head, CI, mergeability, and preliminary findings in the host
+conversation. Do not wait for CI or post this update to GitHub.
+
+If the target changes while fetching, discard the evidence and retry once. If
+it changes again, report the churn and wait for a stable target.
+
+For a trusted PR head, materialize the pinned head in an isolated detached
+worktree. A worktree freezes identity but is not a security sandbox. Treat fork
+heads as untrusted unless the user and environment policy explicitly establish
+otherwise: execute them only in a disposable, secret-free sandbox with restricted
+filesystem, network, and resources; without one, use static SHA-addressed reads
+and CI evidence only. For a local review, freeze the committed, index, worktree,
+and NUL-safe in-scope untracked contents. Follow
+[review-execution.md](references/process/review-execution.md) for trust gates,
+state fingerprints, and byte-for-byte staleness checks.
+
+### 2. Build the diff census
+
+Group files into production code, tests, docs, configuration, build/CI, and
+generated artifacts. Map each changed production file and test group to the PR
+goal. Compare the title/body claims with the actual diff; use linked issues only
+when they define the contract or reproduction.
+
+Mark unrelated scope and unexplained generated artifacts. Do not infer behavior
+from the PR description without tracing the live code.
+
+### 3. Route from the live behavior
+
+Trace each claimed behavior through the changed producer to its live consumer,
+then use [design-contracts.md](references/process/design-contracts.md) and
+[review-routing.md](references/process/review-routing.md) to select one primary
+module contract, a second only for a real documented cross-boundary call path,
+and every matching feature-design and evidence overlay. Treat titles and paths
+as hints; live behavior and the frozen head's current design metadata are
+authoritative. For docs-, tests-, or CI-only changes, route to the production
+contract they protect or use only the applicable evidence checks.
+
+### 4. Run the blocker scan
+
+Apply every category in [general-checks.md](references/process/general-checks.md) before
+lower-priority comments.
+
+If the diff census contains an added, copied, or renamed Python path under
+`examples/`, read and apply the canonical
+[examples policy](../precheck-pr/references/examples-policy.md). Treat a new
+model-specific Python example as blocking. Do not flag model-specific example
+debt that the PR only modifies or removes, and do not run the rest of the
+author-oriented `precheck-pr` workflow.
+
+For each changed value or behavior, trace:
+
+```text
+public ingress -> validation/defaulting -> producer -> transformations
+  -> stage/worker/connector boundary -> final consumer -> terminal cleanup
+```
+
+Cover every applicable offline/online, streaming/non-streaming, sync/async,
+feature-on/off, topology, and compatibility path. Search bounded callers and
+sibling implementations rather than assuming the changed hunk is the only path.
+
+### 5. Apply module and feature contracts
+
+Apply the reference set selected in step 3 and any matching repo-local skill.
+Read the exact module and feature pages in the frozen head, including status,
+ownership boundary, dependencies, candidate invariants, safe-change guide, and
+promotion gate. Candidate or draft rules are questions, not blockers, unless
+current code, tests, or policy enforce them. Inspect both sides of any config,
+registry, serialization, connector, cache, or stage boundary.
+
+When a diff adds or expands a helper, class, fallback, compatibility branch, or
+public behavior, run a subtraction pass: remove out-of-scope behavior and check
+whether each new abstraction can be deleted, merged, moved, or inlined.
+
+### 6. Verify the changed path
+
+Before each validation group, verify the frozen SHA plus the tracked, index,
+untracked, and ignored-file fingerprint, or recreate a pristine snapshot. On a
+trusted head or inside the required sandbox, run an import/version preflight,
+then the narrowest relevant tests and low-cost static checks. Bind every result
+to the head SHA, snapshot fingerprint, and environment fingerprint. Never run
+imports, tests, builds, hooks, or repo-configurable tooling from an untrusted
+head on the reviewer host.
+
+- Treat CI as status evidence; inspect only the first overlapping failure.
+- For docs-only changes, use diff hygiene, links/build checks, and bounded live
+  contract verification instead of dependency setup or pytest.
+- For hardware-dependent paths, run available static/CPU checks and name the
+  exact GPU/NPU gap. Never simulate device evidence.
+- For performance or accuracy claims, require comparable base/head runs with
+  the same environment, workload, warmup, repetitions, and quality criteria.
+
+Stop when each changed semantic path has a supported finding or an explicit
+no-issue conclusion. Do not search further only to increase confidence.
+
+### 7. Consolidate and deliver
+
+Verify each finding against the current diff, deduplicate by root cause, and
+order by severity.
+
+Re-read the remote head and reverify or recreate the pristine validation
+snapshot immediately before delivery. If either changed, mark the review stale
+and restart from the new snapshot.
+
+Return findings first. Use
+[maintainer-style-study.md](references/delivery/maintainer-style-study.md) to keep them
+direct and brief. Each finding must include an exact `path:line`, trigger or
+call path, current behavior, impact, and smallest fix direction. If there are
+no findings, say so briefly and name material validation gaps.
+
+Keep the review read-only unless the user explicitly authorizes posting. Do not
+submit `APPROVE`, `COMMENT`, or `REQUEST_CHANGES`, add labels, edit code, or push
+commits as an implied part of review.
+
+### 8. Optionally request focused owner reviews
+
+Only when the user asks to identify or request reviewers, read
+[review-requests.md](references/delivery/review-requests.md). Rank path-matched
+CODEOWNERS with the frozen module page's owners or required reviewers and
+documented governance expertise; propose one to three focused reviewers with an
+explicit contract rationale.
+
+Identifying or suggesting reviewers is read-only. Requesting reviewers or
+posting `@mention` comments changes external state and requires explicit user
+authorization. When authorized, recheck the head, deduplicate existing
+requests, and post at most one consolidated comment. Do not infer this
+permission from a request to review the code.

.claude/skills/review-pr/agents/openai.yaml

@@ -0,0 +1,4 @@
+interface:
+  display_name: "vLLM-Omni PR Review"
+  short_description: "Review vLLM-Omni PRs and route owner requests"
+  default_prompt: "Use $review-pr to review this vLLM-Omni pull request with owner-specific rules and evidence-backed findings."

.claude/skills/review-pr/references/checks/model-addition-checklist.md

@@ -0,0 +1,121 @@
+# Model Addition Checklist
+
+Read this reference when a PR adds or expands a model architecture, pipeline,
+loader, processor, registry entry, or stage configuration.
+
+Official docs: [model contribution guides](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/),
+[adding an omni model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_omni_model/),
+[adding a diffusion model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_diffusion_model/),
+[adding a TTS model](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/model/adding_tts_model/),
+[supported models](https://docs.vllm.ai/projects/vllm-omni/en/latest/models/supported_models/),
+[diffusion features](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/diffusion_features/),
+and [feature compatibility](https://docs.vllm.ai/projects/vllm-omni/en/latest/user_guide/feature_compatibility/).
+
+## Match claims to the diff
+
+- Reconcile every claimed architecture, modality, variant, feature, and example
+  with changed files and registry entries.
+- Resolve config model types, architecture names, callable paths, exports, and
+  dependency extras against live symbols.
+- Check sibling generations sharing an architecture name are disambiguated by
+  a stable config predicate rather than import order or an accidental default.
+
+## Close the integration chain
+
+Trace:
+
+```text
+public model id -> config/pipeline selection -> registry -> loader/processor
+  -> stage inputs -> model execution -> stage/public output
+```
+
+Verify optional dependencies fail with an actionable message, weight names and
+dtypes map correctly, and each advertised serving mode reaches the production
+dispatcher. Confirm outputs are non-empty and valid for their modality, shape,
+sample rate, or response schema.
+
+## Keep support documentation truthful
+
+- Build an evidence-backed inventory of the new model's exact architecture and
+  checkpoint identifiers, modalities and tasks, hardware backends, serving
+  modes, and supported acceleration, parallelism, cache, offload, and
+  quantization features. Treat untested support as unknown, not supported.
+- Update `docs/models/supported_models.md` with the architecture, model family,
+  example checkpoints, and only the backends validated by the PR.
+- For a diffusion model, add it to the applicable ImageGen, VideoGen, or
+  AudioGen table in `docs/user_guide/diffusion_features.md`. Fill every feature
+  column according to its legend and document partial support or constraints.
+- Update `docs/user_guide/feature_compatibility.md` when the model changes valid
+  feature combinations, configuration constraints, examples, or limitations,
+  and update any other branch-local support table affected by the claims.
+- Cross-check every documented support mark against the registry, configuration,
+  production-path tests, and PR evidence; do not copy a sibling model's support
+  claims without validation.
+
+## Ship the model recipe
+
+- Require every new model PR to add or update one model-family recipe under
+  `recipes/<vendor>/` and its row in the `recipes/README.md` Available Recipes
+  table. Follow [`recipes/TEMPLATE.md`](../../../../../recipes/TEMPLATE.md) and
+  the layout rules in
+  [`recipes/README.md`](../../../../../recipes/README.md); the
+  external recipes repository is a structural reference, not a substitute.
+- Include only tested task, serving-mode, and hardware configurations. Keep the
+  exact model identifiers, flags, commands, verification, feature support,
+  constraints, and limitations consistent with code, tests, examples, and the
+  support documentation.
+- Cover each validated platform in its own hardware section. Use one recipe per
+  model family by default; justify a split when configurations cannot be kept
+  clear in one file, and do not add placeholder sections for untested platforms.
+
+## Compare the reference implementation
+
+Require every new model PR to include a head-versus-reference accuracy or
+quality and performance comparison:
+
+- Name the canonical implementation and pin its repository revision, package
+  version, configuration, and checkpoint. If it cannot run, explain why and use
+  the closest runnable reference; do not silently omit the comparison.
+- Match inputs or dataset, seed, output settings, precision or quantization,
+  hardware, software, warmup, repetitions, synchronization, and timing and
+  memory scope. Include exact commands and environment details for both sides.
+- Compare accuracy or output quality with a task-appropriate metric and explicit
+  tolerance. When no reliable metric exists, provide paired fixed-input outputs,
+  artifacts, and evaluation criteria, and explain expected numerical or
+  qualitative differences.
+- Report end-to-end latency, task-appropriate throughput, peak device memory,
+  run-to-run variability, and head/reference ratios at equivalent output quality.
+- Prefer a timing split for preprocessing or encoding, each model stage,
+  inter-stage transfer or communication, decoding or postprocessing, and output
+  assembly. Define the boundaries, account for overlap, reconcile the split with
+  end-to-end time, and explain any unavailable stage measurement.
+
+Use [perf-verification.md](perf-verification.md) for the comparison protocol and
+repository benchmark and profiling links.
+
+## Remove accidental surface
+
+Search bounded call sites for:
+
+- inference-dead training `forward()` paths, unused factories/wrappers, never-set
+  branch keys, and immediately discarded parameters;
+- duplicate payload strings, validation, or shape coercion across stages;
+- compatibility aliases in brand-new code without an existing caller;
+- private symbols re-exported as public API or module-level side effects.
+
+Keep code only when it has a distinct live caller, invariant, or compatibility
+contract. Prefer one typed producer-consumer schema to repeated string keys.
+
+## Require proportionate evidence
+
+- Run a representative production-path inference and assert output content, not
+  only process survival.
+- Require profiling or A/B tables when the PR makes performance, memory,
+  precision, or quality claims, or when a suspected hot-path/device bug needs
+  that evidence. Do not impose fixed utilization or regression thresholds that
+  the repository does not define.
+- Require focused registry/config/loading tests, a representative E2E path, and
+  user-facing model/usage documentation.
+
+Use [diffusion.md](../modules/diffusion.md) for diffusion models and
+[perf-verification.md](perf-verification.md) for quantitative claims.

.claude/skills/review-pr/references/checks/perf-verification.md

@@ -0,0 +1,51 @@
+# Performance and Accuracy Verification
+
+Activate this reference when the PR adds a model or claims or intentionally
+changes latency, throughput, memory, scaling, precision, or output quality.
+
+Official docs: [profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/profiling/),
+[serving benchmarks](https://docs.vllm.ai/projects/vllm-omni/en/latest/cli/bench/serve/),
+and [metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/metrics/).
+
+## Comparable A/B contract
+
+Compare frozen base and head for an existing path. For a new model absent from
+base, compare head with a pinned canonical reference implementation. Use the same:
+
+- hardware, driver, software versions, model/checkpoint, and dependencies;
+- input or dataset, seed, precision, batch/concurrency, topology, and feature
+  flags;
+- warmup, measured repetitions, synchronization, timing scope, and memory
+  collection method.
+
+Keep exact commands and report variability rather than a cherry-picked run.
+Use the repository's benchmark when it covers the claim; otherwise use the
+smallest reproducible workload that reaches the changed production path.
+
+## Evidence table
+
+| Dimension | Typical evidence |
+| --- | --- |
+| Latency | End-to-end latency and, when available, TTFT plus a defined per-stage and transfer-time split. |
+| Throughput/scaling | Requests, tokens, frames, or audio duration per second across the claimed concurrency/topology. |
+| Memory | Peak allocated/reserved device memory and OOM boundary if relevant. |
+| Quality/accuracy | Repository metric or known-good output comparison with an explicit tolerance; paired samples when no metric exists. |
+
+Every optimization needs correctness or quality evidence. A faster result on a
+different workload, precision, seed, or topology is not a valid comparison.
+
+## Reviewer verification ladder
+
+1. Run base/head A/B on suitable hardware when affordable.
+2. If only one side can run, verify the script and request a comparable pair.
+3. If hardware or assets are unavailable, audit methodology, code path, and
+   contributor evidence; name the exact unverified claim.
+
+Classify discrepancies before reporting: implementation regression, benchmark
+bug, environmental drift, noise, or unsupported claim. Explain material
+regressions against the PR's stated goal or repository contract; do not invent
+universal percentage thresholds.
+
+Report claimed and measured values together, bind them to the frozen SHAs and
+environment, and keep unavailable hardware as a validation gap rather than a
+fabricated pass.

.claude/skills/review-pr/references/checks/test-quality-evaluation.md

@@ -0,0 +1,39 @@
+# Test Quality Evaluation
+
+Read this reference when tests change, a risky production path has no test, or
+a test-only PR may not prove the intended behavior.
+
+Official docs: [test system overview](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_system_overview/)
+and [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/).
+
+## Static proof check
+
+For each changed semantic path, ask whether the test would fail if that behavior
+were reverted or broken. Check:
+
+- assertions pin the contract, not only non-null output, logs, or “no crash”;
+- the production dispatcher, registry, connector, or scheduler is reached
+  instead of the changed behavior being mocked away;
+- fakes preserve relevant types, MRO, shapes, devices, async behavior, and
+  lifecycle transitions;
+- seeds, ordering, timing, synchronization, external services, and numeric
+  tolerances are deterministic or explicitly controlled;
+- normal, invalid, boundary, feature-off, failure/cancellation, and regression
+  paths are covered where the diff changes them;
+- run-level and domain markers place hardware/model tests in the intended CI
+  lane without silently skipping the contract.
+
+Map source symbols to tests with bounded `rg` searches; do not assume the test
+tree mirrors production paths.
+
+## Runtime check
+
+Run the narrowest affected tests supported by the environment. Record skipped
+hardware/model cases as gaps. Classify failures as code, test, infrastructure,
+or flaky before turning them into findings; one passing rerun does not erase a
+flakiness signal.
+
+Keep grades or matrices internal. Report only concrete code bugs or the one or
+two test defects that leave changed behavior materially unprotected. Use
+[tests-docs-checklist.md](tests-docs-checklist.md) for CI and documentation
+coverage.

.claude/skills/review-pr/references/checks/tests-docs-checklist.md

@@ -0,0 +1,48 @@
+# Tests and Documentation Checklist
+
+Use this reference to connect changed behavior to coverage, CI selection,
+examples, documentation, and reproducible PR evidence.
+
+Official docs: [test writing guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_writing_guide/),
+[test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/),
+and [documentation guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/DOCS_GUIDE/).
+
+## Coverage packet
+
+For each high-risk change, record a compact internal packet:
+
+```text
+change and failure risk -> existing unit/E2E coverage -> uncovered boundary
+  -> smallest test and stable assertion that closes the gap
+```
+
+Prefer a regression test that fails on the frozen base for bug fixes. Check
+numeric tolerances, deterministic inputs, realistic mocks, hardware/domain
+markers, and Buildkite/CI wiring using the documentation present on the reviewed
+branch. A skipped test is not a pass.
+
+## Documentation sync
+
+Require user-facing updates when the diff changes a model, feature, CLI/API,
+config key, default, compatibility behavior, or supported platform. Verify:
+
+- the actual docs navigation and support tables used by the branch;
+- exact identifiers, defaults, constraints, known limitations, and migration;
+- a minimal runnable example for newly exposed behavior;
+- examples and docs agree on commands, parameters, output schema, and supported
+  modes;
+- links and the bounded live contract build or resolve correctly.
+
+Do not request docs for internal behavior with no user-visible contract.
+
+## PR evidence
+
+For contributor-run tests or benchmarks, require enough provenance to evaluate
+the claim: hardware, relevant software versions, exact commands, inputs/model,
+result, and approximate runtime. Performance or accuracy evidence must compare
+base and head under equivalent conditions; use
+[perf-verification.md](perf-verification.md).
+
+Turn a missing test or document into a finding only when it protects or explains
+changed behavior. Name the smallest useful addition rather than requesting a
+broad coverage expansion.

.claude/skills/review-pr/references/checks/verification.md

@@ -0,0 +1,36 @@
+# Active Verification
+
+Read this reference when a runnable affected path and suitable local or remote
+hardware are available. Never claim device evidence from static analysis or a
+simulated backend.
+
+Official docs: [installation](https://docs.vllm.ai/projects/vllm-omni/en/latest/getting_started/installation/)
+and [test execution guide](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/ci/test_execution_guide/).
+
+## Choose the narrowest level
+
+| Available environment | Verification |
+| --- | --- |
+| Matching accelerator, model/assets, and server | Targeted unit/integration test plus one representative E2E request. |
+| Matching accelerator without serving setup | Targeted model/runtime test and output or metric inspection. |
+| CPU/static environment only | Import/version preflight, focused CPU tests, config/registry resolution, and static contract checks. |
+| Missing dependencies or inaccessible hardware | Record the exact gap and evaluate contributor/CI evidence. |
+
+## Execute safely
+
+1. Bind the run to the frozen head SHA and record Python, platform, dependency,
+   model, device, and relevant environment fingerprints.
+2. Run the smallest affected unit test before an E2E workload.
+3. Exercise the changed public or production-dispatch path with bounded inputs.
+4. Compare actual output, metrics, cleanup, and exit behavior with the PR claim.
+5. For base/head claims, use the equivalent A/B protocol in
+   [perf-verification.md](perf-verification.md).
+
+Avoid disturbing shared servers, ports, models, or accelerators without user
+authorization. Do not install large dependencies, download gated assets, or
+start remote workloads merely because hardware might exist.
+
+Report the command, environment, result, and any skipped path. Classify failures
+as change-induced, pre-existing, infrastructure, environment, or flaky before
+making a finding. A hardware gap lowers confidence; it is not automatic proof
+of a defect.

.claude/skills/review-pr/references/delivery/maintainer-style-study.md

@@ -0,0 +1,105 @@
+# Maintainer Review Style
+
+Use this reference only when turning proved findings into comments. The baseline
+maintainer samples favor short, direct comments and reserve long explanations
+for architecture or non-obvious failure paths.
+
+Official docs: [contributing and review process](https://docs.vllm.ai/projects/vllm-omni/en/latest/contributing/).
+
+## Evidence basis
+
+This calibration used 928 PRs merged from 2026-05-10 through 2026-08-08.
+Candidates were ranked by substantive human inline-review threads, then by
+distinct reviewers and review rounds. Author self-comments, bots and AI
+reviewers, and bodyless approvals were excluded. Counts selected the sample;
+the practices below came from reading root comments and follow-up replies.
+
+The main set contains the six leaders after filtering; three other high-ranked
+PRs add core-runtime and benchmark coverage. All were merged.
+
+| PR | Review boundary |
+| --- | --- |
+| [#2906](https://github.com/vllm-project/vllm-omni/pull/2906) | New TTS model, shared utilities, recipes, and request state. |
+| [#3907](https://github.com/vllm-project/vllm-omni/pull/3907) | Full-duplex API, session lifecycle, concurrency, and realtime evidence. |
+| [#2162](https://github.com/vllm-project/vllm-omni/pull/2162) | Diffusion world model, OpenPI serving, tests, examples, and docs. |
+| [#3454](https://github.com/vllm-project/vllm-omni/pull/3454) | Diffusion model, generic CLI surface, loaders, and configuration scope. |
+| [#4425](https://github.com/vllm-project/vllm-omni/pull/4425) | Structured configuration, typing, defaults, and compatibility. |
+| [#5397](https://github.com/vllm-project/vllm-omni/pull/5397) | Distributed offload, collectives, failure handling, and load semantics. |
+| [#4281](https://github.com/vllm-project/vllm-omni/pull/4281) | Composable parallelism, configuration contracts, and examples. |
+| [#3628](https://github.com/vllm-project/vllm-omni/pull/3628) | Stage metrics, benchmark definitions, opt-in API behavior, and cleanup. |
+| [#3855](https://github.com/vllm-project/vllm-omni/pull/3855) | Stage runtime, thread safety, startup, and compatibility. |
+
+Historical comments calibrate delivery; they do not create merge policy. Live
+code, tests, repository docs, and the current `CODEOWNERS` file remain
+authoritative. When refreshing this study, promote a practice only when it
+recurs across at least three PRs or is supported by current repository guidance.
+
+## Recurring review behavior
+
+- Start from a changed contract, not a style preference. State the exact
+  request, configuration, topology, or failure path that triggers the issue.
+- Protect repository boundaries. Keep model-specific behavior in its domain,
+  move genuinely reusable logic to a shared module, and reuse existing loaders,
+  protocols, definitions, and config sources instead of bypassing them.
+- Challenge unrelated scope and abstractions. Ask why a new flag, config, class,
+  helper, fallback, or file is needed when an existing path appears sufficient.
+- Trace concurrency and terminal paths: overlapping requests, disconnect,
+  cancellation, partial failure, stale replies, collective schedule mismatch,
+  cleanup, and retry. Prefer an explicit startup error for unsupported
+  combinations over silently running with incomplete state or wrong weights.
+- Preserve user-visible behavior across variants. An accepted CLI/API/config
+  field must be applied, explicitly rejected, or visibly warned about; compare
+  normal and headless, offline and online, and streaming and non-streaming paths.
+- Ask for evidence at the affected boundary: a regression test using the real
+  tensor or protocol shape, correct pytest markers so CI runs it, comparable A/B
+  performance results, or an SLO-oriented latency/quality measurement.
+- Keep interfaces explicit: avoid `Any` and opaque strings where a precise
+  union, enum, or typed envelope exists; remove dead code and duplicated
+  constants; validate requests before expensive execution.
+- Document module scope and non-obvious invariants. Update examples, recipes,
+  supported-model/feature docs, and hardware assumptions when users rely on
+  them.
+- When expertise crosses an ownership boundary, ask a named owner to inspect a
+  specific file or contract and explain why. Do not send a generic `PTAL` list.
+- Keep non-blocking cleanup in a linked follow-up only after the current PR is
+  safe. If a revision fixes only part of a failure chain, continue the existing
+  thread with the remaining trigger instead of opening a duplicate finding.
+
+## Write the finding
+
+- Lead with the issue or decisive question; omit a review preamble.
+- Name the concrete trigger and impact, then the smallest fix direction.
+- Keep obvious fixes to one sentence. Use more detail only when the call path or
+  ownership boundary is otherwise unclear.
+- Prefer one root-cause comment over repeated symptom comments.
+- Hedge only when evidence is genuinely incomplete; mark that item as a
+  question or validation gap rather than overstating severity.
+- Say briefly that no findings were found when the review is clean; zero
+  comments is a valid outcome when posting is authorized.
+
+Useful forms:
+
+```text
+This leaves <state> allocated when <failure/cancellation path>. Release it in
+the shared terminal cleanup path.
+```
+
+```text
+Does this producer also update <consumer>? It still reads the old field here.
+```
+
+```text
+With <configuration>, this bypasses <existing loader/validator>, so <impact>.
+Reuse that path or reject this combination before startup.
+```
+
+```text
+@<owner>, could you check <specific boundary>? This changes <contract> used by
+<consumer>.
+```
+
+Use a priority label only for a proved high-impact defect. Avoid generic praise,
+routine “Nit:” prefixes, decorative severity labels, audit templates, rule IDs,
+comment-count narration, and “I left a few comments.” Keep approvals, review
+events, reviewer requests, mentions, and GitHub replies subject to the user's
+explicit authorization.

.claude/skills/review-pr/references/delivery/review-requests.md

@@ -0,0 +1,99 @@
+# Focused Code-Owner Review Requests
+
+Read only when the user asks to identify, suggest, request, add, or ping
+reviewers. Finding candidates is read-only; GitHub review requests and
+`@mention` comments are external writes.
+
+Owner sources, in order:
+
+1. the reviewed head's
+   [CODEOWNERS](https://github.com/vllm-project/vllm-omni/blob/main/.github/CODEOWNERS)
+   for path ownership;
+2. the active module design page's `owners` and `required_reviewers` metadata
+   for contract ownership and mandatory design review; and
+3. documented
+   [governance expertise](https://docs.vllm.ai/projects/vllm-omni/en/latest/community/governance/)
+   for specialization and tie-breaking.
+
+Resolve all three from the reviewed head when available because ownership
+changes over time. Use published docs only to discover a page absent from an
+older checkout, never to override the frozen head, and do not copy a static
+owner list into review output. CODEOWNERS remains the path authority; module
+metadata identifies contract experts and required design reviewers.
+
+## Rank candidates
+
+1. Freeze the PR head and collect changed files, author, requested reviewers,
+   submitted reviews, and existing comments.
+2. Resolve the **last matching CODEOWNERS rule** for each changed file. Do not
+   union an earlier broad rule with a later specific rule.
+3. Resolve the primary module and feature overlays through
+   [design-contracts.md](../process/design-contracts.md). Read the active module
+   page's status, `owners`, and `required_reviewers`; do not treat owners on an
+   archived, superseded, or unrelated page as current authority.
+4. Group files by the selected primary owner and the exact module or feature
+   contract needing review. Weight production owners and live consumers above
+   tests/docs/CI owners and the repository-wide fallback. Include an applicable
+   `required_reviewers` entry even when it is not the path owner, and label why.
+5. Use documented governance specialties only to choose among matching owners:
+   for example serving versus configuration, diffusion model versus cache or
+   parallelism, TTS versus generic model execution, or CI versus tests.
+6. Exclude the PR author, bots, users who already completed the needed review,
+   and duplicate pending requests. Do not infer availability or invent
+   expertise from a username.
+7. Select one reviewer for a single-owner change and at most three for a real
+   cross-module change. If more are plausible, rank them but do not ping a
+   broad group; ask the user before exceeding three.
+
+If every specific owner is excluded, consider one repository-wide fallback
+owner and label that choice as a fallback rather than a path match. Never fan
+out to the full fallback list. Prefer a qualified owner who covers multiple
+critical path groups over separate reviewers for each file. If CODEOWNERS is
+missing or invalid, state that gap and use governance only as a labeled fallback.
+
+Recent file history may break a tie only when CODEOWNERS and governance remain
+ambiguous. Never use commit count alone to override an explicit current owner.
+
+## Preview before writing
+
+Show the user the exact candidate set and rationale:
+
+```text
+@owner — <owned paths or module role>; please verify <specific module or feature contract>.
+```
+
+Draft one consolidated comment tied to the frozen head:
+
+```text
+Requesting focused review for `<short-head-sha>`:
+
+- @owner1: could you check the <module contract> in `<path/group>`?
+- @owner2: could you check the <feature or cross-boundary contract> in `<path/group>`?
+
+Relevant validation: <one concise result or exact remaining gap>.
+```
+
+Mention only owners being requested. Keep the reason specific enough that each
+person knows what decision or invariant needs attention; do not post a generic
+“please review” or a generated ownership audit.
+
+## External-write gate
+
+- “Who should review?”, “suggest reviewers”, or “draft a request” authorizes
+  only the preview.
+- “Request/add these reviewers” authorizes the GitHub review-request mutation.
+- “Ping/comment/tag the owners” authorizes the consolidated comment.
+- When the user explicitly asks to request reviews **with an owner comment**,
+  both writes are authorized. Obey narrower wording when only one action is
+  requested.
+
+Immediately before an authorized write, verify the PR is still open, the head
+SHA is unchanged, and the candidate is not the author or already satisfied.
+Report draft/WIP state and avoid notifications unless the user explicitly wants
+reviewers requested before ready-for-review.
+
+Use the GitHub connector for reviewer requests and the single top-level comment;
+use `gh` only when connector coverage is unavailable. Do not submit an approval,
+request-changes event, label, assignment, or multiple comments as part of this
+action. After writing, reread the PR and report the reviewers requested and the
+comment URL. If the head changed, discard the draft and rerank before posting.

.claude/skills/review-pr/references/features/communication.md

@@ -0,0 +1,24 @@
+# Communication Features
+
+Use with the OmniConnector, stage-runtime, configuration, and I/O module
+contracts. Load the concrete backend design from the reviewed head:
+
+| Backend | Design |
+| --- | --- |
+| Mooncake store | [MooncakeStoreConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_store_connector/) |
+| Mooncake transfer engine | [MooncakeTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mooncake_transfer_engine_connector/) |
+| Mori transfer engine | [MoriTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/mori_transfer_engine_connector/) |
+| Shared memory | [SharedMemoryConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/shared_memory_connector/) |
+| Yuanrong store | [YuanrongConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_connector/) |
+| Yuanrong transfer engine | [YuanrongTransferEngineConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_connectors/yuanrong_transfer_engine_connector/) |
+
+## Feature checks
+
+- Verify dependency, service, device/network, port, memory-pool, and environment
+  prerequisites with actionable startup errors.
+- Match key/metadata construction, sender and receiver roles, allocation,
+  ownership, completion, and supported fast/serialized paths.
+- Exercise timeout, retry, stale sender/buffer reclamation, partial transfer,
+  cancellation, and connector close semantics.
+- Validate the smallest supported same-host or cross-node topology and document
+  backend limitations and operational diagnostics.

.claude/skills/review-pr/references/features/diffusion-acceleration.md

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+# Diffusion Acceleration Features
+
+Use with the exact diffusion submodule plus platform, cache, quantization, or
+benchmarking contracts selected by the live path.
+
+| Feature family | Designs |
+| --- | --- |
+| Parallelism | [CFG](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cfg_parallel/), [Expert](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/expert_parallel/), [HSDP](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/hsdp/), [Pipeline](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/pipeline_parallel/), [Sequence](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/sequence_parallel/), [Tensor](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/tensor_parallel/), [VAE patch](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/vae_parallel/) |
+| Attention | [Skip-Softmax](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/skip_softmax/) |
+| Quantization | [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/quantization/) |
+| Cache | [Cache-DiT](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/cache_dit/), [TeaCache](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/teacache/) |
+| Batching | [Diffusion Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/diffusion_continuous_batching/) |
+| Offload | [Distributed Layerwise Offload](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/distributed_layerwise_offload/) |
+
+## Feature checks
+
+- Verify feature selection, supported models/platforms/topologies, defaults,
+  dependencies, and every touched feature combination.
+- Preserve feature-off and single-rank correctness; reject unsupported
+  combinations before collectives, loading, or execution begin.
+- Trace shapes, shards, ranks, collectives, precision, cache validity, residency,
+  and per-request state through the selected runtime hooks.
+- Compare output quality or accuracy against the disabled/reference path before
+  accepting latency or memory improvements.
+- Require exact commands, topology, environment, warmup, repetitions, and
+  measurements for performance claims; update feature compatibility docs.

.claude/skills/review-pr/references/features/infrastructure-performance.md

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+# Infrastructure and Performance Features
+
+Load the matching design from the reviewed head:
+
+- [Prometheus Metrics](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/metrics/)
+  for logger wrapping, gating, lifecycle, stage transfer, and metric definitions.
+- [Speech Generation Performance Optimizations](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/qwen3_omni_tts_performance_optimization/)
+  for stage batching, CUDA graphs, async chunk, streaming, re-prefill, compile,
+  and deployment evidence.
+
+## Feature checks
+
+- Route instrumentation changes through observability or profiling and result
+  methodology through benchmarking and the performance evidence check.
+- Preserve opt-in/default behavior, units, labels, aggregation, timing scope,
+  lifecycle resets, and bounded overhead.
+- For stacked optimizations, measure each increment against the same baseline
+  and explain interactions, quality guards, regressions, and limitations.
+- Require exact model, checkpoint, hardware, software, workload, topology,
+  concurrency, warmup, repetitions, and production commands.
+- Update the design page when metric meaning, supported optimization stack, or
+  deployment guidance changes.

.claude/skills/review-pr/references/features/runtime-stage-execution.md

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+# Runtime and Stage Execution Features
+
+Load the matching design from the reviewed head:
+
+| Feature | Design | Primary module intersections |
+| --- | --- | --- |
+| Disaggregated inference | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/disaggregated_inference/) | Configuration, stage runtime, OmniConnector, I/O contracts |
+| Async chunk | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_chunk/) | Model integration, orchestration, stage runtime, connector, I/O |
+| Async diffusion output | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/async_diffusion_output/) | Diffusion runtime, I/O contracts, entrypoints |
+| Async Omni output materialization | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/omni_async_output_materialization/) | AR runtime, model integration, I/O contracts, orchestration |
+| Automatic prefix caching | [Design](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/feature/prefix_caching/) | AR runtime, cache management, I/O contracts |
+
+## Feature checks
+
+- Confirm the feature's supported models, entrypoints, stage types, topology,
+  configuration, default, and compatibility limitations.
+- Trace enablement from public/config ingress through every participating module
+  and verify the advertised production dispatch actually selects it.
+- Preserve a correct disabled or fallback path and make unsupported combinations
+  fail or warn as documented.
+- Test overlap, ordering, identity, backpressure, cancellation, failure, final
+  partial output, and cleanup at each asynchronous boundary.
+- Update the feature page, support matrices, examples, and recipes when behavior
+  or coverage changes.

.claude/skills/review-pr/references/modules/ar-runtime.md

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+# Autoregressive Runtime
+
+Primary design: [Autoregressive Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/ar_runtime/).
+
+Use for AR request queues, scheduling state, token and memory budgets, KV/input
+readiness, workers, runners, and upstream vLLM request/cache transitions.
+
+## Contract checks
+
+- Preserve upstream request-state, scheduling, cache, and preemption semantics
+  unless an Omni-specific difference is documented and tested.
+- Convert modality-specific data through explicit input or output adapters; do
+  not inject it through unrelated scheduler state.
+- Keep workers and model runners focused on assigned execution rather than
+  admission or cross-stage routing.
+- Synchronize token, sequence, batch, and memory budgets with worker-visible
+  state and prevent starvation, double counting, or stale readiness.
+- Preserve legal transitions and one terminal cleanup path across completion,
+  abort, timeout, preemption, worker failure, and shutdown.
+
+Test request lifecycle, scheduling decisions, abort, cache state, adapters, and
+every affected worker execution mode against the pinned upstream contract.

.claude/skills/review-pr/references/modules/benchmarking.md

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+# Benchmarking
+
+Primary design: [Benchmarking](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/benchmarking/).
+
+Use when benchmark code, workloads, metric calculations, CLI benchmark
+entrypoints, or result metadata change. Use the performance check separately
+when a PR uses benchmark results as evidence.
+
+## Contract checks
+
+- Record model and revision, code SHA, hardware, software, configuration,
+  workload, precision, topology, concurrency, warmup, repetitions, and
+  measurement window.
+- Validate successful and semantically acceptable outputs before accepting
+  timing, memory, or scaling results.
+- Define every metric's unit, population, aggregation, synchronization, and
+  timing scope.
+- Keep workload parsing and metric computation deterministic and reusable
+  across base and head.
+- Reject comparisons that change material workload or runtime settings without
+  isolating and explaining that difference.
+
+Test workload parsing and metric calculations with deterministic fixtures and
+publish exact commands plus variability for quantitative claims.

.claude/skills/review-pr/references/modules/cache-management.md

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+# Cache Management
+
+Primary design: [Cache Management](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/cache_management/).
+
+Use for AR or diffusion cache identity, reusable state, validity, lifecycle,
+reset, eviction, and teardown.
+
+## Contract checks
+
+- Include every model, request, input, timestep, precision, device, topology,
+  and feature property that can change a reused value in cache identity.
+- Preserve a correct disabled and miss path; never reuse after validity inputs
+  change.
+- Isolate request-scoped state across concurrent requests and define ownership
+  for model-scoped state.
+- Make reset, invalidation, eviction, capacity, and teardown explicit on
+  completion, cancellation, failure, model reload, and shutdown.
+- Keep cache optimizations behind the owning runtime's hooks rather than adding
+  a second request lifecycle.
+
+Test hit, miss, invalidation, disabled behavior, concurrency, capacity, reset,
+and teardown, with quality comparison when reuse is approximate.

.claude/skills/review-pr/references/modules/configuration.md

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+# vLLM-Omni Configuration
+
+Primary design: [vLLM-Omni Configuration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/vllm_omni_config/).
+
+Use for config construction, deploy YAML, registries, schema, defaults,
+aliases, CLI projection, stage config, and topology. Inspect the reviewed
+page's status: while its contract remains deferred or draft, current code and
+tests—not proposed precedence rules—are authoritative.
+
+## Contract checks
+
+- Resolve each supported structured, legacy, CLI, environment, default, alias,
+  and stage-override producer through its actual runtime consumer.
+- Reject unknown or owner-mismatched fields explicitly; keep process-local
+  runtime objects out of transportable configuration.
+- Verify default, explicit, and feature-off values reach every live factory and
+  consumer without silent reinterpretation.
+- Treat stage count, placement, connector, parallelism, device, and memory
+  fields as one topology contract and reject impossible combinations early.
+- Preserve supported construction-path parity until a documented migration
+  removes a path.
+
+Require normalization and schema tests, a live-consumer assertion, and docs for
+public keys, defaults, constraints, precedence, or migration behavior.

.claude/skills/review-pr/references/modules/diffusion.md

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+# Diffusion Module Family
+
+Primary design: [Diffusion Modules](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/).
+
+Use for diffusion-stage behavior, then load the exact submodule page from the
+reviewed head:
+
+| Submodule | Signals | Design |
+| --- | --- | --- |
+| Runtime | Admission, scheduling, execution, progress, outputs, cancellation, hooks, cleanup | [Diffusion Runtime](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_runtime/) |
+| Model Integration | Pipelines, registry, loader, adapters, shared layers, model-specific processing | [Diffusion Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/diffusion_model_integration/) |
+| Continuous Batching | Compatibility, request/step batches, per-request progress, capacity | [Continuous Batching](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/continuous_batching/) |
+| Parallelism | Rank topology, process groups, sharding, collectives, distributed execution | [Parallelism](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/parallelism/) |
+| Offloader | Residency, transfers, memory accounting, prefetch, teardown | [Offloader](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/diffusion/offloader/) |
+
+## Family-wide checks
+
+- Keep one scheduler-owned lifecycle from admission through completion,
+  cancellation, failure, and cleanup; optional features must use defined hooks.
+- Select pipelines through registry and loader contracts instead of scattered
+  model-name conditionals; keep genuine model differences in model directories.
+- Trace latent, conditioning, timestep, generator, shape, layout, dtype, device,
+  batch expansion, and output conversion through the actual consumer.
+- Make batch compatibility and per-request isolation explicit; never associate
+  output by unstable batch position.
+- Derive topology from validated configuration, define every sharding boundary,
+  keep collectives symmetric, and preserve a supported single-rank path.
+- Track one residency owner per offloaded component, wait for transfer readiness,
+  preserve model state, bound retained copies, and clean up deterministically.
+
+Load matching feature designs for cache, quantization, parallelism, continuous
+batching, or offload. Require feature-off correctness, representative inference,
+terminal-path tests, and quality evidence for optimizations.

.claude/skills/review-pr/references/modules/engine-orchestration.md

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+# Engine Orchestration
+
+Primary design: [Engine Orchestration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/engine_orchestration/).
+
+Use for request-state creation, cross-stage routing, output ordering, companion
+tracking, RPC correlation, cancellation propagation, and terminal convergence.
+Orchestration does not own replica placement, stage-process startup, public
+rendering, connector transport, or payload and error schemas.
+
+## Contract checks
+
+- Keep one owner for cross-stage routing; entrypoints, models, and stage clients
+  must not independently select the next logical stage.
+- Preserve request identity and output order across concurrent stages and
+  correlate every control or RPC result to the originating request and wave.
+- Make terminal state monotonic: no new forwarding after success, abort,
+  request failure, or fatal engine failure.
+- Propagate cancellation and failure to every active stage and release
+  orchestrator-owned tasks, queues, companions, and request state.
+- Preserve shutdown ordering across the orchestrator and stage runtime.
+
+Test representative multi-stage routing, ordering, correlation, cancellation,
+failure, and repeated shutdown.

.claude/skills/review-pr/references/modules/entrypoints.md

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+# Entrypoints and Serving Boundaries
+
+Primary design: [Entrypoints and Serving Boundaries](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/entrypoints/).
+
+Use for offline APIs, CLI and server composition, OpenAI-compatible routes,
+request validation, response conversion, streaming, sessions, and engine
+handoff. Entrypoints adapt public contracts; they do not own cross-stage
+routing, stage lifecycle, configuration precedence, payload schemas, or error
+classification.
+
+## Contract checks
+
+- Normalize and validate public values once before engine submission.
+- Preserve request identity, modality, ordering, and exactly one terminal event
+  across streaming, disconnect, cancellation, and failure.
+- Translate internal outputs and errors explicitly for each advertised offline,
+  HTTP, SSE, WebSocket, or asynchronous-job path.
+- Keep model-specific behavior behind a common adapter or processor instead of
+  adding model-name policy to generic entrypoints.
+- Require API or CLI changes to explain necessity, alternatives, compatibility,
+  migration, and the supported route or transport matrix.
+
+Test the affected public protocol, normalization, engine handoff, rendering,
+and terminal outcomes. Route orchestration, I/O schema, and semantic error
+changes to their module contracts.

.claude/skills/review-pr/references/modules/error-contracts.md

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+# Error Classification, Propagation, and Rendering
+
+Primary design: [Error Classification, Propagation, and Rendering](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/error_contracts/).
+
+Use when a change classifies failures, crosses process boundaries, decides
+fatal versus request-scoped impact, sanitizes context, or renders offline,
+HTTP, SSE, WebSocket, or asynchronous-job errors. The I/O module owns the wire
+schema; this module owns the meaning of its error fields.
+
+## Contract checks
+
+- Keep request-scoped failure isolated unless the underlying engine is no
+  longer usable.
+- Preserve stable classification and request context across process and
+  protocol boundaries without exposing prompts, outputs, secrets, or internal
+  stack details.
+- Keep cancellation distinguishable from internal failure.
+- Terminate each public transport exactly once with consistent status, body,
+  event, finish reason, and metrics classification.
+- Maintain compatibility across mixed or older producers only when the
+  reviewed contract defines that behavior.
+
+Test table-driven classification, wire round trips, fatal/request-scoped
+propagation, cancellation, sanitization, and every affected public renderer.

.claude/skills/review-pr/references/modules/execution-platforms.md

@@ -0,0 +1,23 @@
+# Execution Platforms
+
+Primary design: [Execution Platforms](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/execution_platforms/).
+
+Use for hardware capability detection, worker selection, patches, kernels,
+attention backends, and vendor-specific configuration or connectors.
+
+## Contract checks
+
+- Guard hardware behavior through platform selection or capability detection
+  and fail clearly when unsupported.
+- Keep portable modules free of direct vendor imports when a common selector
+  exists; verify portable imports without the target accelerator.
+- Override only behavior that differs from common or upstream implementations,
+  and compare sibling worker hooks when inheritance does not provide parity.
+- Preserve dtype, layout, rank, stream, synchronization, workspace,
+  graph-capture, and resource-lifetime contracts.
+- Do not let simplified CPU doubles hide platform MRO, initialization, or
+  dispatch failures.
+
+Run portable checks first, then the smallest matching-device test in a fresh
+environment. Report unavailable hardware as a validation gap, never simulated
+device evidence.

.claude/skills/review-pr/references/modules/input-output-modality.md

@@ -0,0 +1,23 @@
+# Input, Output, and Modality Contracts
+
+Primary design: [Input, Output, and Modality Contracts](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/input_output_modality_contracts/).
+
+Use for requests, prompt and modality types, message schemas, serialization,
+output types, accumulation, completion, and compatibility shims crossing
+entrypoint, orchestration, stage, connector, or model boundaries.
+
+## Contract checks
+
+- Give every boundary value an explicit modality and validated typed schema.
+- Preserve request and stage identity across conversion, serialization,
+  streaming updates, batching, cancellation, and errors.
+- Distinguish partial, final, empty, and failed output states for every modality.
+- Verify producer and consumer agreement on shape, layout, dtype, device,
+  ownership, metadata, ordering, and optional or unknown fields.
+- Keep internal objects out of public protocols; translate them at the owning
+  entrypoint.
+- Preserve wire compatibility deliberately and give deprecated shims a bounded
+  migration path.
+
+Require construction, round-trip, accumulation, completion, rejection, and
+compatibility tests at each changed producer-consumer boundary.

.claude/skills/review-pr/references/modules/model-integration.md

@@ -0,0 +1,25 @@
+# Model Integration
+
+Primary design: [Model Integration](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/model_integration/).
+
+Use for model registration, configuration adapters, preprocessing, checkpoint
+loading, stage inputs, model-specific hooks, shared layers, runners, and model
+execution. Use the diffusion family reference for diffusion models.
+
+## Contract checks
+
+- Declare explicitly how the model class, loader, input processor, stage
+  configuration, and runtime capabilities are selected.
+- Preserve weight-name mapping, dtype/device placement, optional-dependency
+  errors, and checkpoint-specific loading semantics.
+- Trace each typed input and output through preprocessing, runner state, model
+  call, and its actual consumer.
+- Reuse upstream vLLM behavior when sufficient; document and test the precise
+  difference for every override or patch.
+- Keep model code out of admission, cross-stage routing, public policy, and
+  terminal lifecycle ownership.
+- Place reusable behavior in shared modules and test it with more than one
+  integration.
+
+Require focused registry, loader, processor, runner, and representative model
+execution tests. Apply the model-addition check for new or expanded support.

.claude/skills/review-pr/references/modules/observability.md

@@ -0,0 +1,21 @@
+# Observability
+
+Primary design: [Observability](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/observability/).
+
+Use for metrics, logs, correlation fields, and cross-stage request visibility.
+
+## Contract checks
+
+- Preserve stable request and stage identity across entrypoints, orchestration,
+  stages, workers, errors, and terminal cleanup.
+- Treat metric names, units, labels, aggregation, and lifecycle as public
+  contracts; document any semantic change.
+- Keep labels bounded: never use request IDs, prompts, outputs, paths, or other
+  unbounded user values.
+- Register and reset metrics exactly once across repeated startup and shutdown,
+  and keep disabled instrumentation inexpensive.
+- Align emitted values with their actual timing and lifecycle owner rather than
+  reconstructing them from ambiguous client strings.
+
+Test registration, label sets, units, resets, error classification, and
+representative multi-stage emission.

.claude/skills/review-pr/references/modules/omni-connector.md

@@ -0,0 +1,24 @@
+# OmniConnector
+
+Primary design: [OmniConnector](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/omni_connector/).
+
+Use for connector contracts and model-agnostic data transfer across stages,
+processes, devices, or nodes. Connector implementations transport and
+synchronize data; they do not choose the next stage or implement model policy.
+
+## Contract checks
+
+- Prove producer and consumer agreement on request/stage identity, shape,
+  layout, dtype, placement, ownership, ordering, and completion.
+- Keep backend selection and metadata/key construction explicit; do not silently
+  reinterpret or drop payloads across SHM, store, network, or vendor paths.
+- Bound connect, put/get, retry, synchronization, and readiness waits with
+  backpressure, cancellation, and actionable errors.
+- Prevent route, endpoint, port, key, and buffer collisions across repeated
+  startup, concurrent requests, and replica replacement.
+- Release connections, handles, buffers, listener work, and stale sender state
+  on success, cancellation, failure, and shutdown.
+
+Load the matching communication feature design for a concrete backend. Require
+focused producer-consumer tests and the smallest runnable multi-process or
+multi-node path.

.claude/skills/review-pr/references/modules/profiling.md

@@ -0,0 +1,21 @@
+# Profiling
+
+Primary design: [Profiling](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/profiling/).
+
+Use for opt-in instrumentation and trace collection across supported profiler
+backends.
+
+## Contract checks
+
+- Keep expensive collection disabled by default and bounded by explicit start,
+  stop, timeout, and cleanup behavior.
+- Preserve scheduling, generated outputs, resource ownership, and concurrency
+  semantics apart from documented synchronization overhead.
+- Include stage, rank, worker, operation, and request correlation where useful
+  without embedding user payloads or secrets.
+- Make repeated start/stop and partial failure safe, including multi-rank trace
+  naming and artifact ownership.
+- Measure disabled and enabled overhead before making performance claims.
+
+Test the disabled path, repeated lifecycle, failure cleanup, trace context, and
+each affected backend.

.claude/skills/review-pr/references/modules/quantization.md

@@ -0,0 +1,22 @@
+# Quantization
+
+Primary design: [Quantization](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/quantization/).
+
+Use for method registration, checkpoint interpretation, layer replacement,
+reduced precision, platform capability, and runtime constraints.
+
+## Contract checks
+
+- Validate checkpoint metadata, selected method, supported layers, device
+  capability, required dependencies, and parallel layout before loading.
+- Preserve loader and weight-name mapping semantics, dtype, scale/granularity,
+  zero point, padding, and buffer handling through the real kernel consumer.
+- Fail clearly for unsupported combinations; do not silently choose a different
+  method or precision.
+- Keep online and pre-quantized checkpoint paths explicit and ensure patches or
+  fallbacks do not bypass model-owned loaders.
+- Require numerical comparison against a defined reference with explicit
+  tolerance before treating performance results as valid.
+
+Test registration, loading, representative layers, metadata, platform guards,
+parallel combinations, and numerical correctness separately from performance.

.claude/skills/review-pr/references/modules/stage-runtime.md

@@ -0,0 +1,23 @@
+# Stage Runtime and Replica Lifecycle
+
+Primary design: [Stage Runtime and Replica Lifecycle](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/module/stage_runtime/).
+
+Use for local or distributed placement, startup, readiness, stage pools,
+replica identity and membership, selection, affinity, liveness, draining, and
+stage-client or process lifecycle. Cross-stage request policy belongs to engine
+orchestration; model scheduling belongs to the owning runtime.
+
+## Contract checks
+
+- Acquire, replace, and retire replicas through one stage-runtime lifecycle.
+- Preserve stable replica identity and request affinity until termination or a
+  declared replica loss.
+- Make readiness explicit before admission; surface partial-startup and
+  membership failures without leaving half-initialized capacity.
+- Keep local and distributed semantics aligned for supported modes.
+- Make drain, shutdown, and cleanup idempotent across repeated signals and
+  process failure.
+
+Test startup, readiness, selection, affinity, membership changes, abort,
+process loss, draining, and repeated shutdown on affected local/distributed
+paths.

.claude/skills/review-pr/references/process/design-contracts.md

@@ -0,0 +1,34 @@
+# Resolve Design Contracts
+
+Use after freezing the review head and before selecting module or feature
+references. The maintained [design index](https://docs.vllm.ai/projects/vllm-omni/en/latest/design/)
+defines two routing axes:
+
+- **Module designs** own code boundaries and invariants. Select one primary
+  module and a second only when the live producer-consumer path crosses it.
+- **Feature designs** describe behavior spanning modules. Load every feature
+  page whose implementation, default, support, or contract changes.
+
+## Resolution order
+
+1. Open `docs/design/index.md` in the reviewed head, not the reviewer's checkout
+   or published latest tree.
+2. Match changed behavior against each module page's `primary_code_paths`,
+   `primary_path_exceptions`, ownership boundary, and `depends_on` metadata.
+3. Read `status`, `architecture_state`, `last_verified_commit`, and any linked
+   in-flight work before treating prose as current behavior.
+4. Use current code and tests when a page is draft, deferred, stale, absent, or
+   conflicts with the frozen implementation. Report material documentation
+   drift separately.
+5. Treat candidate invariants and promotion gates as review questions until
+   repository policy, current code/tests, or owner-approved normative text
+   makes them enforceable. Do not turn a draft identifier into an automatic
+   blocker.
+6. Use published latest docs only for discovery when the reviewed head lacks a
+   page; never import a newer contract into an older branch silently.
+
+When behavior changes an active module or feature contract, require the same PR
+to update that page. Require a new page only when repository policy, an accepted
+RFC/issue, or explicit maintainer acceptance criteria do so; otherwise treat it
+as a non-blocking design suggestion. Do not demand a second page when an existing
+design already owns the exact contract.